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Pandas – Drop last n rows of a DataFrame

In this tutorial, we will look at how to drop the last n rows of a pandas dataframe.

There are a number of ways to remove the last n rows of a dataframe. For example, you can slice the dataframe using .iloc or you can use the pandas drop() function or you can use the pandas head() function. The following is the syntax for the different methods used in this tutorial.

# using iloc
df.iloc[:-n]
# using drop()
df.drop(df.index[-n:])
# using head()
df.head(-n)

Now let’s look at the usage of the above different methods with the help of some examples. First, we will create a sample dataframe that we will be using throughout this tutorial.

import pandas as pd

# create dataframe
df = pd.DataFrame({
    'Name': ['Bulbasaur', 'Charmander', 'Squirtle', 'Caterpie', 'Ekans'],
    'Type': ['Grass', 'Fire', 'Water', 'Bug', 'Poison']
})
# display the dataframe
print(df)

Output:

         Name    Type
0   Bulbasaur   Grass
1  Charmander    Fire
2    Squirtle   Water
3    Caterpie     Bug
4       Ekans  Poison

We now have a dataframe with five rows containing information on Pokemon names and their respective types.

You can drop the last n rows of a dataframe by slicing it using .iloc. For example, let’s remove the last two rows of df and store the resulting dataframe as a separate dataframe.

# remove last two rows
df_sub = df.iloc[:-2]
# display the dataframe
print(df_sub)

Output:

         Name    Type
0   Bulbasaur   Grass
1  Charmander    Fire
2    Squirtle   Water

The resulting dataframe has the bottom two rows removed. Note that, the slice :-2 refers to take all the rows starting from index 0 till but not including the 2nd row from the bottom.

You can also use the pandas drop() function to remove the last n rows of a dataframe. For this, pass the indexes of the rows you want to delete to the drop() function. For example, let’s again drop the last two rows of the dataframe df, this time using the drop() function.

# remove last two rows
df_sub = df.drop(df.index[-2:])
# display the dataframe
print(df_sub)

Output:

         Name    Type
0   Bulbasaur   Grass
1  Charmander    Fire
2    Squirtle   Water

The last two rows are dropped in the returned dataframe. If you want to modify the original dataframe in place, pass inplace=True to the drop() function.

You can also use the pandas head() function to remove the last n rows. Generally, the head() function is used to show the first n rows of a pandas dataframe but you can pass a negative value to skip the rows from the bottom.

# remove last two rows
df_sub = df.head(-2)
# display the dataframe
print(df_sub)

Output:

         Name    Type
0   Bulbasaur   Grass
1  Charmander    Fire
2    Squirtle   Water

You can see that the resulting dataframe has all the rows from the top except for the bottom two rows. This is because we passed -2 to the head() function which equates to performing df[:-n].

For more on the pandas head() function, refer to its documentation.

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having pandas version 1.0.5


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Author

  • Piyush is a data scientist passionate about using data to understand things better and make informed decisions. In the past, he's worked as a Data Scientist for ZS and holds an engineering degree from IIT Roorkee. His hobbies include watching cricket, reading, and working on side projects.