Big Data Practice.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Big Data Assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.75\n"
     ]
    }
   ],
   "source": [
    "#Practice with Basic function\n",
    "no = 10 + 5 * 3 / 4.0\n",
    "\n",
    "print(no)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    }
   ],
   "source": [
    "#Single % sign use to get division. dividend % divisor = remainder.\n",
    "remainder = 15 % 9\n",
    "print(remainder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello everyone My name is Bhagyashree\n"
     ]
    }
   ],
   "source": [
    "#using strings\n",
    "\n",
    "word= ('Hello everyone' +' '+ 'My name is Bhagyashree')\n",
    "print(word)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The count is: 1.25\n",
      "The count is: 2.25\n",
      "The count is: 3.25\n",
      "The count is: 4.25\n",
      "The count is: 5.25\n",
      "The count is: 6.25\n",
      "The count is: 7.25\n",
      "The count is: 8.25\n",
      "The count is: 9.25\n",
      "The count is: 10.25\n",
      "The count is: 11.25\n",
      "The count is: 12.25\n",
      "The count is: 13.25\n",
      "The count is: 14.25\n",
      "The count is: 15.25\n",
      "While Loop Practice\n"
     ]
    }
   ],
   "source": [
    "#creating while loop\n",
    "count = 1\n",
    "while (count <= 15):\n",
    "   print ('The count is:', count+0.25)\n",
    "   count = count + 1\n",
    "\n",
    "print (\"While Loop Practice\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Letter : A\n",
      "Letter : s\n",
      "Letter : s\n",
      "Letter : i\n",
      "Letter : g\n",
      "Letter : n\n",
      "Letter : m\n",
      "Letter : e\n",
      "Letter : n\n",
      "Letter : t\n",
      "Book name : Python\n",
      "Book name : Java\n",
      "Book name : C++\n",
      "For Loop Practice\n"
     ]
    }
   ],
   "source": [
    "#Example\n",
    "for letter in 'Assignment':\n",
    "   print ('Letter :', letter)\n",
    "\n",
    "Books = ['Python','Java','C++']\n",
    "\n",
    "#Example\n",
    "for Book in Books:        \n",
    "   print ('Book name :', Book)\n",
    "\n",
    "print (\"For Loop Practice\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your name is Pallavi, and you are also 25 years old.\n",
      "Your name is either Pallavi or Bhagyashree.\n"
     ]
    }
   ],
   "source": [
    "# creating if condition\n",
    "\n",
    "name = \"Pallavi\"\n",
    "age = 25\n",
    "if name == \"Pallavi\" and age == 25:\n",
    "    print(\"Your name is Pallavi, and you are also 25 years old.\")\n",
    "\n",
    "if name == \"Pallavi\" or name == \"Bhagyashree\":\n",
    "    print(\"Your name is either Pallavi or Bhagyashree.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x equals ten\n",
      "y does not to equal to twenty\n"
     ]
    }
   ],
   "source": [
    "# creating if and else condition\n",
    "\n",
    "x = 10\n",
    "if x == 10:\n",
    "    print(\"x equals ten\")\n",
    "else:\n",
    "    print(\"x does not equal to ten.\")\n",
    "    \n",
    "y =  20\n",
    "\n",
    "if y==10:\n",
    "    print('y equals to tewnty')\n",
    "else:\n",
    "    print('y does not to equal to twenty')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "[0.02059172 0.00200851 0.1312     0.00947522 0.0462963  0.02518519]\n"
     ]
    }
   ],
   "source": [
    "# Create 2 new lists height and weight\n",
    "x = [87,  17, 82, 91, 15, 51]\n",
    "y = [65, 92, 25, 98, 18, 45]\n",
    "\n",
    "# Create 2 numpy arrays from height and weight\n",
    "x1 = np.array(x)\n",
    "y1 = np.array(y)\n",
    "print(type(x))\n",
    "\n",
    "c = x1/y1 **2\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Geography</th>\n",
       "      <th>Timeframe</th>\n",
       "      <th>Current Year Week Ending</th>\n",
       "      <th>Type</th>\n",
       "      <th>ASP Current Year</th>\n",
       "      <th>Total Bulk and Bags Units</th>\n",
       "      <th>4046 Units</th>\n",
       "      <th>4225 Units</th>\n",
       "      <th>4770 Units</th>\n",
       "      <th>TotalBagged Units</th>\n",
       "      <th>SmlBagged Units</th>\n",
       "      <th>LrgBagged Units</th>\n",
       "      <th>X-LrgBagged Units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Total U.S.</td>\n",
       "      <td>Weekly</td>\n",
       "      <td>2019-01-07 00:00:00</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>1.02</td>\n",
       "      <td>44749707.48</td>\n",
       "      <td>14377053.08</td>\n",
       "      <td>11890232.90</td>\n",
       "      <td>893721.10</td>\n",
       "      <td>17588700.40</td>\n",
       "      <td>12829493.40</td>\n",
       "      <td>4617813.55</td>\n",
       "      <td>141393.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albany</td>\n",
       "      <td>Weekly</td>\n",
       "      <td>2019-01-07 00:00:00</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>1.07</td>\n",
       "      <td>129222.29</td>\n",
       "      <td>3789.30</td>\n",
       "      <td>112635.18</td>\n",
       "      <td>158.00</td>\n",
       "      <td>12639.81</td>\n",
       "      <td>8877.95</td>\n",
       "      <td>3761.86</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Weekly</td>\n",
       "      <td>2019-01-07 00:00:00</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>0.92</td>\n",
       "      <td>828971.15</td>\n",
       "      <td>388574.98</td>\n",
       "      <td>38902.85</td>\n",
       "      <td>3482.04</td>\n",
       "      <td>398011.28</td>\n",
       "      <td>299475.26</td>\n",
       "      <td>98477.41</td>\n",
       "      <td>58.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Baltimore/Washington</td>\n",
       "      <td>Weekly</td>\n",
       "      <td>2019-01-07 00:00:00</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>1.31</td>\n",
       "      <td>925391.38</td>\n",
       "      <td>102652.85</td>\n",
       "      <td>530128.43</td>\n",
       "      <td>8212.94</td>\n",
       "      <td>284397.16</td>\n",
       "      <td>263150.78</td>\n",
       "      <td>21233.05</td>\n",
       "      <td>13.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Boise</td>\n",
       "      <td>Weekly</td>\n",
       "      <td>2019-01-07 00:00:00</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>1.23</td>\n",
       "      <td>108261.98</td>\n",
       "      <td>43723.19</td>\n",
       "      <td>7085.86</td>\n",
       "      <td>14435.46</td>\n",
       "      <td>43017.47</td>\n",
       "      <td>23932.54</td>\n",
       "      <td>19049.95</td>\n",
       "      <td>34.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Geography Timeframe Current Year Week Ending          Type  \\\n",
       "0            Total U.S.    Weekly      2019-01-07 00:00:00  Conventional   \n",
       "1                Albany    Weekly      2019-01-07 00:00:00  Conventional   \n",
       "2               Atlanta    Weekly      2019-01-07 00:00:00  Conventional   \n",
       "3  Baltimore/Washington    Weekly      2019-01-07 00:00:00  Conventional   \n",
       "4                 Boise    Weekly      2019-01-07 00:00:00  Conventional   \n",
       "\n",
       "   ASP Current Year  Total Bulk and Bags Units   4046 Units   4225 Units  \\\n",
       "0              1.02                44749707.48  14377053.08  11890232.90   \n",
       "1              1.07                  129222.29      3789.30    112635.18   \n",
       "2              0.92                  828971.15    388574.98     38902.85   \n",
       "3              1.31                  925391.38    102652.85    530128.43   \n",
       "4              1.23                  108261.98     43723.19      7085.86   \n",
       "\n",
       "   4770 Units  TotalBagged Units  SmlBagged Units  LrgBagged Units  \\\n",
       "0   893721.10        17588700.40      12829493.40       4617813.55   \n",
       "1      158.00           12639.81          8877.95          3761.86   \n",
       "2     3482.04          398011.28        299475.26         98477.41   \n",
       "3     8212.94          284397.16        263150.78         21233.05   \n",
       "4    14435.46           43017.47         23932.54         19049.95   \n",
       "\n",
       "   X-LrgBagged Units  \n",
       "0          141393.45  \n",
       "1               0.00  \n",
       "2              58.61  \n",
       "3              13.33  \n",
       "4              34.98  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading file from system\n",
    "#reading sample dataset to perform basic operations\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('Downloads/2019-plu-total-hab-data.csv')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>Geography</th>\n",
       "      <th>Timeframe</th>\n",
       "      <th>Current Year Week Ending</th>\n",
       "      <th>Type</th>\n",
       "      <th>ASP Current Year</th>\n",
       "      <th>Total Bulk and Bags Units</th>\n",
       "      <th>4046 Units</th>\n",
       "      <th>4225 Units</th>\n",
       "      <th>4770 Units</th>\n",
       "      <th>TotalBagged Units</th>\n",
       "      <th>SmlBagged Units</th>\n",
       "      <th>LrgBagged Units</th>\n",
       "      <th>X-LrgBagged Units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Total U.S.</td>\n",
       "      <td>Weekly</td>\n",
       "      <td>2019-01-07 00:00:00</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>1.02</td>\n",
       "      <td>44749707.48</td>\n",
       "      <td>14377053.08</td>\n",
       "      <td>11890232.90</td>\n",
       "      <td>893721.10</td>\n",
       "      <td>17588700.40</td>\n",
       "      <td>12829493.40</td>\n",
       "      <td>4617813.55</td>\n",
       "      <td>141393.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albany</td>\n",
       "      <td>Weekly</td>\n",
       "      <td>2019-01-07 00:00:00</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>1.07</td>\n",
       "      <td>129222.29</td>\n",
       "      <td>3789.30</td>\n",
       "      <td>112635.18</td>\n",
       "      <td>158.00</td>\n",
       "      <td>12639.81</td>\n",
       "      <td>8877.95</td>\n",
       "      <td>3761.86</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Weekly</td>\n",
       "      <td>2019-01-07 00:00:00</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>0.92</td>\n",
       "      <td>828971.15</td>\n",
       "      <td>388574.98</td>\n",
       "      <td>38902.85</td>\n",
       "      <td>3482.04</td>\n",
       "      <td>398011.28</td>\n",
       "      <td>299475.26</td>\n",
       "      <td>98477.41</td>\n",
       "      <td>58.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Baltimore/Washington</td>\n",
       "      <td>Weekly</td>\n",
       "      <td>2019-01-07 00:00:00</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>1.31</td>\n",
       "      <td>925391.38</td>\n",
       "      <td>102652.85</td>\n",
       "      <td>530128.43</td>\n",
       "      <td>8212.94</td>\n",
       "      <td>284397.16</td>\n",
       "      <td>263150.78</td>\n",
       "      <td>21233.05</td>\n",
       "      <td>13.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Boise</td>\n",
       "      <td>Weekly</td>\n",
       "      <td>2019-01-07 00:00:00</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>1.23</td>\n",
       "      <td>108261.98</td>\n",
       "      <td>43723.19</td>\n",
       "      <td>7085.86</td>\n",
       "      <td>14435.46</td>\n",
       "      <td>43017.47</td>\n",
       "      <td>23932.54</td>\n",
       "      <td>19049.95</td>\n",
       "      <td>34.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Boston</td>\n",
       "      <td>Weekly</td>\n",
       "      <td>2019-01-07 00:00:00</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>1.34</td>\n",
       "      <td>767731.38</td>\n",
       "      <td>11483.09</td>\n",
       "      <td>597937.87</td>\n",
       "      <td>2510.57</td>\n",
       "      <td>155799.85</td>\n",
       "      <td>109108.51</td>\n",
       "      <td>46691.34</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Buffalo/Rochester</td>\n",
       "      <td>Weekly</td>\n",
       "      <td>2019-01-07 00:00:00</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>1.19</td>\n",
       "      <td>197137.26</td>\n",
       "      <td>9963.81</td>\n",
       "      <td>92129.51</td>\n",
       "      <td>281.96</td>\n",
       "      <td>94761.98</td>\n",
       "      <td>53069.02</td>\n",
       "      <td>41679.63</td>\n",
       "      <td>13.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>California</td>\n",
       "      <td>Weekly</td>\n",
       "      <td>2019-01-07 00:00:00</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7195166.45</td>\n",
       "      <td>1989731.98</td>\n",
       "      <td>2089603.56</td>\n",
       "      <td>118964.72</td>\n",
       "      <td>2996866.19</td>\n",
       "      <td>2851060.95</td>\n",
       "      <td>77908.55</td>\n",
       "      <td>67896.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Charlotte</td>\n",
       "      <td>Weekly</td>\n",
       "      <td>2019-01-07 00:00:00</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>1.10</td>\n",
       "      <td>323640.60</td>\n",
       "      <td>122312.03</td>\n",
       "      <td>63971.60</td>\n",
       "      <td>9550.23</td>\n",
       "      <td>127806.74</td>\n",
       "      <td>108828.50</td>\n",
       "      <td>18929.10</td>\n",
       "      <td>49.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Chicago</td>\n",
       "      <td>Weekly</td>\n",
       "      <td>2019-01-07 00:00:00</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>1.31</td>\n",
       "      <td>739020.76</td>\n",
       "      <td>186828.35</td>\n",
       "      <td>371930.88</td>\n",
       "      <td>46176.93</td>\n",
       "      <td>134084.60</td>\n",
       "      <td>111203.24</td>\n",
       "      <td>21301.95</td>\n",
       "      <td>1579.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Geography Timeframe Current Year Week Ending          Type  \\\n",
       "0            Total U.S.    Weekly      2019-01-07 00:00:00  Conventional   \n",
       "1                Albany    Weekly      2019-01-07 00:00:00  Conventional   \n",
       "2               Atlanta    Weekly      2019-01-07 00:00:00  Conventional   \n",
       "3  Baltimore/Washington    Weekly      2019-01-07 00:00:00  Conventional   \n",
       "4                 Boise    Weekly      2019-01-07 00:00:00  Conventional   \n",
       "5                Boston    Weekly      2019-01-07 00:00:00  Conventional   \n",
       "6     Buffalo/Rochester    Weekly      2019-01-07 00:00:00  Conventional   \n",
       "7            California    Weekly      2019-01-07 00:00:00  Conventional   \n",
       "8             Charlotte    Weekly      2019-01-07 00:00:00  Conventional   \n",
       "9               Chicago    Weekly      2019-01-07 00:00:00  Conventional   \n",
       "\n",
       "   ASP Current Year  Total Bulk and Bags Units   4046 Units   4225 Units  \\\n",
       "0              1.02                44749707.48  14377053.08  11890232.90   \n",
       "1              1.07                  129222.29      3789.30    112635.18   \n",
       "2              0.92                  828971.15    388574.98     38902.85   \n",
       "3              1.31                  925391.38    102652.85    530128.43   \n",
       "4              1.23                  108261.98     43723.19      7085.86   \n",
       "5              1.34                  767731.38     11483.09    597937.87   \n",
       "6              1.19                  197137.26      9963.81     92129.51   \n",
       "7              1.06                 7195166.45   1989731.98   2089603.56   \n",
       "8              1.10                  323640.60    122312.03     63971.60   \n",
       "9              1.31                  739020.76    186828.35    371930.88   \n",
       "\n",
       "   4770 Units  TotalBagged Units  SmlBagged Units  LrgBagged Units  \\\n",
       "0   893721.10        17588700.40      12829493.40       4617813.55   \n",
       "1      158.00           12639.81          8877.95          3761.86   \n",
       "2     3482.04          398011.28        299475.26         98477.41   \n",
       "3     8212.94          284397.16        263150.78         21233.05   \n",
       "4    14435.46           43017.47         23932.54         19049.95   \n",
       "5     2510.57          155799.85        109108.51         46691.34   \n",
       "6      281.96           94761.98         53069.02         41679.63   \n",
       "7   118964.72         2996866.19       2851060.95         77908.55   \n",
       "8     9550.23          127806.74        108828.50         18929.10   \n",
       "9    46176.93          134084.60        111203.24         21301.95   \n",
       "\n",
       "   X-LrgBagged Units  \n",
       "0          141393.45  \n",
       "1               0.00  \n",
       "2              58.61  \n",
       "3              13.33  \n",
       "4              34.98  \n",
       "5               0.00  \n",
       "6              13.33  \n",
       "7           67896.69  \n",
       "8              49.14  \n",
       "9            1579.41  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is how we read columns data\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Geography</th>\n",
       "      <th>Timeframe</th>\n",
       "      <th>Current Year Week Ending</th>\n",
       "      <th>Type</th>\n",
       "      <th>ASP Current Year</th>\n",
       "      <th>Total Bulk and Bags Units</th>\n",
       "      <th>4046 Units</th>\n",
       "      <th>4225 Units</th>\n",
       "      <th>4770 Units</th>\n",
       "      <th>TotalBagged Units</th>\n",
       "      <th>SmlBagged Units</th>\n",
       "      <th>LrgBagged Units</th>\n",
       "      <th>X-LrgBagged Units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5179</th>\n",
       "      <td>West</td>\n",
       "      <td>Weekly</td>\n",
       "      <td>2019-11-10 00:00:00</td>\n",
       "      <td>Organic</td>\n",
       "      <td>1.91</td>\n",
       "      <td>235865.0</td>\n",
       "      <td>16366.0</td>\n",
       "      <td>19561.0</td>\n",
       "      <td>454.0</td>\n",
       "      <td>199484.0</td>\n",
       "      <td>135025.0</td>\n",
       "      <td>64370.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5180</th>\n",
       "      <td>West Tex/New Mexico</td>\n",
       "      <td>Weekly</td>\n",
       "      <td>2019-12-01 00:00:00</td>\n",
       "      <td>Organic</td>\n",
       "      <td>1.52</td>\n",
       "      <td>17646.0</td>\n",
       "      <td>1162.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>1114.0</td>\n",
       "      <td>14914.0</td>\n",
       "      <td>14358.0</td>\n",
       "      <td>556.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5181</th>\n",
       "      <td>West Tex/New Mexico</td>\n",
       "      <td>Weekly</td>\n",
       "      <td>2019-11-24 00:00:00</td>\n",
       "      <td>Organic</td>\n",
       "      <td>1.62</td>\n",
       "      <td>20587.0</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>444.0</td>\n",
       "      <td>1635.0</td>\n",
       "      <td>17443.0</td>\n",
       "      <td>16791.0</td>\n",
       "      <td>652.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5182</th>\n",
       "      <td>West Tex/New Mexico</td>\n",
       "      <td>Weekly</td>\n",
       "      <td>2019-11-17 00:00:00</td>\n",
       "      <td>Organic</td>\n",
       "      <td>1.68</td>\n",
       "      <td>17278.0</td>\n",
       "      <td>1088.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>1230.0</td>\n",
       "      <td>14620.0</td>\n",
       "      <td>14618.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5183</th>\n",
       "      <td>West Tex/New Mexico</td>\n",
       "      <td>Weekly</td>\n",
       "      <td>2019-11-10 00:00:00</td>\n",
       "      <td>Organic</td>\n",
       "      <td>1.69</td>\n",
       "      <td>20175.0</td>\n",
       "      <td>871.0</td>\n",
       "      <td>383.0</td>\n",
       "      <td>1507.0</td>\n",
       "      <td>17414.0</td>\n",
       "      <td>17101.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Geography Timeframe Current Year Week Ending     Type  \\\n",
       "5179                 West    Weekly      2019-11-10 00:00:00  Organic   \n",
       "5180  West Tex/New Mexico    Weekly      2019-12-01 00:00:00  Organic   \n",
       "5181  West Tex/New Mexico    Weekly      2019-11-24 00:00:00  Organic   \n",
       "5182  West Tex/New Mexico    Weekly      2019-11-17 00:00:00  Organic   \n",
       "5183  West Tex/New Mexico    Weekly      2019-11-10 00:00:00  Organic   \n",
       "\n",
       "      ASP Current Year  Total Bulk and Bags Units  4046 Units  4225 Units  \\\n",
       "5179              1.91                   235865.0     16366.0     19561.0   \n",
       "5180              1.52                    17646.0      1162.0       457.0   \n",
       "5181              1.62                    20587.0      1065.0       444.0   \n",
       "5182              1.68                    17278.0      1088.0       340.0   \n",
       "5183              1.69                    20175.0       871.0       383.0   \n",
       "\n",
       "      4770 Units  TotalBagged Units  SmlBagged Units  LrgBagged Units  \\\n",
       "5179       454.0           199484.0         135025.0          64370.0   \n",
       "5180      1114.0            14914.0          14358.0            556.0   \n",
       "5181      1635.0            17443.0          16791.0            652.0   \n",
       "5182      1230.0            14620.0          14618.0              2.0   \n",
       "5183      1507.0            17414.0          17101.0            312.0   \n",
       "\n",
       "      X-LrgBagged Units  \n",
       "5179               89.0  \n",
       "5180                0.0  \n",
       "5181                0.0  \n",
       "5182                0.0  \n",
       "5183                0.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating tail of dataset\n",
    "df.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Geography</th>\n",
       "      <th>Timeframe</th>\n",
       "      <th>Current Year Week Ending</th>\n",
       "      <th>Type</th>\n",
       "      <th>ASP Current Year</th>\n",
       "      <th>Total Bulk and Bags Units</th>\n",
       "      <th>4046 Units</th>\n",
       "      <th>4225 Units</th>\n",
       "      <th>4770 Units</th>\n",
       "      <th>TotalBagged Units</th>\n",
       "      <th>SmlBagged Units</th>\n",
       "      <th>LrgBagged Units</th>\n",
       "      <th>X-LrgBagged Units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3306</th>\n",
       "      <td>Denver</td>\n",
       "      <td>Weekly</td>\n",
       "      <td>2019-08-04 00:00:00</td>\n",
       "      <td>Organic</td>\n",
       "      <td>2.21</td>\n",
       "      <td>26289.46</td>\n",
       "      <td>5631.94</td>\n",
       "      <td>1340.69</td>\n",
       "      <td>2.33</td>\n",
       "      <td>19314.5</td>\n",
       "      <td>19166.36</td>\n",
       "      <td>148.14</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Geography Timeframe Current Year Week Ending     Type  ASP Current Year  \\\n",
       "3306    Denver    Weekly      2019-08-04 00:00:00  Organic              2.21   \n",
       "\n",
       "      Total Bulk and Bags Units  4046 Units  4225 Units  4770 Units  \\\n",
       "3306                   26289.46     5631.94     1340.69        2.33   \n",
       "\n",
       "      TotalBagged Units  SmlBagged Units  LrgBagged Units  X-LrgBagged Units  \n",
       "3306            19314.5         19166.36           148.14                0.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating sample\n",
    "df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5184 entries, 0 to 5183\n",
      "Data columns (total 13 columns):\n",
      "Geography                    5184 non-null object\n",
      "Timeframe                    5184 non-null object\n",
      "Current Year Week Ending     5184 non-null object\n",
      "Type                         5184 non-null object\n",
      "ASP Current Year             5184 non-null float64\n",
      "Total Bulk and Bags Units    5184 non-null float64\n",
      "4046 Units                   5184 non-null float64\n",
      "4225 Units                   5184 non-null float64\n",
      "4770 Units                   5184 non-null float64\n",
      "TotalBagged Units            5184 non-null float64\n",
      "SmlBagged Units              5184 non-null float64\n",
      "LrgBagged Units              5184 non-null float64\n",
      "X-LrgBagged Units            5184 non-null float64\n",
      "dtypes: float64(9), object(4)\n",
      "memory usage: 526.6+ KB\n"
     ]
    }
   ],
   "source": [
    "#creating information of dataset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ASP Current Year</th>\n",
       "      <th>Total Bulk and Bags Units</th>\n",
       "      <th>4046 Units</th>\n",
       "      <th>4225 Units</th>\n",
       "      <th>4770 Units</th>\n",
       "      <th>TotalBagged Units</th>\n",
       "      <th>SmlBagged Units</th>\n",
       "      <th>LrgBagged Units</th>\n",
       "      <th>X-LrgBagged Units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5184.000000</td>\n",
       "      <td>5.184000e+03</td>\n",
       "      <td>5.184000e+03</td>\n",
       "      <td>5.184000e+03</td>\n",
       "      <td>5.184000e+03</td>\n",
       "      <td>5.184000e+03</td>\n",
       "      <td>5.184000e+03</td>\n",
       "      <td>5.184000e+03</td>\n",
       "      <td>5184.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.412741</td>\n",
       "      <td>1.044700e+06</td>\n",
       "      <td>2.884655e+05</td>\n",
       "      <td>2.659674e+05</td>\n",
       "      <td>2.205838e+04</td>\n",
       "      <td>4.682060e+05</td>\n",
       "      <td>2.952141e+05</td>\n",
       "      <td>1.613626e+05</td>\n",
       "      <td>11629.342990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.371065</td>\n",
       "      <td>4.168012e+06</td>\n",
       "      <td>1.239021e+06</td>\n",
       "      <td>1.097338e+06</td>\n",
       "      <td>9.815348e+04</td>\n",
       "      <td>1.814242e+06</td>\n",
       "      <td>1.125809e+06</td>\n",
       "      <td>6.643936e+05</td>\n",
       "      <td>56767.333535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.540000</td>\n",
       "      <td>2.534500e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.555000e+01</td>\n",
       "      <td>4.555000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.147500</td>\n",
       "      <td>1.802272e+04</td>\n",
       "      <td>5.576625e+02</td>\n",
       "      <td>2.402883e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.337082e+04</td>\n",
       "      <td>1.016043e+04</td>\n",
       "      <td>1.378890e+03</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.370000</td>\n",
       "      <td>1.556058e+05</td>\n",
       "      <td>1.285466e+04</td>\n",
       "      <td>1.773226e+04</td>\n",
       "      <td>2.669300e+02</td>\n",
       "      <td>7.898010e+04</td>\n",
       "      <td>5.354757e+04</td>\n",
       "      <td>1.520447e+04</td>\n",
       "      <td>3.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.630000</td>\n",
       "      <td>5.441795e+05</td>\n",
       "      <td>1.173928e+05</td>\n",
       "      <td>1.237169e+05</td>\n",
       "      <td>5.422078e+03</td>\n",
       "      <td>2.383269e+05</td>\n",
       "      <td>1.463408e+05</td>\n",
       "      <td>7.039264e+04</td>\n",
       "      <td>1865.455000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.780000</td>\n",
       "      <td>6.245151e+07</td>\n",
       "      <td>1.949892e+07</td>\n",
       "      <td>1.788781e+07</td>\n",
       "      <td>1.591800e+06</td>\n",
       "      <td>2.347299e+07</td>\n",
       "      <td>1.526452e+07</td>\n",
       "      <td>8.378356e+06</td>\n",
       "      <td>844929.830000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ASP Current Year  Total Bulk and Bags Units    4046 Units  \\\n",
       "count       5184.000000               5.184000e+03  5.184000e+03   \n",
       "mean           1.412741               1.044700e+06  2.884655e+05   \n",
       "std            0.371065               4.168012e+06  1.239021e+06   \n",
       "min            0.540000               2.534500e+02  0.000000e+00   \n",
       "25%            1.147500               1.802272e+04  5.576625e+02   \n",
       "50%            1.370000               1.556058e+05  1.285466e+04   \n",
       "75%            1.630000               5.441795e+05  1.173928e+05   \n",
       "max            2.780000               6.245151e+07  1.949892e+07   \n",
       "\n",
       "         4225 Units    4770 Units  TotalBagged Units  SmlBagged Units  \\\n",
       "count  5.184000e+03  5.184000e+03       5.184000e+03     5.184000e+03   \n",
       "mean   2.659674e+05  2.205838e+04       4.682060e+05     2.952141e+05   \n",
       "std    1.097338e+06  9.815348e+04       1.814242e+06     1.125809e+06   \n",
       "min    0.000000e+00  0.000000e+00       4.555000e+01     4.555000e+01   \n",
       "25%    2.402883e+03  0.000000e+00       1.337082e+04     1.016043e+04   \n",
       "50%    1.773226e+04  2.669300e+02       7.898010e+04     5.354757e+04   \n",
       "75%    1.237169e+05  5.422078e+03       2.383269e+05     1.463408e+05   \n",
       "max    1.788781e+07  1.591800e+06       2.347299e+07     1.526452e+07   \n",
       "\n",
       "       LrgBagged Units  X-LrgBagged Units  \n",
       "count     5.184000e+03        5184.000000  \n",
       "mean      1.613626e+05       11629.342990  \n",
       "std       6.643936e+05       56767.333535  \n",
       "min       0.000000e+00           0.000000  \n",
       "25%       1.378890e+03           0.000000  \n",
       "50%       1.520447e+04           3.330000  \n",
       "75%       7.039264e+04        1865.455000  \n",
       "max       8.378356e+06      844929.830000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating decribing in numerical format\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Missing Values: 0\n"
     ]
    }
   ],
   "source": [
    "# checking count from dataset \n",
    "\n",
    "#missing count values range\n",
    "\n",
    "\n",
    "count= df.isnull().sum()\n",
    "\n",
    "count\n",
    "\n",
    "total_cells = np.product(df)\n",
    "\n",
    "total_missing = count.sum()\n",
    "\n",
    "print('Total Missing Values:',total_missing)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#importing the following library\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#creating histogram\n",
    "\n",
    "plt.figure()\n",
    "plt.figure(figsize=(20,7))\n",
    "plt.hist(df['Total Bulk and Bags Units'],color = 'blue', edgecolor = 'black', bins= 50)\n",
    "plt.style.use('ggplot')\n",
    "plt.title('Trend of Product')\n",
    "plt.xlabel('Total Bulk and Bags Units')\n",
    "plt.ylabel('TotalBagged Units')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}