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DOC-Modified documentation for the issue GH42106 #42110

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Jun 21, 2021
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DOC-Modified documentation for the issue GH42106 #42110

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DOC-Modified documentation for the issue GH42106
raghuabhishek Jun 18, 2021
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DOC-Modified documentation for the issue GH42106
raghuabhishek Jun 18, 2021
d3364d7
DOC-Modified documentation for the issue GH42106
raghuabhishek Jun 19, 2021
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Merge remote-tracking branch 'upstream/master' into modify-pd.lookup-doc
raghuabhishek Jun 20, 2021
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9 changes: 4 additions & 5 deletions doc/source/user_guide/indexing.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -1523,18 +1523,17 @@ Looking up values by index/column labels
----------------------------------------

Sometimes you want to extract a set of values given a sequence of row labels
and column labels, this can be achieved by ``DataFrame.melt`` combined by filtering the corresponding
rows with ``DataFrame.loc``. For instance:
and column labels, this can be achieved by ``pandas.factorize`` which extracts the distinct values of the intended column and can be indexed by passing the length of the dataframe to the
``numpy.arange`` function and the distinct value array. For instance:
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this is very complicated wording...how about

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and column labels, this can be achieved by ``pandas.factorize`` which extracts the distinct values of the intended column and can be indexed by passing the length of the dataframe to the
``numpy.arange`` function and the distinct value array. For instance:
and column labels, this can be achieved by using ``pandas.factorize`` and NumPy indexing. For instance:

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I have modified the file as per your suggestion.


.. ipython:: python

df = pd.DataFrame({'col': ["A", "A", "B", "B"],
'A': [80, 23, np.nan, 22],
'B': [80, 55, 76, 67]})
df
melt = df.melt('col')
melt = melt.loc[melt['col'] == melt['variable'], 'value']
melt.reset_index(drop=True)
idx, cols = pd.factorize(df['col'])
df.reindex(cols, axis=1).to_numpy()[np.arange(len(df)), idx]
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The text above the example will also need to be changed?

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Yes, you are right. I have modified the text which is there above the example,as per my understanding. Can you review it.

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show the result in the PR itself as this doesn't look to replicate

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On pandas 1.1.5:

In [1]:    df = pd.DataFrame({'col': ["A", "A", "B", "B"],
   ...:    ...:                        'A': [80, 23, np.nan, 22],
   ...:    ...:                        'B': [80, 55, 76, 67]})
   ...: 

In [2]: df.lookup(df.index, df['col'])
Out[2]: array([80., 23., 76., 67.])

the example on master:

In [2]:     melt = df.melt('col')
   ...:     melt = melt.loc[melt['col'] == melt['variable'], 'value']
   ...:     melt.reset_index(drop=True)
Out[2]: 
0    80.0
1    23.0
2    76.0
3    67.0
Name: value, dtype: float64

the example here:

In [3]:     idx, cols = pd.factorize(df['col'])
   ...:     df.reindex(cols, axis=1).to_numpy()[np.arange(len(df)), idx]
Out[3]: array([80., 23., 76., 67.])


Formerly this could be achieved with the dedicated ``DataFrame.lookup`` method
which was deprecated in version 1.2.0.
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