pivot_table() now accepts most iterables for the values parameter (GH12017) • Added Google BigQuery service account authentication support, which enables authentication on remote servers. (GH11881, GH12572) BigQuery Data Sets by way of pandas DataFrames. BigQuery is a high performance SQL-like database service, useful for performing ad-hoc queries against extremely large datasets. See the docs from pandas with pandas doesn’t involve installing pandas at all. Wakari is a free service that provides a hosted IPython Notebook service in the cloud. Simply create an account, and have access to pandas from within

pivot_table() now accepts most iterables for the values parameter (GH12017) • Added Google BigQuery service account authentication support, which enables authentication on remote servers. (GH11881, GH12572) BigQuery Data Sets by way of pandas DataFrames. BigQuery is a high performance SQL-like database service, useful for performing ad-hoc queries against extremely large datasets. See the docs from pandas with pandas doesn’t involve installing pandas at all. Wakari is a free service that provides a hosted IPython Notebook service in the cloud. Simply create an account, and have access to pandas from within

BigQuery Data Sets by way of pandas DataFrames. BigQuery is a high performance SQL-like database service, useful for performing ad-hoc queries against extremely large datasets. See the docs from pandas with pandas doesn’t involve installing pandas at all. Wakari is a free service that provides a hosted IPython Notebook service in the cloud. Simply create an account, and have access to pandas from within DataFrame({’host’:[’other’,’other’,’that’,’this’,’this’], .....: ’service’:[’mail’,’web’,’mail’,’mail’,’web’], .....: ’no’:[1, 2, 1, 2, 1]}).set_index([’host’, ’service’]) .....: In [122]: mask = df.groupby(level=0)

BigQuery Data Sets by way of pandas DataFrames. BigQuery is a high performance SQL-like database service, useful for performing ad-hoc queries against extremely large datasets. See the docs from pandas with pandas doesn’t involve installing pandas at all. Wakari is a free service that provides a hosted IPython Notebook service in the cloud. Simply create an account, and have access to pandas from within DataFrame({’host’:[’other’,’other’,’that’,’this’,’this’], .....: ’service’:[’mail’,’web’,’mail’,’mail’,’web’], .....: ’no’:[1, 2, 1, 2, 1]}).set_index([’host’, ’service’]) .....: In [122]: mask = df.groupby(level=0)

BigQuery Data Sets by way of pandas DataFrames. BigQuery is a high performance SQL-like database service, useful for performing ad-hoc queries against extremely large datasets. See the docs from pandas with pandas doesn’t involve installing pandas at all. Wakari is a free service that provides a hosted IPython Notebook service in the cloud. Simply create an account, and have access to pandas from within DataFrame({'host':['other','other','that','this','this'], .....: 'service':['mail','web','mail','mail','web'], .....: 'no':[1, 2, 1, 2, 1]}).set_index(['host', 'service']) .....: In [122]: mask = df.groupby(level=0)

BigQuery Data Sets by way of pandas DataFrames. BigQuery is a high performance SQL-like database service, useful for performing ad-hoc queries against extremely large datasets. See the docs 1.2. v0.13 version 0.13.0. The pandas.io.gbq module provides a wrapper for Google’s BigQuery analytics web service to simplify retrieving results from BigQuery tables using SQL-like queries. Result sets are parsed datasets as tables if the source datatypes are compatible with BigQuery ones. For specifics on the service itself, see here As an example, suppose you want to load all data from an existing table : test_dataset

BigQuery Data Sets by way of pandas DataFrames. BigQuery is a high performance SQL-like database service, useful for performing ad-hoc queries against extremely large datasets. See the docs from pandas version 0.13.0. The pandas.io.gbq module provides a wrapper for Google’s BigQuery analytics web service to simplify retrieving results from BigQuery tables using SQL-like queries. Result sets are parsed datasets as tables if the source datatypes are compatible with BigQuery ones. For specifics on the service itself, see here As an example, suppose you want to load all data from an existing table : test_dataset

prepared to fix parts of the code, yet the rpy2-2.3.0 introduces improvements such as a better R-Python bridge memory management layer so I might be a good idea to bite the bullet and submit patches for the few

['other', 'other', 'that', 'this', 'this'], .....: 'service': ['mail', 'web', 'mail', 'mail', 'web'], .....: 'no': [1, 2, 1, 2, 1]}).set_index(['host', 'service']) .....: In [140]: mask = df.groupby(level=0) powerful Python data analysis toolkit, Release 0.25.0 (continued from previous page) Out[142]: host service no 0 other web 2 1 that mail 1 2 this mail 2 Grouping like Python’s itertools.groupby In [143]: credentials, such as to use Compute Engine google.auth.compute_engine.Credentials or Service Account google. oauth2.service_account.Credentials directly. New in version 0.8.0 of pandas-gbq. New in version

['other', 'other', 'that', 'this', 'this'], .....: 'service': ['mail', 'web', 'mail', 'mail', 'web'], .....: 'no': [1, 2, 1, 2, 1]}).set_index(['host', 'service']) .....: In [140]: mask = df.groupby(level=0) agg('idxmax') In [141]: df_count = df.loc[mask['no']].reset_index() In [142]: df_count Out[142]: host service no 0 other web 2 1 that mail 1 2 this mail 2 Grouping like Python’s itertools.groupby In [143]: credentials, such as to use Compute Engine google.auth.compute_engine.Credentials or Service Account google. oauth2.service_account.Credentials directly. New in version 0.8.0 of pandas-gbq. New in version