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Pandas VlookUP
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pandasvlookup.py
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pandas trick for the Day"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How to do Excel Vlookup in Pandas"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### First Dataframe contains the Student Names and their respective Scores"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Alice</td>\n",
" <td>300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Branda</td>\n",
" <td>450</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Tim</td>\n",
" <td>500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Mike</td>\n",
" <td>600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Rachel</td>\n",
" <td>725</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Score\n",
"0 Alice 300\n",
"1 Branda 450\n",
"2 Tim 500\n",
"3 Mike 600\n",
"4 Rachel 725"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df=pd.DataFrame({'Score':[300,450,500,600,725],'Name':['Alice','Branda','Tim','Mike','Rachel']})\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Second DataFrame contains the Score and Grades"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Grade</th>\n",
" <th>Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>B</td>\n",
" <td>300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>C</td>\n",
" <td>400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>D</td>\n",
" <td>500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>E</td>\n",
" <td>600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>F</td>\n",
" <td>700</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Grade Score\n",
"0 B 300\n",
"1 C 400\n",
"2 D 500\n",
"3 E 600\n",
"4 F 700"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1=pd.DataFrame({'Score':[300,400,500,600,700],'Grade':['B','C','D','E','F']})\n",
"df1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Merge the Two Dataframes and lookup for corresponding Grades for their Scores\n",
"### Does a left-join except that we match on nearest key rather than equal keys"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Score</th>\n",
" <th>Grade</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Alice</td>\n",
" <td>300</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Branda</td>\n",
" <td>450</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Tim</td>\n",
" <td>500</td>\n",
" <td>D</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Mike</td>\n",
" <td>600</td>\n",
" <td>E</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Rachel</td>\n",
" <td>725</td>\n",
" <td>F</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Score Grade\n",
"0 Alice 300 B\n",
"1 Branda 450 C\n",
"2 Tim 500 D\n",
"3 Mike 600 E\n",
"4 Rachel 725 F"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.merge_asof(df, df1, on='Score')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example 2:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### First Dataframe contains Label and TIN #"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Label</th>\n",
" <th>TIN</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ABC</td>\n",
" <td>I1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>DEF</td>\n",
" <td>I2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>JHK</td>\n",
" <td>I3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>LMN</td>\n",
" <td>I4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>OPQ</td>\n",
" <td>I5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Label TIN\n",
"0 ABC I1\n",
"1 DEF I2\n",
"2 JHK I3\n",
"3 LMN I4\n",
"4 OPQ I5"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df=pd.DataFrame({'Label':['ABC','DEF','JHK','LMN','OPQ'],'TIN':['I1','I2','I3','I4','I5']})\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Second Dataframe Contains TIN Numeric Values"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>TIN</th>\n",
" <th>Value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>I2</td>\n",
" <td>200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>I3</td>\n",
" <td>500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>I4</td>\n",
" <td>900</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" TIN Value\n",
"0 I2 200\n",
"1 I3 500\n",
"2 I4 900"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1=pd.DataFrame({'TIN':['I2','I3','I4'],'Value':[200,500,900]})\n",
"df1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Expected Value:"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
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" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Label</th>\n",
" <th>Value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>DEF</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>JHK</td>\n",
" <td>200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>LMN</td>\n",
" <td>300</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Label Value\n",
"0 DEF 100\n",
"1 JHK 200\n",
"2 LMN 300"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2=pd.DataFrame({'Label':['DEF','JHK','LMN'],'Value':[100,200,300]})\n",
"df2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Merge the Two dataframe using Pandas Merge function: \n",
"### Merges DataFrame objects by performing a database-style join operation by columns or indexes."
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
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" text-align: left;\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Label</th>\n",
" <th>TIN</th>\n",
" <th>Value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>DEF</td>\n",
" <td>I2</td>\n",
" <td>200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>JHK</td>\n",
" <td>I3</td>\n",
" <td>500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>LMN</td>\n",
" <td>I4</td>\n",
" <td>900</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Label TIN Value\n",
"0 DEF I2 200\n",
"1 JHK I3 500\n",
"2 LMN I4 900"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3 = pd.merge(df, df1)\n",
"\n",
"df3"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
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Display the source blob
Display the rendered blob
Raw
Untitled.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pandas trick for the Day"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How to do Excel Vlookup in Pandas"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### First Dataframe contains the Student Names and their respective Scores"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Alice</td>\n",
" <td>300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Branda</td>\n",
" <td>450</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Tim</td>\n",
" <td>500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Mike</td>\n",
" <td>600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Rachel</td>\n",
" <td>725</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Score\n",
"0 Alice 300\n",
"1 Branda 450\n",
"2 Tim 500\n",
"3 Mike 600\n",
"4 Rachel 725"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df=pd.DataFrame({'Score':[300,450,500,600,725],'Name':['Alice','Branda','Tim','Mike','Rachel']})\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Second DataFrame contains the Score and Grades"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Grade</th>\n",
" <th>Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>B</td>\n",
" <td>300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>C</td>\n",
" <td>400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>D</td>\n",
" <td>500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>E</td>\n",
" <td>600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>F</td>\n",
" <td>700</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Grade Score\n",
"0 B 300\n",
"1 C 400\n",
"2 D 500\n",
"3 E 600\n",
"4 F 700"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1=pd.DataFrame({'Score':[300,400,500,600,700],'Grade':['B','C','D','E','F']})\n",
"df1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Merge the Two Dataframes and lookup for corresponding Grades for their Scores\n",
"### Does a left-join except that we match on nearest key rather than equal keys"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
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" text-align: left;\n",
" }\n",
"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Score</th>\n",
" <th>Grade</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Alice</td>\n",
" <td>300</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Branda</td>\n",
" <td>450</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Tim</td>\n",
" <td>500</td>\n",
" <td>D</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Mike</td>\n",
" <td>600</td>\n",
" <td>E</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Rachel</td>\n",
" <td>725</td>\n",
" <td>F</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Score Grade\n",
"0 Alice 300 B\n",
"1 Branda 450 C\n",
"2 Tim 500 D\n",
"3 Mike 600 E\n",
"4 Rachel 725 F"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.merge_asof(df, df1, on='Score')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example 2:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### First Dataframe contains Label and TIN #"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Label</th>\n",
" <th>TIN</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ABC</td>\n",
" <td>I1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>DEF</td>\n",
" <td>I2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>JHK</td>\n",
" <td>I3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>LMN</td>\n",
" <td>I4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>OPQ</td>\n",
" <td>I5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Label TIN\n",
"0 ABC I1\n",
"1 DEF I2\n",
"2 JHK I3\n",
"3 LMN I4\n",
"4 OPQ I5"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df=pd.DataFrame({'Label':['ABC','DEF','JHK','LMN','OPQ'],'TIN':['I1','I2','I3','I4','I5']})\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Second Dataframe Contains TIN Numeric Values"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>TIN</th>\n",
" <th>Value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>I2</td>\n",
" <td>200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>I3</td>\n",
" <td>500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>I4</td>\n",
" <td>900</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" TIN Value\n",
"0 I2 200\n",
"1 I3 500\n",
"2 I4 900"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1=pd.DataFrame({'TIN':['I2','I3','I4'],'Value':[200,500,900]})\n",
"df1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Expected Value:"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Label</th>\n",
" <th>Value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>DEF</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>JHK</td>\n",
" <td>200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>LMN</td>\n",
" <td>300</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Label Value\n",
"0 DEF 100\n",
"1 JHK 200\n",
"2 LMN 300"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2=pd.DataFrame({'Label':['DEF','JHK','LMN'],'Value':[100,200,300]})\n",
"df2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Merge the Two dataframe using Pandas Merge function: \n",
"### Merges DataFrame objects by performing a database-style join operation by columns or indexes."
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Label</th>\n",
" <th>TIN</th>\n",
" <th>Value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>DEF</td>\n",
" <td>I2</td>\n",
" <td>200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>JHK</td>\n",
" <td>I3</td>\n",
" <td>500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>LMN</td>\n",
" <td>I4</td>\n",
" <td>900</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Label TIN Value\n",
"0 DEF I2 200\n",
"1 JHK I3 500\n",
"2 LMN I4 900"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3 = pd.merge(df, df1)\n",
"\n",
"df3"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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