Metadata-Version: 2.1
Name: a-pandas-ex-crosstab-dict
Version: 0.10
Summary: Pandas DataFrame to dict with crosstab DataFrames
Home-page: https://github.com/hansalemaos/a_pandas_ex_crosstab_dict
Author: Johannes Fischer
Author-email: <aulasparticularesdealemaosp@gmail.com>
License: MIT
Keywords: pandas,crosstab,DataFrame,merge,concat,Series
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


# Pandas DataFrame to dict with crosstab DataFrames 



```python



$pip install a-pandas-ex-crosstab-dict



from a_pandas_ex_crosstab_dict import pd_add_crosstab_dict

import pandas as pd

pd_add_crosstab_dict()

df = pd.read_csv(

    "https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv"

)

df.ds_get_crosstab_dict(maincolumn="Cabin", columns=None)

df.ds_get_crosstab_dict( maincolumn="Cabin", columns=["Survived", "Sex",'Fare'])

Out[5]: 

{'Survived': Cabin     A10  A14  A16  A19  A20  A23  A24  ...  F G73  F2  F33  F38  F4  G6  T

 Survived                                     ...                                

 0           1    1    0    1    0    0    1  ...      2   1    0    1   0   2  1

 1           0    0    1    0    1    1    0  ...      0   2    3    0   2   2  0

 

 [2 rows x 147 columns],

 'Sex': Cabin   A10  A14  A16  A19  A20  A23  A24  ...  F G73  F2  F33  F38  F4  G6  T

 Sex                                        ...                                

 female    0    0    1    0    0    0    0  ...      0   0    3    0   1   4  0

 male      1    1    0    1    1    1    1  ...      2   3    0    1   1   0  1

 

 [2 rows x 147 columns],

 'Fare': Cabin     A10  A14  A16  A19  A20  A23  A24  ...  F G73  F2  F33  F38  F4  G6  T

 Fare                                         ...                                

 0.0000      0    0    0    0    0    0    0  ...      0   0    0    0   0   0  0

 5.0000      0    0    0    0    0    0    0  ...      0   0    0    0   0   0  0

 7.6500      0    0    0    0    0    0    0  ...      2   0    0    0   0   0  0

 7.7500      0    0    0    0    0    0    0  ...      0   0    0    1   0   0  0



```

