Count ocurrencies of pattern in pandas dataframe based on condition-CodePudding

Consider this dataframe:

pd.DataFrame(['A(3)BC(1)', 'A(2)BC(5)', 'A(1)BC(3)', 'A(2)BC(5)', 'A(4)BC(2)'], columns=['Column1'])

    Column1
0   A(3)BC(1)
1   A(2)BC(5)
2   A(1)BC(3)
3   A(2)BC(5)
4   A(4)BC(2)

Is there a way to count the number of times A has a number higher than (3) without iterating through every line of the dataframe?

CodePudding user response：

Let's try

out = df['Column1'].str.extract('A\((\d )\)')[0].astype(int).gt(3).sum()

print(out)

1

CodePudding user response：

If the format is always A(X) at the start, you can do:

df['Column1'].apply(lambda st: int(st[st.find("A") 2:st.find(")")])).gt(3).sum()

Output

1