Day 21 - 50 Days of Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "06c42c1f",
   "metadata": {},
   "source": [
    "## 1. \n",
    "\n",
    "#### import the JSON file using Pandas. View the last 5 rows of the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4393d0af",
   "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>products</th>\n",
       "      <th>sales</th>\n",
       "      <th>costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-11-07</td>\n",
       "      <td>cars</td>\n",
       "      <td>15900</td>\n",
       "      <td>10910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-12-06</td>\n",
       "      <td>boats</td>\n",
       "      <td>12087</td>\n",
       "      <td>7087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-12-09</td>\n",
       "      <td>cars</td>\n",
       "      <td>56897</td>\n",
       "      <td>40447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-11-10</td>\n",
       "      <td>cars</td>\n",
       "      <td>11879</td>\n",
       "      <td>5879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021-12-06</td>\n",
       "      <td>houses</td>\n",
       "      <td>19345</td>\n",
       "      <td>13451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date products  sales  costs\n",
       "3 2021-11-07     cars  15900  10910\n",
       "4 2021-12-06    boats  12087   7087\n",
       "5 2021-12-09     cars  56897  40447\n",
       "6 2021-11-10     cars  11879   5879\n",
       "7 2021-12-06   houses  19345  13451"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_json(\"asset_data_analysis.json\", orient= \"split\")\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4050e7f",
   "metadata": {},
   "source": [
    "## 2. \n",
    "\n",
    "#### Using pandas, create a copy of the DataFrame and convert the date column into a pandas datetime format. Set the date as the index of the DataFrame. The date column should not be deleted when it is set as an index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c711d443",
   "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>products</th>\n",
       "      <th>sales</th>\n",
       "      <th>costs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-11-20</th>\n",
       "      <td>2021-11-20</td>\n",
       "      <td>cars</td>\n",
       "      <td>19234</td>\n",
       "      <td>12340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-12</th>\n",
       "      <td>2021-12-12</td>\n",
       "      <td>boats</td>\n",
       "      <td>87598</td>\n",
       "      <td>67568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-06</th>\n",
       "      <td>2021-12-06</td>\n",
       "      <td>houses</td>\n",
       "      <td>20989</td>\n",
       "      <td>11999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-07</th>\n",
       "      <td>2021-11-07</td>\n",
       "      <td>cars</td>\n",
       "      <td>15900</td>\n",
       "      <td>10910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-06</th>\n",
       "      <td>2021-12-06</td>\n",
       "      <td>boats</td>\n",
       "      <td>12087</td>\n",
       "      <td>7087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date products  sales  costs\n",
       "date                                        \n",
       "2021-11-20 2021-11-20     cars  19234  12340\n",
       "2021-12-12 2021-12-12    boats  87598  67568\n",
       "2021-12-06 2021-12-06   houses  20989  11999\n",
       "2021-11-07 2021-11-07     cars  15900  10910\n",
       "2021-12-06 2021-12-06    boats  12087   7087"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a copy of the DataFrame\n",
    "df_copy = df.copy()\n",
    "\n",
    "# Setting date as index\n",
    "df_copy['date'] = pd.to_datetime(df_copy['date'])\n",
    "df_copy = df.set_index('date', drop=False)\n",
    "df_copy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54353911",
   "metadata": {},
   "source": [
    "## 3. \n",
    "\n",
    "#### Using Matplotlib, create a line trend plot of sales and date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3698e73c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
    "\n",
    "# Grouping by the index(date)\n",
    "df_grouped = df_copy.groupby(df_copy.index).sum(numeric_only=True)\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.plot(df_grouped.index, df_grouped.sales)\n",
    "plt.xlabel('date')\n",
    "plt.xticks(rotation = 60)\n",
    "plt.ylabel('sales')\n",
    "plt.title(\"Sales Trend\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a47ce286",
   "metadata": {},
   "source": [
    "## 4. \n",
    "\n",
    "#### Using the original DataFrame (question 1), write another code to set a hierarchical index, with the date column as the outer index and the products column as the inner index. Save this to a new variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "35dc7ffc",
   "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></th>\n",
       "      <th>sales</th>\n",
       "      <th>costs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>products</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-11-20</th>\n",
       "      <th>cars</th>\n",
       "      <td>19234</td>\n",
       "      <td>12340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-12</th>\n",
       "      <th>boats</th>\n",
       "      <td>87598</td>\n",
       "      <td>67568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-06</th>\n",
       "      <th>houses</th>\n",
       "      <td>20989</td>\n",
       "      <td>11999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-07</th>\n",
       "      <th>cars</th>\n",
       "      <td>15900</td>\n",
       "      <td>10910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-06</th>\n",
       "      <th>boats</th>\n",
       "      <td>12087</td>\n",
       "      <td>7087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-09</th>\n",
       "      <th>cars</th>\n",
       "      <td>56897</td>\n",
       "      <td>40447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-10</th>\n",
       "      <th>cars</th>\n",
       "      <td>11879</td>\n",
       "      <td>5879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-06</th>\n",
       "      <th>houses</th>\n",
       "      <td>19345</td>\n",
       "      <td>13451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     sales  costs\n",
       "date       products              \n",
       "2021-11-20 cars      19234  12340\n",
       "2021-12-12 boats     87598  67568\n",
       "2021-12-06 houses    20989  11999\n",
       "2021-11-07 cars      15900  10910\n",
       "2021-12-06 boats     12087   7087\n",
       "2021-12-09 cars      56897  40447\n",
       "2021-11-10 cars      11879   5879\n",
       "2021-12-06 houses    19345  13451"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setting hierarchical index\n",
    "df2 = df.set_index(['date', \"products\"])\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f013f51d",
   "metadata": {},
   "source": [
    "## 5. \n",
    "\n",
    "#### One of the benefits of setting a multiIndexing is that it makes it easy to filter data. Using the hierarchical index you set in question 4, calculate the profit of \"Houses\" on the 12 -o6- 2021."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a3b11285",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Profit on houses on the 12th of June is  14884\n"
     ]
    }
   ],
   "source": [
    "# Using multi-indexing to filter sales\n",
    "sales_on_12 = df2.loc[( [\"12-6-2021\"], \"houses\"), \"sales\"].sum()\n",
    "\n",
    "# Using multi-indexing to filter costs\n",
    "costs_on_12 = df2.loc[( [\"12-6-2021\"], \"houses\"), \"costs\"].sum()\n",
    "\n",
    "# Calculating the difference between costs and sales\n",
    "profit_on_12 = sales_on_12 - costs_on_12\n",
    "print('Profit on houses on the 12th of June is ', profit_on_12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22fadefd",
   "metadata": {},
   "source": [
    "## 6\n",
    "\n",
    "#### Use the apply() function to apply a thousand separator to the sales and cost column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7801f21b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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></th>\n",
       "      <th>sales</th>\n",
       "      <th>costs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>products</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-11-20</th>\n",
       "      <th>cars</th>\n",
       "      <td>19,234</td>\n",
       "      <td>12,340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-12</th>\n",
       "      <th>boats</th>\n",
       "      <td>87,598</td>\n",
       "      <td>67,568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-06</th>\n",
       "      <th>houses</th>\n",
       "      <td>20,989</td>\n",
       "      <td>11,999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-07</th>\n",
       "      <th>cars</th>\n",
       "      <td>15,900</td>\n",
       "      <td>10,910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-06</th>\n",
       "      <th>boats</th>\n",
       "      <td>12,087</td>\n",
       "      <td>7,087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-09</th>\n",
       "      <th>cars</th>\n",
       "      <td>56,897</td>\n",
       "      <td>40,447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-10</th>\n",
       "      <th>cars</th>\n",
       "      <td>11,879</td>\n",
       "      <td>5,879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-06</th>\n",
       "      <th>houses</th>\n",
       "      <td>19,345</td>\n",
       "      <td>13,451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      sales   costs\n",
       "date       products                \n",
       "2021-11-20 cars      19,234  12,340\n",
       "2021-12-12 boats     87,598  67,568\n",
       "2021-12-06 houses    20,989  11,999\n",
       "2021-11-07 cars      15,900  10,910\n",
       "2021-12-06 boats     12,087   7,087\n",
       "2021-12-09 cars      56,897  40,447\n",
       "2021-11-10 cars      11,879   5,879\n",
       "2021-12-06 houses    19,345  13,451"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def thousand_separator(df, sales, costs):\n",
    "    df[sales] = df[sales].apply(lambda x: '{:,}'.format(x))\n",
    "    df[costs] = df[costs].apply(lambda x: '{:,}'.format(x))\n",
    "    return df2\n",
    "\n",
    "thousand_separator(df2, \"sales\", \"costs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e1dcea5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "products\n",
       "cars      4\n",
       "boats     2\n",
       "houses    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df2.groupby(['products']).size().sort_values(ascending=False)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ccd92bb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('cars', 4)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Returning the product that appears the most\n",
    "df3.index[0], df3[0]"
   ]
  }
 ],
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