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)

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

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)

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)

delete unnamed coloumns in pandas

# Best method so far.
df = df.loc[:, ~df.columns.str.contains('^Unnamed')]

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']

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']

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"]

how to delete a column from a dataframe in python

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)

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()

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

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

remove columns from dataframe

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

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]

remove columns that start with pandas

cols = [c for c in df.columns if c.lower()[:6] != 'string']
df=df[cols]

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)

Python Delete column

import pandas as pd
EmployeeData=pd.DataFrame({'Name': ['ram','ravi','sham','sita','gita'],
                            'id': [101,102,103,104,105],
                        'Gender': ['M','M','M','F','F'],
                           'Age': [21,25,24,28,25]
                          })
# Priting data
print(EmployeeData)
 
# Deleting few columns
DeleteList=['Name','Gender']
EmployeeData=EmployeeData.drop(DeleteList, axis=1)
 
# Priting data
print(EmployeeData)

how to delete a column in pandas dataframe

delete column from pandas data frame

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

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]