Assume, you have a dataframe,
one two three 0 1 2 3 1 4 5 6
And the result for removing single column is,
two three 0 2 3 1 5 6
The result for removing after more than one column is,
three 0 3 1 6
To solve this, we will follow the steps given below −
Solution 1
Define a dataframe
Delete a particular column using below method,
del df['one']
Example
Let’s see the below code to get a better understanding −
import pandas as pd
data = [[1,2,3],[4,5,6]]
df = pd.DataFrame(data,columns=('one','two','three'))
print("Before deletion\n", df)
del df['one']
print("After deletion\n", df)Output
Before deletion one two three 0 1 2 3 1 4 5 6 After deletion two three 0 2 3 1 5 6
Solution 2
Define a dataframe
Delete a particular column using pop function. It is defined below
df.pop('one')Example
import pandas as pd
data = [[1,2,3],[4,5,6]]
df = pd.DataFrame(data,columns=('one','two','three'))
print("Before deletion\n", df)
df.pop('one')
print("After deletion\n", df)Output
Before deletion one two three 0 1 2 3 1 4 5 6 After deletion two three 0 2 3 1 5 6
Solution 3
Define a dataframe
Apply the below method to drop more than one columns,
df.drop(columns=['one','two'],inplace = True)
Example
import pandas as pd
data = [[1,2,3],[4,5,6]]
df = pd.DataFrame(data,columns=('one','two','three'))
print("Before deletion\n ", df)
df.drop(columns=['one','two'],inplace = True)
print("After deleting two columns\n", df)Output
Before deletion one two three 0 1 2 3 1 4 5 6 After deletion two three 0 2 3 1 5 6 After deleting two columns three 0 3 1 6