Metadata-Version: 2.1
Name: a-pandas-ex-vertical-to-horizontal
Version: 0.10
Summary: Vertical to horizontal (Series to DataFrame with multiple columns)
Home-page: https://github.com/hansalemaos/a_pandas_ex_vertical_to_horizontal
Author: Johannes Fischer
Author-email: <aulasparticularesdealemaosp@gmail.com>
License: MIT
Keywords: pandas,Series,DataFrame
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Text Editors :: Text Processing
Classifier: Topic :: Text Processing :: General
Classifier: Topic :: Text Processing :: Indexing
Classifier: Topic :: Text Processing :: Filters
Classifier: Topic :: Utilities
Description-Content-Type: text/markdown
License-File: LICENSE.rst
Requires-Dist: pandas


# Vertical to horizontal (Series to DataFrame with multiple columns)



```python

pip install a-pandas-ex-vertical-to-horizontal

from a_pandas_ex_vertical_to_horizontal import pd_add_vertical_to_horizontal

pd_add_vertical_to_horizontal()



df = pd.concat(

    [pd.Series(range(0, 20)), pd.Series(range(1000, 1020))], axis=0

).reset_index(drop=True)





df1 = df.s_vertical_to_horizontal(number_of_cells_to_join=3, drop_redundant=False, negative=False)

df2 = df.s_vertical_to_horizontal(number_of_cells_to_join=6, drop_redundant=True, negative=True)









print(df.to_string())

0        0

1        1

2        2

3        3

4        4

5        5

6        6

7        7

8        8

9        9

10      10

11      11

12      12

13      13

14      14

15      15

16      16

17      17

18      18

19      19

20    1000

21    1001

22    1002

23    1003

24    1004

25    1005

26    1006

27    1007

28    1008

29    1009

30    1010

31    1011

32    1012

33    1013

34    1014

35    1015

36    1016

37    1017

38    1018

39    1019





print(df1.to_string())

    col_0  col_1  col_2

0       0   1019   1018

1       1      0   1019

2       2      1      0

3       3      2      1

4       4      3      2

5       5      4      3

6       6      5      4

7       7      6      5

8       8      7      6

9       9      8      7

10     10      9      8

11     11     10      9

12     12     11     10

13     13     12     11

14     14     13     12

15     15     14     13

16     16     15     14

17     17     16     15

18     18     17     16

19     19     18     17

20   1000     19     18

21   1001   1000     19

22   1002   1001   1000

23   1003   1002   1001

24   1004   1003   1002

25   1005   1004   1003

26   1006   1005   1004

27   1007   1006   1005

28   1008   1007   1006

29   1009   1008   1007

30   1010   1009   1008

31   1011   1010   1009

32   1012   1011   1010

33   1013   1012   1011

34   1014   1013   1012

35   1015   1014   1013

36   1016   1015   1014

37   1017   1016   1015

38   1018   1017   1016

39   1019   1018   1017





print(df2.to_string())

    col_0  col_1  col_2  col_3  col_4  col_5

0       0      1      2      3      4      5

6       6      7      8      9     10     11

12     12     13     14     15     16     17

18     18     19   1000   1001   1002   1003

24   1004   1005   1006   1007   1008   1009

30   1010   1011   1012   1013   1014   1015

36   1016   1017   1018   1019      0      1

```
