Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "forward-probe",
   "metadata": {},
   "source": [
    "# Google Sheets + Python using Gspread Module 🚀\n",
    "\n",
    "--------\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "physical-croatia",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import modules \n",
    "\n",
    "import gspread #pip install gspread\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "behind-pasta",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting up with the connection\n",
    "# The json file downloaded needs to be in the same folder\n",
    "cred_file = \"test-youtube-demo-350317-073112f7ce31.json\"\n",
    "gc = gspread.service_account(cred_file)\n",
    "gc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "outside-sullivan",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Establish the connection\n",
    "# database is the googleSpreadSheet name\n",
    "database = gc.open(\"database\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "serious-mechanics",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Worksheet 'Sheet1' id:0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting a worksheet\n",
    "wks = database.worksheet(\"Sheet1\")\n",
    "wks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "horizontal-magazine",
   "metadata": {},
   "outputs": [],
   "source": [
    "# List all the worksheets\n",
    "list_wks = database.worksheets()\n",
    "list_wks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "exclusive-dover",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding a new worksheet to Google SpreadSheet\n",
    "database.add_worksheet(\"new_wks\",\"100\",\"20\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "proof-fishing",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fetching the url of the of the SpreadSheet \n",
    "database.url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "peaceful-proposal",
   "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>Player</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L. Messi</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Neymar Jr</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>De Gea</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>E. Hazard</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>L. Modrić</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>L. Suárez</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sergio Ramos</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>J. Oblak</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>R. Lewandowski</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>T. Kroos</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>D. Godín</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>David Silva</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>N. Kanté</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>P. Dybala</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>H. Kane</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>A. Griezmann</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>M. ter Stegen</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>T. Courtois</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Sergio Busquets</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Player  Age\n",
       "0            L. Messi   31\n",
       "1   Cristiano Ronaldo   33\n",
       "2           Neymar Jr   26\n",
       "3              De Gea   27\n",
       "4        K. De Bruyne   27\n",
       "5           E. Hazard   27\n",
       "6           L. Modrić   32\n",
       "7           L. Suárez   31\n",
       "8        Sergio Ramos   32\n",
       "9            J. Oblak   25\n",
       "10     R. Lewandowski   29\n",
       "11           T. Kroos   28\n",
       "12           D. Godín   32\n",
       "13        David Silva   32\n",
       "14           N. Kanté   27\n",
       "15          P. Dybala   24\n",
       "16            H. Kane   24\n",
       "17       A. Griezmann   27\n",
       "18      M. ter Stegen   26\n",
       "19        T. Courtois   26\n",
       "20    Sergio Busquets   29"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Acess all the records\n",
    "data = pd.DataFrame(wks.get_all_records())\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "alternate-saint",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Player'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting\n",
    "data.plot.barh(x=\"Player\",y=\"Age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "existing-breed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 8\n"
     ]
    }
   ],
   "source": [
    "# Finding a Keyword from our Selected Worksheet\n",
    "\n",
    "# -----\n",
    "# Selecting a worksheet\n",
    "#wks = database.worksheet(\"Sheet1\")\n",
    "#wks\n",
    "\n",
    "# ----\n",
    "\n",
    "word = \"L. Modrić\"\n",
    "cell = wks.find(word)\n",
    "cell\n",
    "key_row = cell.row\n",
    "key_col = cell.col\n",
    "print(key_col,key_row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "cellular-valuable",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Cristiano Ronaldo'"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtaining the value\n",
    "wks.cell(3,1).value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "communist-fellow",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total number of row\n",
    "n = wks.col_values(2) # Gives all the rows as list in column 2\n",
    "len(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "qualified-edition",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Update a new cell from the \n",
    "new_name = \"Bruno Fernandes\"\n",
    "new_age = 27\n",
    "\n",
    "wks.update_cell(23,1,new_name)\n",
    "wks.update_cell(23,2,new_age)b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "recognized-humanity",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Update batch of cells \n",
    "\n",
    "cell_list = wks.range('B24:B28')\n",
    "cell_values = [20,24,25,26]\n",
    "\n",
    "for i, val in enumerate(cell_values): #gives us a tuple of an index and value\n",
    "    cell_list[i].value = val # use the index on cell_list and the val from cell_values\n",
    "\n",
    "    \n",
    "wks.update_cells(cell_list)"
   ]
  }
 ],
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