u’bar’, u’baz’]) • Index.isin now supports a level argument to specify which index level to use for membership tests (GH7892, GH7890) In [1]: idx = MultiIndex.from_product([[0, 1], [’a’, ’b’, ’c’]]) In [2]: 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

u’bar’, u’baz’]) • Index.isin now supports a level argument to specify which index level to use for membership tests (GH7892, GH7890) In [1]: idx = MultiIndex.from_product([[0, 1], [’a’, ’b’, ’c’]]) In [2]: 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) u'bar', u'baz']) • Index.isin now supports a level argument to specify which index level to use for membership tests (GH7892, GH7890) In [1]: idx = MultiIndex.from_product([[0, 1], ['a', 'b', 'c']]) In [2]: 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

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) u'bar', u'baz']) • Index.isin now supports a level argument to specify which index level to use for membership tests (GH7892, GH7890) In [1]: idx = MultiIndex.from_product([[0, 1], ['a', 'b', 'c']]) In [2]: 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

u'bar', u'baz']) • Index.isin now supports a level argument to specify which index level to use for membership tests (GH7892, GH7890) In [1]: idx = MultiIndex.from_product([[0, 1], ['a', 'b', 'c']]) In [2]: 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

• 1.6.11 MultiIndex • Bug in which incorrect exception raised by Timedelta when testing the membership of MultiIndex (GH24570) • 1.6.12 I/O • Bug in DataFrame.to_html() where values were truncated dtype: float64 In addition to that, MultiIndex allows selecting a separate level to use in the membership check: In [161]: s_mi = pd.Series(np.arange(6), .....: index=pd.MultiIndex.from_product([[0, examples. Using the in operator Using the Python in operator on a Series tests for membership in the index, not membership among the values. In [15]: s = pd.Series(range(5), index=list('abcde')) In [16]:

• 1.6.11 MultiIndex • Bug in which incorrect exception raised by Timedelta when testing the membership of MultiIndex (GH24570) • 1.6.12 I/O • Bug in DataFrame.to_html() where values were truncated dtype: float64 In addition to that, MultiIndex allows selecting a separate level to use in the membership check: In [161]: s_mi = pd.Series(np.arange(6), .....: index=pd.MultiIndex.from_product([[0, examples. Using the in operator Using the Python in operator on a Series tests for membership in the index, not membership among the values. In [15]: s = pd.Series(range(5), index=list('abcde')) In [16]:

the wrong indexes to be read from and written to (GH17148) • Bug in .isin() in which checking membership in empty Series objects raised an error (GH16991) • Bug in CategoricalIndex reindexing in which 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) 'bar', 'baz']) • Index.isin now supports a level argument to specify which index level to use for membership tests (GH7892, GH7890) In [1]: idx = MultiIndex.from_product([[0, 1], ['a', 'b', 'c']]) 1.18

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) 'bar', 'baz']) • Index.isin now supports a level argument to specify which index level to use for membership tests (GH7892, GH7890) In [1]: idx = MultiIndex.from_product([[0, 1], ['a', 'b', 'c']]) 236 Chapter 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

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) 'bar', 'baz']) • Index.isin now supports a level argument to specify which index level to use for membership tests (GH7892, GH7890) In [1]: idx = MultiIndex.from_product([[0, 1], ['a', 'b', 'c']]) In [2]: 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