How to do a conditional count after groupby on a Pandas Dataframe?
I think you need add condition first:
#if need also category c with no values of 'one'
df11=df.groupby('key1')['key2'].apply(lambda x: (x=='one').sum()).reset_index(name='count')
print (df11)
key1 count
0 a 2
1 b 1
2 c 0
Or use categorical
with key1
, then missing value is added by size
:
df['key1'] = df['key1'].astype('category')
df1 = df[df['key2'] == 'one'].groupby(['key1']).size().reset_index(name='count')
print (df1)
key1 count
0 a 2
1 b 1
2 c 0
If need all combinations:
df2 = df.groupby(['key1', 'key2']).size().reset_index(name='count')
print (df2)
key1 key2 count
0 a one 2
1 a two 1
2 b one 1
3 b two 1
4 c two 1
df3 = df.groupby(['key1', 'key2']).size().unstack(fill_value=0)
print (df3)
key2 one two
key1
a 2 1
b 1 1
c 0 1
You can count the occurence of 'one' for the groupby dataframe, in the column 'key2' like this:
df.groupby('key1')['key2'].apply(lambda x: x[x == 'one'].count())
yield
key1
a 2
b 1
c 0
Name: key2, dtype: int64