subclass that will generate the start and end indices used for each window during the rolling aggregation. For more details and example usage, see the custom window rolling documentation 1.2.3 Converting categorical column (GH28787) • Remove error raised due to duplicated input functions in named aggregation in DataFrame.groupby() and Series.groupby(). Previously error will be raised if the same function Tablewise Function Application: pipe() 2. Row or Column-wise Function Application: apply() 3. Aggregation API: agg() and transform() 4. Applying Elementwise Functions: applymap() Tablewise function application

Enhancements 1.1.1 Groupby aggregation with relabeling Pandas has added special groupby behavior, known as “named aggregation”, for naming the output columns when applying multiple aggregation functions to specific should be tuples where the first element is the column selection, and the second element is the aggregation function to apply. Pandas provides the pandas.NamedAgg namedtuple to make it clearer what the arguments max_height average_weight kind cat 9.1 9.5 8.90 dog 6.0 34.0 102.75 [2 rows x 3 columns] Named aggregation is the recommended replacement for the deprecated “dict-of-dicts” approach to naming the output

Enhancements 1.1.1 Groupby aggregation with relabeling Pandas has added special groupby behavior, known as “named aggregation”, for naming the output columns when applying multiple aggregation functions to specific should be tuples where the first element is the column selection, and the second element is the aggregation function to apply. Pandas provides the pandas.NamedAgg namedtuple to make it clearer what the arguments max_height average_weight kind cat 9.1 9.5 8.90 dog 6.0 34.0 102.75 [2 rows x 3 columns] Named aggregation is the recommended replacement for the deprecated “dict-of-dicts” approach to naming the output

group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 752 2.18.4 Aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 752 2.18 values. The user guide has a dedicated section on value_counts , see page on discretization. • Aggregation statistics can be calculated on entire columns or rows • groupby provides the power of the split-apply-combine this specific case, the values on different time steps) pivot_table() can be used, providing an aggregation function (e.g. mean) 44 Chapter 1. Getting started pandas: powerful Python data analysis toolkit

Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653 2.12.3 Aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 668 2.12 group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 687 2.13.4 Aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 688 2.13 60 Chapter 1. Getting started pandas: powerful Python data analysis toolkit, Release 1.0.5 • Aggregation statistics can be calculated on entire columns or rows • groupby provides the power of the split-apply-combine

Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 657 2.12.3 Aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 671 2.12 group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 690 2.13.4 Aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 691 2.13 values. The user guide has a dedicated section on value_counts , see page on discretization. • Aggregation statistics can be calculated on entire columns or rows • groupby provides the power of the split-apply-combine

Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 654 3.12.3 Aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 669 3.12 group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 688 3.13.4 Aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 689 3.13 values. The user guide has a dedicated section on value_counts , see page on discretization. • Aggregation statistics can be calculated on entire columns or rows • groupby provides the power of the split-apply-combine

group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 689 2.17.4 Aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 689 2.17 values. The user guide has a dedicated section on value_counts , see page on discretization. • Aggregation statistics can be calculated on entire columns or rows • groupby provides the power of the split-apply-combine this specific case, the values on different time steps) pivot_table() can be used, providing an aggregation function (e.g. mean) 42 Chapter 1. Getting started pandas: powerful Python data analysis toolkit

group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 782 2.18.4 Aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 782 2.18 values. The user guide has a dedicated section on value_counts , see page on discretization. • Aggregation statistics can be calculated on entire columns or rows • groupby provides the power of the split-apply-combine toolkit, Release 1.3.3 the values on different time steps) pivot_table() can be used, providing an aggregation function (e.g. mean) on how to combine these values. Pivot table is a well known concept in spreadsheet

group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 783 2.18.4 Aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 783 2.18 values. The user guide has a dedicated section on value_counts , see page on discretization. • Aggregation statistics can be calculated on entire columns or rows • groupby provides the power of the split-apply-combine toolkit, Release 1.3.4 the values on different time steps) pivot_table() can be used, providing an aggregation function (e.g. mean) on how to combine these values. Pivot table is a well known concept in spreadsheet