Neonates_growth_v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8043e539",
   "metadata": {},
   "source": [
    "## Data analysis for R&D purpose to analyse the growth of larvea of black soldier flies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b14e1fc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import python basic libraries\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "634c0f7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load and read\n",
    "\n",
    "nta = pd.read_csv('Nutrition_Technologies_SQL_Takehome_A.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d4518ade",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load and read\n",
    "\n",
    "ntb = pd.read_csv('Nutrition_Technologies_SQL_Takehome_B.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80d2904b",
   "metadata": {},
   "source": [
    "## Data cleaning, removing duplicates and correcting typo errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6be5a56a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IY_date</th>\n",
       "      <th>IY_day</th>\n",
       "      <th>IY_SILC_pupae_batch</th>\n",
       "      <th>IY_SILC_pupae_per_cage_kg</th>\n",
       "      <th>IY_SILC_number_of_cages_filled</th>\n",
       "      <th>IY_SILC_days_in_insectary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-06-13</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-06-14</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-06-15</td>\n",
       "      <td>Monday</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-06-16</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-06-17</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      IY_date     IY_day IY_SILC_pupae_batch  IY_SILC_pupae_per_cage_kg  \\\n",
       "0  2020-06-13   Saturday                 NaN                        NaN   \n",
       "1  2020-06-14     Sunday                 NaN                        NaN   \n",
       "2  2020-06-15     Monday                 NaN                        NaN   \n",
       "3  2020-06-16    Tuesday                 NaN                        NaN   \n",
       "4  2020-06-17  Wednesday                 NaN                        NaN   \n",
       "\n",
       "   IY_SILC_number_of_cages_filled  IY_SILC_days_in_insectary  \n",
       "0                             NaN                        NaN  \n",
       "1                             NaN                        NaN  \n",
       "2                             NaN                        NaN  \n",
       "3                             NaN                        NaN  \n",
       "4                             NaN                        NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# review File A\n",
    "\n",
    "nta.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7a0e5b7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date_51h</th>\n",
       "      <th>Daily_Total_51h</th>\n",
       "      <th>L210324</th>\n",
       "      <th>L210325</th>\n",
       "      <th>L210326</th>\n",
       "      <th>L210327</th>\n",
       "      <th>L210328</th>\n",
       "      <th>L210329</th>\n",
       "      <th>L210330</th>\n",
       "      <th>L210331</th>\n",
       "      <th>...</th>\n",
       "      <th>L210905</th>\n",
       "      <th>L210906</th>\n",
       "      <th>L210907</th>\n",
       "      <th>L210908</th>\n",
       "      <th>L210909</th>\n",
       "      <th>L210910</th>\n",
       "      <th>G210910</th>\n",
       "      <th>L210911</th>\n",
       "      <th>L210912</th>\n",
       "      <th>L210913</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-04-15</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-04-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-04-17</td>\n",
       "      <td>27.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-04-18</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-04-19</td>\n",
       "      <td>63.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 176 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Date_51h  Daily_Total_51h  L210324  L210325  L210326  L210327  L210328  \\\n",
       "0  2021-04-15              6.0      NaN      6.0      NaN      NaN      NaN   \n",
       "1  2021-04-16              NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2  2021-04-17             27.0     24.0      NaN      3.0      NaN      NaN   \n",
       "3  2021-04-18              7.0      NaN      2.0      NaN      5.0      NaN   \n",
       "4  2021-04-19             63.0      NaN     12.0     52.0      NaN      NaN   \n",
       "\n",
       "   L210329  L210330  L210331  ...  L210905  L210906  L210907  L210908  \\\n",
       "0      NaN      NaN      NaN  ...      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN  ...      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN  ...      NaN      NaN      NaN      NaN   \n",
       "3      NaN      NaN      NaN  ...      NaN      NaN      NaN      NaN   \n",
       "4      NaN      NaN      NaN  ...      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   L210909  L210910  G210910  L210911  L210912  L210913  \n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN  \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN  \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN  \n",
       "3      NaN      NaN      NaN      NaN      NaN      NaN  \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN  \n",
       "\n",
       "[5 rows x 176 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# review File B\n",
    "\n",
    "ntb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b86dea3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1062 entries, 0 to 1061\n",
      "Data columns (total 6 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   IY_date                         1061 non-null   object \n",
      " 1   IY_day                          1055 non-null   object \n",
      " 2   IY_SILC_pupae_batch             344 non-null    object \n",
      " 3   IY_SILC_pupae_per_cage_kg       306 non-null    float64\n",
      " 4   IY_SILC_number_of_cages_filled  334 non-null    float64\n",
      " 5   IY_SILC_days_in_insectary       335 non-null    float64\n",
      "dtypes: float64(3), object(3)\n",
      "memory usage: 49.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# check for null values in File A\n",
    "\n",
    "nta.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b01fa311",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([488], dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify index of column 'IY_date' where value is null\n",
    "\n",
    "np.where(nta['IY_date'].isnull())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3d53a330",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IY_date                               NaN\n",
       "IY_day                                NaN\n",
       "IY_SILC_pupae_batch               G210910\n",
       "IY_SILC_pupae_per_cage_kg             NaN\n",
       "IY_SILC_number_of_cages_filled        6.0\n",
       "IY_SILC_days_in_insectary            18.0\n",
       "Name: 488, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify batch number where column 'IY_date' is null\n",
    "\n",
    "nta.loc[488]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "763d20ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# delete row index 488 from nta\n",
    "\n",
    "nta.drop(488, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bf8932e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# delete column 'G210910' due to 'IY_date' being null\n",
    "\n",
    "ntb.drop('G210910', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "410cb61a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to datetime format\n",
    "\n",
    "nta.IY_date = pd.to_datetime(nta.IY_date, format='%Y-%m-%d')\n",
    "ntb.Date_51h = pd.to_datetime(ntb.Date_51h, format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "97a03075",
   "metadata": {},
   "outputs": [],
   "source": [
    "# retrieve index to synchronise with first relevant batch in File B with File Aie. batch 'L210324'\n",
    "\n",
    "to_keep = nta[nta['IY_SILC_pupae_batch']=='L210324'].index.tolist()\n",
    "to_keep = str(to_keep)[1:-1]\n",
    "to_keep = int(to_keep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1b0cc9a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# keep values in File A from batch 'L210324' onwards\n",
    "\n",
    "nta = nta.iloc[to_keep:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8745cb28",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IY_date</th>\n",
       "      <th>IY_day</th>\n",
       "      <th>IY_SILC_pupae_batch</th>\n",
       "      <th>IY_SILC_pupae_per_cage_kg</th>\n",
       "      <th>IY_SILC_number_of_cages_filled</th>\n",
       "      <th>IY_SILC_days_in_insectary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>2021-04-13</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>L210324</td>\n",
       "      <td>60.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>2021-04-14</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>L210325</td>\n",
       "      <td>62.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>2021-04-15</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>L210326</td>\n",
       "      <td>51.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>2021-04-16</td>\n",
       "      <td>Friday</td>\n",
       "      <td>L210327</td>\n",
       "      <td>55.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>2021-04-17</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>L210328</td>\n",
       "      <td>40.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       IY_date     IY_day IY_SILC_pupae_batch  IY_SILC_pupae_per_cage_kg  \\\n",
       "304 2021-04-13    Tuesday             L210324                       60.0   \n",
       "305 2021-04-14  Wednesday             L210325                       62.0   \n",
       "306 2021-04-15   Thursday             L210326                       51.0   \n",
       "307 2021-04-16     Friday             L210327                       55.0   \n",
       "308 2021-04-17   Saturday             L210328                       40.0   \n",
       "\n",
       "     IY_SILC_number_of_cages_filled  IY_SILC_days_in_insectary  \n",
       "304                             7.0                       18.0  \n",
       "305                             8.0                       18.0  \n",
       "306                            11.0                       18.0  \n",
       "307                            11.0                       18.0  \n",
       "308                            11.0                       18.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# review File A to ensure it starts with batch 'L210324' as indicated in B file\n",
    "\n",
    "nta.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f423d221",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[nan,\n",
       " 'L210809',\n",
       " 'L210807',\n",
       " 'L210805',\n",
       " 'L210808',\n",
       " 'L210621',\n",
       " 'G210523',\n",
       " 'L210410',\n",
       " 'L210814 & L210815 (RnD pupae)',\n",
       " 'L210914']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify batches which are unique to both File A & B. Batch found in A but not in B.\n",
    "\n",
    "A_list = nta.IY_SILC_pupae_batch.tolist()\n",
    "B_list = ntb.columns.tolist()\n",
    "list(set(A_list)-set(B_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "38336890",
   "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>IY_date</th>\n",
       "      <th>IY_day</th>\n",
       "      <th>IY_SILC_pupae_batch</th>\n",
       "      <th>IY_SILC_pupae_per_cage_kg</th>\n",
       "      <th>IY_SILC_number_of_cages_filled</th>\n",
       "      <th>IY_SILC_days_in_insectary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>2021-09-04</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>L210814 &amp; L210815 (RnD pupae)</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       IY_date    IY_day            IY_SILC_pupae_batch  \\\n",
       "463 2021-09-04  Saturday  L210814 & L210815 (RnD pupae)   \n",
       "\n",
       "     IY_SILC_pupae_per_cage_kg  IY_SILC_number_of_cages_filled  \\\n",
       "463                       52.0                             1.0   \n",
       "\n",
       "     IY_SILC_days_in_insectary  \n",
       "463                       18.0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify anomalies in File A in column 'IY_SILC_pupae_batch' having 'L210814 & L210815 (RnD pupae)'\n",
    "\n",
    "nta[nta['IY_SILC_pupae_batch']=='L210814 & L210815 (RnD pupae)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "da71c6c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# change 'L210814 & L210815 (RnD pupae)' to 'L210815'\n",
    "\n",
    "nta.loc[463,'IY_SILC_pupae_batch']='L210815'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cd4cdc4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Date_51h', 'Daily_Total_51h', 'L210815_', 'G210601']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check whether there are any batches in File B but not in A\n",
    "\n",
    "A_list = nta.IY_SILC_pupae_batch.tolist()\n",
    "B_list = ntb.columns.tolist()\n",
    "list(set(B_list)-set(A_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9bb79e41",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correct anomalies in File B, changing 'L210815_' to 'L210815'\n",
    "\n",
    "ntb.rename(columns={'L210815_':'L210815'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f82c337b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop batch 'G210601' in File B which is not found in File A\n",
    "\n",
    "ntb.drop('G210601', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c71caf2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "L210421\n"
     ]
    }
   ],
   "source": [
    "# identify columns where all rows are null\n",
    "\n",
    "for i in ntb.columns:\n",
    "    x = ntb[i].isnull().sum()\n",
    "    if x > 909:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fc63c714",
   "metadata": {},
   "outputs": [],
   "source": [
    "ntb.drop('L210421', axis=1, inplace=True)\n",
    "to_drop = nta[nta['IY_SILC_pupae_batch']=='L210421'].index.tolist()\n",
    "nta.drop(to_drop, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "63de6549",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[nan,\n",
       " 'L210809',\n",
       " 'L210807',\n",
       " 'L210805',\n",
       " 'L210808',\n",
       " 'L210621',\n",
       " 'G210523',\n",
       " 'L210410',\n",
       " 'L210914']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review after correcting 'L210814 & L210815 (RnD pupae)' to 'L210815'\n",
    "\n",
    "A_list = nta.IY_SILC_pupae_batch.tolist()\n",
    "B_list = ntb.columns.tolist()\n",
    "diff_list = list(set(A_list)-set(B_list))\n",
    "diff_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ff3e98d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['L210809',\n",
       " 'L210807',\n",
       " 'L210805',\n",
       " 'L210808',\n",
       " 'L210621',\n",
       " 'G210523',\n",
       " 'L210410',\n",
       " 'L210914']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove null value and review again\n",
    "\n",
    "diff_list_clean = [x for x in diff_list if x == x]\n",
    "diff_list_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f1320dfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retrieve indexes from File A those batches which are in File A but not in B\n",
    "\n",
    "total = []\n",
    "for i in diff_list_clean:\n",
    "    k = nta[nta['IY_SILC_pupae_batch']==i].index.tolist()\n",
    "    total.append(k)\n",
    "total = [x for y in total for x in y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "deb4b5ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop these batches by their indexes \n",
    "\n",
    "nta.drop(labels=total, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f24d0296",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop any rows where batch numbers in column 'IY_SILC_pupae_batch' is null\n",
    "\n",
    "nta = nta.dropna(axis=0, subset=['IY_SILC_pupae_batch'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7ba62f4c",
   "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>IY_date</th>\n",
       "      <th>IY_day</th>\n",
       "      <th>IY_SILC_pupae_batch</th>\n",
       "      <th>IY_SILC_pupae_per_cage_kg</th>\n",
       "      <th>IY_SILC_number_of_cages_filled</th>\n",
       "      <th>IY_SILC_days_in_insectary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>2021-06-14</td>\n",
       "      <td>Monday</td>\n",
       "      <td>L210522</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>2021-06-28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G210604</td>\n",
       "      <td>58.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>2021-08-08</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>L210718</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>2021-08-14</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>L210717</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       IY_date    IY_day IY_SILC_pupae_batch  IY_SILC_pupae_per_cage_kg  \\\n",
       "368 2021-06-14    Monday             L210522                       56.0   \n",
       "390 2021-06-28       NaN             G210604                       58.0   \n",
       "436 2021-08-08    Sunday             L210718                       44.0   \n",
       "442 2021-08-14  Saturday             L210717                       61.0   \n",
       "\n",
       "     IY_SILC_number_of_cages_filled  IY_SILC_days_in_insectary  \n",
       "368                             1.0                       18.0  \n",
       "390                             6.0                       18.0  \n",
       "436                             2.0                       25.0  \n",
       "442                             2.0                       23.0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# identify duplicate values in File A\n",
    "\n",
    "nta[nta.IY_SILC_pupae_batch.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a02e03ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    171\n",
       "True       4\n",
       "Name: IY_SILC_pupae_batch, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summarize duplicate values in File A. 4 duplicate values\n",
    "\n",
    "nta.IY_SILC_pupae_batch.duplicated().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8b26e6c7",
   "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>IY_date</th>\n",
       "      <th>IY_day</th>\n",
       "      <th>IY_SILC_pupae_batch</th>\n",
       "      <th>IY_SILC_pupae_per_cage_kg</th>\n",
       "      <th>IY_SILC_number_of_cages_filled</th>\n",
       "      <th>IY_SILC_days_in_insectary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>2021-06-11</td>\n",
       "      <td>Friday</td>\n",
       "      <td>L210522</td>\n",
       "      <td>47.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>2021-06-14</td>\n",
       "      <td>Monday</td>\n",
       "      <td>L210522</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       IY_date  IY_day IY_SILC_pupae_batch  IY_SILC_pupae_per_cage_kg  \\\n",
       "363 2021-06-11  Friday             L210522                       47.0   \n",
       "368 2021-06-14  Monday             L210522                       56.0   \n",
       "\n",
       "     IY_SILC_number_of_cages_filled  IY_SILC_days_in_insectary  \n",
       "363                             3.0                       18.0  \n",
       "368                             1.0                       18.0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify 1st duplicate value ie. batch number 'L210522'\n",
    "\n",
    "nta[nta.IY_SILC_pupae_batch=='L210522']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "50e379bc",
   "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>IY_date</th>\n",
       "      <th>IY_day</th>\n",
       "      <th>IY_SILC_pupae_batch</th>\n",
       "      <th>IY_SILC_pupae_per_cage_kg</th>\n",
       "      <th>IY_SILC_number_of_cages_filled</th>\n",
       "      <th>IY_SILC_days_in_insectary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G210604</td>\n",
       "      <td>60.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>2021-06-28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G210604</td>\n",
       "      <td>58.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       IY_date IY_day IY_SILC_pupae_batch  IY_SILC_pupae_per_cage_kg  \\\n",
       "383 2021-06-24    NaN             G210604                       60.0   \n",
       "390 2021-06-28    NaN             G210604                       58.0   \n",
       "\n",
       "     IY_SILC_number_of_cages_filled  IY_SILC_days_in_insectary  \n",
       "383                             5.0                       18.0  \n",
       "390                             6.0                       18.0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify 2nd duplicate value ie. batch number 'G210604'\n",
    "\n",
    "nta[nta.IY_SILC_pupae_batch=='G210604']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "610f4af9",
   "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>IY_date</th>\n",
       "      <th>IY_day</th>\n",
       "      <th>IY_SILC_pupae_batch</th>\n",
       "      <th>IY_SILC_pupae_per_cage_kg</th>\n",
       "      <th>IY_SILC_number_of_cages_filled</th>\n",
       "      <th>IY_SILC_days_in_insectary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>2021-08-07</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>L210718</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>2021-08-08</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>L210718</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       IY_date    IY_day IY_SILC_pupae_batch  IY_SILC_pupae_per_cage_kg  \\\n",
       "434 2021-08-07  Saturday             L210718                       55.0   \n",
       "436 2021-08-08    Sunday             L210718                       44.0   \n",
       "\n",
       "     IY_SILC_number_of_cages_filled  IY_SILC_days_in_insectary  \n",
       "434                             5.0                       25.0  \n",
       "436                             2.0                       25.0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify 3rd duplicate value ie. batch number 'L210718'\n",
    "\n",
    "nta[nta.IY_SILC_pupae_batch=='L210718']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d6213989",
   "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>IY_date</th>\n",
       "      <th>IY_day</th>\n",
       "      <th>IY_SILC_pupae_batch</th>\n",
       "      <th>IY_SILC_pupae_per_cage_kg</th>\n",
       "      <th>IY_SILC_number_of_cages_filled</th>\n",
       "      <th>IY_SILC_days_in_insectary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>2021-08-06</td>\n",
       "      <td>Friday</td>\n",
       "      <td>L210717</td>\n",
       "      <td>53.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>2021-08-14</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>L210717</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       IY_date    IY_day IY_SILC_pupae_batch  IY_SILC_pupae_per_cage_kg  \\\n",
       "433 2021-08-06    Friday             L210717                       53.0   \n",
       "442 2021-08-14  Saturday             L210717                       61.0   \n",
       "\n",
       "     IY_SILC_number_of_cages_filled  IY_SILC_days_in_insectary  \n",
       "433                             8.0                       19.0  \n",
       "442                             2.0                       23.0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify 4th duplicate value ie. batch number 'L210717'\n",
    "\n",
    "nta[nta.IY_SILC_pupae_batch=='L210717']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "eca921f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the 4 duplicate values by their indexes\n",
    "\n",
    "nta.drop([368,390,436,442], axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "27d86625",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 171 entries, 304 to 493\n",
      "Data columns (total 6 columns):\n",
      " #   Column                          Non-Null Count  Dtype         \n",
      "---  ------                          --------------  -----         \n",
      " 0   IY_date                         171 non-null    datetime64[ns]\n",
      " 1   IY_day                          166 non-null    object        \n",
      " 2   IY_SILC_pupae_batch             171 non-null    object        \n",
      " 3   IY_SILC_pupae_per_cage_kg       170 non-null    float64       \n",
      " 4   IY_SILC_number_of_cages_filled  171 non-null    float64       \n",
      " 5   IY_SILC_days_in_insectary       171 non-null    float64       \n",
      "dtypes: datetime64[ns](1), float64(3), object(2)\n",
      "memory usage: 9.4+ KB\n"
     ]
    }
   ],
   "source": [
    "nta.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "58ade4f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 910 entries, 0 to 909\n",
      "Columns: 173 entries, Date_51h to L210913\n",
      "dtypes: datetime64[ns](1), float64(172)\n",
      "memory usage: 1.2 MB\n"
     ]
    }
   ],
   "source": [
    "ntb.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a17fc369",
   "metadata": {},
   "source": [
    "## Data is cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4257eeb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove not needed column 'Daily_Total_51h'\n",
    "\n",
    "ntb.drop('Daily_Total_51h',axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "dcd8095b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add 2 additional empty rows to the beginning of File B to accommodate first batch 'L210324'\n",
    "\n",
    "df1 = pd.DataFrame([[np.nan]* len(ntb.columns)], columns= ntb.columns)\n",
    "ntb = ntb.append(df1, ignore_index=True)\n",
    "ntb = ntb.append(df1, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e99e138d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shift the rows down by 2 rows\n",
    "\n",
    "ntb = ntb.shift()\n",
    "ntb = ntb.shift()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8d651dad",
   "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>days</th>\n",
       "      <th>Date_51h</th>\n",
       "      <th>L210324</th>\n",
       "      <th>L210325</th>\n",
       "      <th>L210326</th>\n",
       "      <th>L210327</th>\n",
       "      <th>L210328</th>\n",
       "      <th>L210329</th>\n",
       "      <th>L210330</th>\n",
       "      <th>L210331</th>\n",
       "      <th>...</th>\n",
       "      <th>L210904</th>\n",
       "      <th>L210905</th>\n",
       "      <th>L210906</th>\n",
       "      <th>L210907</th>\n",
       "      <th>L210908</th>\n",
       "      <th>L210909</th>\n",
       "      <th>L210910</th>\n",
       "      <th>L210911</th>\n",
       "      <th>L210912</th>\n",
       "      <th>L210913</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2021-04-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2021-04-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2021-04-17</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 173 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   days   Date_51h  L210324  L210325  L210326  L210327  L210328  L210329  \\\n",
       "0     1        NaT      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1     2        NaT      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2     3 2021-04-15      NaN      6.0      NaN      NaN      NaN      NaN   \n",
       "3     4 2021-04-16      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4     5 2021-04-17     24.0      NaN      3.0      NaN      NaN      NaN   \n",
       "\n",
       "   L210330  L210331  ...  L210904  L210905  L210906  L210907  L210908  \\\n",
       "0      NaN      NaN  ...      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN  ...      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN  ...      NaN      NaN      NaN      NaN      NaN   \n",
       "3      NaN      NaN  ...      NaN      NaN      NaN      NaN      NaN   \n",
       "4      NaN      NaN  ...      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   L210909  L210910  L210911  L210912  L210913  \n",
       "0      NaN      NaN      NaN      NaN      NaN  \n",
       "1      NaN      NaN      NaN      NaN      NaN  \n",
       "2      NaN      NaN      NaN      NaN      NaN  \n",
       "3      NaN      NaN      NaN      NaN      NaN  \n",
       "4      NaN      NaN      NaN      NaN      NaN  \n",
       "\n",
       "[5 rows x 173 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Insert a new column 'days' as 1st column and render values from 1 - 20 days\n",
    "\n",
    "ntb.insert(0, 'days', np.arange(1, ntb.shape[0]+1))\n",
    "ntb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5724bf97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['L210324', 'L210325', 'L210326', 'L210327', 'L210328', 'L210329',\n",
       "       'L210330', 'L210331', 'L210401', 'L210402',\n",
       "       ...\n",
       "       'L210904', 'L210905', 'L210906', 'L210907', 'L210908', 'L210909',\n",
       "       'L210910', 'L210911', 'L210912', 'L210913'],\n",
       "      dtype='object', length=171)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create new dataframe to extrate columns of File B\n",
    "\n",
    "df2 = ntb.drop(['days','Date_51h'], axis=1)\n",
    "df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1040d68f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create new empty dataframe to populate fresh values for visualization\n",
    "\n",
    "df = ntb[0:0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c2d7672f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Populate days column with days ranging from 1 to 20\n",
    "\n",
    "df.days = pd.Series(np.arange(1,21))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e568acfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reset index\n",
    "\n",
    "nta = nta.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "c7a88a09",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import datetime library\n",
    "\n",
    "from datetime import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d0282b48",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert both tables to datetime format\n",
    "\n",
    "nta.IY_date = pd.to_datetime(nta.IY_date, format='%Y-%m-%d')\n",
    "ntb.Date_51h = pd.to_datetime(ntb.Date_51h, format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ddb6ccf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize object for iteration for File A & B\n",
    "\n",
    "k = np.arange(2,171)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "2ba5acd6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# create dataframe with all values in it\n",
    "# b will interate thru batch names\n",
    "# i will interate thru values along columns of File B\n",
    "\n",
    "for b, i in zip(df2.columns,k):\n",
    "    last_index = ntb.loc[:,b].last_valid_index()\n",
    "    t1 = ntb.loc[last_index,'Date_51h']\n",
    "    t2 = nta.loc[i,'IY_date']\n",
    "    \n",
    "    diff = t1-t2\n",
    "    diff = diff.days\n",
    "    offset = last_index-diff\n",
    "    df.loc[:,b] = pd.Series(ntb.iloc[offset:last_index,i]).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "7abdc5aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df dataframe is fully populated by fresh values. Slice off data at the bottom which are not needed\n",
    "\n",
    "df = df[:17]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "42cdeca6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop column 'Date_51h' which is not needed\n",
    "\n",
    "df = df.drop('Date_51h', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "6916b91b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>days</th>\n",
       "      <th>L210324</th>\n",
       "      <th>L210325</th>\n",
       "      <th>L210326</th>\n",
       "      <th>L210327</th>\n",
       "      <th>L210328</th>\n",
       "      <th>L210329</th>\n",
       "      <th>L210330</th>\n",
       "      <th>L210331</th>\n",
       "      <th>L210401</th>\n",
       "      <th>...</th>\n",
       "      <th>L210904</th>\n",
       "      <th>L210905</th>\n",
       "      <th>L210906</th>\n",
       "      <th>L210907</th>\n",
       "      <th>L210908</th>\n",
       "      <th>L210909</th>\n",
       "      <th>L210910</th>\n",
       "      <th>L210911</th>\n",
       "      <th>L210912</th>\n",
       "      <th>L210913</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 172 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   days  L210324  L210325  L210326  L210327  L210328  L210329  L210330  \\\n",
       "0     1      NaN      NaN      3.0      5.0      NaN      NaN      NaN   \n",
       "1     2      NaN      NaN      NaN      NaN     18.0      NaN      NaN   \n",
       "2     3     24.0      2.0     52.0      NaN     12.0      NaN      NaN   \n",
       "3     4      NaN     12.0      8.0     31.0      NaN     10.0     17.0   \n",
       "4     5      NaN      3.0      4.0      NaN      1.0      6.0      6.0   \n",
       "\n",
       "   L210331  L210401  ...  L210904  L210905  L210906  L210907  L210908  \\\n",
       "0      NaN      NaN  ...      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN  ...      NaN      NaN      NaN      NaN      1.0   \n",
       "2      NaN      NaN  ...      2.0      NaN     38.0     21.0      4.0   \n",
       "3     41.0      NaN  ...      NaN      NaN     16.0     11.0      1.0   \n",
       "4      8.0      NaN  ...      NaN      NaN      8.0      4.0      8.0   \n",
       "\n",
       "   L210909  L210910  L210911  L210912  L210913  \n",
       "0      NaN      NaN      NaN      NaN      NaN  \n",
       "1      NaN      0.0      NaN      NaN      NaN  \n",
       "2      2.0      4.0      NaN      NaN      NaN  \n",
       "3      NaN      NaN     12.0      NaN      NaN  \n",
       "4      1.0     22.0      NaN      NaN      NaN  \n",
       "\n",
       "[5 rows x 172 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review df dataframe\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "7ba146cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create new column in dataframe 'total' to add up all values\n",
    "\n",
    "df['total'] = df[df2.columns].sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "430ddcfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# consolidate to summary by using column 'days' and 'total'\n",
    "\n",
    "summary = df[['days','total']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8752e2d1",
   "metadata": {},
   "source": [
    "## Summary file is uploaded to Google Cloud/Datastudio to add a new column  'percentage' for visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "2205b595",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>days</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>40.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>288.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>499.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>711.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>853.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2852.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>4709.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>6152.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>5832.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>7295.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>13022.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>15973.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>15930.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>14790.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>10264.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>4342.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>2440.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    days    total\n",
       "0      1     40.0\n",
       "1      2    288.0\n",
       "2      3    499.0\n",
       "3      4    711.0\n",
       "4      5    853.0\n",
       "5      6   2852.0\n",
       "6      7   4709.0\n",
       "7      8   6152.0\n",
       "8      9   5832.0\n",
       "9     10   7295.0\n",
       "10    11  13022.0\n",
       "11    12  15973.0\n",
       "12    13  15930.0\n",
       "13    14  14790.0\n",
       "14    15  10264.0\n",
       "15    16   4342.0\n",
       "16    17   2440.0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review summary file\n",
    "\n",
    "summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "8e1a4199",
   "metadata": {},
   "outputs": [],
   "source": [
    "summary.to_csv('nt_summary.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "e53483a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "summation = summary['total'].sum()\n",
    "summary_percentage = summary.copy()\n",
    "\n",
    "# add a new column 'percentage' to capture increase in weight\n",
    "summary_percentage['percentage'] = [i/summation for i in summary.total]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "850f21ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>days</th>\n",
       "      <th>total</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.000377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>288.0</td>\n",
       "      <td>0.002717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>499.0</td>\n",
       "      <td>0.004708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>711.0</td>\n",
       "      <td>0.006708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>853.0</td>\n",
       "      <td>0.008048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2852.0</td>\n",
       "      <td>0.026908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>4709.0</td>\n",
       "      <td>0.044428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>6152.0</td>\n",
       "      <td>0.058042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>5832.0</td>\n",
       "      <td>0.055023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>7295.0</td>\n",
       "      <td>0.068826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>13022.0</td>\n",
       "      <td>0.122858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>15973.0</td>\n",
       "      <td>0.150700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>15930.0</td>\n",
       "      <td>0.150294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>14790.0</td>\n",
       "      <td>0.139539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>10264.0</td>\n",
       "      <td>0.096837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>4342.0</td>\n",
       "      <td>0.040965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>2440.0</td>\n",
       "      <td>0.023021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    days    total  percentage\n",
       "0      1     40.0    0.000377\n",
       "1      2    288.0    0.002717\n",
       "2      3    499.0    0.004708\n",
       "3      4    711.0    0.006708\n",
       "4      5    853.0    0.008048\n",
       "5      6   2852.0    0.026908\n",
       "6      7   4709.0    0.044428\n",
       "7      8   6152.0    0.058042\n",
       "8      9   5832.0    0.055023\n",
       "9     10   7295.0    0.068826\n",
       "10    11  13022.0    0.122858\n",
       "11    12  15973.0    0.150700\n",
       "12    13  15930.0    0.150294\n",
       "13    14  14790.0    0.139539\n",
       "14    15  10264.0    0.096837\n",
       "15    16   4342.0    0.040965\n",
       "16    17   2440.0    0.023021"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "e54acaec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualising growth pattern\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_theme()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "f8c820d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize pattern of growth in weight based on days\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.lineplot(data=summary_percentage, x='days', y='percentage');\n",
    "plt.title('Percentage Increase In Neonates Weight', fontsize=15, fontweight='bold');\n",
    "plt.xlabel('Days', fontsize=13)\n",
    "plt.ylabel('Percentage Of Increase', fontsize=13);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d75a4cc2",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}