26 Oct Delete rows/ columns in a Pandas DataFrame

The drop() method is used in Python to delete rows/ columns in a Pandas DataFrame. It is used to remove a particular row or column. Under the parameters of the drop() method, mention the column you want to delete with the axis.

We will now see two examples:

1. Drop columns
2. Drop rows

Before moving further, we’ve prepared a video tutorial to delete rows/ columns in a Pandas DataFrame:

Drop columns using drop()

The columns are dropped using the column names. The axis is set to 1 since we want to drop a column. The columns axis can also be used for the drop() method to remove the specified column:

axis='columns'
or
axis = 1

Let us see an example to drop columns using the drop() method:

import pandas as pd
 
# Dataset
data = {
  'id': ["S01", "S02", "S03", "S04", "S05"],
  'student': ["Amit", "John", "Jacob", "David", "Steve"],
  'rank': [1, 4, 3, 5, 2],
  'marks': [95, 70, 80, 60, 90]
}
 
dataFrame = pd.DataFrame(data)
print("Student Records\n\n",dataFrame)

# Drop a column using the drop() method
# The marks column is deleted
resDF = dataFrame.drop("marks", axis='columns')

# DataFrame after dropping a column
print("\nUpdated DataFrame:\n",resDF)

Output

Student Records

     id student  rank  marks
0  S01    Amit     1     95
1  S02    John     4     70
2  S03   Jacob     3     80
3  S04   David     5     60
4  S05   Steve     2     90

Updated DataFrame:
     id student  rank
0  S01    Amit     1
1  S02    John     4
2  S03   Jacob     3
3  S04   David     5
4  S05   Steve     2

Drop rows using drop()

The rows are dropped using the index label set as a parameter of the drop() method. The rows of that particular label are removed. Set the axis to 0 since we want to drop a row. The rows axis i.e. index can also be used for the drop() method to remove the specified row:

axis='index'
Or
axis = 0

Let us see an example of dropping a row using the drop() method. Here, the row with the 2^nd index is removed:

import pandas as pd
 
# Dataset
data = {
  'id': ["S01", "S02", "S03", "S04", "S05"],
  'student': ["Amit", "John", "Jacob", "David", "Steve"],
  'rank': [1, 4, 3, 5, 2],
  'marks': [95, 70, 80, 60, 90]
}
 
dataFrame = pd.DataFrame(data)
print("Student Records\n\n",dataFrame)

# Drop a row using the drop() method
# The row with index 2 is removed
resDF = dataFrame.drop(2, axis='index')

# DataFrame after dropping a column
print("\nUpdated DataFrame:\n",resDF)

Output

Student Records

     id student  rank  marks
0  S01    Amit     1     95
1  S02    John     4     70
2  S03   Jacob     3     80
3  S04   David     5     60
4  S05   Steve     2     90

Updated DataFrame:
     id student  rank  marks
0  S01    Amit     1     95
1  S02    John     4     70
3  S04   David     5     60
4  S05   Steve     2     90

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