Drop multiple columns in pandas
Question:
I am trying to drop multiple columns (column 2 and 70 in my data set, indexed as 1 and 69 respectively) by index number in a pandas data frame with the following code:
df.drop([df.columns[[1, 69]]], axis=1, inplace=True)
I get the following error:
TypeError: unhashable type: 'Index'
And in my code the [1, 69] is highlighted and says:
Expected type 'Integral', got 'list[int]' instead
The following code does what I want in two lines of repetitive code (first dropping col index 69, then 1, and order does matter because dropping earlier columns changes the index of later columns).
df.drop([df.columns[69]], axis=1, inplace=True)
df.drop([df.columns[1]], axis=1, inplace=True)
Is there a way to do this in one line similar to the first code snippet above?
Answers:
You don’t need to wrap it in a list with [..], just provide the subselection of the columns index:
df.drop(df.columns[[1, 69]], axis=1, inplace=True)
as the index object is already regarded as list-like.
Try this
df.drop(df.iloc[:, 1:69], inplace=True, axis=1)
This works for me
A readable version is to pass the columns= argument.
df = df.drop(columns=df.columns[[1, 69]])
Putting df.columns[[1, 69]] inside a list (as in OP) becomes useful if we want to drop columns both by integer position and name(s) (simply need to unpack the array with *). For example, the following code drops the 2nd and the 70th columns along with another column named Some_Col.
df = df.drop(columns=[*df.columns[[1, 69]], 'Some_Col'])
Another way to drop columns is via the iloc indexer. For example, to drop the 2nd and 70th columns:
df = df.iloc[:, np.r_[:1, 2:69, 70:df.shape[1]]]
# or
df = df.iloc[:, ~np.isin(np.arange(df.shape[1]), [1,69])]
I am trying to drop multiple columns (column 2 and 70 in my data set, indexed as 1 and 69 respectively) by index number in a pandas data frame with the following code:
df.drop([df.columns[[1, 69]]], axis=1, inplace=True)
I get the following error:
TypeError: unhashable type: 'Index'
And in my code the [1, 69] is highlighted and says:
Expected type 'Integral', got 'list[int]' instead
The following code does what I want in two lines of repetitive code (first dropping col index 69, then 1, and order does matter because dropping earlier columns changes the index of later columns).
df.drop([df.columns[69]], axis=1, inplace=True)
df.drop([df.columns[1]], axis=1, inplace=True)
Is there a way to do this in one line similar to the first code snippet above?
You don’t need to wrap it in a list with [..], just provide the subselection of the columns index:
df.drop(df.columns[[1, 69]], axis=1, inplace=True)
as the index object is already regarded as list-like.
Try this
df.drop(df.iloc[:, 1:69], inplace=True, axis=1)
This works for me
A readable version is to pass the columns= argument.
df = df.drop(columns=df.columns[[1, 69]])
Putting df.columns[[1, 69]] inside a list (as in OP) becomes useful if we want to drop columns both by integer position and name(s) (simply need to unpack the array with *). For example, the following code drops the 2nd and the 70th columns along with another column named Some_Col.
df = df.drop(columns=[*df.columns[[1, 69]], 'Some_Col'])
Another way to drop columns is via the iloc indexer. For example, to drop the 2nd and 70th columns:
df = df.iloc[:, np.r_[:1, 2:69, 70:df.shape[1]]]
# or
df = df.iloc[:, ~np.isin(np.arange(df.shape[1]), [1,69])]