Python Pandas Conditional count after groupby -


i have following dataframe:

   key1  key2 0      1 one      2 two    b   1 3    b   2 4      1 5    c   2

now, want group dataframe key1 , count column key2 value 1 result:

   key1   0      2 1    b   1 2    c   0

i usual count with:

df.groupby(['key1']).size()

but dont know how insert condition.

i tryed things this:

df.groupby(['key1']).apply(df[df['key2'] == 'one'])

but dont futher

it great if here

greetings

i think need add condition first:

#if need category c no values of 'one' df11=df.groupby('key1')['key2'].apply(lambda x: (x=='one').sum()).reset_index(name='count') print (df11)   key1  count 0         2 1    b      1 2    c      0

or use categorical key1, missing value added size:

df['key1'] = df['key1'].astype('category') df1 = df[df['key2'] == 'one'].groupby(['key1']).size().reset_index(name='count')  print (df1)   key1  count 0         2 1    b      1 2    c      0

if need combinations:

df2 = df.groupby(['key1', 'key2']).size().reset_index(name='count')  print (df2)   key1 key2  count 0     1      2 1     2      1 2    b  1      1 3    b  2      1 4    c  2      1  df3 = df.groupby(['key1', 'key2']).size().unstack(fill_value=0) print (df3) key2  1  2 key1                 2    1 b       1    1 c       0    1

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