{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Playing_with_Titanic_Dataset.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/Tanu-N-Prabhu/Python/blob/master/Playing_with_Titanic_Dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hD3dcw0JdAot",
"colab_type": "text"
},
"source": [
"\n",
"<center>\n",
"<img src=\"http://www.datavirtualizationblog.com/wp-content/uploads/2015/11/3-phases-of-data-analysis-b.jpg\" >\n",
"</center>\n",
"\n",
"**This mini project or whatever you call it as was done with the help of the website down below, which helped me in understanding, analysing the data by scraping the data from the internet.**\n",
"\n",
"\n",
"---\n",
"\n",
"\n",
"[Getting started with Data Analysis with Python Pandas](https://towardsdatascience.com/getting-started-to-data-analysis-with-python-pandas-with-titanic-dataset-a195ab043c77\n",
")\n",
"\n",
"---\n",
"\n",
"More importantly the \"Titanic\" dataset was retrived from Kaggle and the link can be found below:\n",
"\n",
"[Titanic Dataset](https://www.kaggle.com/c/titanic/data)\n",
"\n",
"\n",
"\n",
"---\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qI3t3D-UqxZo",
"colab_type": "text"
},
"source": [
"The following two lines of code must be entered in order to upload the file from the local drive, because google colab stores everything in your drive. So make sure that you that you read the article down below before you begin typing the code. Moreover, I dont want you guys to stuck and watch the screen when you get an error.\n",
"\n",
"[Click Here](https://towardsdatascience.com/3-ways-to-load-csv-files-into-colab-7c14fcbdcb92)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "YM_Dq2ZAlaA2",
"colab_type": "code",
"outputId": "3e580035-651c-46fa-b1e1-1e81222dadf6",
"colab": {
"resources": {
"http://localhost:8080/nbextensions/google.colab/files.js": {
"data": "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",
"ok": true,
"headers": [
[
"content-type",
"application/javascript"
]
],
"status": 200,
"status_text": ""
}
},
"base_uri": "https://localhost:8080/",
"height": 75
}
},
"source": [
"from google.colab import files\n",
"uploaded = files.upload()"
],
"execution_count": 3,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" <input type=\"file\" id=\"files-4678cd8f-5114-4504-a19d-15d7330fb3d6\" name=\"files[]\" multiple disabled />\n",
" <output id=\"result-4678cd8f-5114-4504-a19d-15d7330fb3d6\">\n",
" Upload widget is only available when the cell has been executed in the\n",
" current browser session. Please rerun this cell to enable.\n",
" </output>\n",
" <script src=\"/nbextensions/google.colab/files.js\"></script> "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Saving train.csv to train.csv\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CsdIJ9lAsS6G",
"colab_type": "text"
},
"source": [
"\n",
"After entering the above two lines of code, wait till you get the 100% uploaded confirmation. Finally once you get that, please enter the two more lines (I really don't know what the two line does)\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "71vMqOu7ljTo",
"colab_type": "code",
"colab": {}
},
"source": [
"import io\n",
"df2 = pd.read_csv(io.BytesIO(uploaded['train.csv']))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "MjBBMJ-wRKpZ",
"colab_type": "text"
},
"source": [
"# Importing the dataset with read_csv and displaying the data.\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "ORH0fyc7Wlh9",
"colab_type": "code",
"outputId": "1c9048e7-f669-4b7f-8914-b940f1cad169",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 2157
}
},
"source": [
"import pandas as pd\n",
"import csv\n",
"import matplotlib.pyplot as plt\n",
"\n",
"df = pd.read_csv(\"train.csv\")\n",
"df"
],
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Moran, Mr. James</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>330877</td>\n",
" <td>8.4583</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>McCarthy, Mr. Timothy J</td>\n",
" <td>male</td>\n",
" <td>54.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>17463</td>\n",
" <td>51.8625</td>\n",
" <td>E46</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Palsson, Master. Gosta Leonard</td>\n",
" <td>male</td>\n",
" <td>2.0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>349909</td>\n",
" <td>21.0750</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
" <td>female</td>\n",
" <td>27.0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>347742</td>\n",
" <td>11.1333</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
" <td>female</td>\n",
" <td>14.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>237736</td>\n",
" <td>30.0708</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Sandstrom, Miss. Marguerite Rut</td>\n",
" <td>female</td>\n",
" <td>4.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>PP 9549</td>\n",
" <td>16.7000</td>\n",
" <td>G6</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Bonnell, Miss. Elizabeth</td>\n",
" <td>female</td>\n",
" <td>58.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>113783</td>\n",
" <td>26.5500</td>\n",
" <td>C103</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Saundercock, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>20.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>A/5. 2151</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>14</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Andersson, Mr. Anders Johan</td>\n",
" <td>male</td>\n",
" <td>39.0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>347082</td>\n",
" <td>31.2750</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
" <td>female</td>\n",
" <td>14.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>350406</td>\n",
" <td>7.8542</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
" <td>female</td>\n",
" <td>55.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>248706</td>\n",
" <td>16.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>17</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Rice, Master. Eugene</td>\n",
" <td>male</td>\n",
" <td>2.0</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>382652</td>\n",
" <td>29.1250</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>18</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Williams, Mr. Charles Eugene</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>244373</td>\n",
" <td>13.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>19</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
" <td>female</td>\n",
" <td>31.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>345763</td>\n",
" <td>18.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Masselmani, Mrs. Fatima</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2649</td>\n",
" <td>7.2250</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>Fynney, Mr. Joseph J</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>239865</td>\n",
" <td>26.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>22</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Beesley, Mr. Lawrence</td>\n",
" <td>male</td>\n",
" <td>34.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>248698</td>\n",
" <td>13.0000</td>\n",
" <td>D56</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>23</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>McGowan, Miss. Anna \"Annie\"</td>\n",
" <td>female</td>\n",
" <td>15.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>330923</td>\n",
" <td>8.0292</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>24</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Sloper, Mr. William Thompson</td>\n",
" <td>male</td>\n",
" <td>28.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>113788</td>\n",
" <td>35.5000</td>\n",
" <td>A6</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>25</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Palsson, Miss. Torborg Danira</td>\n",
" <td>female</td>\n",
" <td>8.0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>349909</td>\n",
" <td>21.0750</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>26</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>347077</td>\n",
" <td>31.3875</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Emir, Mr. Farred Chehab</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2631</td>\n",
" <td>7.2250</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>28</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Fortune, Mr. Charles Alexander</td>\n",
" <td>male</td>\n",
" <td>19.0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>19950</td>\n",
" <td>263.0000</td>\n",
" <td>C23 C25 C27</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>29</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>330959</td>\n",
" <td>7.8792</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>30</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Todoroff, Mr. Lalio</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>349216</td>\n",
" <td>7.8958</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>861</th>\n",
" <td>862</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>Giles, Mr. Frederick Edward</td>\n",
" <td>male</td>\n",
" <td>21.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>28134</td>\n",
" <td>11.5000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>862</th>\n",
" <td>863</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Swift, Mrs. Frederick Joel (Margaret Welles Ba...</td>\n",
" <td>female</td>\n",
" <td>48.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>17466</td>\n",
" <td>25.9292</td>\n",
" <td>D17</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>863</th>\n",
" <td>864</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>CA. 2343</td>\n",
" <td>69.5500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>864</th>\n",
" <td>865</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>Gill, Mr. John William</td>\n",
" <td>male</td>\n",
" <td>24.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>233866</td>\n",
" <td>13.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>865</th>\n",
" <td>866</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Bystrom, Mrs. (Karolina)</td>\n",
" <td>female</td>\n",
" <td>42.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>236852</td>\n",
" <td>13.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>866</th>\n",
" <td>867</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Duran y More, Miss. Asuncion</td>\n",
" <td>female</td>\n",
" <td>27.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>SC/PARIS 2149</td>\n",
" <td>13.8583</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>867</th>\n",
" <td>868</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Roebling, Mr. Washington Augustus II</td>\n",
" <td>male</td>\n",
" <td>31.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>PC 17590</td>\n",
" <td>50.4958</td>\n",
" <td>A24</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>868</th>\n",
" <td>869</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>van Melkebeke, Mr. Philemon</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>345777</td>\n",
" <td>9.5000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>869</th>\n",
" <td>870</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Johnson, Master. Harold Theodor</td>\n",
" <td>male</td>\n",
" <td>4.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>347742</td>\n",
" <td>11.1333</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>870</th>\n",
" <td>871</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Balkic, Mr. Cerin</td>\n",
" <td>male</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>349248</td>\n",
" <td>7.8958</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>871</th>\n",
" <td>872</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
" <td>female</td>\n",
" <td>47.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>11751</td>\n",
" <td>52.5542</td>\n",
" <td>D35</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>872</th>\n",
" <td>873</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Carlsson, Mr. Frans Olof</td>\n",
" <td>male</td>\n",
" <td>33.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>695</td>\n",
" <td>5.0000</td>\n",
" <td>B51 B53 B55</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>873</th>\n",
" <td>874</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Vander Cruyssen, Mr. Victor</td>\n",
" <td>male</td>\n",
" <td>47.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>345765</td>\n",
" <td>9.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>874</th>\n",
" <td>875</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Abelson, Mrs. Samuel (Hannah Wizosky)</td>\n",
" <td>female</td>\n",
" <td>28.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>P/PP 3381</td>\n",
" <td>24.0000</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>875</th>\n",
" <td>876</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
" <td>female</td>\n",
" <td>15.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2667</td>\n",
" <td>7.2250</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>876</th>\n",
" <td>877</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Gustafsson, Mr. Alfred Ossian</td>\n",
" <td>male</td>\n",
" <td>20.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7534</td>\n",
" <td>9.8458</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>877</th>\n",
" <td>878</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Petroff, Mr. Nedelio</td>\n",
" <td>male</td>\n",
" <td>19.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>349212</td>\n",
" <td>7.8958</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>878</th>\n",
" <td>879</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Laleff, Mr. Kristo</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>349217</td>\n",
" <td>7.8958</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>879</th>\n",
" <td>880</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
" <td>female</td>\n",
" <td>56.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>11767</td>\n",
" <td>83.1583</td>\n",
" <td>C50</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>880</th>\n",
" <td>881</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
" <td>female</td>\n",
" <td>25.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>230433</td>\n",
" <td>26.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>881</th>\n",
" <td>882</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Markun, Mr. Johann</td>\n",
" <td>male</td>\n",
" <td>33.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>349257</td>\n",
" <td>7.8958</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>882</th>\n",
" <td>883</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
" <td>female</td>\n",
" <td>22.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7552</td>\n",
" <td>10.5167</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>883</th>\n",
" <td>884</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>Banfield, Mr. Frederick James</td>\n",
" <td>male</td>\n",
" <td>28.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>C.A./SOTON 34068</td>\n",
" <td>10.5000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>884</th>\n",
" <td>885</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Sutehall, Mr. Henry Jr</td>\n",
" <td>male</td>\n",
" <td>25.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>SOTON/OQ 392076</td>\n",
" <td>7.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>885</th>\n",
" <td>886</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Rice, Mrs. William (Margaret Norton)</td>\n",
" <td>female</td>\n",
" <td>39.0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>382652</td>\n",
" <td>29.1250</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>886</th>\n",
" <td>887</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>Montvila, Rev. Juozas</td>\n",
" <td>male</td>\n",
" <td>27.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>211536</td>\n",
" <td>13.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>887</th>\n",
" <td>888</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Graham, Miss. Margaret Edith</td>\n",
" <td>female</td>\n",
" <td>19.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>112053</td>\n",
" <td>30.0000</td>\n",
" <td>B42</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>888</th>\n",
" <td>889</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>W./C. 6607</td>\n",
" <td>23.4500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>889</th>\n",
" <td>890</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Behr, Mr. Karl Howell</td>\n",
" <td>male</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>111369</td>\n",
" <td>30.0000</td>\n",
" <td>C148</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>890</th>\n",
" <td>891</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Dooley, Mr. Patrick</td>\n",
" <td>male</td>\n",
" <td>32.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>370376</td>\n",
" <td>7.7500</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>891 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass ... Fare Cabin Embarked\n",
"0 1 0 3 ... 7.2500 NaN S\n",
"1 2 1 1 ... 71.2833 C85 C\n",
"2 3 1 3 ... 7.9250 NaN S\n",
"3 4 1 1 ... 53.1000 C123 S\n",
"4 5 0 3 ... 8.0500 NaN S\n",
"5 6 0 3 ... 8.4583 NaN Q\n",
"6 7 0 1 ... 51.8625 E46 S\n",
"7 8 0 3 ... 21.0750 NaN S\n",
"8 9 1 3 ... 11.1333 NaN S\n",
"9 10 1 2 ... 30.0708 NaN C\n",
"10 11 1 3 ... 16.7000 G6 S\n",
"11 12 1 1 ... 26.5500 C103 S\n",
"12 13 0 3 ... 8.0500 NaN S\n",
"13 14 0 3 ... 31.2750 NaN S\n",
"14 15 0 3 ... 7.8542 NaN S\n",
"15 16 1 2 ... 16.0000 NaN S\n",
"16 17 0 3 ... 29.1250 NaN Q\n",
"17 18 1 2 ... 13.0000 NaN S\n",
"18 19 0 3 ... 18.0000 NaN S\n",
"19 20 1 3 ... 7.2250 NaN C\n",
"20 21 0 2 ... 26.0000 NaN S\n",
"21 22 1 2 ... 13.0000 D56 S\n",
"22 23 1 3 ... 8.0292 NaN Q\n",
"23 24 1 1 ... 35.5000 A6 S\n",
"24 25 0 3 ... 21.0750 NaN S\n",
"25 26 1 3 ... 31.3875 NaN S\n",
"26 27 0 3 ... 7.2250 NaN C\n",
"27 28 0 1 ... 263.0000 C23 C25 C27 S\n",
"28 29 1 3 ... 7.8792 NaN Q\n",
"29 30 0 3 ... 7.8958 NaN S\n",
".. ... ... ... ... ... ... ...\n",
"861 862 0 2 ... 11.5000 NaN S\n",
"862 863 1 1 ... 25.9292 D17 S\n",
"863 864 0 3 ... 69.5500 NaN S\n",
"864 865 0 2 ... 13.0000 NaN S\n",
"865 866 1 2 ... 13.0000 NaN S\n",
"866 867 1 2 ... 13.8583 NaN C\n",
"867 868 0 1 ... 50.4958 A24 S\n",
"868 869 0 3 ... 9.5000 NaN S\n",
"869 870 1 3 ... 11.1333 NaN S\n",
"870 871 0 3 ... 7.8958 NaN S\n",
"871 872 1 1 ... 52.5542 D35 S\n",
"872 873 0 1 ... 5.0000 B51 B53 B55 S\n",
"873 874 0 3 ... 9.0000 NaN S\n",
"874 875 1 2 ... 24.0000 NaN C\n",
"875 876 1 3 ... 7.2250 NaN C\n",
"876 877 0 3 ... 9.8458 NaN S\n",
"877 878 0 3 ... 7.8958 NaN S\n",
"878 879 0 3 ... 7.8958 NaN S\n",
"879 880 1 1 ... 83.1583 C50 C\n",
"880 881 1 2 ... 26.0000 NaN S\n",
"881 882 0 3 ... 7.8958 NaN S\n",
"882 883 0 3 ... 10.5167 NaN S\n",
"883 884 0 2 ... 10.5000 NaN S\n",
"884 885 0 3 ... 7.0500 NaN S\n",
"885 886 0 3 ... 29.1250 NaN Q\n",
"886 887 0 2 ... 13.0000 NaN S\n",
"887 888 1 1 ... 30.0000 B42 S\n",
"888 889 0 3 ... 23.4500 NaN S\n",
"889 890 1 1 ... 30.0000 C148 C\n",
"890 891 0 3 ... 7.7500 NaN Q\n",
"\n",
"[891 rows x 12 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Mr_mxZgnQ2uQ",
"colab_type": "text"
},
"source": [
"# **Using Head and Tail methods inorder to display the data.**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "6GO30V6Sczk3",
"colab_type": "code",
"outputId": "58072394-e095-40d0-f5ae-06762aeb3560",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 202
}
},
"source": [
"df.head(5) # Used to display top 5"
],
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass ... Fare Cabin Embarked\n",
"0 1 0 3 ... 7.2500 NaN S\n",
"1 2 1 1 ... 71.2833 C85 C\n",
"2 3 1 3 ... 7.9250 NaN S\n",
"3 4 1 1 ... 53.1000 C123 S\n",
"4 5 0 3 ... 8.0500 NaN S\n",
"\n",
"[5 rows x 12 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "zcxr2dnFhT5h",
"colab_type": "code",
"outputId": "f1006878-059b-4082-f2f8-9c3637c8e972",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 202
}
},
"source": [
"df.tail(5) # Used to display last 5"
],
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>886</th>\n",
" <td>887</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>Montvila, Rev. Juozas</td>\n",
" <td>male</td>\n",
" <td>27.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>211536</td>\n",
" <td>13.00</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>887</th>\n",
" <td>888</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Graham, Miss. Margaret Edith</td>\n",
" <td>female</td>\n",
" <td>19.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>112053</td>\n",
" <td>30.00</td>\n",
" <td>B42</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>888</th>\n",
" <td>889</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>W./C. 6607</td>\n",
" <td>23.45</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>889</th>\n",
" <td>890</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Behr, Mr. Karl Howell</td>\n",
" <td>male</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>111369</td>\n",
" <td>30.00</td>\n",
" <td>C148</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>890</th>\n",
" <td>891</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Dooley, Mr. Patrick</td>\n",
" <td>male</td>\n",
" <td>32.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>370376</td>\n",
" <td>7.75</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass ... Fare Cabin Embarked\n",
"886 887 0 2 ... 13.00 NaN S\n",
"887 888 1 1 ... 30.00 B42 S\n",
"888 889 0 3 ... 23.45 NaN S\n",
"889 890 1 1 ... 30.00 C148 C\n",
"890 891 0 3 ... 7.75 NaN Q\n",
"\n",
"[5 rows x 12 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 9
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Tw84bFRrRUNJ",
"colab_type": "text"
},
"source": [
"# Accessing specific coloums which are needed."
]
},
{
"cell_type": "code",
"metadata": {
"id": "wER50sYvhVPB",
"colab_type": "code",
"outputId": "82e28537-4784-4607-8042-c00a7c635898",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 202
}
},
"source": [
"df = pd.read_csv(\"train.csv\", usecols= [\"PassengerId\", \"Survived\", \"Pclass\", \"Name\", \"Sex\",\"Age\"])\n",
"df.head()"
],
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived ... Sex Age\n",
"0 1 0 ... male 22.0\n",
"1 2 1 ... female 38.0\n",
"2 3 1 ... female 26.0\n",
"3 4 1 ... female 35.0\n",
"4 5 0 ... male 35.0\n",
"\n",
"[5 rows x 6 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 10
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fsNH60Q6RhCL",
"colab_type": "text"
},
"source": [
"# Get more information about the database by using describe method."
]
},
{
"cell_type": "code",
"metadata": {
"id": "Qb4YdAa_hozX",
"colab_type": "code",
"outputId": "6c83d200-eadc-48fd-97d4-1ca966b75048",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 294
}
},
"source": [
"df.describe()"
],
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>714.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>446.000000</td>\n",
" <td>0.383838</td>\n",
" <td>2.308642</td>\n",
" <td>29.699118</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>257.353842</td>\n",
" <td>0.486592</td>\n",
" <td>0.836071</td>\n",
" <td>14.526497</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.420000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>223.500000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>20.125000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>446.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.000000</td>\n",
" <td>28.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>668.500000</td>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>38.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>891.000000</td>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>80.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass Age\n",
"count 891.000000 891.000000 891.000000 714.000000\n",
"mean 446.000000 0.383838 2.308642 29.699118\n",
"std 257.353842 0.486592 0.836071 14.526497\n",
"min 1.000000 0.000000 1.000000 0.420000\n",
"25% 223.500000 0.000000 2.000000 20.125000\n",
"50% 446.000000 0.000000 3.000000 28.000000\n",
"75% 668.500000 1.000000 3.000000 38.000000\n",
"max 891.000000 1.000000 3.000000 80.000000"
]
},
"metadata": {
"tags": []
},
"execution_count": 11
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ORyXDmoyRtAK",
"colab_type": "text"
},
"source": [
"# Sorting the values by using the sort method."
]
},
{
"cell_type": "code",
"metadata": {
"id": "EZ5V3Zmvh1Ef",
"colab_type": "code",
"outputId": "9c059e6b-dcb3-4892-b627-b8423584c0d6",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 202
}
},
"source": [
"df.sort_values(\"Age\")\n",
"df.head()"
],
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived ... Sex Age\n",
"0 1 0 ... male 22.0\n",
"1 2 1 ... female 38.0\n",
"2 3 1 ... female 26.0\n",
"3 4 1 ... female 35.0\n",
"4 5 0 ... male 35.0\n",
"\n",
"[5 rows x 6 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 12
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6-XFGEpdR1fW",
"colab_type": "text"
},
"source": [
"# Sorting the values according to the age."
]
},
{
"cell_type": "code",
"metadata": {
"id": "trrCNJzLiNwa",
"colab_type": "code",
"outputId": "15246d4a-a17c-40aa-b74c-979d8ca04df6",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 202
}
},
"source": [
"df = df.sort_values(\"Age\", ascending = False)\n",
"df.head(5)"
],
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>630</th>\n",
" <td>631</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
" <td>male</td>\n",
" <td>80.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>851</th>\n",
" <td>852</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Svensson, Mr. Johan</td>\n",
" <td>male</td>\n",
" <td>74.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>493</th>\n",
" <td>494</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Artagaveytia, Mr. Ramon</td>\n",
" <td>male</td>\n",
" <td>71.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>97</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Goldschmidt, Mr. George B</td>\n",
" <td>male</td>\n",
" <td>71.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td>117</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Connors, Mr. Patrick</td>\n",
" <td>male</td>\n",
" <td>70.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived ... Sex Age\n",
"630 631 1 ... male 80.0\n",
"851 852 0 ... male 74.0\n",
"493 494 0 ... male 71.0\n",
"96 97 0 ... male 71.0\n",
"116 117 0 ... male 70.5\n",
"\n",
"[5 rows x 6 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 13
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Kz1O1_V6R-HJ",
"colab_type": "text"
},
"source": [
"# Filtering the dataset, or cleaning the dataset by selecting only the name and many more."
]
},
{
"cell_type": "code",
"metadata": {
"id": "2pRJSwAfj0Tg",
"colab_type": "code",
"outputId": "89222e4d-f164-40d6-cae6-4cf9ab0f5fd1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 79
}
},
"source": [
"result = df[df['Name'] == 'Svensson, Mr. Johan'\t]\n",
"result\n"
],
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>851</th>\n",
" <td>852</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Svensson, Mr. Johan</td>\n",
" <td>male</td>\n",
" <td>74.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass Name Sex Age\n",
"851 852 0 3 Svensson, Mr. Johan male 74.0"
]
},
"metadata": {
"tags": []
},
"execution_count": 14
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "l9vxR0OnSoZV",
"colab_type": "text"
},
"source": [
"# Counting the occurences of variables\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "s9D1EHccSp2m",
"colab_type": "code",
"outputId": "22d7f136-96a7-46e0-e13b-a4740832d59a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 69
}
},
"source": [
"df[\"Sex\"].value_counts()\n"
],
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"male 577\n",
"female 314\n",
"Name: Sex, dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 15
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rc8RlzWmS4g8",
"colab_type": "text"
},
"source": [
"# Using .nunique() to count number of unique values that occur in dataset or in a column"
]
},
{
"cell_type": "code",
"metadata": {
"id": "aKWYElPAS3-c",
"colab_type": "code",
"outputId": "5ddbdba8-ba69-43cc-9e96-827f1b341042",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 139
}
},
"source": [
"df.nunique()"
],
"execution_count": 16,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"PassengerId 891\n",
"Survived 2\n",
"Pclass 3\n",
"Name 891\n",
"Sex 2\n",
"Age 88\n",
"dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 16
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fblOPIVxTLnp",
"colab_type": "text"
},
"source": [
"# Filtering"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sDD70RkMTzxI",
"colab_type": "text"
},
"source": [
"**AND operator**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "S6YVgGO9TMnR",
"colab_type": "code",
"outputId": "0f64281f-e6f7-4e8c-b540-6d616fb4308e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1949
}
},
"source": [
"df_age = df[\"Age\"] < 50\n",
"df_sex_mask = df[\"Sex\"] == \"female\"\n",
"df[df_age & df_sex_mask]"
],
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>53</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Harper, Mrs. Henry Sleeper (Myna Haxtun)</td>\n",
" <td>female</td>\n",
" <td>49.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>796</th>\n",
" <td>797</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Leader, Dr. Alice (Farnham)</td>\n",
" <td>female</td>\n",
" <td>49.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>754</th>\n",
" <td>755</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Herman, Mrs. Samuel (Jane Laver)</td>\n",
" <td>female</td>\n",
" <td>48.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>556</th>\n",
" <td>557</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Duff Gordon, Lady. (Lucille Christiana Sutherl...</td>\n",
" <td>female</td>\n",
" <td>48.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>736</th>\n",
" <td>737</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Ford, Mrs. Edward (Margaret Ann Watson)</td>\n",
" <td>female</td>\n",
" <td>48.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>862</th>\n",
" <td>863</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Swift, Mrs. Frederick Joel (Margaret Welles Ba...</td>\n",
" <td>female</td>\n",
" <td>48.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>132</th>\n",
" <td>133</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Robins, Mrs. Alexander A (Grace Charity Laury)</td>\n",
" <td>female</td>\n",
" <td>47.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>871</th>\n",
" <td>872</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
" <td>female</td>\n",
" <td>47.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>706</th>\n",
" <td>707</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Kelly, Mrs. Florence \"Fannie\"</td>\n",
" <td>female</td>\n",
" <td>45.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>276</th>\n",
" <td>277</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Lindblom, Miss. Augusta Charlotta</td>\n",
" <td>female</td>\n",
" <td>45.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>167</th>\n",
" <td>168</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Skoog, Mrs. William (Anna Bernhardina Karlsson)</td>\n",
" <td>female</td>\n",
" <td>45.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362</th>\n",
" <td>363</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Barbara, Mrs. (Catherine David)</td>\n",
" <td>female</td>\n",
" <td>45.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>856</th>\n",
" <td>857</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Wick, Mrs. George Dennick (Mary Hitchcock)</td>\n",
" <td>female</td>\n",
" <td>45.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>440</th>\n",
" <td>441</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Hart, Mrs. Benjamin (Esther Ada Bloomfield)</td>\n",
" <td>female</td>\n",
" <td>45.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>523</th>\n",
" <td>524</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Hippach, Mrs. Louis Albert (Ida Sophia Fischer)</td>\n",
" <td>female</td>\n",
" <td>44.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td>195</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Brown, Mrs. James Joseph (Margaret Tobin)</td>\n",
" <td>female</td>\n",
" <td>44.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>854</th>\n",
" <td>855</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>Carter, Mrs. Ernest Courtenay (Lilian Hughes)</td>\n",
" <td>female</td>\n",
" <td>44.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>779</th>\n",
" <td>780</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Robert, Mrs. Edward Scott (Elisabeth Walton Mc...</td>\n",
" <td>female</td>\n",
" <td>43.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>678</th>\n",
" <td>679</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Goodwin, Mrs. Frederick (Augusta Tyler)</td>\n",
" <td>female</td>\n",
" <td>43.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>432</th>\n",
" <td>433</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Louch, Mrs. Charles Alexander (Alice Adelaide ...</td>\n",
" <td>female</td>\n",
" <td>42.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>865</th>\n",
" <td>866</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Bystrom, Mrs. (Karolina)</td>\n",
" <td>female</td>\n",
" <td>42.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>380</th>\n",
" <td>381</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Bidois, Miss. Rosalie</td>\n",
" <td>female</td>\n",
" <td>42.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>272</th>\n",
" <td>273</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Mellinger, Mrs. (Elizabeth Anne Maidment)</td>\n",
" <td>female</td>\n",
" <td>41.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>337</th>\n",
" <td>338</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Burns, Miss. Elizabeth Margaret</td>\n",
" <td>female</td>\n",
" <td>41.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>254</th>\n",
" <td>255</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Rosblom, Mrs. Viktor (Helena Wilhelmina)</td>\n",
" <td>female</td>\n",
" <td>41.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>638</th>\n",
" <td>639</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Panula, Mrs. Juha (Maria Emilia Ojala)</td>\n",
" <td>female</td>\n",
" <td>41.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>609</th>\n",
" <td>610</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Shutes, Miss. Elizabeth W</td>\n",
" <td>female</td>\n",
" <td>40.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>319</th>\n",
" <td>320</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Spedden, Mrs. Frederic Oakley (Margaretta Corn...</td>\n",
" <td>female</td>\n",
" <td>40.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>161</th>\n",
" <td>162</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Watt, Mrs. James (Elizabeth \"Bessie\" Inglis Mi...</td>\n",
" <td>female</td>\n",
" <td>40.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>346</th>\n",
" <td>347</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Smith, Miss. Marion Elsie</td>\n",
" <td>female</td>\n",
" <td>40.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>634</th>\n",
" <td>635</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Skoog, Miss. Mabel</td>\n",
" <td>female</td>\n",
" <td>9.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>852</th>\n",
" <td>853</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Boulos, Miss. Nourelain</td>\n",
" <td>female</td>\n",
" <td>9.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>541</th>\n",
" <td>542</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Andersson, Miss. Ingeborg Constanzia</td>\n",
" <td>female</td>\n",
" <td>9.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>147</th>\n",
" <td>148</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Ford, Miss. Robina Maggie \"Ruby\"</td>\n",
" <td>female</td>\n",
" <td>9.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>25</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Palsson, Miss. Torborg Danira</td>\n",
" <td>female</td>\n",
" <td>8.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>237</th>\n",
" <td>238</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Collyer, Miss. Marjorie \"Lottie\"</td>\n",
" <td>female</td>\n",
" <td>8.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>535</th>\n",
" <td>536</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Hart, Miss. Eva Miriam</td>\n",
" <td>female</td>\n",
" <td>7.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>720</th>\n",
" <td>721</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Harper, Miss. Annie Jessie \"Nina\"</td>\n",
" <td>female</td>\n",
" <td>6.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>813</th>\n",
" <td>814</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Andersson, Miss. Ebba Iris Alfrida</td>\n",
" <td>female</td>\n",
" <td>6.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>777</th>\n",
" <td>778</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Emanuel, Miss. Virginia Ethel</td>\n",
" <td>female</td>\n",
" <td>5.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>233</th>\n",
" <td>234</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Asplund, Miss. Lillian Gertrud</td>\n",
" <td>female</td>\n",
" <td>5.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>59</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>West, Miss. Constance Mirium</td>\n",
" <td>female</td>\n",
" <td>5.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>448</th>\n",
" <td>449</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Baclini, Miss. Marie Catherine</td>\n",
" <td>female</td>\n",
" <td>5.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>691</th>\n",
" <td>692</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Karun, Miss. Manca</td>\n",
" <td>female</td>\n",
" <td>4.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Sandstrom, Miss. Marguerite Rut</td>\n",
" <td>female</td>\n",
" <td>4.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>750</th>\n",
" <td>751</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Wells, Miss. Joan</td>\n",
" <td>female</td>\n",
" <td>4.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>184</th>\n",
" <td>185</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Kink-Heilmann, Miss. Luise Gretchen</td>\n",
" <td>female</td>\n",
" <td>4.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>618</th>\n",
" <td>619</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Becker, Miss. Marion Louise</td>\n",
" <td>female</td>\n",
" <td>4.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>44</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Laroche, Miss. Simonne Marie Anne Andree</td>\n",
" <td>female</td>\n",
" <td>3.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>374</th>\n",
" <td>375</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Palsson, Miss. Stina Viola</td>\n",
" <td>female</td>\n",
" <td>3.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>642</th>\n",
" <td>643</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Skoog, Miss. Margit Elizabeth</td>\n",
" <td>female</td>\n",
" <td>2.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>205</th>\n",
" <td>206</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Strom, Miss. Telma Matilda</td>\n",
" <td>female</td>\n",
" <td>2.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>530</th>\n",
" <td>531</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Quick, Miss. Phyllis May</td>\n",
" <td>female</td>\n",
" <td>2.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>479</th>\n",
" <td>480</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Hirvonen, Miss. Hildur E</td>\n",
" <td>female</td>\n",
" <td>2.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>297</th>\n",
" <td>298</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Allison, Miss. Helen Loraine</td>\n",
" <td>female</td>\n",
" <td>2.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>119</th>\n",
" <td>120</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Andersson, Miss. Ellis Anna Maria</td>\n",
" <td>female</td>\n",
" <td>2.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>381</th>\n",
" <td>382</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Nakid, Miss. Maria (\"Mary\")</td>\n",
" <td>female</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>172</th>\n",
" <td>173</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Johnson, Miss. Eleanor Ileen</td>\n",
" <td>female</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>644</th>\n",
" <td>645</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Baclini, Miss. Eugenie</td>\n",
" <td>female</td>\n",
" <td>0.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>469</th>\n",
" <td>470</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Baclini, Miss. Helene Barbara</td>\n",
" <td>female</td>\n",
" <td>0.75</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>239 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived ... Sex Age\n",
"52 53 1 ... female 49.00\n",
"796 797 1 ... female 49.00\n",
"754 755 1 ... female 48.00\n",
"556 557 1 ... female 48.00\n",
"736 737 0 ... female 48.00\n",
"862 863 1 ... female 48.00\n",
"132 133 0 ... female 47.00\n",
"871 872 1 ... female 47.00\n",
"706 707 1 ... female 45.00\n",
"276 277 0 ... female 45.00\n",
"167 168 0 ... female 45.00\n",
"362 363 0 ... female 45.00\n",
"856 857 1 ... female 45.00\n",
"440 441 1 ... female 45.00\n",
"523 524 1 ... female 44.00\n",
"194 195 1 ... female 44.00\n",
"854 855 0 ... female 44.00\n",
"779 780 1 ... female 43.00\n",
"678 679 0 ... female 43.00\n",
"432 433 1 ... female 42.00\n",
"865 866 1 ... female 42.00\n",
"380 381 1 ... female 42.00\n",
"272 273 1 ... female 41.00\n",
"337 338 1 ... female 41.00\n",
"254 255 0 ... female 41.00\n",
"638 639 0 ... female 41.00\n",
"609 610 1 ... female 40.00\n",
"319 320 1 ... female 40.00\n",
"161 162 1 ... female 40.00\n",
"346 347 1 ... female 40.00\n",
".. ... ... ... ... ...\n",
"634 635 0 ... female 9.00\n",
"852 853 0 ... female 9.00\n",
"541 542 0 ... female 9.00\n",
"147 148 0 ... female 9.00\n",
"24 25 0 ... female 8.00\n",
"237 238 1 ... female 8.00\n",
"535 536 1 ... female 7.00\n",
"720 721 1 ... female 6.00\n",
"813 814 0 ... female 6.00\n",
"777 778 1 ... female 5.00\n",
"233 234 1 ... female 5.00\n",
"58 59 1 ... female 5.00\n",
"448 449 1 ... female 5.00\n",
"691 692 1 ... female 4.00\n",
"10 11 1 ... female 4.00\n",
"750 751 1 ... female 4.00\n",
"184 185 1 ... female 4.00\n",
"618 619 1 ... female 4.00\n",
"43 44 1 ... female 3.00\n",
"374 375 0 ... female 3.00\n",
"642 643 0 ... female 2.00\n",
"205 206 0 ... female 2.00\n",
"530 531 1 ... female 2.00\n",
"479 480 1 ... female 2.00\n",
"297 298 0 ... female 2.00\n",
"119 120 0 ... female 2.00\n",
"381 382 1 ... female 1.00\n",
"172 173 1 ... female 1.00\n",
"644 645 1 ... female 0.75\n",
"469 470 1 ... female 0.75\n",
"\n",
"[239 rows x 6 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 17
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fZwQ2i6OT9vN",
"colab_type": "text"
},
"source": [
"**OR operator**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "8Tsj5DIuUAyb",
"colab_type": "code",
"outputId": "fcae74bc-81b2-4d49-86a9-2aada60d9309",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 202
}
},
"source": [
"df_sex = df[\"Sex\"] == \"Male\"\n",
"df_age_mask = df[\"Age\"] > 70\n",
"df[df_sex | df_age_mask]"
],
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>630</th>\n",
" <td>631</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
" <td>male</td>\n",
" <td>80.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>851</th>\n",
" <td>852</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Svensson, Mr. Johan</td>\n",
" <td>male</td>\n",
" <td>74.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>493</th>\n",
" <td>494</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Artagaveytia, Mr. Ramon</td>\n",
" <td>male</td>\n",
" <td>71.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>97</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Goldschmidt, Mr. George B</td>\n",
" <td>male</td>\n",
" <td>71.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td>117</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Connors, Mr. Patrick</td>\n",
" <td>male</td>\n",
" <td>70.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived ... Sex Age\n",
"630 631 1 ... male 80.0\n",
"851 852 0 ... male 74.0\n",
"493 494 0 ... male 71.0\n",
"96 97 0 ... male 71.0\n",
"116 117 0 ... male 70.5\n",
"\n",
"[5 rows x 6 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 18
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SF--tZmHUWQg",
"colab_type": "text"
},
"source": [
"# Finding the null values with .isnull()\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "AIH5Uq67UXJ_",
"colab_type": "code",
"outputId": "9f6709aa-a5c3-4287-e8f8-f5a93739d6a2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1949
}
},
"source": [
"null_mask = df[\"Age\"].isnull()\n",
"df[null_mask]"
],
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Moran, Mr. James</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>18</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Williams, Mr. Charles Eugene</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Masselmani, Mrs. Fatima</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Emir, Mr. Farred Chehab</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>29</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>30</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Todoroff, Mr. Lalio</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>32</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Spencer, Mrs. William Augustus (Marie Eugenie)</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>33</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Glynn, Miss. Mary Agatha</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>37</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Mamee, Mr. Hanna</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>43</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Kraeff, Mr. Theodor</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>46</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Rogers, Mr. William John</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>47</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Lennon, Mr. Denis</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>48</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>O'Driscoll, Miss. Bridget</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>49</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Samaan, Mr. Youssef</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>56</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Woolner, Mr. Hugh</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>65</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Stewart, Mr. Albert A</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>66</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Moubarek, Master. Gerios</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>77</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Staneff, Mr. Ivan</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>78</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Moutal, Mr. Rahamin Haim</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>83</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>McDermott, Miss. Brigdet Delia</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>88</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Slocovski, Mr. Selman Francis</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>96</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Shorney, Mr. Charles Joseph</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101</th>\n",
" <td>102</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Petroff, Mr. Pastcho (\"Pentcho\")</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>107</th>\n",
" <td>108</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Moss, Mr. Albert Johan</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109</th>\n",
" <td>110</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Moran, Miss. Bertha</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>121</th>\n",
" <td>122</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Moore, Mr. Leonard Charles</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126</th>\n",
" <td>127</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>McMahon, Mr. Martin</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>128</th>\n",
" <td>129</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Peter, Miss. Anna</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>140</th>\n",
" <td>141</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Boulos, Mrs. Joseph (Sultana)</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>154</th>\n",
" <td>155</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Olsen, Mr. Ole Martin</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>718</th>\n",
" <td>719</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>McEvoy, Mr. Michael</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>727</th>\n",
" <td>728</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Mannion, Miss. Margareth</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>732</th>\n",
" <td>733</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>Knight, Mr. Robert J</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>738</th>\n",
" <td>739</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Ivanoff, Mr. Kanio</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>739</th>\n",
" <td>740</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Nankoff, Mr. Minko</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>740</th>\n",
" <td>741</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Hawksford, Mr. Walter James</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>760</th>\n",
" <td>761</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Garfirth, Mr. John</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>766</th>\n",
" <td>767</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Brewe, Dr. Arthur Jackson</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>769</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Moran, Mr. Daniel J</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>773</th>\n",
" <td>774</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Elias, Mr. Dibo</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>776</th>\n",
" <td>777</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Tobin, Mr. Roger</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>778</th>\n",
" <td>779</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Kilgannon, Mr. Thomas J</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>783</th>\n",
" <td>784</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Johnston, Mr. Andrew G</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>790</th>\n",
" <td>791</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Keane, Mr. Andrew \"Andy\"</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>792</th>\n",
" <td>793</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Sage, Miss. Stella Anna</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>793</th>\n",
" <td>794</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Hoyt, Mr. William Fisher</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>815</th>\n",
" <td>816</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Fry, Mr. Richard</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>825</th>\n",
" <td>826</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Flynn, Mr. John</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>826</th>\n",
" <td>827</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Lam, Mr. Len</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>828</th>\n",
" <td>829</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>McCormack, Mr. Thomas Joseph</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>832</th>\n",
" <td>833</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Saad, Mr. Amin</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>837</th>\n",
" <td>838</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Sirota, Mr. Maurice</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>839</th>\n",
" <td>840</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Marechal, Mr. Pierre</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>846</th>\n",
" <td>847</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Sage, Mr. Douglas Bullen</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>849</th>\n",
" <td>850</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Goldenberg, Mrs. Samuel L (Edwiga Grabowska)</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>859</th>\n",
" <td>860</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Razi, Mr. Raihed</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>863</th>\n",
" <td>864</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>868</th>\n",
" <td>869</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>van Melkebeke, Mr. Philemon</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>878</th>\n",
" <td>879</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Laleff, Mr. Kristo</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>888</th>\n",
" <td>889</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>177 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived ... Sex Age\n",
"5 6 0 ... male NaN\n",
"17 18 1 ... male NaN\n",
"19 20 1 ... female NaN\n",
"26 27 0 ... male NaN\n",
"28 29 1 ... female NaN\n",
"29 30 0 ... male NaN\n",
"31 32 1 ... female NaN\n",
"32 33 1 ... female NaN\n",
"36 37 1 ... male NaN\n",
"42 43 0 ... male NaN\n",
"45 46 0 ... male NaN\n",
"46 47 0 ... male NaN\n",
"47 48 1 ... female NaN\n",
"48 49 0 ... male NaN\n",
"55 56 1 ... male NaN\n",
"64 65 0 ... male NaN\n",
"65 66 1 ... male NaN\n",
"76 77 0 ... male NaN\n",
"77 78 0 ... male NaN\n",
"82 83 1 ... female NaN\n",
"87 88 0 ... male NaN\n",
"95 96 0 ... male NaN\n",
"101 102 0 ... male NaN\n",
"107 108 1 ... male NaN\n",
"109 110 1 ... female NaN\n",
"121 122 0 ... male NaN\n",
"126 127 0 ... male NaN\n",
"128 129 1 ... female NaN\n",
"140 141 0 ... female NaN\n",
"154 155 0 ... male NaN\n",
".. ... ... ... ... ..\n",
"718 719 0 ... male NaN\n",
"727 728 1 ... female NaN\n",
"732 733 0 ... male NaN\n",
"738 739 0 ... male NaN\n",
"739 740 0 ... male NaN\n",
"740 741 1 ... male NaN\n",
"760 761 0 ... male NaN\n",
"766 767 0 ... male NaN\n",
"768 769 0 ... male NaN\n",
"773 774 0 ... male NaN\n",
"776 777 0 ... male NaN\n",
"778 779 0 ... male NaN\n",
"783 784 0 ... male NaN\n",
"790 791 0 ... male NaN\n",
"792 793 0 ... female NaN\n",
"793 794 0 ... male NaN\n",
"815 816 0 ... male NaN\n",
"825 826 0 ... male NaN\n",
"826 827 0 ... male NaN\n",
"828 829 1 ... male NaN\n",
"832 833 0 ... male NaN\n",
"837 838 0 ... male NaN\n",
"839 840 1 ... male NaN\n",
"846 847 0 ... male NaN\n",
"849 850 1 ... female NaN\n",
"859 860 0 ... male NaN\n",
"863 864 0 ... female NaN\n",
"868 869 0 ... male NaN\n",
"878 879 0 ... male NaN\n",
"888 889 0 ... female NaN\n",
"\n",
"[177 rows x 6 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "XhupjmknUt9w",
"colab_type": "code",
"outputId": "550be23c-bf39-4501-a00b-a95ae6fb2c28",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 139
}
},
"source": [
"df.isnull().sum()\n"
],
"execution_count": 20,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"PassengerId 0\n",
"Survived 0\n",
"Pclass 0\n",
"Name 0\n",
"Sex 0\n",
"Age 177\n",
"dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 20
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kG9rV4NZU0YU",
"colab_type": "text"
},
"source": [
"# Dropping a column\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "St1zpvX4U1tI",
"colab_type": "code",
"outputId": "0eca5d14-baa9-4cc7-d005-50a366f2109a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 202
}
},
"source": [
"df.drop(labels = [\"Pclass\"], axis=1).head()"
],
"execution_count": 21,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>630</th>\n",
" <td>631</td>\n",
" <td>1</td>\n",
" <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
" <td>male</td>\n",
" <td>80.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>851</th>\n",
" <td>852</td>\n",
" <td>0</td>\n",
" <td>Svensson, Mr. Johan</td>\n",
" <td>male</td>\n",
" <td>74.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>493</th>\n",
" <td>494</td>\n",
" <td>0</td>\n",
" <td>Artagaveytia, Mr. Ramon</td>\n",
" <td>male</td>\n",
" <td>71.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>97</td>\n",
" <td>0</td>\n",
" <td>Goldschmidt, Mr. George B</td>\n",
" <td>male</td>\n",
" <td>71.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td>117</td>\n",
" <td>0</td>\n",
" <td>Connors, Mr. Patrick</td>\n",
" <td>male</td>\n",
" <td>70.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Name Sex Age\n",
"630 631 1 Barkworth, Mr. Algernon Henry Wilson male 80.0\n",
"851 852 0 Svensson, Mr. Johan male 74.0\n",
"493 494 0 Artagaveytia, Mr. Ramon male 71.0\n",
"96 97 0 Goldschmidt, Mr. George B male 71.0\n",
"116 117 0 Connors, Mr. Patrick male 70.5"
]
},
"metadata": {
"tags": []
},
"execution_count": 21
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cr-iKp_IVJzl",
"colab_type": "text"
},
"source": [
"**Replacing the values by using the replace method**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "hF3no_nLVJQC",
"colab_type": "code",
"outputId": "ed8f7025-c13d-4089-900a-cedff07f9fb1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1949
}
},
"source": [
"df.replace(\"Nan\",df[\"Age\"].median())\n",
"\n",
"df.replace(\"Masselmani, Mrs. Fatima\", \"Tanu\")\n"
],
"execution_count": 22,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>630</th>\n",
" <td>631</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
" <td>male</td>\n",
" <td>80.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>851</th>\n",
" <td>852</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Svensson, Mr. Johan</td>\n",
" <td>male</td>\n",
" <td>74.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>493</th>\n",
" <td>494</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Artagaveytia, Mr. Ramon</td>\n",
" <td>male</td>\n",
" <td>71.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>97</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Goldschmidt, Mr. George B</td>\n",
" <td>male</td>\n",
" <td>71.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td>117</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Connors, Mr. Patrick</td>\n",
" <td>male</td>\n",
" <td>70.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>672</th>\n",
" <td>673</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>Mitchell, Mr. Henry Michael</td>\n",
" <td>male</td>\n",
" <td>70.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>745</th>\n",
" <td>746</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Crosby, Capt. Edward Gifford</td>\n",
" <td>male</td>\n",
" <td>70.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>Wheadon, Mr. Edward H</td>\n",
" <td>male</td>\n",
" <td>66.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>55</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Ostby, Mr. Engelhart Cornelius</td>\n",
" <td>male</td>\n",
" <td>65.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>280</th>\n",
" <td>281</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Duane, Mr. Frank</td>\n",
" <td>male</td>\n",
" <td>65.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>456</th>\n",
" <td>457</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Millet, Mr. Francis Davis</td>\n",
" <td>male</td>\n",
" <td>65.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>438</th>\n",
" <td>439</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Fortune, Mr. Mark</td>\n",
" <td>male</td>\n",
" <td>64.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>545</th>\n",
" <td>546</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Nicholson, Mr. Arthur Ernest</td>\n",
" <td>male</td>\n",
" <td>64.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>275</th>\n",
" <td>276</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Andrews, Miss. Kornelia Theodosia</td>\n",
" <td>female</td>\n",
" <td>63.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>483</th>\n",
" <td>484</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Turkula, Mrs. (Hedwig)</td>\n",
" <td>female</td>\n",
" <td>63.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>570</th>\n",
" <td>571</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Harris, Mr. George</td>\n",
" <td>male</td>\n",
" <td>62.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>252</th>\n",
" <td>253</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Stead, Mr. William Thomas</td>\n",
" <td>male</td>\n",
" <td>62.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>829</th>\n",
" <td>830</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
" <td>female</td>\n",
" <td>62.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>555</th>\n",
" <td>556</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Wright, Mr. George</td>\n",
" <td>male</td>\n",
" <td>62.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>625</th>\n",
" <td>626</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Sutton, Mr. Frederick</td>\n",
" <td>male</td>\n",
" <td>61.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>326</th>\n",
" <td>327</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Nysveen, Mr. Johan Hansen</td>\n",
" <td>male</td>\n",
" <td>61.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>170</th>\n",
" <td>171</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Van der hoef, Mr. Wyckoff</td>\n",
" <td>male</td>\n",
" <td>61.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>684</th>\n",
" <td>685</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>Brown, Mr. Thomas William Solomon</td>\n",
" <td>male</td>\n",
" <td>60.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>694</th>\n",
" <td>695</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Weir, Col. John</td>\n",
" <td>male</td>\n",
" <td>60.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>587</th>\n",
" <td>588</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Frolicher-Stehli, Mr. Maxmillian</td>\n",
" <td>male</td>\n",
" <td>60.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>366</th>\n",
" <td>367</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Warren, Mrs. Frank Manley (Anna Sophia Atkinson)</td>\n",
" <td>female</td>\n",
" <td>60.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td>95</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Coxon, Mr. Daniel</td>\n",
" <td>male</td>\n",
" <td>59.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>232</th>\n",
" <td>233</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>Sjostedt, Mr. Ernst Adolf</td>\n",
" <td>male</td>\n",
" <td>59.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>268</th>\n",
" <td>269</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Graham, Mrs. William Thompson (Edith Junkins)</td>\n",
" <td>female</td>\n",
" <td>58.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Bonnell, Miss. Elizabeth</td>\n",
" <td>female</td>\n",
" <td>58.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>718</th>\n",
" <td>719</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>McEvoy, Mr. Michael</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>727</th>\n",
" <td>728</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Mannion, Miss. Margareth</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>732</th>\n",
" <td>733</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>Knight, Mr. Robert J</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>738</th>\n",
" <td>739</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Ivanoff, Mr. Kanio</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>739</th>\n",
" <td>740</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Nankoff, Mr. Minko</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>740</th>\n",
" <td>741</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Hawksford, Mr. Walter James</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>760</th>\n",
" <td>761</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Garfirth, Mr. John</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>766</th>\n",
" <td>767</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Brewe, Dr. Arthur Jackson</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>769</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Moran, Mr. Daniel J</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>773</th>\n",
" <td>774</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Elias, Mr. Dibo</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>776</th>\n",
" <td>777</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Tobin, Mr. Roger</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>778</th>\n",
" <td>779</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Kilgannon, Mr. Thomas J</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>783</th>\n",
" <td>784</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Johnston, Mr. Andrew G</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>790</th>\n",
" <td>791</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Keane, Mr. Andrew \"Andy\"</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>792</th>\n",
" <td>793</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Sage, Miss. Stella Anna</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>793</th>\n",
" <td>794</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Hoyt, Mr. William Fisher</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>815</th>\n",
" <td>816</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Fry, Mr. Richard</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>825</th>\n",
" <td>826</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Flynn, Mr. John</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>826</th>\n",
" <td>827</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Lam, Mr. Len</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>828</th>\n",
" <td>829</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>McCormack, Mr. Thomas Joseph</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>832</th>\n",
" <td>833</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Saad, Mr. Amin</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>837</th>\n",
" <td>838</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Sirota, Mr. Maurice</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>839</th>\n",
" <td>840</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Marechal, Mr. Pierre</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>846</th>\n",
" <td>847</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Sage, Mr. Douglas Bullen</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>849</th>\n",
" <td>850</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Goldenberg, Mrs. Samuel L (Edwiga Grabowska)</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>859</th>\n",
" <td>860</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Razi, Mr. Raihed</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>863</th>\n",
" <td>864</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>868</th>\n",
" <td>869</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>van Melkebeke, Mr. Philemon</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>878</th>\n",
" <td>879</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Laleff, Mr. Kristo</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>888</th>\n",
" <td>889</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>891 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived ... Sex Age\n",
"630 631 1 ... male 80.0\n",
"851 852 0 ... male 74.0\n",
"493 494 0 ... male 71.0\n",
"96 97 0 ... male 71.0\n",
"116 117 0 ... male 70.5\n",
"672 673 0 ... male 70.0\n",
"745 746 0 ... male 70.0\n",
"33 34 0 ... male 66.0\n",
"54 55 0 ... male 65.0\n",
"280 281 0 ... male 65.0\n",
"456 457 0 ... male 65.0\n",
"438 439 0 ... male 64.0\n",
"545 546 0 ... male 64.0\n",
"275 276 1 ... female 63.0\n",
"483 484 1 ... female 63.0\n",
"570 571 1 ... male 62.0\n",
"252 253 0 ... male 62.0\n",
"829 830 1 ... female 62.0\n",
"555 556 0 ... male 62.0\n",
"625 626 0 ... male 61.0\n",
"326 327 0 ... male 61.0\n",
"170 171 0 ... male 61.0\n",
"684 685 0 ... male 60.0\n",
"694 695 0 ... male 60.0\n",
"587 588 1 ... male 60.0\n",
"366 367 1 ... female 60.0\n",
"94 95 0 ... male 59.0\n",
"232 233 0 ... male 59.0\n",
"268 269 1 ... female 58.0\n",
"11 12 1 ... female 58.0\n",
".. ... ... ... ... ...\n",
"718 719 0 ... male NaN\n",
"727 728 1 ... female NaN\n",
"732 733 0 ... male NaN\n",
"738 739 0 ... male NaN\n",
"739 740 0 ... male NaN\n",
"740 741 1 ... male NaN\n",
"760 761 0 ... male NaN\n",
"766 767 0 ... male NaN\n",
"768 769 0 ... male NaN\n",
"773 774 0 ... male NaN\n",
"776 777 0 ... male NaN\n",
"778 779 0 ... male NaN\n",
"783 784 0 ... male NaN\n",
"790 791 0 ... male NaN\n",
"792 793 0 ... female NaN\n",
"793 794 0 ... male NaN\n",
"815 816 0 ... male NaN\n",
"825 826 0 ... male NaN\n",
"826 827 0 ... male NaN\n",
"828 829 1 ... male NaN\n",
"832 833 0 ... male NaN\n",
"837 838 0 ... male NaN\n",
"839 840 1 ... male NaN\n",
"846 847 0 ... male NaN\n",
"849 850 1 ... female NaN\n",
"859 860 0 ... male NaN\n",
"863 864 0 ... female NaN\n",
"868 869 0 ... male NaN\n",
"878 879 0 ... male NaN\n",
"888 889 0 ... female NaN\n",
"\n",
"[891 rows x 6 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 22
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AX07UhV8iv30",
"colab_type": "text"
},
"source": [
"# Let us calculate how many passengers survived.\n",
"Here 1 = survived, and 0 = Not survived.\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "4s7zbGQvgiKT",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 121
},
"outputId": "5e2f3eae-1259-4fff-92a2-d692389e1b81"
},
"source": [
"count = df['Survived'].value_counts()\n",
"print(count)\n",
"# Let us see that in percentage.\n",
"\n",
"percentage = df['Survived'].value_counts() * 100 / len(df)\n",
"print(percentage)"
],
"execution_count": 41,
"outputs": [
{
"output_type": "stream",
"text": [
"0 549\n",
"1 342\n",
"Name: Survived, dtype: int64\n",
"0 61.616162\n",
"1 38.383838\n",
"Name: Survived, dtype: float64\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ocTuGos1kXMM",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 283
},
"outputId": "c739fbd9-3965-4373-f74a-87f9a8da3636"
},
"source": [
"%matplotlib inline\n",
"color = 0.5\n",
"df['Survived'].value_counts().plot(kind = 'bar')"
],
"execution_count": 42,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f0fcd0e0710>"
]
},
"metadata": {
"tags": []
},
"execution_count": 42
},
{
"output_type": "display_data",
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVMgAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VdEe36TRWnYA",
"colab_type": "text"
},
"source": [
"**I think this much more than enough for a good start to just starting and master data analysis on the web. Further, I will add more concepts, snippets, and examples in class to make things clear**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oOijhEeyYMNz",
"colab_type": "text"
},
"source": [
"\n",
"\n",
"[Click Here to watch the video](https://www.youtube.com/watch?v=DNyKDI9pn0Q)\n"
]
}
]
}