PYTHON

drop a column pandas

df.drop(['column_1', 'Column_2'], axis = 1, inplace = True) 

drop a column from dataframe

#To delete the column without having to reassign df
df.drop('column_name', axis=1, inplace=True) 

Drop a column pandas

df.drop('column_name', axis=1, inplace=True)
#no need to reasign df
#axis 1 is columns, 0 is rows

drop columns pandas

df.drop(columns=['B', 'C'])

drop a column in pandas

note: df is your dataframe

df = df.drop('coloum_name',axis=1)

python - drop a column

# axis=1 tells Python that we want to apply function on columns instead of rows
# To delete the column permanently from original dataframe df, we can use the option inplace=True
df.drop(['A', 'B', 'C'], axis=1, inplace=True)

drop a column from dataframe

df = df.drop('column_name', 1)

df drop column

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

drop column dataframe

df.drop(columns=['Unnamed: 0'])

drop a column from dataframe

#working with "text" syntax for the columns:
df.drop(['column_nameA', 'column_nameB'], axis=1, inplace=True)

drop a column in pandas

df = df.drop(df.columns[[0, 1, 3]], axis=1)  # df.columns is zero-based pd.Index 

padnas drop column

df.drop(columns=['col1', 'col2'])

pd df drop columns

df.drop(['B', 'C'], axis=1)
   A   D
0  0   3
1  4   7
2  8  11

drop 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)

pandas drop column in dataframe

>>> df.drop(['B', 'C'], axis=1)
   A   D
0  0   3
1  4   7
2  8  11

drop column from dataframe

var = dataframe.drop(['col', 'col'], axis=1)
var.sum()

drop column

ALTER TABLE <TableName>
DROP COLUMN <ColumnName>;

drop columns in python pandas

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, inplace=True)
   A   D
0  0   3
1  4   7
2  8  11

df.drop(columns=['B', 'C'], inplace = True)
   A   D
0  0   3
1  4   7
2  8  11

drop column pandas

df.drop(['Col_1', 'Col_2'], axis = 1) # to drop full colum more general way can visulize easily

df.drop(['Col_1', 'Col_2'], axis = 1, inplace = True) # advanced : to generate df without making copies inside memory

drop column

result.drop(['web-scraper-start-url', 'jfy', 'jfy-href'], axis=1, inplace=True)

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)
df

pd df drop columns

df.drop([0, 1]) # drop cols by index

drop columns pandas dataframe

df.iloc[row_start:row_end , column_start:column_end]
#or
data.drop(index=0) 

pandas drop columns

In [212]:
df = pd.DataFrame(np.random.randint(0, 2, (10, 4)), columns=list('abcd'))
df.apply(pd.Series.value_counts)
Out[212]:
   a  b  c  d
0  4  6  4  3
1  6  4  6  7

drop columns

>>> df.drop(index='cow', columns='small')
                big
lama    speed   45.0
        weight  200.0
        length  1.5
falcon  speed   320.0
        weight  1.0
        length  0.3

droping columns

ri.drop('county_name',
  axis='columns', inplace=True)