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craine/titanic-how-to-fillna-in-pandas.ipynb Secret

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Titanic - how to fillna in pandas.ipynb
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titanic-how-to-fillna-in-pandas.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Titanic - how to fillna in pandas.ipynb",
"provenance": [],
"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/gist/craine/3a0a3b3c9adbf9f7b43d7c297d5f38e0/titanic-how-to-fillna-in-pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6DSpykHJR20e",
"colab_type": "text"
},
"source": [
"**Goal**:\n",
"Learn how to fillna in pandas"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "m5w8W0PrSK-O",
"colab_type": "text"
},
"source": [
"We need data. Titanic data works great for this exercise. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "XMDAawmHRtAc",
"colab_type": "code",
"colab": {}
},
"source": [
"#load pandas library\n",
"import pandas as pd\n",
"\n",
"#load the Titanic CSV to the dataset\n",
"titanic = pd.read_csv('https://raw.githubusercontent.com/pcsanwald/kaggle-titanic/master/train.csv')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "SOZaOI2FU9mH",
"colab_type": "code",
"outputId": "7a5d4e47-1a96-4c9a-916f-bc37dc503a22",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"titanic.head()"
],
"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>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>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>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>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>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>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": [
" survived pclass ... cabin embarked\n",
"0 0 3 ... NaN S\n",
"1 1 1 ... C85 C\n",
"2 1 3 ... NaN S\n",
"3 1 1 ... C123 S\n",
"4 0 3 ... NaN S\n",
"\n",
"[5 rows x 11 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "5UMDom5BWrpM",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 221
},
"outputId": "3205921c-f37d-4514-c336-6cc04f7ed37f"
},
"source": [
"titanic.isnull().sum(axis = 0)"
],
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"survived 0\n",
"pclass 0\n",
"name 0\n",
"sex 0\n",
"age 177\n",
"sibsp 0\n",
"parch 0\n",
"ticket 0\n",
"fare 0\n",
"cabin 687\n",
"embarked 2\n",
"dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ogvHOHNYVGRM",
"colab_type": "code",
"colab": {}
},
"source": [
"titanic['age']=titanic['age'].fillna(titanic['age'].mean())"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "mjjzdlZXcWXW",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 221
},
"outputId": "ef7be642-d90c-4942-9aac-75d5cacefa43"
},
"source": [
"titanic.isnull().sum(axis = 0)"
],
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"survived 0\n",
"pclass 0\n",
"name 0\n",
"sex 0\n",
"age 0\n",
"sibsp 0\n",
"parch 0\n",
"ticket 0\n",
"fare 0\n",
"cabin 687\n",
"embarked 2\n",
"dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 14
}
]
}
]
}
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