data-science-intern-question1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "18c477fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing all the tools required\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#making sure that plots are shown in the notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f101e8ad",
   "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>order_id</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_amount</th>\n",
       "      <th>total_items</th>\n",
       "      <th>payment_method</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>746</td>\n",
       "      <td>224</td>\n",
       "      <td>2</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-13 12:36:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>92</td>\n",
       "      <td>925</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-03 17:38:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>44</td>\n",
       "      <td>861</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-14 4:23:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>935</td>\n",
       "      <td>156</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-26 12:43:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>883</td>\n",
       "      <td>156</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-01 4:35:11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  shop_id  user_id  order_amount  total_items payment_method  \\\n",
       "0         1       53      746           224            2           cash   \n",
       "1         2       92      925            90            1           cash   \n",
       "2         3       44      861           144            1           cash   \n",
       "3         4       18      935           156            1    credit_card   \n",
       "4         5       18      883           156            1    credit_card   \n",
       "\n",
       "            created_at  \n",
       "0  2017-03-13 12:36:56  \n",
       "1  2017-03-03 17:38:52  \n",
       "2   2017-03-14 4:23:56  \n",
       "3  2017-03-26 12:43:37  \n",
       "4   2017-03-01 4:35:11  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading the file\n",
    "df=pd.read_csv(\"data/2019 Winter Data Science Intern Challenge Data Set - Sheet1.csv\")\n",
    "#checking the first five lines\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "889786e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5000 entries, 0 to 4999\n",
      "Data columns (total 7 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   order_id        5000 non-null   int64 \n",
      " 1   shop_id         5000 non-null   int64 \n",
      " 2   user_id         5000 non-null   int64 \n",
      " 3   order_amount    5000 non-null   int64 \n",
      " 4   total_items     5000 non-null   int64 \n",
      " 5   payment_method  5000 non-null   object\n",
      " 6   created_at      5000 non-null   object\n",
      "dtypes: int64(5), object(2)\n",
      "memory usage: 273.6+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "order_id          0\n",
       "shop_id           0\n",
       "user_id           0\n",
       "order_amount      0\n",
       "total_items       0\n",
       "payment_method    0\n",
       "created_at        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding information about the dataframe\n",
    "df.columns\n",
    "df.info()\n",
    "#finding out if there are any missing columns\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dcbd6e8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Option 1: 284.00\n",
      "Option 2: 3145.13\n"
     ]
    }
   ],
   "source": [
    "#finding out how the AOV of $3145.13\n",
    "print(f\"Option 1: {df.order_amount.median():.2f}\")\n",
    "print(f\"Option 2: {df.order_amount.mean():.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e5890e7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the average: 3145.13\n"
     ]
    }
   ],
   "source": [
    "#So, we calculated mean and found the AOV of $3145.13\n",
    "#This is how the mean function calculates.\n",
    "avg=df.order_amount.sum()/df.order_amount.count()\n",
    "print(f\"This is the average: {avg:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "565c4c77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the correct average: 357.92\n"
     ]
    }
   ],
   "source": [
    "#This is how we should calculate the average\n",
    "correct_avg=df.order_amount.sum()/df.total_items.sum()\n",
    "print(f\"This is the correct average: {correct_avg:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c164e3a5",
   "metadata": {},
   "source": [
    "Hence, we found the average cost of shoes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "95598976",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f2d3ac7a30>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Lets plot a bar graph to represent the difference\n",
    "frame=pd.DataFrame({'Wrong Average':avg,\n",
    "                    'Correct Average':correct_avg},index=[0])\n",
    "frame.T.plot.bar()\n",
    "plt.xticks(rotation=0)\n",
    "plt.title(\"Comparison between the wrong and correct average\")\n",
    "plt.ylabel(\"Price\")\n",
    "plt.xlabel(\"Label\")\n",
    "plt.legend(['Value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "464daa18",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's find the maximum priced sneaker\n",
    "df[\"Each\"]=df[\"order_amount\"]/df[\"total_items\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c5c663f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    153.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Calc.mode()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d430d12",
   "metadata": {},
   "source": [
    "We can assume this as each store sells only one type of sneaker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5b0d3e8",
   "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}