Xrfgtjtk

I have a problem and I cannot find any solution in the web or documentation, even if I think that it is very trivial.

What do I want to do?

I have a dataframe like this

CLASS FEATURE1 FEATURE2 FEATURE3

  X      A       NaN      NaN

  X     NaN       A       NaN

  B      A        A        A

I want to group by the label(CLASS) and display the number of NaN-Values that are counted in every feature so that it looks like this. The purpose of this is to get a general idea how missing values are distributed over the different classes.

CLASS FEATURE1 FEATURE2 FEATURE3

  X      1        1        2

  B      0        0        0

I know how to recieve the amount of nonnull-Values - df.groupby['CLASS'].count()

Is there something similar for the NaN-Values?

I tried to subtract the count() from the size() but it returned an unformatted output filled with the value NaN

Compute a mask with isna, then group and find the sum:

df.drop('CLASS', 1).isna().groupby(df.CLASS, sort=False).sum().reset_index()



  CLASS  FEATURE1  FEATURE2  FEATURE3

0     X       1.0       1.0       2.0

1     B       0.0       0.0       0.0

Another option is to subtract the size from the count using rsub along the 0^th axis for index aligned subtraction:

df.groupby('CLASS').count().rsub(df.groupby('CLASS').size(), axis=0)

Or,

g = df.groupby('CLASS')

g.count().rsub(g.size(), axis=0)

       FEATURE1  FEATURE2  FEATURE3

CLASS                              

B             0         0         0

X             1         1         2

There are quite a few good answers, so here are some timeits for your perusal:

df_ = df

df = pd.concat([df_] * 10000)



%timeit df.drop('CLASS', 1).isna().groupby(df.CLASS, sort=False).sum()

%timeit df.set_index('CLASS').isna().sum(level=0)    

%%timeit

g = df.groupby('CLASS')

g.count().rsub(g.size(), axis=0)



11.8 ms ± 108 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

9.47 ms ± 379 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

6.54 ms ± 81.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

Actual performance depends on your data and setup, so your mileage may vary.

You can use set_index and sum:

df.set_index('CLASS').isna().sum(level=0)

Output:

       FEATURE1  FEATURE2  FEATURE3

CLASS                              

X           1.0       1.0       2.0

B           0.0       0.0       0.0

Using the diff between count and size

g=df.groupby('CLASS')



-g.count().sub(g.size(),0)



          FEATURE1  FEATURE2  FEATURE3

CLASS                              

B             0         0         0

X             1         1         2

And we can transform this question to the more generic question how to count how many NaN in dataframe with for loop

pd.DataFrame({x: y.isna().sum()for x , y in g }).T.drop('CLASS',1)

Out[468]: 

   FEATURE1  FEATURE2  FEATURE3

B         0         0         0

X         1         1         2