Adventures in Machine Learning

Mastering Pandas: 3 Ways to Remove a Column from a Python Dataframe

In the world of programming, Python is a widely used programming language. It is blessed with several modules and libraries that make programming tasks simpler.

Pandas is one such popular library that is used for data manipulation and analysis. Pandas dataframes are a crucial component in the world of data science as they help store tabular data with rows and columns.

In this article, we will explore different ways to remove a column from a Python dataframe using the pandas module. Ways to Remove a Column from a Python Dataframe:

1.

Using python dataframe.pop() method:

The pop method is a quick and easy way to delete a column from a pandas dataframe. It requires a single argument, which is the label of the column to be removed.

The pop method automatically deletes the column and returns it as a series. 2.

Using Python del keyword:

The del keyword is a powerful tool in Python. It can be used to delete objects directly from memory.

To remove a column from a data frame, we need to reference the column variable with the del keyword. It directly flushes the column from memory.

3. Using Python drop() function:

Another way to remove a column from a pandas dataframe is to use the drop method.

It is more flexible than the pop method as it allows deleting both rows and columns and supports row and column-oriented deletion. We can specify whether to delete the column in place or return a modified copy of the dataframe.

Using python dataframe.pop() method:

The pandas.dataframe.pop() method is a function that is used to delete a column in pandas. It requires an argument that is the name of the column that needs to be removed.

The pop() method automatically deletes the column from memory and returns it as a series. The pop method is useful when we want to retrieve the removed column.

Syntax:

pandas.dataframe.pop(item)

Argument:

– item: It is a mandatory argument, and it represents the column’s label to be removed. Example:

import pandas as pd

df = pd.DataFrame({‘drinks’: [‘Coke’, ‘Pepsi’, ‘7up’], ‘price’: [10,20,15], ‘quantity’: [30, 45, 20]})

print(“Original Dataframe:”)

print(df)

# Using pop() method to delete ‘price’ column

poppedColumn = df.pop(‘price’)

print(“nModified Dataframe:”)

print(df)

print(“Deleted Column:”)

print(poppedColumn)

Output:

Original Dataframe:

drinks price quantity

0 Coke 10 30

1 Pepsi 20 45

2 7up 15 20

Modified Dataframe:

drinks quantity

0 Coke 30

1 Pepsi 45

2 7up 20

Deleted Column:

0 10

1 20

2 15

Name: price, dtype: int64

Using Python del keyword:

The del keyword is a very useful tool in Python. It is not only limited to removing variables but can also be used to delete objects directly from memory.

To delete a column using the del keyword, we need to reference the column variable with the del keyword. This method is useful when we do not need to retrieve the deleted column.

Syntax:

del dataframe[‘column_name’]

Example:

import pandas as pd

df = pd.DataFrame({‘fruits’:[‘banana’,’orange’,’apple’], ‘quantity’:[10,20,30], ‘price’:[5,10,15]})

print(“Original Dataframe:”)

print(df)

#Using del keyword to delete ‘price’ column

del df[‘price’]

print(“nModified Dataframe:”)

print(df)

Output:

Original Dataframe:

fruits quantity price

0 banana 10 5

1 orange 20 10

2 apple 30 15

Modified Dataframe:

fruits quantity

0 banana 10

1 orange 20

2 apple 30

Using Python drop() function:

The pandas.DataFrame.drop() method allows us to remove rows or columns from a dataframe. By default, the method removes rows, but columns can be removed by specifying an `axis` parameter.

The `inplace` parameter is used to indicate whether the drop operation should be performed in place, modifying the original DataFrame, or return a modified copy. Syntax:

pandas.dataframe.drop(labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors=’raise’)

Arguments:

– labels: It is a mandatory argument representing the column(s) to be dropped.

It should be either a string or sequence. – axis: This optional parameter indicates the axis over which the drop function should be applied.

Default value is 0. – index: This optional parameter is used to specify the row index or sequence of row indices to be dropped.

– columns: This optional parameter is used to specify the column index or sequence of column indices to be dropped. – level: This optional parameter is used to specify the level in case of a multi-level index.

– inplace: This optional parameter is used to specify whether to perform the operation in the original data frame or to return a copy. – errors: This optional parameter is used to specify how to handle an error if the specified column(s) do not exist.

Example:

import pandas as pd

df = pd.DataFrame({‘fruits’:[‘banana’,’orange’,’apple’], ‘quantity’:[10,20,30], ‘price’:[5,10,15]})

print(“Original Dataframe:”)

print(df)

#Using drop() method to delete ‘price’ column

df.drop(‘price’, inplace=True, axis=1)

print(“nModified Dataframe:”)

print(df)

Output:

Original Dataframe:

fruits quantity price

0 banana 10 5

1 orange 20 10

2 apple 30 15

Modified Dataframe:

fruits quantity

0 banana 10

1 orange 20

2 apple 30

Conclusion:

In conclusion, these are three ways to remove a column from a Python dataframe using the pandas module. The pop method works well when we want to retrieve the removed column, while the del keyword is useful when we do not need to retrieve the deleted column.

The drop method is the most flexible and allows row and column-oriented deletion. Each of these methods has its merits, and which one to use depends on the task at hand.In the previous section, we discussed different ways to remove a column from a Python dataframe.

We covered three popular methods to delete a column from a dataframe using the pandas module. In this section, we will delve into each of these methods in detail and analyze their working principles.

By the end of this section, we hope that you will understand each of these methods better and be able to choose the most appropriate one for your requirements. 1.

Using python dataframe.pop() method:

One of the most straightforward ways to remove a column from a pandas dataframe is to use the `pop()` method. It is an inbuilt function that is part of the pandas library.

The pop method requires an argument that is the name of the column to be deleted. Syntax:

pandas.dataframe.pop(column_label)

Argument:

– column_label: It is a mandatory argument representing the name or label of the column that needs to be removed.

The `pop()` method automatically removes the column from the dataframe and returns it as a new series. One of the advantages of using this method is that it provides a way to retrieve the removed column.

Example:

import pandas as pd

df = pd.DataFrame({‘name’: [‘Alice’, ‘Bob’, ‘Charlie’], ‘age’: [23, 45, 32], ‘gender’: [‘F’, ‘M’, ‘M’]})

print(“Original Dataframe:”)

print(df)

popped_column = df.pop(‘gender’)

print(“nModified Dataframe:”)

print(df)

print(“nDeleted Column:”)

print(popped_column)

Output:

Original Dataframe:

name age gender

0 Alice 23 F

1 Bob 45 M

2 Charlie 32 M

Modified Dataframe:

name age

0 Alice 23

1 Bob 45

2 Charlie 32

Deleted Column:

0 F

1 M

2 M

Name: gender, dtype: object

2. Using Python del keyword:

The del keyword in Python is a versatile tool that provides a way to delete variables and objects directly from memory.

The del keyword can be used to delete a column from a pandas dataframe by referencing the column variable and using the del keyword. Syntax:

del dataframe[column_name]

Argument:

– column_name: It is a mandatory argument that represents the name or label of the column that needs to be removed.

One of the advantages of using the del keyword is that it offers a quick way to delete a column from a dataframe. However, one of the downsides is that it does not provide a way to retrieve the removed column.

Example:

import pandas as pd

df = pd.DataFrame({‘name’: [‘Alice’, ‘Bob’, ‘Charlie’], ‘age’: [23, 45, 32], ‘gender’: [‘F’, ‘M’, ‘M’]})

print(“Original Dataframe:”)

print(df)

del df[‘gender’]

print(“nModified Dataframe:”)

print(df)

Output:

Original Dataframe:

name age gender

0 Alice 23 F

1 Bob 45 M

2 Charlie 32 M

Modified Dataframe:

name age

0 Alice 23

1 Bob 45

2 Charlie 32

3. Using Python drop() function:

The `drop()` function in the pandas module allows us to delete a row or column from a dataframe based on a given label or index.

By default, the drop function removes rows, but we can specify that we want to remove a column by using the `axis` parameter and setting it to 1. Syntax:

pandas.dataframe.drop(label, axis=1, inplace=False)

Arguments:

– label: It is a mandatory argument that represents the name or label of the column to be removed.

– axis: It is an optional parameter. Default is 0, which means row-oriented deletion.

When set to 1, it means that column-oriented deletion should be performed. – inplace: It is an optional parameter that specifies whether the operation should be performed in place or return a modified copy.

One of the advantages of using the `drop()` function is that it provides more flexibility than the other two methods. For example, we can delete multiple columns at once by passing a list of column names.

Example:

import pandas as pd

df = pd.DataFrame({‘name’: [‘Alice’, ‘Bob’, ‘Charlie’], ‘age’: [23, 45, 32], ‘gender’: [‘F’, ‘M’, ‘M’]})

print(“Original Dataframe:”)

print(df)

df.drop([‘age’, ‘gender’], axis=1, inplace=True)

print(“nModified Dataframe:”)

print(df)

Output:

Original Dataframe:

name age gender

0 Alice 23 F

1 Bob 45 M

2 Charlie 32 M

Modified Dataframe:

name

0 Alice

1 Bob

2 Charlie

Conclusion:

In conclusion, we discussed different ways to remove a column from a pandas dataframe using the pandas module. The pop() method, the del keyword, and the drop() function are three popular methods to achieve this.

The choice of method depends on the requirements of the project. The `pop()` method provides a way to retrieve the deleted column, while the del keyword is a quick way to delete a variable without the need to retrieve it.

The `drop()` function is the most flexible as it allows for both row and column-oriented deletion and can delete multiple columns at once. We hope that this section has provided you with a better understanding of these methods and helped you choose the most appropriate one for your project.

In this article, we explored different ways to remove a column from a Python dataframe using the pandas module. We discussed the pop() method, the del keyword, and the drop() function, highlighting their syntax, usage, and pros and cons.

The pop() method provides a way to retrieve the deleted column, while the del keyword is a quick way to delete a variable without the need to retrieve it. The drop() function is the most flexible method that allows for both row and column-oriented deletion and can delete multiple columns at once.

Choosing the appropriate method depends on the requirements of the project. Through this article, we hope you have gained valuable insights that will help you perform your data manipulation and analysis tasks more efficiently.

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