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

Table of Contents

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

# Drop first row 
# by selecting all rows from first row onwards
df = df.iloc[1: , :]

We selected a portion of dataframe, that included all columns, but it selected only n-1 rows i.e. from first row onwards. Then assigned this back to the same variable. So, basically it removed the first 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:

  • 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 first row from dataframe, just selected the rows from row number 2 till the end and select all columns. As indexing starts from 0, so to select all rows after the first one use –> (1:) i.e. from 2nd row till end. To select all the columns use default values i.e. (:) i.e.

df = df.iloc[1: , :]

Checkout complete example to delete the first 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'])

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

# Drop first row 
# by selecting all rows from first row onwards
df = df.iloc[1: , :]

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

Output:

Contents of the Dataframe : 
   Name  Age    City  Experience
0  Jack   34  Sydney           5
1  Riti   31   Delhi           7
2  Aadi   16  London          11
3  Mark   41   Delhi          12
Modified Dataframe :
   Name  Age    City  Experience
1  Riti   31   Delhi           7
2  Aadi   16  London          11
3  Mark   41   Delhi          12

Use drop() to remove first 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 first 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'])

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

# Drop first row
df.drop(index=df.index[0], 
        axis=0, 
        inplace=True)

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

Output:

Contents of the Dataframe :
   Name  Age    City  Experience
0  Jack   34  Sydney           5
1  Riti   31   Delhi           7
2  Aadi   16  London          11
3  Mark   41   Delhi          12
Modified Dataframe :
   Name  Age    City  Experience
1  Riti   31   Delhi           7
2  Aadi   16  London          11
3  Mark   41   Delhi          12

We fetched the all names of dataframe index as a sequence and passed the first row/index name as the index argument in drop() function, therefore it deleted the first row of dataframe.

Use tail() function to drop first row of pandas dataframe

In python, dataframe provides a function tail(n), it returns the last n rows of dataframe. So, to delete first row of dataframe, just select the last (n-1) rows of dataframe using tail() 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 first row of the dataframe. For example,

Checkout complete example to remove the first 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'])

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

# Delete first row by selecting last n-1 rows
df = df.tail(df.shape[0] -1)

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

Output:

Contents of the Dataframe :
   Name  Age    City  Experience
0  Jack   34  Sydney           5
1  Riti   31   Delhi           7
2  Aadi   16  London          11
3  Mark   41   Delhi          12
4   Sam   56  London          33
Modified Dataframe :
   Name  Age    City  Experience
1  Riti   31   Delhi           7
2  Aadi   16  London          11
3  Mark   41   Delhi          12
4   Sam   56  London          33

We fetched the total number of rows in dataframe using df.shape[0] and then passed (df.shape[0] -1) to the tail() function as argument. Therefore it selected the all rows except the first row of dataframe. Then we assigned back all the selected rows of df. So, this is how it deleted the first row of dataframe in place.

Summary:

We learned about different ways to delete the first row of a dataframe.