and indexing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 516 17 Group By: split-apply-combine 519 17.1 Splitting an object into groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 934 34 API Reference 935 34.1 Input/Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . automatically align the data for you in computations • Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both ag- gregating and transforming data • Make it easy

and indexing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347 13 Group By: split-apply-combine 351 13.1 Splitting an object into groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 645 28 API Reference 647 28.1 Input/Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . automatically align the data for you in computations • Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both ag- gregating and transforming data • Make it easy

and indexing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432 16 Group By: split-apply-combine 435 16.1 Splitting an object into groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 797 32 API Reference 799 32.1 Input/Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . automatically align the data for you in computations • Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both ag- gregating and transforming data • Make it easy

and indexing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 13 Group By: split-apply-combine 321 13.1 Splitting an object into groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 583 28 API Reference 585 28.1 Input/Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . automatically align the data for you in computations • Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both ag- gregating and transforming data • Make it easy

and indexing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 422 16 Group By: split-apply-combine 425 16.1 Splitting an object into groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 783 32 API Reference 785 32.1 Input/Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . automatically align the data for you in computations • Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both ag- gregating and transforming data • Make it easy

weighted windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 686 2.16 Group by: split-apply-combine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 690 2.16 example data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 910 3 API reference 913 3.1 Input/output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . for the Python programming language. To the getting started guides To the user guide To the reference guide To the development guide CONTENTS 1 pandas: powerful Python data analysis toolkit, Release

weighted windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 686 2.16 Group by: split-apply-combine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 690 2.16 example data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 910 3 API reference 913 3.1 Input/output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . for the Python programming language. To the getting started guides To the user guide To the reference guide To the development guide CONTENTS 1 pandas: powerful Python data analysis toolkit, Release

for the Python programming language. To the getting started guides To the user guide To the reference guide To the development guide CONTENTS 1 pandas: powerful Python data analysis toolkit, Release [13]: s.str.split('b', expand=True).dtypes Out[13]: 0 string 1 string Length: 2, dtype: object String accessor methods returning integers will return a value with Int64Dtype In [14]: s.str.count("a") Out[14]: frame.DataFrame'> RangeIndex: 3 entries, 0 to 2 Data columns (total 3 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 int_col 3 non-null int64 1 text_col 3 non-null object

User001 Some text {'Id': 'ID001', 'Name': 'Name001'} b [1 rows x 4 columns] 1.1.6 Series.explode to split list-like values to rows Series and DataFrame have gained the DataFrame.explode() methods to transform form DataFrame is now straightforward using chained operations In [14]: df.assign(var1=df.var1.str.split(',')).explode('var1') Out[14]: var1 var2 0 a 1 0 b 1 0 c 1 1 d 2 1 e 2 1 f 2 [6 rows x 2 columns] Previous behavior: In [3]: df.describe() Out[3]: empty_col count 0 unique 0 New behavior: In [41]: df.describe() Out[41]: empty_col count 0 unique 0 top NaN freq NaN [4 rows x 1 columns] 1.2.10

User001 Some text {'Id': 'ID001', 'Name': 'Name001'} b [1 rows x 4 columns] 1.1.6 Series.explode to split list-like values to rows Series and DataFrame have gained the DataFrame.explode() methods to transform form DataFrame is now straightforward using chained operations In [14]: df.assign(var1=df.var1.str.split(',')).explode('var1') Out[14]: var1 var2 0 a 1 0 b 1 0 c 1 1 d 2 1 e 2 1 f 2 [6 rows x 2 columns] Previous behavior: In [3]: df.describe() Out[3]: empty_col count 0 unique 0 New behavior: In [41]: df.describe() Out[41]: empty_col count 0 unique 0 top NaN freq NaN [4 rows x 1 columns] 1.2.10