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?
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))] 
df[df['C1'].between(0, 20) & df[df['C2'].between(0, 20) & df[df['C3'].between(0, 20)]Shorter version:
In [65]: df[(df>=0)&(df<=20)].dropna() Out[65]: Name C1 C2 C3 1 BBBB 12 1 10 I like to use df.query() for these kind of things
df.query('C1>=0 and C1<=20 and C2>=0 and C2<=20 and C3>=0 and C3<=20') A more concise df.query:
df.query("0 <= C1 <= 20 and 0 <= C2 <= 20 and 0 <= C3 <= 20") or
df.query("0 <= @df <= 20").dropna() Using @foo in df.query refers to the variable foo in the environment.
