semvec/pypi-packages huggingface dataset:
Ekpenisi-Christabell-Edu1803928 0.1.0
semantics ibrary
License: MIT

Semantic library:
    used to extract numbers,convert units and solve equations from text. Three modules are used for the library; number,units and solver modules
     number module:
         used to extract numbers from text. its white space and comma sensitive
         '''
         example :
             from semantics import Number
             
             result = 'three hundred and fifty'
             b = Number()
             b.converts(result)
             
         it prints 350 to the terminal 
         '''
     solve module:
         used to extract equations and solve them from text. all number should be written in text and not digits
         '''
         example:
             from semantics import Solver           
             result = 'three hundred and fifty minus two'
             b = Solver()
             b.solve(result)
        # it prints 348 to the terminal
        '''
        look more at the documention in the code to see more operations to perform
        unit module:
            used to convert between units. its syntax is : convert {magnitude} {unit} to {unit} 
            it contains different units of three quantities; area,length,time
            #look into the code to see the various units 
             '''
             example:
                 from semantics import Units
                 
                 b = Units()
                 result= 'convert twelve cm to m'
                 b.convert(unit)
                 
               this prints 0.12m in the terminal
                 '''
a-pandas-ex-crosstab-dict 0.10
Pandas DataFrame to dict with crosstab DataFrames
License: MIT
Keywords: pandas,crosstab,DataFrame,merge,concat,Series


# 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

```