Exploratory data analysis(EDA) .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory data analysis(EDA) With Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  BranchName  Week  DayWeek  Day  Month  Hour Transaction_Type  Units  Amount\n",
      "0    MyStore     1        3    2      1     9             Card      3   54.00\n",
      "1    MyStore     1        3    2      1    10             Cash      7  -17.80\n",
      "2    MyStore     1        3    2      1    10             Card      7   41.99\n",
      "3    MyStore     1        3    2      1    11             Card     20  412.50\n",
      "4    MyStore     1        3    2      1    12             Cash      1  -18.00\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('data.csv')\n",
    "df['Amount'] = df['Amount'].str.replace('$','').str.replace(',','')\n",
    "df['Amount'] = pd.to_numeric(df['Amount'])\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
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       "    }\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>Week</th>\n",
       "      <th>DayWeek</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Transaction_Type</th>\n",
       "      <th>Units</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>Card</td>\n",
       "      <td>3</td>\n",
       "      <td>54.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>Cash</td>\n",
       "      <td>7</td>\n",
       "      <td>-17.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>Card</td>\n",
       "      <td>7</td>\n",
       "      <td>41.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>Card</td>\n",
       "      <td>20</td>\n",
       "      <td>412.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Cash</td>\n",
       "      <td>1</td>\n",
       "      <td>-18.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Week  DayWeek  Day  Month  Hour Transaction_Type  Units  Amount\n",
       "0     1        3    2      1     9             Card      3   54.00\n",
       "1     1        3    2      1    10             Cash      7  -17.80\n",
       "2     1        3    2      1    10             Card      7   41.99\n",
       "3     1        3    2      1    11             Card     20  412.50\n",
       "4     1        3    2      1    12             Cash      1  -18.00"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove unwanted \n",
    "df.drop('BranchName',axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4100, 8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Week</th>\n",
       "      <th>DayWeek</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Units</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4100.000000</td>\n",
       "      <td>4100.000000</td>\n",
       "      <td>4100.000000</td>\n",
       "      <td>4100.000000</td>\n",
       "      <td>4100.000000</td>\n",
       "      <td>4100.000000</td>\n",
       "      <td>4100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>34.017805</td>\n",
       "      <td>4.183902</td>\n",
       "      <td>15.812195</td>\n",
       "      <td>8.231463</td>\n",
       "      <td>12.949024</td>\n",
       "      <td>12.779512</td>\n",
       "      <td>35.237046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.714289</td>\n",
       "      <td>1.967864</td>\n",
       "      <td>8.810817</td>\n",
       "      <td>3.396586</td>\n",
       "      <td>2.631853</td>\n",
       "      <td>17.854968</td>\n",
       "      <td>183.538724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1041.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>-2.385000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>99.512500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>53.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>274.000000</td>\n",
       "      <td>1487.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Week      DayWeek          Day        Month         Hour  \\\n",
       "count  4100.000000  4100.000000  4100.000000  4100.000000  4100.000000   \n",
       "mean     34.017805     4.183902    15.812195     8.231463    12.949024   \n",
       "std      14.714289     1.967864     8.810817     3.396586     2.631853   \n",
       "min       1.000000     1.000000     1.000000     1.000000     8.000000   \n",
       "25%      29.000000     3.000000     8.000000     7.000000    11.000000   \n",
       "50%      37.000000     4.000000    16.000000     9.000000    13.000000   \n",
       "75%      45.000000     6.000000    23.000000    11.000000    15.000000   \n",
       "max      53.000000     7.000000    31.000000    12.000000    19.000000   \n",
       "\n",
       "             Units       Amount  \n",
       "count  4100.000000  4100.000000  \n",
       "mean     12.779512    35.237046  \n",
       "std      17.854968   183.538724  \n",
       "min       1.000000 -1041.800000  \n",
       "25%       3.000000   -47.000000  \n",
       "50%       8.000000    -2.385000  \n",
       "75%      16.000000    99.512500  \n",
       "max     274.000000  1487.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4100"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Units'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4a17f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_bins = 10\n",
    "plt.hist(df['Amount'], num_bins, normed=1, facecolor='blue', alpha=0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data varies from `-1000` to `1000` sums up how much the amount varies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sales by Month, Day and Hour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### By Month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Month\n",
      "1     604\n",
      "7     606\n",
      "8     572\n",
      "9     580\n",
      "10    581\n",
      "11    587\n",
      "12    570\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Total Sales')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x49e4ff0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_by_month = df.groupby('Month').size()\n",
    "print(sales_by_month)\n",
    "#Plotting the Graph\n",
    "plot_by_month = sales_by_month.plot(title='Monthly Sales',xticks=(1,2,3,4,5,6,7,8,9,10,11,12))\n",
    "plot_by_month.set_xlabel('Months')\n",
    "plot_by_month.set_ylabel('Total Sales')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### By Day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Total Sales')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4b53630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_by_day = df.groupby('Day').size()\n",
    "plot_by_day = sales_by_day.plot(title='Daily Sales',xticks=(range(1,31)),rot=55)\n",
    "plot_by_day.set_xlabel('Day')\n",
    "plot_by_day.set_ylabel('Total Sales')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As such, the 18th day was the best day, that day 151 units were sold and the last sale of the month was drastically reduced."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### By Hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Total Sales')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9srjjsqNIV10nOURamvHdj05hY1UN97y2OupwpIMSmizMbBRwLvDb8L4BpwKPhA+5D7gwvH1BeJ9w/2mmE/GTgrvzrYcXsnHXfn515QwGq7aTtOBD4wdzevEw7nz5XbbvORB1ONIBiT6yuAP4DtAY3h8M7HL3+vD+emBkeHsksA4g3F8VPl56uLvmrOK5pVu46aPFzBwzsO0nSEr7t49OpqaugdufeyfqUKQDEpYszOw8YKu7l8Vubuah3o59se1ebWalZla6bdu2OEQqXfHGqh3c+swKzj2ikM8dPzbqcCQJjC/ox6dmjWH2m2tZuaU66nCknRJ5ZHE8cL6ZrQEeJOh+ugMYYGZNM8dHAU2nRqwHigDC/f2BD5yU7e53u3uJu5cUFBQculu60dbdNVw7ez5jBvXhlouPUPkOabevnTaRvtkZ/PSpZW0/WHqEhCULd/83dx/l7mOBy4EX3f1K4CXgE+HDrgIeC28/Ht4n3P+ia+WUHqu+oZFrZ8+nuqaOX39qBnk5mVGHJElkUN8srj11Ai+t2MZrK7dHHY60QxTzLG4ArjezcoIxiXvC7fcAg8Pt1wM3RhCbtNNtz77D3NU7+elFRzB5eH7U4UgSuurDYykalMtPnlxKgybq9Xjdkizc/WV3Py+8vcrdj3X3Ce5+ibsfCLfXhPcnhPtXdUds0nHPLd3Cb155lyuOG83HZ4yKOhxJUtkZ6dxw9mSWb67mkbJ1UYcjbdAMbumQtTv2cf1DC5g2Mp8fnDcl6nAkyZ17RCEzRg/gtmffYe+B+rafIJFRspB2q6lr4Mv3l2HAnVfO1Op20mVmxvfOm8K26gPcNUedCT2ZkoW024+eWMKSjbu5/bKjKBrUJ+pwpJeYMXog5x1ZyN1z3mVzVU3U4UgLlCykXR4pW8/sN9dxzSnjOa14WNThSC9zw9mTaWyE255dEXUo0gIlC2nTsk27+d5fF/GhcYO5/gwVCJT4KxrUh88dP5Y/z1vPko1VUYcjzVCykFbtrgkKBObnZPLLTx5NRrr+ZCQxrvnIBAbkZnLzk8vQFKueR598aZG7852H32btzn38zxUzKMhTgUBJnP65mXz99MN5/d0dvLh8a9ThyCGULKRF97y2mr8v2cwNZ0/S+tnSLa44bjTjCvpy81PLqGtobPsJ0m2ULKRZpWt2csvTyzlzyjD+9cRxUYcjKSIzPY2bzilm1ba9zH5zbdThSAwlC/mA7XsO8JUH5jFyYC63XjJdBQKlW51WPJQPjRvMHc+vZHdNXdThSEjJQt6nodG57sH57NpXx51XzqR/rgoESvcyM757bjGV+2r51UvlUYcjISULeZ87nn+Hf5Tv4N8vnMaUESoQKNGYNrI/Hz96FP/32hrW7dwXdTiCkoXEeGn5Vv77xXIuLRnFpSVFUYcjKe7bZ00iLQ3+4xlN1OsJlCwEgHU79/H1Py2guDCfH18wLepwRBjeP4erTxzHEws3Mn9tZdThpDwlC8Hd+dqD82lsdO68coYKBEqP8cWTx1OQl83Pnl4edSgpT8lC2LBrP/PX7uK60ycydkjfqMMROahvdgZXHDuat9bsZF+tSphHSclCKKsIDvFnjRsccSQiHzR1RD7usHxzddShpDQlC6F0TSV9s9KZPDwv6lBEPqC4MDgrb9mm3RFHktqULITSikqOHj1QRQKlRxo1MJe8nAwli4jp2yHFVdfUsWLzbmaMGRh1KCLNMjOKh+ezbJO6oaKkZJHiFqzbRaNDiZKF9GCTC/NYvmk3jY0qXR4VJYsUV7qmkjSDo0cPiDoUkRYVF+azt7aB9ZX7ow4lZSlZpLiyikomDc8nL0c1oKTnahrkXqpxi8goWaSw+oZG5q+tVBeU9HiThuWRZjojKkpKFilsxZZq9tY2MFPJQnq43Kx0xg7pq2QRISWLFNY0GU/JQpJBcWE+yzYrWURFySKFla6pZFh+NqMG5kYdikibphTms27nfqq1IFIklCxSWFlFJSVjBmklPEkKTRUGVPYjGkoWKWpT1X427NqvLihJGk1nRC3XuEUklCxSlMYrJNkU9s+hf24mSzWTOxJKFimqdE0luZnpWjpVkoaZUVyYpzOiIqJkkaLKKiqZXtSfTBUPlCRSXJjPis3VNKjsR7fr0DeFBdq1Oo6Z5ZjZm2a20MyWmNmPwu2HmdlcM1tpZn8ys6xwe3Z4vzzcP7ajb0baZ++BepZu2k3JmEFRhyLSIcWF+eyva6Bix96oQ0k5bSYLM/u9meWbWR9gCbDazK5vR9sHgFPdfTpwFHC2mc0Cfg7c7u4TgUrgC+HjvwBUuvsE4PbwcZIAC9ftoqHRmTlW4xWSXKYcXNtC4xbdrT1HFke4+27gQuBZYBTw2bae5IE94d3M8OLAqcAj4fb7wnYBLgjvE+4/zXROZ0I0DW7PKFKykOQyYWg/0tNM4xYRaE+yyDKzDIIv87+6ey3Q2J7GzSzdzBYAW4HngHeBXe7etJjuemBkeHsksA4g3F8FfGCdTzO72sxKzax027Zt7QlDDlFaUcnhw/rRv4+KB0pyyclMZ5zKfkSiPcnit8BaYCDwipmNBva0/pSAuze4+1EERyPHAsXNPSy8bu4o4gOjWO5+t7uXuHtJQUFBe8KQGI2Nzry1lczUeIUkqeLCfE3Mi0CbycLdb3f3Ee5+prs7wdHAqR15EXffBbwMzAIGhEcqECSRjeHt9UARQLi/P7CzI68jbXtnazXVNfWqNCtJq7gwnw279lO1T2U/ulN7BrgLzOwuM/tbuGkycEU7nzcgvJ0LnA4sA14CPhE+7CrgsfD24+F9wv0vhslJ4qh0jSbjSXIrLgzKfqioYPdqTzfUvcArhL/6gZXAN9vxvELgJTN7G3gLeM7d/wbcAFxvZuUEYxL3hI+/Bxgcbr8euLG9b0Lab15FJUP6ZTFmcJ+oQxHplPfOiFKy6E4ZbT+Eoe7+gJl9G8Dd68ysoa0nufvbwNHNbF9FMH5x6PYa4JJ2xCNdUFpRycwxA1U8UJJWQV42g/tmKVl0s/YcWew1s0GEg81mdgyg0aUktLW6hrU792kyniS1oOxHvuZadLP2JItvAU8A48zsFWA2cG1Co5KEKGsar9BkPElyk4fnsWJLNfUN7TqLX+KgzW4ody81s48QnPZqwNJwroUkmdKKSrIy0piq4oGS5IoL86mtb2TNjr1MGJoXdTgpocVkYWbnt7BrtJnh7o8nKCZJkLKKSqaP6k92RnrUoYh0SdPaFks3VStZdJPWjixaG2x2glNdJUnU1DWwZGMVXzhhXNShiHTZhKH9yEwPyn6cP31E1OGkhBaThbt/ujsDkcRauG4XdQ2uyXjSK2RlpDG+oJ/OiOpG7Tl1FjM7C5gK5DRtc/efJiooib/SpuKBShbSS0wpzOcf726POoyU0Z4Z3L8mmFl9PZALfAqYkOC4JM7KKioZV9CXQX2zog5FJC6KC/PZsvsAO/fqfJvu0J5TZ09w9yuAHe7+feA4gppOkiSaigeqC0p6k2LN5O5W7UkW+8PrGjMbDtQAYxMWkcTdqu172LWvTpPxpFeZ3FQjSsmiW7RnzOLpsCDgbcACoIH3FimSJFCqyXjSCw3pl01BXrZmcneT9kzK+2F48+Gw8myuu6t0eBIprahkYJ9Mxg1p1/LpIkkjKPuhI4vu0GI3lJnNNLNhMfevBP4IfK+p9LgkhzIVD5Reqrgwj/Kte6hT2Y+Ea23M4m6gHsDMTiDohnqIYMzi7sSHJvGwY88BVm/fq5XxpFeaUphPbUMj725r1+Kd0gWtJYsMd98R3r4cuNvd/+TuNwGTEh+axENZOL+iROMV0gvpjKju01qySDezpiJCpwEvxu5LXEgST2UVlWSlp3HEyP5RhyISd+OG9CUrI02D3N2gtQHuhwhWutsG1AKvApjZeEBpPEmUVlQybWQ+OZnK79L7ZKSncfgwlf3oDi0eWbj7j4GbgAcJJuY1jSBlAl/rhtiki2rqGli0vkrrbUuvNnm4FkLqDq1OynP319z9YXevjtm23N1LEx+adNWSjVXUNjRqcFt6teLCfLbvOcC26gNRh9KrtWcGtySpg5PxdGQhvVixZnJ3CyWLXqy0opKxg/tQkJcddSgiCTNFZ0R1CyWLXsrdmVdRqZLk0usN6JNFYf8cJYsEa21Z1UqCFfE+sAtwd1dHeA+2evteduytVfFASQlB2Q8NcidSa6fODum2KCTuNBlPUklxYR5z3tnGgfoGrTGfIK2dOtsQewH6A8NiLtKDlVVUkp+TwYSCflGHIpJwk4fnU9/olG9V2Y9Eac9Keeea2TvAemBueP1i68+SqJWGxQPT0lQ8UHq/98p+qCsqUdozwH0zcDywwt2LgLOAlxMZlHTNrn21lG/do1NmJWUcNqQvOZlpGuROoPYki3p33wakmZm5+3PAjATHJV3QNF6hyXiSKtLTjEnD8pQsEqg9K+VVmVlf4DXg92a2FVDx+B6srKKSjDTjqCItOyKpo7gwn2eWbMbdtXZLArTnyOJCgjUsvk7Q/bQBOC+BMUkXlVZUMnVEPrlZOitEUkdxYT6V++rYsltlPxKhPcni38Izourc/R53/0/g+kQHJp1TW9/IwnW71AUlKUdrWyRWe5LF2c1sOzfegUh8LNlYxYH6Rg1uS8qZ3FQjarOSRSK0tgb3F81sPjDJzObFXFYCS9tq2MyKzOwlM1tmZkvM7Lpw+yAze87MVobXA8PtZma/NLNyM3vbzDSI3gmajCepKj8nk5EDcnX6bIK0tfjRC8DPgBtjtle7+9Z2tF0PfNPd55lZHlBmZs8BnwVecPdbzOzGsO0bgHOAieHlOODO8Fo6oKyiklEDcxmWnxN1KCLdLij7oSOLRGhtBnelu5e7+yVALnBGeCloT8Puvsnd54W3q4FlwEjgAuC+8GH3EQygE27/vQfeAAaYWWEn3lPKcndKKyopUReUpKgphXms2raHmrqGqEPpddozg/srBEcZo8PLQ2Z2TUdexMzGAkcTzAAf5u6bIEgowNDwYSOBdTFPWx9uO7Stq82s1MxKt23b1pEwer11O/ezrfoAM8dqcFtSU3FhPo0O72xRV1S8tWeA+4vAse5+k7vfRNA19KX2voCZ9QP+DHzd3Vs7PmzuxOgPVL1197vdvcTdSwoK2nWQkzJKK3YCMHO0jiwkNemMqMRpT7IwoC7mfh3Nf7F/8IlmmQSJ4n53/0u4eUtT91J43TT+sR4oinn6KGBje15HAqUVleRlZzBpeF7UoYhEYvSgPvTNStcgdwK0djZU0+D3H4A3zOx7ZvY94HXeG3NokQVTKO8BloVzM5o8DlwV3r4KeCxm+2fCs6JmAVVN3VXSPvMqKjlq9ADSVTxQUlRamjFpeB5LdWQRd60dWbwJ4O7/AVwN7AP2A19y99va0fbxwKeBU81sQXj5KHALcEZ4Cu4Z4X2Ap4BVQDnwv0CHxkVSXdX+OlZsqdZiR5LyJhfms3zTbtybW7tNOqu1U2cP/jx197eAtzrSsLu/RsvdVac183gHvtKR15D3zF9bibvmV4gUF+bzwNy1bKyqYeSA3KjD6TVaSxYFZtZiWY9DupYkYmUVlaQZTFfxQElxU5pmcm/crWQRR611Q6UD/YC8Fi7Sg5SuqaS4MJ9+2e0pJCzSe00arjOiEqG1b5ZN7v7jbotEOq2+oZEF63ZxacmoqEMRiVy/7AzGDO6jGlFx1tqRhU6pSRLLNlWzv65Bk/FEQsXD83X6bJy1liw+MAgtPdPByXgq8yECBIPca3bsZV9tfdSh9Bqt1Yba2Z2BSOeVVlRS2D9Hg3kioeLCPNxhxWYdXcRLe2ZwSw83r6JSRxUiMd4r+6FkES9KFkluw679bKqqUaVZkRijBuaSl52hM6LiSMkiyZWuCXoLSzS4LXKQmTG5ME/JIo6ULJJcWUUlfbLSmazigSLvU1yYz/LN1TQ2quxHPChZJLnSNZUcVTSAjHT9V4rEKi7MZ8+BetZX7o86lF5B3zBJbM+BepZv3q3xCpFmNA1yqwJtfChZJLEFa3fR6GgynkgzJg3Lw0xlP+JFySKJlVbsxAyOHq3igSKHys1K57DBfVmush9xoWSRxMoqKpk0LI/8nMyoQxHpkYrwMNrzAAAQmklEQVQLVfYjXpQsklRDozN/7S5NxhNpRXFhHmt37qO6pq7tB0urlCyS1IrN1ew5UK/FjkRa0TTIrbIfXadkkaTKwuKBWkZVpGXvlf3QuEVXKVkkqdKKSobmZTNqoIoHirSksH8O/XMzWapxiy5TskhSpWuC4oFmWnZEpCVmRrHKfsSFkkUS2lxVw4Zd+zW4LdIOxYX5rFDZjy5TskhCZRWVgIoHirRH8fB89tc1ULFzX9ShJDUliyRUWrGTnMw0po7IjzoUkR5Pg9zxoWSRhMoqKpk+agCZKh4o0qaJw/qRnmZKFl2UEXUA0jGNjU7frAxmjRscdSgiSSEnM51xQ/oqWXSRkkWSSUszZl89K+owRJJKcWH+wbE+6Rz1Y4hIr1dcmM+GXfup2qeyH52lZCEivV5xYbCSpCrQdp6ShYj0elN0RlSXKVmISK9XkJfNoL5ZKlfeBUoWItLrHSz7oW6oTktYsjCz35nZVjNbHLNtkJk9Z2Yrw+uB4XYzs1+aWbmZvW1mMxIVl4ikpuLhQdmP+obGqENJSok8srgXOPuQbTcCL7j7ROCF8D7AOcDE8HI1cGcC4xKRFFRcmM+B+kbW7NgbdShJKWHJwt3nADsP2XwBcF94+z7gwpjtv/fAG8AAMytMVGwiknqayn6oXHnndPeYxTB33wQQXg8Nt48E1sU8bn24TUQkLiYM7Udmusp+dFZPGeBublGGZusJm9nVZlZqZqXbtm1LcFgi0ltkZaQxvqAfy5UsOqW7k8WWpu6l8HpruH09UBTzuFHAxuYacPe73b3E3UsKCgoSGqyI9C7Fhfk6fbaTujtZPA5cFd6+CngsZvtnwrOiZgFVTd1VIiLxUlyYx+bdNVTurY06lKSTyFNnZwP/BCaZ2Xoz+wJwC3CGma0EzgjvAzwFrALKgf8FrklUXCKSurS2ReclrOqsu3+yhV2nNfNYB76SqFhERCD2jKjdfHjCkIijSS49ZYBbRCThhvTLpiAvW+MWnaBkISIpJRjkVjdURylZiEhKKS7Mo3zrHupU9qNDlCxEJKVMKcyntqGRVdtU9qMjlCxEJKVMHq4zojpDyUJEUsq4gr5kpacpWXSQkoWIpJTM9DQmDuvHUiWLDlGyEJGUc+xhg3j93R28ufrQwtjSEiULEUk5159xOEUDc7l29jy27zkQdThJQclCRFJOXk4mv75yJpX76vjGnxbQ0NhskWuJoWQhIilpyoh8fnz+VF5duZ1fvVQedTg9npKFiKSsy44p4qKjR3L78+/wj/LtUYfToylZiEjKMjN+cuE0xhf047oH57N1d03UIfVYShYiktL6Zmdw55Uz2HuggWtnz6deZUCapWQhIilv4rA8fnLhNOau3skdz6+MOpweSclCRAS4eOYoLisp4n9eKuflFVvbfkKKUbIQEQn96IKpTB6exzf+tICNu/ZHHU6PomQhIhLKyUzn11fOoLa+kWtnz1cZ8xhKFiIiMcYV9OOWi4+krKKSW59ZEXU4PYaShYjIIT42fQSfnjWGu+es4rmlW6IOp0dQshARacb3zitm2sh8vvnQAtbt3Bd1OJFTshARaUZ2Rjq/vmImDnz1gXkcqG+IOqRIKVmIiLRg9OA+3PqJ6SxcX8XPnloedTiRUrIQEWnF2dOG8/njD+Pe19fw1KJNUYcTGSULEZE23HjOZI4qGsB3HnmbNdv3Rh1OJJQsRETakJWRxq+unEFGunHN/fOoqUu98QslCxGRdhg5IJf/vHQ6Szft5sd/Wxp1ON1OyUJEpJ1OnTyML508ngfmruWv8zdEHU63UrIQEemAb515OMeOHcRNjy6ifOueqMPpNkoWIiIdkJGexi8/eTS5melcc38Z+2tTY/xCyUJEpIOG98/h9suOYuXWPXz/scVRh9MtlCxERDrhpMMLuPbUiTxStp6HStdFHU7C9ahkYWZnm9kKMys3sxujjkdEpDXXnTaRD48fzA8eW8zyzbujDiehekyyMLN04FfAOcAU4JNmNiXaqEREWpaeZvzX5UeTl5PJNffPY8+B+qhDSpgekyyAY4Fyd1/l7rXAg8AFEcckItKqgrxsfnn50azZvpeb/rIId486pITIiDqAGCOB2I6/9cBxhz7IzK4GrgYYPXp090QmItKKD40fzLfOmkRtfSPuYBZ1RPHXk5JFc/+8H0jR7n43cDdASUlJ70zhIpJ0rjllQtQhJFRP6oZaDxTF3B8FbIwoFhERidGTksVbwEQzO8zMsoDLgccjjklEROhB3VDuXm9mXwWeAdKB37n7kojDEhERelCyAHD3p4Cnoo5DRETeryd1Q4mISA+lZCEiIm1SshARkTYpWYiISJssmaemm1k1sCLOzQ4BtqtNtdnD2lObajOeJrl7Xkee0KPOhuqEFe5eEs8GzaxUbarNntae2lSb8W6zo89RN5SIiLRJyUJERNqU7MnibrWpNntom8kQo9pUm+2W1APcIiLSPZL9yEJERLqBkoWIiLQpaZOFma0xs0VmtqAzp4G10OYAM3vEzJab2TIz+1AX25sUxtd02W1mX+9im98wsyVmttjMZptZTlfaC9u8LmxvSVfiM7PfmdlWM1scs22QmT1nZivD64FdbO+SMM5GM+vw6YQttHlr+H/+tpk9amYD4tDmv4ftLTCzZ81sRFfbjNn3LTNzMxsShzh/aGYbYv5GPxqPOM3sWjNbEf5f/Ucc4vxTTIxrzGxBHNo8yszeaPoOMbNj49DmdDP7Z/jd9ISZ5XegvSIzeyn87lliZteF27vyGWqpzY5/jtw9KS/AGmBInNu8D/iX8HYWMCCObacDm4ExXWhjJLAayA3vPwR8totxTQMWA30I5t08D0zsZFsnATOAxTHb/gO4Mbx9I/DzLrZXDEwCXgZK4hTjmUBGePvnHYmxlTbzY25/DfhNV9sMtxcRlPGv6Ojffwtx/hD4Vhf+fppr8yPh31F2eH9oPN57zP5fAD+IQ5zPAueEtz8KvByHNt8CTg5vfx749w60VwjMCG/nAe8AU7r4GWqpzQ5/jpL2yCLewl8AJwH3ALh7rbvviuNLnAa86+4VXWwnA8g1swyCL/iuriZYDLzh7vvcvR54BbioMw25+xxg5yGbLyBIwoTXF3alPXdf5u6dnrXfQpvPhu8d4A2CVRq72ubumLt9aWaJ4I62Gbod+E5H22ujzU5roc0vA7e4+4HwMVvj0CYAZmbApcDsOLTpQNMv//508LPUQpuTgDnh7eeAizvQ3iZ3nxfergaWEfxA7MpnqNk2O/M5SuZk4cCzZlZmZlfHob1xwDbg/8xsvpn91sz6xqHdJpfTwT/wQ7n7BuA2YC2wCahy92e7GNdi4CQzG2xmfQh+YRW18ZyOGObumyD4wwWGxrHtRPg88HQ8GjKzm81sHXAl8IM4tHc+sMHdF3Y5uPf7athl9ruOdHG04nDgRDOba2avmNkxcWizyYnAFndfGYe2vg7cGv4f3Qb8WxzaXAycH96+hE5+lsxsLHA0MJc4fYYOabPDkjlZHO/uM4BzgK+Y2UldbC+D4JDyTnc/GthLcMjXZRYsE3s+8HAX2xlI8CvjMGAE0NfMPtWVNt19GUHXy3PA34GFQH2rT+qlzOy7BO/9/ni05+7fdfeisL2vdjG2PsB3iUPSOcSdwHjgKIIfIL+IQ5sZwEBgFvBt4KHwiCAePkkXf3TF+DLwjfD/6BuEvQpd9HmC76Mygm6f2o42YGb9gD8DXz/kCLXT4tFm0iYLd98YXm8FHgU6NDjVjPXAendvyrqPECSPeDgHmOfuW7rYzunAanff5u51wF+AD3c1OHe/x91nuPtJBIfV8fjV1mSLmRUChNcd6pLoLmZ2FXAecKWHHbxx9AAd6I5owXiCHwkLzWwNQVfZPDMb3pVG3X2Luze4eyPwv3T9cwTBZ+kvHngTaCQohtclYdfrx4E/dbWt0FUEnyEIfsh1+b27+3J3P9PdZxIktXc78nwzyyT4Ur/f3Zti69JnqIU2Oywpk4WZ9TWzvKbbBAOUHzhrpCPcfTOwzswmhZtOA5Z2KdD3xOvX0Fpglpn1CX+pnUbQB9klZjY0vB5N8GGM1y83gMcJPpSE14/Fse24MLOzgRuA8919X5zanBhz93xgeVfac/dF7j7U3ce6+1iCL+QZ4d9tpzV9CYUuooufo9BfgVPD9g8nOFkkHlVTTweWu/v6OLQFwRjFyeHtU4nDj6SYz1Ia8D3gNx14rhEc3Sxz9/+M2dXpz1ArbXZcR0b/e8qFYHxhYXhZAnw3Tu0eBZQCbxP8wQ+MQ5t9gB1A/zjF+COCL57FwB8IzzjpYpuvEiTGhcBpXWhnNkFXRh3Bl9kXgMHACwQfxBeAQV1s76Lw9gFgC/BMHGIsB9YBC8JLR89caq7NP4f/R28DTxAMKnapzUP2r6HjZ0M1F+cfgEVhnI8DhXFoMwv4Y/j+5wGnxuO9A/cCX4rj3+YJQFn4dz8XmBmHNq8jOOPoHeAWwioZ7WzvBIKx2Ldj/hY/2sXPUEttdvhzpHIfIiLSpqTshhIRke6lZCEiIm1SshARkTYpWYiISJuULEREpE1KFtIrmNntFlMx18yeMbPfxtz/hZld34H2xh5aSTXcXmJmv+x6xAcrvn7rkG1rrIPVZEW6g5KF9BavE85mDydEDQGmxuz/MPCP9jRkZukt7XP3Unf/Whfi7BbhbGeRuFGykN7iH7xX+mQqwYSwajMbaGbZBNV151vgVgvW71hkZpcBmNkpYd3/BwgmqR1kZuPC4pLHhI/7W7j9h2HxvZfNbJWZfS3mOd+3YI2M5yxYd+R9RxDtYWbXh3EubjpqOvSIx4K1LX4Y3n7ZzH5qZq8A14VrFiw2s4VmNqf5VxFpH/36kF7B3TeaWX1YsuTDwD8Jyjt/CKgC3nb3WjO7mGCm/nSCo4+3Yr5IjwWmufvqsEInYfmXB4HPufsCMzvlkJeeTLB+Qx6wwszuDNu+mKDCZwbBLOayFkL/xiHFIEeErzsT+BxwHGDA3DAJVLbxTzHA3U8O21gEnOXuG6yDCzqJHEpHFtKbNB1dNCWLf8bcfz18zAnAbA+K520hWL+jqYT2m+6+Oqa9AoI6PJ9y95ZWZnvS3Q+4+3aCAm/Dwtd4zN33e7CGwBOtxHy7ux/VdOG9NRVOAB51973uvoeg4N2J7fg3iC2y9w/gXjP7V4LFt0Q6TclCepOmcYsjCLqh3iA4sogdr2itVPbeQ+5XEdSMOr6V5xyIud1AcCQRj3LcLbVRz/s/t4cuq3vwPbj7lwiK2RUBC8xscBzikhSlZCG9yT8IyozvDI8cdgIDCBLGP8PHzAEuM7N0MysgWB3xzRbaqyVYlewzZnZFB+J4DfiYmeWE6wic24n3Mge4MKww3Jeg8NurBEXfhlqwWFU2wfttlpmNd/e57v4Dgqqv8VzUSlKMxiykN1lEMA7xwCHb+oXdRBCsffIhgkqjDnzH3Teb2eTmGnT3vWZ2HvCcme0lONpolbu/ZWaPh69RQVDJuM3nHdLGPDO7l/cS2W/dfT6Amf2YoErqalovfX5rWCrdCKqVxnuFPUkhqjorkgBm1s/d94Qr3M0BrvZwLWSRZKQjC5HEuNvMphCMKdynRCHJTkcWIiLSJg1wi4hIm5QsRESkTUoWIiLSJiULERFpk5KFiIi06f8D1URzgpMIgnkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5f755d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_by_hour = df.groupby('Hour').size()\n",
    "plot_by_hour = sales_by_hour.plot(title='Hourly Sales',xticks=(range(5,22)))\n",
    "plot_by_hour.set_xlabel('Working Hours')\n",
    "plot_by_hour.set_ylabel('Total Sales')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Majority of the customers come in afternoon. The frequency gets quite low during closing time."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}