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Created July 28, 2023 18:18
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pandas_for_data_science_pandas.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/JenniferNorthrup/243eb63af57476ce53cf285e5818498d/pandas_for_data_science_pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "oMiXoONyeGXa"
},
"outputs": [],
"source": [
"# importing the libraries\n",
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RWg1XRJ-u1iM"
},
"source": [
"### 2.6 Pandas - Series and DataFrames"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WKUBbjGgaaTh"
},
"source": [
"**Pandas Series**\n",
"* Pandas Series is a one-dimensional labeled array/list capable of holding data of any type (integer, string, float, python objects, etc.).\n",
"* The labels are collectively called index.\n",
"* Pandas Series can be thought as a single column of an excel spreadsheet and each entry in a series corresponds to an individual row in the spreadsheet."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "X9J5Mm9EsjOU",
"outputId": "d7aa4ed1-9aa8-4f61-bd12-3ca176abc275"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"0 55\n",
"1 25\n",
"2 75\n",
"3 40\n",
"4 90\n",
"dtype: int64\n",
"0 55\n",
"1 25\n",
"2 75\n",
"3 40\n",
"4 90\n",
"dtype: int64\n"
]
}
],
"source": [
"# creating a list of price of different medicines\n",
"med_price_list = [55,25,75,40,90]\n",
"\n",
"# converting the med_price_list to an array\n",
"med_price_arr = np.array(med_price_list)\n",
"\n",
"# converting the list and array into a Pandas Series object\n",
"series_list = pd.Series(med_price_list)\n",
"series_arr = pd.Series(med_price_arr)\n",
"\n",
"# printing the converted series object\n",
"print(series_list)\n",
"print(series_arr)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KhZ6-xThvSmd"
},
"source": [
"* We can see that the list and array have been converted to a Pandas Series object.\n",
"* We also see that the series has automatically got index labels. Let's see how these can be modified."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5cjQVBUryeif",
"outputId": "92a29aa6-adba-485c-c628-a5865428c77b"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Omeprazole 55\n",
"Azithromycin 25\n",
"Metformin 75\n",
"Ibuprofen 40\n",
"Cetirizine 90\n",
"dtype: int64\n"
]
}
],
"source": [
"# changing the index of a series\n",
"med_price_list_labeled = pd.Series(med_price_list, index = ['Omeprazole','Azithromycin','Metformin','Ibuprofen','Cetirizine'])\n",
"print(med_price_list_labeled)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "agSG9KlEaaTj"
},
"source": [
"**Performing mathematical operations on Pandas Series**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lzAjVJtwaaTj"
},
"source": [
"* The price of each medicine was increased by $2.5. Let's add this to the existing price."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lhK_joCfaaTj",
"outputId": "57d5c484-ffef-4766-b6c5-97e10e685a31"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Omeprazole 57.5\n",
"Azithromycin 27.5\n",
"Metformin 77.5\n",
"Ibuprofen 42.5\n",
"Cetirizine 92.5\n",
"dtype: float64"
]
},
"metadata": {},
"execution_count": 4
}
],
"source": [
"# adding 2.5 to existing prices\n",
"med_price_list_labeled_updated = med_price_list_labeled + 2.5\n",
"med_price_list_labeled_updated"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5sVET8vraaTk"
},
"source": [
"* A new price list was released by vendors for each medicine. Let's find the difference between new price and the old price"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "wGCw6jlfaaTk",
"outputId": "11d64c18-a29e-4809-e6c6-d312b8177530"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Omeprazole 77.0\n",
"Azithromycin 45.5\n",
"Metformin 100.0\n",
"Ibuprofen 50.0\n",
"Cetirizine 80.0\n",
"dtype: float64\n"
]
}
],
"source": [
"new_price_list = [77, 45.5, 100, 50, 80]\n",
"new_price_list_labeled = pd.Series(new_price_list, index = ['Omeprazole','Azithromycin','Metformin','Ibuprofen','Cetirizine'])\n",
"print(new_price_list_labeled)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "oynSb82OunnO",
"outputId": "1fe22cec-c6e9-49d4-ff97-0aa22c4a73ab",
"scrolled": true
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Difference between new price and old price - \n",
"Omeprazole 19.5\n",
"Azithromycin 18.0\n",
"Metformin 22.5\n",
"Ibuprofen 7.5\n",
"Cetirizine -12.5\n",
"dtype: float64\n"
]
}
],
"source": [
"print('Difference between new price and old price - ')\n",
"print(new_price_list_labeled - med_price_list_labeled_updated)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "P0OPO95sztGf"
},
"source": [
"**Pandas DataFrame**\n",
"\n",
"Pandas DataFrame is a two-dimensional tabular data structure with labeled axes (rows and columns)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Vb-R08TD0MK6"
},
"source": [
"**Creating a Pandas DataFrame using a list**"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "YR7Elttt0YWA",
"outputId": "87488dbe-758e-447c-c01a-30b3fe53585b"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Student\n",
"0 Mary\n",
"1 Peter\n",
"2 Susan\n",
"3 Toby\n",
"4 Vishal"
],
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"metadata": {},
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],
"source": [
"student = ['Mary', 'Peter', 'Susan', 'Toby', 'Vishal']\n",
"df1 = pd.DataFrame(student,columns=['Student'])\n",
"df1"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tbff68pm230z"
},
"source": [
"**Creating a Pandas DataFrame using a dictionary**"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6ebCP2pi01_L",
"outputId": "88229ea3-ffd3-44f9-d3d7-ad5b3839a775"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"0 B-\n",
"1 A+\n",
"2 A-\n",
"3 B+\n",
"4 C\n",
"Name: Grade, dtype: object\n"
]
}
],
"source": [
"# defining another list\n",
"grades = ['B-','A+','A-', 'B+', 'C']\n",
"\n",
"# creating the dataframe using a dictionary\n",
"df2 = pd.DataFrame({'Student':student,'Grade':grades})\n",
"df2\n",
"print(df2['Grade'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lj7iRqkS145x"
},
"source": [
"**Creating a Pandas DataFrame using Series**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YbJy3YLMaaTm"
},
"source": [
"The data for total energy consumption for the U.S. was collected from 2012 - 2018. Let's see how this data can be presented in form of data frame."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 269
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"id": "Lg18pRxJ3YVd",
"outputId": "6c16a1e1-e00b-4c73-e400-d91f07280303"
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{
"output_type": "execute_result",
"data": {
"text/plain": [
" Year Energy_Consumption(Mtoe)\n",
"0 2012 2152\n",
"1 2013 2196\n",
"2 2014 2217\n",
"3 2015 2194\n",
"4 2016 2172\n",
"5 2017 2180\n",
"6 2018 2258"
],
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" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
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" }\n",
" </script>\n",
" </div>\n",
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]
},
"metadata": {},
"execution_count": 9
}
],
"source": [
"year = pd.Series([2012,2013,2014,2015,2016,2017,2018])\n",
"energy_consumption = pd.Series([2152,2196,2217,2194,2172,2180,2258])\n",
"\n",
"df3 = pd.DataFrame({'Year':year,'Energy_Consumption(Mtoe)':energy_consumption})\n",
"df3"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wD1Z1hsA55A-"
},
"source": [
"**Creating a Pandas DataFrame using random values**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "W_UWLjAOaaTn"
},
"source": [
"For encryption purposes a web browser company wants to generate random values which have mean equal to 0 and variance equal to 1. They want 5 randomly generated numbers in 2 different trials."
]
},
{
"cell_type": "code",
"execution_count": 10,
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"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "5cdmJWu83xAD",
"outputId": "1af1a488-53e7-4745-b4cf-d020edf8d028"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Trial 1 Trial 2\n",
"0 1.117188 0.131725\n",
"1 -0.049699 -0.150862\n",
"2 0.100401 -1.478799\n",
"3 0.235745 -0.558268\n",
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},
"metadata": {},
"execution_count": 10
}
],
"source": [
"# we can create a new dataframe using random values\n",
"df4 = pd.DataFrame(np.random.randn(5,2),columns = ['Trial 1', 'Trial 2'])\n",
"df4"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "c7MwEmop6GWe"
},
"source": [
"### 2.7 Pandas - Accessing and Modifying"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jy3tHXkyJLu9"
},
"source": [
"**Accessing Series**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UWxqQH5aaaTo"
},
"source": [
"The revenue (in billion dollars) of different telecommunication operators in U.S. was collected for the year of 2020. The following lists consist of the names of the telecommunication operators and their respective revenue (in billion dollars)."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8hlsWm_MaaTo",
"outputId": "e0ea9ad2-68a4-4dc7-9e15-df50e00f55d2"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"AT&T 171.76\n",
"Verizon 128.29\n",
"T-Mobile US 68.40\n",
"US Cellular 4.04\n",
"dtype: float64"
]
},
"metadata": {},
"execution_count": 11
}
],
"source": [
"operators = ['AT&T', 'Verizon', 'T-Mobile US', 'US Cellular']\n",
"revenue = [171.76, 128.29, 68.4, 4.04]\n",
"\n",
"#creating a Series from lists\n",
"telecom = pd.Series(revenue, index=operators)\n",
"telecom"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LSIn5H4_I5cc"
},
"source": [
"**Accessing Pandas Series using its index**"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "iFy5pkGRKBWQ",
"outputId": "81741fce-4676-47cc-bc47-b89702b251b8"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"171.76"
]
},
"metadata": {},
"execution_count": 12
}
],
"source": [
"# accessing the first element of series\n",
"telecom[0]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UZTdWeaQKicd",
"outputId": "63681658-04a7-4d1b-c729-1fb044d8ac94"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"AT&T 171.76\n",
"Verizon 128.29\n",
"T-Mobile US 68.40\n",
"dtype: float64"
]
},
"metadata": {},
"execution_count": 13
}
],
"source": [
"# accessing firt 3 elements of a series\n",
"telecom[:3]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xxyTDCkKKrKP",
"outputId": "58c0fcb4-b1ab-4b2b-bb21-49e69f2a2d38"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"T-Mobile US 68.40\n",
"US Cellular 4.04\n",
"dtype: float64"
]
},
"metadata": {},
"execution_count": 14
}
],
"source": [
"# accessing the last two elements of a series\n",
"telecom[-2:]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "hIJny1Z3LTPW",
"outputId": "d26ec540-03ae-4231-91dd-6f2ddb1b10a8"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"AT&T 171.76\n",
"T-Mobile US 68.40\n",
"US Cellular 4.04\n",
"dtype: float64"
]
},
"metadata": {},
"execution_count": 15
}
],
"source": [
"# accessing multiple elements of a series\n",
"telecom[[0,2,3]]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kbrvcVEzLJrO"
},
"source": [
"**Accessing Pandas Series using its labeled index**"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WXRSET1iJT6t",
"outputId": "ba817ed9-fe40-4ebe-d0f2-99f00d3980b6"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"171.76"
]
},
"metadata": {},
"execution_count": 16
}
],
"source": [
"# accessing the revenue of AT&T\n",
"telecom['AT&T']"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "I3RQr51ILfJl",
"outputId": "b574a64f-3d57-4ab0-ef84-674a905079ed"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"AT&T 171.76\n",
"Verizon 128.29\n",
"T-Mobile US 68.40\n",
"dtype: float64"
]
},
"metadata": {},
"execution_count": 17
}
],
"source": [
"# accessing firt 3 revenues of operators in the series\n",
"telecom[:'T-Mobile US']"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dap494agJWhA",
"outputId": "ea082223-8724-455b-a696-9acea509d7ab"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"AT&T 171.76\n",
"US Cellular 4.04\n",
"Verizon 128.29\n",
"dtype: float64"
]
},
"metadata": {},
"execution_count": 18
}
],
"source": [
"# accessing multiple values\n",
"telecom[['AT&T','US Cellular','Verizon']]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "E-MAFZwTJhfN"
},
"source": [
"**Accessing DataFrames**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "k1lOvovyaaTq"
},
"source": [
"The data of the customers visiting 24/7 Stores from different locations was collected. The data includes Customer ID, location of store, gender of the customer, type of product purchased, quantity of products purchased, total bill amount. Let's create the dataset and see how to access different entries of it."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "Bozahsu4aaTq",
"outputId": "64520a14-0751-4df8-ff43-d314f03e4f85"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" CustomerID location gender type quantity total_bill\n",
"0 CustID00 Chicago M Electronics 1 100\n",
"1 CustID01 Boston M Food&Beverages 3 75\n",
"2 CustID02 Seattle F Food&Beverages 4 125\n",
"3 CustID03 San Francisco M Medicine 2 50\n",
"4 CustID04 Austin F Beauty 1 80"
],
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" vertical-align: middle;\n",
" }\n",
"\n",
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"\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>CustomerID</th>\n",
" <th>location</th>\n",
" <th>gender</th>\n",
" <th>type</th>\n",
" <th>quantity</th>\n",
" <th>total_bill</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>CustID00</td>\n",
" <td>Chicago</td>\n",
" <td>M</td>\n",
" <td>Electronics</td>\n",
" <td>1</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>CustID01</td>\n",
" <td>Boston</td>\n",
" <td>M</td>\n",
" <td>Food&amp;Beverages</td>\n",
" <td>3</td>\n",
" <td>75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>CustID02</td>\n",
" <td>Seattle</td>\n",
" <td>F</td>\n",
" <td>Food&amp;Beverages</td>\n",
" <td>4</td>\n",
" <td>125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>CustID03</td>\n",
" <td>San Francisco</td>\n",
" <td>M</td>\n",
" <td>Medicine</td>\n",
" <td>2</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>CustID04</td>\n",
" <td>Austin</td>\n",
" <td>F</td>\n",
" <td>Beauty</td>\n",
" <td>1</td>\n",
" <td>80</td>\n",
" </tr>\n",
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"</table>\n",
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" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b97d9e78-0ebb-4418-a428-2e589a477e3f')\"\n",
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"\n",
"\n",
"\n",
" <div id=\"df-07437ec0-28f1-4ae0-9f25-3b573fd69693\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-07437ec0-28f1-4ae0-9f25-3b573fd69693')\"\n",
" title=\"Suggest charts.\"\n",
" style=\"display:none;\">\n",
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" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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" background-color: #3B4455;\n",
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" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const containerElement = document.querySelector('#' + key);\n",
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" 'suggestCharts', [key], {});\n",
" }\n",
" </script>\n",
"\n",
" <script>\n",
"\n",
"function displayQuickchartButton(domScope) {\n",
" let quickchartButtonEl =\n",
" domScope.querySelector('#df-07437ec0-28f1-4ae0-9f25-3b573fd69693 button.colab-df-quickchart');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"}\n",
"\n",
" displayQuickchartButton(document);\n",
" </script>\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
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" background-color: #E8F0FE;\n",
" border: none;\n",
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" background-color: #434B5C;\n",
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" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
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"\n",
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" const buttonEl =\n",
" document.querySelector('#df-b97d9e78-0ebb-4418-a428-2e589a477e3f button.colab-df-convert');\n",
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" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
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" const element = document.querySelector('#df-b97d9e78-0ebb-4418-a428-2e589a477e3f');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 19
}
],
"source": [
"# creating the dataframe using dictionary\n",
"store_data = pd.DataFrame({'CustomerID': ['CustID00','CustID01','CustID02','CustID03','CustID04']\n",
" ,'location': ['Chicago', 'Boston', 'Seattle', 'San Francisco', 'Austin']\n",
" ,'gender': ['M','M','F','M','F']\n",
" ,'type': ['Electronics','Food&Beverages','Food&Beverages','Medicine','Beauty']\n",
" ,'quantity':[1,3,4,2,1],'total_bill':[100,75,125,50,80]})\n",
"store_data"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81
},
"id": "U_v2ac5mUMtp",
"outputId": "539d6374-1a73-4d80-8877-a17247413024"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" CustomerID location gender type quantity total_bill\n",
"0 CustID00 Chicago M Electronics 1 100"
],
"text/html": [
"\n",
"\n",
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" <div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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"\n",
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" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
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" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
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"\n",
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" background-color: #E2EBFA;\n",
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" background-color: #434B5C;\n",
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" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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"\n",
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" document.querySelector('#df-8ab62534-99e4-4e9e-882e-3dcc7f76212f button.colab-df-convert');\n",
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" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 20
}
],
"source": [
"# accessing first row of the dataframe\n",
"store_data[:1]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "AOPng29EUVMG",
"outputId": "44601401-895e-4b10-9d9e-3b686d02f694"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 Chicago\n",
"1 Boston\n",
"2 Seattle\n",
"3 San Francisco\n",
"4 Austin\n",
"Name: location, dtype: object"
]
},
"metadata": {},
"execution_count": 21
}
],
"source": [
"# accessing first column of the dataframe\n",
"store_data['location']"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 143
},
"id": "mjDrEFdnNvBl",
"outputId": "ca90081d-ec6e-4027-9196-89c6c5305cf5"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" CustomerID location gender type quantity total_bill\n",
"0 CustID00 Chicago M Electronics 1 100\n",
"2 CustID02 Seattle F Food&Beverages 4 125\n",
"4 CustID04 Austin F Beauty 1 80"
],
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" width=\"24px\">\n",
" <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
" </svg>\n",
" </button>\n",
"\n",
"\n",
"\n",
" <div id=\"df-8ba293f7-8fba-47a5-98f9-24598178240b\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8ba293f7-8fba-47a5-98f9-24598178240b')\"\n",
" title=\"Suggest charts.\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <g>\n",
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
" </div>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const containerElement = document.querySelector('#' + key);\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" }\n",
" </script>\n",
"\n",
" <script>\n",
"\n",
"function displayQuickchartButton(domScope) {\n",
" let quickchartButtonEl =\n",
" domScope.querySelector('#df-8ba293f7-8fba-47a5-98f9-24598178240b button.colab-df-quickchart');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"}\n",
"\n",
" displayQuickchartButton(document);\n",
" </script>\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-6c0fd167-a842-456f-8cc7-eb50e39cd07e button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-6c0fd167-a842-456f-8cc7-eb50e39cd07e');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 22
}
],
"source": [
"# accessing rows with the step size of 2\n",
"store_data[::2]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 143
},
"id": "4Zki01TwM7Ql",
"outputId": "aa566e93-07d4-4538-fb00-53631005c0d3"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" CustomerID location gender type quantity total_bill\n",
"4 CustID04 Austin F Beauty 1 80\n",
"2 CustID02 Seattle F Food&Beverages 4 125\n",
"0 CustID00 Chicago M Electronics 1 100"
],
"text/html": [
"\n",
"\n",
" <div id=\"df-57623182-be61-4899-82ff-50d4fbecc472\">\n",
" <div class=\"colab-df-container\">\n",
" <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>CustomerID</th>\n",
" <th>location</th>\n",
" <th>gender</th>\n",
" <th>type</th>\n",
" <th>quantity</th>\n",
" <th>total_bill</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>CustID04</td>\n",
" <td>Austin</td>\n",
" <td>F</td>\n",
" <td>Beauty</td>\n",
" <td>1</td>\n",
" <td>80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>CustID02</td>\n",
" <td>Seattle</td>\n",
" <td>F</td>\n",
" <td>Food&amp;Beverages</td>\n",
" <td>4</td>\n",
" <td>125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>CustID00</td>\n",
" <td>Chicago</td>\n",
" <td>M</td>\n",
" <td>Electronics</td>\n",
" <td>1</td>\n",
" <td>100</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-57623182-be61-4899-82ff-50d4fbecc472')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
"\n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
" </svg>\n",
" </button>\n",
"\n",
"\n",
"\n",
" <div id=\"df-cf50fde7-7ab4-438c-9589-d1aa7e1584d7\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cf50fde7-7ab4-438c-9589-d1aa7e1584d7')\"\n",
" title=\"Suggest charts.\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <g>\n",
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
" </div>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const containerElement = document.querySelector('#' + key);\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" }\n",
" </script>\n",
"\n",
" <script>\n",
"\n",
"function displayQuickchartButton(domScope) {\n",
" let quickchartButtonEl =\n",
" domScope.querySelector('#df-cf50fde7-7ab4-438c-9589-d1aa7e1584d7 button.colab-df-quickchart');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"}\n",
"\n",
" displayQuickchartButton(document);\n",
" </script>\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-57623182-be61-4899-82ff-50d4fbecc472 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-57623182-be61-4899-82ff-50d4fbecc472');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 23
}
],
"source": [
"# accessing the rows in reverse\n",
"store_data[::-2]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ed3PjeXmPhHS"
},
"source": [
"**Using loc and iloc method**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WHWSlf0PPxWv"
},
"source": [
"**loc method**\n",
"\n",
"* loc is a method to access rows and columns on pandas objects. When using the loc method on a dataframe, we specify which rows and which columns we want by using the following format:\n",
"\n",
" * **dataframe.loc[row selection, column selection]**\n",
"\n",
"* DataFrame.loc[] method is a method that takes **only index labels** and returns row or dataframe if the index label exists in the data frame."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "wwnArsdDO23-",
"outputId": "106a93d5-e784-4451-b2f8-b89547d6469a"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"CustomerID CustID00\n",
"location Chicago\n",
"gender M\n",
"type Electronics\n",
"quantity 1\n",
"total_bill 100\n",
"Name: 0, dtype: object"
]
},
"metadata": {},
"execution_count": 24
}
],
"source": [
"# accessing first index value using loc method (indexing starts from 0 in python)\n",
"store_data.loc[0]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Q0yJ-rLlaaTs"
},
"source": [
"**Accessing selected rows and columns using loc method**"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 112
},
"id": "nb1S6qqCS3j2",
"outputId": "d7a61c5e-b72b-4d46-ae61-386584c9ac8f"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" location type\n",
"1 Boston Food&Beverages\n",
"4 Austin Beauty"
],
"text/html": [
"\n",
"\n",
" <div id=\"df-d2bfc38f-0487-431f-822f-9fa9f79ab4fa\">\n",
" <div class=\"colab-df-container\">\n",
" <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>location</th>\n",
" <th>type</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Boston</td>\n",
" <td>Food&amp;Beverages</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Austin</td>\n",
" <td>Beauty</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d2bfc38f-0487-431f-822f-9fa9f79ab4fa')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
"\n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
" </svg>\n",
" </button>\n",
"\n",
"\n",
"\n",
" <div id=\"df-50c0d1de-4dc4-4dda-b793-bc808e0b195a\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-50c0d1de-4dc4-4dda-b793-bc808e0b195a')\"\n",
" title=\"Suggest charts.\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <g>\n",
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
" </div>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const containerElement = document.querySelector('#' + key);\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" }\n",
" </script>\n",
"\n",
" <script>\n",
"\n",
"function displayQuickchartButton(domScope) {\n",
" let quickchartButtonEl =\n",
" domScope.querySelector('#df-50c0d1de-4dc4-4dda-b793-bc808e0b195a button.colab-df-quickchart');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"}\n",
"\n",
" displayQuickchartButton(document);\n",
" </script>\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-d2bfc38f-0487-431f-822f-9fa9f79ab4fa button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-d2bfc38f-0487-431f-822f-9fa9f79ab4fa');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 25
}
],
"source": [
"# accessing 1st and 4th index values along with location and type columns\n",
"store_data.loc[[1,4],['location','type']]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XL5mkENwQ1Yt"
},
"source": [
"**iloc method**\n",
"\n",
"* The iloc indexer for Pandas Dataframe is used for **integer location-based** indexing/selection by position. When using the loc method on a dataframe, we specify which rows and which columns we want by using the following format:\n",
"\n",
" * **dataframe.iloc[row selection, column selection]**\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 112
},
"id": "PP6eMjy-RNVd",
"outputId": "e920881a-b2af-4b96-8876-8d288ac48d08"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" CustomerID gender\n",
"1 CustID01 M\n",
"4 CustID04 F"
],
"text/html": [
"\n",
"\n",
" <div id=\"df-e19b5cac-8a34-4115-b11b-8c538deb3b6f\">\n",
" <div class=\"colab-df-container\">\n",
" <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>CustomerID</th>\n",
" <th>gender</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>CustID01</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>CustID04</td>\n",
" <td>F</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e19b5cac-8a34-4115-b11b-8c538deb3b6f')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
"\n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
" </svg>\n",
" </button>\n",
"\n",
"\n",
"\n",
" <div id=\"df-966a75af-4165-4e7f-9dfc-e6191146cbe8\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-966a75af-4165-4e7f-9dfc-e6191146cbe8')\"\n",
" title=\"Suggest charts.\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <g>\n",
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
" </div>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const containerElement = document.querySelector('#' + key);\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" }\n",
" </script>\n",
"\n",
" <script>\n",
"\n",
"function displayQuickchartButton(domScope) {\n",
" let quickchartButtonEl =\n",
" domScope.querySelector('#df-966a75af-4165-4e7f-9dfc-e6191146cbe8 button.colab-df-quickchart');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"}\n",
"\n",
" displayQuickchartButton(document);\n",
" </script>\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-e19b5cac-8a34-4115-b11b-8c538deb3b6f button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-e19b5cac-8a34-4115-b11b-8c538deb3b6f');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 26
}
],
"source": [
"# accessing selected rows and columns using iloc method\n",
"store_data.iloc[[1,4],[0,2]]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YUjPItvHXNa_"
},
"source": [
"**Difference between loc and iloc indexing methods**\n",
"\n",
"* loc is label-based, which means that you have to specify rows and columns based on their row and column labels.\n",
"* iloc is integer position-based, so you have to specify rows and columns by their integer position values (0-based integer position).\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PUL471fKaaTt"
},
"source": [
"If we use labels instead of index values in .iloc it will throw an error."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 336
},
"id": "TMDNQ34raaTt",
"outputId": "40a6928d-644b-4be8-b5ca-44ef3c161482"
},
"outputs": [
{
"output_type": "error",
"ename": "IndexError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-27-53acc0d7ec5b>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# accessing selected rows and columns using iloc method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mstore_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'location'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'type'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1065\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1066\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtakeable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_takeable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1067\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1068\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1069\u001b[0m \u001b[0;31m# we by definition only have the 0th axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m 1561\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1562\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1563\u001b[0;31m \u001b[0mtup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_tuple_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1564\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0msuppress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mIndexingError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1565\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_lowerdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_tuple_indexer\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 871\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 872\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 873\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 874\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 875\u001b[0m raise ValueError(\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_key\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1475\u001b[0m \u001b[0;31m# check that the key has a numeric dtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1476\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_numeric_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1477\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\".iloc requires numeric indexers, got {arr}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1478\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1479\u001b[0m \u001b[0;31m# check that the key does not exceed the maximum size of the index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mIndexError\u001b[0m: .iloc requires numeric indexers, got ['location' 'type']"
]
}
],
"source": [
"# accessing selected rows and columns using iloc method\n",
"store_data.iloc[[1,4],['location','type']]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Gitf4BMjkAlE"
},
"source": [
"* As expected, .iloc has given error on using 'labels'."
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"id": "uA2-H3DwRuDJ"
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "RBPiD5DiTUGN"
},
"source": [
"We can modify entries of a dataframe using loc or iloc too"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0UkHqdZNQfYh",
"outputId": "0e1e1284-3236-443e-cab2-a16d46085741"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Beauty\n"
]
}
],
"source": [
"print(store_data.loc[4,'type'])\n",
"store_data.loc[4,'type'] = 'Electronics'"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "RjRVVLptRaPw",
"outputId": "f5ee07dc-1109-4f7f-e7c1-094e3b84781b"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" CustomerID location gender type quantity total_bill\n",
"0 CustID00 Chicago M Electronics 1 100\n",
"1 CustID01 Boston M Food&Beverages 3 75\n",
"2 CustID02 Seattle F Food&Beverages 4 125\n",
"3 CustID03 San Francisco M Medicine 2 50\n",
"4 CustID04 Austin F Electronics 1 80"
],
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"\n",
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]
},
"metadata": {},
"execution_count": 29
}
],
"source": [
"store_data"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "2W8vdM6uTuSt",
"outputId": "0b7c1112-2314-47b7-c4aa-af162c548730"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" CustomerID location gender type quantity total_bill\n",
"0 CustID00 Chicago M Electronics 1 100\n",
"1 CustID01 Boston M Food&Beverages 3 75\n",
"2 CustID02 Seattle F Food&Beverages 4 125\n",
"3 CustID03 San Francisco M Medicine 2 50\n",
"4 CustID04 Austin F Beauty 1 80"
],
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]
},
"metadata": {},
"execution_count": 30
}
],
"source": [
"store_data.iloc[4,3] = 'Beauty'\n",
"store_data"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "96RCw04-XlVb"
},
"source": [
"**Condition based indexing**"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2cH76CDLYOVD",
"outputId": "92b8e6e6-d9b9-478e-ce0f-86f06ffda8b2"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 False\n",
"1 True\n",
"2 True\n",
"3 True\n",
"4 False\n",
"Name: quantity, dtype: bool"
]
},
"metadata": {},
"execution_count": 31
}
],
"source": [
"store_data['quantity']>1"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mI04a8_0YXG6"
},
"source": [
"* Wherever the condition of greater than 1 is satisfied in quantity column, 'True' is returned. Let's retrieve the original values wherever the condition is satisfied."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 143
},
"id": "c3Wba0oJYRlx",
"outputId": "5f6da77c-0d7b-4910-da39-14927d201234"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" CustomerID location gender type quantity total_bill\n",
"1 CustID01 Boston M Food&Beverages 3 75\n",
"2 CustID02 Seattle F Food&Beverages 4 125\n",
"3 CustID03 San Francisco M Medicine 2 50"
],
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"metadata": {},
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],
"source": [
"store_data.loc[store_data['quantity']>1]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "78kpdkOuYU5G"
},
"source": [
"* Wherever the condition is satisfied we get the original values, and wherever the condition is not satisfied we do not get those records in the output."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OGT6WSHFZApo"
},
"source": [
"**Column addition and removal from a Pandas DataFrame**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "X29WMLIgZbPi"
},
"source": [
"**Adding a new column in a DataFrame**"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "JH2SiWOBZl1U",
"outputId": "a64b7e90-af52-4771-9c85-2e19d9ed250a"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" CustomerID location gender type quantity total_bill\n",
"0 CustID00 Chicago M Electronics 1 100\n",
"1 CustID01 Boston M Food&Beverages 3 75\n",
"2 CustID02 Seattle F Food&Beverages 4 125\n",
"3 CustID03 San Francisco M Medicine 2 50\n",
"4 CustID04 Austin F Beauty 1 80"
],
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},
"metadata": {},
"execution_count": 33
}
],
"source": [
"store_data"
]
},
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"height": 206
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"id": "zwcPawNlZn8S",
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{
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"data": {
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" CustomerID location gender type quantity total_bill \\\n",
"0 CustID00 Chicago M Electronics 1 100 \n",
"1 CustID01 Boston M Food&Beverages 3 75 \n",
"2 CustID02 Seattle F Food&Beverages 4 125 \n",
"3 CustID03 San Francisco M Medicine 2 50 \n",
"4 CustID04 Austin F Beauty 1 80 \n",
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},
"metadata": {},
"execution_count": 34
}
],
"source": [
"# adding a new column in data frame store_data which is a rating (out of 5) given by customer based on their shopping experience\n",
"store_data['rating'] = [2,5,3,4,4]\n",
"store_data"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8-RPaeeMaAms"
},
"source": [
"**Removing a column from a DataFrame**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "luayOzSOaaTu"
},
"source": [
"* The CustomerID column is a unique identifier of each customer. This unique identifier will not help 24/7 Stores in getting useful insights about their customers. So, they have decided to remove this column from the data frame."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
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"height": 206
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"id": "hCXhoJ7naHCW",
"outputId": "2ce01850-383f-45eb-93e1-0a0e3d147689"
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{
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"data": {
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" location gender type quantity total_bill rating\n",
"0 Chicago M Electronics 1 100 2\n",
"1 Boston M Food&Beverages 3 75 5\n",
"2 Seattle F Food&Beverages 4 125 3\n",
"3 San Francisco M Medicine 2 50 4\n",
"4 Austin F Beauty 1 80 4"
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"source": [
"store_data.drop('CustomerID',axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "72v8jOTOaMpc"
},
"source": [
"* We sucessfully removed the 'CustomerID' from dataframe. But this change is not permanent in the dataframe, let's have a look at the store_data again."
]
},
{
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{
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" CustomerID location gender type quantity total_bill \\\n",
"0 CustID00 Chicago M Electronics 1 100 \n",
"1 CustID01 Boston M Food&Beverages 3 75 \n",
"2 CustID02 Seattle F Food&Beverages 4 125 \n",
"3 CustID03 San Francisco M Medicine 2 50 \n",
"4 CustID04 Austin F Beauty 1 80 \n",
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"metadata": {},
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],
"source": [
"store_data"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "R-Auuivladg5"
},
"source": [
"* We see that store_data still has column 'CustomerID' in it.\n",
"* To make permanent changes to a dataframe there are two methods will have to use a parameter `inplace` and set its value to `True`."
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"id": "ye21aHVbCw4d"
},
"outputs": [],
"source": []
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{
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"height": 206
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"id": "d7pr_yifasub",
"outputId": "c15697bb-85c9-4479-da18-1b03f87a5ecd"
},
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{
"output_type": "execute_result",
"data": {
"text/plain": [
" location gender type quantity total_bill rating\n",
"0 Chicago M Electronics 1 100 2\n",
"1 Boston M Food&Beverages 3 75 5\n",
"2 Seattle F Food&Beverages 4 125 3\n",
"3 San Francisco M Medicine 2 50 4\n",
"4 Austin F Beauty 1 80 4"
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},
"metadata": {},
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}
],
"source": [
"store_data.drop('CustomerID',axis=1,inplace=True)\n",
"store_data"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cC2Cr5ySa0V-"
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"source": [
"* Now the column has been permanently removed from the dataframe."
]
},
{
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" location gender type quantity total_bill rating\n",
"0 Chicago M Electronics 1 100 2\n",
"1 Boston M Food&Beverages 3 75 5\n",
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},
"metadata": {},
"execution_count": 38
}
],
"source": [
"# we can also remove multiple columns simultaneously\n",
"# it is always a good idea to store the new/updated data frames in new variables to avoid changes to the existing data frame\n",
"\n",
"# creating a copy of the existing data frame\n",
"new_store_data = store_data.copy()\n",
"store_data"
]
},
{
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{
"output_type": "execute_result",
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" gender type quantity total_bill\n",
"0 M Electronics 1 100\n",
"1 M Food&Beverages 3 75\n",
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},
"metadata": {},
"execution_count": 39
}
],
"source": [
"# dropping location and rating columns simultaneously\n",
"new_store_data.drop(['location','rating'],axis=1,inplace=True)\n",
"new_store_data"
]
},
{
"cell_type": "code",
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{
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" location gender type quantity total_bill rating\n",
"0 Chicago M Electronics 1 100 2\n",
"1 Boston M Food&Beverages 3 75 5\n",
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" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Austin</td>\n",
" <td>F</td>\n",
" <td>Beauty</td>\n",
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" }\n",
" </script>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 40
}
],
"source": [
"# lets check if store_data was impacted\n",
"store_data"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JBLXfOlob0NY"
},
"source": [
"* There were no changes to data frame store_data."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7HFM6r_qaaTw"
},
"source": [
"* Deep copy stores copies of the object’s value.\n",
"* Shallow Copy stores the references of objects to the original memory address."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hT8Sq8-Ab3Rj"
},
"source": [
"**Removing rows from a dataframe**"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 175
},
"id": "1KeeacRlcDgw",
"outputId": "a1e3a6b6-0956-4d6b-aaf6-f61bb0979bca"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" location gender type quantity total_bill rating\n",
"0 Chicago M Electronics 1 100 2\n",
"2 Seattle F Food&Beverages 4 125 3\n",
"3 San Francisco M Medicine 2 50 4\n",
"4 Austin F Beauty 1 80 4"
],
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" }\n",
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]
},
"metadata": {},
"execution_count": 41
}
],
"source": [
"store_data.drop(1,axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "3F4WNiEONe4g",
"outputId": "2d80ee35-a263-414b-df9e-52661579785e"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" location gender type quantity total_bill rating\n",
"0 Chicago M Electronics 1 100 2\n",
"1 Boston M Food&Beverages 3 75 5\n",
"2 Seattle F Food&Beverages 4 125 3\n",
"3 San Francisco M Medicine 2 50 4\n",
"4 Austin F Beauty 1 80 4"
],
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" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
" </div>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const containerElement = document.querySelector('#' + key);\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" }\n",
" </script>\n",
"\n",
" <script>\n",
"\n",
"function displayQuickchartButton(domScope) {\n",
" let quickchartButtonEl =\n",
" domScope.querySelector('#df-5f3c851f-f8e2-4e34-ba3a-ad40828e0d9f button.colab-df-quickchart');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"}\n",
"\n",
" displayQuickchartButton(document);\n",
" </script>\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
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"\n",
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" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
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" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
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" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-c1c3bf90-a2f6-4684-bee1-892564119196 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-c1c3bf90-a2f6-4684-bee1-892564119196');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 96
}
],
"source": [
"store_data"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qsGlmJzucG3t"
},
"source": [
"* Notice that we used **`axis=0`** to drop a row from a data frame, while we were using **`axis=1`** for dropping a column from the data frame.\n",
"* Also, to make permanent changes to the data frame we will have to use `inplace=True` parameter.\n",
"* We also see that the index are not correct now as first row has been removed. So, we will have to reset the index of the data frame. Let's see how this can be done."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 175
},
"id": "QHj_lQWQd8Li",
"outputId": "d060ab0d-11aa-43e5-f040-5e74eacdcb6a"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" location gender type quantity total_bill rating\n",
"0 Chicago M Electronics 1 100 2\n",
"2 Seattle F Food&Beverages 4 125 3\n",
"3 San Francisco M Medicine 2 50 4\n",
"4 Austin F Beauty 1 80 4"
],
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"\n",
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" <div id=\"df-a262a76f-ef76-41fe-ad21-58326cd7217a\">\n",
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" <td>4</td>\n",
" <td>125</td>\n",
" <td>3</td>\n",
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" <tr>\n",
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" <td>2</td>\n",
" <td>50</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Austin</td>\n",
" <td>F</td>\n",
" <td>Beauty</td>\n",
" <td>1</td>\n",
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" </g>\n",
"</svg>\n",
" </button>\n",
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" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
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" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
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" [theme=dark] .colab-df-quickchart:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
"</style>\n",
"\n",
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" const containerElement = document.querySelector('#' + key);\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" }\n",
" </script>\n",
"\n",
" <script>\n",
"\n",
"function displayQuickchartButton(domScope) {\n",
" let quickchartButtonEl =\n",
" domScope.querySelector('#df-c658dc95-7453-41b3-a9b5-02663df70112 button.colab-df-quickchart');\n",
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"}\n",
"\n",
" displayQuickchartButton(document);\n",
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" display:flex;\n",
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" background-color: #E8F0FE;\n",
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" }\n",
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"\n",
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" const buttonEl =\n",
" document.querySelector('#df-a262a76f-ef76-41fe-ad21-58326cd7217a button.colab-df-convert');\n",
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"\n",
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" const element = document.querySelector('#df-a262a76f-ef76-41fe-ad21-58326cd7217a');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
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" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 42
}
],
"source": [
"# creating a new dataframe\n",
"store_data_new = store_data.drop(1,axis=0)\n",
"store_data_new"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 175
},
"id": "a3XJVG6UfTrX",
"outputId": "4d30b65c-b05b-47b2-a783-c685e9c86031"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" index location gender type quantity total_bill rating\n",
"0 0 Chicago M Electronics 1 100 2\n",
"1 2 Seattle F Food&Beverages 4 125 3\n",
"2 3 San Francisco M Medicine 2 50 4\n",
"3 4 Austin F Beauty 1 80 4"
],
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" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-ee31ccc5-0f76-49eb-bb9f-c56f18e715aa button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-ee31ccc5-0f76-49eb-bb9f-c56f18e715aa');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 43
}
],
"source": [
"# resetting the index of data frame\n",
"store_data_new.reset_index()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XOh2G_b1fbqk"
},
"source": [
"* We see that the index of the data frame is now resetted but the index has become a column in the data frame. We do not need the index to become a column so we can simply set the parameter **`drop=True`** in reset_index() function."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 175
},
"id": "sZU0tctKf0A2",
"outputId": "a027534d-0b2d-4e7f-d6f3-13ef99519bc8"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" location gender type quantity total_bill rating\n",
"0 Chicago M Electronics 1 100 2\n",
"1 Seattle F Food&Beverages 4 125 3\n",
"2 San Francisco M Medicine 2 50 4\n",
"3 Austin F Beauty 1 80 4"
],
"text/html": [
"\n",
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" <td>Seattle</td>\n",
" <td>F</td>\n",
" <td>Food&amp;Beverages</td>\n",
" <td>4</td>\n",
" <td>125</td>\n",
" <td>3</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>San Francisco</td>\n",
" <td>M</td>\n",
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" <td>2</td>\n",
" <td>50</td>\n",
" <td>4</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>Austin</td>\n",
" <td>F</td>\n",
" <td>Beauty</td>\n",
" <td>1</td>\n",
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"function displayQuickchartButton(domScope) {\n",
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" quickchartButtonEl.style.display =\n",
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" displayQuickchartButton(document);\n",
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" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-fee9ecdc-d945-4ef2-9ebb-4e1febc7d13b');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 44
}
],
"source": [
"# setting inplace = True to make the changes permanent\n",
"store_data_new.reset_index(drop=True,inplace=True)\n",
"store_data_new"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1PVPsH6Rf9Us"
},
"source": [
"### 2.8 Pandas - Combining DataFrames"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1V8PJFedawJY"
},
"source": [
"We will examine 3 methods for combining dataframes\n",
"\n",
"1. concat\n",
"2. join\n",
"3. merge"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"id": "F7dzbCINgNIj"
},
"outputs": [],
"source": [
"data_cust = pd.DataFrame({\"customerID\":['101','102','103','104'],\n",
" 'category': ['Medium','Medium','High','Low'],\n",
" 'first_visit': ['yes','no','yes','yes'],\n",
" 'sales': [123,52,214,663]},index=[0,1,2,3])\n",
"\n",
"data_cust_new = pd.DataFrame({\"customerID\":['101','103','104','105'],\n",
" 'distance': [12,9,44,21],\n",
" 'sales': [123,214,663,331]},index=[4,5,6,7])"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 175
},
"id": "A65b10V4Twoq",
"outputId": "52e1fa4a-8015-4000-bfd6-2429a3854405"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" customerID category first_visit sales\n",
"0 101 Medium yes 123\n",
"1 102 Medium no 52\n",
"2 103 High yes 214\n",
"3 104 Low yes 663"
],
"text/html": [
"\n",
"\n",
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" <tr>\n",
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" <td>103</td>\n",
" <td>High</td>\n",
" <td>yes</td>\n",
" <td>214</td>\n",
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" <tr>\n",
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" <td>104</td>\n",
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" <td>yes</td>\n",
" <td>663</td>\n",
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"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d4fc04a3-7046-4591-bb63-a4ac423e7f9e')\"\n",
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" width=\"24px\">\n",
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"</svg>\n",
" </button>\n",
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" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
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"\n",
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"\n",
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" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-d4fc04a3-7046-4591-bb63-a4ac423e7f9e button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-d4fc04a3-7046-4591-bb63-a4ac423e7f9e');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 46
}
],
"source": [
"data_cust"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 175
},
"id": "0MMWONOvTy8x",
"outputId": "e8753574-a5cf-439e-f3ec-58dd763a2eeb"
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"metadata": {},
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],
"source": [
"data_cust_new"
]
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{
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"data": {
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" customerID category first_visit sales distance\n",
"0 101 Medium yes 123 NaN\n",
"1 102 Medium no 52 NaN\n",
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},
"metadata": {},
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],
"source": [
"pd.concat([data_cust,data_cust_new],axis=0)"
]
},
{
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"height": 300
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"id": "x4d_fwk9hyIn",
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},
"outputs": [
{
"output_type": "execute_result",
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"text/plain": [
" customerID category first_visit sales customerID distance sales\n",
"0 101 Medium yes 123.0 NaN NaN NaN\n",
"1 102 Medium no 52.0 NaN NaN NaN\n",
"2 103 High yes 214.0 NaN NaN NaN\n",
"3 104 Low yes 663.0 NaN NaN NaN\n",
"4 NaN NaN NaN NaN 101 12.0 123.0\n",
"5 NaN NaN NaN NaN 103 9.0 214.0\n",
"6 NaN NaN NaN NaN 104 44.0 663.0\n",
"7 NaN NaN NaN NaN 105 21.0 331.0"
],
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"metadata": {},
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}
],
"source": [
"pd.concat([data_cust,data_cust_new],axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rYWgB69Uh8eb"
},
"source": [
"**Merge and Join**\n",
"\n",
"* Merge combines dataframes using a column's values to identify common entries\n",
"\n",
"* Join combines dataframes using the index to identify common entries"
]
},
{
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" customerID category first_visit sales_x distance sales_y\n",
"0 101 Medium yes 123.0 12.0 123.0\n",
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"metadata": {},
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}
],
"source": [
"pd.merge(data_cust,data_cust_new,how='outer',on='customerID') # outer merge is union of on"
]
},
{
"cell_type": "code",
"execution_count": 51,
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"id": "arBjOKCCiKeB",
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{
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"0 101 Medium yes 123 12 123\n",
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]
},
"metadata": {},
"execution_count": 51
}
],
"source": [
"pd.merge(data_cust,data_cust_new,how='inner',on='customerID') # inner merge is intersection of on"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 175
},
"id": "BXWe2W7hirk5",
"outputId": "e4ed221e-5c95-4d56-ab39-d1129afc0e9a"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" customerID category first_visit sales_x distance sales_y\n",
"0 101 Medium yes 123.0 12 123\n",
"1 103 High yes 214.0 9 214\n",
"2 104 Low yes 663.0 44 663\n",
"3 105 NaN NaN NaN 21 331"
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]
},
"metadata": {},
"execution_count": 56
}
],
"source": [
"pd.merge(data_cust,data_cust_new,how='right',on='customerID')"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"id": "KCrVkI7_izBx"
},
"outputs": [],
"source": [
"data_quarters = pd.DataFrame({'Q1': [101,102,103],\n",
" 'Q2': [201,202,203]},\n",
" index=['I0','I1','I2'])\n",
"\n",
"data_quarters_new = pd.DataFrame({'Q3': [301,302,303],\n",
" 'Q4': [401,402,403]},\n",
" index=['I0','I2','I3'])"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 143
},
"id": "udScH_N_YJd2",
"outputId": "19aee7c2-5ab5-41e7-ac86-c31cc29485e2"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Q1 Q2\n",
"I0 101 201\n",
"I1 102 202\n",
"I2 103 203"
],
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" + ' to learn more about interactive tables.';\n",
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" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
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" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 54
}
],
"source": [
"data_quarters"
]
},
{
"cell_type": "code",
"execution_count": 57,
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"height": 143
},
"id": "F46S08gHYJku",
"outputId": "647be86e-de56-4545-b951-db2b3648bd6a"
},
"outputs": [
{
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" + ' to learn more about interactive tables.';\n",
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" element.appendChild(docLink);\n",
" }\n",
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" </div>\n"
]
},
"metadata": {},
"execution_count": 57
}
],
"source": [
"data_quarters_new"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xhilQNPcjDb9"
},
"source": [
"* `join` behaves just like merge, except instead of using the values of one of the columns to combine data frames, it uses the index labels"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 143
},
"id": "sEgY9t7pi_pH",
"outputId": "84102796-de69-46a9-93c2-abfb50fd9b3c"
},
"outputs": [
{
"output_type": "execute_result",
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"\n",
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"\n",
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" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 62
}
],
"source": [
"data_quarters.join(data_quarters_new,how='right') # outer, inner, left, and right work the same as merge"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TQdLaw2WaaT0"
},
"source": [
"### 2.9 Pandas - Saving and Loading DataFrames"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hzUVpfIqcLM-"
},
"source": [
"**Note**\n",
"\n",
"In real-life scenario, we deal with much larger datasets that have thousands of rows and multiple columns. It will not be feasible for us to create datasets using multiple lists, especially if the number of columns and rows increases.\n",
"\n",
"So, it is clear we need a more efficient way of handling the data simultaneously at the columns and row levels. In Python, we can import dataset from our local system, from links, or from databases and work on them directly instead of creating our own dataset."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2vu0qeSHaaT0"
},
"source": [
"**Loading a CSV file in Python**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0s1W4Ya3aaT0"
},
"source": [
"**For Jupyter Notebook**\n",
"* When the data file and jupyter notebook are in the same folder."
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"id": "6hg4rM1NkMpK"
},
"outputs": [],
"source": [
"# Using pd.read_csv() function will work without any path if the notebook and dataset are in the folder\n",
"\n",
"# data = pd.read_csv('StockData.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sfi3h_bGaaT0"
},
"source": [
"**For Google Colab with Google Drive**\n",
"\n",
"First, we have to give google colab access to our google drive:"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "KSpDmB4XcExv",
"outputId": "de5326b7-2f78-4adf-d2f2-97c4b0528f01"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n"
]
}
],
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9oKnCEzrcKq9"
},
"source": [
"Once we have access we can load files from google drive using read_csv() function."
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"id": "fTw5q0pBcI5h"
},
"outputs": [],
"source": [
"path=\"/content/drive/MyDrive/StockData.csv\"\n",
"data=pd.read_csv(path)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"id": "K2BX7gs2aaT1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"outputId": "30dd504d-1534-4178-fbfe-0baa462c17b7"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" stock date price\n",
"0 AAPL 08-02-2013 67.8542\n",
"1 AAPL 11-02-2013 68.5614\n",
"2 AAPL 12-02-2013 66.8428\n",
"3 AAPL 13-02-2013 66.7156\n",
"4 AAPL 14-02-2013 66.6556"
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" <thead>\n",
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" <th></th>\n",
" <th>stock</th>\n",
" <th>date</th>\n",
" <th>price</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>AAPL</td>\n",
" <td>08-02-2013</td>\n",
" <td>67.8542</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>AAPL</td>\n",
" <td>11-02-2013</td>\n",
" <td>68.5614</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>AAPL</td>\n",
" <td>12-02-2013</td>\n",
" <td>66.8428</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>AAPL</td>\n",
" <td>13-02-2013</td>\n",
" <td>66.7156</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>AAPL</td>\n",
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" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
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"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
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" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
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" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 69
}
],
"source": [
"# head() function helps us to see the first 5 rows of the data\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ulKq6vDgaaT1"
},
"source": [
"**Loading an excel file in Python**"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"id": "AthmlNozaaT1"
},
"outputs": [],
"source": [
"path_excel=\"/content/drive/MyDrive/StockData.xlsx\"\n",
"data_excel = pd.read_excel(path_excel)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"id": "57m8zw-ZaaT1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"outputId": "eb106bee-7af0-4511-98af-fc407a27e3a7"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" stock date price\n",
"0 AAPL 2013-02-08 67.8542\n",
"1 AAPL 2013-02-11 68.5614\n",
"2 AAPL 2013-02-12 66.8428\n",
"3 AAPL 2013-02-13 66.7156\n",
"4 AAPL 2013-02-14 66.6556"
],
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" docLink.innerHTML = docLinkHtml;\n",
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]
},
"metadata": {},
"execution_count": 71
}
],
"source": [
"data_excel.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0NNWK5FpaaT1"
},
"source": [
"**Saving a dataset in Python**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WTVMrSqJaaT2"
},
"source": [
"**Saving the dataset as a csv file**\n",
"\n",
"To save a dataset as .csv file the syntax used is -\n",
"\n",
"**data.to_csv('name of the file.csv', index=False)**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jrreBwusfM28"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"id": "2MosW0HdaaT2"
},
"outputs": [],
"source": [
"data.to_csv('/content/drive/MyDrive/Saved_StockData.csv',index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Bbk-Y4SaaaT2"
},
"source": [
"* In jupyter notebook, the dataset will be saved in the folder where the jupyter notebook is located.\n",
"* We can also save the dataset to a desired folder by providing the path/location of the folder."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vKWc9M2vaaT2"
},
"source": [
"**Saving the dataset as an excel spreadsheet**\n",
"\n",
"To save a dataset as .xlsx file the syntax used is -\n",
"\n",
"**data.to_excel('name of the file.xlsx',index=False)**"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"id": "tU7dwEa1aaT2"
},
"outputs": [],
"source": [
"data.to_excel('/content/drive/MyDrive/Saved_StockData.xlsx',index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JI-WmKp4aaT2"
},
"source": [
"### 2.10 Pandas - Functions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mqT0F5lTaaT2"
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "bEZ3-RglaaT3"
},
"source": [
"**head() - to check the first 5 rows of the dataset**"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "70J7-E8NaaT3",
"outputId": "316b0410-a62f-45b5-bd9c-68551803776c"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" stock date price\n",
"0 AAPL 08-02-2013 67.8542\n",
"1 AAPL 11-02-2013 68.5614\n",
"2 AAPL 12-02-2013 66.8428\n",
"3 AAPL 13-02-2013 66.7156\n",
"4 AAPL 14-02-2013 66.6556"
],
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" <th>0</th>\n",
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" <td>08-02-2013</td>\n",
" <td>67.8542</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>AAPL</td>\n",
" <td>11-02-2013</td>\n",
" <td>68.5614</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>AAPL</td>\n",
" <td>12-02-2013</td>\n",
" <td>66.8428</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>AAPL</td>\n",
" <td>13-02-2013</td>\n",
" <td>66.7156</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>AAPL</td>\n",
" <td>14-02-2013</td>\n",
" <td>66.6556</td>\n",
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},
"metadata": {},
"execution_count": 74
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],
"source": [
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "g2zJbqfpaaT3"
},
"source": [
"**tail() - to check the last 5 rows of the dataset**"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"id": "8zSpQq0PaaT3",
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"base_uri": "https://localhost:8080/",
"height": 206
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"outputId": "3471c999-3e56-4f1c-c28b-89f909e6be62"
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"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" stock date price\n",
"5031 ZTS 01-02-2018 77.82\n",
"5032 ZTS 02-02-2018 76.78\n",
"5033 ZTS 05-02-2018 73.83\n",
"5034 ZTS 06-02-2018 73.27\n",
"5035 ZTS 07-02-2018 73.86"
],
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]
},
"metadata": {},
"execution_count": 75
}
],
"source": [
"data.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qwiUkUxBaaT3"
},
"source": [
"**shape - to check the number of rows and columns in the dataset**"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"id": "z5n0z2yraaT3",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "1f0cf0f7-23ca-422f-be1c-4706f915a78b"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(5036, 3)"
]
},
"metadata": {},
"execution_count": 76
}
],
"source": [
"data.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "res1b5v6aaT4"
},
"source": [
"* The dataset has 5036 rows and 3 columns."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tHCwDyZbaaT4"
},
"source": [
"**info() - to check the data type of the columns**"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"id": "WWEGpWAOaaT4",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "db71a11e-d8a8-4fd1-988c-43ed0921e643"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 5036 entries, 0 to 5035\n",
"Data columns (total 3 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 stock 5036 non-null object \n",
" 1 date 5036 non-null object \n",
" 2 price 5036 non-null float64\n",
"dtypes: float64(1), object(2)\n",
"memory usage: 118.2+ KB\n"
]
}
],
"source": [
"data.info()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "q3WS5pgjaaT4"
},
"source": [
"* The price column is numeric in nature while the stock and date columns are of object types."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yzt0Db5zaaT4"
},
"source": [
"**min() - to check the minimum value of a numeric column**"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"id": "b6_fiiy7aaT4",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "36e2cf99-9b7c-4591-9ddb-39092c2f116b"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"28.4"
]
},
"metadata": {},
"execution_count": 78
}
],
"source": [
"data['price'].min()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5Ilfa_9yaaT4"
},
"source": [
"**max() - to check the maximum value of a numeric column**"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"id": "2t8A8QyaaaT4",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "09274866-e23b-4728-b490-246099a57ca2"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"179.26"
]
},
"metadata": {},
"execution_count": 79
}
],
"source": [
"data['price'].max()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1EwaP056aaT5"
},
"source": [
"**unique() - to check the number of unique values that are present in a column**"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"id": "ZTkwMMXAaaT5",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "b7d4a46d-622d-44bc-e9b3-0e0de3c864c5"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['AAPL', 'SNI', 'TJX', 'ZTS'], dtype=object)"
]
},
"metadata": {},
"execution_count": 80
}
],
"source": [
"data['stock'].unique()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WtqlXzUEaaT5"
},
"source": [
"**value_counts() - to check the number of values that each unique quantity has in a column**"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"id": "9jFLbvr2aaT5",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "5ed23d45-0517-46f6-9ada-01cc441ee9da"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"AAPL 1259\n",
"SNI 1259\n",
"TJX 1259\n",
"ZTS 1259\n",
"Name: stock, dtype: int64"
]
},
"metadata": {},
"execution_count": 81
}
],
"source": [
"data['stock'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UPGoY2kxaaT5"
},
"source": [
"**value_counts(normalize=True) - using the `normalize` parameter and initializing it to True will return the relative frequencies of the unique values.**"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"id": "NNeKczQfaaT5",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "e33bf537-85a7-44cb-e3c2-1e6b0e9e6d53"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"AAPL 0.25\n",
"SNI 0.25\n",
"TJX 0.25\n",
"ZTS 0.25\n",
"Name: stock, dtype: float64"
]
},
"metadata": {},
"execution_count": 82
}
],
"source": [
"data['stock'].value_counts(normalize=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2ZJXTZ_faaT5"
},
"source": [
"**Statistical Functions**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LWHaFXvMaaT6"
},
"source": [
"**mean() - to check the mean (average) value of the column**"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"id": "fQgxZuw1aaT6",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "dfb85ebb-cb78-4446-fc40-2807ef597008"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"73.05702966640192"
]
},
"metadata": {},
"execution_count": 83
}
],
"source": [
"data['price'].mean()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "K-KFoQB2aaT6"
},
"source": [
"**median() - to check the median value of the column**"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"id": "fuajaPzpaaT6",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "622501ca-328f-4531-8b65-e5d4144e64a4"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"69.08500000000001"
]
},
"metadata": {},
"execution_count": 84
}
],
"source": [
"data['price'].median()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2mNcZjzFaaT6"
},
"source": [
"**mode() - to check the mode value of the column**"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"id": "T8iMcq6maaT6",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "b7a0e12c-0461-47b3-a9e6-66a2df9b49ea"
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"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 AAPL\n",
"1 SNI\n",
"2 TJX\n",
"3 ZTS\n",
"Name: stock, dtype: object"
]
},
"metadata": {},
"execution_count": 85
}
],
"source": [
"data['stock'].mode()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Nw9F_I3DaaT6"
},
"source": [
"**To access a particular mode when the dataset has more than 1 mode**"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"id": "xyjKRXJLaaT6",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "afded4d4-4809-479a-e94c-749e1d971909"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"74.59"
]
},
"metadata": {},
"execution_count": 86
}
],
"source": [
"#to access the first mode\n",
"data['price'].mode()[0]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hDReuxYbaaT7"
},
"source": [
"**Group By function**\n",
"* Pandas dataframe.groupby() function is used to split the data into groups based on some criteria."
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"id": "WDYZKZSCaaT7",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "665e8c79-c8e8-4a5d-ee49-48746bb8c0e5"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"stock\n",
"AAPL 109.066698\n",
"SNI 71.319206\n",
"TJX 66.743566\n",
"ZTS 45.098648\n",
"Name: price, dtype: float64"
]
},
"metadata": {},
"execution_count": 87
}
],
"source": [
"data.groupby(['stock'])['price'].mean()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zIeiAZLJaaT7"
},
"source": [
"* Here the groupby function is used to split the data into the 4 stocks that are present in the dataset and then the mean price of each of the 4 stock is calculated."
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"id": "vRa5naunaaT7",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "35a2b715-307f-4af3-f88e-13eca525e310"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"stock\n",
"AAPL 109.01\n",
"SNI 72.31\n",
"TJX 68.85\n",
"ZTS 45.62\n",
"Name: price, dtype: float64"
]
},
"metadata": {},
"execution_count": 88
}
],
"source": [
"# similarly we can get the median price of each stock\n",
"data.groupby(['stock'])['price'].median()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SoUenYJtaaT7"
},
"source": [
"* Here the groupby function is used to split the data into the 4 stocks that are present in the dataset and then the median price of each of the 4 stock is calculated."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MQXyWBP4aaT7"
},
"source": [
"**Let's create a function to increase the price of the stock by 10%**"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"id": "GGZUiVtkaaT8"
},
"outputs": [],
"source": [
"def profit(s):\n",
" return s + s*0.10 # increase of 10%"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ShnkGc_OaaT8"
},
"source": [
"**The Pandas apply() function lets you to manipulate columns and rows in a DataFrame.**"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"id": "xnqjB-8eaaT8",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "0bf00885-cc2c-4ec7-f23a-177f950e8eb1"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 74.63962\n",
"1 75.41754\n",
"2 73.52708\n",
"3 73.38716\n",
"4 73.32116\n",
" ... \n",
"5031 85.60200\n",
"5032 84.45800\n",
"5033 81.21300\n",
"5034 80.59700\n",
"5035 81.24600\n",
"Name: price, Length: 5036, dtype: float64"
]
},
"metadata": {},
"execution_count": 90
}
],
"source": [
"data['price'].apply(profit)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JQPJPeA0aaT8"
},
"source": [
"* We can now add this updated values in the dataset."
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"id": "SJcPDTRjaaT8",
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{
"output_type": "execute_result",
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" stock date price new_price\n",
"0 AAPL 08-02-2013 67.8542 74.63962\n",
"1 AAPL 11-02-2013 68.5614 75.41754\n",
"2 AAPL 12-02-2013 66.8428 73.52708\n",
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},
{
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"metadata": {
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]
},
{
"cell_type": "code",
"execution_count": 92,
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"1244 AAPL 18-01-2018 179.26 197.186\n",
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"1245 AAPL 19-01-2018 178.46 196.306\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5036 rows × 4 columns</p>\n",
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},
"metadata": {},
"execution_count": 92
}
],
"source": [
"data.sort_values(by='new_price',ascending=False) # by default ascending is set to True"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jq__TYL7aaT9"
},
"source": [
"### 2.11 Pandas - Date-time Functions"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"id": "qiFur3ffaaT9"
},
"outputs": [],
"source": [
"# reading the StockData\n",
"path=\"/content/drive/MyDrive/StockData.csv\"\n",
"data=pd.read_csv(path)"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"id": "O9sK3H9WaaT9",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
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"outputId": "57c433f3-370a-44c0-f725-ebfa1bda925e"
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{
"output_type": "execute_result",
"data": {
"text/plain": [
" stock date price\n",
"0 AAPL 08-02-2013 67.8542\n",
"1 AAPL 11-02-2013 68.5614\n",
"2 AAPL 12-02-2013 66.8428\n",
"3 AAPL 13-02-2013 66.7156\n",
"4 AAPL 14-02-2013 66.6556"
],
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},
"metadata": {},
"execution_count": 94
}
],
"source": [
"# checking the first 5 rows of the dataset\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"id": "DUTj4xHuaaT9",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "14ee2ad5-0ab7-4910-efe4-b9651d965dbc"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 5036 entries, 0 to 5035\n",
"Data columns (total 3 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 stock 5036 non-null object \n",
" 1 date 5036 non-null object \n",
" 2 price 5036 non-null float64\n",
"dtypes: float64(1), object(2)\n",
"memory usage: 118.2+ KB\n"
]
}
],
"source": [
"# checking the data type of columns in the dataset\n",
"data.info()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zJwyGtcUaaT9"
},
"source": [
"* We observe that the date column is of object type whereas it should be of date time data type."
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"id": "wf_sF48baaT9"
},
"outputs": [],
"source": [
"# converting the date column to datetime format\n",
"data['date'] = pd.to_datetime(data['date'],dayfirst=True)"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"id": "x0JpES2paaT9",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "0981fb87-cf01-4ac9-838a-e6cf18937a4f"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 5036 entries, 0 to 5035\n",
"Data columns (total 3 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 stock 5036 non-null object \n",
" 1 date 5036 non-null datetime64[ns]\n",
" 2 price 5036 non-null float64 \n",
"dtypes: datetime64[ns](1), float64(1), object(1)\n",
"memory usage: 118.2+ KB\n"
]
}
],
"source": [
"data.info()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "c3LKyXDsaaT-"
},
"source": [
"* We observe that the date column has been converted to datetime format"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"id": "PwvtmGCZaaT-",
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"base_uri": "https://localhost:8080/",
"height": 206
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{
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"data": {
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" stock date price\n",
"0 AAPL 2013-02-08 67.8542\n",
"1 AAPL 2013-02-11 68.5614\n",
"2 AAPL 2013-02-12 66.8428\n",
"3 AAPL 2013-02-13 66.7156\n",
"4 AAPL 2013-02-14 66.6556"
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},
"metadata": {},
"execution_count": 99
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],
"source": [
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MOltU1f_aaT-"
},
"source": [
"**The column 'date' is now in datetime format. Now we can change the format of the date to any other format**"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"id": "lRzPtIN1aaT-",
"colab": {
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"outputId": "c3adf8c4-875b-425f-e048-aef9a482e16a"
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{
"output_type": "execute_result",
"data": {
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"0 02/08/2013\n",
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"2 02/12/2013\n",
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" ... \n",
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"metadata": {},
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{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"id": "jmZPqQ_raaT-",
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"outputId": "db202fcd-903b-4a9f-a70d-0bbc6e2330fe"
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"metadata": {},
"execution_count": 101
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"source": [
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]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hLr6kwcRaaT-"
},
"source": [
"**Extracting year from the date column**"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"id": "TawW5CJEaaT-",
"colab": {
"base_uri": "https://localhost:8080/"
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"outputId": "68f8a520-f4cb-494c-d088-8a21cac61ac6"
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{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 2013\n",
"1 2013\n",
"2 2013\n",
"3 2013\n",
"4 2013\n",
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},
"metadata": {},
"execution_count": 102
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],
"source": [
"data['date'].dt.year"
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},
{
"cell_type": "markdown",
"metadata": {
"id": "yE47GlNpaaT-"
},
"source": [
"Creating a new column and adding the extracted year values into the dataframe."
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},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"id": "YB5h8YxHaaT_"
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"outputs": [],
"source": [
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},
{
"cell_type": "markdown",
"metadata": {
"id": "hY_74cFsaaT_"
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"source": [
"**Extracting month from the date column**"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"id": "NiswAWBkaaT_",
"scrolled": true,
"colab": {
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"metadata": {},
"execution_count": 105
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"source": [
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},
{
"cell_type": "markdown",
"metadata": {
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"source": [
"Creating a new column and adding the extracted month values into the dataframe."
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{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"id": "othBpB1KaaT_"
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"outputs": [],
"source": [
"data['month'] = data['date'].dt.month"
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},
{
"cell_type": "markdown",
"metadata": {
"id": "uB5H7zjgaaT_"
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"source": [
"**Extracting day from the date column**"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"id": "Zqna7c2KaaT_",
"colab": {
"base_uri": "https://localhost:8080/"
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"outputId": "b649559e-79e7-4b96-8fb3-b826ffc3584c"
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"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 8\n",
"1 11\n",
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},
"metadata": {},
"execution_count": 108
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],
"source": [
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},
{
"cell_type": "markdown",
"metadata": {
"id": "y3z5F-NFaaT_"
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"source": [
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},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"id": "BjbZlnl8aaUA"
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"outputs": [],
"source": [
"data['day'] = data['date'].dt.day"
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"cell_type": "code",
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" stock date price year month day\n",
"0 AAPL 2013-02-08 67.8542 2013 2 8\n",
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"2 AAPL 2013-02-12 66.8428 2013 2 12\n",
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" </script>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 110
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "q1ukYGe1jr-1"
},
"source": [
"* We can see that year, month, and day columns have been added in the dataset."
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"id": "01DjN1VPlKlw",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "42e19ff5-7daf-4faf-a976-3970ae931d25"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Timedelta('3 days 00:00:00')"
]
},
"metadata": {},
"execution_count": 111
}
],
"source": [
"# The datetime format is convenient for many tasks!\n",
"data['date'][1]-data['date'][0]"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"id": "tQMoqQeLlYVH"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "owbbDqkwlqtk"
},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"collapsed_sections": [
"RWg1XRJ-u1iM",
"c7MwEmop6GWe",
"1PVPsH6Rf9Us",
"TQdLaw2WaaT0",
"JI-WmKp4aaT2",
"jq__TYL7aaT9"
],
"provenance": [],
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.13"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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