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]: dtype: float64 In addition to that, MultiIndex allows selecting a separate level to use in the membership check: In [107]: s_mi = Series(np.arange(6), .....: index=pd.MultiIndex.from_product([[0, 1] 27.1.2 Using the in operator Using the Python in operator on a Series tests for membership in the index, not membership among the values. If this behavior is surprising, keep in mind that using in on

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]: dtype: float64 In addition to that, MultiIndex allows selecting a separate level to use in the membership check: In [107]: s_mi = Series(np.arange(6), .....: index=pd.MultiIndex.from_product([[0, 1] 27.1.2 Using the in operator Using the Python in operator on a Series tests for membership in the index, not membership among the values. If this behavior is surprising, keep in mind that using in on

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]: dtype: float64 In addition to that, MultiIndex allows selecting a separate level to use in the membership check: 13.11. Indexing with isin 425 pandas: powerful Python data analysis toolkit, Release 0 28.1.2 Using the in operator Using the Python in operator on a Series tests for membership in the index, not membership among the values. If this behavior is surprising, keep in mind that using in on

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]: that using in on a Python dictionary tests keys, not values, and Series are dict-like. To test for membership in the values, use the method isin(): 852 Chapter 3. User Guide pandas: powerful Python data

• 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 '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 dtype: float64 In addition to that, MultiIndex allows selecting a separate level to use in the membership check: In [165]: s_mi = pd.Series(np.arange(6), .....: index=pd.MultiIndex.from_product([[0,

23.1.2 Using the in operator Using the Python in operator on a Series tests for membership in the index, not membership among the values. If this behavior is surprising, keep in mind that using in on and Series are dict-like. To test for membership in the values, use the method isin(): For DataFrames, likewise, in applies to the column axis, testing for membership in the list of column names. 23.2 NaN For example 1000 values for 10 quantiles would produce a Categorical object indicating quantile membership for each data point. Parameters x : ndarray or Series q : integer or array of quantiles Number

dtype: float64 In addition to that, MultiIndex allows selecting a separate level to use in the membership check: In [171]: 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]: that using in on a Python dictionary tests keys, not values, and Series are dict-like. To test for membership in the values, use the method isin(): In [18]: s.isin([2]) Out[18]: a False b False c True

dtype: float64 In addition to that, MultiIndex allows selecting a separate level to use in the membership check: In [171]: 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]: that using in on a Python dictionary tests keys, not values, and Series are dict-like. To test for membership in the values, use the method isin(): In [18]: s.isin([2]) Out[18]: a False b False c True