10960 lines
525 KiB
Plaintext
10960 lines
525 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# records"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import vectorbt as vbt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"Collapsed": "false"
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"from numba import njit\n",
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"from collections import namedtuple\n",
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"from datetime import datetime"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Disable caching for performance testing\n",
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"# NOTE: Expect waterfall of executions, since some attributes depend on other attributes \n",
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"# that have to be calculated again and again\n",
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"vbt.settings.caching['enabled'] = False"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"example_dt = np.dtype([\n",
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" ('id', np.int64),\n",
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" ('idx', np.int64),\n",
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" ('col', np.int64),\n",
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" ('some_field1', np.float64),\n",
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" ('some_field2', np.float64)\n",
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"], align=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[(0, 0, 0, 10., 21.) (1, 1, 0, 11., 20.) (2, 2, 0, 12., 19.)\n",
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" (3, 0, 1, 13., 18.) (4, 1, 1, 14., 17.) (5, 2, 1, 13., 18.)\n",
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" (6, 0, 2, 12., 19.) (7, 1, 2, 11., 20.) (8, 2, 2, 10., 21.)]\n",
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"(9,)\n"
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]
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}
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],
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"source": [
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"records_arr = np.asarray([\n",
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" (0, 0, 0, 10, 21),\n",
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" (1, 1, 0, 11, 20),\n",
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" (2, 2, 0, 12, 19),\n",
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" (3, 0, 1, 13, 18),\n",
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" (4, 1, 1, 14, 17),\n",
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" (5, 2, 1, 13, 18),\n",
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" (6, 0, 2, 12, 19),\n",
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" (7, 1, 2, 11, 20),\n",
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" (8, 2, 2, 10, 21)\n",
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"], dtype=example_dt)\n",
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"print(records_arr)\n",
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"print(records_arr.shape)\n",
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"\n",
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"columns = pd.MultiIndex.from_arrays([[0, 1, 1, 1], ['a', 'b', 'c', 'd']], names=['lvl1', 'lvl2'])\n",
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"wrapper = vbt.ArrayWrapper(index=['x', 'y', 'z'], columns=columns, ndim=2, freq='1 days')\n",
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"records = vbt.Records(wrapper, records_arr)\n",
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"\n",
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"records_grouped = vbt.Records(wrapper.copy(group_by=0), records_arr)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(1000000,)\n"
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]
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}
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],
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"source": [
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"big_records_arr = np.asarray(list(zip(*(\n",
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" np.arange(1000000),\n",
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" np.tile(np.arange(1000), 1000),\n",
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" np.repeat(np.arange(1000), 1000),\n",
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" np.random.randint(0, 100, size=1000000),\n",
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" np.random.randint(0, 100, size=1000000)))), dtype=example_dt)\n",
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"print(big_records_arr.shape)\n",
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"\n",
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"big_columns = pd.MultiIndex.from_arrays([np.repeat(np.array([0, 1]), 500), np.arange(1000)], names=['lvl1', 'lvl2'])\n",
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"big_wrapper = vbt.ArrayWrapper(index=np.arange(1000), columns=big_columns, ndim=2, freq='1 days')\n",
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"big_records = vbt.Records(big_wrapper, big_records_arr)\n",
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"\n",
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"big_records_grouped = vbt.Records(big_wrapper.copy(group_by=0), big_records_arr)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[(8, 2, 2, 10., 21.) (7, 1, 2, 11., 20.) (6, 0, 2, 12., 19.)\n",
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" (5, 2, 1, 13., 18.) (4, 1, 1, 14., 17.) (3, 0, 1, 13., 18.)\n",
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" (2, 2, 0, 12., 19.) (1, 1, 0, 11., 20.) (0, 0, 0, 10., 21.)]\n"
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]
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}
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],
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"source": [
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"records_nosort = records.copy(records_arr=records.records_arr[::-1])\n",
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"print(records_nosort.records_arr)\n",
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"\n",
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"big_records_nosort = big_records.copy(records_arr=big_records.records_arr[::-1])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"group_by = pd.Series(['first', 'first', 'second', 'second'], name='group')\n",
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"big_group_by = pd.Series(np.repeat(np.array([0, 1]), 500))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## ColumnMapper"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[0 0 0 1 1 1 2 2 2]\n"
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]
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}
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],
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"source": [
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"print(records.col_mapper.col_arr)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[0 0 0 1 1 1 2 2 2]\n",
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"[0 0 0 1 1 1 1 1 1]\n",
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"7.57 µs ± 81.5 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
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]
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}
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],
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"source": [
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"print(records.col_mapper.get_col_arr())\n",
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"print(records_grouped.col_mapper.get_col_arr())\n",
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"\n",
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"%timeit big_records_grouped.col_mapper.get_col_arr()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[ 0 3]\n",
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" [ 3 6]\n",
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" [ 6 9]\n",
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" [-1 -1]]\n",
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"6.99 µs ± 73.1 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
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]
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}
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],
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"source": [
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"print(records.col_mapper.col_range)\n",
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"\n",
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"%timeit big_records.col_mapper.col_range"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[ 0 3]\n",
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" [ 3 6]\n",
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" [ 6 9]\n",
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" [-1 -1]]\n",
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"[[0 3]\n",
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" [3 9]]\n",
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"7.38 µs ± 56.5 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
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]
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}
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],
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"source": [
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"print(records.col_mapper.get_col_range())\n",
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"print(records_grouped.col_mapper.get_col_range())\n",
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"\n",
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"%timeit big_records_grouped.col_mapper.get_col_range()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(array([0, 1, 2, 3, 4, 5, 6, 7, 8]), array([3, 3, 3, 0]))\n",
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"7.03 µs ± 22.4 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
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]
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}
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],
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"source": [
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"print(records.col_mapper.col_map)\n",
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"\n",
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"%timeit big_records.col_mapper.col_map"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(array([0, 1, 2, 3, 4, 5, 6, 7, 8]), array([3, 3, 3, 0]))\n",
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"(array([0, 1, 2, 3, 4, 5, 6, 7, 8]), array([3, 6]))\n",
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"7.43 µs ± 46.4 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
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]
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}
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],
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"source": [
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"print(records.col_mapper.get_col_map())\n",
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"print(records_grouped.col_mapper.get_col_map())\n",
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"\n",
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"%timeit big_records_grouped.col_mapper.get_col_map()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"True\n",
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"7.48 µs ± 48.2 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
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"False\n",
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"7.49 µs ± 8.8 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
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]
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}
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],
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"source": [
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"print(records.col_mapper.is_sorted())\n",
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"%timeit big_records.col_mapper.is_sorted()\n",
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"\n",
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"print(records_nosort.col_mapper.is_sorted())\n",
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"%timeit big_records_nosort.col_mapper.is_sorted()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## MappedArray"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"mapped_array = records.map_field('some_field1')\n",
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"big_mapped_array = big_records.map_field('some_field1')\n",
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"\n",
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"mapped_array_nosort = records_nosort.map_field('some_field1')\n",
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"big_mapped_array_nosort = big_records_nosort.map_field('some_field1')\n",
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"\n",
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"mapped_array_grouped = records_grouped.map_field('some_field1')\n",
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"big_mapped_array_grouped = big_records_grouped.map_field('some_field1')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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||
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[10. 11. 12.]\n",
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"[0 0 0]\n",
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"MultiIndex([(0, 'a')],\n",
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" names=['lvl1', 'lvl2'])\n",
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"[13. 14. 13.]\n",
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"[0 0 0]\n",
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"MultiIndex([(1, 'b')],\n",
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" names=['lvl1', 'lvl2'])\n",
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"[10. 11. 12. 10. 11. 12.]\n",
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"[0 0 0 1 1 1]\n",
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"MultiIndex([(0, 'a'),\n",
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" (0, 'a')],\n",
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|
" names=['lvl1', 'lvl2'])\n",
|
||
|
|
"[10. 11. 12. 13. 14. 13.]\n",
|
||
|
|
"[0 0 0 1 1 1]\n",
|
||
|
|
"MultiIndex([(0, 'a'),\n",
|
||
|
|
" (1, 'b')],\n",
|
||
|
|
" names=['lvl1', 'lvl2'])\n",
|
||
|
|
"954 µs ± 11.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
|
||
|
|
"13.7 ms ± 1.09 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array[(0, 'a')].values)\n",
|
||
|
|
"print(mapped_array[(0, 'a')].col_arr)\n",
|
||
|
|
"print(mapped_array[(0, 'a')].wrapper.columns)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array[(1, 'b')].values)\n",
|
||
|
|
"print(mapped_array[(1, 'b')].col_arr)\n",
|
||
|
|
"print(mapped_array[(1, 'b')].wrapper.columns)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array[[(0, 'a'), (0, 'a')]].values)\n",
|
||
|
|
"print(mapped_array[[(0, 'a'), (0, 'a')]].col_arr)\n",
|
||
|
|
"print(mapped_array[[(0, 'a'), (0, 'a')]].wrapper.columns)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array[[(0, 'a'), (1, 'b')]].values)\n",
|
||
|
|
"print(mapped_array[[(0, 'a'), (1, 'b')]].col_arr)\n",
|
||
|
|
"print(mapped_array[[(0, 'a'), (1, 'b')]].wrapper.columns)\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_mapped_array.iloc[0]\n",
|
||
|
|
"%timeit big_mapped_array.iloc[:]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[12. 11. 10.]\n",
|
||
|
|
"[0 0 0]\n",
|
||
|
|
"MultiIndex([(0, 'a')],\n",
|
||
|
|
" names=['lvl1', 'lvl2'])\n",
|
||
|
|
"[13. 14. 13.]\n",
|
||
|
|
"[0 0 0]\n",
|
||
|
|
"MultiIndex([(1, 'b')],\n",
|
||
|
|
" names=['lvl1', 'lvl2'])\n",
|
||
|
|
"[12. 11. 10. 12. 11. 10.]\n",
|
||
|
|
"[0 0 0 1 1 1]\n",
|
||
|
|
"MultiIndex([(0, 'a'),\n",
|
||
|
|
" (0, 'a')],\n",
|
||
|
|
" names=['lvl1', 'lvl2'])\n",
|
||
|
|
"[12. 11. 10. 13. 14. 13.]\n",
|
||
|
|
"[0 0 0 1 1 1]\n",
|
||
|
|
"MultiIndex([(0, 'a'),\n",
|
||
|
|
" (1, 'b')],\n",
|
||
|
|
" names=['lvl1', 'lvl2'])\n",
|
||
|
|
"954 µs ± 7.38 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
|
||
|
|
"14.2 ms ± 448 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array_nosort[(0, 'a')].values)\n",
|
||
|
|
"print(mapped_array_nosort[(0, 'a')].col_arr)\n",
|
||
|
|
"print(mapped_array_nosort[(0, 'a')].wrapper.columns)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array_nosort[(1, 'b')].values)\n",
|
||
|
|
"print(mapped_array_nosort[(1, 'b')].col_arr)\n",
|
||
|
|
"print(mapped_array_nosort[(1, 'b')].wrapper.columns)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array_nosort[[(0, 'a'), (0, 'a')]].values)\n",
|
||
|
|
"print(mapped_array_nosort[[(0, 'a'), (0, 'a')]].col_arr)\n",
|
||
|
|
"print(mapped_array_nosort[[(0, 'a'), (0, 'a')]].wrapper.columns)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array_nosort[[(0, 'a'), (1, 'b')]].values)\n",
|
||
|
|
"print(mapped_array_nosort[[(0, 'a'), (1, 'b')]].col_arr)\n",
|
||
|
|
"print(mapped_array_nosort[[(0, 'a'), (1, 'b')]].wrapper.columns)\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_mapped_array_nosort.iloc[0]\n",
|
||
|
|
"%timeit big_mapped_array_nosort.iloc[:]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"MultiIndex([(0, 'a')],\n",
|
||
|
|
" names=['lvl1', 'lvl2'])\n",
|
||
|
|
"1\n",
|
||
|
|
"1\n",
|
||
|
|
"Index([0], dtype='int64', name='lvl1')\n",
|
||
|
|
"MultiIndex([(1, 'b'),\n",
|
||
|
|
" (1, 'c'),\n",
|
||
|
|
" (1, 'd')],\n",
|
||
|
|
" names=['lvl1', 'lvl2'])\n",
|
||
|
|
"2\n",
|
||
|
|
"1\n",
|
||
|
|
"Index([1, 1, 1], dtype='int64', name='lvl1')\n",
|
||
|
|
"MultiIndex([(0, 'a')],\n",
|
||
|
|
" names=['lvl1', 'lvl2'])\n",
|
||
|
|
"2\n",
|
||
|
|
"2\n",
|
||
|
|
"Index([0], dtype='int64', name='lvl1')\n",
|
||
|
|
"MultiIndex([(0, 'a'),\n",
|
||
|
|
" (1, 'b'),\n",
|
||
|
|
" (1, 'c'),\n",
|
||
|
|
" (1, 'd')],\n",
|
||
|
|
" names=['lvl1', 'lvl2'])\n",
|
||
|
|
"2\n",
|
||
|
|
"2\n",
|
||
|
|
"Index([0, 1, 1, 1], dtype='int64', name='lvl1')\n",
|
||
|
|
"5.57 ms ± 375 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"12.1 ms ± 451 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array_grouped[0].wrapper.columns) # indexing on groups, not columns!\n",
|
||
|
|
"print(mapped_array_grouped[0].wrapper.ndim)\n",
|
||
|
|
"print(mapped_array_grouped[0].wrapper.grouped_ndim)\n",
|
||
|
|
"print(mapped_array_grouped[0].wrapper.grouper.group_by)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array_grouped[1].wrapper.columns)\n",
|
||
|
|
"print(mapped_array_grouped[1].wrapper.ndim)\n",
|
||
|
|
"print(mapped_array_grouped[1].wrapper.grouped_ndim)\n",
|
||
|
|
"print(mapped_array_grouped[1].wrapper.grouper.group_by)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array_grouped[[0]].wrapper.columns)\n",
|
||
|
|
"print(mapped_array_grouped[[0]].wrapper.ndim)\n",
|
||
|
|
"print(mapped_array_grouped[[0]].wrapper.grouped_ndim)\n",
|
||
|
|
"print(mapped_array_grouped[[0]].wrapper.grouper.group_by)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array_grouped[[0, 1]].wrapper.columns)\n",
|
||
|
|
"print(mapped_array_grouped[[0, 1]].wrapper.ndim)\n",
|
||
|
|
"print(mapped_array_grouped[[0, 1]].wrapper.grouped_ndim)\n",
|
||
|
|
"print(mapped_array_grouped[[0, 1]].wrapper.grouper.group_by)\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_mapped_array_grouped.iloc[0]\n",
|
||
|
|
"%timeit big_mapped_array_grouped.iloc[:]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 15,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Index(['x', 'y', 'z'], dtype='object')\n",
|
||
|
|
"MultiIndex([(0, 'a'),\n",
|
||
|
|
" (1, 'b'),\n",
|
||
|
|
" (1, 'c'),\n",
|
||
|
|
" (1, 'd')],\n",
|
||
|
|
" names=['lvl1', 'lvl2'])\n",
|
||
|
|
"2\n",
|
||
|
|
"None\n",
|
||
|
|
"Index(['x', 'y', 'z'], dtype='object')\n",
|
||
|
|
"MultiIndex([(0, 'a'),\n",
|
||
|
|
" (1, 'b'),\n",
|
||
|
|
" (1, 'c'),\n",
|
||
|
|
" (1, 'd')],\n",
|
||
|
|
" names=['lvl1', 'lvl2'])\n",
|
||
|
|
"2\n",
|
||
|
|
"Index([0, 1, 1, 1], dtype='int64', name='lvl1')\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array.wrapper.index)\n",
|
||
|
|
"print(mapped_array.wrapper.columns)\n",
|
||
|
|
"print(mapped_array.wrapper.ndim)\n",
|
||
|
|
"print(mapped_array.wrapper.grouper.group_by)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array_grouped.wrapper.index)\n",
|
||
|
|
"print(mapped_array_grouped.wrapper.columns)\n",
|
||
|
|
"print(mapped_array_grouped.wrapper.ndim)\n",
|
||
|
|
"print(mapped_array_grouped.wrapper.grouper.group_by)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 16,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[10. 11. 12. 13. 14. 13. 12. 11. 10.]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array.values)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[0 0 0 1 1 1 2 2 2]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array.col_arr)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 18,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[0 1 2 3 4 5 6 7 8]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array.id_arr)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 19,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[0 1 2 0 1 2 0 1 2]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array.idx_arr)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 20,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"True\n",
|
||
|
|
"1.04 ms ± 7.48 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
|
||
|
|
"False\n",
|
||
|
|
"7.84 µs ± 31 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
|
||
|
|
"True\n",
|
||
|
|
"1.58 ms ± 65.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
|
||
|
|
"False\n",
|
||
|
|
"6.99 µs ± 20.4 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array.is_sorted())\n",
|
||
|
|
"%timeit big_mapped_array.is_sorted()\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array_nosort.is_sorted())\n",
|
||
|
|
"%timeit big_mapped_array_nosort.is_sorted()\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array.is_sorted(incl_id=True))\n",
|
||
|
|
"%timeit big_mapped_array.is_sorted(incl_id=True)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array_nosort.is_sorted(incl_id=True))\n",
|
||
|
|
"%timeit big_mapped_array_nosort.is_sorted(incl_id=True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 21,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[0 0 0 1 1 1 2 2 2]\n",
|
||
|
|
"[0 1 2 3 4 5 6 7 8]\n",
|
||
|
|
"1.41 ms ± 48.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
|
||
|
|
"[0 0 0 1 1 1 2 2 2]\n",
|
||
|
|
"[2 1 0 5 4 3 8 7 6]\n",
|
||
|
|
"20.1 ms ± 544 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
|
||
|
|
"[0 0 0 1 1 1 2 2 2]\n",
|
||
|
|
"[0 1 2 3 4 5 6 7 8]\n",
|
||
|
|
"1.91 ms ± 3.77 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
|
||
|
|
"[0 0 0 1 1 1 2 2 2]\n",
|
||
|
|
"[0 1 2 3 4 5 6 7 8]\n",
|
||
|
|
"16.1 ms ± 1.42 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array.sort().col_arr)\n",
|
||
|
|
"print(mapped_array.sort().id_arr)\n",
|
||
|
|
"%timeit big_mapped_array.sort()\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array_nosort.sort().col_arr)\n",
|
||
|
|
"print(mapped_array_nosort.sort().id_arr)\n",
|
||
|
|
"%timeit big_mapped_array_nosort.sort()\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array.sort(incl_id=True).col_arr)\n",
|
||
|
|
"print(mapped_array.sort(incl_id=True).id_arr)\n",
|
||
|
|
"%timeit big_mapped_array.sort(incl_id=True)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array_nosort.sort(incl_id=True).col_arr)\n",
|
||
|
|
"print(mapped_array_nosort.sort(incl_id=True).id_arr)\n",
|
||
|
|
"%timeit big_mapped_array_nosort.sort(incl_id=True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[12. 13. 14. 13. 12.]\n",
|
||
|
|
"18.2 ms ± 487 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"17.9 ms ± 143 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"mask = mapped_array.values >= mapped_array.values.mean()\n",
|
||
|
|
"print(mapped_array.apply_mask(mask).values)\n",
|
||
|
|
"\n",
|
||
|
|
"big_mask = big_mapped_array.values >= big_mapped_array.values.mean()\n",
|
||
|
|
"%timeit big_mapped_array.apply_mask(big_mask)\n",
|
||
|
|
"%timeit big_mapped_array_nosort.apply_mask(big_mask)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 24,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[ True False True True False True True False True]\n",
|
||
|
|
"1.11 ms ± 3.83 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
|
||
|
|
"1.64 ms ± 43.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"@njit\n",
|
||
|
|
"def every_2_nb(inout, idxs, col, mapped_arr):\n",
|
||
|
|
" inout[idxs[::2]] = True\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array.map_to_mask(every_2_nb))\n",
|
||
|
|
"%timeit big_mapped_array.map_to_mask(every_2_nb)\n",
|
||
|
|
"%timeit big_mapped_array_nosort.map_to_mask(every_2_nb)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 25,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[10. 11. 12. 13. 14. 13. 12. 11. 10.]\n",
|
||
|
|
"[False False True False True False True False False]\n",
|
||
|
|
"50.8 ms ± 76.3 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
|
||
|
|
"[ True False False True False False False False True]\n",
|
||
|
|
"51.6 ms ± 358 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array.values)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array.top_n_mask(1))\n",
|
||
|
|
"%timeit big_mapped_array.top_n_mask(100)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array.bottom_n_mask(1))\n",
|
||
|
|
"%timeit big_mapped_array.bottom_n_mask(100)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 26,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[12. 14. 12.]\n",
|
||
|
|
"61.2 ms ± 642 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
|
||
|
|
"[10. 13. 10.]\n",
|
||
|
|
"59.6 ms ± 362 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array.top_n(1).values)\n",
|
||
|
|
"%timeit big_mapped_array.top_n(100)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array.bottom_n(1).values)\n",
|
||
|
|
"%timeit big_mapped_array.bottom_n(100)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 27,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"True\n",
|
||
|
|
"2.44 ms ± 11.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array.is_expandable())\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_mapped_array.is_expandable()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 28,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"lvl1 0 1 \n",
|
||
|
|
"lvl2 a b c d\n",
|
||
|
|
"x 10.0 13.0 12.0 NaN\n",
|
||
|
|
"y 11.0 14.0 11.0 NaN\n",
|
||
|
|
"z 12.0 13.0 10.0 NaN\n",
|
||
|
|
"lvl1 0 1 \n",
|
||
|
|
"lvl2 a b c d\n",
|
||
|
|
"x 10.0 13.0 12.0 0.0\n",
|
||
|
|
"y 11.0 14.0 11.0 0.0\n",
|
||
|
|
"z 12.0 13.0 10.0 0.0\n",
|
||
|
|
"5.41 ms ± 239 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array.to_pd())\n",
|
||
|
|
"print(mapped_array.to_pd(fill_value=0.))\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_mapped_array.to_pd()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 29,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"0 10.0\n",
|
||
|
|
"1 11.0\n",
|
||
|
|
"2 12.0\n",
|
||
|
|
"Name: (0, a), dtype: float64\n",
|
||
|
|
"8.29 ms ± 1.09 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
|
||
|
|
"lvl1 0 1 \n",
|
||
|
|
"lvl2 a b c d\n",
|
||
|
|
"0 10.0 13.0 12.0 NaN\n",
|
||
|
|
"1 11.0 14.0 11.0 NaN\n",
|
||
|
|
"2 12.0 13.0 10.0 NaN\n",
|
||
|
|
"3.48 ms ± 202 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"lvl1 0 1\n",
|
||
|
|
"0 10.0 13.0\n",
|
||
|
|
"1 11.0 14.0\n",
|
||
|
|
"2 12.0 13.0\n",
|
||
|
|
"3 NaN 12.0\n",
|
||
|
|
"4 NaN 11.0\n",
|
||
|
|
"5 NaN 10.0\n",
|
||
|
|
"lvl1 0 1\n",
|
||
|
|
"0 10.0 13.0\n",
|
||
|
|
"1 11.0 14.0\n",
|
||
|
|
"2 12.0 13.0\n",
|
||
|
|
"3 0.0 12.0\n",
|
||
|
|
"4 0.0 11.0\n",
|
||
|
|
"5 0.0 10.0\n",
|
||
|
|
"3.34 ms ± 79.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array[(0, 'a')].to_pd(ignore_index=True))\n",
|
||
|
|
"%timeit big_mapped_array[0].to_pd(ignore_index=True)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array.to_pd(ignore_index=True))\n",
|
||
|
|
"%timeit big_mapped_array.to_pd(ignore_index=True)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array_grouped.to_pd(ignore_index=True))\n",
|
||
|
|
"print(mapped_array_grouped.to_pd(ignore_index=True, fill_value=0))\n",
|
||
|
|
"%timeit big_mapped_array_grouped.to_pd(ignore_index=True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 30,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"11.0\n",
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 11.000000\n",
|
||
|
|
"1 b 13.333333\n",
|
||
|
|
"Name: reduce, dtype: float64\n",
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 11.000000\n",
|
||
|
|
"1 b 13.333333\n",
|
||
|
|
" c 11.000000\n",
|
||
|
|
" d NaN\n",
|
||
|
|
"Name: reduce, dtype: float64\n",
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 11.000000\n",
|
||
|
|
"1 b 13.333333\n",
|
||
|
|
" c 11.000000\n",
|
||
|
|
" d 0.000000\n",
|
||
|
|
"Name: reduce, dtype: float64\n",
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 11.000000\n",
|
||
|
|
"1 b 13.333333\n",
|
||
|
|
" c 11.000000\n",
|
||
|
|
" d 0.000000\n",
|
||
|
|
"Name: reduce, dtype: float64\n",
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 11 days 00:00:00\n",
|
||
|
|
"1 b 13 days 08:00:00\n",
|
||
|
|
" c 11 days 00:00:00\n",
|
||
|
|
" d NaT\n",
|
||
|
|
"Name: reduce, dtype: timedelta64[ns]\n",
|
||
|
|
"2.07 ms ± 18.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
|
||
|
|
"2.52 ms ± 38 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"@njit\n",
|
||
|
|
"def mean_reduce_nb(col, a):\n",
|
||
|
|
" return np.mean(a)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array[(0, 'a')].reduce(mean_reduce_nb))\n",
|
||
|
|
"print(mapped_array[[(0, 'a'), (1, 'b')]].reduce(mean_reduce_nb))\n",
|
||
|
|
"print(mapped_array.reduce(mean_reduce_nb))\n",
|
||
|
|
"print(mapped_array.reduce(mean_reduce_nb, fill_value=0.))\n",
|
||
|
|
"print(mapped_array.reduce(mean_reduce_nb, fill_value=0., wrap_kwargs=dict(dtype=np.int64)))\n",
|
||
|
|
"print(mapped_array.reduce(mean_reduce_nb, wrap_kwargs=dict(to_timedelta=True)))\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_mapped_array.reduce(mean_reduce_nb)\n",
|
||
|
|
"%timeit big_mapped_array_nosort.reduce(mean_reduce_nb)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 31,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"11.0\n",
|
||
|
|
"lvl1\n",
|
||
|
|
"0 11.0\n",
|
||
|
|
"Name: reduce, dtype: float64\n",
|
||
|
|
"lvl1\n",
|
||
|
|
"0 11.000000\n",
|
||
|
|
"1 12.166667\n",
|
||
|
|
"Name: reduce, dtype: float64\n",
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 11.000000\n",
|
||
|
|
"1 b 13.333333\n",
|
||
|
|
" c 11.000000\n",
|
||
|
|
" d NaN\n",
|
||
|
|
"Name: reduce, dtype: float64\n",
|
||
|
|
"2.13 ms ± 141 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array_grouped[0].reduce(mean_reduce_nb))\n",
|
||
|
|
"print(mapped_array_grouped[[0]].reduce(mean_reduce_nb))\n",
|
||
|
|
"print(mapped_array_grouped.reduce(mean_reduce_nb))\n",
|
||
|
|
"print(mapped_array_grouped.reduce(mean_reduce_nb, group_by=False))\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_mapped_array_grouped.reduce(mean_reduce_nb)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 32,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a x\n",
|
||
|
|
"1 b x\n",
|
||
|
|
" c z\n",
|
||
|
|
" d NaN\n",
|
||
|
|
"Name: reduce, dtype: object\n",
|
||
|
|
"3.9 ms ± 76.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 0\n",
|
||
|
|
"1 b 0\n",
|
||
|
|
" c 2\n",
|
||
|
|
" d -1\n",
|
||
|
|
"Name: reduce, dtype: int64\n",
|
||
|
|
"2.89 ms ± 113 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"lvl1\n",
|
||
|
|
"0 x\n",
|
||
|
|
"1 z\n",
|
||
|
|
"Name: reduce, dtype: object\n",
|
||
|
|
"3.15 ms ± 100 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"@njit\n",
|
||
|
|
"def argmin_reduce_nb(col, a):\n",
|
||
|
|
" return np.argmin(a)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array.reduce(argmin_reduce_nb, returns_idx=True))\n",
|
||
|
|
"%timeit big_mapped_array.reduce(argmin_reduce_nb, returns_idx=True)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array.reduce(argmin_reduce_nb, returns_idx=True, to_index=False))\n",
|
||
|
|
"%timeit big_mapped_array.reduce(argmin_reduce_nb, returns_idx=True, to_index=False)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array_grouped.reduce(argmin_reduce_nb, returns_idx=True))\n",
|
||
|
|
"%timeit big_mapped_array_grouped.reduce(argmin_reduce_nb, returns_idx=True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 33,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"0 10.0\n",
|
||
|
|
"1 12.0\n",
|
||
|
|
"Name: (0, a), dtype: float64\n",
|
||
|
|
"lvl1 0 1\n",
|
||
|
|
"lvl2 a b\n",
|
||
|
|
"0 10.0 13.0\n",
|
||
|
|
"1 12.0 14.0\n",
|
||
|
|
"lvl1 0 1 \n",
|
||
|
|
"lvl2 a b c d\n",
|
||
|
|
"0 10.0 13.0 10.0 NaN\n",
|
||
|
|
"1 12.0 14.0 12.0 NaN\n",
|
||
|
|
"lvl1 0 1 \n",
|
||
|
|
"lvl2 a b c d\n",
|
||
|
|
"min 10.0 13.0 10.0 NaN\n",
|
||
|
|
"max 12.0 14.0 12.0 NaN\n",
|
||
|
|
"lvl1 0 1 \n",
|
||
|
|
"lvl2 a b c d\n",
|
||
|
|
"min 10.0 13.0 10.0 0.0\n",
|
||
|
|
"max 12.0 14.0 12.0 0.0\n",
|
||
|
|
"lvl1 0 1 \n",
|
||
|
|
"lvl2 a b c d\n",
|
||
|
|
"0 10 days 13 days 10 days NaT\n",
|
||
|
|
"1 12 days 14 days 12 days NaT\n",
|
||
|
|
"2.45 ms ± 12.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"@njit\n",
|
||
|
|
"def min_max_reduce_nb(col, a):\n",
|
||
|
|
" return np.array([np.min(a), np.max(a)])\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array[(0, 'a')].reduce(min_max_reduce_nb, returns_array=True))\n",
|
||
|
|
"print(mapped_array[[(0, 'a'), (1, 'b')]].reduce(min_max_reduce_nb, returns_array=True))\n",
|
||
|
|
"print(mapped_array.reduce(min_max_reduce_nb, returns_array=True))\n",
|
||
|
|
"print(mapped_array.reduce(min_max_reduce_nb, returns_array=True, wrap_kwargs=dict(name_or_index=['min', 'max'])))\n",
|
||
|
|
"print(mapped_array.reduce(min_max_reduce_nb, returns_array=True, wrap_kwargs=dict(name_or_index=['min', 'max']), fill_value=0.))\n",
|
||
|
|
"print(mapped_array.reduce(min_max_reduce_nb, returns_array=True, wrap_kwargs=dict(to_timedelta=True)))\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_mapped_array.reduce(min_max_reduce_nb, returns_array=True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 34,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"0 10.0\n",
|
||
|
|
"1 12.0\n",
|
||
|
|
"dtype: float64\n",
|
||
|
|
"lvl1 0\n",
|
||
|
|
"0 10.0\n",
|
||
|
|
"1 12.0\n",
|
||
|
|
"lvl1 0 1\n",
|
||
|
|
"0 10.0 10.0\n",
|
||
|
|
"1 12.0 14.0\n",
|
||
|
|
"lvl1 0 1 \n",
|
||
|
|
"lvl2 a b c d\n",
|
||
|
|
"0 10.0 13.0 10.0 NaN\n",
|
||
|
|
"1 12.0 14.0 12.0 NaN\n",
|
||
|
|
"2.38 ms ± 3.66 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array_grouped[0].reduce(min_max_reduce_nb, returns_array=True))\n",
|
||
|
|
"print(mapped_array_grouped[[0]].reduce(min_max_reduce_nb, returns_array=True))\n",
|
||
|
|
"print(mapped_array_grouped.reduce(min_max_reduce_nb, returns_array=True))\n",
|
||
|
|
"print(mapped_array_grouped.reduce(min_max_reduce_nb, returns_array=True, group_by=False))\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_mapped_array_grouped.reduce(min_max_reduce_nb, returns_array=True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 35,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"lvl1 0 1 \n",
|
||
|
|
"lvl2 a b c d\n",
|
||
|
|
"0 x x z NaN\n",
|
||
|
|
"1 z y x NaN\n",
|
||
|
|
"82.4 ms ± 388 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
|
||
|
|
"lvl1 0 1 \n",
|
||
|
|
"lvl2 a b c d\n",
|
||
|
|
"0 0 0 2 -1\n",
|
||
|
|
"1 2 1 0 -1\n",
|
||
|
|
"4.04 ms ± 17.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"lvl1 0 1\n",
|
||
|
|
"0 x z\n",
|
||
|
|
"1 z y\n",
|
||
|
|
"4.45 ms ± 3.78 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"@njit\n",
|
||
|
|
"def idxmin_idxmax_reduce_nb(col, a):\n",
|
||
|
|
" return np.array([np.argmin(a), np.argmax(a)])\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array.reduce(idxmin_idxmax_reduce_nb, returns_array=True, returns_idx=True))\n",
|
||
|
|
"%timeit big_mapped_array.reduce(idxmin_idxmax_reduce_nb, returns_array=True, returns_idx=True)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array.reduce(idxmin_idxmax_reduce_nb, returns_array=True, returns_idx=True, to_index=False))\n",
|
||
|
|
"%timeit big_mapped_array.reduce(idxmin_idxmax_reduce_nb, returns_array=True, returns_idx=True, to_index=False)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array_grouped.reduce(idxmin_idxmax_reduce_nb, returns_array=True, returns_idx=True))\n",
|
||
|
|
"%timeit big_mapped_array_grouped.reduce(idxmin_idxmax_reduce_nb, returns_array=True, returns_idx=True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 36,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 10.0\n",
|
||
|
|
"1 b 13.0\n",
|
||
|
|
" c 12.0\n",
|
||
|
|
" d NaN\n",
|
||
|
|
"Name: nth, dtype: float64\n",
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 12.0\n",
|
||
|
|
"1 b 13.0\n",
|
||
|
|
" c 10.0\n",
|
||
|
|
" d NaN\n",
|
||
|
|
"Name: nth, dtype: float64\n",
|
||
|
|
"1.03 ms ± 1.44 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
|
||
|
|
"lvl1\n",
|
||
|
|
"0 10.0\n",
|
||
|
|
"1 13.0\n",
|
||
|
|
"Name: nth, dtype: float64\n",
|
||
|
|
"1.11 ms ± 3.55 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array.nth(0))\n",
|
||
|
|
"print(mapped_array.nth(-1))\n",
|
||
|
|
"%timeit big_mapped_array.nth(0)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array_grouped.nth(0))\n",
|
||
|
|
"%timeit big_mapped_array_grouped.nth(0)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 37,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 10.0\n",
|
||
|
|
"1 b 13.0\n",
|
||
|
|
" c 10.0\n",
|
||
|
|
" d NaN\n",
|
||
|
|
"Name: min, dtype: float64\n",
|
||
|
|
"10.1 ms ± 458 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 10.0\n",
|
||
|
|
"1 b 13.0\n",
|
||
|
|
" c 10.0\n",
|
||
|
|
" d NaN\n",
|
||
|
|
"Name: min, dtype: float64\n",
|
||
|
|
"2.98 ms ± 4.29 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"lvl1\n",
|
||
|
|
"0 10.0\n",
|
||
|
|
"1 10.0\n",
|
||
|
|
"Name: min, dtype: float64\n",
|
||
|
|
"3.02 ms ± 5.68 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array.to_pd().vbt.min())\n",
|
||
|
|
"%timeit big_mapped_array.to_pd().vbt.min()\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array.min())\n",
|
||
|
|
"%timeit big_mapped_array.min()\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array_grouped.min())\n",
|
||
|
|
"%timeit big_mapped_array_grouped.min()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 38,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 12.0\n",
|
||
|
|
"1 b 14.0\n",
|
||
|
|
" c 12.0\n",
|
||
|
|
" d NaN\n",
|
||
|
|
"Name: max, dtype: float64\n",
|
||
|
|
"9.97 ms ± 92 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 12.0\n",
|
||
|
|
"1 b 14.0\n",
|
||
|
|
" c 12.0\n",
|
||
|
|
" d NaN\n",
|
||
|
|
"Name: max, dtype: float64\n",
|
||
|
|
"2.98 ms ± 6.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"lvl1\n",
|
||
|
|
"0 12.0\n",
|
||
|
|
"1 14.0\n",
|
||
|
|
"Name: max, dtype: float64\n",
|
||
|
|
"3.03 ms ± 3.58 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array.to_pd().vbt.max())\n",
|
||
|
|
"%timeit big_mapped_array.to_pd().vbt.max()\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array.max())\n",
|
||
|
|
"%timeit big_mapped_array.max()\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array_grouped.max())\n",
|
||
|
|
"%timeit big_mapped_array_grouped.max()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 39,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 11.000000\n",
|
||
|
|
"1 b 13.333333\n",
|
||
|
|
" c 11.000000\n",
|
||
|
|
" d NaN\n",
|
||
|
|
"Name: mean, dtype: float64\n",
|
||
|
|
"8.4 ms ± 336 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 11.000000\n",
|
||
|
|
"1 b 13.333333\n",
|
||
|
|
" c 11.000000\n",
|
||
|
|
" d NaN\n",
|
||
|
|
"Name: mean, dtype: float64\n",
|
||
|
|
"3.27 ms ± 10.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"lvl1\n",
|
||
|
|
"0 11.000000\n",
|
||
|
|
"1 12.166667\n",
|
||
|
|
"Name: mean, dtype: float64\n",
|
||
|
|
"3.32 ms ± 5.36 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array.to_pd().vbt.mean())\n",
|
||
|
|
"%timeit big_mapped_array.to_pd().vbt.mean()\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array.mean())\n",
|
||
|
|
"%timeit big_mapped_array.mean()\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array_grouped.mean())\n",
|
||
|
|
"%timeit big_mapped_array_grouped.mean()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 40,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 11.0\n",
|
||
|
|
"1 b 13.0\n",
|
||
|
|
" c 11.0\n",
|
||
|
|
" d NaN\n",
|
||
|
|
"Name: median, dtype: float64\n",
|
||
|
|
"16.9 ms ± 151 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 11.0\n",
|
||
|
|
"1 b 13.0\n",
|
||
|
|
" c 11.0\n",
|
||
|
|
" d NaN\n",
|
||
|
|
"Name: median, dtype: float64\n",
|
||
|
|
"12.5 ms ± 7.77 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"lvl1\n",
|
||
|
|
"0 11.0\n",
|
||
|
|
"1 12.5\n",
|
||
|
|
"Name: median, dtype: float64\n",
|
||
|
|
"11.7 ms ± 13.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array.to_pd().vbt.median())\n",
|
||
|
|
"%timeit big_mapped_array.to_pd().vbt.median()\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array.median())\n",
|
||
|
|
"%timeit big_mapped_array.median()\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array_grouped.median())\n",
|
||
|
|
"%timeit big_mapped_array_grouped.median()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 41,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 1.00000\n",
|
||
|
|
"1 b 0.57735\n",
|
||
|
|
" c 1.00000\n",
|
||
|
|
" d NaN\n",
|
||
|
|
"Name: std, dtype: float64\n",
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 0.816497\n",
|
||
|
|
"1 b 0.471405\n",
|
||
|
|
" c 0.816497\n",
|
||
|
|
" d NaN\n",
|
||
|
|
"Name: std, dtype: float64\n",
|
||
|
|
"8.7 ms ± 23.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 1.00000\n",
|
||
|
|
"1 b 0.57735\n",
|
||
|
|
" c 1.00000\n",
|
||
|
|
" d NaN\n",
|
||
|
|
"Name: std, dtype: float64\n",
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 0.816497\n",
|
||
|
|
"1 b 0.471405\n",
|
||
|
|
" c 0.816497\n",
|
||
|
|
" d NaN\n",
|
||
|
|
"Name: std, dtype: float64\n",
|
||
|
|
"6.59 ms ± 134 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"lvl1\n",
|
||
|
|
"0 1.00000\n",
|
||
|
|
"1 1.47196\n",
|
||
|
|
"Name: std, dtype: float64\n",
|
||
|
|
"6.24 ms ± 3.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array.to_pd().vbt.std())\n",
|
||
|
|
"print(mapped_array.to_pd().vbt.std(ddof=0))\n",
|
||
|
|
"%timeit big_mapped_array.to_pd().vbt.std()\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array.std())\n",
|
||
|
|
"print(mapped_array.std(ddof=0))\n",
|
||
|
|
"%timeit big_mapped_array.std()\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array_grouped.std())\n",
|
||
|
|
"%timeit big_mapped_array_grouped.std()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 42,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 33.0\n",
|
||
|
|
"1 b 40.0\n",
|
||
|
|
" c 33.0\n",
|
||
|
|
" d 0.0\n",
|
||
|
|
"Name: sum, dtype: float64\n",
|
||
|
|
"8.63 ms ± 36.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 33.0\n",
|
||
|
|
"1 b 40.0\n",
|
||
|
|
" c 33.0\n",
|
||
|
|
" d 0.0\n",
|
||
|
|
"Name: sum, dtype: float64\n",
|
||
|
|
"3.26 ms ± 6.28 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"lvl1\n",
|
||
|
|
"0 33.0\n",
|
||
|
|
"1 73.0\n",
|
||
|
|
"Name: sum, dtype: float64\n",
|
||
|
|
"3.31 ms ± 3.33 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array.to_pd().vbt.sum())\n",
|
||
|
|
"%timeit big_mapped_array.to_pd().vbt.sum()\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array.sum())\n",
|
||
|
|
"%timeit big_mapped_array.sum()\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array_grouped.sum())\n",
|
||
|
|
"%timeit big_mapped_array_grouped.sum()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 43,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a x\n",
|
||
|
|
"1 b x\n",
|
||
|
|
" c z\n",
|
||
|
|
" d NaN\n",
|
||
|
|
"Name: idxmin, dtype: object\n",
|
||
|
|
"9.22 ms ± 130 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a x\n",
|
||
|
|
"1 b x\n",
|
||
|
|
" c z\n",
|
||
|
|
" d NaN\n",
|
||
|
|
"Name: idxmin, dtype: object\n",
|
||
|
|
"4.86 ms ± 28.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"lvl1\n",
|
||
|
|
"0 x\n",
|
||
|
|
"1 z\n",
|
||
|
|
"Name: idxmin, dtype: object\n",
|
||
|
|
"4.21 ms ± 7.54 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array.to_pd().vbt.idxmin())\n",
|
||
|
|
"%timeit big_mapped_array.to_pd().vbt.idxmin()\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array.idxmin())\n",
|
||
|
|
"%timeit big_mapped_array.idxmin()\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array_grouped.idxmin())\n",
|
||
|
|
"%timeit big_mapped_array_grouped.idxmin()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 44,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a z\n",
|
||
|
|
"1 b y\n",
|
||
|
|
" c x\n",
|
||
|
|
" d NaN\n",
|
||
|
|
"Name: idxmax, dtype: object\n",
|
||
|
|
"9.4 ms ± 395 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a z\n",
|
||
|
|
"1 b y\n",
|
||
|
|
" c x\n",
|
||
|
|
" d NaN\n",
|
||
|
|
"Name: idxmax, dtype: object\n",
|
||
|
|
"4.88 ms ± 41.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"lvl1\n",
|
||
|
|
"0 z\n",
|
||
|
|
"1 y\n",
|
||
|
|
"Name: idxmax, dtype: object\n",
|
||
|
|
"4.22 ms ± 7.03 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array.to_pd().vbt.idxmax())\n",
|
||
|
|
"%timeit big_mapped_array.to_pd().vbt.idxmax()\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array.idxmax())\n",
|
||
|
|
"%timeit big_mapped_array.idxmax()\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array_grouped.idxmax())\n",
|
||
|
|
"%timeit big_mapped_array_grouped.idxmax()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 45,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"lvl1 0 1 \n",
|
||
|
|
"lvl2 a b c d\n",
|
||
|
|
"count 3.0 3.000000 3.0 0.0\n",
|
||
|
|
"mean 11.0 13.333333 11.0 NaN\n",
|
||
|
|
"std 1.0 0.577350 1.0 NaN\n",
|
||
|
|
"min 10.0 13.000000 10.0 NaN\n",
|
||
|
|
"25% 10.5 13.000000 10.5 NaN\n",
|
||
|
|
"50% 11.0 13.000000 11.0 NaN\n",
|
||
|
|
"75% 11.5 13.500000 11.5 NaN\n",
|
||
|
|
"max 12.0 14.000000 12.0 NaN\n",
|
||
|
|
"lvl1 0 1 \n",
|
||
|
|
"lvl2 a b c d\n",
|
||
|
|
"count 3.0 3.000000 3.0 0.0\n",
|
||
|
|
"mean 11.0 13.333333 11.0 NaN\n",
|
||
|
|
"std 1.0 0.577350 1.0 NaN\n",
|
||
|
|
"min 10.0 13.000000 10.0 NaN\n",
|
||
|
|
"30% 10.6 13.000000 10.6 NaN\n",
|
||
|
|
"50% 11.0 13.000000 11.0 NaN\n",
|
||
|
|
"70% 11.4 13.400000 11.4 NaN\n",
|
||
|
|
"max 12.0 14.000000 12.0 NaN\n",
|
||
|
|
"41.4 ms ± 24.4 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
|
||
|
|
"lvl1 0 1 \n",
|
||
|
|
"lvl2 a b c d\n",
|
||
|
|
"count 3.0 3.000000 3.0 0.0\n",
|
||
|
|
"mean 11.0 13.333333 11.0 NaN\n",
|
||
|
|
"std 1.0 0.577350 1.0 NaN\n",
|
||
|
|
"min 10.0 13.000000 10.0 NaN\n",
|
||
|
|
"25% 10.5 13.000000 10.5 NaN\n",
|
||
|
|
"50% 11.0 13.000000 11.0 NaN\n",
|
||
|
|
"75% 11.5 13.500000 11.5 NaN\n",
|
||
|
|
"max 12.0 14.000000 12.0 NaN\n",
|
||
|
|
"lvl1 0 1 \n",
|
||
|
|
"lvl2 a b c d\n",
|
||
|
|
"count 3.0 3.000000 3.0 0.0\n",
|
||
|
|
"mean 11.0 13.333333 11.0 NaN\n",
|
||
|
|
"std 1.0 0.577350 1.0 NaN\n",
|
||
|
|
"min 10.0 13.000000 10.0 NaN\n",
|
||
|
|
"30% 10.6 13.000000 10.6 NaN\n",
|
||
|
|
"50% 11.0 13.000000 11.0 NaN\n",
|
||
|
|
"70% 11.4 13.400000 11.4 NaN\n",
|
||
|
|
"max 12.0 14.000000 12.0 NaN\n",
|
||
|
|
"35.6 ms ± 432 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
|
||
|
|
"lvl1 0 1\n",
|
||
|
|
"count 3.0 6.000000\n",
|
||
|
|
"mean 11.0 12.166667\n",
|
||
|
|
"std 1.0 1.471960\n",
|
||
|
|
"min 10.0 10.000000\n",
|
||
|
|
"25% 10.5 11.250000\n",
|
||
|
|
"50% 11.0 12.500000\n",
|
||
|
|
"75% 11.5 13.000000\n",
|
||
|
|
"max 12.0 14.000000\n",
|
||
|
|
"37.9 ms ± 123 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array.to_pd().vbt.describe())\n",
|
||
|
|
"print(mapped_array.to_pd().vbt.describe(percentiles=[0.3, 0.7]))\n",
|
||
|
|
"%timeit big_mapped_array.to_pd().vbt.describe()\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array.describe())\n",
|
||
|
|
"print(mapped_array.describe(percentiles=[0.3, 0.7]))\n",
|
||
|
|
"%timeit big_mapped_array.describe()\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array_grouped.describe())\n",
|
||
|
|
"%timeit big_mapped_array_grouped.describe()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 46,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 3\n",
|
||
|
|
"1 b 3\n",
|
||
|
|
" c 3\n",
|
||
|
|
" d 0\n",
|
||
|
|
"Name: count, dtype: int64\n",
|
||
|
|
"7.45 ms ± 215 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 3\n",
|
||
|
|
"1 b 3\n",
|
||
|
|
" c 3\n",
|
||
|
|
" d 0\n",
|
||
|
|
"Name: count, dtype: int64\n",
|
||
|
|
"49.6 µs ± 200 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
|
||
|
|
"lvl1\n",
|
||
|
|
"0 3\n",
|
||
|
|
"1 6\n",
|
||
|
|
"Name: count, dtype: int64\n",
|
||
|
|
"48.3 µs ± 83.7 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array.to_pd().vbt.count())\n",
|
||
|
|
"%timeit big_mapped_array.to_pd().vbt.count()\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array.count())\n",
|
||
|
|
"%timeit big_mapped_array.count()\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array_grouped.count())\n",
|
||
|
|
"%timeit big_mapped_array_grouped.count()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 47,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"mapping = {x: str(x) for x in np.unique(mapped_array.values)}\n",
|
||
|
|
"big_mapping = {x: str(x) for x in np.unique(big_mapped_array.values)}"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 48,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"10.0 1\n",
|
||
|
|
"11.0 1\n",
|
||
|
|
"12.0 1\n",
|
||
|
|
"Name: (0, a), dtype: int64\n",
|
||
|
|
"13.4 ms ± 584 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"10.0 1\n",
|
||
|
|
"11.0 1\n",
|
||
|
|
"12.0 1\n",
|
||
|
|
"Name: (0, a), dtype: int64\n",
|
||
|
|
"12.9 ms ± 353 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"lvl1 0 1 \n",
|
||
|
|
"lvl2 a b c d\n",
|
||
|
|
"10.0 1 0 1 0\n",
|
||
|
|
"11.0 1 0 1 0\n",
|
||
|
|
"12.0 1 0 1 0\n",
|
||
|
|
"13.0 0 2 0 0\n",
|
||
|
|
"14.0 0 1 0 0\n",
|
||
|
|
"10.9 ms ± 23.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"lvl1 0 1 \n",
|
||
|
|
"lvl2 a b c d\n",
|
||
|
|
"10.0 1 0 1 0\n",
|
||
|
|
"11.0 1 0 1 0\n",
|
||
|
|
"12.0 1 0 1 0\n",
|
||
|
|
"13.0 0 2 0 0\n",
|
||
|
|
"14.0 0 1 0 0\n",
|
||
|
|
"11.2 ms ± 121 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"lvl1 0 1\n",
|
||
|
|
"10.0 1 1\n",
|
||
|
|
"11.0 1 1\n",
|
||
|
|
"12.0 1 1\n",
|
||
|
|
"13.0 0 2\n",
|
||
|
|
"14.0 0 1\n",
|
||
|
|
"10.9 ms ± 451 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array[(0, 'a')].value_counts())\n",
|
||
|
|
"%timeit big_mapped_array[0].value_counts()\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array[(0, 'a')].value_counts(mapping=mapping))\n",
|
||
|
|
"%timeit big_mapped_array[0].value_counts(mapping=big_mapping)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array.value_counts())\n",
|
||
|
|
"%timeit big_mapped_array.value_counts()\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array.value_counts(mapping=mapping))\n",
|
||
|
|
"%timeit big_mapped_array.value_counts(mapping=big_mapping)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array_grouped.value_counts())\n",
|
||
|
|
"%timeit big_mapped_array_grouped.value_counts()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 49,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Start x\n",
|
||
|
|
"End z\n",
|
||
|
|
"Period 3 days 00:00:00\n",
|
||
|
|
"Count 3\n",
|
||
|
|
"Mean 11.0\n",
|
||
|
|
"Std 1.0\n",
|
||
|
|
"Min 10.0\n",
|
||
|
|
"Median 11.0\n",
|
||
|
|
"Max 12.0\n",
|
||
|
|
"Min Index x\n",
|
||
|
|
"Max Index z\n",
|
||
|
|
"Name: (0, a), dtype: object\n",
|
||
|
|
"29 ms ± 287 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
|
||
|
|
"Start x\n",
|
||
|
|
"End z\n",
|
||
|
|
"Period 3 days 00:00:00\n",
|
||
|
|
"Count 3\n",
|
||
|
|
"Mean 11.0\n",
|
||
|
|
"Std 1.0\n",
|
||
|
|
"Min 10.0\n",
|
||
|
|
"Median 11.0\n",
|
||
|
|
"Max 12.0\n",
|
||
|
|
"Min Index x\n",
|
||
|
|
"Max Index z\n",
|
||
|
|
"Name: (0, a), dtype: object\n",
|
||
|
|
"46 ms ± 1.01 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
|
||
|
|
"Start x\n",
|
||
|
|
"End z\n",
|
||
|
|
"Period 3 days 00:00:00\n",
|
||
|
|
"Count 2.25\n",
|
||
|
|
"Mean 11.777778\n",
|
||
|
|
"Std 0.859117\n",
|
||
|
|
"Min 11.0\n",
|
||
|
|
"Median 11.666667\n",
|
||
|
|
"Max 12.666667\n",
|
||
|
|
"Name: agg_func_mean, dtype: object\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"/Users/olegpolakow/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:7: UserWarning: Metric 'idx_min' returned multiple values despite having no aggregation function\n",
|
||
|
|
" import sys\n",
|
||
|
|
"/Users/olegpolakow/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:7: UserWarning: Metric 'idx_max' returned multiple values despite having no aggregation function\n",
|
||
|
|
" import sys\n",
|
||
|
|
"/Users/olegpolakow/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:7: UserWarning: Object has multiple columns. Aggregating using <function mean at 0x7feb1013b598>. Pass column to select a single column/group.\n",
|
||
|
|
" import sys\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"44.3 ms ± 343 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
|
||
|
|
"Start x\n",
|
||
|
|
"End z\n",
|
||
|
|
"Period 3 days 00:00:00\n",
|
||
|
|
"Count 2.25\n",
|
||
|
|
"Value Counts: 10.0 0.5\n",
|
||
|
|
"Value Counts: 11.0 0.5\n",
|
||
|
|
"Value Counts: 12.0 0.5\n",
|
||
|
|
"Value Counts: 13.0 0.5\n",
|
||
|
|
"Value Counts: 14.0 0.25\n",
|
||
|
|
"Name: agg_func_mean, dtype: object\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"/Users/olegpolakow/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:10: UserWarning: Object has multiple columns. Aggregating using <function mean at 0x7feb1013b598>. Pass column to select a single column/group.\n",
|
||
|
|
" # Remove the CWD from sys.path while we load stuff.\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"23.1 ms ± 475 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(mapped_array[(0, 'a')].stats())\n",
|
||
|
|
"%timeit big_mapped_array[0].stats(silence_warnings=True)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array.stats(column=(0, 'a')))\n",
|
||
|
|
"%timeit big_mapped_array.stats(column=0, silence_warnings=True)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array.stats())\n",
|
||
|
|
"%timeit big_mapped_array.stats(silence_warnings=True)\n",
|
||
|
|
"\n",
|
||
|
|
"print(mapped_array.copy(mapping=mapping).stats())\n",
|
||
|
|
"%timeit big_mapped_array.copy(mapping=big_mapping).stats(silence_warnings=True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 50,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/svg+xml": [
|
||
|
|
"<svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"700\" height=\"350\" style=\"\" viewBox=\"0 0 700 350\"><rect x=\"0\" y=\"0\" width=\"700\" height=\"350\" style=\"fill: rgb(255, 255, 255); fill-opacity: 1;\"/><defs id=\"defs-c70295\"><g class=\"clips\"><clipPath id=\"clipc70295xyplot\" class=\"plotclip\"><rect width=\"637\" height=\"274\"/></clipPath><clipPath class=\"axesclip\" id=\"clipc70295x\"><rect x=\"33\" y=\"0\" width=\"637\" height=\"350\"/></clipPath><clipPath class=\"axesclip\" id=\"clipc70295y\"><rect x=\"0\" y=\"46\" width=\"700\" height=\"274\"/></clipPath><clipPath class=\"axesclip\" id=\"clipc70295xy\"><rect x=\"33\" y=\"46\" width=\"637\" height=\"274\"/></clipPath></g><g class=\"gradients\"/><g class=\"patterns\"/></defs><g class=\"bglayer\"><rect class=\"bg\" x=\"33\" y=\"46\" width=\"637\" height=\"274\" style=\"fill: rgb(229, 236, 246); fill-opacity: 1; stroke-width: 0;\"/></g><g class=\"layer-below\"><g class=\"imagelayer\"/><g class=\"shapelayer\"/></g><g class=\"cartesianlayer\"><g class=\"subplot xy\"><g class=\"layer-subplot\"><g class=\"shapelayer\"/><g class=\"imagelayer\"/></g><g class=\"gridlayer\"><g class=\"x\"/><g class=\"y\"><path class=\"ygrid crisp\" transform=\"translate(0,254.93)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,189.85)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,124.77)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,59.7)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/></g></g><g class=\"zerolinelayer\"><path class=\"yzl zl crisp\" transform=\"translate(0,320)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 2px;\"/></g><path class=\"xlines-below\"/><path class=\"ylines-below\"/><g class=\"overlines-below\"/><g class=\"xaxislayer-below\"/><g class=\"yaxislayer-below\"/><g class=\"overaxes-below\"/><g class=\"plot\" transform=\"translate(33,46)\" clip-path=\"url(#clipc70295xyplot)\"><g class=\"barlayer mlayer\"><g class=\"trace bars\" shape-rendering=\"crispEdges\" style=\"opacity: 1;\"><g class=\"points\"><g class=\"point\"><path d=\"M0,274V13.7H318.5V274Z\" style=\"vector-effect: non-scaling-stroke; opacity: 1; stroke-width: 0px; fill: rgb(31, 119, 180); fill-opacity: 1;\"/></g><g class=\"point\"><path d=\"M318.5,274V143.85H637V274Z\" style=\"vector-effect: non-scaling-stroke; opacity: 1; stroke-width: 0px; fill: rgb(31, 119, 180); fill-opacity: 1;\"/></g></g></g></g></g><g class=\"overplot\"/><path class=\"xlines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><path class=\"ylines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><g class=\"overlines-above\"/><g class=\"xaxislayer-above\"><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"333\" transform=\"translate(33,0)\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\">9.5</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"333\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(112.63,0)\">10</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"333\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(192.25,0)\">10.5</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"333\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(271.88,0)\">11</
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/svg+xml": [
|
||
|
|
"<svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"700\" height=\"350\" style=\"\" viewBox=\"0 0 700 350\"><rect x=\"0\" y=\"0\" width=\"700\" height=\"350\" style=\"fill: rgb(255, 255, 255); fill-opacity: 1;\"/><defs id=\"defs-6addc6\"><g class=\"clips\"><clipPath id=\"clip6addc6xyplot\" class=\"plotclip\"><rect width=\"637\" height=\"274\"/></clipPath><clipPath class=\"axesclip\" id=\"clip6addc6x\"><rect x=\"33\" y=\"0\" width=\"637\" height=\"350\"/></clipPath><clipPath class=\"axesclip\" id=\"clip6addc6y\"><rect x=\"0\" y=\"46\" width=\"700\" height=\"274\"/></clipPath><clipPath class=\"axesclip\" id=\"clip6addc6xy\"><rect x=\"33\" y=\"46\" width=\"637\" height=\"274\"/></clipPath></g><g class=\"gradients\"/><g class=\"patterns\"/></defs><g class=\"bglayer\"><rect class=\"bg\" x=\"33\" y=\"46\" width=\"637\" height=\"274\" style=\"fill: rgb(229, 236, 246); fill-opacity: 1; stroke-width: 0;\"/></g><g class=\"layer-below\"><g class=\"imagelayer\"/><g class=\"shapelayer\"/></g><g class=\"cartesianlayer\"><g class=\"subplot xy\"><g class=\"layer-subplot\"><g class=\"shapelayer\"/><g class=\"imagelayer\"/></g><g class=\"gridlayer\"><g class=\"x\"/><g class=\"y\"><path class=\"ygrid crisp\" transform=\"translate(0,254.93)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,189.85)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,124.77)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,59.7)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/></g></g><g class=\"zerolinelayer\"><path class=\"yzl zl crisp\" transform=\"translate(0,320)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 2px;\"/></g><path class=\"xlines-below\"/><path class=\"ylines-below\"/><g class=\"overlines-below\"/><g class=\"xaxislayer-below\"/><g class=\"yaxislayer-below\"/><g class=\"overaxes-below\"/><g class=\"plot\" transform=\"translate(33,46)\" clip-path=\"url(#clip6addc6xyplot)\"><g class=\"barlayer mlayer\"><g class=\"trace bars\" shape-rendering=\"crispEdges\" style=\"opacity: 0.75;\"><g class=\"points\"><g class=\"point\"><path d=\"M0,274V13.7H254.8V274Z\" style=\"vector-effect: non-scaling-stroke; opacity: 1; stroke-width: 0px; fill: rgb(31, 119, 180); fill-opacity: 1;\"/></g><g class=\"point\"><path d=\"M254.8,274V143.85H509.6V274Z\" style=\"vector-effect: non-scaling-stroke; opacity: 1; stroke-width: 0px; fill: rgb(31, 119, 180); fill-opacity: 1;\"/></g></g></g><g class=\"trace bars\" shape-rendering=\"crispEdges\" style=\"opacity: 0.75;\"><g class=\"points\"><g class=\"point\"><path d=\"M382.2,274V13.7H509.6V274Z\" style=\"vector-effect: non-scaling-stroke; opacity: 1; stroke-width: 0px; fill: rgb(255, 127, 14); fill-opacity: 1;\"/></g><g class=\"point\"><path d=\"M509.6,274V143.85H637V274Z\" style=\"vector-effect: non-scaling-stroke; opacity: 1; stroke-width: 0px; fill: rgb(255, 127, 14); fill-opacity: 1;\"/></g></g></g><g class=\"trace bars\" shape-rendering=\"crispEdges\" style=\"opacity: 0.75;\"><g class=\"points\"><g class=\"point\"><path d=\"M0,274V13.7H254.8V274Z\" style=\"vector-effect: non-scaling-stroke; opacity: 1; stroke-width: 0px; fill: rgb(44, 160, 44); fill-opacity: 1;\"/></g><g class=\"point\"><path d=\"M254.8,274V143.85H509.6V274Z\" style=\"vector-effect: non-scaling-stroke; opacity: 1; stroke-width: 0px; fill: rgb(44, 160, 44); fill-opacity: 1;\"/></g></g></g></g></g><g class=\"overplot\"/><path class=\"xlines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><path class=\"ylines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><g class=\"overlines-above\"/><g class=\"xaxislayer-above\"><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"333\" transform=\"translate(96.7,0)\" style=\
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/svg+xml": [
|
||
|
|
"<svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"700\" height=\"350\" style=\"\" viewBox=\"0 0 700 350\"><rect x=\"0\" y=\"0\" width=\"700\" height=\"350\" style=\"fill: rgb(255, 255, 255); fill-opacity: 1;\"/><defs id=\"defs-61099c\"><g class=\"clips\"><clipPath id=\"clip61099cxyplot\" class=\"plotclip\"><rect width=\"637\" height=\"274\"/></clipPath><clipPath class=\"axesclip\" id=\"clip61099cx\"><rect x=\"33\" y=\"0\" width=\"637\" height=\"350\"/></clipPath><clipPath class=\"axesclip\" id=\"clip61099cy\"><rect x=\"0\" y=\"46\" width=\"700\" height=\"274\"/></clipPath><clipPath class=\"axesclip\" id=\"clip61099cxy\"><rect x=\"33\" y=\"46\" width=\"637\" height=\"274\"/></clipPath></g><g class=\"gradients\"/><g class=\"patterns\"/></defs><g class=\"bglayer\"><rect class=\"bg\" x=\"33\" y=\"46\" width=\"637\" height=\"274\" style=\"fill: rgb(229, 236, 246); fill-opacity: 1; stroke-width: 0;\"/></g><g class=\"layer-below\"><g class=\"imagelayer\"/><g class=\"shapelayer\"/></g><g class=\"cartesianlayer\"><g class=\"subplot xy\"><g class=\"layer-subplot\"><g class=\"shapelayer\"/><g class=\"imagelayer\"/></g><g class=\"gridlayer\"><g class=\"x\"/><g class=\"y\"><path class=\"ygrid crisp\" transform=\"translate(0,276.62)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,233.23)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,189.85)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,146.47)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,103.08)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,59.7)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/></g></g><g class=\"zerolinelayer\"><path class=\"yzl zl crisp\" transform=\"translate(0,320)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 2px;\"/></g><path class=\"xlines-below\"/><path class=\"ylines-below\"/><g class=\"overlines-below\"/><g class=\"xaxislayer-below\"/><g class=\"yaxislayer-below\"/><g class=\"overaxes-below\"/><g class=\"plot\" transform=\"translate(33,46)\" clip-path=\"url(#clip61099cxyplot)\"><g class=\"barlayer mlayer\"><g class=\"trace bars\" shape-rendering=\"crispEdges\" style=\"opacity: 0.75;\"><g class=\"points\"><g class=\"point\"><path d=\"M0,274V100.47H212.33V274Z\" style=\"vector-effect: non-scaling-stroke; opacity: 1; stroke-width: 0px; fill: rgb(31, 119, 180); fill-opacity: 1;\"/></g><g class=\"point\"><path d=\"M212.33,274V187.23H424.67V274Z\" style=\"vector-effect: non-scaling-stroke; opacity: 1; stroke-width: 0px; fill: rgb(31, 119, 180); fill-opacity: 1;\"/></g></g></g><g class=\"trace bars\" shape-rendering=\"crispEdges\" style=\"opacity: 0.75;\"><g class=\"points\"><g class=\"point\"><path d=\"M0,274V100.47H212.33V274Z\" style=\"vector-effect: non-scaling-stroke; opacity: 1; stroke-width: 0px; fill: rgb(255, 127, 14); fill-opacity: 1;\"/></g><g class=\"point\"><path d=\"M212.33,274V13.7H424.67V274Z\" style=\"vector-effect: non-scaling-stroke; opacity: 1; stroke-width: 0px; fill: rgb(255, 127, 14); fill-opacity: 1;\"/></g><g class=\"point\"><path d=\"M424.67,274V187.23H637V274Z\" style=\"vector-effect: non-scaling-stroke; opacity: 1; stroke-width: 0px; fill: rgb(255, 127, 14); fill-opacity: 1;\"/></g></g></g></g></g><g class=\"overplot\"/><path class=\"xlines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><path class=\"ylines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><g class=\"overlines-above\"/><g class=\"xaxislayer-above\"><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"333\" transfo
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"mapped_array[(0, 'a')].histplot().show_svg()\n",
|
||
|
|
"mapped_array.histplot().show_svg()\n",
|
||
|
|
"mapped_array_grouped.histplot().show_svg()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 51,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/svg+xml": [
|
||
|
|
"<svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"700\" height=\"350\" style=\"\" viewBox=\"0 0 700 350\"><rect x=\"0\" y=\"0\" width=\"700\" height=\"350\" style=\"fill: rgb(255, 255, 255); fill-opacity: 1;\"/><defs id=\"defs-2d103d\"><g class=\"clips\"><clipPath id=\"clip2d103dxyplot\" class=\"plotclip\"><rect width=\"630\" height=\"274\"/></clipPath><clipPath class=\"axesclip\" id=\"clip2d103dx\"><rect x=\"40\" y=\"0\" width=\"630\" height=\"350\"/></clipPath><clipPath class=\"axesclip\" id=\"clip2d103dy\"><rect x=\"0\" y=\"46\" width=\"700\" height=\"274\"/></clipPath><clipPath class=\"axesclip\" id=\"clip2d103dxy\"><rect x=\"40\" y=\"46\" width=\"630\" height=\"274\"/></clipPath></g><g class=\"gradients\"/><g class=\"patterns\"/></defs><g class=\"bglayer\"><rect class=\"bg\" x=\"40\" y=\"46\" width=\"630\" height=\"274\" style=\"fill: rgb(229, 236, 246); fill-opacity: 1; stroke-width: 0;\"/></g><g class=\"layer-below\"><g class=\"imagelayer\"/><g class=\"shapelayer\"/></g><g class=\"cartesianlayer\"><g class=\"subplot xy\"><g class=\"layer-subplot\"><g class=\"shapelayer\"/><g class=\"imagelayer\"/></g><g class=\"gridlayer\"><g class=\"x\"/><g class=\"y\"><path class=\"ygrid crisp\" transform=\"translate(0,306.3)\" d=\"M40,0h630\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,244.65)\" d=\"M40,0h630\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,183)\" d=\"M40,0h630\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,121.35)\" d=\"M40,0h630\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,59.7)\" d=\"M40,0h630\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/></g></g><g class=\"zerolinelayer\"/><path class=\"xlines-below\"/><path class=\"ylines-below\"/><g class=\"overlines-below\"/><g class=\"xaxislayer-below\"/><g class=\"yaxislayer-below\"/><g class=\"overaxes-below\"/><g class=\"plot\" transform=\"translate(40,46)\" clip-path=\"url(#clip2d103dxyplot)\"><g class=\"boxlayer mlayer\"><g class=\"trace boxes\" style=\"opacity: 1;\"><path class=\"box\" d=\"M160.65,137H469.35M160.65,229.47H469.35V44.52H160.65ZM315,229.47V260.3M315,44.52V13.7M237.83,260.3H392.17M237.83,13.7H392.17\" style=\"vector-effect: non-scaling-stroke; stroke-width: 2px; stroke: rgb(31, 119, 180); stroke-opacity: 1; fill: rgb(31, 119, 180); fill-opacity: 0.5;\"/><g class=\"points\"/></g></g></g><g class=\"overplot\"/><path class=\"xlines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><path class=\"ylines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><g class=\"overlines-above\"/><g class=\"xaxislayer-above\"><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"333\" transform=\"translate(355,0)\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\">(0, 'a')</text></g></g><g class=\"yaxislayer-above\"><g class=\"ytick\"><text text-anchor=\"end\" x=\"39\" y=\"4.199999999999999\" transform=\"translate(0,306.3)\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\">10</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"39\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,244.65)\">10.5</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"39\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,183)\">11</text></g><g class=
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/svg+xml": [
|
||
|
|
"<svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"700\" height=\"350\" style=\"\" viewBox=\"0 0 700 350\"><rect x=\"0\" y=\"0\" width=\"700\" height=\"350\" style=\"fill: rgb(255, 255, 255); fill-opacity: 1;\"/><defs id=\"defs-7cd63b\"><g class=\"clips\"><clipPath id=\"clip7cd63bxyplot\" class=\"plotclip\"><rect width=\"640\" height=\"274\"/></clipPath><clipPath class=\"axesclip\" id=\"clip7cd63bx\"><rect x=\"30\" y=\"0\" width=\"640\" height=\"350\"/></clipPath><clipPath class=\"axesclip\" id=\"clip7cd63by\"><rect x=\"0\" y=\"46\" width=\"700\" height=\"274\"/></clipPath><clipPath class=\"axesclip\" id=\"clip7cd63bxy\"><rect x=\"30\" y=\"46\" width=\"640\" height=\"274\"/></clipPath></g><g class=\"gradients\"/><g class=\"patterns\"/></defs><g class=\"bglayer\"><rect class=\"bg\" x=\"30\" y=\"46\" width=\"640\" height=\"274\" style=\"fill: rgb(229, 236, 246); fill-opacity: 1; stroke-width: 0;\"/></g><g class=\"layer-below\"><g class=\"imagelayer\"/><g class=\"shapelayer\"/></g><g class=\"cartesianlayer\"><g class=\"subplot xy\"><g class=\"layer-subplot\"><g class=\"shapelayer\"/><g class=\"imagelayer\"/></g><g class=\"gridlayer\"><g class=\"x\"/><g class=\"y\"><path class=\"ygrid crisp\" transform=\"translate(0,306.3)\" d=\"M30,0h640\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,244.65)\" d=\"M30,0h640\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,183)\" d=\"M30,0h640\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,121.35)\" d=\"M30,0h640\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,59.7)\" d=\"M30,0h640\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/></g></g><g class=\"zerolinelayer\"/><path class=\"xlines-below\"/><path class=\"ylines-below\"/><g class=\"overlines-below\"/><g class=\"xaxislayer-below\"/><g class=\"yaxislayer-below\"/><g class=\"overaxes-below\"/><g class=\"plot\" transform=\"translate(30,46)\" clip-path=\"url(#clip7cd63bxyplot)\"><g class=\"boxlayer mlayer\"><g class=\"trace boxes\" style=\"opacity: 1;\"><path class=\"box\" d=\"M54.4,198.65H158.93M54.4,244.89H158.93V152.41H54.4ZM106.67,244.89V260.3M106.67,152.41V137M80.53,260.3H132.8M80.53,137H132.8\" style=\"vector-effect: non-scaling-stroke; stroke-width: 2px; stroke: rgb(31, 119, 180); stroke-opacity: 1; fill: rgb(31, 119, 180); fill-opacity: 0.5;\"/><g class=\"points\"/></g><g class=\"trace boxes\" style=\"opacity: 1;\"><path class=\"box\" d=\"M267.73,74.35H372.27M267.73,75.35H372.27V29.11H267.73ZM320,75.35V75.35M320,29.11V13.7M293.87,75.35H346.13M293.87,13.7H346.13\" style=\"vector-effect: non-scaling-stroke; stroke-width: 2px; stroke: rgb(255, 127, 14); stroke-opacity: 1; fill: rgb(255, 127, 14); fill-opacity: 0.5;\"/><g class=\"points\"/></g><g class=\"trace boxes\" style=\"opacity: 1;\"><path class=\"box\" d=\"M481.07,198.65H585.6M481.07,244.89H585.6V152.41H481.07ZM533.33,244.89V260.3M533.33,152.41V137M507.2,260.3H559.47M507.2,137H559.47\" style=\"vector-effect: non-scaling-stroke; stroke-width: 2px; stroke: rgb(44, 160, 44); stroke-opacity: 1; fill: rgb(44, 160, 44); fill-opacity: 0.5;\"/><g class=\"points\"/></g></g></g><g class=\"overplot\"/><path class=\"xlines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><path class=\"ylines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><g class=\"overlines-above\"/><g class=\"xaxislayer-above\"><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"333\" transform=\"translate(136.67000000000002,0)\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\">(0, 'a')</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"333\" transform=\"translate(350,0)\"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/svg+xml": [
|
||
|
|
"<svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"700\" height=\"350\" style=\"\" viewBox=\"0 0 700 350\"><rect x=\"0\" y=\"0\" width=\"700\" height=\"350\" style=\"fill: rgb(255, 255, 255); fill-opacity: 1;\"/><defs id=\"defs-bff574\"><g class=\"clips\"><clipPath id=\"clipbff574xyplot\" class=\"plotclip\"><rect width=\"640\" height=\"274\"/></clipPath><clipPath class=\"axesclip\" id=\"clipbff574x\"><rect x=\"30\" y=\"0\" width=\"640\" height=\"350\"/></clipPath><clipPath class=\"axesclip\" id=\"clipbff574y\"><rect x=\"0\" y=\"46\" width=\"700\" height=\"274\"/></clipPath><clipPath class=\"axesclip\" id=\"clipbff574xy\"><rect x=\"30\" y=\"46\" width=\"640\" height=\"274\"/></clipPath></g><g class=\"gradients\"/><g class=\"patterns\"/></defs><g class=\"bglayer\"><rect class=\"bg\" x=\"30\" y=\"46\" width=\"640\" height=\"274\" style=\"fill: rgb(229, 236, 246); fill-opacity: 1; stroke-width: 0;\"/></g><g class=\"layer-below\"><g class=\"imagelayer\"/><g class=\"shapelayer\"/></g><g class=\"cartesianlayer\"><g class=\"subplot xy\"><g class=\"layer-subplot\"><g class=\"shapelayer\"/><g class=\"imagelayer\"/></g><g class=\"gridlayer\"><g class=\"x\"/><g class=\"y\"><path class=\"ygrid crisp\" transform=\"translate(0,306.3)\" d=\"M30,0h640\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,244.65)\" d=\"M30,0h640\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,183)\" d=\"M30,0h640\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,121.35)\" d=\"M30,0h640\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,59.7)\" d=\"M30,0h640\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/></g></g><g class=\"zerolinelayer\"/><path class=\"xlines-below\"/><path class=\"ylines-below\"/><g class=\"overlines-below\"/><g class=\"xaxislayer-below\"/><g class=\"yaxislayer-below\"/><g class=\"overaxes-below\"/><g class=\"plot\" transform=\"translate(30,46)\" clip-path=\"url(#clipbff574xyplot)\"><g class=\"boxlayer mlayer\"><g class=\"trace boxes\" style=\"opacity: 1;\"><path class=\"box\" d=\"M81.6,198.65H238.4M81.6,244.89H238.4V152.41H81.6ZM160,244.89V260.3M160,152.41V137M120.8,260.3H199.2M120.8,137H199.2\" style=\"vector-effect: non-scaling-stroke; stroke-width: 2px; stroke: rgb(31, 119, 180); stroke-opacity: 1; fill: rgb(31, 119, 180); fill-opacity: 0.5;\"/><g class=\"points\"/></g><g class=\"trace boxes\" style=\"opacity: 1;\"><path class=\"box\" d=\"M401.6,106.17H558.4M401.6,198.65H558.4V75.35H401.6ZM480,198.65V260.3M480,75.35V13.7M440.8,260.3H519.2M440.8,13.7H519.2\" style=\"vector-effect: non-scaling-stroke; stroke-width: 2px; stroke: rgb(255, 127, 14); stroke-opacity: 1; fill: rgb(255, 127, 14); fill-opacity: 0.5;\"/><g class=\"points\"/></g></g></g><g class=\"overplot\"/><path class=\"xlines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><path class=\"ylines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><g class=\"overlines-above\"/><g class=\"xaxislayer-above\"><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"333\" transform=\"translate(190,0)\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\">0</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"333\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(510,0)\">1</text></g></g><g class=\"yaxislayer-above\"><g class=\"ytick\"><text text-anchor=\"end\" x=\"29\" y=\"4.199999999999999\" transform=\"translate(0,306.3)\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-o
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"mapped_array[(0, 'a')].boxplot().show_svg()\n",
|
||
|
|
"mapped_array.boxplot().show_svg()\n",
|
||
|
|
"mapped_array_grouped.boxplot().show_svg()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/svg+xml": [
|
||
|
|
"<svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"750\" height=\"380\" style=\"\" viewBox=\"0 0 750 380\"><rect x=\"0\" y=\"0\" width=\"750\" height=\"380\" style=\"fill: rgb(255, 255, 255); fill-opacity: 1;\"/><defs id=\"defs-f7d873\"><g class=\"clips\"><clipPath id=\"clipf7d873xyplot\" class=\"plotclip\"><rect width=\"680\" height=\"260\"/></clipPath><clipPath class=\"axesclip\" id=\"clipf7d873x\"><rect x=\"40\" y=\"0\" width=\"680\" height=\"380\"/></clipPath><clipPath class=\"axesclip\" id=\"clipf7d873y\"><rect x=\"0\" y=\"68\" width=\"750\" height=\"260\"/></clipPath><clipPath class=\"axesclip\" id=\"clipf7d873xy\"><rect x=\"40\" y=\"68\" width=\"680\" height=\"260\"/></clipPath></g><g class=\"gradients\"/><g class=\"patterns\"/></defs><g class=\"bglayer\"><rect class=\"bg\" x=\"40\" y=\"68\" width=\"680\" height=\"260\" style=\"fill: rgb(229, 236, 246); fill-opacity: 1; stroke-width: 0;\"/></g><g class=\"layer-below\"><g class=\"imagelayer\"/><g class=\"shapelayer\"/></g><g class=\"cartesianlayer\"><g class=\"subplot xy\"><g class=\"layer-subplot\"><g class=\"shapelayer\"/><g class=\"imagelayer\"/></g><g class=\"gridlayer\"><g class=\"x\"><path class=\"xgrid crisp\" transform=\"translate(77.75,0)\" d=\"M0,68v260\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(380,0)\" d=\"M0,68v260\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(682.25,0)\" d=\"M0,68v260\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/></g><g class=\"y\"><path class=\"ygrid crisp\" transform=\"translate(0,311.25)\" d=\"M40,0h680\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,254.63)\" d=\"M40,0h680\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,198)\" d=\"M40,0h680\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,141.38)\" d=\"M40,0h680\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,84.75)\" d=\"M40,0h680\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/></g></g><g class=\"zerolinelayer\"/><path class=\"xlines-below\"/><path class=\"ylines-below\"/><g class=\"overlines-below\"/><g class=\"xaxislayer-below\"/><g class=\"yaxislayer-below\"/><g class=\"overaxes-below\"/><g class=\"plot\" transform=\"translate(40,68)\" clip-path=\"url(#clipf7d873xyplot)\"><g class=\"scatterlayer mlayer\"><g class=\"trace scatter tracefdacad43-8d85-4678-b212-b17c94fa4875\" style=\"stroke-miterlimit: 2; opacity: 1;\"><g class=\"fills\"/><g class=\"errorbars\"/><g class=\"lines\"><path class=\"js-line\" d=\"M37.75,243.25L642.25,16.75\" style=\"vector-effect: non-scaling-stroke; fill: none; stroke: rgb(31, 119, 180); stroke-opacity: 1; stroke-width: 2px; opacity: 1;\"/></g><g class=\"points\"><path class=\"point\" transform=\"translate(37.75,243.25)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(31, 119, 180); fill-opacity: 1;\"/><path class=\"point\" transform=\"translate(340,130)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(31, 119, 180); fill-opacity: 1;\"/><path class=\"point\" transform=\"translate(642.25,16.75)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(31, 119, 180); fill-opacity: 1;\"/></g><g class=\"text\"/></g></g></g><g class=\"overplot\"/><path class=\"xlines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><path class=\"ylines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><g class=\"overlines-above\"/><g class=\"xaxislayer-above\"><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"341\" transform
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/svg+xml": [
|
||
|
|
"<svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"750\" height=\"380\" style=\"\" viewBox=\"0 0 750 380\"><rect x=\"0\" y=\"0\" width=\"750\" height=\"380\" style=\"fill: rgb(255, 255, 255); fill-opacity: 1;\"/><defs id=\"defs-a82e1d\"><g class=\"clips\"><clipPath id=\"clipa82e1dxyplot\" class=\"plotclip\"><rect width=\"690\" height=\"260\"/></clipPath><clipPath class=\"axesclip\" id=\"clipa82e1dx\"><rect x=\"30\" y=\"0\" width=\"690\" height=\"380\"/></clipPath><clipPath class=\"axesclip\" id=\"clipa82e1dy\"><rect x=\"0\" y=\"68\" width=\"750\" height=\"260\"/></clipPath><clipPath class=\"axesclip\" id=\"clipa82e1dxy\"><rect x=\"30\" y=\"68\" width=\"690\" height=\"260\"/></clipPath></g><g class=\"gradients\"/><g class=\"patterns\"/></defs><g class=\"bglayer\"><rect class=\"bg\" x=\"30\" y=\"68\" width=\"690\" height=\"260\" style=\"fill: rgb(229, 236, 246); fill-opacity: 1; stroke-width: 0;\"/></g><g class=\"layer-below\"><g class=\"imagelayer\"/><g class=\"shapelayer\"/></g><g class=\"cartesianlayer\"><g class=\"subplot xy\"><g class=\"layer-subplot\"><g class=\"shapelayer\"/><g class=\"imagelayer\"/></g><g class=\"gridlayer\"><g class=\"x\"><path class=\"xgrid crisp\" transform=\"translate(68.25,0)\" d=\"M0,68v260\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(375,0)\" d=\"M0,68v260\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(681.75,0)\" d=\"M0,68v260\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/></g><g class=\"y\"><path class=\"ygrid crisp\" transform=\"translate(0,311.25)\" d=\"M30,0h690\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,254.63)\" d=\"M30,0h690\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,198)\" d=\"M30,0h690\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,141.37)\" d=\"M30,0h690\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,84.75)\" d=\"M30,0h690\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/></g></g><g class=\"zerolinelayer\"/><path class=\"xlines-below\"/><path class=\"ylines-below\"/><g class=\"overlines-below\"/><g class=\"xaxislayer-below\"/><g class=\"yaxislayer-below\"/><g class=\"overaxes-below\"/><g class=\"plot\" transform=\"translate(30,68)\" clip-path=\"url(#clipa82e1dxyplot)\"><g class=\"scatterlayer mlayer\"><g class=\"trace scatter traceb037390a-7b3e-4e76-8666-79cdfea531ae\" style=\"stroke-miterlimit: 2; opacity: 1;\"><g class=\"fills\"/><g class=\"errorbars\"/><g class=\"lines\"><path class=\"js-line\" d=\"M38.25,243.25L651.75,130\" style=\"vector-effect: non-scaling-stroke; fill: none; stroke: rgb(31, 119, 180); stroke-opacity: 1; stroke-width: 2px; opacity: 1;\"/></g><g class=\"points\"><path class=\"point\" transform=\"translate(38.25,243.25)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(31, 119, 180); fill-opacity: 1;\"/><path class=\"point\" transform=\"translate(345,186.63)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(31, 119, 180); fill-opacity: 1;\"/><path class=\"point\" transform=\"translate(651.75,130)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(31, 119, 180); fill-opacity: 1;\"/></g><g class=\"text\"/></g><g class=\"trace scatter trace0131fe6f-7c6c-4715-b573-e77b83a0326c\" style=\"stroke-miterlimit: 2; opacity: 1;\"><g class=\"fills\"/><g class=\"errorbars\"/><g class=\"lines\"><path class=\"js-line\" d=\"M38.25,73.37L345,16.75L651.75,73.37\" style=\"vector-effect: non-scaling-stroke; fill: none; stroke:
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"mapped_array[(0, 'a')].plots().show_svg()\n",
|
||
|
|
"mapped_array.plots().show_svg()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Records"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 52,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[(0, 0, 0, 10., 21.) (1, 1, 0, 11., 20.) (2, 2, 0, 12., 19.)]\n",
|
||
|
|
"MultiIndex([(0, 'a')],\n",
|
||
|
|
" names=['lvl1', 'lvl2'])\n",
|
||
|
|
"[(3, 0, 0, 13., 18.) (4, 1, 0, 14., 17.) (5, 2, 0, 13., 18.)]\n",
|
||
|
|
"MultiIndex([(1, 'b')],\n",
|
||
|
|
" names=['lvl1', 'lvl2'])\n",
|
||
|
|
"[(0, 0, 0, 10., 21.) (1, 1, 0, 11., 20.) (2, 2, 0, 12., 19.)\n",
|
||
|
|
" (0, 0, 1, 10., 21.) (1, 1, 1, 11., 20.) (2, 2, 1, 12., 19.)]\n",
|
||
|
|
"MultiIndex([(0, 'a'),\n",
|
||
|
|
" (0, 'a')],\n",
|
||
|
|
" names=['lvl1', 'lvl2'])\n",
|
||
|
|
"[(0, 0, 0, 10., 21.) (1, 1, 0, 11., 20.) (2, 2, 0, 12., 19.)\n",
|
||
|
|
" (3, 0, 1, 13., 18.) (4, 1, 1, 14., 17.) (5, 2, 1, 13., 18.)]\n",
|
||
|
|
"MultiIndex([(0, 'a'),\n",
|
||
|
|
" (1, 'b')],\n",
|
||
|
|
" names=['lvl1', 'lvl2'])\n",
|
||
|
|
"810 µs ± 9.11 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
|
||
|
|
"13.7 ms ± 259 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(records[(0, 'a')].values)\n",
|
||
|
|
"print(records[(0, 'a')].wrapper.columns)\n",
|
||
|
|
"\n",
|
||
|
|
"print(records[(1, 'b')].values)\n",
|
||
|
|
"print(records[(1, 'b')].wrapper.columns)\n",
|
||
|
|
"\n",
|
||
|
|
"print(records[[(0, 'a'), (0, 'a')]].values)\n",
|
||
|
|
"print(records[[(0, 'a'), (0, 'a')]].wrapper.columns)\n",
|
||
|
|
"\n",
|
||
|
|
"print(records[[(0, 'a'), (1, 'b')]].values)\n",
|
||
|
|
"print(records[[(0, 'a'), (1, 'b')]].wrapper.columns)\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_records.iloc[0]\n",
|
||
|
|
"%timeit big_records.iloc[:]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 53,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[(2, 2, 0, 12., 19.) (1, 1, 0, 11., 20.) (0, 0, 0, 10., 21.)]\n",
|
||
|
|
"MultiIndex([(0, 'a')],\n",
|
||
|
|
" names=['lvl1', 'lvl2'])\n",
|
||
|
|
"[(5, 2, 0, 13., 18.) (4, 1, 0, 14., 17.) (3, 0, 0, 13., 18.)]\n",
|
||
|
|
"MultiIndex([(1, 'b')],\n",
|
||
|
|
" names=['lvl1', 'lvl2'])\n",
|
||
|
|
"[(2, 2, 0, 12., 19.) (1, 1, 0, 11., 20.) (0, 0, 0, 10., 21.)\n",
|
||
|
|
" (2, 2, 1, 12., 19.) (1, 1, 1, 11., 20.) (0, 0, 1, 10., 21.)]\n",
|
||
|
|
"MultiIndex([(0, 'a'),\n",
|
||
|
|
" (0, 'a')],\n",
|
||
|
|
" names=['lvl1', 'lvl2'])\n",
|
||
|
|
"[(2, 2, 0, 12., 19.) (1, 1, 0, 11., 20.) (0, 0, 0, 10., 21.)\n",
|
||
|
|
" (5, 2, 1, 13., 18.) (4, 1, 1, 14., 17.) (3, 0, 1, 13., 18.)]\n",
|
||
|
|
"MultiIndex([(0, 'a'),\n",
|
||
|
|
" (1, 'b')],\n",
|
||
|
|
" names=['lvl1', 'lvl2'])\n",
|
||
|
|
"842 µs ± 5.42 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
|
||
|
|
"15.9 ms ± 678 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(records_nosort[(0, 'a')].values)\n",
|
||
|
|
"print(records_nosort[(0, 'a')].wrapper.columns)\n",
|
||
|
|
"\n",
|
||
|
|
"print(records_nosort[(1, 'b')].values)\n",
|
||
|
|
"print(records_nosort[(1, 'b')].wrapper.columns)\n",
|
||
|
|
"\n",
|
||
|
|
"print(records_nosort[[(0, 'a'), (0, 'a')]].values)\n",
|
||
|
|
"print(records_nosort[[(0, 'a'), (0, 'a')]].wrapper.columns)\n",
|
||
|
|
"\n",
|
||
|
|
"print(records_nosort[[(0, 'a'), (1, 'b')]].values)\n",
|
||
|
|
"print(records_nosort[[(0, 'a'), (1, 'b')]].wrapper.columns)\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_records_nosort.iloc[0]\n",
|
||
|
|
"%timeit big_records_nosort.iloc[:]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 54,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"MultiIndex([(0, 'a')],\n",
|
||
|
|
" names=['lvl1', 'lvl2'])\n",
|
||
|
|
"1\n",
|
||
|
|
"1\n",
|
||
|
|
"Index([0], dtype='int64', name='lvl1')\n",
|
||
|
|
"MultiIndex([(1, 'b'),\n",
|
||
|
|
" (1, 'c'),\n",
|
||
|
|
" (1, 'd')],\n",
|
||
|
|
" names=['lvl1', 'lvl2'])\n",
|
||
|
|
"2\n",
|
||
|
|
"1\n",
|
||
|
|
"Index([1, 1, 1], dtype='int64', name='lvl1')\n",
|
||
|
|
"MultiIndex([(0, 'a')],\n",
|
||
|
|
" names=['lvl1', 'lvl2'])\n",
|
||
|
|
"2\n",
|
||
|
|
"2\n",
|
||
|
|
"Index([0], dtype='int64', name='lvl1')\n",
|
||
|
|
"MultiIndex([(0, 'a'),\n",
|
||
|
|
" (1, 'b'),\n",
|
||
|
|
" (1, 'c'),\n",
|
||
|
|
" (1, 'd')],\n",
|
||
|
|
" names=['lvl1', 'lvl2'])\n",
|
||
|
|
"2\n",
|
||
|
|
"2\n",
|
||
|
|
"Index([0, 1, 1, 1], dtype='int64', name='lvl1')\n",
|
||
|
|
"6.25 ms ± 70.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"13.9 ms ± 264 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(records_grouped[0].wrapper.columns) # indexing on groups, not columns!\n",
|
||
|
|
"print(records_grouped[0].wrapper.ndim)\n",
|
||
|
|
"print(records_grouped[0].wrapper.grouped_ndim)\n",
|
||
|
|
"print(records_grouped[0].wrapper.grouper.group_by)\n",
|
||
|
|
"\n",
|
||
|
|
"print(records_grouped[1].wrapper.columns)\n",
|
||
|
|
"print(records_grouped[1].wrapper.ndim)\n",
|
||
|
|
"print(records_grouped[1].wrapper.grouped_ndim)\n",
|
||
|
|
"print(records_grouped[1].wrapper.grouper.group_by)\n",
|
||
|
|
"\n",
|
||
|
|
"print(records_grouped[[0]].wrapper.columns)\n",
|
||
|
|
"print(records_grouped[[0]].wrapper.ndim)\n",
|
||
|
|
"print(records_grouped[[0]].wrapper.grouped_ndim)\n",
|
||
|
|
"print(records_grouped[[0]].wrapper.grouper.group_by)\n",
|
||
|
|
"\n",
|
||
|
|
"print(records_grouped[[0, 1]].wrapper.columns)\n",
|
||
|
|
"print(records_grouped[[0, 1]].wrapper.ndim)\n",
|
||
|
|
"print(records_grouped[[0, 1]].wrapper.grouped_ndim)\n",
|
||
|
|
"print(records_grouped[[0, 1]].wrapper.grouper.group_by)\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_records_grouped.iloc[0]\n",
|
||
|
|
"%timeit big_records_grouped.iloc[:]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 55,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Index(['x', 'y', 'z'], dtype='object')\n",
|
||
|
|
"MultiIndex([(0, 'a'),\n",
|
||
|
|
" (1, 'b'),\n",
|
||
|
|
" (1, 'c'),\n",
|
||
|
|
" (1, 'd')],\n",
|
||
|
|
" names=['lvl1', 'lvl2'])\n",
|
||
|
|
"2\n",
|
||
|
|
"None\n",
|
||
|
|
"Index(['x', 'y', 'z'], dtype='object')\n",
|
||
|
|
"MultiIndex([(0, 'a'),\n",
|
||
|
|
" (1, 'b'),\n",
|
||
|
|
" (1, 'c'),\n",
|
||
|
|
" (1, 'd')],\n",
|
||
|
|
" names=['lvl1', 'lvl2'])\n",
|
||
|
|
"2\n",
|
||
|
|
"Index([0, 1, 1, 1], dtype='int64', name='lvl1')\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(records.wrapper.index)\n",
|
||
|
|
"print(records.wrapper.columns)\n",
|
||
|
|
"print(records.wrapper.ndim)\n",
|
||
|
|
"print(records.wrapper.grouper.group_by)\n",
|
||
|
|
"\n",
|
||
|
|
"print(records_grouped.wrapper.index)\n",
|
||
|
|
"print(records_grouped.wrapper.columns)\n",
|
||
|
|
"print(records_grouped.wrapper.ndim)\n",
|
||
|
|
"print(records_grouped.wrapper.grouper.group_by)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 56,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[(0, 0, 0, 10., 21.) (1, 1, 0, 11., 20.) (2, 2, 0, 12., 19.)\n",
|
||
|
|
" (3, 0, 1, 13., 18.) (4, 1, 1, 14., 17.) (5, 2, 1, 13., 18.)\n",
|
||
|
|
" (6, 0, 2, 12., 19.) (7, 1, 2, 11., 20.) (8, 2, 2, 10., 21.)]\n",
|
||
|
|
"[(0, 0, 0, 10., 21.) (1, 1, 0, 11., 20.) (2, 2, 0, 12., 19.)\n",
|
||
|
|
" (3, 0, 1, 13., 18.) (4, 1, 1, 14., 17.) (5, 2, 1, 13., 18.)\n",
|
||
|
|
" (6, 0, 2, 12., 19.) (7, 1, 2, 11., 20.) (8, 2, 2, 10., 21.)]\n",
|
||
|
|
"3.54 µs ± 13.6 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(records.values)\n",
|
||
|
|
"\n",
|
||
|
|
"print(records.recarray)\n",
|
||
|
|
"%timeit big_records.recarray"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 57,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" id idx col some_field1 some_field2\n",
|
||
|
|
"0 0 0 0 10.0 21.0\n",
|
||
|
|
"1 1 1 0 11.0 20.0\n",
|
||
|
|
"2 2 2 0 12.0 19.0\n",
|
||
|
|
"3 3 0 1 13.0 18.0\n",
|
||
|
|
"4 4 1 1 14.0 17.0\n",
|
||
|
|
"5 5 2 1 13.0 18.0\n",
|
||
|
|
"6 6 0 2 12.0 19.0\n",
|
||
|
|
"7 7 1 2 11.0 20.0\n",
|
||
|
|
"8 8 2 2 10.0 21.0\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(records.records)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 58,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"True\n",
|
||
|
|
"1.06 ms ± 1.99 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
|
||
|
|
"False\n",
|
||
|
|
"8.89 µs ± 232 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
|
||
|
|
"True\n",
|
||
|
|
"1.56 ms ± 1.74 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
|
||
|
|
"False\n",
|
||
|
|
"8.41 µs ± 37.3 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(records.is_sorted())\n",
|
||
|
|
"%timeit big_records.is_sorted()\n",
|
||
|
|
"\n",
|
||
|
|
"print(records_nosort.is_sorted())\n",
|
||
|
|
"%timeit big_records_nosort.is_sorted()\n",
|
||
|
|
"\n",
|
||
|
|
"print(records.is_sorted(incl_id=True))\n",
|
||
|
|
"%timeit big_records.is_sorted(incl_id=True)\n",
|
||
|
|
"\n",
|
||
|
|
"print(records_nosort.is_sorted(incl_id=True))\n",
|
||
|
|
"%timeit big_records_nosort.is_sorted(incl_id=True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 59,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[(0, 0, 0, 10., 21.) (1, 1, 0, 11., 20.) (2, 2, 0, 12., 19.)\n",
|
||
|
|
" (3, 0, 1, 13., 18.) (4, 1, 1, 14., 17.) (5, 2, 1, 13., 18.)\n",
|
||
|
|
" (6, 0, 2, 12., 19.) (7, 1, 2, 11., 20.) (8, 2, 2, 10., 21.)]\n",
|
||
|
|
"1.3 ms ± 771 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
|
||
|
|
"[(2, 2, 0, 12., 19.) (1, 1, 0, 11., 20.) (0, 0, 0, 10., 21.)\n",
|
||
|
|
" (5, 2, 1, 13., 18.) (4, 1, 1, 14., 17.) (3, 0, 1, 13., 18.)\n",
|
||
|
|
" (8, 2, 2, 10., 21.) (7, 1, 2, 11., 20.) (6, 0, 2, 12., 19.)]\n",
|
||
|
|
"70.6 ms ± 1.59 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
|
||
|
|
"[(0, 0, 0, 10., 21.) (1, 1, 0, 11., 20.) (2, 2, 0, 12., 19.)\n",
|
||
|
|
" (3, 0, 1, 13., 18.) (4, 1, 1, 14., 17.) (5, 2, 1, 13., 18.)\n",
|
||
|
|
" (6, 0, 2, 12., 19.) (7, 1, 2, 11., 20.) (8, 2, 2, 10., 21.)]\n",
|
||
|
|
"1.83 ms ± 12.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
|
||
|
|
"[(0, 0, 0, 10., 21.) (1, 1, 0, 11., 20.) (2, 2, 0, 12., 19.)\n",
|
||
|
|
" (3, 0, 1, 13., 18.) (4, 1, 1, 14., 17.) (5, 2, 1, 13., 18.)\n",
|
||
|
|
" (6, 0, 2, 12., 19.) (7, 1, 2, 11., 20.) (8, 2, 2, 10., 21.)]\n",
|
||
|
|
"66 ms ± 1.53 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(records.sort().records_arr)\n",
|
||
|
|
"%timeit big_records.sort()\n",
|
||
|
|
"\n",
|
||
|
|
"print(records_nosort.sort().records_arr)\n",
|
||
|
|
"%timeit big_records_nosort.sort()\n",
|
||
|
|
"\n",
|
||
|
|
"print(records.sort(incl_id=True).records_arr)\n",
|
||
|
|
"%timeit big_records.sort(incl_id=True)\n",
|
||
|
|
"\n",
|
||
|
|
"print(records_nosort.sort(incl_id=True).records_arr)\n",
|
||
|
|
"%timeit big_records_nosort.sort(incl_id=True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[(2, 2, 0, 12., 19.) (3, 0, 1, 13., 18.) (4, 1, 1, 14., 17.)\n",
|
||
|
|
" (5, 2, 1, 13., 18.) (6, 0, 2, 12., 19.)]\n",
|
||
|
|
"10.4 ms ± 232 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"mask = records.values['some_field1'] >= records.values['some_field1'].mean()\n",
|
||
|
|
"print(records.apply_mask(mask).values)\n",
|
||
|
|
"\n",
|
||
|
|
"big_mask = big_records.values['some_field1'] >= big_records.values['some_field1'].mean()\n",
|
||
|
|
"%timeit big_records.apply_mask(big_mask)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 61,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 153.0\n",
|
||
|
|
"1 b 146.0\n",
|
||
|
|
" c 153.0\n",
|
||
|
|
" d 0.0\n",
|
||
|
|
"Name: sum, dtype: float64\n",
|
||
|
|
"lvl1\n",
|
||
|
|
"0 153.0\n",
|
||
|
|
"1 299.0\n",
|
||
|
|
"Name: sum, dtype: float64\n",
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 153.0\n",
|
||
|
|
"1 b 146.0\n",
|
||
|
|
" c 153.0\n",
|
||
|
|
" d 0.0\n",
|
||
|
|
"Name: sum, dtype: float64\n",
|
||
|
|
"3.25 ms ± 735 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"4.53 ms ± 278 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"@njit\n",
|
||
|
|
"def map_nb(record):\n",
|
||
|
|
" return record.some_field1 + record.some_field2 * 2\n",
|
||
|
|
"\n",
|
||
|
|
"print(records.map(map_nb).sum())\n",
|
||
|
|
"print(records_grouped.map(map_nb).sum())\n",
|
||
|
|
"print(records_grouped.map(map_nb, group_by=False).sum())\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit vbt.records.MappedArray(\\\n",
|
||
|
|
" big_wrapper,\\\n",
|
||
|
|
" big_records_arr['some_field1'] + big_records_arr['some_field2'] * 2,\\\n",
|
||
|
|
" big_records_arr['col'],\\\n",
|
||
|
|
")\n",
|
||
|
|
"%timeit big_records.map(map_nb) # faster"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 62,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[0 0 0 1 1 1 2 2 2]\n",
|
||
|
|
"[0 1 2 0 1 2 0 1 2]\n",
|
||
|
|
"[10. 11. 12. 13. 14. 13. 12. 11. 10.]\n",
|
||
|
|
"[21. 20. 19. 18. 17. 18. 19. 20. 21.]\n",
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 33.0\n",
|
||
|
|
"1 b 40.0\n",
|
||
|
|
" c 33.0\n",
|
||
|
|
" d 0.0\n",
|
||
|
|
"Name: sum, dtype: float64\n",
|
||
|
|
"lvl1\n",
|
||
|
|
"0 33.0\n",
|
||
|
|
"1 73.0\n",
|
||
|
|
"Name: sum, dtype: float64\n",
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 33.0\n",
|
||
|
|
"1 b 40.0\n",
|
||
|
|
" c 33.0\n",
|
||
|
|
" d 0.0\n",
|
||
|
|
"Name: sum, dtype: float64\n",
|
||
|
|
"293 µs ± 4.43 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(records.map_field('col').values)\n",
|
||
|
|
"print(records.map_field('idx').values)\n",
|
||
|
|
"print(records.map_field('some_field1').values)\n",
|
||
|
|
"print(records.map_field('some_field2').values)\n",
|
||
|
|
"\n",
|
||
|
|
"print(records.map_field('some_field1').sum())\n",
|
||
|
|
"print(records_grouped.map_field('some_field1').sum())\n",
|
||
|
|
"print(records_grouped.map_field('some_field1', group_by=False).sum())\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_records.map_field('some_field1')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 63,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[52. 51. 50. 49. 48. 49. 50. 51. 52.]\n",
|
||
|
|
"lvl1\n",
|
||
|
|
"0 153.0\n",
|
||
|
|
"1 299.0\n",
|
||
|
|
"Name: sum, dtype: float64\n",
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 153.0\n",
|
||
|
|
"1 b 146.0\n",
|
||
|
|
" c 153.0\n",
|
||
|
|
" d 0.0\n",
|
||
|
|
"Name: sum, dtype: float64\n",
|
||
|
|
"2.55 ms ± 544 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(records.map_array(records_arr['some_field1'] + records_arr['some_field2'] * 2).values)\n",
|
||
|
|
"print(records_grouped.map_array(records_arr['some_field1'] + records_arr['some_field2'] * 2).sum())\n",
|
||
|
|
"print(records_grouped.map_array(records_arr['some_field1'] + records_arr['some_field2'] * 2, group_by=False).sum())\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_records.map_array(big_records_arr['some_field1'] + big_records_arr['some_field2'] * 2)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 64,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 3\n",
|
||
|
|
"1 b 3\n",
|
||
|
|
" c 3\n",
|
||
|
|
" d 0\n",
|
||
|
|
"Name: count, dtype: int64\n",
|
||
|
|
"lvl1\n",
|
||
|
|
"0 3\n",
|
||
|
|
"1 6\n",
|
||
|
|
"Name: count, dtype: int64\n",
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 3\n",
|
||
|
|
"1 b 3\n",
|
||
|
|
" c 3\n",
|
||
|
|
" d 0\n",
|
||
|
|
"Name: count, dtype: int64\n",
|
||
|
|
"49.5 µs ± 133 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
|
||
|
|
"48.5 µs ± 595 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(records.count())\n",
|
||
|
|
"print(records_grouped.count())\n",
|
||
|
|
"print(records_grouped.count(group_by=False))\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_records.count()\n",
|
||
|
|
"%timeit big_records_grouped.count()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 1\n",
|
||
|
|
"1 b 0\n",
|
||
|
|
" c 1\n",
|
||
|
|
" d 0\n",
|
||
|
|
"Name: count, dtype: int64\n",
|
||
|
|
"lvl1\n",
|
||
|
|
"0 1\n",
|
||
|
|
"1 1\n",
|
||
|
|
"Name: count, dtype: int64\n",
|
||
|
|
"lvl1 lvl2\n",
|
||
|
|
"0 a 1\n",
|
||
|
|
"1 b 0\n",
|
||
|
|
" c 1\n",
|
||
|
|
" d 0\n",
|
||
|
|
"Name: count, dtype: int64\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"filter_mask = np.array([True, False, False, False, False, False, False, False, True])\n",
|
||
|
|
"\n",
|
||
|
|
"print(records.apply_mask(filter_mask).count())\n",
|
||
|
|
"print(records_grouped.apply_mask(filter_mask).count())\n",
|
||
|
|
"print(records_grouped.apply_mask(filter_mask, group_by=False).count())"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" id idx col some_field1 some_field2\n",
|
||
|
|
"0 0 0 0 10.0 21.0\n",
|
||
|
|
"1 8 2 2 10.0 21.0\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"filtered_records = records.apply_mask(filter_mask)\n",
|
||
|
|
"print(filtered_records.records)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 67,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" id idx col some_field1 some_field2\n",
|
||
|
|
"0 0 0 0 10.0 21.0\n",
|
||
|
|
"[10.]\n",
|
||
|
|
"10.0\n",
|
||
|
|
"1\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(filtered_records[(0, 'a')].records)\n",
|
||
|
|
"print(filtered_records[(0, 'a')].map_field('some_field1').values)\n",
|
||
|
|
"print(filtered_records[(0, 'a')].map_field('some_field1').min())\n",
|
||
|
|
"print(filtered_records[(0, 'a')].count())"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 68,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Empty DataFrame\n",
|
||
|
|
"Columns: [id, idx, col, some_field1, some_field2]\n",
|
||
|
|
"Index: []\n",
|
||
|
|
"[]\n",
|
||
|
|
"nan\n",
|
||
|
|
"0\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(filtered_records[(1, 'b')].records)\n",
|
||
|
|
"print(filtered_records[(1, 'b')].map_field('some_field1').values)\n",
|
||
|
|
"print(filtered_records[(1, 'b')].map_field('some_field1').min())\n",
|
||
|
|
"print(filtered_records[(1, 'b')].count())"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 69,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" id idx col some_field1 some_field2\n",
|
||
|
|
"0 8 2 0 10.0 21.0\n",
|
||
|
|
"[10.]\n",
|
||
|
|
"10.0\n",
|
||
|
|
"1\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(filtered_records[(1, 'c')].records)\n",
|
||
|
|
"print(filtered_records[(1, 'c')].map_field('some_field1').values)\n",
|
||
|
|
"print(filtered_records[(1, 'c')].map_field('some_field1').min())\n",
|
||
|
|
"print(filtered_records[(1, 'c')].count())"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 70,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Empty DataFrame\n",
|
||
|
|
"Columns: [id, idx, col, some_field1, some_field2]\n",
|
||
|
|
"Index: []\n",
|
||
|
|
"[]\n",
|
||
|
|
"nan\n",
|
||
|
|
"0\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(filtered_records[(1, 'd')].records)\n",
|
||
|
|
"print(filtered_records[(1, 'd')].map_field('some_field1').values)\n",
|
||
|
|
"print(filtered_records[(1, 'd')].map_field('some_field1').min())\n",
|
||
|
|
"print(filtered_records[(1, 'd')].count())"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Start x\n",
|
||
|
|
"End z\n",
|
||
|
|
"Period 3 days 00:00:00\n",
|
||
|
|
"Count 3\n",
|
||
|
|
"Name: (0, a), dtype: object\n",
|
||
|
|
"12.7 ms ± 680 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"Start x\n",
|
||
|
|
"End z\n",
|
||
|
|
"Period 3 days 00:00:00\n",
|
||
|
|
"Count 3\n",
|
||
|
|
"Name: (0, a), dtype: object\n",
|
||
|
|
"2.18 ms ± 42.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"Start x\n",
|
||
|
|
"End z\n",
|
||
|
|
"Period 3 days 00:00:00\n",
|
||
|
|
"Count 2.25\n",
|
||
|
|
"Name: agg_func_mean, dtype: object\n",
|
||
|
|
"2.1 ms ± 1.67 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"/Users/olegpolakow/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:7: UserWarning:\n",
|
||
|
|
"\n",
|
||
|
|
"Object has multiple columns. Aggregating using <function mean at 0x7fbd30c34598>. Pass column to select a single column/group.\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(records[(0, 'a')].stats())\n",
|
||
|
|
"%timeit big_records[0].stats(silence_warnings=True)\n",
|
||
|
|
"\n",
|
||
|
|
"print(records.stats(column=(0, 'a')))\n",
|
||
|
|
"%timeit big_records.stats(column=0, silence_warnings=True)\n",
|
||
|
|
"\n",
|
||
|
|
"print(records.stats())\n",
|
||
|
|
"%timeit big_records.stats(silence_warnings=True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"/Users/olegpolakow/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: UserWarning:\n",
|
||
|
|
"\n",
|
||
|
|
"No subplots to plot\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"records[(0, 'a')].plots()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Drawdowns"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"ts = pd.DataFrame({\n",
|
||
|
|
" 'a': [2, 1, 3, 1, 4, 1], \n",
|
||
|
|
" 'b': [1, 2, 1, 3, 1, 4],\n",
|
||
|
|
" 'c': [1, 2, 3, 2, 1, 2],\n",
|
||
|
|
" 'd': [1, 2, 3, 4, 5, 6]\n",
|
||
|
|
"}, index=[\n",
|
||
|
|
" datetime(2020, 1, 1),\n",
|
||
|
|
" datetime(2020, 1, 2),\n",
|
||
|
|
" datetime(2020, 1, 3),\n",
|
||
|
|
" datetime(2020, 1, 4),\n",
|
||
|
|
" datetime(2020, 1, 5),\n",
|
||
|
|
" datetime(2020, 1, 6)\n",
|
||
|
|
"])\n",
|
||
|
|
"\n",
|
||
|
|
"np.random.seed(42)\n",
|
||
|
|
"big_ts = pd.DataFrame(np.random.randint(1, 10, size=(1000, 1000)))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 15,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"(6,)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"drawdowns = vbt.Drawdowns.from_ts(ts, freq='1 days')\n",
|
||
|
|
"print(drawdowns.values.shape)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 16,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"(99845,)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"big_drawdowns = vbt.Drawdowns.from_ts(big_ts, freq='1 days')\n",
|
||
|
|
"print(big_drawdowns.values.shape)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 74,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"5.37 ms ± 57.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"%timeit vbt.Drawdowns.from_ts(big_ts, freq='1 days')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 75,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" id col peak_idx start_idx valley_idx end_idx peak_val valley_val \\\n",
|
||
|
|
"0 0 0 0 1 1 2 2.0 1.0 \n",
|
||
|
|
"1 1 0 2 3 3 4 3.0 1.0 \n",
|
||
|
|
"2 2 0 4 5 5 5 4.0 1.0 \n",
|
||
|
|
"3 3 1 1 2 2 3 2.0 1.0 \n",
|
||
|
|
"4 4 1 3 4 4 5 3.0 1.0 \n",
|
||
|
|
"5 5 2 2 3 4 5 3.0 1.0 \n",
|
||
|
|
"\n",
|
||
|
|
" end_val status \n",
|
||
|
|
"0 2.0 1 \n",
|
||
|
|
"1 3.0 1 \n",
|
||
|
|
"2 1.0 0 \n",
|
||
|
|
"3 2.0 1 \n",
|
||
|
|
"4 3.0 1 \n",
|
||
|
|
"5 2.0 0 \n",
|
||
|
|
" a b c d\n",
|
||
|
|
"2020-01-01 2 1 1 1\n",
|
||
|
|
"2020-01-02 1 2 2 2\n",
|
||
|
|
"2020-01-03 3 1 3 3\n",
|
||
|
|
"2020-01-04 1 3 2 4\n",
|
||
|
|
"2020-01-05 4 1 1 5\n",
|
||
|
|
"2020-01-06 1 4 2 6\n",
|
||
|
|
" id col peak_idx start_idx valley_idx end_idx peak_val valley_val \\\n",
|
||
|
|
"0 0 0 0 1 1 2 2.0 1.0 \n",
|
||
|
|
"1 1 0 2 3 3 4 3.0 1.0 \n",
|
||
|
|
"2 2 0 4 5 5 5 4.0 1.0 \n",
|
||
|
|
"\n",
|
||
|
|
" end_val status \n",
|
||
|
|
"0 2.0 1 \n",
|
||
|
|
"1 3.0 1 \n",
|
||
|
|
"2 1.0 0 \n",
|
||
|
|
"2020-01-01 2\n",
|
||
|
|
"2020-01-02 1\n",
|
||
|
|
"2020-01-03 3\n",
|
||
|
|
"2020-01-04 1\n",
|
||
|
|
"2020-01-05 4\n",
|
||
|
|
"2020-01-06 1\n",
|
||
|
|
"Name: a, dtype: int64\n",
|
||
|
|
"1.21 ms ± 12.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
|
||
|
|
"3.81 ms ± 418 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(drawdowns.records)\n",
|
||
|
|
"print(drawdowns.ts)\n",
|
||
|
|
"\n",
|
||
|
|
"print(drawdowns['a'].records)\n",
|
||
|
|
"print(drawdowns['a'].ts)\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_drawdowns.iloc[0]\n",
|
||
|
|
"%timeit big_drawdowns.iloc[:]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 76,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" Drawdown Id Column Peak Timestamp Start Timestamp Valley Timestamp \\\n",
|
||
|
|
"0 0 a 2020-01-01 2020-01-02 2020-01-02 \n",
|
||
|
|
"1 1 a 2020-01-03 2020-01-04 2020-01-04 \n",
|
||
|
|
"2 2 a 2020-01-05 2020-01-06 2020-01-06 \n",
|
||
|
|
"3 3 b 2020-01-02 2020-01-03 2020-01-03 \n",
|
||
|
|
"4 4 b 2020-01-04 2020-01-05 2020-01-05 \n",
|
||
|
|
"5 5 c 2020-01-03 2020-01-04 2020-01-05 \n",
|
||
|
|
"\n",
|
||
|
|
" End Timestamp Peak Value Valley Value End Value Status \n",
|
||
|
|
"0 2020-01-03 2.0 1.0 2.0 Recovered \n",
|
||
|
|
"1 2020-01-05 3.0 1.0 3.0 Recovered \n",
|
||
|
|
"2 2020-01-06 4.0 1.0 1.0 Active \n",
|
||
|
|
"3 2020-01-04 2.0 1.0 2.0 Recovered \n",
|
||
|
|
"4 2020-01-06 3.0 1.0 3.0 Recovered \n",
|
||
|
|
"5 2020-01-06 3.0 1.0 2.0 Active \n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(drawdowns.records_readable)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 77,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"3\n",
|
||
|
|
"a 3\n",
|
||
|
|
"b 2\n",
|
||
|
|
"c 1\n",
|
||
|
|
"d 0\n",
|
||
|
|
"Name: count, dtype: int64\n",
|
||
|
|
"group\n",
|
||
|
|
"first 5\n",
|
||
|
|
"second 1\n",
|
||
|
|
"Name: count, dtype: int64\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(drawdowns['a'].count())\n",
|
||
|
|
"\n",
|
||
|
|
"print(drawdowns.count())\n",
|
||
|
|
"print(drawdowns.count(group_by=group_by))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 80,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"2020-01-01 NaN\n",
|
||
|
|
"2020-01-02 NaN\n",
|
||
|
|
"2020-01-03 -0.500000\n",
|
||
|
|
"2020-01-04 NaN\n",
|
||
|
|
"2020-01-05 -0.666667\n",
|
||
|
|
"2020-01-06 -0.750000\n",
|
||
|
|
"Name: a, dtype: float64\n",
|
||
|
|
" a b c d\n",
|
||
|
|
"2020-01-01 NaN NaN NaN NaN\n",
|
||
|
|
"2020-01-02 NaN NaN NaN NaN\n",
|
||
|
|
"2020-01-03 -0.500000 NaN NaN NaN\n",
|
||
|
|
"2020-01-04 NaN -0.500000 NaN NaN\n",
|
||
|
|
"2020-01-05 -0.666667 NaN NaN NaN\n",
|
||
|
|
"2020-01-06 -0.750000 -0.666667 -0.666667 NaN\n",
|
||
|
|
"463 µs ± 21.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(drawdowns['a'].drawdown.to_pd())\n",
|
||
|
|
"print(drawdowns.drawdown.to_pd())\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_drawdowns.drawdown"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 81,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"-0.6388888888888888\n",
|
||
|
|
"a -0.638889\n",
|
||
|
|
"b -0.583333\n",
|
||
|
|
"c -0.666667\n",
|
||
|
|
"d NaN\n",
|
||
|
|
"Name: avg_drawdown, dtype: float64\n",
|
||
|
|
"1.12 ms ± 200 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
|
||
|
|
"group\n",
|
||
|
|
"first -0.616667\n",
|
||
|
|
"second -0.666667\n",
|
||
|
|
"Name: avg_drawdown, dtype: float64\n",
|
||
|
|
"4.65 ms ± 1.91 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(drawdowns['a'].avg_drawdown())\n",
|
||
|
|
"print(drawdowns.avg_drawdown())\n",
|
||
|
|
"%timeit big_drawdowns.avg_drawdown()\n",
|
||
|
|
"\n",
|
||
|
|
"print(drawdowns.avg_drawdown(group_by=group_by))\n",
|
||
|
|
"%timeit big_drawdowns.avg_drawdown(group_by=big_group_by)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 82,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"-0.75\n",
|
||
|
|
"a -0.750000\n",
|
||
|
|
"b -0.666667\n",
|
||
|
|
"c -0.666667\n",
|
||
|
|
"d NaN\n",
|
||
|
|
"Name: max_drawdown, dtype: float64\n",
|
||
|
|
"1.15 ms ± 214 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
|
||
|
|
"group\n",
|
||
|
|
"first -0.750000\n",
|
||
|
|
"second -0.666667\n",
|
||
|
|
"Name: max_drawdown, dtype: float64\n",
|
||
|
|
"3.81 ms ± 652 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(drawdowns['a'].max_drawdown())\n",
|
||
|
|
"print(drawdowns.max_drawdown())\n",
|
||
|
|
"%timeit big_drawdowns.max_drawdown()\n",
|
||
|
|
"\n",
|
||
|
|
"print(drawdowns.max_drawdown(group_by=group_by))\n",
|
||
|
|
"%timeit big_drawdowns.max_drawdown(group_by=big_group_by)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 83,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"2020-01-01 NaN\n",
|
||
|
|
"2020-01-02 NaN\n",
|
||
|
|
"2020-01-03 1.0\n",
|
||
|
|
"2020-01-04 NaN\n",
|
||
|
|
"2020-01-05 2.0\n",
|
||
|
|
"2020-01-06 0.0\n",
|
||
|
|
"Name: a, dtype: float64\n",
|
||
|
|
" a b c d\n",
|
||
|
|
"2020-01-01 NaN NaN NaN NaN\n",
|
||
|
|
"2020-01-02 NaN NaN NaN NaN\n",
|
||
|
|
"2020-01-03 1.0 NaN NaN NaN\n",
|
||
|
|
"2020-01-04 NaN 1.0 NaN NaN\n",
|
||
|
|
"2020-01-05 2.0 NaN NaN NaN\n",
|
||
|
|
"2020-01-06 0.0 2.0 1.0 NaN\n",
|
||
|
|
"479 µs ± 45.9 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(drawdowns['a'].recovery_return.to_pd())\n",
|
||
|
|
"print(drawdowns.recovery_return.to_pd())\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_drawdowns.recovery_return"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 84,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"1.0\n",
|
||
|
|
"a 1.0\n",
|
||
|
|
"b 1.5\n",
|
||
|
|
"c 1.0\n",
|
||
|
|
"d NaN\n",
|
||
|
|
"Name: avg_recovery_return, dtype: float64\n",
|
||
|
|
"1.01 ms ± 8.88 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
|
||
|
|
"group\n",
|
||
|
|
"first 1.2\n",
|
||
|
|
"second 1.0\n",
|
||
|
|
"Name: avg_recovery_return, dtype: float64\n",
|
||
|
|
"3.48 ms ± 80 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(drawdowns['a'].avg_recovery_return())\n",
|
||
|
|
"print(drawdowns.avg_recovery_return())\n",
|
||
|
|
"%timeit big_drawdowns.avg_recovery_return()\n",
|
||
|
|
"\n",
|
||
|
|
"print(drawdowns.avg_recovery_return(group_by=group_by))\n",
|
||
|
|
"%timeit big_drawdowns.avg_recovery_return(group_by=big_group_by)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 85,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"2.0\n",
|
||
|
|
"a 2.0\n",
|
||
|
|
"b 2.0\n",
|
||
|
|
"c 1.0\n",
|
||
|
|
"d NaN\n",
|
||
|
|
"Name: max_recovery_return, dtype: float64\n",
|
||
|
|
"1.06 ms ± 35.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
|
||
|
|
"group\n",
|
||
|
|
"first 2.0\n",
|
||
|
|
"second 1.0\n",
|
||
|
|
"Name: max_recovery_return, dtype: float64\n",
|
||
|
|
"3.72 ms ± 302 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(drawdowns['a'].max_recovery_return())\n",
|
||
|
|
"print(drawdowns.max_recovery_return())\n",
|
||
|
|
"%timeit big_drawdowns.max_recovery_return()\n",
|
||
|
|
"\n",
|
||
|
|
"print(drawdowns.max_recovery_return(group_by=group_by))\n",
|
||
|
|
"%timeit big_drawdowns.max_recovery_return(group_by=big_group_by)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 86,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"2020-01-01 NaN\n",
|
||
|
|
"2020-01-02 NaN\n",
|
||
|
|
"2020-01-03 1.0\n",
|
||
|
|
"2020-01-04 NaN\n",
|
||
|
|
"2020-01-05 1.0\n",
|
||
|
|
"2020-01-06 1.0\n",
|
||
|
|
"Name: a, dtype: float64\n",
|
||
|
|
" a b c d\n",
|
||
|
|
"2020-01-01 NaN NaN NaN NaN\n",
|
||
|
|
"2020-01-02 NaN NaN NaN NaN\n",
|
||
|
|
"2020-01-03 1.0 NaN NaN NaN\n",
|
||
|
|
"2020-01-04 NaN 1.0 NaN NaN\n",
|
||
|
|
"2020-01-05 1.0 NaN NaN NaN\n",
|
||
|
|
"2020-01-06 1.0 1.0 3.0 NaN\n",
|
||
|
|
"483 µs ± 6.53 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(drawdowns['a'].duration.to_pd())\n",
|
||
|
|
"print(drawdowns.duration.to_pd())\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_drawdowns.duration"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 87,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"1 days 00:00:00\n",
|
||
|
|
"a 1 days\n",
|
||
|
|
"b 1 days\n",
|
||
|
|
"c 3 days\n",
|
||
|
|
"d NaT\n",
|
||
|
|
"Name: avg_duration, dtype: timedelta64[ns]\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/base/array_wrapper.py:661: UserWarning:\n",
|
||
|
|
"\n",
|
||
|
|
"Couldn't parse the frequency of index. Pass it as `freq` or define it globally under `settings.array_wrapper`.\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"905 µs ± 3.32 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
|
||
|
|
"group\n",
|
||
|
|
"first 1 days\n",
|
||
|
|
"second 3 days\n",
|
||
|
|
"Name: avg_duration, dtype: timedelta64[ns]\n",
|
||
|
|
"3.34 ms ± 35.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(drawdowns['a'].avg_duration())\n",
|
||
|
|
"print(drawdowns.avg_duration())\n",
|
||
|
|
"%timeit big_drawdowns.avg_duration()\n",
|
||
|
|
"\n",
|
||
|
|
"print(drawdowns.avg_duration(group_by=group_by))\n",
|
||
|
|
"%timeit big_drawdowns.avg_duration(group_by=big_group_by)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 88,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"1 days 00:00:00\n",
|
||
|
|
"a 1 days\n",
|
||
|
|
"b 1 days\n",
|
||
|
|
"c 3 days\n",
|
||
|
|
"d NaT\n",
|
||
|
|
"Name: max_duration, dtype: timedelta64[ns]\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/base/array_wrapper.py:661: UserWarning:\n",
|
||
|
|
"\n",
|
||
|
|
"Couldn't parse the frequency of index. Pass it as `freq` or define it globally under `settings.array_wrapper`.\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"992 µs ± 72.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
|
||
|
|
"group\n",
|
||
|
|
"first 1 days\n",
|
||
|
|
"second 3 days\n",
|
||
|
|
"Name: max_duration, dtype: timedelta64[ns]\n",
|
||
|
|
"3.35 ms ± 18.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(drawdowns['a'].max_duration())\n",
|
||
|
|
"print(drawdowns.max_duration())\n",
|
||
|
|
"%timeit big_drawdowns.max_duration()\n",
|
||
|
|
"\n",
|
||
|
|
"print(drawdowns.max_duration(group_by=group_by))\n",
|
||
|
|
"%timeit big_drawdowns.max_duration(group_by=big_group_by)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 89,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"0.5\n",
|
||
|
|
"a 0.500000\n",
|
||
|
|
"b 0.333333\n",
|
||
|
|
"c 0.500000\n",
|
||
|
|
"d NaN\n",
|
||
|
|
"Name: coverage, dtype: float64\n",
|
||
|
|
"13.8 ms ± 181 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"group\n",
|
||
|
|
"first 0.416667\n",
|
||
|
|
"second 0.250000\n",
|
||
|
|
"Name: coverage, dtype: float64\n",
|
||
|
|
"16.8 ms ± 164 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(drawdowns['a'].coverage())\n",
|
||
|
|
"print(drawdowns.coverage())\n",
|
||
|
|
"%timeit big_drawdowns.coverage()\n",
|
||
|
|
"\n",
|
||
|
|
"print(drawdowns.coverage(group_by=group_by))\n",
|
||
|
|
"%timeit big_drawdowns.coverage(group_by=big_group_by)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 90,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"2020-01-01 NaN\n",
|
||
|
|
"2020-01-02 NaN\n",
|
||
|
|
"2020-01-03 1.0\n",
|
||
|
|
"2020-01-04 NaN\n",
|
||
|
|
"2020-01-05 1.0\n",
|
||
|
|
"2020-01-06 1.0\n",
|
||
|
|
"Name: a, dtype: float64\n",
|
||
|
|
" a b c d\n",
|
||
|
|
"2020-01-01 NaN NaN NaN NaN\n",
|
||
|
|
"2020-01-02 NaN NaN NaN NaN\n",
|
||
|
|
"2020-01-03 1.0 NaN NaN NaN\n",
|
||
|
|
"2020-01-04 NaN 1.0 NaN NaN\n",
|
||
|
|
"2020-01-05 1.0 NaN NaN NaN\n",
|
||
|
|
"2020-01-06 1.0 1.0 2.0 NaN\n",
|
||
|
|
"518 µs ± 30.9 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(drawdowns['a'].decline_duration.to_pd())\n",
|
||
|
|
"print(drawdowns.decline_duration.to_pd())\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_drawdowns.decline_duration"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 91,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"2020-01-01 NaN\n",
|
||
|
|
"2020-01-02 NaN\n",
|
||
|
|
"2020-01-03 1.0\n",
|
||
|
|
"2020-01-04 NaN\n",
|
||
|
|
"2020-01-05 1.0\n",
|
||
|
|
"2020-01-06 0.0\n",
|
||
|
|
"Name: a, dtype: float64\n",
|
||
|
|
" a b c d\n",
|
||
|
|
"2020-01-01 NaN NaN NaN NaN\n",
|
||
|
|
"2020-01-02 NaN NaN NaN NaN\n",
|
||
|
|
"2020-01-03 1.0 NaN NaN NaN\n",
|
||
|
|
"2020-01-04 NaN 1.0 NaN NaN\n",
|
||
|
|
"2020-01-05 1.0 NaN NaN NaN\n",
|
||
|
|
"2020-01-06 0.0 1.0 1.0 NaN\n",
|
||
|
|
"479 µs ± 17.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(drawdowns['a'].recovery_duration.to_pd())\n",
|
||
|
|
"print(drawdowns.recovery_duration.to_pd())\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_drawdowns.recovery_duration"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 92,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"2020-01-01 NaN\n",
|
||
|
|
"2020-01-02 NaN\n",
|
||
|
|
"2020-01-03 1.0\n",
|
||
|
|
"2020-01-04 NaN\n",
|
||
|
|
"2020-01-05 1.0\n",
|
||
|
|
"2020-01-06 0.0\n",
|
||
|
|
"Name: a, dtype: float64\n",
|
||
|
|
" a b c d\n",
|
||
|
|
"2020-01-01 NaN NaN NaN NaN\n",
|
||
|
|
"2020-01-02 NaN NaN NaN NaN\n",
|
||
|
|
"2020-01-03 1.0 NaN NaN NaN\n",
|
||
|
|
"2020-01-04 NaN 1.0 NaN NaN\n",
|
||
|
|
"2020-01-05 1.0 NaN NaN NaN\n",
|
||
|
|
"2020-01-06 0.0 1.0 0.5 NaN\n",
|
||
|
|
"699 µs ± 43.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(drawdowns['a'].recovery_duration_ratio.to_pd())\n",
|
||
|
|
"print(drawdowns.recovery_duration_ratio.to_pd())\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_drawdowns.recovery_duration_ratio"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Drawdowns(**Config({\n",
|
||
|
|
" \"wrapper\": \"<vectorbt.base.array_wrapper.ArrayWrapper object at 0x7fa004833748> of shape (6, 4)\",\n",
|
||
|
|
" \"records_arr\": \"<numpy.ndarray object at 0x7fa021f50870> of shape (2,)\",\n",
|
||
|
|
" \"col_mapper\": null,\n",
|
||
|
|
" \"ts\": \"<pandas.core.frame.DataFrame object at 0x7f9ff803f898> of shape (6, 4)\",\n",
|
||
|
|
" \"freq\": \"1 days\"\n",
|
||
|
|
"}))\n",
|
||
|
|
" id col peak_idx start_idx valley_idx end_idx peak_val valley_val \\\n",
|
||
|
|
"0 2 0 4 5 5 5 4.0 1.0 \n",
|
||
|
|
"\n",
|
||
|
|
" end_val status \n",
|
||
|
|
"0 1.0 0 \n",
|
||
|
|
" id col peak_idx start_idx valley_idx end_idx peak_val valley_val \\\n",
|
||
|
|
"0 2 0 4 5 5 5 4.0 1.0 \n",
|
||
|
|
"\n",
|
||
|
|
" end_val status \n",
|
||
|
|
"0 1.0 0 \n",
|
||
|
|
" id col peak_idx start_idx valley_idx end_idx peak_val valley_val \\\n",
|
||
|
|
"0 2 0 4 5 5 5 4.0 1.0 \n",
|
||
|
|
"1 5 2 2 3 4 5 3.0 1.0 \n",
|
||
|
|
"\n",
|
||
|
|
" end_val status \n",
|
||
|
|
"0 1.0 0 \n",
|
||
|
|
"1 2.0 0 \n",
|
||
|
|
"707 µs ± 42.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(drawdowns.active)\n",
|
||
|
|
"print(drawdowns['a'].active.records)\n",
|
||
|
|
"print(drawdowns.active['a'].records)\n",
|
||
|
|
"print(drawdowns.active.records)\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_drawdowns.active"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 18,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Drawdowns(**Config({\n",
|
||
|
|
" \"wrapper\": \"<vectorbt.base.array_wrapper.ArrayWrapper object at 0x7fa004833748> of shape (6, 4)\",\n",
|
||
|
|
" \"records_arr\": \"<numpy.ndarray object at 0x7fa021f4bad8> of shape (4,)\",\n",
|
||
|
|
" \"col_mapper\": null,\n",
|
||
|
|
" \"ts\": \"<pandas.core.frame.DataFrame object at 0x7f9ff803f898> of shape (6, 4)\",\n",
|
||
|
|
" \"freq\": \"1 days\"\n",
|
||
|
|
"}))\n",
|
||
|
|
" id col peak_idx start_idx valley_idx end_idx peak_val valley_val \\\n",
|
||
|
|
"0 0 0 0 1 1 2 2.0 1.0 \n",
|
||
|
|
"1 1 0 2 3 3 4 3.0 1.0 \n",
|
||
|
|
"\n",
|
||
|
|
" end_val status \n",
|
||
|
|
"0 2.0 1 \n",
|
||
|
|
"1 3.0 1 \n",
|
||
|
|
" id col peak_idx start_idx valley_idx end_idx peak_val valley_val \\\n",
|
||
|
|
"0 0 0 0 1 1 2 2.0 1.0 \n",
|
||
|
|
"1 1 0 2 3 3 4 3.0 1.0 \n",
|
||
|
|
"\n",
|
||
|
|
" end_val status \n",
|
||
|
|
"0 2.0 1 \n",
|
||
|
|
"1 3.0 1 \n",
|
||
|
|
" id col peak_idx start_idx valley_idx end_idx peak_val valley_val \\\n",
|
||
|
|
"0 0 0 0 1 1 2 2.0 1.0 \n",
|
||
|
|
"1 1 0 2 3 3 4 3.0 1.0 \n",
|
||
|
|
"2 3 1 1 2 2 3 2.0 1.0 \n",
|
||
|
|
"3 4 1 3 4 4 5 3.0 1.0 \n",
|
||
|
|
"\n",
|
||
|
|
" end_val status \n",
|
||
|
|
"0 2.0 1 \n",
|
||
|
|
"1 3.0 1 \n",
|
||
|
|
"2 2.0 1 \n",
|
||
|
|
"3 3.0 1 \n",
|
||
|
|
"2.26 ms ± 266 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(drawdowns.recovered)\n",
|
||
|
|
"print(drawdowns['a'].recovered.records)\n",
|
||
|
|
"print(drawdowns.recovered['a'].records)\n",
|
||
|
|
"print(drawdowns.recovered.records)\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_drawdowns.recovered"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 95,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"-0.75\n",
|
||
|
|
"a -0.750000\n",
|
||
|
|
"b NaN\n",
|
||
|
|
"c -0.333333\n",
|
||
|
|
"d NaN\n",
|
||
|
|
"Name: active_drawdown, dtype: float64\n",
|
||
|
|
"2.03 ms ± 48.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(drawdowns['a'].active_drawdown())\n",
|
||
|
|
"print(drawdowns.active_drawdown())\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_drawdowns.active_drawdown()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 96,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"1 days 00:00:00\n",
|
||
|
|
"a 1 days\n",
|
||
|
|
"b NaT\n",
|
||
|
|
"c 3 days\n",
|
||
|
|
"d NaT\n",
|
||
|
|
"Name: active_duration, dtype: timedelta64[ns]\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/base/array_wrapper.py:661: UserWarning:\n",
|
||
|
|
"\n",
|
||
|
|
"Couldn't parse the frequency of index. Pass it as `freq` or define it globally under `settings.array_wrapper`.\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"1.41 ms ± 65.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(drawdowns['a'].active_duration())\n",
|
||
|
|
"print(drawdowns.active_duration())\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_drawdowns.active_duration()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 97,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"0.0\n",
|
||
|
|
"a 0.0\n",
|
||
|
|
"b NaN\n",
|
||
|
|
"c 1.0\n",
|
||
|
|
"d NaN\n",
|
||
|
|
"Name: active_recovery_return, dtype: float64\n",
|
||
|
|
"1.43 ms ± 43.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(drawdowns['a'].active_recovery_return())\n",
|
||
|
|
"print(drawdowns.active_recovery_return())\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_drawdowns.active_recovery_return()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 98,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"0 days 00:00:00\n",
|
||
|
|
"a 0 days\n",
|
||
|
|
"b NaT\n",
|
||
|
|
"c 1 days\n",
|
||
|
|
"d NaT\n",
|
||
|
|
"Name: active_recovery_duration, dtype: timedelta64[ns]\n",
|
||
|
|
"1.41 ms ± 53.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(drawdowns['a'].active_recovery_duration())\n",
|
||
|
|
"print(drawdowns.active_recovery_duration())\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_drawdowns.active_recovery_duration()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 19,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Start 2020-01-01 00:00:00\n",
|
||
|
|
"End 2020-01-06 00:00:00\n",
|
||
|
|
"Period 6 days 00:00:00\n",
|
||
|
|
"Coverage [%] 50.0\n",
|
||
|
|
"Total Records 3\n",
|
||
|
|
"Total Recovered Drawdowns 2\n",
|
||
|
|
"Total Active Drawdowns 1\n",
|
||
|
|
"Active Drawdown [%] 75.0\n",
|
||
|
|
"Active Duration 1 days 00:00:00\n",
|
||
|
|
"Active Recovery [%] 0.0\n",
|
||
|
|
"Active Recovery Return [%] 0.0\n",
|
||
|
|
"Active Recovery Duration 0 days 00:00:00\n",
|
||
|
|
"Max Drawdown [%] 66.666667\n",
|
||
|
|
"Avg Drawdown [%] 58.333333\n",
|
||
|
|
"Max Drawdown Duration 1 days 00:00:00\n",
|
||
|
|
"Avg Drawdown Duration 1 days 00:00:00\n",
|
||
|
|
"Max Recovery Return [%] 200.0\n",
|
||
|
|
"Avg Recovery Return [%] 150.0\n",
|
||
|
|
"Max Recovery Duration 1 days 00:00:00\n",
|
||
|
|
"Avg Recovery Duration 1 days 00:00:00\n",
|
||
|
|
"Avg Recovery Duration Ratio 1.0\n",
|
||
|
|
"Name: a, dtype: object\n",
|
||
|
|
"21.6 ms ± 115 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
|
||
|
|
"Start 2020-01-01 00:00:00\n",
|
||
|
|
"End 2020-01-06 00:00:00\n",
|
||
|
|
"Period 6 days 00:00:00\n",
|
||
|
|
"Coverage [%] 50.0\n",
|
||
|
|
"Total Records 3\n",
|
||
|
|
"Total Recovered Drawdowns 2\n",
|
||
|
|
"Total Active Drawdowns 1\n",
|
||
|
|
"Active Drawdown [%] 75.0\n",
|
||
|
|
"Active Duration 1 days 00:00:00\n",
|
||
|
|
"Active Recovery [%] 0.0\n",
|
||
|
|
"Active Recovery Return [%] 0.0\n",
|
||
|
|
"Active Recovery Duration 0 days 00:00:00\n",
|
||
|
|
"Max Drawdown [%] 66.666667\n",
|
||
|
|
"Avg Drawdown [%] 58.333333\n",
|
||
|
|
"Max Drawdown Duration 1 days 00:00:00\n",
|
||
|
|
"Avg Drawdown Duration 1 days 00:00:00\n",
|
||
|
|
"Max Recovery Return [%] 200.0\n",
|
||
|
|
"Avg Recovery Return [%] 150.0\n",
|
||
|
|
"Max Recovery Duration 1 days 00:00:00\n",
|
||
|
|
"Avg Recovery Duration 1 days 00:00:00\n",
|
||
|
|
"Avg Recovery Duration Ratio 1.0\n",
|
||
|
|
"Name: a, dtype: object\n",
|
||
|
|
"44.9 ms ± 2.18 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
|
||
|
|
"Start 2020-01-01 00:00:00\n",
|
||
|
|
"End 2020-01-06 00:00:00\n",
|
||
|
|
"Period 6 days 00:00:00\n",
|
||
|
|
"Coverage [%] 44.444444\n",
|
||
|
|
"Total Records 1.5\n",
|
||
|
|
"Total Recovered Drawdowns 1.0\n",
|
||
|
|
"Total Active Drawdowns 0.5\n",
|
||
|
|
"Active Drawdown [%] 54.166667\n",
|
||
|
|
"Active Duration 2 days 00:00:00\n",
|
||
|
|
"Active Recovery [%] 25.0\n",
|
||
|
|
"Active Recovery Return [%] 50.0\n",
|
||
|
|
"Active Recovery Duration 0 days 12:00:00\n",
|
||
|
|
"Max Drawdown [%] 66.666667\n",
|
||
|
|
"Avg Drawdown [%] 58.333333\n",
|
||
|
|
"Max Drawdown Duration 1 days 00:00:00\n",
|
||
|
|
"Avg Drawdown Duration 1 days 00:00:00\n",
|
||
|
|
"Max Recovery Return [%] 200.0\n",
|
||
|
|
"Avg Recovery Return [%] 150.0\n",
|
||
|
|
"Max Recovery Duration 1 days 00:00:00\n",
|
||
|
|
"Avg Recovery Duration 1 days 00:00:00\n",
|
||
|
|
"Avg Recovery Duration Ratio 1.0\n",
|
||
|
|
"Name: agg_func_mean, dtype: object\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"/Users/olegpolakow/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:7: UserWarning: Object has multiple columns. Aggregating using <function mean at 0x7fa020856598>. Pass column to select a single column/group.\n",
|
||
|
|
" import sys\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"45.2 ms ± 757 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(drawdowns['a'].stats())\n",
|
||
|
|
"%timeit big_drawdowns[0].stats(silence_warnings=True)\n",
|
||
|
|
"\n",
|
||
|
|
"print(drawdowns.stats(column='a'))\n",
|
||
|
|
"%timeit big_drawdowns.stats(column=0, silence_warnings=True)\n",
|
||
|
|
"\n",
|
||
|
|
"print(drawdowns.stats())\n",
|
||
|
|
"%timeit big_drawdowns.stats(silence_warnings=True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/svg+xml": [
|
||
|
|
"<svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"700\" height=\"350\" style=\"\" viewBox=\"0 0 700 350\"><rect x=\"0\" y=\"0\" width=\"700\" height=\"350\" style=\"fill: rgb(255, 255, 255); fill-opacity: 1;\"/><defs id=\"defs-e02067\"><g class=\"clips\"><clipPath id=\"clipe02067xyplot\" class=\"plotclip\"><rect width=\"637\" height=\"261\"/></clipPath><clipPath class=\"axesclip\" id=\"clipe02067x\"><rect x=\"33\" y=\"0\" width=\"637\" height=\"350\"/></clipPath><clipPath class=\"axesclip\" id=\"clipe02067y\"><rect x=\"0\" y=\"46\" width=\"700\" height=\"261\"/></clipPath><clipPath class=\"axesclip\" id=\"clipe02067xy\"><rect x=\"33\" y=\"46\" width=\"637\" height=\"261\"/></clipPath></g><g class=\"gradients\"/><g class=\"patterns\"/></defs><g class=\"bglayer\"><rect class=\"bg\" x=\"33\" y=\"46\" width=\"637\" height=\"261\" style=\"fill: rgb(229, 236, 246); fill-opacity: 1; stroke-width: 0;\"/></g><g class=\"layer-below\"><g class=\"imagelayer\"/><g class=\"shapelayer\"><path data-index=\"0\" fill-rule=\"evenodd\" d=\"M69.22,307H182.13V46H69.22Z\" clip-path=\"url(#clipe02067x)\" style=\"opacity: 0.2; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(255, 0, 0); fill-opacity: 1; stroke-width: 0px;\"/><path data-index=\"1\" fill-rule=\"evenodd\" d=\"M295.04,307H407.96V46H295.04Z\" clip-path=\"url(#clipe02067x)\" style=\"opacity: 0.2; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(255, 0, 0); fill-opacity: 1; stroke-width: 0px;\"/><path data-index=\"2\" fill-rule=\"evenodd\" d=\"M182.13,307H295.04V46H182.13Z\" clip-path=\"url(#clipe02067x)\" style=\"opacity: 0.2; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(0, 128, 0); fill-opacity: 1; stroke-width: 0px;\"/><path data-index=\"3\" fill-rule=\"evenodd\" d=\"M407.96,307H520.87V46H407.96Z\" clip-path=\"url(#clipe02067x)\" style=\"opacity: 0.2; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(0, 128, 0); fill-opacity: 1; stroke-width: 0px;\"/><path data-index=\"4\" fill-rule=\"evenodd\" d=\"M520.87,307H633.78V46H520.87Z\" clip-path=\"url(#clipe02067x)\" style=\"opacity: 0.2; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(255, 165, 0); fill-opacity: 1; stroke-width: 0px;\"/></g></g><g class=\"cartesianlayer\"><g class=\"subplot xy\"><g class=\"layer-subplot\"><g class=\"shapelayer\"/><g class=\"imagelayer\"/></g><g class=\"gridlayer\"><g class=\"x\"><path class=\"xgrid crisp\" transform=\"translate(69.22,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(182.13,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(295.04,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(407.96,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(520.87,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(633.78,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/></g><g class=\"y\"><path class=\"ygrid crisp\" transform=\"translate(0,289.58000000000004)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,251.88)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,214.19)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,176.5)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,138.81)\" d=\"M33,0h637\" style=\"stroke:
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"drawdowns['a'].plot().show_svg()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/svg+xml": [
|
||
|
|
"<svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"700\" height=\"350\" style=\"\" viewBox=\"0 0 700 350\"><rect x=\"0\" y=\"0\" width=\"700\" height=\"350\" style=\"fill: rgb(255, 255, 255); fill-opacity: 1;\"/><defs id=\"defs-36aee7\"><g class=\"clips\"><clipPath id=\"clip36aee7xyplot\" class=\"plotclip\"><rect width=\"637\" height=\"261\"/></clipPath><clipPath class=\"axesclip\" id=\"clip36aee7x\"><rect x=\"33\" y=\"0\" width=\"637\" height=\"350\"/></clipPath><clipPath class=\"axesclip\" id=\"clip36aee7y\"><rect x=\"0\" y=\"46\" width=\"700\" height=\"261\"/></clipPath><clipPath class=\"axesclip\" id=\"clip36aee7xy\"><rect x=\"33\" y=\"46\" width=\"637\" height=\"261\"/></clipPath></g><g class=\"gradients\"/><g class=\"patterns\"/></defs><g class=\"bglayer\"><rect class=\"bg\" x=\"33\" y=\"46\" width=\"637\" height=\"261\" style=\"fill: rgb(229, 236, 246); fill-opacity: 1; stroke-width: 0;\"/></g><g class=\"layer-below\"><g class=\"imagelayer\"/><g class=\"shapelayer\"><path data-index=\"0\" fill-rule=\"evenodd\" d=\"M520.74,307H633.78V46H520.74Z\" clip-path=\"url(#clip36aee7x)\" style=\"opacity: 0.2; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(255, 165, 0); fill-opacity: 1; stroke-width: 0px;\"/></g></g><g class=\"cartesianlayer\"><g class=\"subplot xy\"><g class=\"layer-subplot\"><g class=\"shapelayer\"/><g class=\"imagelayer\"/></g><g class=\"gridlayer\"><g class=\"x\"><path class=\"xgrid crisp\" transform=\"translate(68.6,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(181.64,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(294.67,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(407.71,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(520.74,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(633.78,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/></g><g class=\"y\"><path class=\"ygrid crisp\" transform=\"translate(0,289.58000000000004)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,251.88)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,214.19)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,176.5)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,138.81)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,101.12)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,63.43)\" d=\"M33,0h637\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/></g></g><g class=\"zerolinelayer\"/><path class=\"xlines-below\"/><path class=\"ylines-below\"/><g class=\"overlines-below\"/><g class=\"xaxislayer-below\"/><g class=\"yaxislayer-below\"/><g class=\"overaxes-below\"/><g class=\"plot\" transform=\"translate(33,46)\" clip-path=\"url(#clip36aee7xyplot)\"><g class=\"scatterlayer mlayer\"><g class=\"trace scatter trace36f1672b-06da-42f9-8948-bb7e08e1231a\" style=\"stroke-miterlimit: 2; opacity: 1;\"><g class=\"fills\"/><g class=\"errorbars\"/><g class=\"lines\"><path class=\"js-line\" d=\"M35.6,168.19L148.64,243.58L261.67,92
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"drawdowns.plot(column='a', top_n=1).show_svg()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 19,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/svg+xml": [
|
||
|
|
"<svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"750\" height=\"380\" style=\"\" viewBox=\"0 0 750 380\"><rect x=\"0\" y=\"0\" width=\"750\" height=\"380\" style=\"fill: rgb(255, 255, 255); fill-opacity: 1;\"/><defs id=\"defs-ae7538\"><g class=\"clips\"><clipPath id=\"clipae7538xyplot\" class=\"plotclip\"><rect width=\"687\" height=\"245\"/></clipPath><clipPath class=\"axesclip\" id=\"clipae7538x\"><rect x=\"33\" y=\"0\" width=\"687\" height=\"380\"/></clipPath><clipPath class=\"axesclip\" id=\"clipae7538y\"><rect x=\"0\" y=\"67\" width=\"750\" height=\"245\"/></clipPath><clipPath class=\"axesclip\" id=\"clipae7538xy\"><rect x=\"33\" y=\"67\" width=\"687\" height=\"245\"/></clipPath></g><g class=\"gradients\"/><g class=\"patterns\"/></defs><g class=\"bglayer\"><rect class=\"bg\" x=\"33\" y=\"67\" width=\"687\" height=\"245\" style=\"fill: rgb(229, 236, 246); fill-opacity: 1; stroke-width: 0;\"/></g><g class=\"layer-below\"><g class=\"imagelayer\"/><g class=\"shapelayer\"><path data-index=\"0\" fill-rule=\"evenodd\" d=\"M71.72999999999999,312H193.64V67H71.72999999999999Z\" clip-path=\"url(#clipae7538x)\" style=\"opacity: 0.2; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(255, 0, 0); fill-opacity: 1; stroke-width: 0px;\"/><path data-index=\"1\" fill-rule=\"evenodd\" d=\"M315.55,312H437.45V67H315.55Z\" clip-path=\"url(#clipae7538x)\" style=\"opacity: 0.2; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(255, 0, 0); fill-opacity: 1; stroke-width: 0px;\"/><path data-index=\"2\" fill-rule=\"evenodd\" d=\"M193.64,312H315.55V67H193.64Z\" clip-path=\"url(#clipae7538x)\" style=\"opacity: 0.2; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(0, 128, 0); fill-opacity: 1; stroke-width: 0px;\"/><path data-index=\"3\" fill-rule=\"evenodd\" d=\"M437.45,312H559.36V67H437.45Z\" clip-path=\"url(#clipae7538x)\" style=\"opacity: 0.2; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(0, 128, 0); fill-opacity: 1; stroke-width: 0px;\"/><path data-index=\"4\" fill-rule=\"evenodd\" d=\"M559.36,312H681.27V67H559.36Z\" clip-path=\"url(#clipae7538x)\" style=\"opacity: 0.2; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(255, 165, 0); fill-opacity: 1; stroke-width: 0px;\"/></g></g><g class=\"cartesianlayer\"><g class=\"subplot xy\"><g class=\"layer-subplot\"><g class=\"shapelayer\"/><g class=\"imagelayer\"/></g><g class=\"gridlayer\"><g class=\"x\"><path class=\"xgrid crisp\" transform=\"translate(71.72999999999999,0)\" d=\"M0,67v245\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(193.64,0)\" d=\"M0,67v245\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(315.55,0)\" d=\"M0,67v245\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(437.45,0)\" d=\"M0,67v245\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(559.36,0)\" d=\"M0,67v245\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(681.27,0)\" d=\"M0,67v245\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/></g><g class=\"y\"><path class=\"ygrid crisp\" transform=\"translate(0,295.38)\" d=\"M33,0h687\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,260.08000000000004)\" d=\"M33,0h687\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,224.79)\" d=\"M33,0h687\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,189.5)\" d=\"M33,0h687\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,154.2
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"drawdowns['a'].plots().show_svg()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Orders"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 20,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" a b c d\n",
|
||
|
|
"2020-01-01 1 1 1 1\n",
|
||
|
|
"2020-01-02 2 2 2 2\n",
|
||
|
|
"2020-01-03 3 3 3 3\n",
|
||
|
|
"2020-01-04 4 4 4 4\n",
|
||
|
|
"2020-01-05 5 5 5 5\n",
|
||
|
|
"2020-01-06 6 6 6 6\n",
|
||
|
|
"2020-01-07 7 7 7 7\n",
|
||
|
|
"2020-01-08 8 8 8 8\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"close = pd.Series([1, 2, 3, 4, 5, 6, 7, 8], index=[\n",
|
||
|
|
" datetime(2020, 1, 1),\n",
|
||
|
|
" datetime(2020, 1, 2),\n",
|
||
|
|
" datetime(2020, 1, 3),\n",
|
||
|
|
" datetime(2020, 1, 4),\n",
|
||
|
|
" datetime(2020, 1, 5),\n",
|
||
|
|
" datetime(2020, 1, 6),\n",
|
||
|
|
" datetime(2020, 1, 7),\n",
|
||
|
|
" datetime(2020, 1, 8)\n",
|
||
|
|
"]).vbt.tile(4, keys=['a', 'b', 'c', 'd'])\n",
|
||
|
|
"print(close)\n",
|
||
|
|
"\n",
|
||
|
|
"big_close = pd.DataFrame(np.random.uniform(1, 10, size=(1000, 1000)))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 21,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"(21,)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"from vectorbt.portfolio.enums import order_dt\n",
|
||
|
|
"\n",
|
||
|
|
"records_arr = np.asarray([\n",
|
||
|
|
" (0, 0, 0, 1. , 1., 0.01 , 0), (1, 0, 1, 0.1, 2., 0.002, 0),\n",
|
||
|
|
" (2, 0, 2, 1. , 3., 0.03 , 1), (3, 0, 3, 0.1, 4., 0.004, 1),\n",
|
||
|
|
" (4, 0, 5, 1. , 6., 0.06 , 0), (5, 0, 6, 1. , 7., 0.07 , 1),\n",
|
||
|
|
" (6, 0, 7, 2. , 8., 0.16 , 0), (7, 1, 0, 1. , 1., 0.01 , 1),\n",
|
||
|
|
" (8, 1, 1, 0.1, 2., 0.002, 1), (9, 1, 2, 1. , 3., 0.03 , 0),\n",
|
||
|
|
" (10, 1, 3, 0.1, 4., 0.004, 0), (11, 1, 5, 1. , 6., 0.06 , 1),\n",
|
||
|
|
" (12, 1, 6, 1. , 7., 0.07 , 0), (13, 1, 7, 2. , 8., 0.16 , 1),\n",
|
||
|
|
" (14, 2, 0, 1. , 1., 0.01 , 0), (15, 2, 1, 0.1, 2., 0.002, 0),\n",
|
||
|
|
" (16, 2, 2, 1. , 3., 0.03 , 1), (17, 2, 3, 0.1, 4., 0.004, 1),\n",
|
||
|
|
" (18, 2, 5, 1. , 6., 0.06 , 0), (19, 2, 6, 2. , 7., 0.14 , 1),\n",
|
||
|
|
" (20, 2, 7, 2. , 8., 0.16 , 0)\n",
|
||
|
|
"], dtype=order_dt)\n",
|
||
|
|
"print(records_arr.shape)\n",
|
||
|
|
"\n",
|
||
|
|
"wrapper = vbt.ArrayWrapper.from_obj(close, freq='1 days')\n",
|
||
|
|
"orders = vbt.Orders(wrapper, records_arr, close)\n",
|
||
|
|
"orders_grouped = vbt.Orders(wrapper.regroup(group_by), records_arr, close)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 22,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"(1000000,)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"big_records_arr = np.asarray(list(zip(*(\n",
|
||
|
|
" np.arange(1000000),\n",
|
||
|
|
" np.tile(np.arange(1000), 1000),\n",
|
||
|
|
" np.repeat(np.arange(1000), 1000),\n",
|
||
|
|
" np.full(1000000, 10),\n",
|
||
|
|
" np.random.uniform(1, 10, size=1000000),\n",
|
||
|
|
" np.full(1000000, 1),\n",
|
||
|
|
" np.full(1000000, 1)\n",
|
||
|
|
"))), dtype=order_dt)\n",
|
||
|
|
"big_records_arr['side'][::2] = 0\n",
|
||
|
|
"print(big_records_arr.shape)\n",
|
||
|
|
"\n",
|
||
|
|
"big_wrapper = vbt.ArrayWrapper.from_obj(big_close, freq='1 days')\n",
|
||
|
|
"big_orders = vbt.Orders(big_wrapper, big_records_arr, big_close)\n",
|
||
|
|
"big_orders_grouped = vbt.Orders(big_wrapper.copy(group_by=big_group_by), big_records_arr, big_close)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 106,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" id col idx size price fees side\n",
|
||
|
|
"0 0 0 0 1.0 1.0 0.010 0\n",
|
||
|
|
"1 1 0 1 0.1 2.0 0.002 0\n",
|
||
|
|
"2 2 0 2 1.0 3.0 0.030 1\n",
|
||
|
|
"3 3 0 3 0.1 4.0 0.004 1\n",
|
||
|
|
"4 4 0 5 1.0 6.0 0.060 0\n",
|
||
|
|
"5 5 0 6 1.0 7.0 0.070 1\n",
|
||
|
|
"6 6 0 7 2.0 8.0 0.160 0\n",
|
||
|
|
"7 7 1 0 1.0 1.0 0.010 1\n",
|
||
|
|
"8 8 1 1 0.1 2.0 0.002 1\n",
|
||
|
|
"9 9 1 2 1.0 3.0 0.030 0\n",
|
||
|
|
"10 10 1 3 0.1 4.0 0.004 0\n",
|
||
|
|
"11 11 1 5 1.0 6.0 0.060 1\n",
|
||
|
|
"12 12 1 6 1.0 7.0 0.070 0\n",
|
||
|
|
"13 13 1 7 2.0 8.0 0.160 1\n",
|
||
|
|
"14 14 2 0 1.0 1.0 0.010 0\n",
|
||
|
|
"15 15 2 1 0.1 2.0 0.002 0\n",
|
||
|
|
"16 16 2 2 1.0 3.0 0.030 1\n",
|
||
|
|
"17 17 2 3 0.1 4.0 0.004 1\n",
|
||
|
|
"18 18 2 5 1.0 6.0 0.060 0\n",
|
||
|
|
"19 19 2 6 2.0 7.0 0.140 1\n",
|
||
|
|
"20 20 2 7 2.0 8.0 0.160 0\n",
|
||
|
|
" a b c d\n",
|
||
|
|
"2020-01-01 1 1 1 1\n",
|
||
|
|
"2020-01-02 2 2 2 2\n",
|
||
|
|
"2020-01-03 3 3 3 3\n",
|
||
|
|
"2020-01-04 4 4 4 4\n",
|
||
|
|
"2020-01-05 5 5 5 5\n",
|
||
|
|
"2020-01-06 6 6 6 6\n",
|
||
|
|
"2020-01-07 7 7 7 7\n",
|
||
|
|
"2020-01-08 8 8 8 8\n",
|
||
|
|
" id col idx size price fees side\n",
|
||
|
|
"0 0 0 0 1.0 1.0 0.010 0\n",
|
||
|
|
"1 1 0 1 0.1 2.0 0.002 0\n",
|
||
|
|
"2 2 0 2 1.0 3.0 0.030 1\n",
|
||
|
|
"3 3 0 3 0.1 4.0 0.004 1\n",
|
||
|
|
"4 4 0 5 1.0 6.0 0.060 0\n",
|
||
|
|
"5 5 0 6 1.0 7.0 0.070 1\n",
|
||
|
|
"6 6 0 7 2.0 8.0 0.160 0\n",
|
||
|
|
"2020-01-01 1\n",
|
||
|
|
"2020-01-02 2\n",
|
||
|
|
"2020-01-03 3\n",
|
||
|
|
"2020-01-04 4\n",
|
||
|
|
"2020-01-05 5\n",
|
||
|
|
"2020-01-06 6\n",
|
||
|
|
"2020-01-07 7\n",
|
||
|
|
"2020-01-08 8\n",
|
||
|
|
"Name: a, dtype: int64\n",
|
||
|
|
"1.06 ms ± 17.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
|
||
|
|
"12.5 ms ± 2.47 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
|
||
|
|
"13.5 ms ± 1.52 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"13 ms ± 4.86 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(orders.records)\n",
|
||
|
|
"print(orders.close)\n",
|
||
|
|
"\n",
|
||
|
|
"print(orders['a'].records)\n",
|
||
|
|
"print(orders['a'].close)\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_orders.iloc[0]\n",
|
||
|
|
"%timeit big_orders.iloc[:]\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_orders_grouped.iloc[0]\n",
|
||
|
|
"%timeit big_orders_grouped.iloc[:]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 107,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" Order Id Column Timestamp Size Price Fees Side\n",
|
||
|
|
"0 0 a 2020-01-01 1.0 1.0 0.010 Buy\n",
|
||
|
|
"1 1 a 2020-01-02 0.1 2.0 0.002 Buy\n",
|
||
|
|
"2 2 a 2020-01-03 1.0 3.0 0.030 Sell\n",
|
||
|
|
"3 3 a 2020-01-04 0.1 4.0 0.004 Sell\n",
|
||
|
|
"4 4 a 2020-01-06 1.0 6.0 0.060 Buy\n",
|
||
|
|
"5 5 a 2020-01-07 1.0 7.0 0.070 Sell\n",
|
||
|
|
"6 6 a 2020-01-08 2.0 8.0 0.160 Buy\n",
|
||
|
|
"7 7 b 2020-01-01 1.0 1.0 0.010 Sell\n",
|
||
|
|
"8 8 b 2020-01-02 0.1 2.0 0.002 Sell\n",
|
||
|
|
"9 9 b 2020-01-03 1.0 3.0 0.030 Buy\n",
|
||
|
|
"10 10 b 2020-01-04 0.1 4.0 0.004 Buy\n",
|
||
|
|
"11 11 b 2020-01-06 1.0 6.0 0.060 Sell\n",
|
||
|
|
"12 12 b 2020-01-07 1.0 7.0 0.070 Buy\n",
|
||
|
|
"13 13 b 2020-01-08 2.0 8.0 0.160 Sell\n",
|
||
|
|
"14 14 c 2020-01-01 1.0 1.0 0.010 Buy\n",
|
||
|
|
"15 15 c 2020-01-02 0.1 2.0 0.002 Buy\n",
|
||
|
|
"16 16 c 2020-01-03 1.0 3.0 0.030 Sell\n",
|
||
|
|
"17 17 c 2020-01-04 0.1 4.0 0.004 Sell\n",
|
||
|
|
"18 18 c 2020-01-06 1.0 6.0 0.060 Buy\n",
|
||
|
|
"19 19 c 2020-01-07 2.0 7.0 0.140 Sell\n",
|
||
|
|
"20 20 c 2020-01-08 2.0 8.0 0.160 Buy\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(orders.records_readable)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 23,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Orders(**Config({\n",
|
||
|
|
" \"wrapper\": \"<vectorbt.base.array_wrapper.ArrayWrapper object at 0x7f9f78606d30> of shape (8, 4)\",\n",
|
||
|
|
" \"records_arr\": \"<numpy.ndarray object at 0x7f9f780709d0> of shape (11,)\",\n",
|
||
|
|
" \"col_mapper\": null,\n",
|
||
|
|
" \"close\": \"<pandas.core.frame.DataFrame object at 0x7f9f782a4390> of shape (8, 4)\"\n",
|
||
|
|
"}))\n",
|
||
|
|
" id col idx size price fees side\n",
|
||
|
|
"0 0 0 0 1.0 1.0 0.010 0\n",
|
||
|
|
"1 1 0 1 0.1 2.0 0.002 0\n",
|
||
|
|
"2 4 0 5 1.0 6.0 0.060 0\n",
|
||
|
|
"3 6 0 7 2.0 8.0 0.160 0\n",
|
||
|
|
" id col idx size price fees side\n",
|
||
|
|
"0 0 0 0 1.0 1.0 0.010 0\n",
|
||
|
|
"1 1 0 1 0.1 2.0 0.002 0\n",
|
||
|
|
"2 4 0 5 1.0 6.0 0.060 0\n",
|
||
|
|
"3 6 0 7 2.0 8.0 0.160 0\n",
|
||
|
|
" id col idx size price fees side\n",
|
||
|
|
"0 0 0 0 1.0 1.0 0.010 0\n",
|
||
|
|
"1 1 0 1 0.1 2.0 0.002 0\n",
|
||
|
|
"2 4 0 5 1.0 6.0 0.060 0\n",
|
||
|
|
"3 6 0 7 2.0 8.0 0.160 0\n",
|
||
|
|
"4 9 1 2 1.0 3.0 0.030 0\n",
|
||
|
|
"5 10 1 3 0.1 4.0 0.004 0\n",
|
||
|
|
"6 12 1 6 1.0 7.0 0.070 0\n",
|
||
|
|
"7 14 2 0 1.0 1.0 0.010 0\n",
|
||
|
|
"8 15 2 1 0.1 2.0 0.002 0\n",
|
||
|
|
"9 18 2 5 1.0 6.0 0.060 0\n",
|
||
|
|
"10 20 2 7 2.0 8.0 0.160 0\n",
|
||
|
|
"11.1 ms ± 387 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(orders.buy)\n",
|
||
|
|
"print(orders['a'].buy.records)\n",
|
||
|
|
"print(orders.buy['a'].records)\n",
|
||
|
|
"print(orders.buy.records)\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_orders.buy"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 24,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Orders(**Config({\n",
|
||
|
|
" \"wrapper\": \"<vectorbt.base.array_wrapper.ArrayWrapper object at 0x7f9f78606d30> of shape (8, 4)\",\n",
|
||
|
|
" \"records_arr\": \"<numpy.ndarray object at 0x7fa0223c06b8> of shape (10,)\",\n",
|
||
|
|
" \"col_mapper\": null,\n",
|
||
|
|
" \"close\": \"<pandas.core.frame.DataFrame object at 0x7f9f782a4390> of shape (8, 4)\"\n",
|
||
|
|
"}))\n",
|
||
|
|
" id col idx size price fees side\n",
|
||
|
|
"0 2 0 2 1.0 3.0 0.030 1\n",
|
||
|
|
"1 3 0 3 0.1 4.0 0.004 1\n",
|
||
|
|
"2 5 0 6 1.0 7.0 0.070 1\n",
|
||
|
|
" id col idx size price fees side\n",
|
||
|
|
"0 2 0 2 1.0 3.0 0.030 1\n",
|
||
|
|
"1 3 0 3 0.1 4.0 0.004 1\n",
|
||
|
|
"2 5 0 6 1.0 7.0 0.070 1\n",
|
||
|
|
" id col idx size price fees side\n",
|
||
|
|
"0 2 0 2 1.0 3.0 0.030 1\n",
|
||
|
|
"1 3 0 3 0.1 4.0 0.004 1\n",
|
||
|
|
"2 5 0 6 1.0 7.0 0.070 1\n",
|
||
|
|
"3 7 1 0 1.0 1.0 0.010 1\n",
|
||
|
|
"4 8 1 1 0.1 2.0 0.002 1\n",
|
||
|
|
"5 11 1 5 1.0 6.0 0.060 1\n",
|
||
|
|
"6 13 1 7 2.0 8.0 0.160 1\n",
|
||
|
|
"7 16 2 2 1.0 3.0 0.030 1\n",
|
||
|
|
"8 17 2 3 0.1 4.0 0.004 1\n",
|
||
|
|
"9 19 2 6 2.0 7.0 0.140 1\n",
|
||
|
|
"10.7 ms ± 117 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(orders.sell)\n",
|
||
|
|
"print(orders['a'].sell.records)\n",
|
||
|
|
"print(orders.sell['a'].records)\n",
|
||
|
|
"print(orders.sell.records)\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_orders.sell"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 25,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Start 2020-01-01 00:00:00\n",
|
||
|
|
"End 2020-01-08 00:00:00\n",
|
||
|
|
"Period 8 days 00:00:00\n",
|
||
|
|
"Total Records 7\n",
|
||
|
|
"Total Buy Orders 4\n",
|
||
|
|
"Total Sell Orders 3\n",
|
||
|
|
"Min Size 0.1\n",
|
||
|
|
"Max Size 2.0\n",
|
||
|
|
"Avg Size 0.885714\n",
|
||
|
|
"Avg Buy Size 1.025\n",
|
||
|
|
"Avg Sell Size 0.7\n",
|
||
|
|
"Avg Buy Price 4.25\n",
|
||
|
|
"Avg Sell Price 4.666667\n",
|
||
|
|
"Total Fees 0.336\n",
|
||
|
|
"Min Fees 0.002\n",
|
||
|
|
"Max Fees 0.16\n",
|
||
|
|
"Avg Fees 0.048\n",
|
||
|
|
"Avg Buy Fees 0.058\n",
|
||
|
|
"Avg Sell Fees 0.034667\n",
|
||
|
|
"Name: a, dtype: object\n",
|
||
|
|
"14 ms ± 171 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"Start 2020-01-01 00:00:00\n",
|
||
|
|
"End 2020-01-08 00:00:00\n",
|
||
|
|
"Period 8 days 00:00:00\n",
|
||
|
|
"Total Records 7\n",
|
||
|
|
"Total Buy Orders 4\n",
|
||
|
|
"Total Sell Orders 3\n",
|
||
|
|
"Min Size 0.1\n",
|
||
|
|
"Max Size 2.0\n",
|
||
|
|
"Avg Size 0.885714\n",
|
||
|
|
"Avg Buy Size 1.025\n",
|
||
|
|
"Avg Sell Size 0.7\n",
|
||
|
|
"Avg Buy Price 4.25\n",
|
||
|
|
"Avg Sell Price 4.666667\n",
|
||
|
|
"Total Fees 0.336\n",
|
||
|
|
"Min Fees 0.002\n",
|
||
|
|
"Max Fees 0.16\n",
|
||
|
|
"Avg Fees 0.048\n",
|
||
|
|
"Avg Buy Fees 0.058\n",
|
||
|
|
"Avg Sell Fees 0.034667\n",
|
||
|
|
"Name: a, dtype: object\n",
|
||
|
|
"85.6 ms ± 3.18 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
|
||
|
|
"Start 2020-01-01 00:00:00\n",
|
||
|
|
"End 2020-01-08 00:00:00\n",
|
||
|
|
"Period 8 days 00:00:00\n",
|
||
|
|
"Total Records 5.25\n",
|
||
|
|
"Total Buy Orders 2.75\n",
|
||
|
|
"Total Sell Orders 2.5\n",
|
||
|
|
"Min Size 0.1\n",
|
||
|
|
"Max Size 2.0\n",
|
||
|
|
"Avg Size 0.933333\n",
|
||
|
|
"Avg Buy Size 0.916667\n",
|
||
|
|
"Avg Sell Size 0.919444\n",
|
||
|
|
"Avg Buy Price 4.388889\n",
|
||
|
|
"Avg Sell Price 4.527778\n",
|
||
|
|
"Total Fees 0.2695\n",
|
||
|
|
"Min Fees 0.002\n",
|
||
|
|
"Max Fees 0.16\n",
|
||
|
|
"Avg Fees 0.051333\n",
|
||
|
|
"Avg Buy Fees 0.050222\n",
|
||
|
|
"Avg Sell Fees 0.050222\n",
|
||
|
|
"Name: agg_func_mean, dtype: object\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"/Users/olegpolakow/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:7: UserWarning: Object has multiple columns. Aggregating using <function mean at 0x7fa020856598>. Pass column to select a single column/group.\n",
|
||
|
|
" import sys\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"84.7 ms ± 2.13 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(orders['a'].stats())\n",
|
||
|
|
"%timeit big_orders[0].stats(silence_warnings=True)\n",
|
||
|
|
"\n",
|
||
|
|
"print(orders.stats(column='a'))\n",
|
||
|
|
"%timeit big_orders.stats(column=0, silence_warnings=True)\n",
|
||
|
|
"\n",
|
||
|
|
"print(orders.stats())\n",
|
||
|
|
"%timeit big_orders.stats(silence_warnings=True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 27,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/svg+xml": [
|
||
|
|
"<svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"700\" height=\"350\" style=\"\" viewBox=\"0 0 700 350\"><rect x=\"0\" y=\"0\" width=\"700\" height=\"350\" style=\"fill: rgb(255, 255, 255); fill-opacity: 1;\"/><defs id=\"defs-f204e7\"><g class=\"clips\"><clipPath id=\"clipf204e7xyplot\" class=\"plotclip\"><rect width=\"640\" height=\"261\"/></clipPath><clipPath class=\"axesclip\" id=\"clipf204e7x\"><rect x=\"30\" y=\"0\" width=\"640\" height=\"350\"/></clipPath><clipPath class=\"axesclip\" id=\"clipf204e7y\"><rect x=\"0\" y=\"46\" width=\"700\" height=\"261\"/></clipPath><clipPath class=\"axesclip\" id=\"clipf204e7xy\"><rect x=\"30\" y=\"46\" width=\"640\" height=\"261\"/></clipPath></g><g class=\"gradients\"/><g class=\"patterns\"/></defs><g class=\"bglayer\"><rect class=\"bg\" x=\"30\" y=\"46\" width=\"640\" height=\"261\" style=\"fill: rgb(229, 236, 246); fill-opacity: 1; stroke-width: 0;\"/></g><g class=\"layer-below\"><g class=\"imagelayer\"/><g class=\"shapelayer\"/></g><g class=\"cartesianlayer\"><g class=\"subplot xy\"><g class=\"layer-subplot\"><g class=\"shapelayer\"/><g class=\"imagelayer\"/></g><g class=\"gridlayer\"><g class=\"x\"><path class=\"xgrid crisp\" transform=\"translate(67,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(147.86,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(228.71,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(309.57,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(390.43,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(471.29,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(552.14,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(633,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/></g><g class=\"y\"><path class=\"ygrid crisp\" transform=\"translate(0,256.82)\" d=\"M30,0h640\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,192.56)\" d=\"M30,0h640\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,128.31)\" d=\"M30,0h640\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,64.05)\" d=\"M30,0h640\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/></g></g><g class=\"zerolinelayer\"/><path class=\"xlines-below\"/><path class=\"ylines-below\"/><g class=\"overlines-below\"/><g class=\"xaxislayer-below\"/><g class=\"yaxislayer-below\"/><g class=\"overaxes-below\"/><g class=\"plot\" transform=\"translate(30,46)\" clip-path=\"url(#clipf204e7xyplot)\"><g class=\"scatterlayer mlayer\"><g class=\"trace scatter trace615bd783-5476-4ca3-8638-ccc06f1ffe19\" style=\"stroke-miterlimit: 2; opacity: 1;\"><g class=\"fills\"/><g class=\"errorbars\"/><g class=\"lines\"><path class=\"js-line\" d=\"M37,242.95L603,18.05\" style=\"vector-effect: non-scaling-stroke; fill: none; stroke: rgb(31, 119, 180); stroke-opacity: 1; stroke-width: 2px; opacity: 1;\"/></g><g class=\"points\"><path class=\"point\" transform=\"translate(37,242.95)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(31, 119, 180); fill-opacity: 1;\"/><path class=\"point\" transform=\"translate(117.86,210.82)\" d=\"M3,0A3,3 0 1,1 0,-3A
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"orders['a'].plot().show_svg()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 28,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/svg+xml": [
|
||
|
|
"<svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"700\" height=\"350\" style=\"\" viewBox=\"0 0 700 350\"><rect x=\"0\" y=\"0\" width=\"700\" height=\"350\" style=\"fill: rgb(255, 255, 255); fill-opacity: 1;\"/><defs id=\"defs-88ebb4\"><g class=\"clips\"><clipPath id=\"clip88ebb4xyplot\" class=\"plotclip\"><rect width=\"640\" height=\"261\"/></clipPath><clipPath class=\"axesclip\" id=\"clip88ebb4x\"><rect x=\"30\" y=\"0\" width=\"640\" height=\"350\"/></clipPath><clipPath class=\"axesclip\" id=\"clip88ebb4y\"><rect x=\"0\" y=\"46\" width=\"700\" height=\"261\"/></clipPath><clipPath class=\"axesclip\" id=\"clip88ebb4xy\"><rect x=\"30\" y=\"46\" width=\"640\" height=\"261\"/></clipPath></g><g class=\"gradients\"/><g class=\"patterns\"/></defs><g class=\"bglayer\"><rect class=\"bg\" x=\"30\" y=\"46\" width=\"640\" height=\"261\" style=\"fill: rgb(229, 236, 246); fill-opacity: 1; stroke-width: 0;\"/></g><g class=\"layer-below\"><g class=\"imagelayer\"/><g class=\"shapelayer\"/></g><g class=\"cartesianlayer\"><g class=\"subplot xy\"><g class=\"layer-subplot\"><g class=\"shapelayer\"/><g class=\"imagelayer\"/></g><g class=\"gridlayer\"><g class=\"x\"><path class=\"xgrid crisp\" transform=\"translate(67,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(147.86,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(228.71,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(309.57,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(390.43,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(471.29,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(552.14,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xgrid crisp\" transform=\"translate(633,0)\" d=\"M0,46v261\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/></g><g class=\"y\"><path class=\"ygrid crisp\" transform=\"translate(0,256.82)\" d=\"M30,0h640\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,192.56)\" d=\"M30,0h640\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,128.31)\" d=\"M30,0h640\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ygrid crisp\" transform=\"translate(0,64.05)\" d=\"M30,0h640\" style=\"stroke: rgb(255, 255, 255); stroke-opacity: 1; stroke-width: 1px;\"/></g></g><g class=\"zerolinelayer\"/><path class=\"xlines-below\"/><path class=\"ylines-below\"/><g class=\"overlines-below\"/><g class=\"xaxislayer-below\"/><g class=\"yaxislayer-below\"/><g class=\"overaxes-below\"/><g class=\"plot\" transform=\"translate(30,46)\" clip-path=\"url(#clip88ebb4xyplot)\"><g class=\"scatterlayer mlayer\"><g class=\"trace scatter trace0d852ff7-75c2-4f03-97f1-dd0253cece74\" style=\"stroke-miterlimit: 2; opacity: 1;\"><g class=\"fills\"/><g class=\"errorbars\"/><g class=\"lines\"><path class=\"js-line\" d=\"M37,242.95L603,18.05\" style=\"vector-effect: non-scaling-stroke; fill: none; stroke: rgb(31, 119, 180); stroke-opacity: 1; stroke-width: 2px; opacity: 1;\"/></g><g class=\"points\"><path class=\"point\" transform=\"translate(37,242.95)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(31, 119, 180); fill-opacity: 1;\"/><path class=\"point\" transform=\"translate(117.86,210.82)\" d=\"M3,0A3,3 0 1,1 0,-3A
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"orders.plot(column='a').show_svg()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 23,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/svg+xml": [
|
||
|
|
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|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"orders.plots(column='a').show_svg()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Trades"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 24,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"(13,)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"trades = vbt.ExitTrades.from_orders(orders)\n",
|
||
|
|
"trades_grouped = vbt.ExitTrades.from_orders(orders_grouped)\n",
|
||
|
|
"print(trades.values.shape)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 25,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"(1000,)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"big_trades = vbt.ExitTrades.from_orders(big_orders)\n",
|
||
|
|
"big_trades_grouped = vbt.ExitTrades.from_orders(big_orders_grouped)\n",
|
||
|
|
"print(big_trades.values.shape)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 22,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"4.55 ms ± 46.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"%timeit vbt.ExitTrades.from_orders(big_orders)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 32,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" id col size entry_idx entry_price entry_fees exit_idx exit_price \\\n",
|
||
|
|
"0 0 0 1.0 0 1.090909 0.010909 2 3.0 \n",
|
||
|
|
"1 1 0 0.1 0 1.090909 0.001091 3 4.0 \n",
|
||
|
|
"2 2 0 1.0 5 6.000000 0.060000 6 7.0 \n",
|
||
|
|
"3 3 0 2.0 7 8.000000 0.160000 7 8.0 \n",
|
||
|
|
"4 4 1 1.0 0 1.090909 0.010909 2 3.0 \n",
|
||
|
|
"5 5 1 0.1 0 1.090909 0.001091 3 4.0 \n",
|
||
|
|
"6 6 1 1.0 5 6.000000 0.060000 6 7.0 \n",
|
||
|
|
"7 7 1 2.0 7 8.000000 0.160000 7 8.0 \n",
|
||
|
|
"8 8 2 1.0 0 1.090909 0.010909 2 3.0 \n",
|
||
|
|
"9 9 2 0.1 0 1.090909 0.001091 3 4.0 \n",
|
||
|
|
"10 10 2 1.0 5 6.000000 0.060000 6 7.0 \n",
|
||
|
|
"11 11 2 1.0 6 7.000000 0.070000 7 8.0 \n",
|
||
|
|
"12 12 2 1.0 7 8.000000 0.080000 7 8.0 \n",
|
||
|
|
"\n",
|
||
|
|
" exit_fees pnl return direction status position_id \n",
|
||
|
|
"0 0.030 1.868182 1.712500 0 1 0 \n",
|
||
|
|
"1 0.004 0.285818 2.620000 0 1 0 \n",
|
||
|
|
"2 0.070 0.870000 0.145000 0 1 1 \n",
|
||
|
|
"3 0.000 -0.160000 -0.010000 0 0 2 \n",
|
||
|
|
"4 0.030 -1.950000 -1.787500 1 1 3 \n",
|
||
|
|
"5 0.004 -0.296000 -2.713333 1 1 3 \n",
|
||
|
|
"6 0.070 -1.130000 -0.188333 1 1 4 \n",
|
||
|
|
"7 0.000 -0.160000 -0.010000 1 0 5 \n",
|
||
|
|
"8 0.030 1.868182 1.712500 0 1 6 \n",
|
||
|
|
"9 0.004 0.285818 2.620000 0 1 6 \n",
|
||
|
|
"10 0.070 0.870000 0.145000 0 1 7 \n",
|
||
|
|
"11 0.080 -1.150000 -0.164286 1 1 8 \n",
|
||
|
|
"12 0.000 -0.080000 -0.010000 0 0 9 \n",
|
||
|
|
" a b c d\n",
|
||
|
|
"2020-01-01 1 1 1 1\n",
|
||
|
|
"2020-01-02 2 2 2 2\n",
|
||
|
|
"2020-01-03 3 3 3 3\n",
|
||
|
|
"2020-01-04 4 4 4 4\n",
|
||
|
|
"2020-01-05 5 5 5 5\n",
|
||
|
|
"2020-01-06 6 6 6 6\n",
|
||
|
|
"2020-01-07 7 7 7 7\n",
|
||
|
|
"2020-01-08 8 8 8 8\n",
|
||
|
|
" id col size entry_idx entry_price entry_fees exit_idx exit_price \\\n",
|
||
|
|
"0 0 0 1.0 0 1.090909 0.010909 2 3.0 \n",
|
||
|
|
"1 1 0 0.1 0 1.090909 0.001091 3 4.0 \n",
|
||
|
|
"2 2 0 1.0 5 6.000000 0.060000 6 7.0 \n",
|
||
|
|
"3 3 0 2.0 7 8.000000 0.160000 7 8.0 \n",
|
||
|
|
"\n",
|
||
|
|
" exit_fees pnl return direction status position_id \n",
|
||
|
|
"0 0.030 1.868182 1.7125 0 1 0 \n",
|
||
|
|
"1 0.004 0.285818 2.6200 0 1 0 \n",
|
||
|
|
"2 0.070 0.870000 0.1450 0 1 1 \n",
|
||
|
|
"3 0.000 -0.160000 -0.0100 0 0 2 \n",
|
||
|
|
"2020-01-01 1\n",
|
||
|
|
"2020-01-02 2\n",
|
||
|
|
"2020-01-03 3\n",
|
||
|
|
"2020-01-04 4\n",
|
||
|
|
"2020-01-05 5\n",
|
||
|
|
"2020-01-06 6\n",
|
||
|
|
"2020-01-07 7\n",
|
||
|
|
"2020-01-08 8\n",
|
||
|
|
"Name: a, dtype: int64\n",
|
||
|
|
"1.56 ms ± 288 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
|
||
|
|
"3.37 ms ± 567 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"2.84 ms ± 218 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"3.65 ms ± 225 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(trades.records)\n",
|
||
|
|
"print(trades.close)\n",
|
||
|
|
"\n",
|
||
|
|
"print(trades['a'].records)\n",
|
||
|
|
"print(trades['a'].close)\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_trades.iloc[0]\n",
|
||
|
|
"%timeit big_trades.iloc[:]\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_trades_grouped.iloc[0]\n",
|
||
|
|
"%timeit big_trades_grouped.iloc[:]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 33,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" Trade Id Column Size Entry Timestamp Avg Entry Price Entry Fees \\\n",
|
||
|
|
"0 0 a 1.0 2020-01-01 1.090909 0.010909 \n",
|
||
|
|
"1 1 a 0.1 2020-01-01 1.090909 0.001091 \n",
|
||
|
|
"2 2 a 1.0 2020-01-06 6.000000 0.060000 \n",
|
||
|
|
"3 3 a 2.0 2020-01-08 8.000000 0.160000 \n",
|
||
|
|
"4 4 b 1.0 2020-01-01 1.090909 0.010909 \n",
|
||
|
|
"5 5 b 0.1 2020-01-01 1.090909 0.001091 \n",
|
||
|
|
"6 6 b 1.0 2020-01-06 6.000000 0.060000 \n",
|
||
|
|
"7 7 b 2.0 2020-01-08 8.000000 0.160000 \n",
|
||
|
|
"8 8 c 1.0 2020-01-01 1.090909 0.010909 \n",
|
||
|
|
"9 9 c 0.1 2020-01-01 1.090909 0.001091 \n",
|
||
|
|
"10 10 c 1.0 2020-01-06 6.000000 0.060000 \n",
|
||
|
|
"11 11 c 1.0 2020-01-07 7.000000 0.070000 \n",
|
||
|
|
"12 12 c 1.0 2020-01-08 8.000000 0.080000 \n",
|
||
|
|
"\n",
|
||
|
|
" Exit Timestamp Avg Exit Price Exit Fees PnL Return Direction \\\n",
|
||
|
|
"0 2020-01-03 3.0 0.030 1.868182 1.712500 Long \n",
|
||
|
|
"1 2020-01-04 4.0 0.004 0.285818 2.620000 Long \n",
|
||
|
|
"2 2020-01-07 7.0 0.070 0.870000 0.145000 Long \n",
|
||
|
|
"3 2020-01-08 8.0 0.000 -0.160000 -0.010000 Long \n",
|
||
|
|
"4 2020-01-03 3.0 0.030 -1.950000 -1.787500 Short \n",
|
||
|
|
"5 2020-01-04 4.0 0.004 -0.296000 -2.713333 Short \n",
|
||
|
|
"6 2020-01-07 7.0 0.070 -1.130000 -0.188333 Short \n",
|
||
|
|
"7 2020-01-08 8.0 0.000 -0.160000 -0.010000 Short \n",
|
||
|
|
"8 2020-01-03 3.0 0.030 1.868182 1.712500 Long \n",
|
||
|
|
"9 2020-01-04 4.0 0.004 0.285818 2.620000 Long \n",
|
||
|
|
"10 2020-01-07 7.0 0.070 0.870000 0.145000 Long \n",
|
||
|
|
"11 2020-01-08 8.0 0.080 -1.150000 -0.164286 Short \n",
|
||
|
|
"12 2020-01-08 8.0 0.000 -0.080000 -0.010000 Long \n",
|
||
|
|
"\n",
|
||
|
|
" Status Position Id \n",
|
||
|
|
"0 Closed 0 \n",
|
||
|
|
"1 Closed 0 \n",
|
||
|
|
"2 Closed 1 \n",
|
||
|
|
"3 Open 2 \n",
|
||
|
|
"4 Closed 3 \n",
|
||
|
|
"5 Closed 3 \n",
|
||
|
|
"6 Closed 4 \n",
|
||
|
|
"7 Open 5 \n",
|
||
|
|
"8 Closed 6 \n",
|
||
|
|
"9 Closed 6 \n",
|
||
|
|
"10 Closed 7 \n",
|
||
|
|
"11 Closed 8 \n",
|
||
|
|
"12 Open 9 \n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(trades.records_readable)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 34,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"4\n",
|
||
|
|
"a 4\n",
|
||
|
|
"b 4\n",
|
||
|
|
"c 5\n",
|
||
|
|
"d 0\n",
|
||
|
|
"Name: count, dtype: int64\n",
|
||
|
|
"49.4 µs ± 504 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
|
||
|
|
"group\n",
|
||
|
|
"first 8\n",
|
||
|
|
"second 5\n",
|
||
|
|
"Name: count, dtype: int64\n",
|
||
|
|
"49 µs ± 303 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(trades['a'].count())\n",
|
||
|
|
"print(trades.count())\n",
|
||
|
|
"%timeit big_trades.count()\n",
|
||
|
|
"\n",
|
||
|
|
"print(trades_grouped.count())\n",
|
||
|
|
"%timeit big_trades_grouped.count()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 35,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Trades(**Config({\n",
|
||
|
|
" \"wrapper\": \"<vectorbt.base.array_wrapper.ArrayWrapper object at 0x7f9f78606d30> of shape (8, 4)\",\n",
|
||
|
|
" \"records_arr\": \"<numpy.ndarray object at 0x7f9f7cf155b0> of shape (6,)\",\n",
|
||
|
|
" \"col_mapper\": null,\n",
|
||
|
|
" \"ts\": null,\n",
|
||
|
|
" \"close\": \"<pandas.core.frame.DataFrame object at 0x7f9f782a4390> of shape (8, 4)\"\n",
|
||
|
|
"}))\n",
|
||
|
|
" id col size entry_idx entry_price entry_fees exit_idx exit_price \\\n",
|
||
|
|
"0 0 0 1.0 0 1.090909 0.010909 2 3.0 \n",
|
||
|
|
"1 1 0 0.1 0 1.090909 0.001091 3 4.0 \n",
|
||
|
|
"2 2 0 1.0 5 6.000000 0.060000 6 7.0 \n",
|
||
|
|
"\n",
|
||
|
|
" exit_fees pnl return direction status position_id \n",
|
||
|
|
"0 0.030 1.868182 1.7125 0 1 0 \n",
|
||
|
|
"1 0.004 0.285818 2.6200 0 1 0 \n",
|
||
|
|
"2 0.070 0.870000 0.1450 0 1 1 \n",
|
||
|
|
" id col size entry_idx entry_price entry_fees exit_idx exit_price \\\n",
|
||
|
|
"0 0 0 1.0 0 1.090909 0.010909 2 3.0 \n",
|
||
|
|
"1 1 0 0.1 0 1.090909 0.001091 3 4.0 \n",
|
||
|
|
"2 2 0 1.0 5 6.000000 0.060000 6 7.0 \n",
|
||
|
|
"\n",
|
||
|
|
" exit_fees pnl return direction status position_id \n",
|
||
|
|
"0 0.030 1.868182 1.7125 0 1 0 \n",
|
||
|
|
"1 0.004 0.285818 2.6200 0 1 0 \n",
|
||
|
|
"2 0.070 0.870000 0.1450 0 1 1 \n",
|
||
|
|
" id col size entry_idx entry_price entry_fees exit_idx exit_price \\\n",
|
||
|
|
"0 0 0 1.0 0 1.090909 0.010909 2 3.0 \n",
|
||
|
|
"1 1 0 0.1 0 1.090909 0.001091 3 4.0 \n",
|
||
|
|
"2 2 0 1.0 5 6.000000 0.060000 6 7.0 \n",
|
||
|
|
"3 8 2 1.0 0 1.090909 0.010909 2 3.0 \n",
|
||
|
|
"4 9 2 0.1 0 1.090909 0.001091 3 4.0 \n",
|
||
|
|
"5 10 2 1.0 5 6.000000 0.060000 6 7.0 \n",
|
||
|
|
"\n",
|
||
|
|
" exit_fees pnl return direction status position_id \n",
|
||
|
|
"0 0.030 1.868182 1.7125 0 1 0 \n",
|
||
|
|
"1 0.004 0.285818 2.6200 0 1 0 \n",
|
||
|
|
"2 0.070 0.870000 0.1450 0 1 1 \n",
|
||
|
|
"3 0.030 1.868182 1.7125 0 1 6 \n",
|
||
|
|
"4 0.004 0.285818 2.6200 0 1 6 \n",
|
||
|
|
"5 0.070 0.870000 0.1450 0 1 7 \n",
|
||
|
|
"756 µs ± 11 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(trades.winning)\n",
|
||
|
|
"print(trades['a'].winning.records)\n",
|
||
|
|
"print(trades.winning['a'].records)\n",
|
||
|
|
"print(trades.winning.records)\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_trades.winning"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 36,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Trades(**Config({\n",
|
||
|
|
" \"wrapper\": \"<vectorbt.base.array_wrapper.ArrayWrapper object at 0x7f9f78606d30> of shape (8, 4)\",\n",
|
||
|
|
" \"records_arr\": \"<numpy.ndarray object at 0x7fa007ca3a28> of shape (7,)\",\n",
|
||
|
|
" \"col_mapper\": null,\n",
|
||
|
|
" \"ts\": null,\n",
|
||
|
|
" \"close\": \"<pandas.core.frame.DataFrame object at 0x7f9f782a4390> of shape (8, 4)\"\n",
|
||
|
|
"}))\n",
|
||
|
|
" id col size entry_idx entry_price entry_fees exit_idx exit_price \\\n",
|
||
|
|
"0 3 0 2.0 7 8.0 0.16 7 8.0 \n",
|
||
|
|
"\n",
|
||
|
|
" exit_fees pnl return direction status position_id \n",
|
||
|
|
"0 0.0 -0.16 -0.01 0 0 2 \n",
|
||
|
|
" id col size entry_idx entry_price entry_fees exit_idx exit_price \\\n",
|
||
|
|
"0 3 0 2.0 7 8.0 0.16 7 8.0 \n",
|
||
|
|
"\n",
|
||
|
|
" exit_fees pnl return direction status position_id \n",
|
||
|
|
"0 0.0 -0.16 -0.01 0 0 2 \n",
|
||
|
|
" id col size entry_idx entry_price entry_fees exit_idx exit_price \\\n",
|
||
|
|
"0 3 0 2.0 7 8.000000 0.160000 7 8.0 \n",
|
||
|
|
"1 4 1 1.0 0 1.090909 0.010909 2 3.0 \n",
|
||
|
|
"2 5 1 0.1 0 1.090909 0.001091 3 4.0 \n",
|
||
|
|
"3 6 1 1.0 5 6.000000 0.060000 6 7.0 \n",
|
||
|
|
"4 7 1 2.0 7 8.000000 0.160000 7 8.0 \n",
|
||
|
|
"5 11 2 1.0 6 7.000000 0.070000 7 8.0 \n",
|
||
|
|
"6 12 2 1.0 7 8.000000 0.080000 7 8.0 \n",
|
||
|
|
"\n",
|
||
|
|
" exit_fees pnl return direction status position_id \n",
|
||
|
|
"0 0.000 -0.160 -0.010000 0 0 2 \n",
|
||
|
|
"1 0.030 -1.950 -1.787500 1 1 3 \n",
|
||
|
|
"2 0.004 -0.296 -2.713333 1 1 3 \n",
|
||
|
|
"3 0.070 -1.130 -0.188333 1 1 4 \n",
|
||
|
|
"4 0.000 -0.160 -0.010000 1 0 5 \n",
|
||
|
|
"5 0.080 -1.150 -0.164286 1 1 8 \n",
|
||
|
|
"6 0.000 -0.080 -0.010000 0 0 9 \n",
|
||
|
|
"751 µs ± 8.53 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(trades.losing)\n",
|
||
|
|
"print(trades['a'].losing.records)\n",
|
||
|
|
"print(trades.losing['a'].records)\n",
|
||
|
|
"print(trades.losing.records)\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_trades.losing"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 39,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"0 1\n",
|
||
|
|
"1 2\n",
|
||
|
|
"2 3\n",
|
||
|
|
"3 0\n",
|
||
|
|
"Name: a, dtype: int64\n",
|
||
|
|
" a b c d\n",
|
||
|
|
"0 1.0 0.0 1.0 NaN\n",
|
||
|
|
"1 2.0 0.0 2.0 NaN\n",
|
||
|
|
"2 3.0 0.0 3.0 NaN\n",
|
||
|
|
"3 0.0 0.0 0.0 NaN\n",
|
||
|
|
"4 NaN NaN 0.0 NaN\n",
|
||
|
|
"565 µs ± 7.54 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(trades['a'].winning_streak.to_pd(ignore_index=True))\n",
|
||
|
|
"print(trades.winning_streak.to_pd(ignore_index=True))\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_trades.winning_streak"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 41,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"0 0\n",
|
||
|
|
"1 0\n",
|
||
|
|
"2 0\n",
|
||
|
|
"3 1\n",
|
||
|
|
"Name: a, dtype: int64\n",
|
||
|
|
" a b c d\n",
|
||
|
|
"0 0.0 1.0 0.0 NaN\n",
|
||
|
|
"1 0.0 2.0 0.0 NaN\n",
|
||
|
|
"2 0.0 3.0 0.0 NaN\n",
|
||
|
|
"3 1.0 4.0 1.0 NaN\n",
|
||
|
|
"4 NaN NaN 2.0 NaN\n",
|
||
|
|
"551 µs ± 5.51 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(trades['a'].losing_streak.to_pd(ignore_index=True))\n",
|
||
|
|
"print(trades.losing_streak.to_pd(ignore_index=True))\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_trades.losing_streak"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 37,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"0.75\n",
|
||
|
|
"a 0.75\n",
|
||
|
|
"b 0.00\n",
|
||
|
|
"c 0.60\n",
|
||
|
|
"d NaN\n",
|
||
|
|
"Name: win_rate, dtype: float64\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/portfolio/trades.py:530: RuntimeWarning:\n",
|
||
|
|
"\n",
|
||
|
|
"invalid value encountered in true_divide\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"951 µs ± 3.9 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
|
||
|
|
"group\n",
|
||
|
|
"first 0.375\n",
|
||
|
|
"second 0.600\n",
|
||
|
|
"Name: win_rate, dtype: float64\n",
|
||
|
|
"5.47 ms ± 21.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(trades['a'].win_rate())\n",
|
||
|
|
"print(trades.win_rate())\n",
|
||
|
|
"%timeit big_trades.win_rate()\n",
|
||
|
|
"\n",
|
||
|
|
"print(trades.win_rate(group_by=group_by))\n",
|
||
|
|
"%timeit big_trades.win_rate(group_by=big_group_by)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 42,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"18.9\n",
|
||
|
|
"a 18.900000\n",
|
||
|
|
"b 0.000000\n",
|
||
|
|
"c 2.458537\n",
|
||
|
|
"d NaN\n",
|
||
|
|
"Name: profit_factor, dtype: float64\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/portfolio/trades.py:544: RuntimeWarning:\n",
|
||
|
|
"\n",
|
||
|
|
"invalid value encountered in true_divide\n",
|
||
|
|
"\n",
|
||
|
|
"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/portfolio/trades.py:544: RuntimeWarning:\n",
|
||
|
|
"\n",
|
||
|
|
"divide by zero encountered in true_divide\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"2.65 ms ± 136 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"group\n",
|
||
|
|
"first 0.818182\n",
|
||
|
|
"second 2.458537\n",
|
||
|
|
"Name: profit_factor, dtype: float64\n",
|
||
|
|
"2.57 ms ± 65.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(trades['a'].profit_factor())\n",
|
||
|
|
"print(trades.profit_factor())\n",
|
||
|
|
"%timeit big_trades.profit_factor()\n",
|
||
|
|
"\n",
|
||
|
|
"print(trades_grouped.profit_factor())\n",
|
||
|
|
"%timeit big_trades_grouped.profit_factor()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 43,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"0.716\n",
|
||
|
|
"a 0.7160\n",
|
||
|
|
"b -0.8840\n",
|
||
|
|
"c 0.3588\n",
|
||
|
|
"d NaN\n",
|
||
|
|
"Name: expectancy, dtype: float64\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/portfolio/trades.py:530: RuntimeWarning:\n",
|
||
|
|
"\n",
|
||
|
|
"invalid value encountered in true_divide\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"3.55 ms ± 34.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"group\n",
|
||
|
|
"first -0.0840\n",
|
||
|
|
"second 0.3588\n",
|
||
|
|
"Name: expectancy, dtype: float64\n",
|
||
|
|
"3.54 ms ± 37.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(trades['a'].expectancy())\n",
|
||
|
|
"print(trades.expectancy())\n",
|
||
|
|
"%timeit big_trades.expectancy()\n",
|
||
|
|
"\n",
|
||
|
|
"print(trades_grouped.expectancy())\n",
|
||
|
|
"%timeit big_trades_grouped.expectancy()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 44,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"1.634155521947584\n",
|
||
|
|
"a 1.634156\n",
|
||
|
|
"b -2.130073\n",
|
||
|
|
"c 0.716604\n",
|
||
|
|
"d NaN\n",
|
||
|
|
"Name: sqn, dtype: float64\n",
|
||
|
|
"1.32 ms ± 11.9 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
|
||
|
|
"group\n",
|
||
|
|
"first -0.204047\n",
|
||
|
|
"second 0.716604\n",
|
||
|
|
"Name: sqn, dtype: float64\n",
|
||
|
|
"924 µs ± 9.78 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(trades['a'].sqn())\n",
|
||
|
|
"print(trades.sqn())\n",
|
||
|
|
"%timeit big_trades.sqn()\n",
|
||
|
|
"\n",
|
||
|
|
"print(trades_grouped.sqn())\n",
|
||
|
|
"%timeit big_trades_grouped.sqn()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 45,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Trades(**Config({\n",
|
||
|
|
" \"wrapper\": \"<vectorbt.base.array_wrapper.ArrayWrapper object at 0x7f9f78606d30> of shape (8, 4)\",\n",
|
||
|
|
" \"records_arr\": \"<numpy.ndarray object at 0x7f9f68734be0> of shape (8,)\",\n",
|
||
|
|
" \"col_mapper\": null,\n",
|
||
|
|
" \"ts\": null,\n",
|
||
|
|
" \"close\": \"<pandas.core.frame.DataFrame object at 0x7f9f782a4390> of shape (8, 4)\"\n",
|
||
|
|
"}))\n",
|
||
|
|
" id col size entry_idx entry_price entry_fees exit_idx exit_price \\\n",
|
||
|
|
"0 0 0 1.0 0 1.090909 0.010909 2 3.0 \n",
|
||
|
|
"1 1 0 0.1 0 1.090909 0.001091 3 4.0 \n",
|
||
|
|
"2 2 0 1.0 5 6.000000 0.060000 6 7.0 \n",
|
||
|
|
"3 3 0 2.0 7 8.000000 0.160000 7 8.0 \n",
|
||
|
|
"\n",
|
||
|
|
" exit_fees pnl return direction status position_id \n",
|
||
|
|
"0 0.030 1.868182 1.7125 0 1 0 \n",
|
||
|
|
"1 0.004 0.285818 2.6200 0 1 0 \n",
|
||
|
|
"2 0.070 0.870000 0.1450 0 1 1 \n",
|
||
|
|
"3 0.000 -0.160000 -0.0100 0 0 2 \n",
|
||
|
|
" id col size entry_idx entry_price entry_fees exit_idx exit_price \\\n",
|
||
|
|
"0 0 0 1.0 0 1.090909 0.010909 2 3.0 \n",
|
||
|
|
"1 1 0 0.1 0 1.090909 0.001091 3 4.0 \n",
|
||
|
|
"2 2 0 1.0 5 6.000000 0.060000 6 7.0 \n",
|
||
|
|
"3 3 0 2.0 7 8.000000 0.160000 7 8.0 \n",
|
||
|
|
"\n",
|
||
|
|
" exit_fees pnl return direction status position_id \n",
|
||
|
|
"0 0.030 1.868182 1.7125 0 1 0 \n",
|
||
|
|
"1 0.004 0.285818 2.6200 0 1 0 \n",
|
||
|
|
"2 0.070 0.870000 0.1450 0 1 1 \n",
|
||
|
|
"3 0.000 -0.160000 -0.0100 0 0 2 \n",
|
||
|
|
" id col size entry_idx entry_price entry_fees exit_idx exit_price \\\n",
|
||
|
|
"0 0 0 1.0 0 1.090909 0.010909 2 3.0 \n",
|
||
|
|
"1 1 0 0.1 0 1.090909 0.001091 3 4.0 \n",
|
||
|
|
"2 2 0 1.0 5 6.000000 0.060000 6 7.0 \n",
|
||
|
|
"3 3 0 2.0 7 8.000000 0.160000 7 8.0 \n",
|
||
|
|
"4 8 2 1.0 0 1.090909 0.010909 2 3.0 \n",
|
||
|
|
"5 9 2 0.1 0 1.090909 0.001091 3 4.0 \n",
|
||
|
|
"6 10 2 1.0 5 6.000000 0.060000 6 7.0 \n",
|
||
|
|
"7 12 2 1.0 7 8.000000 0.080000 7 8.0 \n",
|
||
|
|
"\n",
|
||
|
|
" exit_fees pnl return direction status position_id \n",
|
||
|
|
"0 0.030 1.868182 1.7125 0 1 0 \n",
|
||
|
|
"1 0.004 0.285818 2.6200 0 1 0 \n",
|
||
|
|
"2 0.070 0.870000 0.1450 0 1 1 \n",
|
||
|
|
"3 0.000 -0.160000 -0.0100 0 0 2 \n",
|
||
|
|
"4 0.030 1.868182 1.7125 0 1 6 \n",
|
||
|
|
"5 0.004 0.285818 2.6200 0 1 6 \n",
|
||
|
|
"6 0.070 0.870000 0.1450 0 1 7 \n",
|
||
|
|
"7 0.000 -0.080000 -0.0100 0 0 9 \n",
|
||
|
|
"764 µs ± 12.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(trades.long)\n",
|
||
|
|
"print(trades['a'].long.records)\n",
|
||
|
|
"print(trades.long['a'].records)\n",
|
||
|
|
"print(trades.long.records)\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_trades.long"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 46,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Trades(**Config({\n",
|
||
|
|
" \"wrapper\": \"<vectorbt.base.array_wrapper.ArrayWrapper object at 0x7f9f78606d30> of shape (8, 4)\",\n",
|
||
|
|
" \"records_arr\": \"<numpy.ndarray object at 0x7f9f7a2d8608> of shape (5,)\",\n",
|
||
|
|
" \"col_mapper\": null,\n",
|
||
|
|
" \"ts\": null,\n",
|
||
|
|
" \"close\": \"<pandas.core.frame.DataFrame object at 0x7f9f782a4390> of shape (8, 4)\"\n",
|
||
|
|
"}))\n",
|
||
|
|
"Empty DataFrame\n",
|
||
|
|
"Columns: [id, col, size, entry_idx, entry_price, entry_fees, exit_idx, exit_price, exit_fees, pnl, return, direction, status, position_id]\n",
|
||
|
|
"Index: []\n",
|
||
|
|
"Empty DataFrame\n",
|
||
|
|
"Columns: [id, col, size, entry_idx, entry_price, entry_fees, exit_idx, exit_price, exit_fees, pnl, return, direction, status, position_id]\n",
|
||
|
|
"Index: []\n",
|
||
|
|
" id col size entry_idx entry_price entry_fees exit_idx exit_price \\\n",
|
||
|
|
"0 4 1 1.0 0 1.090909 0.010909 2 3.0 \n",
|
||
|
|
"1 5 1 0.1 0 1.090909 0.001091 3 4.0 \n",
|
||
|
|
"2 6 1 1.0 5 6.000000 0.060000 6 7.0 \n",
|
||
|
|
"3 7 1 2.0 7 8.000000 0.160000 7 8.0 \n",
|
||
|
|
"4 11 2 1.0 6 7.000000 0.070000 7 8.0 \n",
|
||
|
|
"\n",
|
||
|
|
" exit_fees pnl return direction status position_id \n",
|
||
|
|
"0 0.030 -1.950 -1.787500 1 1 3 \n",
|
||
|
|
"1 0.004 -0.296 -2.713333 1 1 3 \n",
|
||
|
|
"2 0.070 -1.130 -0.188333 1 1 4 \n",
|
||
|
|
"3 0.000 -0.160 -0.010000 1 0 5 \n",
|
||
|
|
"4 0.080 -1.150 -0.164286 1 1 8 \n",
|
||
|
|
"762 µs ± 9.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(trades.short)\n",
|
||
|
|
"print(trades['a'].short.records)\n",
|
||
|
|
"print(trades.short['a'].records)\n",
|
||
|
|
"print(trades.short.records)\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_trades.short"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 47,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Trades(**Config({\n",
|
||
|
|
" \"wrapper\": \"<vectorbt.base.array_wrapper.ArrayWrapper object at 0x7f9f78606d30> of shape (8, 4)\",\n",
|
||
|
|
" \"records_arr\": \"<numpy.ndarray object at 0x7f9f7cd48978> of shape (3,)\",\n",
|
||
|
|
" \"col_mapper\": null,\n",
|
||
|
|
" \"ts\": null,\n",
|
||
|
|
" \"close\": \"<pandas.core.frame.DataFrame object at 0x7f9f782a4390> of shape (8, 4)\"\n",
|
||
|
|
"}))\n",
|
||
|
|
" id col size entry_idx entry_price entry_fees exit_idx exit_price \\\n",
|
||
|
|
"0 3 0 2.0 7 8.0 0.16 7 8.0 \n",
|
||
|
|
"\n",
|
||
|
|
" exit_fees pnl return direction status position_id \n",
|
||
|
|
"0 0.0 -0.16 -0.01 0 0 2 \n",
|
||
|
|
" id col size entry_idx entry_price entry_fees exit_idx exit_price \\\n",
|
||
|
|
"0 3 0 2.0 7 8.0 0.16 7 8.0 \n",
|
||
|
|
"\n",
|
||
|
|
" exit_fees pnl return direction status position_id \n",
|
||
|
|
"0 0.0 -0.16 -0.01 0 0 2 \n",
|
||
|
|
" id col size entry_idx entry_price entry_fees exit_idx exit_price \\\n",
|
||
|
|
"0 3 0 2.0 7 8.0 0.16 7 8.0 \n",
|
||
|
|
"1 7 1 2.0 7 8.0 0.16 7 8.0 \n",
|
||
|
|
"2 12 2 1.0 7 8.0 0.08 7 8.0 \n",
|
||
|
|
"\n",
|
||
|
|
" exit_fees pnl return direction status position_id \n",
|
||
|
|
"0 0.0 -0.16 -0.01 0 0 2 \n",
|
||
|
|
"1 0.0 -0.16 -0.01 1 0 5 \n",
|
||
|
|
"2 0.0 -0.08 -0.01 0 0 9 \n",
|
||
|
|
"777 µs ± 10.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(trades.open)\n",
|
||
|
|
"print(trades['a'].open.records)\n",
|
||
|
|
"print(trades.open['a'].records)\n",
|
||
|
|
"print(trades.open.records)\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_trades.open"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 48,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Trades(**Config({\n",
|
||
|
|
" \"wrapper\": \"<vectorbt.base.array_wrapper.ArrayWrapper object at 0x7f9f78606d30> of shape (8, 4)\",\n",
|
||
|
|
" \"records_arr\": \"<numpy.ndarray object at 0x7f9f7a4e3608> of shape (10,)\",\n",
|
||
|
|
" \"col_mapper\": null,\n",
|
||
|
|
" \"ts\": null,\n",
|
||
|
|
" \"close\": \"<pandas.core.frame.DataFrame object at 0x7f9f782a4390> of shape (8, 4)\"\n",
|
||
|
|
"}))\n",
|
||
|
|
" id col size entry_idx entry_price entry_fees exit_idx exit_price \\\n",
|
||
|
|
"0 0 0 1.0 0 1.090909 0.010909 2 3.0 \n",
|
||
|
|
"1 1 0 0.1 0 1.090909 0.001091 3 4.0 \n",
|
||
|
|
"2 2 0 1.0 5 6.000000 0.060000 6 7.0 \n",
|
||
|
|
"\n",
|
||
|
|
" exit_fees pnl return direction status position_id \n",
|
||
|
|
"0 0.030 1.868182 1.7125 0 1 0 \n",
|
||
|
|
"1 0.004 0.285818 2.6200 0 1 0 \n",
|
||
|
|
"2 0.070 0.870000 0.1450 0 1 1 \n",
|
||
|
|
" id col size entry_idx entry_price entry_fees exit_idx exit_price \\\n",
|
||
|
|
"0 0 0 1.0 0 1.090909 0.010909 2 3.0 \n",
|
||
|
|
"1 1 0 0.1 0 1.090909 0.001091 3 4.0 \n",
|
||
|
|
"2 2 0 1.0 5 6.000000 0.060000 6 7.0 \n",
|
||
|
|
"\n",
|
||
|
|
" exit_fees pnl return direction status position_id \n",
|
||
|
|
"0 0.030 1.868182 1.7125 0 1 0 \n",
|
||
|
|
"1 0.004 0.285818 2.6200 0 1 0 \n",
|
||
|
|
"2 0.070 0.870000 0.1450 0 1 1 \n",
|
||
|
|
" id col size entry_idx entry_price entry_fees exit_idx exit_price \\\n",
|
||
|
|
"0 0 0 1.0 0 1.090909 0.010909 2 3.0 \n",
|
||
|
|
"1 1 0 0.1 0 1.090909 0.001091 3 4.0 \n",
|
||
|
|
"2 2 0 1.0 5 6.000000 0.060000 6 7.0 \n",
|
||
|
|
"3 4 1 1.0 0 1.090909 0.010909 2 3.0 \n",
|
||
|
|
"4 5 1 0.1 0 1.090909 0.001091 3 4.0 \n",
|
||
|
|
"5 6 1 1.0 5 6.000000 0.060000 6 7.0 \n",
|
||
|
|
"6 8 2 1.0 0 1.090909 0.010909 2 3.0 \n",
|
||
|
|
"7 9 2 0.1 0 1.090909 0.001091 3 4.0 \n",
|
||
|
|
"8 10 2 1.0 5 6.000000 0.060000 6 7.0 \n",
|
||
|
|
"9 11 2 1.0 6 7.000000 0.070000 7 8.0 \n",
|
||
|
|
"\n",
|
||
|
|
" exit_fees pnl return direction status position_id \n",
|
||
|
|
"0 0.030 1.868182 1.712500 0 1 0 \n",
|
||
|
|
"1 0.004 0.285818 2.620000 0 1 0 \n",
|
||
|
|
"2 0.070 0.870000 0.145000 0 1 1 \n",
|
||
|
|
"3 0.030 -1.950000 -1.787500 1 1 3 \n",
|
||
|
|
"4 0.004 -0.296000 -2.713333 1 1 3 \n",
|
||
|
|
"5 0.070 -1.130000 -0.188333 1 1 4 \n",
|
||
|
|
"6 0.030 1.868182 1.712500 0 1 6 \n",
|
||
|
|
"7 0.004 0.285818 2.620000 0 1 6 \n",
|
||
|
|
"8 0.070 0.870000 0.145000 0 1 7 \n",
|
||
|
|
"9 0.080 -1.150000 -0.164286 1 1 8 \n",
|
||
|
|
"759 µs ± 11.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(trades.closed)\n",
|
||
|
|
"print(trades['a'].closed.records)\n",
|
||
|
|
"print(trades.closed['a'].records)\n",
|
||
|
|
"print(trades.closed.records)\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_trades.closed"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 49,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/portfolio/trades.py:544: RuntimeWarning:\n",
|
||
|
|
"\n",
|
||
|
|
"divide by zero encountered in true_divide\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Start 2020-01-01 00:00:00\n",
|
||
|
|
"End 2020-01-08 00:00:00\n",
|
||
|
|
"Period 8 days 00:00:00\n",
|
||
|
|
"First Trade Start 2020-01-01 00:00:00\n",
|
||
|
|
"Last Trade End 2020-01-08 00:00:00\n",
|
||
|
|
"Coverage 5 days 00:00:00\n",
|
||
|
|
"Overlap Coverage 2 days 00:00:00\n",
|
||
|
|
"Total Records 4\n",
|
||
|
|
"Total Long Trades 4\n",
|
||
|
|
"Total Short Trades 0\n",
|
||
|
|
"Total Closed Trades 3\n",
|
||
|
|
"Total Open Trades 1\n",
|
||
|
|
"Open Trade PnL -0.16\n",
|
||
|
|
"Win Rate [%] 100.0\n",
|
||
|
|
"Max Win Streak 3\n",
|
||
|
|
"Max Loss Streak 0\n",
|
||
|
|
"Best Trade [%] 262.0\n",
|
||
|
|
"Worst Trade [%] 14.5\n",
|
||
|
|
"Avg Winning Trade [%] 149.25\n",
|
||
|
|
"Avg Losing Trade [%] NaN\n",
|
||
|
|
"Avg Winning Trade Duration 2 days 00:00:00\n",
|
||
|
|
"Avg Losing Trade Duration NaT\n",
|
||
|
|
"Profit Factor inf\n",
|
||
|
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"Expectancy 1.008\n",
|
||
|
|
"SQN 2.181955\n",
|
||
|
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"Name: a, dtype: object\n"
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||
|
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]
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},
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{
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"name": "stderr",
|
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"output_type": "stream",
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"text": [
|
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"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/portfolio/trades.py:530: RuntimeWarning:\n",
|
||
|
|
"\n",
|
||
|
|
"invalid value encountered in true_divide\n",
|
||
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|
"\n",
|
||
|
|
"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/portfolio/trades.py:544: RuntimeWarning:\n",
|
||
|
|
"\n",
|
||
|
|
"invalid value encountered in true_divide\n",
|
||
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|
"\n",
|
||
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"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/portfolio/trades.py:530: RuntimeWarning:\n",
|
||
|
|
"\n",
|
||
|
|
"invalid value encountered in true_divide\n",
|
||
|
|
"\n",
|
||
|
|
"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/portfolio/trades.py:544: RuntimeWarning:\n",
|
||
|
|
"\n",
|
||
|
|
"invalid value encountered in true_divide\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"42.1 ms ± 2.02 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
|
||
|
|
"Start 2020-01-01 00:00:00\n",
|
||
|
|
"End 2020-01-08 00:00:00\n",
|
||
|
|
"Period 8 days 00:00:00\n",
|
||
|
|
"First Trade Start 2020-01-01 00:00:00\n",
|
||
|
|
"Last Trade End 2020-01-08 00:00:00\n",
|
||
|
|
"Coverage 5 days 00:00:00\n",
|
||
|
|
"Overlap Coverage 2 days 00:00:00\n",
|
||
|
|
"Total Records 4\n",
|
||
|
|
"Total Long Trades 4\n",
|
||
|
|
"Total Short Trades 0\n",
|
||
|
|
"Total Closed Trades 3\n",
|
||
|
|
"Total Open Trades 1\n",
|
||
|
|
"Open Trade PnL -0.16\n",
|
||
|
|
"Win Rate [%] 100.0\n",
|
||
|
|
"Max Win Streak 3\n",
|
||
|
|
"Max Loss Streak 0\n",
|
||
|
|
"Best Trade [%] 262.0\n",
|
||
|
|
"Worst Trade [%] 14.5\n",
|
||
|
|
"Avg Winning Trade [%] 149.25\n",
|
||
|
|
"Avg Losing Trade [%] NaN\n",
|
||
|
|
"Avg Winning Trade Duration 2 days 00:00:00\n",
|
||
|
|
"Avg Losing Trade Duration NaT\n",
|
||
|
|
"Profit Factor inf\n",
|
||
|
|
"Expectancy 1.008\n",
|
||
|
|
"SQN 2.181955\n",
|
||
|
|
"Name: a, dtype: object\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/portfolio/trades.py:530: RuntimeWarning:\n",
|
||
|
|
"\n",
|
||
|
|
"invalid value encountered in true_divide\n",
|
||
|
|
"\n",
|
||
|
|
"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/portfolio/trades.py:544: RuntimeWarning:\n",
|
||
|
|
"\n",
|
||
|
|
"divide by zero encountered in true_divide\n",
|
||
|
|
"\n",
|
||
|
|
"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/portfolio/trades.py:544: RuntimeWarning:\n",
|
||
|
|
"\n",
|
||
|
|
"invalid value encountered in true_divide\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"44.4 ms ± 1.17 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
|
||
|
|
"Start 2020-01-01 00:00:00\n",
|
||
|
|
"End 2020-01-08 00:00:00\n",
|
||
|
|
"Period 8 days 00:00:00\n",
|
||
|
|
"First Trade Start 2020-01-01 00:00:00\n",
|
||
|
|
"Last Trade End 2020-01-08 00:00:00\n",
|
||
|
|
"Coverage 5 days 08:00:00\n",
|
||
|
|
"Overlap Coverage 2 days 00:00:00\n",
|
||
|
|
"Total Records 3.25\n",
|
||
|
|
"Total Long Trades 2.0\n",
|
||
|
|
"Total Short Trades 1.25\n",
|
||
|
|
"Total Closed Trades 2.5\n",
|
||
|
|
"Total Open Trades 0.75\n",
|
||
|
|
"Open Trade PnL -0.1\n",
|
||
|
|
"Win Rate [%] 58.333333\n",
|
||
|
|
"Max Win Streak 2.0\n",
|
||
|
|
"Max Loss Streak 1.333333\n",
|
||
|
|
"Best Trade [%] 168.388889\n",
|
||
|
|
"Worst Trade [%] -91.087302\n",
|
||
|
|
"Avg Winning Trade [%] 149.25\n",
|
||
|
|
"Avg Losing Trade [%] -86.367063\n",
|
||
|
|
"Avg Winning Trade Duration 2 days 00:00:00\n",
|
||
|
|
"Avg Losing Trade Duration 1 days 12:00:00\n",
|
||
|
|
"Profit Factor inf\n",
|
||
|
|
"Expectancy 0.117056\n",
|
||
|
|
"SQN 0.189316\n",
|
||
|
|
"Name: agg_func_mean, dtype: object\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/portfolio/trades.py:530: RuntimeWarning:\n",
|
||
|
|
"\n",
|
||
|
|
"invalid value encountered in true_divide\n",
|
||
|
|
"\n",
|
||
|
|
"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/portfolio/trades.py:544: RuntimeWarning:\n",
|
||
|
|
"\n",
|
||
|
|
"divide by zero encountered in true_divide\n",
|
||
|
|
"\n",
|
||
|
|
"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/portfolio/trades.py:544: RuntimeWarning:\n",
|
||
|
|
"\n",
|
||
|
|
"invalid value encountered in true_divide\n",
|
||
|
|
"\n",
|
||
|
|
"/Users/olegpolakow/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:7: UserWarning:\n",
|
||
|
|
"\n",
|
||
|
|
"Object has multiple columns. Aggregating using <function mean at 0x7fa020856598>. Pass column to select a single column/group.\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"44.9 ms ± 1.26 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(trades['a'].stats())\n",
|
||
|
|
"%timeit big_trades[0].stats(silence_warnings=True)\n",
|
||
|
|
"\n",
|
||
|
|
"print(trades.stats(column='a'))\n",
|
||
|
|
"%timeit big_trades.stats(column=0, silence_warnings=True)\n",
|
||
|
|
"\n",
|
||
|
|
"print(trades.stats())\n",
|
||
|
|
"%timeit big_trades.stats(silence_warnings=True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 27,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/svg+xml": [
|
||
|
|
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|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"trades['c'].plot().show_svg()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 28,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/svg+xml": [
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||
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|
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|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"trades['c'].plot_pnl().show_svg()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 29,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/svg+xml": [
|
||
|
|
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|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"trades.plots(column='c').show_svg()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Positions"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"(10,)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"positions = vbt.Positions.from_trades(trades)\n",
|
||
|
|
"positions_grouped = vbt.Positions.from_trades(trades_grouped)\n",
|
||
|
|
"print(positions.values.shape)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 31,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"(1000,)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"big_positions = vbt.Positions.from_trades(big_trades)\n",
|
||
|
|
"big_positions_grouped = vbt.Positions.from_trades(big_trades_grouped)\n",
|
||
|
|
"print(big_positions.values.shape)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 31,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"724 µs ± 12.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"%timeit vbt.Positions.from_trades(big_trades)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 55,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" id col size entry_idx entry_price entry_fees exit_idx exit_price \\\n",
|
||
|
|
"0 0 0 1.1 0 1.090909 0.012 3 3.090909 \n",
|
||
|
|
"1 1 0 1.0 5 6.000000 0.060 6 7.000000 \n",
|
||
|
|
"2 2 0 2.0 7 8.000000 0.160 7 8.000000 \n",
|
||
|
|
"3 3 1 1.1 0 1.090909 0.012 3 3.090909 \n",
|
||
|
|
"4 4 1 1.0 5 6.000000 0.060 6 7.000000 \n",
|
||
|
|
"5 5 1 2.0 7 8.000000 0.160 7 8.000000 \n",
|
||
|
|
"6 6 2 1.1 0 1.090909 0.012 3 3.090909 \n",
|
||
|
|
"7 7 2 1.0 5 6.000000 0.060 6 7.000000 \n",
|
||
|
|
"8 8 2 1.0 6 7.000000 0.070 7 8.000000 \n",
|
||
|
|
"9 9 2 1.0 7 8.000000 0.080 7 8.000000 \n",
|
||
|
|
"\n",
|
||
|
|
" exit_fees pnl return direction status \n",
|
||
|
|
"0 0.034 2.154 1.795000 0 1 \n",
|
||
|
|
"1 0.070 0.870 0.145000 0 1 \n",
|
||
|
|
"2 0.000 -0.160 -0.010000 0 0 \n",
|
||
|
|
"3 0.034 -2.246 -1.871667 1 1 \n",
|
||
|
|
"4 0.070 -1.130 -0.188333 1 1 \n",
|
||
|
|
"5 0.000 -0.160 -0.010000 1 0 \n",
|
||
|
|
"6 0.034 2.154 1.795000 0 1 \n",
|
||
|
|
"7 0.070 0.870 0.145000 0 1 \n",
|
||
|
|
"8 0.080 -1.150 -0.164286 1 1 \n",
|
||
|
|
"9 0.000 -0.080 -0.010000 0 0 \n",
|
||
|
|
" a b c d\n",
|
||
|
|
"2020-01-01 1 1 1 1\n",
|
||
|
|
"2020-01-02 2 2 2 2\n",
|
||
|
|
"2020-01-03 3 3 3 3\n",
|
||
|
|
"2020-01-04 4 4 4 4\n",
|
||
|
|
"2020-01-05 5 5 5 5\n",
|
||
|
|
"2020-01-06 6 6 6 6\n",
|
||
|
|
"2020-01-07 7 7 7 7\n",
|
||
|
|
"2020-01-08 8 8 8 8\n",
|
||
|
|
" id col size entry_idx entry_price entry_fees exit_idx exit_price \\\n",
|
||
|
|
"0 0 0 1.1 0 1.090909 0.012 3 3.090909 \n",
|
||
|
|
"1 1 0 1.0 5 6.000000 0.060 6 7.000000 \n",
|
||
|
|
"2 2 0 2.0 7 8.000000 0.160 7 8.000000 \n",
|
||
|
|
"\n",
|
||
|
|
" exit_fees pnl return direction status \n",
|
||
|
|
"0 0.034 2.154 1.795 0 1 \n",
|
||
|
|
"1 0.070 0.870 0.145 0 1 \n",
|
||
|
|
"2 0.000 -0.160 -0.010 0 0 \n",
|
||
|
|
"2020-01-01 1\n",
|
||
|
|
"2020-01-02 2\n",
|
||
|
|
"2020-01-03 3\n",
|
||
|
|
"2020-01-04 4\n",
|
||
|
|
"2020-01-05 5\n",
|
||
|
|
"2020-01-06 6\n",
|
||
|
|
"2020-01-07 7\n",
|
||
|
|
"2020-01-08 8\n",
|
||
|
|
"Name: a, dtype: int64\n",
|
||
|
|
"1.4 ms ± 43.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
|
||
|
|
"3.06 ms ± 757 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"2.52 ms ± 53.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
||
|
|
"3.22 ms ± 89.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(positions.records)\n",
|
||
|
|
"print(positions.close)\n",
|
||
|
|
"\n",
|
||
|
|
"print(positions['a'].records)\n",
|
||
|
|
"print(positions['a'].close)\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_positions.iloc[0]\n",
|
||
|
|
"%timeit big_positions.iloc[:]\n",
|
||
|
|
"\n",
|
||
|
|
"%timeit big_positions_grouped.iloc[0]\n",
|
||
|
|
"%timeit big_positions_grouped.iloc[:]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3 (ipykernel)",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.7.3"
|
||
|
|
},
|
||
|
|
"widgets": {
|
||
|
|
"application/vnd.jupyter.widget-state+json": {
|
||
|
|
"state": {
|
||
|
|
"01f2862665d847b1bb454f087835c35c": {
|
||
|
|
"buffers": [
|
||
|
|
{
|
||
|
|
"data": "AAAAAAAAJEAAAAAAAAAmQAAAAAAAAChA",
|
||
|
|
"encoding": "base64",
|
||
|
|
"path": [
|
||
|
|
"_data",
|
||
|
|
0,
|
||
|
|
"x",
|
||
|
|
"value"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"model_module": "plotlywidget",
|
||
|
|
"model_module_version": "^4.12.0",
|
||
|
|
"model_name": "FigureModel",
|
||
|
|
"state": {
|
||
|
|
"_config": {
|
||
|
|
"plotlyServerURL": "https://plot.ly"
|
||
|
|
},
|
||
|
|
"_data": [
|
||
|
|
{
|
||
|
|
"name": "(0, 'a')",
|
||
|
|
"opacity": 1,
|
||
|
|
"showlegend": true,
|
||
|
|
"type": "histogram",
|
||
|
|
"uid": "d6ad8d12-ddd8-4fb4-aff9-ddb84ce5205f",
|
||
|
|
"x": {
|
||
|
|
"dtype": "float64",
|
||
|
|
"shape": [
|
||
|
|
3
|
||
|
|
],
|
||
|
|
"value": {}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"_js2py_layoutDelta": {},
|
||
|
|
"_js2py_pointsCallback": {},
|
||
|
|
"_js2py_relayout": {},
|
||
|
|
"_js2py_restyle": {},
|
||
|
|
"_js2py_traceDeltas": {},
|
||
|
|
"_js2py_update": {},
|
||
|
|
"_last_layout_edit_id": 4,
|
||
|
|
"_last_trace_edit_id": 4,
|
||
|
|
"_layout": {
|
||
|
|
"barmode": "overlay",
|
||
|
|
"height": 350,
|
||
|
|
"legend": {
|
||
|
|
"orientation": "h",
|
||
|
|
"traceorder": "normal",
|
||
|
|
"x": 1,
|
||
|
|
"xanchor": "right",
|
||
|
|
"y": 1.02,
|
||
|
|
"yanchor": "bottom"
|
||
|
|
},
|
||
|
|
"margin": {
|
||
|
|
"b": 30,
|
||
|
|
"l": 30,
|
||
|
|
"r": 30,
|
||
|
|
"t": 30
|
||
|
|
},
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||
|
|
"template": {
|
||
|
|
"data": {
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||
|
|
"bar": [
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||
|
|
{
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||
|
|
"error_x": {
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|
|
"color": "#2a3f5f"
|
||
|
|
},
|
||
|
|
"error_y": {
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|
|
"color": "#2a3f5f"
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|
|
},
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|
|
"marker": {
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|
|
"line": {
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|
|
"color": "#E5ECF6",
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|
|
"width": 0.5
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|
|
}
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|
|
},
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|
|
"type": "bar"
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|
}
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|
|
],
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||
|
|
"barpolar": [
|
||
|
|
{
|
||
|
|
"marker": {
|
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|
|
"line": {
|
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|
|
"color": "#E5ECF6",
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|
|
"width": 0.5
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"type": "barpolar"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"carpet": [
|
||
|
|
{
|
||
|
|
"aaxis": {
|
||
|
|
"endlinecolor": "#2a3f5f",
|
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|
|
"gridcolor": "white",
|
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|
|
"linecolor": "white",
|
||
|
|
"minorgridcolor": "white",
|
||
|
|
"startlinecolor": "#2a3f5f"
|
||
|
|
},
|
||
|
|
"baxis": {
|
||
|
|
"endlinecolor": "#2a3f5f",
|
||
|
|
"gridcolor": "white",
|
||
|
|
"linecolor": "white",
|
||
|
|
"minorgridcolor": "white",
|
||
|
|
"startlinecolor": "#2a3f5f"
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||
|
|
},
|
||
|
|
"type": "carpet"
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||
|
|
}
|
||
|
|
],
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||
|
|
"choropleth": [
|
||
|
|
{
|
||
|
|
"colorbar": {
|
||
|
|
"outlinewidth": 0,
|
||
|
|
"ticks": ""
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||
|
|
},
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||
|
|
"type": "choropleth"
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||
|
|
}
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||
|
|
],
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||
|
|
"contour": [
|
||
|
|
{
|
||
|
|
"colorbar": {
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||
|
|
"outlinewidth": 0,
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|
|
"ticks": ""
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||
|
|
},
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|
|
"colorscale": [
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||
|
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