[DEPR] Deprecate functions

[samukweku]

Please describe the changes proposed in the pull request:

Add deprecation warnings for :

• process_text,
• rename_column,
• rename_columns,
• filter_on,
• remove_columns,
• fill_direction
• add_column
• add_columns
• toset
• find_replace
• join_apply
• change_type
• fill_empty
• groupby_agg

This PR is related to #1045 .

Please tag maintainers to review.

• @ericmjl

[ericmjl]

🚀 Deployed on https://deploy-preview-1212--pyjanitor.netlify.app

[codecov]

Codecov Report

Merging #1212 (0852f09) into dev (8d298c7) will increase coverage by 0.02%.
The diff coverage is 97.91%.

@@            Coverage Diff             @@
##              dev    #1212      +/-   ##
==========================================
+ Coverage   97.67%   97.69%   +0.02%     
==========================================
  Files          78       78              
  Lines        3693     3730      +37     
==========================================
+ Hits         3607     3644      +37     
  Misses         86       86              

[ericmjl]

Wow, extensive work, @samukweku! Thank you for handling the deprecations!

[ericmjl]

Hmmm, in trying to write an example for how to write fill_direction using df.assign or df.transform_column, I wasn't able to easily replicate the functionality that you had here. Are we sure we want to deprecate fill_direction?

[samukweku]

fill_direction can be replicated with assign:

In [175]: >>> df = pd.DataFrame(
...:             {
...:                 'col1': [1, 2, 3, 4],
...:                 'col2': [None, 5, 6, 7],
...:                 'col3': [8, 9, 10, None],
...:                 'col4': [None, None, 11, None],
...:                 'col5': [None, 12, 13, None]
...:             }
...:          )
...: df
Out[175]:
   col1  col2  col3  col4  col5
0     1   NaN   8.0   NaN   NaN
1     2   5.0   9.0   NaN  12.0
2     3   6.0  10.0  11.0  13.0
3     4   7.0   NaN   NaN   NaN

In [176]: df.assign(
               col2 = df.col2.bfill(), 
               col3 = df.col3.ffill(), 
               col4 = df.col4.ffill().bfill(), 
               col5 = df.col5.bfill().ffill())
Out[176]:
   col1  col2  col3  col4  col5
0     1   5.0   8.0  11.0  12.0
1     2   5.0   9.0  11.0  12.0
2     3   6.0  10.0  11.0  13.0
3     4   7.0  10.0  11.0  13.0

[ericmjl]

Okie dokes, this PR has been open long enough. Let's merge, @samukweku?

[samukweku]

@ericmjl yes pls