Selecting Rows Based On Multiple Column Values In Pandas Dataframe

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) d

Solution 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))]

Solution 2:

Shorter version:

In [65]:

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

Solution 3:

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')

Solution 4:

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.