{
"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",
" age | \n",
" city | \n",
" name | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 30 | \n",
" new york | \n",
" john | \n",
"
\n",
" \n",
" 1 | \n",
" 25 | \n",
" los angeles | \n",
" mary | \n",
"
\n",
" \n",
" 2 | \n",
" 40 | \n",
" london | \n",
" paul | \n",
"
\n",
" \n",
"
\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",
" age | \n",
" city | \n",
" name | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 30 | \n",
" new york | \n",
" john | \n",
"
\n",
" \n",
" 1 | \n",
" 40 | \n",
" los angeles | \n",
" mary | \n",
"
\n",
" \n",
" 2 | \n",
" 40 | \n",
" london | \n",
" paul | \n",
"
\n",
" \n",
"
\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",
" age | \n",
" city | \n",
" name | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 30 | \n",
" new york | \n",
" johnny | \n",
"
\n",
" \n",
" 1 | \n",
" 26 | \n",
" los angeles | \n",
" mary | \n",
"
\n",
" \n",
" 2 | \n",
" 40 | \n",
" london | \n",
" paul | \n",
"
\n",
" \n",
"
\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",
" age | \n",
" city | \n",
" name | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 30 | \n",
" new york | \n",
" FOO | \n",
"
\n",
" \n",
" 1 | \n",
" 25 | \n",
" los angeles | \n",
" mary | \n",
"
\n",
" \n",
" 2 | \n",
" 40 | \n",
" london | \n",
" paul | \n",
"
\n",
" \n",
"
\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",
" name | \n",
" num_children | \n",
" num_pets | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" john | \n",
" 0 | \n",
" 0 | \n",
"
\n",
" \n",
" 1 | \n",
" mary | \n",
" 4 | \n",
" 1 | \n",
"
\n",
" \n",
" 2 | \n",
" paul | \n",
" 5 | \n",
" 2 | \n",
"
\n",
" \n",
"
\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",
" name | \n",
" num_children | \n",
" num_pets | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" john | \n",
" 0 | \n",
" 1 | \n",
"
\n",
" \n",
" 1 | \n",
" mary | \n",
" 4 | \n",
" 1 | \n",
"
\n",
" \n",
" 2 | \n",
" paul | \n",
" 5 | \n",
" 2 | \n",
"
\n",
" \n",
"
\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
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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