{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Pandas - Apply function to a Series or DataFrame" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- **DataFrame** methods\n", " - `apply()` works along a **row** / **column** basis of a DataFrame\n", " - `applymap()` works element-wise on a DataFrame\n", "- **Series** methods\n", " - `apply()` works element-wise on a Series. lets you pass in `args` and `kwargs` into the function while `map()` doesn't\n", " - `map()` Can use not only a **function**, but also a **dictionary** or another Series (Map values of Series using input correspondence)\n", "\n", "> `apply()` can make a **DataFrame** out of a Series; however, `map()` will just put a **Series in every cell** of another Series, which is probably not what you want." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "0 0 A/5 21171 7.2500 NaN S \n", "1 0 PC 17599 71.2833 C85 C \n", "2 0 STON/O2. 3101282 7.9250 NaN S \n", "3 0 113803 53.1000 C123 S \n", "4 0 373450 8.0500 NaN S " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train = pd.read_csv('http://bit.ly/kaggletrain')\n", "train.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### map()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "train['sex_num'] = train.Sex.map({'female': 0, 'male': 1})" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Sexsex_num
0male1
1female0
2female0
3female0
4male1
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" ], "text/plain": [ " Sex sex_num\n", "0 male 1\n", "1 female 0\n", "2 female 0\n", "3 female 0\n", "4 male 1" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train.loc[0:4, ['Sex', 'sex_num']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### apply() for Series" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "train['name_length'] = train.Name.apply(len)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Namename_length
0Braund, Mr. Owen Harris23
1Cumings, Mrs. John Bradley (Florence Briggs Th...51
2Heikkinen, Miss. Laina22
3Futrelle, Mrs. Jacques Heath (Lily May Peel)44
4Allen, Mr. William Henry24
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" ], "text/plain": [ " Name name_length\n", "0 Braund, Mr. Owen Harris 23\n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... 51\n", "2 Heikkinen, Miss. Laina 22\n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) 44\n", "4 Allen, Mr. William Henry 24" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train.loc[0:4, ['Name', 'name_length']]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "train['fare_ceil'] = train.Fare.apply(np.ceil)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Farefare_ceil
07.25008.0
171.283372.0
27.92508.0
353.100054.0
48.05009.0
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" ], "text/plain": [ " Fare fare_ceil\n", "0 7.2500 8.0\n", "1 71.2833 72.0\n", "2 7.9250 8.0\n", "3 53.1000 54.0\n", "4 8.0500 9.0" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train.loc[0:4, ['Fare', 'fare_ceil']]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 [Braund, Mr. Owen Harris]\n", "1 [Cumings, Mrs. John Bradley (Florence Briggs ...\n", "2 [Heikkinen, Miss. Laina]\n", "3 [Futrelle, Mrs. Jacques Heath (Lily May Peel)]\n", "4 [Allen, Mr. William Henry]\n", "Name: Name, dtype: object" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train.Name.str.split(',').head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Braund\n", "1 Cumings\n", "2 Heikkinen\n", "3 Futrelle\n", "4 Allen\n", "Name: Name, dtype: object" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train.Name.str.split(',').apply(lambda x: x[0]).head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### apply() for Dataframe" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinent
0Afghanistan0000.0Asia
1Albania89132544.9Europe
2Algeria250140.7Africa
3Andorra24513831212.4Europe
4Angola21757455.9Africa
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" ], "text/plain": [ " country beer_servings spirit_servings wine_servings \\\n", "0 Afghanistan 0 0 0 \n", "1 Albania 89 132 54 \n", "2 Algeria 25 0 14 \n", "3 Andorra 245 138 312 \n", "4 Angola 217 57 45 \n", "\n", " total_litres_of_pure_alcohol continent \n", "0 0.0 Asia \n", "1 4.9 Europe \n", "2 0.7 Africa \n", "3 12.4 Europe \n", "4 5.9 Africa " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "drinks = pd.read_csv('http://bit.ly/drinksbycountry')\n", "drinks.head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 0\n", "1 132\n", "2 25\n", "3 312\n", "4 217\n", "dtype: int64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get the max value for each row\n", "drinks.loc[:, 'beer_servings':'wine_servings'].apply(max, axis=1).head()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 0\n", "1 1\n", "2 0\n", "3 2\n", "4 0\n", "dtype: int64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get the \"position\" of the max value for each row\n", "drinks.loc[:, 'beer_servings':'wine_servings'].apply(lambda row: row.values.argmax(), axis=1).head()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 beer_servings\n", "1 spirit_servings\n", "2 beer_servings\n", "3 wine_servings\n", "4 beer_servings\n", "dtype: object" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Use Series.idxmax() to get the \"index\" instead of the \"position\" by argmax()\n", "drinks.loc[:, 'beer_servings':'wine_servings'].apply(lambda row: row.idxmax(), axis=1).head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### applymap()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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beer_servingsspirit_servingswine_servings
00.00.00.0
189.0132.054.0
225.00.014.0
3245.0138.0312.0
4217.057.045.0
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" ], "text/plain": [ " beer_servings spirit_servings wine_servings\n", "0 0.0 0.0 0.0\n", "1 89.0 132.0 54.0\n", "2 25.0 0.0 14.0\n", "3 245.0 138.0 312.0\n", "4 217.0 57.0 45.0" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "drinks.loc[:, 'beer_servings':'wine_servings'].applymap(float).head()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.2" } }, "nbformat": 4, "nbformat_minor": 2 }