Day 24 - 50 Days of Data Analysis .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bb215d09",
   "metadata": {},
   "source": [
    "## 1. \n",
    "\n",
    "#### Using pandas import the Excel file above. Write a code to view the first 5 rows of your DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "da98e530",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11-20-2021</td>\n",
       "      <td>cars</td>\n",
       "      <td>19234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12-12-2021</td>\n",
       "      <td>boats</td>\n",
       "      <td>87598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11-07-2021</td>\n",
       "      <td>cars</td>\n",
       "      <td>15900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12-06-2021</td>\n",
       "      <td>boats</td>\n",
       "      <td>12087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12-09-2021</td>\n",
       "      <td>cars</td>\n",
       "      <td>56897</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date products  sales\n",
       "0  11-20-2021     cars  19234\n",
       "1  12-12-2021    boats  87598\n",
       "2  11-07-2021     cars  15900\n",
       "3  12-06-2021    boats  12087\n",
       "4  12-09-2021     cars  56897"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_excel(\"Asset_sales_data.xlsx\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72f74913",
   "metadata": {},
   "source": [
    "## 2.\n",
    "\n",
    "#### Write another code that will return the data types of all the columns in the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "10746c99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date        object\n",
      "products    object\n",
      "sales        int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e56c44b",
   "metadata": {},
   "source": [
    "## 3. \n",
    "\n",
    "#### Using pandas, what month had the highest value of sales?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "85ecb89b",
   "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>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11-20-2021</td>\n",
       "      <td>cars</td>\n",
       "      <td>19234</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12-12-2021</td>\n",
       "      <td>boats</td>\n",
       "      <td>87598</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11-07-2021</td>\n",
       "      <td>cars</td>\n",
       "      <td>15900</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12-06-2021</td>\n",
       "      <td>boats</td>\n",
       "      <td>12087</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12-09-2021</td>\n",
       "      <td>cars</td>\n",
       "      <td>56897</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date products  sales  month\n",
       "0  11-20-2021     cars  19234     11\n",
       "1  12-12-2021    boats  87598     12\n",
       "2  11-07-2021     cars  15900     11\n",
       "3  12-06-2021    boats  12087     12\n",
       "4  12-09-2021     cars  56897     12"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert date to pandas datetime object.\n",
    "df[\"month\"] = pd.to_datetime(df['date']).dt.month\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c4882984",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sales    12\n",
       "dtype: int32"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use  groupy and idxmax to find month with max sales\n",
    "max_sales_month = df.groupby(df['month']).sum(numeric_only=True).idxmax()\n",
    "max_sales_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "61cbe0c3",
   "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>sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>111736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>565688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sales\n",
       "month        \n",
       "11     111736\n",
       "12     565688"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use  groupy and idxmax to find column with max sales\n",
    "sales_per_month = df.groupby(df['month']).sum(numeric_only=True)\n",
    "sales_per_month"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc31349c",
   "metadata": {},
   "source": [
    "## 4.\n",
    "#### Write a code to return the value of sales between 11-20-2021 to 12-06-2021. Create a Dataframe and return the total sales value for this period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "31f53774",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          date products  sales  month\n",
      "0   11-20-2021     cars  19234     11\n",
      "3   12-06-2021    boats  12087     12\n",
      "5   12-06-2021   houses  19345     12\n",
      "6   11-20-2021     cars  19234     11\n",
      "8   12-06-2021   houses  20989     12\n",
      "10  12-06-2021    boats  12087     12\n",
      "12  11-25-2021     cars  22234     11\n",
      "14  12-06-2021    boats  14987     12\n",
      "16  11-20-2021     cars  19234     11\n"
     ]
    }
   ],
   "source": [
    "# Using loc to fiter DataFrame by Date\n",
    "df2 = df.loc[(df['date'] >= '11-20-2021') & (df['date'] <= '12-06-2021')]\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e547a11b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "159431"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[\"sales\"].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "707974bd",
   "metadata": {},
   "source": [
    "## 5. \n",
    "\n",
    "#### Using Matplotlib, create a pie chart of the products and their sales values as percentages. Your chart should have labels and a title."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3f30d47d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "prod_sales = df.groupby(['products'])['sales'].sum()\n",
    "\n",
    "explode = (0, 0.1, 0)  \n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.pie(prod_sales, \n",
    "        explode=explode, \n",
    "        labels=prod_sales.index, \n",
    "        autopct='%1.1f%%', \n",
    "        shadow=True)\n",
    "\n",
    "plt.title(\"Sales Pie Chart\", \n",
    "          fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aa119c7",
   "metadata": {},
   "source": [
    "## 6.\n",
    "#### Use pandas to create a pivot table and calculate the sum of sales column grouped by the products column. Use pandas and Matplotlib to plot this on a bar plot. Your plot size must be width = 12, height =10. Your plot title will be \"Total Sales Per Product\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "22518d42",
   "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>sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>products</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>boats</th>\n",
       "      <td>301955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cars</th>\n",
       "      <td>290427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>houses</th>\n",
       "      <td>85042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           sales\n",
       "products        \n",
       "boats     301955\n",
       "cars      290427\n",
       "houses     85042"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_per_product = df.pivot_table(values='sales', \n",
    "                                   index='products', \n",
    "                                   aggfunc='sum')\n",
    "sales_per_product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e217b986",
   "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": [
    "sales_per_product.plot(kind=\"bar\", figsize=(12, 8))\n",
    "plt.title(\"Total Sales Per Product\", fontsize = 20)\n",
    "plt.xticks(rotation = 0)\n",
    "plt.ylabel(\"sales\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc9d79ba",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}