selecting rows based on multiple column values in pandas dataframe

Question

I have a pandas DataFrame df:

import pandas as pd  data = {"Name": ["AAAA", "BBBB"],         "C1": [25, 12],         "C2": [2, 1],         "C3": [1, 10]}  df = pd.DataFrame(data) df.set_index("Name") 

which looks like this when printed (for reference):

      C1  C2  C3 Name             AAAA  25   2   1 BBBB  12   1  10 

I would like to choose rows for which C1, C2 and C3 have values between 0 and 20.

Can you suggest an elegant way to select those rows?

Answer 1

I think below should do it, but its elegance is up for debate.

new_df = old_df[((old_df['C1'] > 0) & (old_df['C1'] < 20)) & ((old_df['C2'] > 0) & (old_df['C2'] < 20)) & ((old_df['C3'] > 0) & (old_df['C3'] < 20))] 

Answer 2

Shorter version:

In [65]:  df[(df>=0)&(df<=20)].dropna() Out[65]:    Name  C1  C2  C3 1  BBBB  12   1  10