drop a column pandas
df.drop(['column_1', 'Column_2'], axis = 1, inplace = True)drop a column pandas
df.drop(['column_1', 'Column_2'], axis = 1, inplace = True)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 column
df = df.drop(['B', 'C'], axis=1)drop a column in pandas
df = df.drop(df.columns[[0, 1, 3]], axis=1) # df.columns is zero-based pd.Indexdrop column pandas
df.drop(['column_1', 'Column_2'], axis = 1, inplace = True)
# Remove all columns between column index 1 to 3
df.drop(df.iloc[:, 1:3], inplace = True, axis = 1)drop 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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