Dealing-With-Missing-Data-Using-Date-Range.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dealing With Missing Data Using `date_range`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from datetime import timedelta\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an incomplete series of random data (some dates missing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = []\n",
    "numbers = []\n",
    "start_date = pd.Timestamp(\"2020-01-01\")\n",
    "date = start_date\n",
    "\n",
    "for i in range(31):\n",
    "    dates.append(date)\n",
    "    numbers.append(np.sin((date - start_date).days / 20))\n",
    "    \n",
    "    # Increment date or skip ahead to simulate missing data\n",
    "    if date == pd.Timestamp('2020-01-13'):\n",
    "        inc = 10  # days\n",
    "    else:\n",
    "        inc = 1\n",
    "    date = date + pd.Timedelta(days=inc)\n",
    "\n",
    "index = pd.Index(dates, name='Date')\n",
    "df_random_data = pd.DataFrame(numbers, index=index, columns=['Numbers'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>Numbers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-12</th>\n",
       "      <td>0.522687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-13</th>\n",
       "      <td>0.564642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>0.891207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>0.912764</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Numbers\n",
       "Date                \n",
       "2020-01-12  0.522687\n",
       "2020-01-13  0.564642\n",
       "2020-01-23  0.891207\n",
       "2020-01-24  0.912764"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_random_data.loc['2020-01-12':'2020-01-24']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What if we try to plot this?\n",
    "df_random_data.plot(grid=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Looks okay, but is it?\n",
    "# Note: There is a gap in the data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# To see the gap, we can make a bar plot\n",
    "# Bar plots assume the data is categorical\n",
    "df_random_data.plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Before we plot this, we might want to add NaNs or zeros \n",
    "# for the days with no data - for example, maybe we know \n",
    "# there was a shutdown during these days and therefore \n",
    "# production is zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04',\n",
       "               '2020-01-05', '2020-01-06', '2020-01-07', '2020-01-08',\n",
       "               '2020-01-09', '2020-01-10', '2020-01-11', '2020-01-12',\n",
       "               '2020-01-13', '2020-01-14', '2020-01-15', '2020-01-16',\n",
       "               '2020-01-17', '2020-01-18', '2020-01-19', '2020-01-20',\n",
       "               '2020-01-21', '2020-01-22', '2020-01-23', '2020-01-24',\n",
       "               '2020-01-25', '2020-01-26', '2020-01-27', '2020-01-28',\n",
       "               '2020-01-29', '2020-01-30', '2020-01-31', '2020-02-01',\n",
       "               '2020-02-02', '2020-02-03', '2020-02-04', '2020-02-05',\n",
       "               '2020-02-06', '2020-02-07', '2020-02-08', '2020-02-09'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First create a date range that is complete\n",
    "start_date = df_random_data.first_valid_index()\n",
    "end_date = df_random_data.last_valid_index()\n",
    "\n",
    "my_date_range = pd.date_range(start_date, end_date, freq='D')\n",
    "my_date_range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Numbers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-12</th>\n",
       "      <td>0.522687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-13</th>\n",
       "      <td>0.564642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-14</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-15</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-16</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-17</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-18</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-19</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-20</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-21</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>0.891207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>0.912764</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Numbers\n",
       "date                \n",
       "2020-01-12  0.522687\n",
       "2020-01-13  0.564642\n",
       "2020-01-14       NaN\n",
       "2020-01-15       NaN\n",
       "2020-01-16       NaN\n",
       "2020-01-17       NaN\n",
       "2020-01-18       NaN\n",
       "2020-01-19       NaN\n",
       "2020-01-20       NaN\n",
       "2020-01-21       NaN\n",
       "2020-01-22       NaN\n",
       "2020-01-23  0.891207\n",
       "2020-01-24  0.912764"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = pd.Index(my_date_range, name=\"date\")\n",
    "\n",
    "df_complete = pd.DataFrame(data=df_random_data, index=index)\n",
    "df_complete.loc['2020-01-12':'2020-01-24']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace NaNs with zeros\n",
    "#df_complete = df_complete.fillna(0)\n",
    "#df_complete.loc['2015-01-12':'2015-01-24']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Numbers</th>\n",
       "      <th>Week Number</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-12</th>\n",
       "      <td>0.522687</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-13</th>\n",
       "      <td>0.564642</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>0.891207</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>0.912764</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Numbers  Week Number\n",
       "date                             \n",
       "2020-01-12  0.522687            2\n",
       "2020-01-13  0.564642            3\n",
       "2020-01-14       NaN            3\n",
       "2020-01-15       NaN            3\n",
       "2020-01-16       NaN            3\n",
       "2020-01-17       NaN            3\n",
       "2020-01-18       NaN            3\n",
       "2020-01-19       NaN            3\n",
       "2020-01-20       NaN            4\n",
       "2020-01-21       NaN            4\n",
       "2020-01-22       NaN            4\n",
       "2020-01-23  0.891207            4\n",
       "2020-01-24  0.912764            4"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_complete = df_complete.reset_index()\n",
    "df_complete['Week Number'] = df_complete['date'].dt.week\n",
    "df_complete = df_complete.set_index('date', drop=True)\n",
    "df_complete.loc['2020-01-12':'2020-01-24']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAEDCAYAAAAcI05xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAYe0lEQVR4nO3de3SV1Z3/8feXhBIUqgMJDpjB4HS8MbmI4SYwBVEaK62riuVSEWacptUfQn/O/Fz215lW2+qi0w6gY2fNisqlDpIKFRxh2UKXxQsCkmAoIChig8RLuTgiiAy37/xxTtIQD5yTcC77JJ/XWizOeZ59nvN9QvJhZ5/97MfcHRERCVenTBcgIiJnpqAWEQmcglpEJHAKahGRwCmoRUQCp6AWEQlcbioOmp+f70VFRak4tIhIu1RbW7vP3Qti7UtJUBcVFVFTU5OKQ4uItEtmtut0+zT0ISISOAW1iEjgFNQiIoFLyRi1tH/Hjh2joaGBI0eOZLqUdi8vL4/CwkI6d+6c6VIkQxTU0iYNDQ10796doqIizCzT5bRb7s7+/ftpaGigX79+mS5HMiTu0IeZXWpmdc3+fGxm30lHcRKuI0eO0LNnT4V0ipkZPXv21G8uHVzcHrW7vwGUAZhZDvAusDTFdUkWUEinh77O0toPE0cDO939tPP9RNIlJyeHsrIy+vfvT2lpKbNmzeLkyZNnfE19fT1PPvlkmipM3Jw5czh8+HCmy5BAtXaMegKwKNYOM6sEKgH69u17lmW1P0X3rkioXf3MG1JcSWoken6JSuTr0LVrV+rq6gDYs2cPkyZN4sCBA9x///2nP240qCdNmpS0WpNhzpw53HrrrZxzzjkZef9E/v2y9XsTsv/8Eu5Rm9nngK8Ci2Ptd/cqdy939/KCgphXQYqkTK9evaiqquKRRx7B3amvr2fEiBEMGDCAAQMG8MorrwBw77338tJLL1FWVsbs2bNP266lX/ziF5SUlFBaWsrkyZMB2LVrF6NHj6akpITRo0fzzjvvADB16lSWLFnS9Npu3boBsHr1akaOHMm4ceO47LLL+MY3voG78/DDD/Pee+8xatQoRo0alcovk2Sp1vSorwc2uvsfU1WMyNm4+OKLOXnyJHv27KFXr16sWrWKvLw8duzYwcSJE6mpqWHmzJn87Gc/Y/ny5QAcPnw4Zrvmtm7dygMPPMCaNWvIz8/nww8/BGDatGncdtttTJkyhblz5zJ9+nSWLVt2xhpfe+01tm7dSp8+fRg2bBhr1qxh+vTpzJo1i9/97nfk5+en5osjWa01QT2R0wx7iISi8R6gx44dY9q0adTV1ZGTk8Obb74Zs30i7Z5//nnGjRvXFKI9evQAYO3atTz99NMATJ48mXvuuSdufYMGDaKwsBCAsrIy6uvrGT58eOtPNFDFC4oTard5yuYUV9K+JBTUZnYOcB3wrdSWI9J2b7/9Njk5OfTq1Yv777+fCy64gE2bNnHy5Eny8vJivmb27Nlx27l7QjMvGtvk5uY2fajp7hw9erSpTZcuXZoe5+TkcPz48Vado3RMCY1Ru/thd+/p7gdSXZBIW+zdu5dvf/vbTJs2DTPjwIED9O7dm06dOvHEE09w4sQJALp3787BgwebXne6ds2NHj2ap556iv379wM0DX1cffXVVFdXA7Bw4cKmnnFRURG1tbUAPPPMMxw7dixu/S3rEmlOa31I1vr000+bpudde+21jBkzhh/84AcA3HnnnSxYsIAhQ4bw5ptvcu655wJQUlJCbm4upaWlzJ49+7Ttmuvfvz/f+973+OIXv0hpaSl33303AA8//DDz5s2jpKSEJ554goceegiAb37zm7zwwgsMGjSI9evXxzxmS5WVlVx//fX6MFFissYxvWQqLy93rUd9qvY2PW/btm1cfvnlmS6jw0j11ztZ09dCHaNO5/m19dzMrNbdy2PtU49aRCRwCmoRkcApqEVEAqegFhEJnIJaRCRwCmoRkcApqCWrLV26FDNj+/btmS5FJGV0Ky5JjvvOS/LxErsIdtGiRQwfPpzq6mruu+++s3rLEydOkJOTc1bHEEkF9aglax06dIg1a9bw+OOPU11dzXPPPcfXv/71pv2rV6/mK1/5CgArV65k6NChDBgwgFtuuYVDhw4Bkcu9f/jDHzJ8+HAWL17Mo48+ysCBAyktLeXmm29uWsx/586dDBkyhIEDB/L973+/aelSgJ/+9KcMHDiQkpKSpisjRZJJQS1Za9myZVRUVHDJJZfQo0cPevbsybp16/jkk08A+OUvf8n48ePZt28fP/7xj/ntb3/Lxo0bKS8vZ9asWU3HycvL4+WXX2bChAncdNNNbNiwgU2bNnH55Zfz+OOPAzBjxgxmzJjBhg0b6NOnT9NrV65cyY4dO3j11Vepq6ujtraWF198Mb1fCGn3FNSStRYtWsSECRMAmDBhAosXL6aiooJnn32W48ePs2LFCm688UbWrVvH66+/zrBhwygrK2PBggXs2vWnu8mNHz++6fGWLVsYMWIExcXFLFy4kK1btwKRJU1vueUWgFPuDrNy5UpWrlzJlVdeyYABA9i+fTs7duxIx+lLB6IxaslK+/fv5/nnn2fLli2YGSdOnMDMmDdvHj//+c/p0aMHAwcOpHv37rg71113HYsWxV5OvfmiSVOnTmXZsmWUlpYyf/58Vq9efcY63J3vfve7fOtbWgFYUkc9aslKS5Ys4bbbbmPXrl3U19eze/du+vXrR25uLhs3buTRRx9t6ikPGTKENWvW8NZbbwGRu7qc7kYCBw8epHfv3hw7doyFCxc2bR8yZAi/+tWvAJqWNgX40pe+xNy5c5vGvN9991327NmTknOWjktBLVlp0aJFfO1rXztl280330x1dTVjx47lueeeY+zYsQAUFBQwf/58Jk6cSElJCUOGDDntdL4f/ehHDB48mOuuu47LLrusafucOXOYNWsWgwYN4v333+e88yKzXMaMGcOkSZMYOnQoxcXFjBs3TutKS9Jp6EOSI8HpdMkSa0hi+vTpTY8feeSRU/Zdc801bNiw4TOvqa+vP+X5HXfcwR133PGZdhdeeCHr1q3DzKiurqa8/E+rUTZ+0CiSKgpqkQTU1tYybdo03J3zzz+fuXPnZrok6UAU1CIJGDFiBJs2bcp0GdJBaYxaRCRwCQW1mZ1vZkvMbLuZbTOzoakuTEREIhId+ngI+LW7jzOzzwHnpLAmERFpJm5Qm9nngb8BpgK4+1HgaGrLEhGRRokMfVwM7AXmmdlrZvaYmZ0b70Ui6fDAAw/Qv39/SkpKKCsrY/369ZkuSSTpEhn6yAUGAHe5+3ozewi4F/jn5o3MrBKoBOjbt2+y65TAFS8oTurxNk/ZHLfN2rVrWb58ORs3bqRLly7s27ePo0f1y560P4n0qBuABndv7KosIRLcp3D3Kncvd/fygoKCZNYoEtP7779Pfn4+Xbp0ASA/P58+ffpQVFTEvn37AKipqWHkyJEA3HfffUyZMoUxY8ZQVFTE008/zT333ENxcTEVFRUcO3YsU6cickZxg9rdPwB2m9ml0U2jgddTWpVIAsaMGcPu3bu55JJLuPPOO3nhhRfivmbnzp2sWLGCZ555hltvvZVRo0axefNmunbtyooVK9JQtUjrJTqP+i5goZn9HigDHkxdSSKJ6datG7W1tVRVVVFQUMD48eOZP3/+GV9z/fXX07lzZ4qLizlx4gQVFRUAFBcXf+ZycpFQJDQ9z93rgPK4DUXSLCcnh5EjRzJy5EiKi4tZsGABubm5nDx5EoAjR46c0r5xmKRTp0507twZM2t6fvz48fQWL5IgXZkoWeuNN944ZZH+uro6LrroIoqKiqitrQVoWppUJJtprQ/JWocOHeKuu+7io48+Ijc3ly984QtUVVWxbds2br/9dh588EEGDx6c6TJFzpqCWpIikel0yXbVVVfxyiuvfGb7iBEjYt4YoOVdyhsX+4+1TyQkGvoQEQmcglpEJHAKahGRwCmopc3cPdMldAj6OouCWtokLy+P/fv3K0RSzN3Zv38/eXl5mS5FMkizPqRNCgsLaWhoYO/evZkupd3Ly8ujsLAw02VIBimopU06d+5Mv379Ml2GSIegoQ8RkcApqEVEAqegFhEJnIJaRCRwCmoRkcApqEVEAqegFhEJnIJaRCRwCmoRkcApqEVEAqegFhEJXEJrfZhZPXAQOAEcd3fdkVxEJE1asyjTKHffl7JKREQkJg19iIgELtGgdmClmdWaWWUqCxIRkVMlOvQxzN3fM7NewCoz2+7uLzZvEA3wSoC+ffu2qZiie1fEbVM/84Y2HVtEJFsl1KN29/eif+8BlgKDYrSpcvdydy8vKChIbpUiIh1Y3KA2s3PNrHvjY2AMsCXVhYmISEQiQx8XAEvNrLH9k+7+65RWJSIiTeIGtbu/DZSmoRYREYlB0/NERAKnoBYRCZyCWkQkcApqEZHAKahFRAKnoBYRCZyCWkQkcApqEZHAKahFRAKnoBYRCZyCWkQkcApqEZHAKahFRAKnoBYRCZyCWkQkcApqEZHAKahFRAKnoBYRCZyCWkQkcApqEZHAJRzUZpZjZq+Z2fJUFiQiIqdqTY96BrAtVYWIiEhsCQW1mRUCNwCPpbYcERFpKdEe9RzgHuBkCmsREZEYcuM1MLOxwB53rzWzkWdoVwlUAvTt2zdpBYqEoOjeFXHb1M+8IQ2VSEeUSI96GPBVM6sHqoFrzOw/WzZy9yp3L3f38oKCgiSXKSLSccUNanf/rrsXunsRMAF43t1vTXllIiICaB61iEjw4o5RN+fuq4HVKalERERiUo9aRCRwCmoRkcApqEVEAqegFhEJXKs+TBSRs1e8oDhum81TNqehEskW6lGLiAROQS0iEjgFtYhI4BTUIiKBU1CLiAROQS0iEjgFtYhI4BTUIiKBU1CLiAROQS0iEjgFtYhI4BTUIiKBU1CLiAROQS0iEjgFtYhI4BTUIiKBixvUZpZnZq+a2SYz22pm96ejMBERiUjkDi//A1zj7ofMrDPwspk95+7rUlybiIiQQFC7uwOHok87R/94KosSEZE/SWiM2sxyzKwO2AOscvf1qS1LREQaJRTU7n7C3cuAQmCQmf11yzZmVmlmNWZWs3fv3mTXKSLSYbVq1oe7fwSsBipi7Kty93J3Ly8oKEhSeSIiksisjwIzOz/6uCtwLbA91YWJiEhEIrM+egMLzCyHSLA/5e7LU1uWiIg0SmTWx++BK9NQi4iIxKArE0VEAqegFhEJnIJaRCRwiXyYKIEpXlAct83mKZvTUImIpIN61CIigVNQi4gETkEtIhI4BbWISOAU1CIigVNQi4gETkEtIhI4BbWISOAU1CIigVNQi4gETkEtIhI4BbWISOAU1CIigVNQi4gETkEtIhI4BbWISODiBrWZ/YWZ/c7MtpnZVjObkY7CREQkIpE7vBwH/sHdN5pZd6DWzFa5++sprk1EREigR+3u77v7xujjg8A24MJUFyYiIhGtGqM2syLgSmB9KooREZHPSjiozawb8CvgO+7+cYz9lWZWY2Y1e/fuTWaNIiIdWkJBbWadiYT0Qnd/OlYbd69y93J3Ly8oKEhmjSIiHVoisz4MeBzY5u6zUl+SiIg0l0iPehgwGbjGzOqif76c4rpERCQq7vQ8d38ZsDTUIiIiMejKRBGRwCmoRUQCp6AWEQmcglpEJHAKahGRwCmoRUQCp6AWEQmcglpEJHAKahGRwCmoRUQCp6AWEQmcglpEJHAKahGRwCmoRUQCp6AWEQmcglpEJHAKahGRwCmoRUQCp6AWEQmcglpEJHAKahGRwMUNajOba2Z7zGxLOgoSEZFTJdKjng9UpLgOERE5jbhB7e4vAh+moRYREYlBY9QiIoHLTdaBzKwSqATo27dvsg4rWaLo3hVx29TPvCENlUjK3Hde/Db99LOfCkkLanevAqoAysvLPVnHFRFJi4D/I0paUIt0eIn8oIN6ndJqiUzPWwSsBS41swYzuz31ZYmISKO4PWp3n5iOQkREJDbN+hARCZyCWkQkcApqEZHAKahFRAKnoBYRCZyCWkQkcApqEZHAKahFRAKnS8glfRK8xLo4gUusN0/ZfLbViGQN9ahFRALXLnvUxQuK47ZRj0xEsoV61CIigVNQi4gErl0OfWS1gBcvF5HMUI9aRCRwCmoRkcApqEVEAqegFhEJnIJaRCRwCmoRkcApqEVEApfQPGozqwAeAnKAx9x9ZkqrOhPNMxaRDiZuj9rMcoCfA9cDVwATzeyKVBcmIiIRiQx9DALecve33f0oUA3cmNqyRESkkbn7mRuYjQMq3P3vo88nA4PdfVqLdpVAZfTppcAbyS83pnxgX5reKxN0ftlN55e90n1uF7l7QawdiYxRW4xtn0l3d68CqlpZ2Fkzsxp3L0/3+6aLzi+76fyyV0jnlsjQRwPwF82eFwLvpaYcERFpKZGg3gD8lZn1M7PPAROA/0ptWSIi0iju0Ie7HzezacBviEzPm+vuW1NeWeLSPtySZjq/7Kbzy17BnFvcDxNFRCSzdGWiiEjgFNQiIoFTUIuIBE5BHRgzu8zMRptZtxbbKzJVUzKZ2SAzGxh9fIWZ3W1mX850XalgZr/IdA2pYmbDo/92YzJdSzKY2WAz+3z0cVczu9/MnjWzn5hZAgsMpbi+9vRhopn9rbvPy3QdbWVm04H/A2wDyoAZ7v5MdN9Gdx+QyfrOlpn9gMiaMbnAKmAwsBq4FviNuz+QuerOjpm1nLJqwCjgeQB3/2rai0oiM3vV3QdFH3+TyPfpUmAM8GxGF2pLAjPbCpRGZ7lVAYeBJcDo6PabMlpfOwvqd9w9a5fOM7PNwFB3P2RmRUS+UZ5w94fM7DV3vzKjBZ6l6PmVAV2AD4BCd//YzLoC6929JKMFngUz2wi8DjxG5MpdAxYRue4Ad38hc9Wdvebff2a2Afiyu+81s3OBde5enNkKz46ZbXP3y6OPT+kUmVmdu5dlrroElzkNiZn9/nS7gAvSWUsK5Lj7IQB3rzezkcASM7uI2JfyZ5vj7n4COGxmO939YwB3/9TMTma4trNVDswAvgf8P3evM7NPsz2gm+lkZn9GZLjU3H0vgLt/YmbHM1taUmxp9hv5JjMrd/caM7sEOJbp4rIuqImE8ZeA/26x3YBX0l9OUn1gZmXuXgcQ7VmPBeYCWd1jiTpqZue4+2HgqsaN0THArA5qdz8JzDazxdG//0h2/nydznlALZGfMzezP3f3D6KfpbSHTsTfAw+Z2T8RWYhprZntBnZH92VU1g19mNnjwDx3fznGvifdfVIGykoKMysk0uv8IMa+Ye6+JgNlJY2ZdXH3/4mxPR/o7e6bM1BWSpjZDcAwd///ma4llczsHOACd/9DpmtJBjPrDlxM5D/ZBnf/Y4ZLArIwqEVEOhpNzxMRCZyCWkQkcApqSTkzm21m32n2/Ddm9liz5/9qZne34bjzo3cgitfmXTPrEn2eb2b1rX2v0xx7pJktT8axRM5EQS3p8ApwNYCZdSJyi6P+zfZfDaTyg9ITwN+l8PhtEr1xtEhcCmpJhzVEg5pIQG8BDprZn0V7upcDr5nZVWb2gpnVRnvdvQHM7C/N7NfR7S+Z2WUt38DMfhTtPcf6np4D/F8zy23xmlN6xGb2iJlNjT6uN7MHzWytmdWY2YBoTTvN7NvNDvN5M1tqZq+b2X80vr+ZjYm+dqOZLW5cEiB63O+b2cvALW36akqHo6CWlHP394DjZtaXSGCvBdYDQ4lcKPJ7Ilfz/Rswzt2vIjJ3vPGS8irgruj2fwT+vfnxzexfgF7A30bnM7f0DvAyMLmVpe9296HAS8B8YBwwBPhhszaDgH8gMs/9L4GbotMN/wm4NnqFWw3QfGjniLsPd/fqVtYjHVR7mpAvYWvsVV8NzAIujD4+QGRo5FLgr4FVZgaRuwm9H+2JXg0sjm6HyCXojf6ZyOXnlXHe/0Eit5Bb0YqaG9fv2Ax0c/eDRH4TOGJm50f3verubwOY2SJgOHAEuAJYE635c0T+c2r0y1bUIKKglrRpHKcuJjL0sZtIT/RjIr1nA7ZGe7BNoiuafXSGtRY2AFeZWQ93//B0b+7ub5lZHfD1ZpuPc+pvlXktXtZ4cc7JZo8bnzf+7LS8EKFxnY9V7j7xNOV8cro6RWLR0IekyxpgLPChu5+Ihur5RIY/1gJvAAVmNhTAzDqbWf/oeiB/MLNbotvNzEqbHffXwExgRfSqsjN5gMjQSaNdwBVm1iV6GfvoNpzXIIvc+LkTMJ7IEMs6YJiZfSFa8znRNSNE2kRBLemymchsj3Utth1w933ufpTIGPBPzGwTUMefPoD8BnB7dPtW4MbmB3b3xcCjwH9ZZCW+mKI3Zd7Y7Plu4CkiY+QLgdfacF5rifxHsQX4A7A0umDRVGBRdBGxdcBnPgAVSZQuIRcRCZx61CIigVNQi4gETkEtIhI4BbWISOAU1CIigVNQi4gETkEtIhI4BbWISOD+FxdcHGIZM+nqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weekly_data = pd.DataFrame({\n",
    "    'Data count': df_complete.groupby('Week Number')['Numbers'].count(),\n",
    "    'Average':  df_complete.groupby('Week Number')['Numbers'].mean(),\n",
    "    'Sum':  df_complete.groupby('Week Number')['Numbers'].sum()\n",
    "})\n",
    "weekly_data.plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:torch]",
   "language": "python",
   "name": "conda-env-torch-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}