{
"cells": [
{
"cell_type": "markdown",
"id": "92f5ec54",
"metadata": {},
"source": [
"## String functions on Columns"
]
},
{
"cell_type": "markdown",
"id": "877b8bc5",
"metadata": {},
"source": [
"### EDA for columns"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "06e8f3f1",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "373789d6",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>First Name</th>\n",
" <th>Age</th>\n",
" <th>Gender</th>\n",
" <th>City</th>\n",
" <th>Place of Work</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Sahil</td>\n",
" <td>10</td>\n",
" <td>M</td>\n",
" <td>J</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Sonia</td>\n",
" <td>20</td>\n",
" <td>F</td>\n",
" <td>K</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Sourav</td>\n",
" <td>30</td>\n",
" <td>M</td>\n",
" <td>L</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Vishal</td>\n",
" <td>40</td>\n",
" <td>M</td>\n",
" <td>P</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" First Name Age Gender City Place of Work\n",
"0 Sahil 10 M J True\n",
"1 Sonia 20 F K False\n",
"2 Sourav 30 M L False\n",
"3 Vishal 40 M P True"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df=pd.DataFrame({\n",
"'First Name':['Sahil','Sonia','Sourav','Vishal'],\n",
"'Age':[10,20,30,40],\n",
"'Gender':['M','F','M','M'],\n",
"'City':['J','K','L','P'],\n",
"'Place of Work':[True,False,False,True],\n",
"}\n",
")\n",
"df"
]
},
{
"cell_type": "markdown",
"id": "38e34482",
"metadata": {},
"source": [
"#### Get columns as list"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e4d76140",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['First Name', 'Age', 'Gender', 'City', 'Place of Work']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns.tolist()"
]
},
{
"cell_type": "markdown",
"id": "102fad7d",
"metadata": {},
"source": [
"#### Convert column names to series | df:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "ffe89310",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"First Name First Name\n",
"Age Age\n",
"Gender Gender\n",
"City City\n",
"Place of Work Place of Work\n",
"dtype: object"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns.to_series()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "f8870e68",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>First Name</th>\n",
" <td>First Name</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Age</th>\n",
" <td>Age</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gender</th>\n",
" <td>Gender</td>\n",
" </tr>\n",
" <tr>\n",
" <th>City</th>\n",
" <td>City</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Place of Work</th>\n",
" <td>Place of Work</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0\n",
"First Name First Name\n",
"Age Age\n",
"Gender Gender\n",
"City City\n",
"Place of Work Place of Work"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns.to_frame()"
]
},
{
"cell_type": "markdown",
"id": "d794ed40",
"metadata": {},
"source": [
"#### Check if specific column is there or not"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "939a91ff",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ True, False, False, False, False])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns.str.contains('Name') "
]
},
{
"cell_type": "markdown",
"id": "4d7d7886",
"metadata": {},
"source": [
"#### Check if any duplicate column is there"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "1d5b6490",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([False, False, False, False, False])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns.duplicated()"
]
},
{
"cell_type": "markdown",
"id": "55079b22",
"metadata": {},
"source": [
"#### Check methods/attributes of String"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "12107440",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['__annotations__', '__class__', '__delattr__', '__dict__', '__dir__']"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dir(df.columns.str)[0:5]"
]
},
{
"cell_type": "markdown",
"id": "e185987b",
"metadata": {},
"source": [
"#### Make column names to lower case"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "9b96b260",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['first name', 'age', 'gender', 'city', 'place of work'], dtype='object')"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns.str.lower()"
]
},
{
"cell_type": "markdown",
"id": "f34c789f",
"metadata": {},
"source": [
"#### Make column names to Upper case"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "e74ca52a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['FIRST NAME', 'AGE', 'GENDER', 'CITY', 'PLACE OF WORK'], dtype='object')"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns.str.upper()"
]
},
{
"cell_type": "markdown",
"id": "f41bfa14",
"metadata": {},
"source": [
"#### Make column names to Title case"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "b1c5a382",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['First Name', 'Age', 'Gender', 'City', 'Place Of Work'], dtype='object')"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns.str.title() # Camel Case"
]
},
{
"cell_type": "markdown",
"id": "bce613bd",
"metadata": {},
"source": [
"#### Make column names to Capitalize"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "42cce642",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['First name', 'Age', 'Gender', 'City', 'Place of work'], dtype='object')"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns.str.capitalize() # Only first letter big"
]
},
{
"cell_type": "markdown",
"id": "3367e158",
"metadata": {},
"source": [
"#### Replace empty spaces with underscores"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "ce8a4321",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['First-Name', 'Age', 'Gender', 'City', 'Place-of-Work'], dtype='object')"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns.str.replace(' ','-')"
]
},
{
"cell_type": "markdown",
"id": "3ce3d474",
"metadata": {},
"source": [
"#### Rename columns"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "f1c0b479",
"metadata": {},
"outputs": [],
"source": [
"df.rename(columns={'oldname':'newname'},inplace=True)"
]
},
{
"cell_type": "markdown",
"id": "17232ae6",
"metadata": {},
"source": [
"#### Check total number of columns"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "fb541c61",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(df.columns)"
]
},
{
"cell_type": "markdown",
"id": "4448982c",
"metadata": {},
"source": [
"#### Select particular columns"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "8c25c05f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['First Name', 'Age', 'Gender', 'City'], dtype=object)"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns.values[0:4]"
]
},
{
"cell_type": "markdown",
"id": "a271a055",
"metadata": {},
"source": [
"#### Get 2nd column and rename it"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "80ddb35d",
"metadata": {},
"outputs": [],
"source": [
"df.columns.values[2]='DOB'"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "16c3bccc",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>First Name</th>\n",
" <th>Age</th>\n",
" <th>DOB</th>\n",
" <th>City</th>\n",
" <th>Place of Work</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Sahil</td>\n",
" <td>10</td>\n",
" <td>M</td>\n",
" <td>J</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Sonia</td>\n",
" <td>20</td>\n",
" <td>F</td>\n",
" <td>K</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Sourav</td>\n",
" <td>30</td>\n",
" <td>M</td>\n",
" <td>L</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Vishal</td>\n",
" <td>40</td>\n",
" <td>M</td>\n",
" <td>P</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" First Name Age DOB City Place of Work\n",
"0 Sahil 10 M J True\n",
"1 Sonia 20 F K False\n",
"2 Sourav 30 M L False\n",
"3 Vishal 40 M P True"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"id": "e9eea57a",
"metadata": {},
"source": [
"#### Select all columns except one"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "0cc2184f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['First Name', 'Age', 'City', 'Place of Work'], dtype='object')"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns[df.columns!= 'DOB']"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "04f9d131",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>First Name</th>\n",
" <th>Age</th>\n",
" <th>DOB</th>\n",
" <th>City</th>\n",
" <th>Place of Work</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Sahil</td>\n",
" <td>10</td>\n",
" <td>M</td>\n",
" <td>J</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Sonia</td>\n",
" <td>20</td>\n",
" <td>F</td>\n",
" <td>K</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Sourav</td>\n",
" <td>30</td>\n",
" <td>M</td>\n",
" <td>L</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Vishal</td>\n",
" <td>40</td>\n",
" <td>M</td>\n",
" <td>P</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" First Name Age DOB City Place of Work\n",
"0 Sahil 10 M J True\n",
"1 Sonia 20 F K False\n",
"2 Sourav 30 M L False\n",
"3 Vishal 40 M P True"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"id": "d41853db",
"metadata": {},
"source": [
"#### Select all columns except multiple"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "06ca1e33",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'numpy.ndarray' object has no attribute 'columns'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-43-3cca24522c75>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'DOB'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'City'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'columns'"
]
}
],
"source": [
"#?\n",
"df.loc[:,-df.columns.isin(['DOB','City']).columns]"
]
},
{
"cell_type": "markdown",
"id": "1d592bf9",
"metadata": {},
"source": [
"#### Select column names that begins with particular word"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "a71af597",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ True, False, False, False, False])"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns.str.startswith('First')\n",
"# Gives array of booleans"
]
},
{
"cell_type": "markdown",
"id": "ca67d9aa",
"metadata": {},
"source": [
"#### Select group of column names"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "9d6ffb19",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['First Name', 'Age', 'DOB'], dtype=object)"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns.values[[0,1,2]]"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "71ea54bb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['First Name', 'Age', 'DOB'], dtype='object')"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns[0:3]"
]
}
],
"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": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}