operations. In addition to arithmetic operations, pd.NA also propagates as “missing” or “unknown” in comparison operations: 4 Chapter 1. What’s new in 1.0.0 (January 29, 2020) pandas: powerful Python data between pandas.NA and numpy. nan. 1.5.7 arrays.IntegerArray comparisons return arrays.BooleanArray Comparison operations on a arrays.IntegerArray now returns a arrays.BooleanArray rather than a NumPy array (GH24596) • 1.8 Performance improvements • Performance improvement in DataFrame arithmetic and comparison operations with scalars (GH24990, GH29853) • Performance improvement in indexing with a non-unique

Series.at() that would raise exception if the index was a CategoricalIndex (GH20629) • Fixed bug in comparison of ordered Categorical that contained missing values with a scalar which some- times incorrectly and will never copy data. Series.to_numpy() will always return a NumPy array, potentially at the cost of copying / coercing values. 2. When your DataFrame contains a mixture of data types, DataFrame 3.113362 d NaN -4.787893 -4.158491 Flexible comparisons Series and DataFrame have the binary comparison methods eq, ne, lt, gt, le, and ge whose behavior is analogous to the binary arithmetic operations

Series.at() that would raise exception if the index was a CategoricalIndex (GH20629) • Fixed bug in comparison of ordered Categorical that contained missing values with a scalar which some- times incorrectly and will never copy data. Series.to_numpy() will always return a NumPy array, potentially at the cost of copying / coercing values. 2. When your DataFrame contains a mixture of data types, DataFrame -0.792383 d NaN 2.577169 -2.077592 Flexible comparisons Series and DataFrame have the binary comparison methods eq, ne, lt, gt, le, and ge whose behavior is analogous to the binary arithmetic operations

pandas operations. # arithmetic In [3]: s + 1 Out[3]: 0 2 1 3 2 NaN Length: 3, dtype: Int64 # comparison In [4]: s == 1 \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\Out[4]: 0 True 1 False 'TimedeltaIndex' and 'float' 1.2.13 DataFrame Comparison Operations Broadcasting Changes Previously, the broadcasting behavior of DataFrame comparison operations (==, !=, ...) was inconsistent with the the behavior of arithmetic operations (+, -, ...). The behavior of the comparison operations has been changed to match the arithmetic operations in these cases. (GH22880) The affected cases are: • operating

started tutorials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.4.4 Comparison with other tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 1.4.5 is provided in the user guide section on database style merging of tables. Or have a look at the comparison with SQL page. • Multiple tables can be concatenated both column-wise and row-wise using the concat full overview is provided in the user guide pages on working with text data. 1.4.4 Comparison with other tools Comparison with R / R libraries Since pandas aims to provide a lot of the data manipulation

started tutorials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.4.4 Comparison with other tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 1.4.5 is provided in the user guide section on database style merging of tables. Or have a look at the comparison with SQL page. • Multiple tables can be concatenated both column-wise and row-wise using the concat full overview is provided in the user guide pages on working with text data. 1.4.4 Comparison with other tools Comparison with R / R libraries Since pandas aims to provide a lot of the data manipulation

structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 1.4.7 Comparison with other tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 1.4.8 is provided in the user guide section on database style merging of tables. Or have a look at the comparison with SQL page. • Multiple tables can be concatenated both column as row wise using the concat and will never copy data. Series.to_numpy() will always return a NumPy array, potentially at the cost of copying / coercing values. 2. When your DataFrame contains a mixture of data types, DataFrame

structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 1.4.7 Comparison with other tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 1.4.8 is provided in the user guide section on database style merging of tables. Or have a look at the comparison with SQL page. • Multiple tables can be concatenated both column as row wise using the concat and will never copy data. Series.to_numpy() will always return a NumPy array, potentially at the cost of copying / coercing values. 2. When your DataFrame contains a mixture of data types, DataFrame

structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 2.4.7 Comparison with other tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 2.4.8 is provided in the user guide section on database style merging of tables. Or have a look at the comparison with SQL page. • Multiple tables can be concatenated both column as row wise using the concat and will never copy data. Series.to_numpy() will always return a NumPy array, potentially at the cost of copying / coercing values. 2. When your DataFrame contains a mixture of data types, DataFrame

started tutorials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.4.4 Comparison with other tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 1.4.5 is provided in the user guide section on database style merging of tables. Or have a look at the comparison with SQL page. • Multiple tables can be concatenated both column-wise and row-wise using the concat full overview is provided in the user guide pages on working with text data. 1.4.4 Comparison with other tools Comparison with R / R libraries Since pandas aims to provide a lot of the data manipulation