Day 30 - 50 Days of Data Analysis .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4561a571",
   "metadata": {},
   "source": [
    "## 1\n",
    "\n",
    "#### Import the furniture_data data file using pandas. Check the length of the DataFrame. Find the sum of duplicates in the \"Product\" column. Using the pandas pivot_table() function, return a table that shows how many times each product appears in the \"Product\" column. This table will reveal which products are duplicated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e90f012c",
   "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>Product</th>\n",
       "      <th>Sale Price</th>\n",
       "      <th>Cost per Product</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Total Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sofa</td>\n",
       "      <td>800</td>\n",
       "      <td>600</td>\n",
       "      <td>32</td>\n",
       "      <td>25600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chair</td>\n",
       "      <td>300</td>\n",
       "      <td>200</td>\n",
       "      <td>12</td>\n",
       "      <td>3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Table</td>\n",
       "      <td>500</td>\n",
       "      <td>300</td>\n",
       "      <td>41</td>\n",
       "      <td>20500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bed</td>\n",
       "      <td>1000</td>\n",
       "      <td>700</td>\n",
       "      <td>23</td>\n",
       "      <td>23000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wardrobe</td>\n",
       "      <td>800</td>\n",
       "      <td>600</td>\n",
       "      <td>19</td>\n",
       "      <td>15200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Product  Sale Price  Cost per Product  Quantity  Total Revenue\n",
       "0      Sofa         800               600        32          25600\n",
       "1     Chair         300               200        12           3600\n",
       "2     Table         500               300        41          20500\n",
       "3       Bed        1000               700        23          23000\n",
       "4  Wardrobe         800               600        19          15200"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "\n",
    "df = pd.read_csv(\"furniture_data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "55b4226a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding length of the DataFrame\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1fc2091d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Product.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "43895d21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product\n",
      "Bed             1\n",
      "Bookshelf       2\n",
      "Chair           1\n",
      "Coffee Table    2\n",
      "Dresser         2\n",
      "Ottoman         2\n",
      "Rug             2\n",
      "Sofa            1\n",
      "Table           1\n",
      "Wardrobe        2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Counting duplicates in the product column\n",
    "df_duplicates = df.pivot_table(index = ['Product'], aggfunc ='size')\n",
    "print(df_duplicates)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71e6a162",
   "metadata": {},
   "source": [
    "## 2. \n",
    "\n",
    "#### Use Seaborn regplot() to fit a linear regression model and visualize the relationship between the price of each item and total revenue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "24c1f1c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 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 = plt.figure(figsize=(15, 10))\n",
    "sns.regplot(x=df[\"Sale Price\"],\n",
    "            y=df[\"Total Revenue\"],\n",
    "            data=df)\n",
    "\n",
    "plt.title(\"Relationship Between Sale Price and Total Revenue\", fontsize= 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aefb45d",
   "metadata": {},
   "source": [
    "## 3.\n",
    "\n",
    "#### What is the difference in revenue between wardrobes and beds?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0f269fcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2600\n"
     ]
    }
   ],
   "source": [
    "# Grouping data by product and summing the total revenue column\n",
    "df_group = df.groupby(\"Product\")[\"Total Revenue\"].sum()\n",
    "\n",
    "# Calculating the difference\n",
    "wardrobes_less_bed_revenue = df_group.Wardrobe -  df_group.Bed \n",
    "print(wardrobes_less_bed_revenue)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71ad56bf",
   "metadata": {},
   "source": [
    "Another way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ba7a5816",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2600\n"
     ]
    }
   ],
   "source": [
    "# using loc to locate wardrobe and bed\n",
    "wardrobes_revenue = df.loc[df[\"Product\"] == \"Wardrobe\",\"Total Revenue\"].sum()  \n",
    "beds_revenue = df.loc[df[\"Product\"] == \"Bed\",\"Total Revenue\"].sum()\n",
    "print(wardrobes_revenue - beds_revenue)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "729e8bbc",
   "metadata": {},
   "source": [
    "## 4. \n",
    "\n",
    "#### Calculate the \"Total_Cost\" column and insert it in the DataFrame between the \"Quantity\" and \"Total Revenue.\". Use the pandas insert() function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dc63dd02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product</th>\n",
       "      <th>Sale Price</th>\n",
       "      <th>Cost per Product</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Total_Costs</th>\n",
       "      <th>Total Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sofa</td>\n",
       "      <td>800</td>\n",
       "      <td>600</td>\n",
       "      <td>32</td>\n",
       "      <td>19200</td>\n",
       "      <td>25600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chair</td>\n",
       "      <td>300</td>\n",
       "      <td>200</td>\n",
       "      <td>12</td>\n",
       "      <td>2400</td>\n",
       "      <td>3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Table</td>\n",
       "      <td>500</td>\n",
       "      <td>300</td>\n",
       "      <td>41</td>\n",
       "      <td>12300</td>\n",
       "      <td>20500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bed</td>\n",
       "      <td>1000</td>\n",
       "      <td>700</td>\n",
       "      <td>23</td>\n",
       "      <td>16100</td>\n",
       "      <td>23000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wardrobe</td>\n",
       "      <td>800</td>\n",
       "      <td>600</td>\n",
       "      <td>19</td>\n",
       "      <td>11400</td>\n",
       "      <td>15200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Product  Sale Price  Cost per Product  Quantity  Total_Costs   \n",
       "0      Sofa         800               600        32        19200  \\\n",
       "1     Chair         300               200        12         2400   \n",
       "2     Table         500               300        41        12300   \n",
       "3       Bed        1000               700        23        16100   \n",
       "4  Wardrobe         800               600        19        11400   \n",
       "\n",
       "   Total Revenue  \n",
       "0          25600  \n",
       "1           3600  \n",
       "2          20500  \n",
       "3          23000  \n",
       "4          15200  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the total costs\n",
    "Total_Costs = df[\"Cost per Product\"] * df[\"Quantity\"]\n",
    "\n",
    "# Using insert to insert column on index 4\n",
    "df.insert(4, 'Total_Costs', Total_Costs)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54879314",
   "metadata": {},
   "source": [
    "## 5.\n",
    "\n",
    "#### Which product has the least profit margin?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5c9761c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Revenue</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Profit_Margin</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dresser</th>\n",
       "      <td>53400</td>\n",
       "      <td>8900</td>\n",
       "      <td>16.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Total Revenue  Profit  Profit_Margin\n",
       "Product                                      \n",
       "Dresser          53400    8900      16.666667"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the profit for each product\n",
    "df[\"Profit\"] = df[\"Total Revenue\"] - df[\"Total_Costs\"]\n",
    "\n",
    "# Grouping data by product and summing revenue and profit\n",
    "df_group = df.groupby(\"Product\")[[\"Total Revenue\",\"Profit\"]].sum()\n",
    "\n",
    "# Calculating profit margin for each product\n",
    "df_group[\"Profit_Margin\"] = (df_group[\"Profit\"]/df_group[\"Total Revenue\"])*100\n",
    "\n",
    "# Sorting data by profit margin and returning the first product\n",
    "df_group.sort_values(by=\"Profit_Margin\", ascending=True).head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f3c1f95",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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