基于熊猫数据框中的多列值选择行

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

我认为下面应该这样做，但它的优雅有待商榷。

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

Answer 3

我喜欢用 df.query() 做这些事情

df.query('C1>=0 and C1<=20 and C2>=0 and C2<=20 and C3>=0 and C3<=20')

Answer 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.