(8)_Matplotlib_extra_examples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example 1: Ads Data \n",
    "\n",
    "Replace missing values for EstimatedSalary with its mean value \n",
    "\n",
    "Find: \n",
    "(i)   mean \n",
    "(ii)  median \n",
    "(iii) max\n",
    "(iv)  min "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2202,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "pd.plotting.register_matplotlib_converters()\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2203,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "    User ID  Gender    Age  EstimatedSalary  Purchased\n0  15624510    Male   19.0          19000.0          0\n1  15810944    Male    NaN          20000.0          0\n2  15668575  Female -888.0          43000.0          0\n3  15603246  Female   27.0              NaN          0\n4  15804002    Male   19.0          76000.0          0",
      "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>User ID</th>\n      <th>Gender</th>\n      <th>Age</th>\n      <th>EstimatedSalary</th>\n      <th>Purchased</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>15624510</td>\n      <td>Male</td>\n      <td>19.0</td>\n      <td>19000.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>15810944</td>\n      <td>Male</td>\n      <td>NaN</td>\n      <td>20000.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>15668575</td>\n      <td>Female</td>\n      <td>-888.0</td>\n      <td>43000.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>15603246</td>\n      <td>Female</td>\n      <td>27.0</td>\n      <td>NaN</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>15804002</td>\n      <td>Male</td>\n      <td>19.0</td>\n      <td>76000.0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 2203
    }
   ],
   "source": [
    "# Importing Ads Data\n",
    "\n",
    "csv_path = ('/Users/antonioelias/Desktop/MOOCs/Python Coding Bootcamp/Data Science Libs/ads.csv')\n",
    "\n",
    "ads_data = pd.read_csv(csv_path, delimiter=';')\n",
    "ads_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2204,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "            User ID         Age  EstimatedSalary   Purchased\ncount  4.000000e+02  394.000000       394.000000  400.000000\nmean   1.569154e+07   30.695431     69309.652284    0.357500\nstd    7.165832e+04   81.249427     34408.554232    0.479864\nmin    1.556669e+07 -888.000000      -999.000000    0.000000\n25%    1.562676e+07   29.000000     43000.000000    0.000000\n50%    1.569434e+07   37.000000     70000.000000    0.000000\n75%    1.575036e+07   46.000000     87750.000000    1.000000\nmax    1.581524e+07   60.000000    150000.000000    1.000000",
      "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>User ID</th>\n      <th>Age</th>\n      <th>EstimatedSalary</th>\n      <th>Purchased</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>4.000000e+02</td>\n      <td>394.000000</td>\n      <td>394.000000</td>\n      <td>400.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>1.569154e+07</td>\n      <td>30.695431</td>\n      <td>69309.652284</td>\n      <td>0.357500</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>7.165832e+04</td>\n      <td>81.249427</td>\n      <td>34408.554232</td>\n      <td>0.479864</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.556669e+07</td>\n      <td>-888.000000</td>\n      <td>-999.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>1.562676e+07</td>\n      <td>29.000000</td>\n      <td>43000.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1.569434e+07</td>\n      <td>37.000000</td>\n      <td>70000.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1.575036e+07</td>\n      <td>46.000000</td>\n      <td>87750.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.581524e+07</td>\n      <td>60.000000</td>\n      <td>150000.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 2204
    }
   ],
   "source": [
    "# Summary Statistics\n",
    "\n",
    "ads_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2205,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 400 entries, 0 to 399\nData columns (total 5 columns):\n #   Column           Non-Null Count  Dtype  \n---  ------           --------------  -----  \n 0   User ID          400 non-null    int64  \n 1   Gender           395 non-null    object \n 2   Age              394 non-null    float64\n 3   EstimatedSalary  394 non-null    float64\n 4   Purchased        400 non-null    int64  \ndtypes: float64(2), int64(2), object(1)\nmemory usage: 15.8+ KB\n"
    }
   ],
   "source": [
    "# Inspecting columns for NaN/NA values and dtypes\n",
    "\n",
    "ads_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2206,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Gender             5\nAge                6\nEstimatedSalary    6\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 2206
    }
   ],
   "source": [
    "# Counting Null values\n",
    "\n",
    "missing_val_count_by_column = ads_data.isnull().sum()\n",
    "mask = missing_val_count_by_column > 0\n",
    "missing_series = missing_val_count_by_column[mask]\n",
    "\n",
    "missing_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2207,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "\nMean EstimatedSalary: 69309.65\n\nMissing values for EstimatedSalary: 0\n"
    }
   ],
   "source": [
    "# Filling in missing values with EstimatedSalary mean\n",
    "mean_salary = ads_data.loc[:, 'EstimatedSalary'].mean()\n",
    "ads_data.loc[:, 'EstimatedSalary'] = ads_data.loc[:, 'EstimatedSalary'].replace(np.nan, mean_salary)\n",
    "\n",
    "salary_missing_values = ads_data.loc[:, 'EstimatedSalary'].isnull().sum()\n",
    "\n",
    "print('\\nMean EstimatedSalary: {:.2f}'.format(mean_salary))\n",
    "print('\\nMissing values for EstimatedSalary: {}'.format(salary_missing_values))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2208,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                mean        median       max      min\nGender                                               \nFemale  72067.404286  70000.000000  149000.0  15000.0\nMale    67279.962125  69654.826142  150000.0   -999.0",
      "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>mean</th>\n      <th>median</th>\n      <th>max</th>\n      <th>min</th>\n    </tr>\n    <tr>\n      <th>Gender</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Female</th>\n      <td>72067.404286</td>\n      <td>70000.000000</td>\n      <td>149000.0</td>\n      <td>15000.0</td>\n    </tr>\n    <tr>\n      <th>Male</th>\n      <td>67279.962125</td>\n      <td>69654.826142</td>\n      <td>150000.0</td>\n      <td>-999.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 2208
    }
   ],
   "source": [
    "# Grouping data by Gender and comparing EstimatedSalary metrics\n",
    "\n",
    "metrics = ['mean', 'median', 'max', 'min']\n",
    "\n",
    "gender_salary = ads_data.groupby('Gender')['EstimatedSalary'].agg(metrics)\n",
    "gender_salary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example 2: Tips Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2209,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   total_bill   tip     sex smoker  day    time  size\n0       16.99  1.01  Female     No  Sun  Dinner     2\n1       10.34  1.66    Male     No  Sun  Dinner     3\n2       21.01  3.50    Male     No  Sun  Dinner     3\n3       23.68  3.31    Male     No  Sun  Dinner     2\n4       24.59  3.61  Female     No  Sun  Dinner     4",
      "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>total_bill</th>\n      <th>tip</th>\n      <th>sex</th>\n      <th>smoker</th>\n      <th>day</th>\n      <th>time</th>\n      <th>size</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>16.99</td>\n      <td>1.01</td>\n      <td>Female</td>\n      <td>No</td>\n      <td>Sun</td>\n      <td>Dinner</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10.34</td>\n      <td>1.66</td>\n      <td>Male</td>\n      <td>No</td>\n      <td>Sun</td>\n      <td>Dinner</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>21.01</td>\n      <td>3.50</td>\n      <td>Male</td>\n      <td>No</td>\n      <td>Sun</td>\n      <td>Dinner</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>23.68</td>\n      <td>3.31</td>\n      <td>Male</td>\n      <td>No</td>\n      <td>Sun</td>\n      <td>Dinner</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>24.59</td>\n      <td>3.61</td>\n      <td>Female</td>\n      <td>No</td>\n      <td>Sun</td>\n      <td>Dinner</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 2209
    }
   ],
   "source": [
    "# Importing Tips Data\n",
    "\n",
    "csv_path = ('/Users/antonioelias/Desktop/MOOCs/Python Coding Bootcamp/Data Science Libs/tips.csv')\n",
    "\n",
    "tips_data = pd.read_csv(csv_path, index_col=0)\n",
    "tips_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2210,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "       total_bill         tip        size\ncount  244.000000  244.000000  244.000000\nmean    19.785943    2.998279    2.569672\nstd      8.902412    1.383638    0.951100\nmin      3.070000    1.000000    1.000000\n25%     13.347500    2.000000    2.000000\n50%     17.795000    2.900000    2.000000\n75%     24.127500    3.562500    3.000000\nmax     50.810000   10.000000    6.000000",
      "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>total_bill</th>\n      <th>tip</th>\n      <th>size</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>244.000000</td>\n      <td>244.000000</td>\n      <td>244.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>19.785943</td>\n      <td>2.998279</td>\n      <td>2.569672</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>8.902412</td>\n      <td>1.383638</td>\n      <td>0.951100</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>3.070000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>13.347500</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>17.795000</td>\n      <td>2.900000</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>24.127500</td>\n      <td>3.562500</td>\n      <td>3.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>50.810000</td>\n      <td>10.000000</td>\n      <td>6.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 2210
    }
   ],
   "source": [
    "# Summary Statistics \n",
    "\n",
    "tips_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2211,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 244 entries, 0 to 243\nData columns (total 7 columns):\n #   Column      Non-Null Count  Dtype  \n---  ------      --------------  -----  \n 0   total_bill  244 non-null    float64\n 1   tip         244 non-null    float64\n 2   sex         244 non-null    object \n 3   smoker      244 non-null    object \n 4   day         244 non-null    object \n 5   time        244 non-null    object \n 6   size        244 non-null    int64  \ndtypes: float64(2), int64(1), object(4)\nmemory usage: 15.2+ KB\n"
    }
   ],
   "source": [
    "# Inspecting columns for NaN/NAs and dtypes\n",
    "\n",
    "tips_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2212,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inspecting object type columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2213,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Male      157\nFemale     87\nName: sex, dtype: int64"
     },
     "metadata": {},
     "execution_count": 2213
    }
   ],
   "source": [
    "tips_data.loc[:, 'sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2214,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "No     151\nYes     93\nName: smoker, dtype: int64"
     },
     "metadata": {},
     "execution_count": 2214
    }
   ],
   "source": [
    "tips_data.loc[:, 'smoker'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2215,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Sat     87\nSun     76\nThur    62\nFri     19\nName: day, dtype: int64"
     },
     "metadata": {},
     "execution_count": 2215
    }
   ],
   "source": [
    "tips_data.loc[:, 'day'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2216,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Dinner    176\nLunch      68\nName: time, dtype: int64"
     },
     "metadata": {},
     "execution_count": 2216
    }
   ],
   "source": [
    "tips_data.loc[:, 'time'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2217,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "\nTotal Sales: $4827.77\n\nTotal Tips: $731.58\n"
    }
   ],
   "source": [
    "# Total sales\n",
    "\n",
    "total_sales = tips_data.total_bill.sum()\n",
    "\n",
    "# Total tips\n",
    "\n",
    "total_tips = tips_data.tip.sum()\n",
    "\n",
    "print('\\nTotal Sales: ${:.2f}'.format(total_sales))\n",
    "print('\\nTotal Tips: ${:.2f}'.format(total_tips))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2218,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "\nTotal Dinner Sales: $ 3660.30\n\nTotal Lunch Sales: $ 1167.47\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "time\nDinner    3660.30\nLunch     1167.47\nName: total_bill, dtype: float64"
     },
     "metadata": {},
     "execution_count": 2218
    }
   ],
   "source": [
    "# Comparing Lunch vs Dinner Total Sales - Part 1 \n",
    "\n",
    "# Method 1: \n",
    "\n",
    "lunch_mask = tips_data.time == ('Lunch')\n",
    "lunch_tips_data = tips_data[lunch_mask]\n",
    "total_sales_lunch = lunch_tips_data.total_bill.sum()\n",
    "\n",
    "dinner_mask = tips_data.time == ('Dinner')\n",
    "dinner_tips_data = tips_data[dinner_mask]\n",
    "total_sales_dinner = dinner_tips_data.total_bill.sum()\n",
    "\n",
    "print('\\nTotal Dinner Sales: $ {:.2f}'.format(total_sales_dinner))\n",
    "print('\\nTotal Lunch Sales: $ {:.2f}'.format(total_sales_lunch))\n",
    "\n",
    "# Method 2:\n",
    "\n",
    "bill_time = tips_data.groupby('time')['total_bill'].sum()\n",
    "bill_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2219,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "\nMean Dinner Sales: $ 20.80\n\nMean Lunch Sales: $ 17.17\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "time\nDinner    20.797159\nLunch     17.168676\nName: total_bill, dtype: float64"
     },
     "metadata": {},
     "execution_count": 2219
    }
   ],
   "source": [
    "# Comparing Lunch vs Dinner Total Sales - Part 2\n",
    "\n",
    "# Method 1:\n",
    " \n",
    "mean_lunch_sales = lunch_tips_data.total_bill.mean()\n",
    "mean_dinner_sales = dinner_tips_data.total_bill.mean()\n",
    "\n",
    "print('\\nMean Dinner Sales: $ {:.2f}'.format(mean_dinner_sales))\n",
    "print('\\nMean Lunch Sales: $ {:.2f}'.format(mean_lunch_sales))\n",
    "\n",
    "# Method 2: \n",
    "\n",
    "mean_sales_time = tips_data.groupby('time')['total_bill'].mean()\n",
    "mean_sales_time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example 3: Ads Data\n",
    "- Salary Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2220,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x432 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Cleaning Age Date\n",
    "\n",
    "f_mask = ads_data.Gender == 'Female'\n",
    "f_data = ads_data[f_mask]\n",
    "f_mean_salary = f_data.EstimatedSalary.mean()\n",
    "f_data.loc[:, 'EstimatedSalary'] = f_data.loc[:, 'EstimatedSalary'].replace([np.nan, -888, -888.00], f_mean_salary)\n",
    "\n",
    "m_mask = ads_data.Gender == 'Male'\n",
    "m_data = ads_data[m_mask]\n",
    "m_mean_salary = m_data.EstimatedSalary.mean()\n",
    "m_data.loc[:, 'EstimatedSalary'] = m_data.loc[:, 'EstimatedSalary'].replace([np.nan, -888, -888.00], m_mean_salary)\n",
    "\n",
    "mf_data = pd.concat([m_data, f_data])\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.kdeplot(data=m_data['EstimatedSalary'], shade=True, label='Male')\n",
    "sns.kdeplot(data=f_data['EstimatedSalary'], shade=True, label='Female')\n",
    "\n",
    "plt.title('Salary by Gender - Kernel Density Estimates')\n",
    "plt.xlabel('Salary (in $)')\n",
    "plt.ylabel('Frequency/Count')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example 4: Iris Data\n",
    "- Scatter Plots "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2221,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "    SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\nId                                                                       \n1             5.1           3.5            1.4           0.2  Iris-setosa\n2             4.9           3.0            1.4           0.2  Iris-setosa\n3             4.7           3.2            1.3           0.2  Iris-setosa\n4             4.6           3.1            1.5           0.2  Iris-setosa\n5             5.0           3.6            1.4           0.2  Iris-setosa",
      "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>SepalLengthCm</th>\n      <th>SepalWidthCm</th>\n      <th>PetalLengthCm</th>\n      <th>PetalWidthCm</th>\n      <th>Species</th>\n    </tr>\n    <tr>\n      <th>Id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>5.1</td>\n      <td>3.5</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>Iris-setosa</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.9</td>\n      <td>3.0</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>Iris-setosa</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.7</td>\n      <td>3.2</td>\n      <td>1.3</td>\n      <td>0.2</td>\n      <td>Iris-setosa</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4.6</td>\n      <td>3.1</td>\n      <td>1.5</td>\n      <td>0.2</td>\n      <td>Iris-setosa</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5.0</td>\n      <td>3.6</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>Iris-setosa</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 2221
    }
   ],
   "source": [
    "# Importing Iris Data\n",
    "\n",
    "# Note: Used different csv file (couldn't read from the one provided in class...)\n",
    "\n",
    "# Used this one instead : https://www.kaggle.com/uciml/iris?select=Iris.csv\n",
    "\n",
    "csv_path = ('/Users/antonioelias/Desktop/MOOCs/Python Coding Bootcamp/Data Science Libs/iris.csv')\n",
    "\n",
    "iris_data = pd.read_csv(csv_path, delimiter=',', index_col='Id')\n",
    "iris_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2222,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 150 entries, 1 to 150\nData columns (total 5 columns):\n #   Column         Non-Null Count  Dtype  \n---  ------         --------------  -----  \n 0   SepalLengthCm  150 non-null    float64\n 1   SepalWidthCm   150 non-null    float64\n 2   PetalLengthCm  150 non-null    float64\n 3   PetalWidthCm   150 non-null    float64\n 4   Species        150 non-null    object \ndtypes: float64(4), object(1)\nmemory usage: 12.0+ KB\n"
    }
   ],
   "source": [
    "# Inspecting for NaN/NA and column dtypes \n",
    "\n",
    "iris_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2225,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Iris-setosa        50\nIris-virginica     50\nIris-versicolor    50\nName: Species, dtype: int64"
     },
     "metadata": {},
     "execution_count": 2225
    }
   ],
   "source": [
    "# Species \n",
    "\n",
    "iris_data.Species.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2228,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Sepal Scatter Plot \n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x=iris_data['SepalLengthCm'], y=iris_data['SepalWidthCm'], hue=iris_data['Species'])\n",
    "\n",
    "plt.title('Scatter Plot: Sepal measurements by Species')\n",
    "plt.xlabel('Sepal Length (cm)')\n",
    "plt.ylabel('Sepal Width (cm)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2230,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x432 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Petal Scatter Plot \n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x=iris_data['PetalLengthCm'], y=iris_data['PetalWidthCm'], hue=iris_data['Species'])\n",
    "\n",
    "plt.title('Scatter Plot: Petal measurements by Species')\n",
    "plt.xlabel('Petal Length (cm)')\n",
    "plt.ylabel('Petal Width (cm)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
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