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Marketing toolset using Pandas & Google Sheets API, with classes for a variety of other Google APIs

Project description

Google Sheets with Pandas dataframes, useful when ‘prospecting’ in analytics work and hacking.


  1. Create or select a project in Google’s developer console

    • Also, you will need to enable the APIs you plan to use

  2. Get a client_secrets.json credentials file from the credentials section

  3. Load the prospecting module in a Python session to initialize the ~/.prospecting/ folder in your home directory

  4. Place the client_secrets.json file in the ~/.prospecting/credentials/ directory

  5. Load an API class in a Python session, then run apiclass.authenticate() and follow steps

    • You only need to setup authentication once per API unless creds change


import prospecting as p

Use stats sheet to store stats and misc statistics (scopelist defaults to read-only, so pass scopes for writing):

ss_stats = p.SheetsApi(spreadsheetid = 'PASTE_GOOGLE_SHEETID_HERE',
ss_stats.update('Sheet1', somedataframe)

Use a reference sheet to provide a named entity list (or stopwords, vocabulary) for NLP preprocessing:

ss_reference = p.SheetsApi(spreadsheetid = 'PASTE_GOOGLE_SHEETID_HERE',
named_entity_list = list(ss_reference.get('ne!A:B').iloc[:,0].values)

Get keywords sheet as dataframe, filter, take sampled subset, upload new df to other tab in spreadsheet:

ss_kw = p.SheetsApi(spreadsheetid = 'PASTE_GOOGLE_SHEETID_HERE',

#  Get data using spreadsheet syntax like ('sheetname') or ('sheetname!A:B25')
df_query = ss_kw.get('queries')
df_query_subset = df_query[(df_query['raw_len'] > 1) &
                           (df_query['reject'] != 1)]

#  Take a subsample of data
df_query_subset_sample = df_query_subset.sample(frac=0.5)
df_query_subset_sample.reset_index(drop=True, inplace=True)

#  Update 'sheetname' with dataframe object
ss_kw.update('queries_shuffled', df_query_subset_sample)

Key changes between 0.1.4 and 0.1.2:

  • Switched order of input arguments for ss.update() function:

       ss.update(dataframe, 'sheetname')
       ss.update('sheetname', dataframe)
  • Removed Docker files to simplify

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