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) 

delete column pandas dataframe

df.drop(columns='column_name', 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

python code to drop columns from dataframe

# Let df be a dataframe
# Let new_df be a dataframe after dropping a column

new_df = df.drop(labels='column_name', axis=1)

# Or if you don't want to change the name of the dataframe
df = df.drop(labels='column_name', axis=1)

# Or to remove several columns
df = df.drop(['list_of_column_names'], axis=1)

# axis=0 for 'rows' and axis=1 for columns

drop columns pandas

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

remove column from dataframe

df.drop('column_name', axis=1, inplace=True)

drop a column in pandas

note: df is your dataframe

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

drop unamed columns in pandas

df = df.loc[:, ~df.columns.str.contains('^Unnamed')]

In [162]: df
Out[162]:
   colA  ColB  colC  colD  colE  colF  colG
0    44    45    26    26    40    26    46
1    47    16    38    47    48    22    37
2    19    28    36    18    40    18    46
3    50    14    12    33    12    44    23
4    39    47    16    42    33    48    38

how to drop a column by name in pandas

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

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'])

pandas drop column by name

df.drop(columns=['Column_Name1','Column_Name2'], axis=1, inplace=True)

drop a column from dataframe

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

pandas remove column

del df['column_name']

drop a column in pandas

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

pandas dataframe delete column

del df['column_name']

padnas drop column

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

how to drop a column in python

# 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(['column_1', 'Column_2'], axis = 1, inplace = True) 

delete pandas column

del df["column"]

pd df drop columns

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

remove a column from dataframe

del df['column_name'] #to remove a column from dataframe

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()

delete a column in pandas

# Remove the unwanted columns
data.drop(['Country code', 'Continental region'], axis=1, inplace=True)
data.head()

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

delete column in dataframe pandas

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

python how to drop columns from dataframe

# When you have many columns, and only want to keep a few:
# drop columns which are not needed.

# df = pandas.Dataframe()
columnsToKeep = ['column_1', 'column_13', 'column_99']
df_subset = df[columnsToKeep]

# Or:
df = df[columnsToKeep]

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

How to drop columns from pandas dataframe

df.drop(cols_to_drop, axis=1)

pd df drop columns

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

how to delete a column in pandas dataframe

delete column from pandas data frame

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

how to drop a column by name in pandas

>>> midx = pd.MultiIndex(levels=[['lama', 'cow', 'falcon'],
...                              ['speed', 'weight', 'length']],
...                      codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
...                             [0, 1, 2, 0, 1, 2, 0, 1, 2]])
>>> df = pd.DataFrame(index=midx, columns=['big', 'small'],
...                   data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
...                         [250, 150], [1.5, 0.8], [320, 250],
...                         [1, 0.8], [0.3, 0.2]])
>>> df
                big     small
lama    speed   45.0    30.0
        weight  200.0   100.0
        length  1.5     1.0
cow     speed   30.0    20.0
        weight  250.0   150.0
        length  1.5     0.8
falcon  speed   320.0   250.0
        weight  1.0     0.8
        length  0.3     0.2