How can I count a specific value in group_by in pandas?

Question

I have a dataframe and I use groupby to group it by Season. One of the columns of the original df is named Check and consists of True and False. My aim it to count the True values for each group and put it in the new dataframe.

import pandas as pd

df = ....
df['Check'] = df['Actual'] == df['Prediction']
grouped_per_year = df.groupby('Season')

df_2= pd.DataFrame()
df_2['Seasons'] = total_matches_per_year.keys()
df_2['Successes'] = ''
df_2['Total_Matches'] = list(grouped_per_year.size())
df_2['SR'] = df_2['Successes'] / df_2['Total_Matches']
df_2['Money_In'] = list(grouped_per_year['Money_In'].apply(sum))
df_2['Profit (%)'] = (df_profit['Money_In'] - df_profit['Total_Matches']) / df_profit['Total_Matches'] * 100.

I have tried:

successes_per_year = grouped_per_year['Pred_Check'].value_counts()

but I don't know how to get only the True count.

Answer 1

For counting True, you can also use sum (as True=1 and False=0 when doing a numerical operation):

grouped_per_year['Pred_Check'].sum()