Is there a way in Pandas to count (Countifs in excel) in one dataframe and add counts as new column in another dataframe of different length?

Question

I'm translating an excel formula in pandas. I have two dataframe df1 and df2 , and I will need to count values in a column first dataframe df1 and populate dataframe df2 where the values counted in df1 is equal to a value in df2 . How do I check and fill a new column in df2 with the counted values from df1 ?

df1 :

      id      member        seq
0   48299      Koif          1
1   48299      Iki           1
2   48299      Juju          2
3   48299      PNik          3 
4   48865      Lok           1 
5   48865      Mkoj          2
6   48865      Kino          1
7   64865      Boni          1
8   64865      Afriya        2
9   50774      Amah          2
10  23697      Pilato        1
11  23697      Clems         1

df2 :

   group_id      group_name    count
0   48299      e_sys          
1   50774      Y3N
2   64865      nana
3   48865      juzti

There could be members from df1 for example Clems and Pilato whose counts are not needed since this group is not in df2 .

I can do the counts alright (see code below), my problem is comparing counted id in df1 with group_id in df2 and filling the counted values.

Counting:

 df1.groupby('id')['id'].count()

My current solution is:

df2['count'] = df1[(df2['group_id'].isin(df1['id']))].count() Or

df2['count'] = df1[(df2['group_id'].isin(df1['id']))].transform('count')

Both doesn't give the desired result.

Results df2 :

   group_id      group_name    count
0   48299      e_sys              4
1   50774      Y3N                1
2   64865      nana               2
3   48865      juzti              3

Answer 1

Use map by Series :

df2['count'] = df2['group_id'].map(df1.groupby('id')['id'].count())

Alternative with Series.value_counts :

df2['count'] = df2['group_id'].map(df1['id'].value_counts())

---

print (df2)
   group_id group_name  count
0     48299      e_sys      4
1     50774        Y3N      1
2     64865       nana      2
3     48865      juzti      3

Answer 2

Merge the two dataframe using a left-join:

counts = df1.groupby('id').size().reset_index()
df2.merge(counts, how='left', left_on='group_id', right_on='id')

Output:

 #      group_id group_name     id  0
 #   0     48299      e_sys  48299  4
 #   1     50774        Y3N  50774  1
 #   2     64865       nana  64865  2
 #   3     48865      juzti  48865  3

Left join makes sure you only keep the counts that appear in df2 . Note I used the groupby().size() as a somewhat clearer and more concise version of counting.