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 1How would I go about deleting every column besides a and b?
This would result in:
a b
1 2
4 3
8 9I 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.
6 Answers
In [48]: df.drop(df.columns.difference(['a','b']), 1, inplace=True)
Out[48]: a b
0 1 2
1 4 3
2 8 9or:
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 9PS 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']]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.
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 If you only want to keep more columns than you're dropping put a "~" before the .isin statement to select every column except the ones you want:
df = df.loc[:, ~df.columns.isin(['a','b'])]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)Hey what you are looking for is:
df = df[["a","b"]]You will recive a dataframe which only contains the columns a and b
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