Panda drop values in columns but keep columns

Question

Has the title say, I would like to find a way to drop the row (erase it) in a data frame from a column to the end of the data frame but I don't find any way to do so.

I would like to start with

A    B    C
-----------
1    1    1
1    1    1
1    1    1

and get

A    B    C
-----------
1        
1        
1

I was trying with

df.drop(df.loc[:, 'B':].columns, axis = 1, inplace = True)

But this delete the column itself too

am I missing something?

There must be something in those cells, either a ' ', or np.nan, or any kind of character. — sophocles
– sophocles, Commented Feb 16, 2021 at 17:12
I would like to make the drop but I only know the name of the last column I need to keep the row — Guillaume Lemer
– Guillaume Lemer, Commented Feb 16, 2021 at 17:33
I would like to keep the first row event if the column is empty — Guillaume Lemer
– Guillaume Lemer, Commented Feb 16, 2021 at 17:36

Amir P · Accepted Answer · 2021-02-16 18:49:03Z

3

If you only know the column name that you want to keep:

import pandas as pd

new_df = pd.DataFrame(df["A"])

If you only know the column names that you want to drop:

new_df = df.drop(["B", "C"], axis=1)

For your case, to keep the columns, but remove the content, one possible way is:

new_df = pd.DataFrame(df["A"], columns=df.columns)

Resulting df contains columns "A" and "B" but without values (NaN instead)

edited Feb 16, 2021 at 18:49

answered Feb 16, 2021 at 17:47

Amir P

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2 Comments

Guillaume Lemer Over a year ago

When I do that, as I said, I drop the first line ( B C ) to, but I want to keep it

Amir P Over a year ago

Ok, I edited the answer. This is in fact very special use case.

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