{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
agecityname
030new yorkjohn
125los angelesmary
240londonpaul
\n", "
" ], "text/plain": [ " age city name\n", "0 30 new york john\n", "1 25 los angeles mary\n", "2 40 london paul" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame({\n", " 'name':['john','mary','paul'],\n", " 'age':[30,25,40],\n", " 'city':['new york','los angeles','london']\n", "})\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### simplest possible example: replace one value with another" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
agecityname
030new yorkjohn
140los angelesmary
240londonpaul
\n", "
" ], "text/plain": [ " age city name\n", "0 30 new york john\n", "1 40 los angeles mary\n", "2 40 london paul" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.replace([25],40)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### replace with dict" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
agecityname
030new yorkjohnny
126los angelesmary
240londonpaul
\n", "
" ], "text/plain": [ " age city name\n", "0 30 new york johnny\n", "1 26 los angeles mary\n", "2 40 london paul" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.replace({\n", " 25:26,\n", " 'john':'johnny'\n", "})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### with regex" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
agecityname
030new yorkFOO
125los angelesmary
240londonpaul
\n", "
" ], "text/plain": [ " age city name\n", "0 30 new york FOO\n", "1 25 los angeles mary\n", "2 40 london paul" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.replace('jo.+','FOO',regex=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### replace in column" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
namenum_childrennum_pets
0john00
1mary41
2paul52
\n", "
" ], "text/plain": [ " name num_children num_pets\n", "0 john 0 0\n", "1 mary 4 1\n", "2 paul 5 2" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame({\n", " 'name':['john','mary','paul'],\n", " 'num_children':[0,4,5],\n", " 'num_pets':[0,1,2]\n", "})\n", "df" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
namenum_childrennum_pets
0john01
1mary41
2paul52
\n", "
" ], "text/plain": [ " name num_children num_pets\n", "0 john 0 1\n", "1 mary 4 1\n", "2 paul 5 2" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.replace({'num_pets':{0:1}})" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Global TF Kernel (Python 3)", "language": "python", "name": "global-tf-python-3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }