Drop last N rows of pandas dataframe

In this article, we will discuss different ways to delete last N rows of a dataframe in python.

Use iloc to drop last N rows of pandas dataframe

In Pandas, the Dataframe provides an attribute iloc to select a portion of the dataframe using position based indexing. This selected portion can be a few columns or rows . We can use this attribute to select all the rows except last N rows of a dataframe and then assign back that to the original variable. It will give an effect that we have deleted the last N rows from the dataframe. For example,

# Drop last 3 rows
# by selecting all rows except last 3 rows
N = 3
df = df.iloc[:-N , :]

We selected a portion of dataframe, that included all columns, but it selected only first (size – N) rows. Then assigned this back to the same variable. So, basically it removed the last N rows of dataframe.

How did it work?

The syntax of dataframe.iloc[] is like,

df.iloc[row_start:row_end , col_start, col_end]

• row_start: The row index/position from where it should start selection. Default is 0.
• row_end: The row index/position from where it should end the selection i.e. select till row_end-1. Default is till the last row of the dataframe.
• col_start: The column index/position from where it should start selection. Default is 0.
• col_end: The column index/position from where it should end the selection i.e. select till col_end-1. Default is till the last column of the dataframe.

It returns a portion of dataframe that includes rows from row_start to row_end-1 and columns from col_start to col_end-1.

To delete the last N rows of the dataframe, just select the rows from row number 0 till the end -N ( with negative indexing it is -N ) and select all columns i.e.

df = df.iloc[:-N , :]

Checkout complete example to delete the last 3 rows of dataframe,

import pandas as pd

''' Using iloc[] '''

# List of Tuples
empoyees = [('Jack',    34, 'Sydney',   5),
            ('Riti',    31, 'Delhi' ,   7),
            ('Aadi',    16, 'London',   11),
            ('Mark',    41, 'Delhi' ,   12),
            ('Sam',     56, 'London',   33)]

# Create a DataFrame object
df = pd.DataFrame(  empoyees, 
                    columns=['Name', 'Age', 'City', 'Experience'],
                    index = ['A', 'B', 'C', 'D', 'E'])

print("Contents of the Dataframe : ")
print(df)

# Drop last 3 rows
# by selecting all rows except last 3 rows
N = 3
df = df.iloc[:-N , :]

print("Modified Dataframe : ")
print(df)

Output:

Contents of the Dataframe :
   Name  Age    City  Experience
A  Jack   34  Sydney           5
B  Riti   31   Delhi           7
C  Aadi   16  London          11
D  Mark   41   Delhi          12
E   Sam   56  London          33
Modified Dataframe :
   Name  Age    City  Experience
A  Jack   34  Sydney           5
B  Riti   31   Delhi           7

Use drop() to remove last N rows of pandas dataframe

In pandas, the dataframe’s drop() function accepts a sequence of row names that it needs to delete from the dataframe. To make sure that it removes the rows only, use argument axis=0 and to make changes in place i.e. in calling dataframe object, pass argument inplace=True.

Checkout complete example to delete the last 3 rows of dataframe,

import pandas as pd

# List of Tuples
empoyees = [('Jack',    34, 'Sydney',   5),
            ('Riti',    31, 'Delhi' ,   7),
            ('Aadi',    16, 'London',   11),
            ('Mark',    41, 'Delhi' ,   12),
            ('Sam',     56, 'London',   33)]


# Create a DataFrame object
df = pd.DataFrame(  empoyees, 
                    columns=['Name', 'Age', 'City', 'Experience'],
                    index = ['A', 'B', 'C', 'D', 'E'])

print("Contents of the Dataframe : ")
print(df)

# Drop last 3 rows of dataframe
N = 3
df.drop(index=df.index[-N:], 
        axis=0, 
        inplace=True)

print("Modified Dataframe : ")
print(df)

Output:

Contents of the Dataframe :
   Name  Age    City  Experience
A  Jack   34  Sydney           5
B  Riti   31   Delhi           7
C  Aadi   16  London          11
D  Mark   41   Delhi          12
E   Sam   56  London          33
Modified Dataframe :
   Name  Age    City  Experience
A  Jack   34  Sydney           5
B  Riti   31   Delhi           7

We fetched the row names of dataframe as a sequence and passed the last N row names ( df.index[-N:] ) as the index argument in drop() function, therefore it deleted the last N rows (3 rows) of dataframe.

Use head() to remove last N rows of pandas dataframe

In Pandas, dataframe provides a function head(N) to select first N rows of dataframe. To delete last N rows of dataframe, we can select first (Size-N) rows of dataframe using head() function. For example,

import pandas as pd

# List of Tuples
empoyees = [('Jack',    34, 'Sydney',   5),
            ('Riti',    31, 'Delhi' ,   7),
            ('Aadi',    16, 'London',   11),
            ('Mark',    41, 'Delhi' ,   12),
            ('Sam',     56, 'London',   33)]

# Create a DataFrame object
df = pd.DataFrame(  empoyees, 
                    columns=['Name', 'Age', 'City', 'Experience'],
                    index = ['A', 'B', 'C', 'D', 'E'])

print("Contents of the Dataframe : ")
print(df)

# Drop last 3 rows of dataframe
N = 3
df = df.head(df.shape[0] -N)

print("Modified Dataframe : ")
print(df)

Output:

Contents of the Dataframe :
   Name  Age    City  Experience
A  Jack   34  Sydney           5
B  Riti   31   Delhi           7
C  Aadi   16  London          11
D  Mark   41   Delhi          12
E   Sam   56  London          33
Modified Dataframe :
   Name  Age    City  Experience
A  Jack   34  Sydney           5
B  Riti   31   Delhi           7

It removed the last 3 rows of dataframe in place.

Summary:

We learned about four different ways to delete last N rows of a dataframe.