Loading a generic Google Spreadsheet in Pandas

Question

When I try to load a Google Spreadsheet in pandas

from StringIO import StringIO  
import requests
r = requests.get('https://docs.google.com/spreadsheet/ccc?key=<some_long_code>&output=csv')
data = r.content
df = pd.read_csv(StringIO(data), index_col=0)

I get the following:

CParserError: Error tokenizing data. C error: Expected 1316 fields in line 73, saw 1386

Why? I would think that one could identify the spreadsheet set of rows and columns with data and use the spreadsheets rows and columns as the dataframe index and columns respectively (with NaN for anything empty). Why does it fail?

Answer 1

This question of mine shows how Getting Google Spreadsheet CSV into A Pandas Dataframe

As one of the commentators noted you have not asked for the data in CSV format you have the "edit" request at the end of the url You can use this code and see it work on the spreadsheet (which by the way needs to be public..) It is possible to do private sheets as well but that is another topic.

from StringIO import StringIO  # got moved around in python3 if you're using that.

import requests
r = requests.get('https://docs.google.com/spreadsheet/ccc?key=0Ak1ecr7i0wotdGJmTURJRnZLYlV3M2daNTRubTdwTXc&output=csv')
data = r.content

In [10]: df = pd.read_csv(StringIO(data), index_col=0,parse_dates=['Quradate'])

In [11]: df.head()
Out[11]: 
          City                                            region     Res_Comm  \
0       Dothan  South_Central-Montgomery-Auburn-Wiregrass-Dothan  Residential   
10       Foley                              South_Mobile-Baldwin  Residential   
12  Birmingham      North_Central-Birmingham-Tuscaloosa-Anniston   Commercial   
38       Brent      North_Central-Birmingham-Tuscaloosa-Anniston  Residential   
44      Athens                 North_Huntsville-Decatur-Florence  Residential   

          mkt_type            Quradate  National_exp  Alabama_exp  Sales_exp  \
0            Rural 2010-01-15 00:00:00             2            2          3   
10  Suburban_Urban 2010-01-15 00:00:00             4            4          4   
12  Suburban_Urban 2010-01-15 00:00:00             2            2          3   
38           Rural 2010-01-15 00:00:00             3            3          3   
44  Suburban_Urban 2010-01-15 00:00:00             4            5          4   

The new Google spreadsheet url format for getting the csv output is

https://docs.google.com/spreadsheets/d/177_dFZ0i-duGxLiyg6tnwNDKruAYE-_Dd8vAQziipJQ/export?format=csv&id

Well they changed the url format slightly again now you need:

https://docs.google.com/spreadsheets/d/177_dFZ0i-duGxLiyg6tnwNDKruAYE-_Dd8vAQziipJQ/export?format=csv&gid=0 #for the 1st sheet

I also found I needed to do the following to deal with Python 3 a slight revision to the above:

from io import StringIO 

and to get the file:

guid=0 #for the 1st sheet
act = requests.get('https://docs.google.com/spreadsheets/d/177_dFZ0i-duGxLiyg6tnwNDKruAYE-_Dd8vAQziipJQ/export?format=csv&gid=%s' % guid)
dataact = act.content.decode('utf-8') #To convert to string for Stringio
actdf = pd.read_csv(StringIO(dataact),index_col=0,parse_dates=[0], thousands=',').sort()

actdf is now a full pandas dataframe with headers (column names)

Answer 2

Warning: this solution will make your data accessible by anyone.

In google sheet click file>publish to web. Then select what do you need to publish and select export format .csv. You'll have the link something like: https://docs.google.com/spreadsheets/d/<your sheets key yhere>/pub?gid=1317664180&single=true&output=csv

Then simply:

import pandas as pd
pathtoCsv = r'https://docs.google.com/spreadsheets/d/<sheets key>/pub?gid=1317664180&single=true&output=csv'
dev = pd.read_csv(pathtoCsv)
print dev