Metadata-Version: 2.1
Name: a-pandas-ex-apply-against-all
Version: 0.10
Summary: Apply each value in a column against the whole column
Home-page: https://github.com/hansalemaos/a_pandas_ex_apply_against_all
Author: Johannes Fischer
Author-email: <aulasparticularesdealemaosp@gmail.com>
License: MIT
Keywords: pandas,DataFrame,apply
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


# Apply each value in a column against the whole column



```python

pip install a-pandas-ex-apply-against-all

```



```python



from a_pandas_ex_apply_against_all import pd_add_apply_each

import pandas as pd

pd_add_apply_each()

df = pd.read_csv(

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

)

df1 = df.PassengerId.s_apply_each(

    expression="str(x) + str(y)", # use always x/y in your expression

    exception_value=pd.NA,

    diagonal_value=pd.NA,

    print_exception=True,

)

print(df1)

      0     1     2     3     4    ...     886     887     888     889     890

0    <NA>    12    13    14    15  ...    1887    1888    1889    1890    1891

1      21  <NA>    23    24    25  ...    2887    2888    2889    2890    2891

2      31    32  <NA>    34    35  ...    3887    3888    3889    3890    3891

3      41    42    43  <NA>    45  ...    4887    4888    4889    4890    4891

4      51    52    53    54  <NA>  ...    5887    5888    5889    5890    5891

..    ...   ...   ...   ...   ...  ...     ...     ...     ...     ...     ...

886  8871  8872  8873  8874  8875  ...    <NA>  887888  887889  887890  887891

887  8881  8882  8883  8884  8885  ...  888887    <NA>  888889  888890  888891

888  8891  8892  8893  8894  8895  ...  889887  889888    <NA>  889890  889891

889  8901  8902  8903  8904  8905  ...  890887  890888  890889    <NA>  890891

890  8911  8912  8913  8914  8915  ...  891887  891888  891889  891890    <NA>

[891 rows x 891 columns]





```

