In this article, we will discuss different ways to delete first column of a pandas dataframe in python.
Table of Contents
- Use iloc to drop first column of pandas dataframe.
- Use drop() to remove first column of pandas dataframe.
- Use del keyword to remove first column of pandas dataframe.
- Use pop() to remove first column of pandas dataframe.
Use iloc to drop first column 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 columns except the first one and then assign back the selected columns to the original variable. It will give an effect that we have deleted the first column from the dataframe. For example,
# Drop first column of dataframe df = df.iloc[: , 1:]
We selected a portion of dataframe, that included all rows, but it selected only n-1 columns i.e. from first column onwards. Then assigned this back to the same variable. So, basically it removed the first column 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 column from dataframe, just selected the columns from column number 2 till the end and select all rows. As indexing starts from 0, so to select all columns after the first one use –> (1:) i.e. from 2nd column till end. To select all the rows use default values i.e. (:) i.e.
df = df.iloc[: , 1:]
Checkout complete example to delete the first column 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 column of dataframe
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 : Age City Experience 0 34 Sydney 5 1 31 Delhi 7 2 16 London 11 3 41 Delhi 12
Use drop() to remove first column of pandas dataframe
In pandas, the dataframe’s drop() function accepts a sequence of column names that it needs to delete from the dataframe. To make sure that it removes the columns only, use argument axis=1 and to make changes in place i.e. in calling dataframe object, pass argument inplace=True.
Checkout complete example to delete the first column 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 column
df.drop(columns=df.columns[0],
axis=1,
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 : Age City Experience 0 34 Sydney 5 1 31 Delhi 7 2 16 London 11 3 41 Delhi 12
We fetched the column names of dataframe as a sequence and passed the first column name as the columns argument in drop() function, therefore it deleted the first column of dataframe.
Use del keyword to drop first column of pandas dataframe
Fetch the name of first column of dataframe i.e. at position 0, from the dataframe.columns sequence. Then select that column by passing column name in subscript operator i.e. df[df.columns[0]]. Then call del keyword on selected column,
del df[df.columns[0]]
It will delete the first column of dataframe.
Checkout complete example to remove the first column 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)
# Delete first column
del df[df.columns[0]]
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 : Age City Experience 0 34 Sydney 5 1 31 Delhi 7 2 16 London 11 3 41 Delhi 12
It deleted the first column of dataframe in place.
Use pop() to drop first column of pandas dataframe
In Pandas, dataframe provides a function pop(column_name). It expects a column name as an argument and deletes that column from the calling dataframe object. It also returns the deleted column as a series. Let’s use this to delete first column 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 column
deleted_column = df.pop(df.columns[0])
print("Modified Dataframe : ")
print(df)
Output:
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 : Age City Experience 0 34 Sydney 5 1 31 Delhi 7 2 16 London 11 3 41 Delhi 12
It removed the first column of dataframe in place and also returned the deleted column as a series.
Summary:
We learned about different ways to delete the first column of a dataframe.
Thanks for reading.