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[WIP] summarize function #1230

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[WIP] summarize function #1230

wants to merge 65 commits into from

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samukweku
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@samukweku samukweku commented Jan 10, 2023 •
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PR Description

Please describe the changes proposed in the pull request:

  • aggregation is with an col class
  • support for multiple column aggregations
  • support for aggregation in the presence of a grouping
  • inspiration from dplyr's across and rdatatable's SD

At its core, it is nothing more than a for loop. All the hard work is passed on to Pandas. It does not supplant agg - users should reach for summarize only if agg does not do the job - it's major addition is for grouping flexibly on multiple columns , via the col class - thanks to the select_columns syntax.

**This PR resolves #1225 **

Examples:

import pandas as pd
import janitor as jn
from janitor import col

In [107]: url = "https://gist.githubusercontent.com/seankross/a412dfbd88b3db70b74b/raw/5f23f993cd87c283ce766e7ac6b329ee7cc2e1d1/
     ...: mtcars.csv"
     ...: mtcars = pd.read_csv(url)
     ...: mtcars.head()
Out[107]: 
               model   mpg  cyl   disp   hp  drat     wt   qsec  vs  am  gear  carb
0          Mazda RX4  21.0    6  160.0  110  3.90  2.620  16.46   0   1     4     4
1      Mazda RX4 Wag  21.0    6  160.0  110  3.90  2.875  17.02   0   1     4     4
2         Datsun 710  22.8    4  108.0   93  3.85  2.320  18.61   1   1     4     1
3     Hornet 4 Drive  21.4    6  258.0  110  3.08  3.215  19.44   1   0     3     1
4  Hornet Sportabout  18.7    8  360.0  175  3.15  3.440  17.02   0   0     3     2

cols = [col("*p").compute("sum"), col("*t").compute("mean")]

mtcars.summarize(*cols, by = "cyl")
       disp    hp      drat        wt
cyl                                  
4    1156.5   909  4.070909  2.285727
6    1283.2   856  3.585714  3.117143
8    4943.4  2929  3.229286  3.999214

cols = [col("*p").compute("sum").rename("{_fn}_{_col}"), 
        col("*t").compute("mean").rename("{_col}_{_fn}")]

mtcars.summarize(*cols, by = "cyl")
     sum_disp  sum_hp  drat_mean   wt_mean
cyl                                       
4      1156.5     909   4.070909  2.285727
6      1283.2     856   3.585714  3.117143
8      4943.4    2929   3.229286  3.999214

summarize can be a useful abstraction for scenarios where agg doesnt quite do the job easily - an example is from this blogpost:

df
   x  y  n
0  1  1  3
1  1  2  2
2  1  3  1
3  2  1  1
4  2  2  2
5  3  1  1


cols = [col("y").compute("sum").rename("freq"), col("y").compute("nth", n=1)]

df.summarize(*cols, by = 'x')
   freq    y
x           
1     6  2.0
2     3  2.0
3     1  NaN

PR Checklist

Please ensure that you have done the following:

  1. PR in from a fork off your branch. Do not PR from <your_username>:dev, but rather from <your_username>:<feature-branch_name>.
  1. If you're not on the contributors list, add yourself to AUTHORS.md.
  1. Add a line to CHANGELOG.md under the latest version header (i.e. the one that is "on deck") describing the contribution.
    • Do use some discretion here; if there are multiple PRs that are related, keep them in a single line.

Automatic checks

There will be automatic checks run on the PR. These include:

  • Building a preview of the docs on Netlify
  • Automatically linting the code
  • Making sure the code is documented
  • Making sure that all tests are passed
  • Making sure that code coverage doesn't go down.

Relevant Reviewers

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@samukweku samukweku self-assigned this Jan 10, 2023
@ericmjl
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ericmjl commented Jan 10, 2023 •
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🚀 Deployed on https://deploy-preview-1230--pyjanitor.netlify.app

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codecov bot commented Jan 10, 2023 •
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Codecov Report

Merging #1230 (6ca01c9) into dev (936fa8b) will decrease coverage by 14.66%.
The diff coverage is 100.00%.

@@             Coverage Diff             @@
##              dev    #1230       +/-   ##
===========================================
- Coverage   97.71%   83.06%   -14.66%     
===========================================
  Files          78       79        +1     
  Lines        3770     3886      +116     
===========================================
- Hits         3684     3228      -456     
- Misses         86      658      +572

@samukweku samukweku changed the title [ENH] summarize function [WIP] summarize function Jan 16, 2023
@samukweku samukweku marked this pull request as draft January 29, 2023 04:05
samuel.oranyeli and others added 25 commits February 1, 2023 14:42
@samukweku samukweku closed this Feb 22, 2023
@samukweku samukweku deleted the samukweku/summarise branch February 22, 2023 20:04
@ericmjl
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ericmjl commented Feb 22, 2023

@samukweku apologies for the delay in reviewing. I was originally planning to get to it after this week’s storm of events is over. Was there a reason for closing?

@samukweku
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haha ... not at all @ericmjl this was a draft ... I'll resurrect it when I feel I have gotten the logic right ... truth be told i'm wary of introducing a function that mimics what Pandas already does (in this case agg) - summarize is supposed to cover cases agg doesnt touch; still I want to just finesse the idea a bit more before resurrecting the draft, and try out other options that could be passed to agg, without having to use summarize

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@ericmjl ericmjl ericmjl left review comments

@Zeroto521 Zeroto521 Awaiting requested review from Zeroto521

@thatlittleboy thatlittleboy Awaiting requested review from thatlittleboy

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summarize
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