使用 pd.Index 与 df.loc 的优缺点是什么

Question

What is the difference between using pd.Index vs df.loc ? Is it effectively the same thing?

idx = pd.Index(('a', 'b'))
df = pd.DataFrame({'a': [0, 1], 'b': [2, 3], 'c': [0, 5]})

print(df.loc[:, ('a', 'b')],)
print(df[idx])

   a  b
0  0  2
1  1  3

Answer 1

When you do loc , you can do with index slice and columns slice or combine, however pd.index can only do for column slice

df.loc[[0]]
   a  b  c
0  0  2  0

df.loc[[0],['a','b']]
   a  b
0  0  2

IMO, loc is more flexible to using, and I will select loc which will more clear for the long run or check back stage.

Answer 2

How loc is the preferred method is described in the documentation . Using multiple slices can lead to a SettingWithCopyWarning :

idx = ['a', 'b']
d = df[idx]
d.iloc[0,0] = 9

SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

In contrast, using loc doesn't trigger the SettingWithCopyWarning :

idx = ['a', 'b']
d = df.loc[:,idx]
d.iloc[0,0] = 9

Of note, loc also enables you to pass a specific axis as parameter:

df.loc(axis=1)[idx]