count() becomes zero after transposing df

I have a dataset df1 that looks like this:

fake_id    date          type     value
xxx        12.1.22       A
zzz        12.2.22       A
           13.4.22       B        12

Then I have a df2that looks like this:

name       downloads 
Name1      23

I want to count all occurrences of all types (eg: A and B).

Then I want to add the the counts to my first dataset. Something like this:

name       value     count_A        count_B   
Name1      23        2              1

I was trying this:

    df1 = df1.groupby('type').count()
    df1_transposed = df1.T
    df1_transposed = df1_transposed[['A', 'B']]
    df1_transposed = df1_transposed.reset_index()
    df2 = pd.merge(df2, df1_transposed, left_index=True, right_index=True)
    df2 = df2.drop('index', 1)

and it gives me an output that looks like this:

    name  value     A     B  
0  Name1     12     2     0     

Although the value for group A is correct, the value for B is incorrect. This is probably because there are some NULL values in the fake_id column for type B. Hence, after transposing, it takes the value of 0 instead of 1. How can I fix this?

For example, after this part, the table looks like this:

type                             A                  B
fake_id                          2                  0
date                             2                  1
value                            0                  1

>Solution :

You could use the value_counts() function to count occurences of all values in a column.

values_counts = df1.type.value_counts()
a_counts = values_counts.a
b_counts = values_counts.b
df2["count_A"] = [a_counts]
df2["count_B"] = [b_counts]

This should do the trick.