Applied Data Science With Python  CAT II.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applied Python For Data Science CAT 2\n",
    "         August 13 2019\n",
    "         zalego Institute of technology"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Name of Student: Beryl Kanali\n",
    "                 Fredrick Gachoka"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using List Comprehensions convert Celsius to Fahrenheit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "celsius = [0,10,20.1,34.5]\n",
    "def celsius_to_fahrenheit(celsius):\n",
    "    \"\"\"\n",
    "    This is a function that converts a list with values in celcius to a list with values in fahrenheit\n",
    "    param celcius:list with values in celcius\n",
    "    param type:list\n",
    "    return fahreinheit:contains values in fahreinheit\n",
    "    \"\"\"\n",
    "    fahrenheit=[(9/5)*temp+32 for temp in celsius]\n",
    "    return fahrenheit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[32.0, 50.0, 68.18, 94.1]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fahrenheit=celsius_to_fahrenheit(celsius)\n",
    "fahrenheit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Split this string:**\n",
    "\n",
    "    s = \"Who is your data!\"\n",
    "    \n",
    "**into a list. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "s='Who is your data!'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Who', 'is', 'your', 'data!']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.split()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Given this nested dictionary grab the word \"hello\". Be prepared, this will be annoying/tricky "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'k1': [1, {'tricky': ['oh', 'man', {'target': [1, 2, 'hello']}]}]}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'k1':[1,{'tricky':['oh','man',{'target':[1,2,'hello']}]}]}\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tricky': ['oh', 'man', {'target': [1, 2, 'hello']}]}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E=d['k1'][1]\n",
    "E"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'target': [1, 2, 'hello']}"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F=E['tricky'][2]\n",
    "F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hello'"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F['target'][2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Given this nested list, use indexing to grab the word \"hello\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, [3, 4], [5, [100, 200, ['hello']], 23, 11], 1, 7]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst = [1,2,[3,4],[5,[100,200,['hello']],23,11],1,7]\n",
    "lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, [100, 200, ['hello']], 23, 11]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1=lst[3]\n",
    "list1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[100, 200, ['hello']]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list2=list1[1]\n",
    "list2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hello']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list2[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using any string formatting method produce the following output. Note that 12 and Sam are fed from variables.\n",
    "My number is: 12, and my name is: Sam\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter your number12\n",
      "enter your nameSam\n",
      "My number is: 12 and my name is: Sam\n"
     ]
    }
   ],
   "source": [
    "number=input('enter your number')\n",
    "name=input('enter your name')\n",
    "print('My number is:',number, 'and my name is:',name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define a function called hotel_cost with one argument nights as input. The hotel costs Kshs14000 per night. So, the function hotel_cost should return 14000 * nights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hotel_cost(nights):\n",
    "    \"\"\"\n",
    "    This is a function that takes in number of nights and calculates the total cost based on number of nights and cost per night\n",
    "    param nights:this is the nuber of nights\n",
    "    param type:integer\n",
    "    return HotelCost:contains the total hotel cost based on the number of nights passed in the argument\n",
    "     \"\"\"\n",
    "    nights=int(input('enter number of nights'))\n",
    "    HotelCost=14000*nights\n",
    "    return HotelCost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter number of nights10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "140000"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotel_cost(nights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create an array of the integers from 10 to 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n",
       "       27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,\n",
       "       44, 45, 46, 47, 48, 49, 50])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(10,51)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create an array of 20 linearly spaced points between 0 and 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.05263158, 0.10526316, 0.15789474, 0.21052632,\n",
       "       0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,\n",
       "       0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,\n",
       "       0.78947368, 0.84210526, 0.89473684, 0.94736842, 1.        ])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,1,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Visualization\n",
    "#### The Data We will be working with a famous titanic data set for these exercises."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic = sns.load_dataset('titanic')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the style to darkgrid/whitegrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Permorm basic descriptive analytics on the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 15 columns):\n",
      "survived       891 non-null int64\n",
      "pclass         891 non-null int64\n",
      "sex            891 non-null object\n",
      "age            714 non-null float64\n",
      "sibsp          891 non-null int64\n",
      "parch          891 non-null int64\n",
      "fare           891 non-null float64\n",
      "embarked       889 non-null object\n",
      "class          891 non-null category\n",
      "who            891 non-null object\n",
      "adult_male     891 non-null bool\n",
      "deck           203 non-null category\n",
      "embark_town    889 non-null object\n",
      "alive          891 non-null object\n",
      "alone          891 non-null bool\n",
      "dtypes: bool(2), category(2), float64(2), int64(4), object(5)\n",
      "memory usage: 80.6+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic['age'].fillna(value=titanic['age'].mean(),inplace=True)## incase null values in age affected our plots then we would have to fillthem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 15 columns):\n",
      "survived       891 non-null int64\n",
      "pclass         891 non-null int64\n",
      "sex            891 non-null object\n",
      "age            891 non-null float64\n",
      "sibsp          891 non-null int64\n",
      "parch          891 non-null int64\n",
      "fare           891 non-null float64\n",
      "embarked       889 non-null object\n",
      "class          891 non-null category\n",
      "who            891 non-null object\n",
      "adult_male     891 non-null bool\n",
      "deck           203 non-null category\n",
      "embark_town    889 non-null object\n",
      "alive          891 non-null object\n",
      "alone          891 non-null bool\n",
      "dtypes: bool(2), category(2), float64(2), int64(4), object(5)\n",
      "memory usage: 80.6+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare  class    who  \\\n",
       "0         0       3    male  22.0      1      0   7.2500  Third    man   \n",
       "1         1       1  female  38.0      1      0  71.2833  First  woman   \n",
       "2         1       3  female  26.0      0      0   7.9250  Third  woman   \n",
       "3         1       1  female  35.0      1      0  53.1000  First  woman   \n",
       "4         0       3    male  35.0      0      0   8.0500  Third    man   \n",
       "\n",
       "   adult_male alive  alone  \n",
       "0        True    no  False  \n",
       "1       False   yes  False  \n",
       "2       False   yes   True  \n",
       "3       False   yes  False  \n",
       "4        True    no   True  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.dropna(axis=1,inplace=True)\n",
    "titanic.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Recreate the plots below using the titanic dataframe. There are very few hints since most of the plots can be done with just one or two lines of code and a hint would basically give away the solution. Keep careful attention to the x and y labels for hints.**\n",
    "\n",
    "** *Note! In order to not lose the plot image, make sure you don't code in the cell that is directly above the plot, there is an extra cell above that one which won't overwrite that plot!* **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CODE HERE\n",
    "# REPLICATE EXERCISE PLOT IMAGE BELOW Age against sex for both male and female\n",
    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g=sns.FacetGrid(titanic,col='sex')\n",
    "g=g.map(plt.hist,'age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a box plot comparing the age against class\n",
    "# CODE HERE \n",
    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21a026a3668>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEICAYAAAC3Y/QeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAHjlJREFUeJzt3Xt0VNXdxvHvTAKByUUSvKDcmgRsA1SxUkAXgtSEYKn1ssDAsCKIl2IRDUpMQAygQEAQq7QKaq01kXt5XbqkRQIso0BTSgUxK4Ikmsj9kgjJDAyTZN4/KKHxAjMwJ4fJeT5/5Uxm9v6dOeGZzZ6zz7H5fD4fIiJiKXazCxARkaan8BcRsSCFv4iIBSn8RUQsSOEvImJBCn8REQtS+IuIWJDCX0TEghT+IiIWFG52AT9m27ZtREREmF2GiEhI8Xg89OzZ87zPu2TDPyIigqSkJLPLEBEJKSUlJX49T9M+IiIWpPAXEbEghb+IiAUp/EVELEjhLyJiQYac7eP1esnOzmbv3r3Y7Xaee+45wsPDyc7Oxmaz0bVrV6ZOnYrdrs8eEREzGBL+H330EbW1tSxdupSNGzfyhz/8Aa/XS0ZGBn369CEnJ4d169aRkpJiRPciInIehoR/fHw8dXV11NfXU1NTQ3h4ONu2baN3794A9O/fn40bN17y4V9QUMCaNWuC3m5VVRUAsbGxQW87NTWV5OTkoLcrIs2LIeHvcDjYu3cvt99+O1VVVSxcuJAtW7Zgs9kAiIyMpLq6+pxteDwevxcrGGXfvn243e6gt3vkyBEAQ1Yw79u3z/T3TUQufYaE/1tvvUW/fv148skn2b9/P6NGjcLr9Tb83uVyERMTc842LoUVvklJSdx3331BbzczMxOAuXPnBr1tEbE2U1f4xsTEEB0dDcBll11GbW0t3bp1o6ioCIDCwkJ69eplRNciIuIHQ0b+o0ePZvLkyTidTrxeLxMmTKBHjx4888wzzJ8/n4SEBFJTU43oWkRE/GBI+EdGRvLSSy997/H8/HwjuhMRkQDpRHsREQtS+IuIWJDCX0TEghT+IiIWpPAXEbEghb+IiAUp/EXEMo4ePcrEiROprKw0uxTTKfxFxDIWL15McXExixcvNrsU0yn8RcQSjh49ytq1a/H5fHz44YeWH/0r/EXEEhYvXkx9fT0A9fX1lh/9K/xFxBI2bNhAbW0tALW1taxfv97kisyl8BcRSxg4cCDh4acvZxYeHs6vfvUrkysyl8JfRCzB6XQ23DfcbrfjdDpNrshcCn8RsYS2bduSkpKCzWZj0KBBxMXFmV2SqQy5pLOIyKXI6XRSXl5u+VE/aOQvImJJCn+RAGmVaOjSIq+zDAn/VatWkZ6eTnp6Ovfeey8///nP2bZtG8OGDWP48OH88Y9/NKJbkSahAAlNWuTVmCHhf88995CXl0deXh7du3dnypQpTJ06lRdeeIElS5awfft2iouLjehaxFAKkNClRV6NGTrts2PHDnbv3s2QIUM4deoUnTp1wmaz0a9fPzZv3mxk1yKGUICELi3yaszQs30WLVrEuHHjqKmpISoqquHxyMhIvvnmm3O+1uPxUFJSYmR5pnG73QDNdv+as4KCgkYBsnbtWm677TaTqxJ/3HDDDfzzn/+krq6OsLAwbrjhBkv/GzQs/I8fP05ZWRl9+/alpqYGl8vV8DuXy0VMTMw5Xx8REUFSUpJR5ZnK4XAANNv9a86Sk5NZs2YNtbW1hIeHk5KSouMYIn7/+9+zZcuWhvAfN25cszzX398PNMOmfbZs2cLNN98MQFRUFC1atKCiogKfz8cnn3xCr169jOpaxDBaJRq6tMirMcNG/l999RUdOnRo2J4+fToTJ06krq6Ofv36cf311xvVtYhhzgTI6tWrFSAhSIu8zjIs/B988MFG2z179mT58uVGdSfSZBQgoatt27bMmzfP7DIuCbq8g0iAFCDSHGiFr0iAtMJXmgOFv0iAtMJXmgOFv0gAtMJXmguFv0gAtMI3tGnK7iyFv0gAdImA0KYpu7MU/iIB0H1gQ5em7BpT+IsEQCt8Q5em7BpT+IsEQJcICF2asmtM4S8SIKfTSffu3TXqDzEDBw7EZrMBYLPZLD9lp/AXCdCZFb4a9YeW22+/HZ/PB4DP5+PXv/61yRWZS+EvEiCdLhia/v73vzca+a9evdrkisyl8BcJkE4XDE0bNmxoNPLXnL+I+E2nC4YunabbmMJfJAA6XTB0OZ3ORsfO6l/YK/xFAqDTBaW5UPiLBGDgwIGEhYUBEBYWZvmpg1CyePHiRl/4Wv1/bYaF/6JFi0hLS+Oee+5hxYoVlJeXM2LECJxOJ1OnTm3475dIKHE6nY2+NLT61EEo2bBhA3V1dQDU1dVZ/n9thoR/UVERn376KUuWLCEvL48DBw6Qm5tLRkYGixcvxufzsW7dOiO6FhH5QfrCtzFDwv+TTz7h2muvZdy4cYwdO5Zbb72V4uJievfuDUD//v3ZtGmTEV2LGOrM4AVOj/ytPnUQSvSFb2OG3MO3qqqKffv2sXDhQvbs2cMjjzyCz+drmG+LjIykurr6nG14PB5KSkqMKM90brcboNnuX3NWUFDQKPzXrl3LbbfdZnJV4o9jx441OnZffvklMTExJldlHkPCv02bNiQkJNCyZUsSEhKIiIjgwIEDDb93uVznfdMjIiJISkoyojzTORwOgGa7f83ZVVddRUVFRcN2u3btdBxDxIIFC7Db7dTV1WG329myZQuPPvqo2WUFnb+DSkOmfW688UY+/vhjfD4fBw8e5MSJE9x0000UFRUBUFhYSK9evYzoWsRQhw8fbrR96NAhkyqRQOkL38YMCf+BAweSlJTE0KFDeeSRR8jJySErK4sFCxaQlpaG1+slNTXViK5FDPXdLwk15RM69IVvY4ZM+wA89dRT33ssPz/fqO5EmoTT6eSDDz5otC2hwel0snbtWkA34gEt8hIJSFVVVaPtb7/91qRKJFC6EU9jCn+RADz//PONtufMmWNSJXIhdCOesxT+IgH43zN9AMrLy02qROTiKPxFAtCpU6dG2507dzapErkQuhfDWQp/kQB890SGrKwskyqRQOleDI0p/EUC0KVLl4bRf+fOnUlISDC5IvGX7sXQmGGneoqYqaCggDVr1hjS9smTJ7HZbLRo0YLMzMygtp2amkpycnJQ25TTfuheDM1xha+/NPIXCZDL5cLhcNC6dWuzS5EAaJFXYxr5S7OUnJxs2Aj6zGh/7ty5hrQvxtAir8Y08hcRS9Air8Y08hcRy3A6nZSXl1t+1A8KfxGxkLZt2zJv3jyzy7gkaNpHRMSCFP4iIhak8BcRsSCFv4iIBekLXxG55Bi1QvvM/RhiY2OD3jaE1gptw8L/rrvuIjo6GoAOHTqQlpbGzJkzCQsLo1+/fpZeVi0i5jA6/EOJIeHv8XgAyMvLa3jszjvvZMGCBXTs2JGHH36Y4uJiunfvbkT3IhLijFqhrdXZZxky5//FF19w4sQJxowZw3333ceWLVs4deoUnTp1wmaz0a9fPzZv3mxE1yIi4gdDRv6tWrXigQceYNiwYXz99dc89NBDxMTENPw+MjKSb7755pxteDweSkpKjCjPdG63G6DZ7l9zp+MXunTszjIk/OPj4+ncuTM2m434+Hiio6Mb3eja5XI1+jD4IRERESQlJZ23r4ULF1JaWnrRNTelAwcOAPDmm2+aXElgEhMTGTt2rNllmM7hcAD49fcplxYrHDt/P9gMCf+VK1eya9cupk2bxsGDBzlx4gQOh4OKigo6duzIJ598ErQvfEtLS9n2xZecjLs6KO01hfCw05cCPnSoxuRK/Neqcr/ZJYhIEBkS/kOHDmXSpEmMGDECm83GrFmzsNvtTJw4kbq6Ovr168f1118ftP5Oxl1NRerDQWtPvq/TmtfMLkFEgsiQ8G/ZsiUvvPDC9x5fvny5Ed2JiEiAtMJXRMSCFP4iIhak8BcRsSCFv4iIBSn8RUQsSOEvImJBCn8REQtS+IuIWJDCX0TEghT+IiIWpPAXEbEghb+IiAUp/EVELMjv8P/666/56KOPOHDgAD6fz8iaRETEYH5d0jk/P5+1a9dy7Ngx7rrrLioqKsjJyTG6NhERMYhfI/8PPviAt956i+joaEaPHs327duNrktERAzkV/ifmeax2WzA6Zu1nM/Ro0cZMGAApaWllJeXM2LECJxOJ1OnTqW+vv4iShYRkYvlV/gPGTKEkSNHUlFRwUMPPURycvI5n+/1esnJyaFVq1YA5ObmkpGRweLFi/H5fKxbt+7iKxcRkQvm15x/eno6N998M7t27SIhIYGf/vSn53z+nDlzGD58OK+9dvq+r8XFxfTu3RuA/v37s3HjRlJSUi6ydBERuVB+hf+kSZMafi4sLKRFixa0a9eOkSNHctlllzV67qpVq4iLi+OWW25pCH+fz9cwZRQZGUl1dXWw6hcRkQvgV/h7PB46duxIr1692L59Ozt27CAuLo6srCwWLlzY6Ll/+9vfsNlsbN68mZKSErKysqisrGz4vcvlIiYmxq8+S0pKzvs8t9vtzy5IELjdbr+OSXN35m9O70Xo0bE7y6/wr6ysZP78+QDccsstjBkzhoyMDEaOHPm9577zzjsNP6enpzNt2jTmzp1LUVERffr0obCwkL59+563z4iICJKSks77PIfDATU1/uyGXCSHw+HXMWnuHA4HgN6LEGSFY+fvB5tfX/jW1NRQWloKQGlpKS6Xi6qqKr9H3VlZWSxYsIC0tDS8Xi+pqal+vU5ERIzh18g/JyeHzMxMDh06xNVXX80zzzzD6tWrGTt27Dlfl5eX1/Bzfn7+xVUqIiJB41f4FxcX43K5aNmyJUePHmXixIl8+OGHRtcmIiIG8WvaZ8WKFeTl5TFgwAByc3Pp0qWL0XWJiIiB/Br5x8bGcuWVV+JyuejTpw8vv/yy0XX5raqqilaVR+i05jWzS2nWWlXup6rF5WaXISJB4tfIPzo6moKCAmw2G0uXLm106qaIiIQev0b+M2bMoKKigieffJI333yTadOmGVyW/2JjY9npbUFF6sNml9KsdVrzGrGxUWaXISJB4lf4R0VF0a1bNwCys7MNLUisZeHChQ2nEYeKsrIyADIzM02uxH+JiYnnPTtPrMWv8BcxSmlpKSWlO4npfIXZpfgv5vRVbffWhsb05/Hyw2aXIJcghb+YLqbzFfSZOszsMpqtoukrzC5BLkG6h6+IiAUp/EVELEjhLyJiQQp/ERELUviLiFiQwl9ExIJ0qqeIXLBQW6QXigv0wJhFegp/EblgpaWllO4sptPlrc0uxS8xLbwAeI+WmVyJ/yqOnDCkXYW/iFyUTpe3ZsrdCWaX0WzN+D9jPqgMCf+6ujqmTJnCV199RVhYGLm5ufh8PrKzs7HZbHTt2pWpU6dit+srBxERMxgS/hs2bABg6dKlFBUVNYR/RkYGffr0IScnh3Xr1pGSkmJE9yIich6GDL2Tk5N57rnnANi3bx+XX345xcXF9O7dG4D+/fuzadMmI7oWERE/GDbnHx4eTlZWFmvXruXll19mw4YN2Gw2ACIjI6murj7n6z0eDyUlJeftx+12B6VeOT+32+3XMQm0TVoGtUn5AUYcuzPttgh6q/JdRhw/Q7/wnTNnDhMnTuTee+/F4/E0PO5yuYiJiTnnayMiIkhKSjpvHw6HA2pqLrpWOT+Hw+HXMQm0zarak0FtU77PiGN3pl2vMSejyP8I5Pj5+yFhSPi/++67HDx4kN/97ne0bt0am81Gjx49KCoqok+fPhQWFtK3b18jupYQU1VVxfHKw7rssIGOlx/GEWczuwy5xBgS/oMGDWLSpEmMHDmS2tpaJk+eTGJiIs888wzz588nISGB1NRUI7oWERE/GBL+DoeDl1566XuP5+fnG9GdhLDY2Fjc0T7dzMVARdNXEBsea3YZconRifYiIhak8BcRsaBmcXmHVpX76bTmNbPL8Fv4idOnuda2jja5Ev+1qtwPV3Y1uwwRCZKQD//ExESzSwhYWdkhABKuvNrkSgJwZdeQfK9F5IeFfPgH+zKnTeHM5WTnzp1rciUiYlWa8xcRsSCFv4iIBSn8RUQsSOEvImJBIf+Fr4iYp6qqiqNHThh2tymB8iMnaGuvCnq7GvmLiFiQRv4icsFiY2OJqq/SPXwNNOP/ymgRG/xrMyn8xXTHy0Prks6eb0/fQCiijcPkSvxzvPww7RPjzC5DLjEKfzFVKK4aLjv+LQDtL+9gciX+aZ8YF5LvsxhL4S+m0gptEXPoC18REQtS+IuIWFDQp328Xi+TJ09m7969nDp1ikceeYQuXbqQnZ2NzWaja9euTJ06FbtdnzsiImYJevi/9957tGnThrlz51JVVcXdd9/Nz372MzIyMujTpw85OTmsW7eOlJSUYHctIiJ+Cvrwe/DgwTz++OMN22FhYRQXF9O7d28A+vfvz6ZNm4LdrYiIBCDoI//IyEgAampqeOyxx8jIyGDOnDnYbLaG31dXV5+3HY/HQ0lJSbDLuyS43afPE2+u+9fc6fid5Xa7aWF2ERbgdruD/vdmyKme+/fvZ9y4cTidTu64445Gp8S5XC5iYmLO20ZERARJSUlGlGc6h+P04qDmun/NnY7fWQ6HA+8Js6to/hwOh99/b/5+SAR92ufIkSOMGTOGzMxMhg4dCkC3bt0oKioCoLCwkF69egW7WxERCUDQw3/hwoUcP36cV155hfT0dNLT08nIyGDBggWkpaXh9XpJTU0NdrciIhKAoE/7TJkyhSlTpnzv8fz8/GB3JSKXgIoQuqTzMbcXgMscofNNRcWREyS2DX67uryDiFywULtm0PFjpz+kLm8bOlchTWxrzPus8BeRCxZq12bSdZnO0jJbERELUviLiFiQwl9ExIIU/iIiFqTwFxGxIIW/iIgFKfxFRCxI4S8iYkEKfxERC1L4i4hYkMJfRMSCFP4iIhak8BcRsSCFv4iIBSn8RUQsyLDw3759O+np6QCUl5czYsQInE4nU6dOpb6+3qhuRUTED4aE/+uvv86UKVPweDwA5ObmkpGRweLFi/H5fKxbt86IbkVExE+GhH+nTp1YsGBBw3ZxcTG9e/cGoH///mzatMmIbkVExE+G3MYxNTWVPXv2NGz7fD5sNhsAkZGRVFdXn7cNj8dDSUmJEeWZzu12AzTb/WvudPxCl47dWU1yD1+7/ex/MFwuFzExMed9TUREBElJSUaWZRqHwwHQbPevudPxC11WOHb+frA1ydk+3bp1o6ioCIDCwkJ69erVFN2KiMiPaJLwz8rKYsGCBaSlpeH1eklNTW2KbkVE5EcYNu3ToUMHli9fDkB8fDz5+flGdSUiIgHSIi8REQtS+IuIWJDCX0TEghT+IiIWpPAXEbEghb+IiAUp/EVELEjhLyJiQQp/ERELapILu4k0tYKCAtasWWNI27t27cLj8TB27Fiio6OD2nZqairJyclBbVPkh2jkLxKgU6dOAVBRUWFyJSIXTiN/aZaSk5MNGUFv3bqVp59+GoD6+nqcTic33HBD0PsRMZpG/iIByM3NbbQ9c+ZMkyoRuTgKf5EA1NTUnHNbJFQo/EUCcOZ2pD+2LRIqFP4iAfD5fOfcFgkVCn+RAPzv/ah/aFskVDTZ2T719fVMmzaNnTt30rJlS2bMmEHnzp2bqvsLYtS54mVlZQBkZmYGvW2dJ26s+vr6c26LhIomC/+CggJOnTrFsmXL2LZtG7Nnz+bVV19tqu4vKbGxsWaXIHJJC8WBF4TW4KvJwn/r1q3ccsstAPTs2ZPPP/+8qbq+YEadKy6hq3Xr1pw4caLRtoQODbzOarLwr6mpISoqqmE7LCyM2tpawsN/uASPx0NJSUlTlSfil/vuu49FixY1bI8ePVp/pwZo3749Y8aMMbuMCxIqfw9NFv5RUVG4XK6G7fr6+h8NfoCIiAiSkpKaojQRvyUlJfH2229z4sQJWrduzZ133ml2SSKN+Pvh02SnKvziF7+gsLAQgG3btnHttdc2VdciQTVlyhTsdjs5OTlmlyJywZps5J+SksLGjRsZPnw4Pp+PWbNmNVXXIkF14403snr1arPLELkoTRb+drudZ599tqm6ExGRc9AKFRERC1L4i4hYkMJfRMSCFP4iIhZ0yd7JS4u8REQC5/F4/Hqezadr0oqIWI6mfURELEjhLyJiQQp/ERELUviLiFiQwl9ExIIu2VM9Q9mePXv47W9/S/fu3Rse69OnDwCPPvroeV//7bff8vHHH3PHHXcYVqPVvfbaa2zatAm73Y7NZmPChAn06NHD8H4nTJjA8OHDG/4eJDhmz55NcXExhw8f5uTJk3Ts2JEvv/ySm266iRdffLHRc2fOnMn999/PNddc86Pt3XvvvcyfP58OHToYXbppFP4G6dKlC3l5eRf02p07d7J+/XqFv0F2797N+vXrWbJkCTabjZKSErKysnjvvffMLk0uUHZ2NgCrVq2irKyMiRMnUlRUxNKlS7/33Keffrqpy7skKfybyJk/xBdffJGBAweSkJBAQkICv/zlL3n99dcJDw+nffv2PP/88yxcuJAvvviCZcuWkZaWZnbpzU5cXBz79u1j5cqV9O/fn6SkJFauXMnOnTuZMWMGAG3atGHWrFlERkYyY8YMPvvsM7xeL+PHjyc5OZnZs2ezdetWAH7zm98watQosrOzadmyJXv37uXQoUPMnj2b7t27884777BixQquuOIKjh49auauW055eTkPPvgglZWVDBw4kPHjx5Oens60adNYvXo1n376KW63m5kzZ/Lee+/x8ccf065dO6qqqswu3XAKf4Ps3r2b9PT0hu1hw4Y1/Lx//35WrVpFbGwsjz32GKNHj2bIkCG8++671NTUMHbsWJYuXargN0hcXByvvvoq+fn5/OlPf6JVq1ZMmDCBP//5z8yaNYsuXbqwYsUK3njjDXr06EFVVRUrV67k8OHD5OfnExYWxp49e1i+fDm1tbU4nU769u0LwDXXXMOzzz7L8uXLWbZsGZmZmbz99tu8//772Gw27rnnHpP33lo8Hg+vvPIKdXV13HrrrYwfP77R7xMSEpgyZQq7du1iy5YtrFy5ErfbzaBBg0yquOko/A3y3WmfoqKihp9jY2MbbiQ9adIkFi1axJIlS0hISNAN45tAeXk5UVFR5ObmArBjxw4efvhhTp48yfTp0wHwer3Ex8fz1Vdf0bNnTwCuuOIKJkyYwBtvvEGvXr2w2Wy0aNGC66+/ntLSUoCGW4+2a9eO//znP5SVldGlSxdatmwJwHXXXdfUu2tpXbt2bXjvf+i2sfHx8cDpwVqPHj2w2+1ERUVZ4k6DOtvHBHb72bd92bJljB8/nvz8fADWrl2L3W6nvr7erPKavZ07dzJt2rSGa6DEx8cTHR1N586dmTNnDnl5eWRmZjJgwAASEhLYsWMHANXV1TzwwAMkJiY2TPl4vV4+/fRTOnfuDIDNZmvUV8eOHdm9ezcnT56krq5O16tqYt89Ht915t9ifHw8n332GfX19bjdbnbv3t0U5ZlKI3+TXXfdddx///20adOGyMhIbr31Vk6dOsWuXbt46623GD16tNklNjuDBg2itLSUYcOG4XA48Pl8PPXUU7Rr146srCzq6uqA02eF/OQnP2Hz5s2MGDGCuro6xo0bx4ABA/jXv/5FWloaXq+XwYMHNzqz63/FxcXx+OOPM3z4cOLi4mjdunVT7qr4KSkpicGDBzN06FCuvPJK2rZta3ZJhtOF3URELEjTPiIiFqTwFxGxIIW/iIgFKfxFRCxI4S8iYkEKf5EfsWrVKubNm2d2GSKGUPiLiFiQFnmJ/NfJkyeZNGkS+/btw+v1kpqa2vC7F154gc8//xyXy0ViYiK5ubls3bqVOXPmEB4eTkxMDPPmzePw4cNMmjSJ8PBwwsLCeP7557nqqqtM3CuRH6bwF/mvpUuX0r59e1588UV27drFpk2bqK6upqamhpiYGP7yl79QX1/PkCFDOHjwIAUFBaSkpPDAAw+wfv16jh8/zqZNm+jevTvZ2dn8+9//5tixYwp/uSRp2kfkv8rKyhou4nbttdcSExMDQEREBJWVlTzxxBPk5OTgdrvxer2MHTuWyspKRo0axT/+8Q/Cw8MZOnQosbGxPPjgg7zzzjuEhYWZuUsiP0rhL/JfiYmJDRdx++abb5g/fz4AhYWF7N+/n/nz5/PEE09w8uRJfD4f77//PnfffTd5eXl07dqV5cuXs27dOm688Ub++te/MnjwYN544w0zd0nkR+naPiL/5fF4mDx5MgcPHqSuro7k5GSqqqoYNWoUY8eOJSwsjJYtWzZ8NxAeHs706dNxOBy0aNGCZ599Fp/PR2ZmJmFhYdjtdiZNmvSjF30TMZPCX0TEgjTtIyJiQQp/ERELUviLiFiQwl9ExIIU/iIiFqTwFxGxIIW/iIgFKfxFRCzo/wGX2FgIUbtnigAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='class',y='age',data=titanic,palette='rainbow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Read Salaries.csv as a dataframe called sal.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sal=pd.read_csv('Salaries.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the head of the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>EmployeeName</th>\n",
       "      <th>JobTitle</th>\n",
       "      <th>BasePay</th>\n",
       "      <th>OvertimePay</th>\n",
       "      <th>OtherPay</th>\n",
       "      <th>Benefits</th>\n",
       "      <th>TotalPay</th>\n",
       "      <th>TotalPayBenefits</th>\n",
       "      <th>Year</th>\n",
       "      <th>Notes</th>\n",
       "      <th>Agency</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>NATHANIEL FORD</td>\n",
       "      <td>GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY</td>\n",
       "      <td>167411.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>400184.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>567595.43</td>\n",
       "      <td>567595.43</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GARY JIMENEZ</td>\n",
       "      <td>CAPTAIN III (POLICE DEPARTMENT)</td>\n",
       "      <td>155966.02</td>\n",
       "      <td>245131.88</td>\n",
       "      <td>137811.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>538909.28</td>\n",
       "      <td>538909.28</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>ALBERT PARDINI</td>\n",
       "      <td>CAPTAIN III (POLICE DEPARTMENT)</td>\n",
       "      <td>212739.13</td>\n",
       "      <td>106088.18</td>\n",
       "      <td>16452.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>335279.91</td>\n",
       "      <td>335279.91</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>CHRISTOPHER CHONG</td>\n",
       "      <td>WIRE ROPE CABLE MAINTENANCE MECHANIC</td>\n",
       "      <td>77916.00</td>\n",
       "      <td>56120.71</td>\n",
       "      <td>198306.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>332343.61</td>\n",
       "      <td>332343.61</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>PATRICK GARDNER</td>\n",
       "      <td>DEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT)</td>\n",
       "      <td>134401.60</td>\n",
       "      <td>9737.00</td>\n",
       "      <td>182234.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>326373.19</td>\n",
       "      <td>326373.19</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id       EmployeeName                                        JobTitle  \\\n",
       "0   1     NATHANIEL FORD  GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY   \n",
       "1   2       GARY JIMENEZ                 CAPTAIN III (POLICE DEPARTMENT)   \n",
       "2   3     ALBERT PARDINI                 CAPTAIN III (POLICE DEPARTMENT)   \n",
       "3   4  CHRISTOPHER CHONG            WIRE ROPE CABLE MAINTENANCE MECHANIC   \n",
       "4   5    PATRICK GARDNER    DEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT)   \n",
       "\n",
       "     BasePay  OvertimePay   OtherPay  Benefits   TotalPay  TotalPayBenefits  \\\n",
       "0  167411.18         0.00  400184.25       NaN  567595.43         567595.43   \n",
       "1  155966.02    245131.88  137811.38       NaN  538909.28         538909.28   \n",
       "2  212739.13    106088.18   16452.60       NaN  335279.91         335279.91   \n",
       "3   77916.00     56120.71  198306.90       NaN  332343.61         332343.61   \n",
       "4  134401.60      9737.00  182234.59       NaN  326373.19         326373.19   \n",
       "\n",
       "   Year  Notes         Agency  Status  \n",
       "0  2011    NaN  San Francisco     NaN  \n",
       "1  2011    NaN  San Francisco     NaN  \n",
       "2  2011    NaN  San Francisco     NaN  \n",
       "3  2011    NaN  San Francisco     NaN  \n",
       "4  2011    NaN  San Francisco     NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many Entries do we have in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 148654 entries, 0 to 148653\n",
      "Data columns (total 13 columns):\n",
      "Id                  148654 non-null int64\n",
      "EmployeeName        148654 non-null object\n",
      "JobTitle            148654 non-null object\n",
      "BasePay             148045 non-null float64\n",
      "OvertimePay         148650 non-null float64\n",
      "OtherPay            148650 non-null float64\n",
      "Benefits            112491 non-null float64\n",
      "TotalPay            148654 non-null float64\n",
      "TotalPayBenefits    148654 non-null float64\n",
      "Year                148654 non-null int64\n",
      "Notes               0 non-null float64\n",
      "Agency              148654 non-null object\n",
      "Status              0 non-null float64\n",
      "dtypes: float64(8), int64(2), object(3)\n",
      "memory usage: 14.7+ MB\n"
     ]
    }
   ],
   "source": [
    "sal.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the average BasePay ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66325.44884050643"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Average_BasePay=sal['BasePay'].mean()\n",
    "Average_BasePay"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the highest amount of OvertimePay in the dataset ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "245131.88"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Highest_Overtime=sal['OvertimePay'].max()\n",
    "Highest_Overtime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many people have the word Chief in their job title? (This is pretty tricky)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "423\n"
     ]
    }
   ],
   "source": [
    "count=0\n",
    "for i in sal['JobTitle']:\n",
    "    if 'Chief' not in i:\n",
    "        continue\n",
    "    else:\n",
    "        count=count+1\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the top 5 most common jobs?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Transit Operator                7036\n",
       "Special Nurse                   4389\n",
       "Registered Nurse                3736\n",
       "Public Svc Aide-Public Works    2518\n",
       "Police Officer 3                2421\n",
       "Name: JobTitle, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common=sal['JobTitle'].value_counts()\n",
    "common[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is there a correlation between length of the Job Title string and Salary?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title_len</th>\n",
       "      <th>TotalPayBenefits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>title_len</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.036878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalPayBenefits</th>\n",
       "      <td>-0.036878</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  title_len  TotalPayBenefits\n",
       "title_len          1.000000         -0.036878\n",
       "TotalPayBenefits  -0.036878          1.000000"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal['title_len']=sal['JobTitle'].apply(len)\n",
    "sal[['title_len','TotalPayBenefits']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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