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Finding the maximum value in a Python column

I have a data frame ( combined_ranking_df) like this in pandas python:

                Id  Rank                         Activity
0              14035   8.0                         deployed
1              47728   8.0                         deployed
2              24259   1.0                         NaN
3              24259   6.0                         WIP
4              14251   8.0                         deployed
5              14250   1.0                         NaN
6              14250   6.0                         WIP
7              14250   5.0                         NaN
8              14250   5.0                         NaN
9              14250   1.0                         NaN

I am trying to get the maximum value for each id. for example, 14250 it should be 6.0. 24259 it should be 6.0.

                Id  Rank                         Activity
0              14035   8.0                         deployed
1              47728   8.0                         deployed
3              24259   6.0                         WIP
4              14251   8.0                         deployed
6              14250   6.0                         WIP

I tried to do it combined_ranking_df.groupby(['Id'], sort=False)['Rank'].max(), but the result I achieved was the first dataframe(nothing has changed).

What am I doing wrong?

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4 answers

1
, @ayhan
, , 'Id'. pd.DataFrame.drop_duplicates . , , . , 'Id'.

df.sort_values('Rank').drop_duplicates('Id', 'last')

      Id  Rank  Activity
3  24259   6.0       WIP
6  14250   6.0       WIP
0  14035   8.0  deployed
1  47728   8.0  deployed
4  14251   8.0  deployed


df.sort_values('Rank').drop_duplicates('Id', 'last').sort_index()

      Id  Rank  Activity
0  14035   8.0  deployed
1  47728   8.0  deployed
3  24259   6.0       WIP
4  14251   8.0  deployed
6  14250   6.0       WIP

2
groupby idxmax
, . @MaxU - , n 'Id'.

df.loc[df.groupby('Id', sort=False).Rank.idxmax()]

      Id  Rank  Activity
0  14035   8.0  deployed
1  47728   8.0  deployed
3  24259   6.0       WIP
4  14251   8.0  deployed
6  14250   6.0       WIP
+8

IIUC:

In [40]: df.groupby('Id', as_index=False, sort=False) \
           .apply(lambda x: x.nlargest(1, ['Rank'])) \
    ...:   .reset_index(level=1, drop=True)
Out[40]:
      Id  Rank  Activity
0  14035   8.0  deployed
1  47728   8.0  deployed
2  24259   6.0       WIP
3  14251   8.0  deployed
4  14250   6.0       WIP

@piRSquared:

In [41]: df.groupby('Id', group_keys=False, sort=False) \
           .apply(pd.DataFrame.nlargest, n=1, columns='Rank')
Out[41]:
      Id  Rank  Activity
0  14035   8.0  deployed
1  47728   8.0  deployed
3  24259   6.0       WIP
4  14251   8.0  deployed
6  14250   6.0       WIP
+6

, groupedby

groups = combined_ranking_df.groupby(['Id'], as_index=False, sort=False).max()[['Id','Rank']].

      Id  Rank
0  14035   8.0
1  47728   8.0
2  24259   6.0
3  14251   8.0
4  14250   6.0
+4

You can create a logical index to check if its maximum value matches a Rankgiven Idone. Then use boolean indexing to extract the maximum values ​​from the data frame.

The mask is created using groupbyon Idwith the help transform, which saves the original size of the data block.

>>> df[(df[['Rank']] == df[['Id', 'Rank']].groupby('Id').transform(max)).squeeze().tolist()]
      Id  Rank  Activity
0  14035     8  deployed
1  47728     8  deployed
3  24259     6       WIP
4  14251     8  deployed
6  14250     6       WIP
+3
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Source: https://habr.com/ru/post/1681291/

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