Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 821 22.11.6 Side Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 822 ValueError when index are different. • Series logical operators align both index of left and right hand side. Warning: Until 0.18.1, comparing Series with the same length, would succeed even if the .index are Logical operators Logical operators align both .index of left and right hand side. Previous behavior (Series), only left hand side index was kept: In [4]: s1 = pd.Series([True, False, True], index=list('ABC'))

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 823 22.11.6 Side Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 824 ValueError when index are different. • Series logical operators align both index of left and right hand side. Warning: Until 0.18.1, comparing Series with the same length, would succeed even if the .index are Logical operators Logical operators align both .index of left and right hand side. Previous behavior (Series), only left hand side index was kept: In [4]: s1 = pd.Series([True, False, True], index=list('ABC'))

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 910 21.11.6 Side Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 911 ValueError when index are different. • Series logical operators align both index of left and right hand side. Warning: Until 0.18.1, comparing Series with the same length, would succeed even if the .index are Logical operators Logical operators align both .index of left and right hand side. Previous behavior (Series), only left hand side index was kept: In [4]: s1 = pd.Series([True, False, True], index=list('ABC'))

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 944 21.12.5 Side Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 944 datetime or Period values. Prior to pandas 0.21.0, these were implicitly registered with matplotlib, as a side effect of import pandas. In pandas 0.21.0, we required users to explicitly register the converter registering the converters) with import performance and best practices (importing pandas shouldn’t have the side effect of overwriting any custom converters you’ve already set). In the future we hope to have most

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 906 21.11.6 Side Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 907 ValueError when index are different. • Series logical operators align both index of left and right hand side. Warning: Until 0.18.1, comparing Series with the same length, would succeed even if the .index are Logical operators Logical operators align both .index of left and right hand side. Previous behavior (Series), only left hand side index was kept: In [4]: s1 = pd.Series([True, False, True], index=list('ABC'))

on the first group to infer if it is safe to use a fast code path. Particularly for functions with side effects, this was an undesired behavior and may have led to surprises. (GH2936, GH2656, GH7739, GH10519 division and modulo operation at the same time returning a two-tuple of the same type as the left hand side. For example: In [29]: s = pd.Series(np.arange(10)) In [30]: s Out[30]: 0 0 1 1 2 2 3 3 4 4 False d True False False These operations produce a pandas object of the same type as the left-hand-side input that is of dtype bool. These boolean objects can be used in indexing operations, see the section

on the first group to infer if it is safe to use a fast code path. Particularly for functions with side effects, this was an undesired behavior and may have led to surprises. (GH2936, GH2656, GH7739, GH10519 division and modulo operation at the same time returning a two-tuple of the same type as the left hand side. For example: In [29]: s = pd.Series(np.arange(10)) In [30]: s Out[30]: 0 0 1 1 2 2 3 3 4 4 False d True False False These operations produce a pandas object of the same type as the left-hand-side input that is of dtype bool. These boolean objects can be used in indexing operations, see the section

division and modulo operation at the same time returning a two-tuple of the same type as the left hand side. For example: In [33]: s = pd.Series(np.arange(10)) In [34]: s Out[34]: 0 0 1 1 2 2 3 3 4 4 False d True False False These operations produce a pandas object of the same type as the left-hand-side input that is of dtype bool. These boolean objects can be used in indexing operations, see the section 3]) In [316]: ser.searchsorted([1, 3], side='right') \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\Out[316]: array([1, 3]) In [317]: ser.searchsorted([1, 3], side='left') \\\\\\\\\\\\\\\\\\\\\\\\\\\\

NaN 61.9 NaN NaN To create a new column, use the [] brackets with the new column name at the left side of the assignment. 1.4. Tutorials 35 pandas: powerful Python data analysis toolkit, Release 1.5 • Ease-of-use: Is one tool easier/harder to use (you may have to be the judge of this, given side-by-side code comparisons) This page is also here to offer a bit of a translation guide for users of these Using the tips dataset again, let’s find the average gratuity by size of the party and sex of the server. In Excel, we use the following configuration for the PivotTable: The equivalent in pandas: In

permitted (GH8444) • Bug in item assignment of a DataFrame with multi-index columns where right-hand-side columns were not aligned (GH7655) • Suppress FutureWarning generated by NumPy when comparing object All databases supported by SQLAlchemy can be used, such as PostgreSQL, MySQL, Oracle, Microsoft SQL server (see documentation of SQLAlchemy on included dialects). The functionality of providing DBAPI connection 247 246 C3 D0 -10 -10 -10 -10 D1 -10 -10 -10 -10 [64 rows x 4 columns] You can use a right-hand-side of an alignable object as well. In [65]: df2 = df.copy() In [66]: df2.loc[idx[:,:,[’C1’,’C3’]],:]