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finished titanic exercise
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‎Visualization/Titanic_Desaster/Exercises.ipynb‎

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"### Step 1. Import the necessary libraries"
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sbn\n",
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"\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Visualization/Titanic%20Desaster/train.csv). "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Step 3. Assign it to a variable titanic "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>PassengerId</th>\n",
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" <th>Survived</th>\n",
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" <th>Pclass</th>\n",
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" <th>Name</th>\n",
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" <th>Sex</th>\n",
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" <th>Age</th>\n",
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" <th>SibSp</th>\n",
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" <th>Parch</th>\n",
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" <th>Ticket</th>\n",
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" <th>Fare</th>\n",
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" <th>Cabin</th>\n",
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" <th>Embarked</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>3</td>\n",
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" <td>Braund, Mr. Owen Harris</td>\n",
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" <td>male</td>\n",
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" <td>22.0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>A/5 21171</td>\n",
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" <td>7.2500</td>\n",
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" <td>NaN</td>\n",
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" <td>S</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>2</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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" <td>female</td>\n",
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" <td>38.0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>PC 17599</td>\n",
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" <td>71.2833</td>\n",
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" <td>C85</td>\n",
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" <td>C</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>3</td>\n",
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" <td>1</td>\n",
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" <td>3</td>\n",
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" <td>Heikkinen, Miss. Laina</td>\n",
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" <td>female</td>\n",
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" <td>26.0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>STON/O2. 3101282</td>\n",
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" <td>7.9250</td>\n",
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" <td>NaN</td>\n",
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" <td>S</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>4</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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" <td>female</td>\n",
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" <td>35.0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>113803</td>\n",
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" <td>53.1000</td>\n",
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" <td>C123</td>\n",
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" <td>S</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>5</td>\n",
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" <td>0</td>\n",
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" <td>3</td>\n",
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" <td>Allen, Mr. William Henry</td>\n",
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" <td>male</td>\n",
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" <td>35.0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>373450</td>\n",
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" <td>8.0500</td>\n",
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" <td>NaN</td>\n",
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" <td>S</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" PassengerId Survived Pclass \\\n",
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"0 1 0 3 \n",
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"1 2 1 1 \n",
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"2 3 1 3 \n",
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"3 4 1 1 \n",
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"4 5 0 3 \n",
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"\n",
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" Name Sex Age SibSp \\\n",
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"0 Braund, Mr. Owen Harris male 22.0 1 \n",
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"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
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"2 Heikkinen, Miss. Laina female 26.0 0 \n",
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"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
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"4 Allen, Mr. William Henry male 35.0 0 \n",
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"\n",
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" Parch Ticket Fare Cabin Embarked \n",
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"0 0 A/5 21171 7.2500 NaN S \n",
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"1 0 PC 17599 71.2833 C85 C \n",
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"2 0 STON/O2. 3101282 7.9250 NaN S \n",
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"3 0 113803 53.1000 C123 S \n",
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"4 0 373450 8.0500 NaN S "
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"titanic = pd.read_csv('https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Visualization/Titanic%20Desaster/train.csv')\n",
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"\n",
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"titanic.head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Step 4. "
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]
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},
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"cell_type": "code",
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"execution_count": null,
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Step 5. "
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"### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Visualization/Titanic_Desaster/train.csv). "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Step 6. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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},
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"cell_type": "markdown",
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"source": [
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"### Step 7. "
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]
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": []
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},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Step 8. "
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"### Step 3. Assign it to a variable titanic "
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Step 9. "
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"### Step 4. Set PassengerId as the index "
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"cell_type": "markdown",
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"metadata": {},
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"### Step 10. "
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"### Step 5. Create a pie chart presenting the male/female proportion"
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"cell_type": "markdown",
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"### Step 11. "
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"### Step 6. Create a scatterplot with the Fare payed and the Age, differ the plot color by gender"
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"cell_type": "markdown",
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"### Step 12. "
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"### Step 7. How many people survived?"
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"cell_type": "markdown",
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"### Step 13. "
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"### Step 8. Create a histogram with the Fare payed"
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"outputs": [],
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"source": []
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"cell_type": "markdown",
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"source": [
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"### Step 14. "
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]
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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"outputs": [],
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"source": []
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"metadata": {},
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"source": [
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"### Step 15. "
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"execution_count": null,
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"metadata": {
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"collapsed": true
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"outputs": [],
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"source": []
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"cell_type": "markdown",
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"source": [
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"### Step 16. "
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"outputs": [],
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"cell_type": "markdown",
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"metadata": {},

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