How do I select the counts of values in a column in pandas for a specific value in another column?

Question

I have a pandas dataframe with two columns named "column one" and "column two". I want to select the counts of all values in "column two" where "column one" has value b. I can do this in two steps with this code:

data = [['a', 'val1'], ['b', 'val2'], ['b', 'val2'], ['b','val3'], ['b','val4'], ['a', 'val5'], ['a', 'val6']]
ex = pd.DataFrame(data, columns = ['column one', 'column two'])
exa = ex[ex['column one']=='b']
exa['column two'].value_counts()

This will give me the output:

val2 2

val3 1

val4 1

Now how do I write this such that my output includes the values val1, val5 and val6 showing 0

Answer 1

Use Series.reindex by unique values of original column:

s = exa['column two'].value_counts().reindex(ex['column two'].unique(), fill_value=0)
print (s)
val1    0
val2    2
val3    1
val4    1
val5    0
val6    0
Name: column two, dtype: int64

Just out of curiosity is there a way to do this without having to create the second dataframe exa?

Yes, you can chain code together and add DataFrame.loc for select column by condition:

s = (ex.loc[ex['column one']=='b', 'column two']
        .value_counts()
        .reindex(ex['column two'].unique(), fill_value=0))

Solution with aggregation:

s = ex['column one'].eq('b').view('i1').groupby(ex['column two']).sum()
#alternative
s = ex['column one'].eq('b').astype(int).groupby(ex['column two']).sum()
print (s)
column two
val1    0
val2    2
val3    1
val4    1
val5    0
val6    0
Name: column one, dtype: int8

Answer 2

Or with groupby

import pandas as pd
import numpy as np

# data
data = [['a', 'val1'], ['b', 'val2'], ['b', 'val2'], ['b','val3'], ['b','val4'], ['a', 'val5'], ['a', 'val6']]
ex = pd.DataFrame(data, columns = ['column one', 'column two'])
#

ex.groupby('column two')['column one'].apply(lambda x: np.sum(x=='b'))

This will return a pandas series