Pandas: Drop first N rows of dataframe

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

Use iloc to drop first 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 first N rows of a dataframe and then assign back that to the original variable. It will give an effect that we have deleted the first N rows from the dataframe. For example,

# Drop first 3 rows
# by selecting all rows from 4th row onwards
N = 3
df = df.iloc[N: , :]

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

How did it work?

The syntax of dataframe.iloc[] is like,

Advertisements
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 first N rows of the dataframe, just select the rows from row number N till the end and select all columns. As indexing starts from 0, so to select all rows after the N, use –> (N:) i.e. from Nth row till the end. To select all the columns use default values i.e. (:) i.e.

df = df.iloc[N: , :]

Checkout complete example to delete the first 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 first 3 rows
# by selecting all rows from 4th row onwards
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
D  Mark   41   Delhi          12
E   Sam   56  London          33

Use drop() to remove first 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 first 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 first 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
D  Mark   41   Delhi          12
E   Sam   56  London          33

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

Use tail() to remove first N rows of pandas dataframe

In Pandas, dataframe provides a function tail(N) to select last N rows of dataframe. To delete first N rows of dataframe, we can select last (Size-N) rows of dataframe using tail 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 first 3 rows of dataframe
N = 3
df = df.tail(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
D  Mark   41   Delhi          12
E   Sam   56  London          33

It removed the first 3 rows of dataframe in place.

Summary:

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

Pandas Tutorials -Learn Data Analysis with Python

   

Are you looking to make a career in Data Science with Python?

Data Science is the future, and the future is here now. Data Scientists are now the most sought-after professionals today. To become a good Data Scientist or to make a career switch in Data Science one must possess the right skill set. We have curated a list of Best Professional Certificate in Data Science with Python. These courses will teach you the programming tools for Data Science like Pandas, NumPy, Matplotlib, Seaborn and how to use these libraries to implement Machine learning models.

Checkout the Detailed Review of Best Professional Certificate in Data Science with Python.

Remember, Data Science requires a lot of patience, persistence, and practice. So, start learning today.

Join a LinkedIn Community of Python Developers

Leave a Comment

Your email address will not be published.

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Scroll to Top