如何删除DataFrame中除某些列外的所有列？

Question

Let's say I have a DataFrame that looks like this:

a  b  c  d  e  f  g  
1  2  3  4  5  6  7
4  3  7  1  6  9  4
8  9  0  2  4  2  1

How would I go about deleting every column besides a and b ?

This would result in:

a  b
1  2
4  3
8  9

I would like a way to delete these using a simple line of code that says, delete all columns besides a and b , because let's say hypothetically I have 1000 columns of data.

Thank you.

Answer 1

In [48]: df.drop(df.columns.difference(['a','b']), 1, inplace=True)
Out[48]:
   a  b
0  1  2
1  4  3
2  8  9

or:

In [55]: df = df.loc[:, df.columns.intersection(['a','b'])]

In [56]: df
Out[56]:
   a  b
0  1  2
1  4  3
2  8  9

PS please be aware that the most idiomatic Pandas way to do that was already proposed by @Wen :

df = df[['a','b']]

or

df = df.loc[:, ['a','b']]

Answer 2

Another option to add to the mix. I prefer this approach for readability.

df = df.filter(['a', 'b'])

Where the first positional argument is items=[]

---

Bonus

You can also use a like argument or regex to filter.
Helpful if you have a set of columns like ['a_1','a_2','b_1','b_2']

You can do

df = df.filter(like='b_')

and end up with ['b_1','b_2']

Pandas documentation for filter.

Answer 3

there are multiple solution .

df = df[['a','b']] #1

df = df[list('ab')] #2

df = df.loc[:,df.columns.isin(['a','b'])] #3

df = pd.DataFrame(data=df.eval('a,b').T,columns=['a','b']) #4 PS:I do not recommend this method , but still a way to achieve this 

Answer 4

If you have more than two columns that you want to drop, let's say 20 or 30 , you can use lists as well. Make sure that you also specify the axis value.

drop_list = ["a","b"]
df = df.drop(df.columns.difference(drop_list), axis=1)

Answer 5

Hey what you are looking for is:

df = df[["a","b"]]

You will recive a dataframe which only contains the columns a and b

Answer 6

如果您只想保留多于您删除的列，请在 .isin 语句前添加“~”以选择除您想要的列之外的每一列：

df = df.loc[:, ~df.columns.isin(['a','b'])]