Pandas – Drop first n rows of a DataFrame

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

How to drop the first n rows of a dataframe?

There are a number of ways to remove the first 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 tail() 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 tail()
df.tail(-n)

Examples

Let’s look at some examples of using the above methods to remove the first n rows of a dataframe. 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.

1. Remove first n rows with .iloc

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

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

Output:

       Name    Type
2  Squirtle   Water
3  Caterpie     Bug
4     Ekans  Poison

The resulting dataframe has the top two rows removed. Note that, the slice 2: refers to take all the rows starting from index 2 (that is, the third row). That is why the resulting dataframe has rows from index 2.

2. Remove first n rows with drop()

You can also use the pandas drop() function to remove the first 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 top two rows of the dataframe df, this time using the drop() function.

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

Output:

       Name    Type
2  Squirtle   Water
3  Caterpie     Bug
4     Ekans  Poison

The first 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.

3. Remove first n rows with tail()

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

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

Output:

       Name    Type
2  Squirtle   Water
3  Caterpie     Bug
4     Ekans  Poison

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

For more on the pandas tail() 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

Tutorials on removing data from pandas dataframe –

• Pandas – Drop last n rows of a DataFrame
• Pandas – Drop first n rows of a DataFrame
• Pandas – Delete rows based on column values
• Drop Duplicates from a Pandas DataFrame
• Drop Rows with NaNs in Pandas DataFrame
• Pandas – Drop one or more Columns from a Dataframe