In this article, we will discuss different ways to delete last row of a pandas dataframe in python.
Table of Contents
- Use iloc to drop last row of pandas dataframe.
- Use drop() to remove last row of pandas dataframe.
- Use head() function to remove last row of pandas dataframe.
Use iloc to drop last row 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 few columns or rows . We can use this attribute to select all the rows except the last one and then assign back the selected rows to the original variable. It will give an effect that we have deleted the last row from the dataframe. For example,
# Drop last row # by selecting all rows except last row df = df.iloc[:-1 , :]
We selected a portion of dataframe, that included all columns, but it selected only size-1 rows i.e. from row at position 0 to -1 (one less than last row). Then assigned this back to the same variable. So, basically it removed the last row of dataframe.
How did it work?
The syntax of dataframe.iloc[] is like,
df.iloc[row_start:row_end , col_start, col_end]
Arguments:
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- 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 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 row from dataframe, just selected the rows from row number 0 till the end-1 and select all columns. To represent the index number of last row, we used the -1 ( negative indexing) . As indexing starts from 0, so to select all rows after the last one use –> (:-1) i.e. from 0th row till 2nd last row. To select all the columns use default values i.e. (:) i.e.
df = df.iloc[:-1 , :]
Checkout complete example to delete the last row 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)]
# Create a DataFrame object
df = pd.DataFrame( empoyees,
columns=['Name', 'Age', 'City', 'Experience'],
index = ['A', 'B', 'C', 'D'])
print("Contents of the Dataframe : ")
print(df)
# Drop last row
# by selecting all rows except last row
df = df.iloc[:-1 , :]
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 Modified Dataframe : Name Age City Experience A Jack 34 Sydney 5 B Riti 31 Delhi 7 C Aadi 16 London 11
Use drop() to remove last row 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 row of dataframe is as follows,
import pandas as pd
# List of Tuples
empoyees = [('Jack', 34, 'Sydney', 5) ,
('Riti', 31, 'Delhi' , 7) ,
('Aadi', 16, 'London', 11) ,
('Mark', 41, 'Delhi' , 12)]
# Create a DataFrame object
df = pd.DataFrame( empoyees,
columns=['Name', 'Age', 'City', 'Experience'],
index = ['A', 'B', 'C', 'D'])
print("Contents of the Dataframe : ")
print(df)
# Drop last row
df.drop(index=df.index[-1],
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 Modified Dataframe : Name Age City Experience A Jack 34 Sydney 5 B Riti 31 Delhi 7 C Aadi 16 London 11
We fetched the all names of dataframe index as a sequence and passed the last row/index name as the index argument in drop() function, therefore it deleted the last row of dataframe.
Use head() function to drop last row of pandas dataframe
In python, dataframe provides a function head(n), it returns the first n rows of dataframe. So, to delete last row of dataframe, just select the first (n-1) rows of dataframe using head() function, where n is the total rows of dataframe. Then assign these selected rows back to the same variable. It will give an effect that we have deleted last row of the dataframe. For example,
Checkout complete example to remove the last row of dataframe is as follows,
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)
# Delete last row
df = df.head(df.shape[0] -1)
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 C Aadi 16 London 11 D Mark 41 Delhi 12
We fetched the total number of rows in dataframe using df.shape[0] and then passed (df.shape[0] -1) to the head() function as argument. Therefore it selected the all rows except the last row of dataframe. Then we assigned back all the selected rows of df. So, this is how it deleted the last row of dataframe in place.
Summary:
We learned about different ways to delete the last row of a dataframe.