PYTHON

drop a column from dataframe

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

pandas remove column

df.drop(columns='column_name', inplace=True)

remove column from dataframe

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

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)

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 delete column by name

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

delete columns pandas

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

pandas remove column

del df['column_name']

remove columns from a dataframe python

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']

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"]

remove a column from dataframe

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

delete columns pandas

df = df.drop(df.columns[[0, 1, 3]], axis=1)

drop column from dataframe

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

remove columns from a dataframe python

# Import pandas package
import pandas as pd

# create a dictionary with five fields each
data = {
'A':['A1', 'A2', 'A3', 'A4', 'A5'],
'B':['B1', 'B2', 'B3', 'B4', 'B5'],
'C':['C1', 'C2', 'C3', 'C4', 'C5'],
'D':['D1', 'D2', 'D3', 'D4', 'D5'],
'E':['E1', 'E2', 'E3', 'E4', 'E5'] }

# Convert the dictionary into DataFrame
df = pd.DataFrame(data)
#drop the 'A' column from your dataframe df 
df.drop(['A'],axis=1,inplace=True)
df

#-->df contains 'B','C','D' and 'E'
#in this example you will change your dataframe , if you don't want to ,
#just remove the in place parameter and assign your result to an other variable 

df1=df.drop(['B'],axis=1)
#-->df1 contains 'C','D','E'
df1

remove columns from dataframe

df.drop('col_name',1) #1 drop column / 0 drop row

remove name of a column

M
      a b c
1[1,] 1 4 7
2[2,] 2 5 8
3[3,] 3 6 9

row.names(M)<- NULL ; colnames(M)<- NULL
M

     [,1] [,2] [,3]
[1,]    1    4    7
[2,]    2    5    8
[3,]    3    6    9

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

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

droping columns

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

drop columns by name

import pandas as pd

# create a sample dataframe
data = {
    'A': ['a1', 'a2', 'a3'],
    'B': ['b1', 'b2', 'b3'],
    'C': ['c1', 'c2', 'c3'],
    'D': ['d1', 'd2', 'd3']
}

df = pd.DataFrame(data)

# print the dataframe
print("Original Dataframe:
")
print(df)

# remove column C
df = df.drop('C', axis=1)

print("
After dropping C:
")
print(df)

remove a columns in pandas

DataFrame.drop(self, labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise')[source]