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)

['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

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)

['other', 'other', 'that', 'this', 'this'], .....: 'service': ['mail', 'web', 'mail', 'mail', 'web'], .....: 'no': [1, 2, 1, 2, 1]}).set_index(['host', 'service']) .....: In [150]: mask = df.groupby(level=0) agg('idxmax') In [151]: df_count = df.loc[mask['no']].reset_index() In [152]: df_count Out[152]: host service no 0 other web 2 1 that mail 1 2 this mail 2 Grouping like Python’s itertools.groupby In [153]: 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

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 DataFrame({'host':['other','other','that','this','this'], .....: 'service':['mail','web','mail','mail','web'], .....: 'no':[1, 2, 1, 2, 1]}).set_index(['host', 'service']) .....: In [119]: mask = df.groupby(level=0)

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 DataFrame({'host':['other','other','that','this','this'], .....: 'service':['mail','web','mail','mail','web'], .....: 'no':[1, 2, 1, 2, 1]}).set_index(['host', 'service']) .....: In [119]: mask = df.groupby(level=0)