I\'m developing a Python 2.7
script that analyzes data from an SQL table and at the end, generates a CSV file.
Once the file is gener
Another alternative to Sam Berlin's answer. If you're using Python, you can use the Drive API via gspread to import a CSV file. Here's an example:
import gspread
# Check how to get `credentials`:
# https://github.com/burnash/gspread
gc = gspread.authorize(credentials)
# Read CSV file contents
content = open('file_to_import.csv', 'r').read()
gc.import_csv('<SPREADSHEET_ID>', content)
Related question: Upload CSV to Google Sheets using gspread
An alternative to Sam Berlin's answer, you can turn your CSV into a list of lists and set that to your POST payload.
Such a function looks something like this:
def preprocess(table):
table.to_csv('pivoted.csv') # I use Pandas but use whatever you'd like
_file = open('pivoted.csv')
contents = _file.read()
array = contents.split('\n')
master_array = []
for row in array:
master_array.append(row.split(','))
return master_array
That master array gets thrown into the following:
body = {
'values': newValues
}
result2 = service.spreadsheets().values().update(spreadsheetId=spreadsheetId, range=rangeName + str(len(values) + start + 1), valueInputOption="USER_ENTERED", body=body).execute()
It works just fine for me.
I like Burnash's gspread library, but the import_csv
function in his answer is limited. It always starts the paste at A1
of the first worksheet (tab) and deletes all other tabs.
I needed to paste starting at a particular tab and cell, so I took Sam Berlin's suggestion to use a PasteDataRequest. Here's my function:
def pasteCsv(csvFile, sheet, cell):
'''
csvFile - path to csv file to upload
sheet - a gspread.Spreadsheet object
cell - string giving starting cell, optionally including sheet/tab name
ex: 'A1', 'MySheet!C3', etc.
'''
if '!' in cell:
(tabName, cell) = cell.split('!')
wks = sheet.worksheet(tabName)
else:
wks = sheet.sheet1
(firstRow, firstColumn) = gspread.utils.a1_to_rowcol(cell)
with open(csvFile, 'r') as f:
csvContents = f.read()
body = {
'requests': [{
'pasteData': {
"coordinate": {
"sheetId": wks.id,
"rowIndex": firstRow-1,
"columnIndex": firstColumn-1,
},
"data": csvContents,
"type": 'PASTE_NORMAL',
"delimiter": ',',
}
}]
}
return sheet.batch_update(body)
Note that I used a raw pasteData request rather than the higher-level update_cells
method to take advantage of Google's automatic (correct) handling of input data that contains quoted strings, which may contain non-delimeter commas.
You have two options for importing g CSV file. You can use the Drive API to create a spreadsheet from a CSV, or you can use the Sheets API to create an empty spreadsheet and then use spreadsheets.batchUpdate with a PasteDataRequest to add CSV data.
I've spent couple of hours trying to make any of the other answers work. Libraries do not explain the authentication well, and don't work with google-provided way of handling credentials. On the other hand, Sam's answer doesn't elaborate on the details of using the API, which might be confusing at times. So, here is a full recipe of uploading CSVs to gSheets. It uses both Sam's and CapoChino's answers plus some of my own research.
credentials.json
with no extra stepsquickstart.py
can easily be adapted into authenticate.py
https://www.googleapis.com/auth/spreadsheets
Hopefully by now you have your credentials stored, so let's move to the actual code
import pickle
from googleapiclient.discovery import build
SPREADSHEET_ID = '1BxiMVs0XRA5nFMdKvBdBZjgmUUqptlbs74OgvE2upms' # Get this one from the link in browser
worksheet_name = 'Sheet2'
path_to_csv = 'New Folder/much_data.csv'
path_to_credentials = 'Credentials/token.pickle'
# convenience routines
def find_sheet_id_by_name(sheet_name):
# ugly, but works
sheets_with_properties = API \
.spreadsheets() \
.get(spreadsheetId=SPREADSHEET_ID, fields='sheets.properties') \
.execute() \
.get('sheets')
for sheet in sheets_with_properties:
if 'title' in sheet['properties'].keys():
if sheet['properties']['title'] == sheet_name:
return sheet['properties']['sheetId']
def push_csv_to_gsheet(csv_path, sheet_id):
with open(csv_path, 'r') as csv_file:
csvContents = csv_file.read()
body = {
'requests': [{
'pasteData': {
"coordinate": {
"sheetId": sheet_id,
"rowIndex": "0", # adapt this if you need different positioning
"columnIndex": "0", # adapt this if you need different positioning
},
"data": csvContents,
"type": 'PASTE_NORMAL',
"delimiter": ',',
}
}]
}
request = API.spreadsheets().batchUpdate(spreadsheetId=SPREADSHEET_ID, body=body)
response = request.execute()
return response
# upload
with open(path_to_credentials, 'rb') as token:
credentials = pickle.load(token)
API = build('sheets', 'v4', credentials=credentials)
push_csv_to_gsheet(
csv_path=path_to_csv,
sheet_id=find_sheet_id_by_name(worksheet_name)
)
Good thing about directly using batchUpdate
is that it uploads thousands of rows in a second. On a low level gspread
does the same and should be as performant. Also there is gspread-pandas.
p.s. the code is tested with python 3.5
, but this thread seemed to be most appropriate to submit it to.