How does Python Pandas Transform work internally when passed a lambda question?-CodePudding

I found the following example online which explains how to essentially achieve a SQL equivalent of PARTITION BY

df['percent_of_points'] = df.groupby('team')['points'].transform(lambda x: x/x.sum())

#view updated DataFrame
print(df)

  team  points  percent_of_points
0    A      30           0.352941
1    A      22           0.258824
2    A      19           0.223529
3    A      14           0.164706
4    B      14           0.191781
5    B      11           0.150685
6    B      20           0.273973
7    B      28           0.383562

I struggle to understand what the 'x' refers to in the lambda function lambda x: x/x.sum() because it appears to refer to an individual element when used as the numerator i.e. 'x' but also appears to be a list of values when used as a denominator i.e. x.sum().

I think I am not thinking about this is in the right way or have a gap in my understanding of python or pandas.

CodePudding user response：

it appears to refer to an individual element when used as the numerator i.e. 'x' but also appears to be a list of values when used as a denominator i.e. x.sum()

It doesn't, it returns a pd.Series of length the size of the group, and x / x.sum() is not a single value, it a pd.Series of the same size.

.transform will assign the values of this series to the corresponding values in that column from the group-by operation.

So, consider:

In [15]: df
Out[15]:
  team  points
0    A      30
1    A      22
2    A      19
3    A      14
4    B      14
5    B      11
6    B      20
7    B      28

In [16]: for k, g in df.groupby('team')['points']:
    ...:     print(g)
    ...:     print(g / g.sum())
    ...:
0    30
1    22
2    19
3    14
Name: points, dtype: int64
0    0.352941
1    0.258824
2    0.223529
3    0.164706
Name: points, dtype: float64
4    14
5    11
6    20
7    28
Name: points, dtype: int64
4    0.191781
5    0.150685
6    0.273973
7    0.383562
Name: points, dtype: float64

In [17]: