Day 29 - 50 Days of Data Analysis .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f55c7e8e",
   "metadata": {},
   "source": [
    "## 1.\t\n",
    "\n",
    "#### Import the car_service_data CSV file. Write a code to return all the column names in the DataFrame. Check the data types of the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "718421c3",
   "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>Service ID</th>\n",
       "      <th>Service Type</th>\n",
       "      <th>Service Cost</th>\n",
       "      <th>Service Revenue</th>\n",
       "      <th>Number of Customers</th>\n",
       "      <th>Advertising Cost</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Oil Change</td>\n",
       "      <td>$25</td>\n",
       "      <td>$50</td>\n",
       "      <td>10</td>\n",
       "      <td>$100</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Brake Repair</td>\n",
       "      <td>$200</td>\n",
       "      <td>$500</td>\n",
       "      <td>5</td>\n",
       "      <td>$50</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Tire Rotation</td>\n",
       "      <td>$30</td>\n",
       "      <td>$75</td>\n",
       "      <td>12</td>\n",
       "      <td>$150</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Transmission Repair</td>\n",
       "      <td>$1000</td>\n",
       "      <td>$2500</td>\n",
       "      <td>2</td>\n",
       "      <td>$200</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Battery Replacement</td>\n",
       "      <td>$150</td>\n",
       "      <td>$300</td>\n",
       "      <td>8</td>\n",
       "      <td>$75</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Service ID         Service Type Service Cost Service Revenue   \n",
       "0           1           Oil Change          $25             $50  \\\n",
       "1           2         Brake Repair         $200            $500   \n",
       "2           3        Tire Rotation          $30             $75   \n",
       "3           4  Transmission Repair        $1000           $2500   \n",
       "4           5  Battery Replacement         $150            $300   \n",
       "\n",
       "   Number of Customers Advertising Cost  Location  \n",
       "0                   10             $100     Urban  \n",
       "1                    5              $50  Suburban  \n",
       "2                   12             $150     Rural  \n",
       "3                    2             $200     Urban  \n",
       "4                    8              $75  Suburban  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"car_service_data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25285842",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    " code to return all the column names in the Dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f2b4e58b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Service ID', 'Service Type', 'Service Cost', 'Service Revenue',\n",
       "       'Number of Customers', 'Advertising Cost', 'Location'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ee708af9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Service ID              int64\n",
       "Service Type           object\n",
       "Service Cost           object\n",
       "Service Revenue        object\n",
       "Number of Customers     int64\n",
       "Advertising Cost       object\n",
       "Location               object\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a25b4f2",
   "metadata": {},
   "source": [
    "## 2.\t\n",
    "\n",
    "#### Which location has the highest service costs? Plot a bar plot using Seaborn to visualize the service costs by location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bea01fed",
   "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>Service ID</th>\n",
       "      <th>Service Type</th>\n",
       "      <th>Service Cost</th>\n",
       "      <th>Service Revenue</th>\n",
       "      <th>Number of Customers</th>\n",
       "      <th>Advertising Cost</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Oil Change</td>\n",
       "      <td>25</td>\n",
       "      <td>50</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Brake Repair</td>\n",
       "      <td>200</td>\n",
       "      <td>500</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Tire Rotation</td>\n",
       "      <td>30</td>\n",
       "      <td>75</td>\n",
       "      <td>12</td>\n",
       "      <td>150</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Transmission Repair</td>\n",
       "      <td>1000</td>\n",
       "      <td>2500</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Battery Replacement</td>\n",
       "      <td>150</td>\n",
       "      <td>300</td>\n",
       "      <td>8</td>\n",
       "      <td>75</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Service ID         Service Type  Service Cost  Service Revenue   \n",
       "0           1           Oil Change            25               50  \\\n",
       "1           2         Brake Repair           200              500   \n",
       "2           3        Tire Rotation            30               75   \n",
       "3           4  Transmission Repair          1000             2500   \n",
       "4           5  Battery Replacement           150              300   \n",
       "\n",
       "   Number of Customers  Advertising Cost  Location  \n",
       "0                   10               100     Urban  \n",
       "1                    5                50  Suburban  \n",
       "2                   12               150     Rural  \n",
       "3                    2               200     Urban  \n",
       "4                    8                75  Suburban  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a copy of the DataFrame\n",
    "df2 = df.copy()\n",
    "\n",
    "# Creating a function to clean the data\n",
    "def modify_df(df):\n",
    "    df[\"Service Revenue\"] = df[\"Service Revenue\"].str.replace(\"$\", \"\").astype(int)\n",
    "    df[\"Service Cost\"] = df[\"Service Cost\"].str.replace(\"$\", \"\").astype(int)\n",
    "    df[\"Advertising Cost\"] = df[\"Advertising Cost\"].str.replace(\"$\", \"\").astype(int)\n",
    "    return df\n",
    "\n",
    "modify_df(df2)\n",
    "\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f5520875",
   "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>Service ID</th>\n",
       "      <th>Service Type</th>\n",
       "      <th>Service Cost</th>\n",
       "      <th>Service Revenue</th>\n",
       "      <th>Number of Customers</th>\n",
       "      <th>Advertising Cost</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th></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>Urban</th>\n",
       "      <td>70</td>\n",
       "      <td>Oil ChangeTransmission RepairAC RechargeDetail...</td>\n",
       "      <td>2225</td>\n",
       "      <td>5350</td>\n",
       "      <td>42</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suburban</th>\n",
       "      <td>77</td>\n",
       "      <td>Brake RepairBattery ReplacementSuspension Repa...</td>\n",
       "      <td>2050</td>\n",
       "      <td>4950</td>\n",
       "      <td>67</td>\n",
       "      <td>625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rural</th>\n",
       "      <td>63</td>\n",
       "      <td>Tire RotationEngine DiagnosticRadiator FlushFu...</td>\n",
       "      <td>655</td>\n",
       "      <td>1650</td>\n",
       "      <td>72</td>\n",
       "      <td>575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Service ID                                       Service Type   \n",
       "Location                                                                  \n",
       "Urban             70  Oil ChangeTransmission RepairAC RechargeDetail...  \\\n",
       "Suburban          77  Brake RepairBattery ReplacementSuspension Repa...   \n",
       "Rural             63  Tire RotationEngine DiagnosticRadiator FlushFu...   \n",
       "\n",
       "          Service Cost  Service Revenue  Number of Customers  Advertising Cost  \n",
       "Location                                                                        \n",
       "Urban             2225             5350                   42               750  \n",
       "Suburban          2050             4950                   67               625  \n",
       "Rural              655             1650                   72               575  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by location\n",
    "group_by_location = df2.groupby(\"Location\").sum()\n",
    "\n",
    "# Sort data by location in ascending order\n",
    "group_by_location.sort_values(by=\"Service Cost\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1bda775d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "sns.barplot(x=group_by_location.index, \n",
    "            y=group_by_location[\"Service Cost\"], \n",
    "            data=group_by_location)\n",
    "ax.set_title('Costs Per Location', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc05ca38",
   "metadata": {},
   "source": [
    "## 3.\t\n",
    "\n",
    "#### Which location has the highest number of customers? Using pandas and Matplotlib, plot a line plot of location and number of customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0e33fb01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grouping the data by location\n",
    "df_group = df2.groupby(\"Location\")[\"Number of Customers\"].sum()\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.pie(df_group, explode=(0.1, 0, 0), \n",
    "       labels=df_group.index, \n",
    "       autopct='%1.1f%%',\n",
    "       shadow=True, \n",
    "       )\n",
    "\n",
    "plt.title(\"Customers Per Location\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7983a52",
   "metadata": {},
   "source": [
    "## 4.\t\n",
    "\n",
    "#### Using the pandas groupby() method, group the data by location and find the average profit margin per location. Format your output to 1 decimal place and present it as a percentage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b339f9e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location\n",
       "Rural       60.4%\n",
       "Suburban    55.8%\n",
       "Urban       56.5%\n",
       "Name: Profit Margin, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the profit\n",
    "df2['Profit'] = df2['Service Revenue'] - df2['Service Cost']\n",
    "\n",
    "# Calculate the profit margin\n",
    "df2['Profit Margin'] = df2['Profit'] / df2['Service Revenue']*100\n",
    "                                        \n",
    "# Grouping data by products and calculating mean\n",
    "df2.groupby('Location')['Profit Margin'].mean().map('{:,.1f}%'.format)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f716b491",
   "metadata": {},
   "source": [
    "## 5.\t\n",
    "\n",
    "#### What is the advertising cost of each region as a percentage of total revenue? Using the rank() method, rank each region by the cost of advertising as a percentage of revenue in descending order( add a rank column to grouped data. Which region has the lowest rank?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8bb66448",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location\n",
       "Rural       34.8%\n",
       "Suburban    12.6%\n",
       "Urban       14.0%\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using groupby to group by location and sum advertising and revenue columns\n",
    "df_group2 = df2.groupby([\"Location\" ])[['Advertising Cost','Service Revenue']].sum()\n",
    "\n",
    "# Calculating the advertising cost as  percentage of revenue\n",
    "(df_group2[\"Advertising Cost\"]/df_group2[\"Service Revenue\"] * 100).map('{:,.1f}%'.format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "30a966e2",
   "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>Advertising Cost</th>\n",
       "      <th>Service Revenue</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rural</th>\n",
       "      <td>575</td>\n",
       "      <td>1650</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suburban</th>\n",
       "      <td>625</td>\n",
       "      <td>4950</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urban</th>\n",
       "      <td>750</td>\n",
       "      <td>5350</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Advertising Cost  Service Revenue  rank\n",
       "Location                                         \n",
       "Rural                  575             1650   1.0\n",
       "Suburban               625             4950   3.0\n",
       "Urban                  750             5350   2.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group2[\"rank\"] = (df_group2[\"Advertising Cost\"]/df_group2\n",
    "                     [\"Service Revenue\"]).rank(ascending=False)\n",
    "df_group2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eff9456a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e748bc8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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   "name": "python3"
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  "language_info": {
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