Template code for downloading view data and pivoting it using Python and tableau-api-lib

tableau-api-lib-download-view-crosstab.py

import pandas as pd

from tableau_api_lib import TableauServerConnection
from tableau_api_lib.sample import sample_config
from tableau_api_lib.utils import querying
from tableau_api_lib.utils.common import flatten_dict_column


# sample config
tableau_server_config = {
  'tableau_env': {
    'server': 'https://<YOUR_SERVER>.com',
    'api_version': '<YOUR_API_VERSION>',
    'username': '<YOUR_USERNAME>',
    'password': '<YOUR_PASSWORD>',
    'site_name': '<YOUR_SITE_NAME>',
    'site_url': '<YOUR_SITE_URL>'
  }
}


# configure connection and sign in
conn = TableauServerConnection(config, 'tableau_env')
conn.sign_in()

# query views on the site
site_views_df = querying.get_views_dataframe(conn)

# flatten the nested workbook json / dict to gain access to workbook info that helps identify the desired view 
site_views_detailed_df = flatten_dict_column(site_views_df, keys=['name', 'id'], col_name='workbook')

# get views for the workbook relevant to this example
site_views_detailed_df[site_views_detailed_df['workbook_name'] == 'query_view_as_crosstab']

# the view we want to query in this case is 'Visual C', so let's get its view ID
relevant_views_df = site_views_detailed_df[site_views_detailed_df['workbook_name'] == 'query_view_as_crosstab']
visual_c_id = relevant_views_df[relevant_views_df['name'] == 'Visual C']['id'].to_list()[0]

# use the view id to query data in the view
view_data_raw = querying.get_view_data_dataframe(conn, view_id=visual_c_id)

# optionally, check the data types of your resulting Pandas DataFrame
# in this example, 'Sales' begins as an object (or string) column and we will change it to numerical
print(view_data_raw.dtypes)
view_data_raw['Sales'] = view_data_raw['Sales'].apply(lambda x: x.replace(',', ''))
view_data_raw['Sales'] = view_data_raw['Sales'].astype('float')

# optionally, rename any columns by calling the rename() method on your Pandas DataFrame
view_data_raw.rename(columns={'Year of Order Date': 'Year'}, inplace=True)

# finally, we can pivot our data into the form we want
# in this example we have 'Sub-Category' providing each row, 'Year' providing columns, and 'Sales' as the values
view_data_raw\
.pivot_table(values=['Sales'], index=['Sub-Category'], columns=['Year'])

# sign out of Tableau Server
conn.sign_out()