Python/gspread - how can I update multiple cells with DIFFERENT VALUES at once?

Question

To update a range of cells, you use the following command.

## Select a range
cell_list = worksheet.range(\'A1:A7\')

for cell in cell_list:
    cell.value =          


        

Answer 1

You can use enumerate on a separate list containing the different values you want in the cells and use the index part of the tuple to match to the appropriate cells in cell_list.

cell_list = worksheet.range('A1:A7')
cell_values = [1,2,3,4,5,6,7]

for i, val in enumerate(cell_values):  #gives us a tuple of an index and value
    cell_list[i].value = val    #use the index on cell_list and the val from cell_values

worksheet.update_cells(cell_list)

Answer 2

1. Import modules

import gspread
from gspread.models import Cell
from oauth2client.service_account import ServiceAccountCredentials
import string as string
import random

2. create cell array with values

cells = []
cells.append(Cell(row=1, col=1, value='Row-1 -- Col-1'))
cells.append(Cell(row=1, col=2, value='Row-1 -- Col-2'))
cells.append(Cell(row=9, col=20, value='Row-9 -- Col-20'))

3. Find the sheet

# use creds to create a client to interact with the Google Drive API
scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']
creds = ServiceAccountCredentials.from_json_keyfile_name('Sheet-Update-Secret.json', scope)
client = gspread.authorize(creds)

4. Update the cells

sheet.update_cells(cells)

You could refer these link for more details.

Answer 3

Here's my solution if you want to export a pandas data frame to a google sheet with gspread:

• We can't access and replace elements in cell_list with values in the data frame intuitively, with [row, col] notation.
• However, the elements are stored 'cell_list' are stored in a 'row-wise' order. The relative ordering depends on how many columns in your dataframe. Element (0,0) => 0, element (3,2) in a 5x5 dataframe is 17.
  • We can construct a function that maps a [row, col] value from a data frame to its position in the list:

def getListIndex(nrow, ncol,row_pos, col_pos):
    list_pos = row_pos*ncol + col_pos
    return(list_pos)

We can use this function to update the correct element in the list, cell_list, with the respective value in the dataframe, df.

count_row = df.shape[0]
count_col = df.shape[1]

# note this outputs data from the 1st row
cell_list = worksheet.range(1,1,count_row,count_col)

for row in range(0,count_row):
    for col in range(0,count_col):
        list_index = getListIndex(count_row, count_col, row, col)
        cell_list[list_index].value = df.iloc[row,col]

We can output the results of the list, cell_list, to our worksheet.

worksheet.update_cells(cell_list)

Answer 4

Assuming a table with a header row, as follows:

Name  | Weight
------+-------
Apple | 56
Pear  | 23
Leaf  | 88

Then, the following should be self explanatory

cell_list = []

# get the headers from row #1
headers = worksheet.row_values(1)
# find the column "Weight", we will remember this column #
colToUpdate = headers.index('Weight')

# task 1 of 2
cellLookup = worksheet.find('Leaf')
# get the cell to be updated
cellToUpdate = worksheet.cell(cellLookup.row, colToUpdate)
# update the cell's value
cellToUpdate.value = 77
# put it in the queue
cell_list.append(cellToUpdate)

# task 2 of 2
cellLookup = worksheet.find('Pear')
# get the cell to be updated
cellToUpdate = worksheet.cell(cellLookup.row, colToUpdate)
# update the cell's value
cellToUpdate.value = 28
# put it in the queue
cell_list.append(cellToUpdate)

# now, do it
worksheet.update_cells(cell_list)