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 is observed. In [43]: weekmask_egypt = ’Sun Mon Tue Wed Thu’ # They also observe International Workers’ Day so let’s # add that for a couple of years In [44]: holidays = [’2012-05-01’, datetime(2013, 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 is observed. In [43]: weekmask_egypt = ’Sun Mon Tue Wed Thu’ # They also observe International Workers’ Day so let’s # add that for a couple of years In [44]: holidays = [’2012-05-01’, datetime(2013, 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 is observed. In [40]: weekmask_egypt = 'Sun Mon Tue Wed Thu' # They also observe International Workers' Day so let's # add that for a couple of years In [41]: holidays = ['2012-05-01', datetime(2013,

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 is observed. In [40]: weekmask_egypt = 'Sun Mon Tue Wed Thu' # They also observe International Workers' Day so let's # add that for a couple of years In [41]: holidays = ['2012-05-01', datetime(2013,

is observed. In [185]: weekmask_egypt = 'Sun Mon Tue Wed Thu' # They also observe International Workers' Day so let's # add that for a couple of years In [186]: holidays = ['2012-05-01', .....: datetime ['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]:

is observed. In [185]: weekmask_egypt = 'Sun Mon Tue Wed Thu' # They also observe International Workers' Day so let's # add that for a couple of years In [186]: holidays = ['2012-05-01', .....: datetime ['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]:

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 is observed. In [40]: weekmask_egypt = 'Sun Mon Tue Wed Thu' # They also observe International Workers' Day so let's # add that for a couple of years In [41]: holidays = ['2012-05-01', datetime(2013, 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 1.2. v0.13 is observed. In [42]: weekmask_egypt = ’Sun Mon Tue Wed Thu’ # They also observe International Workers’ Day so let’s # add that for a couple of years In [43]: holidays = [’2012-05-01’, datetime(2013, is observed. In [84]: weekmask_egypt = ’Sun Mon Tue Wed Thu’ # They also observe International Workers’ Day so let’s # add that for a couple of years In [85]: holidays = [’2012-05-01’, datetime(2013,

is observed. In [183]: weekmask_egypt = 'Sun Mon Tue Wed Thu' # They also observe International Workers' Day so let's # add that for a couple of years In [184]: holidays = ['2012-05-01', .....: datetime ['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]:

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 is observed. In [43]: weekmask_egypt = ’Sun Mon Tue Wed Thu’ # They also observe International Workers’ Day so let’s # add that for a couple of years In [44]: holidays = [’2012-05-01’, datetime(2013, Release 0.14.0 In [84]: weekmask_egypt = ’Sun Mon Tue Wed Thu’ # They also observe International Workers’ Day so let’s # add that for a couple of years In [85]: holidays = [’2012-05-01’, datetime(2013,