python pandas drop
df = pd.DataFrame(np.arange(12).reshape(3, 4),
... columns=['A', 'B', 'C', 'D'])
>>> df
A B C D
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
Drop columns
>>> df.drop(['B', 'C'], axis=1)
A D
0 0 3
1 4 7
2 8 11
>>> df.drop(columns=['B', 'C'])
A D
0 0 3
1 4 7
2 8 11df.drop
>>>df = pd.DataFrame(np.arange(12).reshape(3, 4),
columns=['A', 'B', 'C', 'D'])
>>>df
A B C D
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
>>> df.drop(['B', 'C'], axis=1)
A D
0 0 3
1 4 7
2 8 11
OR
>>> df.drop(columns=['B', 'C'])
A D
0 0 3
1 4 7
2 8 11Python pandas how to drop a column
df = pd.DataFrame(np.arange(12).reshape(3, 4), columns=['A', 'B', 'C', 'D'])
>>> df
A B C D
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
cols=['B', 'C']
>>> df.drop(columns = cols) # or # df.drop(columns = ['B', 'C'])
A D
0 0 3
1 4 7
2 8 11drop dataframe columns
# Drop The Original Categorical Columns which had Whitespace Issues in their values
df.drop(cat_columns, axis = 1, inplace = True)
dict_1 = {'workclass_stripped':'workclass', 'education_stripped':'education',
'marital-status_stripped':'marital_status', 'occupation_stripped':'occupation',
'relationship_stripped':'relationship', 'race_stripped':'race',
'sex_stripped':'sex', 'native-country_stripped':'native-country',
'Income_stripped':'Income'}
df.rename(columns = dict_1, inplace = True)
dfCopyright © 2021 Codeinu
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