Day 46 - 50 Days of Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d4dda9e0",
   "metadata": {},
   "source": [
    "## 1. \n",
    "\n",
    "#### What is the average monthly revenue, expenses, and profit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5901c49c",
   "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>Date</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Expenses</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31 01 2022</td>\n",
       "      <td>19000</td>\n",
       "      <td>5000</td>\n",
       "      <td>14000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28 02 2022</td>\n",
       "      <td>16000</td>\n",
       "      <td>5500</td>\n",
       "      <td>10500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31 03 2022</td>\n",
       "      <td>10000</td>\n",
       "      <td>7000</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30 04 2022</td>\n",
       "      <td>18000</td>\n",
       "      <td>8000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31 05 2022</td>\n",
       "      <td>25000</td>\n",
       "      <td>9000</td>\n",
       "      <td>16000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Revenue  Expenses  Profit\n",
       "0  31 01 2022    19000      5000   14000\n",
       "1  28 02 2022    16000      5500   10500\n",
       "2  31 03 2022    10000      7000    3000\n",
       "3  30 04 2022    18000      8000   10000\n",
       "4  31 05 2022    25000      9000   16000"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "df = pd.read_csv(\"financial_data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3b9ca7df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average monthly revenue: 35200.00\n",
      "Average monthly expenses: 15425.00\n",
      "Average monthly profit: 19775.00\n"
     ]
    }
   ],
   "source": [
    "# calculate the average monthly revenue, expenses, and profit\n",
    "avg_revenue = df['Revenue'].mean()\n",
    "avg_expenses = df['Expenses'].mean()\n",
    "avg_profit = df['Profit'].mean()\n",
    "\n",
    "print(f\"Average monthly revenue: {avg_revenue:.2f}\")\n",
    "print(f\"Average monthly expenses: {avg_expenses:.2f}\")\n",
    "print(f\"Average monthly profit: {avg_profit:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51e725ae",
   "metadata": {},
   "source": [
    "## 2. \n",
    "\n",
    "#### How does the profit margin vary over time? Plot a line plot of the changes in profit margin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5dfbd75b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the profit margin\n",
    "df['Profit Margin'] = (df['Profit'] / df['Revenue']) * 100\n",
    "\n",
    "# Converting date to datetime format\n",
    "df[\"Date\"] = pd.to_datetime(df[\"Date\"], dayfirst=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c0b486ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# plot the profit margin over time\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.plot(df[\"Date\"], df['Profit Margin'])\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Profit Margin (%)')\n",
    "plt.title('Profit Margin Over Time', fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9697d606",
   "metadata": {},
   "source": [
    "## 3.\n",
    "\n",
    "#### What is the difference between the revenue of the highest month and and that of the lowest months? Which month has the highest revenue and which month has the lowest revenue?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "41801bec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The difference in Revenue is 48000\n"
     ]
    }
   ],
   "source": [
    "highest_revenue = df.loc[df['Revenue'] == df['Revenue'].max(), 'Revenue'].values[0]\n",
    "lowest_revenue = df.loc[df['Revenue'] == df['Revenue'].min(), 'Revenue'].values[0]\n",
    "\n",
    "difference = highest_revenue - lowest_revenue\n",
    "print(f'The difference in Revenue is {difference}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f198a611",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The month with highest revenue is: August\n",
      "The month with highest revenue is: March\n"
     ]
    }
   ],
   "source": [
    "# Extract month from Date\n",
    "df[\"Month\"] = df[\"Date\"].dt.month_name()\n",
    "\n",
    "highest_month = df.loc[df['Revenue'] == df['Revenue'].max(), 'Month'].values[0]\n",
    "lowest_month = df.loc[df['Revenue'] == df['Revenue'].min(), 'Month'].values[0]\n",
    "print( f'The month with highest revenue is: {highest_month}')\n",
    "print(f'The month with highest revenue is: {lowest_month}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "657e209b",
   "metadata": {},
   "source": [
    "## 4. \n",
    "\n",
    "#### Which months had expenses above $10,000?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b701a78f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6          July\n",
       "7        August\n",
       "8     September\n",
       "9       October\n",
       "10     November\n",
       "11     December\n",
       "12      January\n",
       "13     February\n",
       "14        March\n",
       "15        April\n",
       "16          May\n",
       "17         June\n",
       "18         July\n",
       "19       August\n",
       "Name: Month, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month_expenses_above_10 = df.loc[df['Expenses'] > 10000, 'Month']\n",
    "month_expenses_above_10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4794df37",
   "metadata": {},
   "source": [
    "## 5.\n",
    "\n",
    "#### How much profit was made in the last quarter of 2022?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "650e1624",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2022Q1     45000\n",
       "2022Q2     65000\n",
       "2022Q3     97000\n",
       "2022Q4    112000\n",
       "2023Q1    126000\n",
       "2023Q2    150000\n",
       "2023Q3    109000\n",
       "Freq: Q-DEC, Name: Revenue, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group the data by quarters\n",
    "df2 = df.groupby(df[\"Date\"].dt.to_period('Q'))[\"Revenue\"].sum()\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a983f459",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Revenue in the last quarter of 2022 is 112000\n"
     ]
    }
   ],
   "source": [
    "print(f'Revenue in the last quarter of 2022 is {df2.loc[\"2022Q4\"]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4648f505",
   "metadata": {},
   "source": [
    "## 6.\n",
    "\n",
    "#### What was the percentage change in revenue from quarter to quarter?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b0fbd9fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage change in revenue from quarter to quarter:\n",
      "Date\n",
      "2022Q1         NaN\n",
      "2022Q2    0.444444\n",
      "2022Q3    0.492308\n",
      "2022Q4    0.154639\n",
      "2023Q1    0.125000\n",
      "2023Q2    0.190476\n",
      "2023Q3   -0.273333\n",
      "Freq: Q-DEC, Name: Revenue, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# calculate the percentage change in revenue from quarter to quarter\n",
    "quarterly_revenue_change = df2.pct_change()\n",
    "\n",
    "print(\"Percentage change in revenue from quarter to quarter:\")\n",
    "print(f'{quarterly_revenue_change:}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16919fde",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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