Reorganize Your Data Easily: Changing the Order of Columns in Pandas DataFrame

Changing the Order of Columns in a Pandas DataFrame: A Guide

Are you struggling to organize your Pandas DataFrame? Do you need to re-arrange the order of your columns for better analysis?

Don’t worry; in this article, we’ll show you how to change the order of columns in a Pandas DataFrame with ease.

1. Change the Order of Columns by Name

The first method you can use to change the order of columns in a Pandas DataFrame is by name. This is useful if you have a particular order in mind and know the names of the columns.

1.1 Steps to Change the Order of Columns by Name

1. Create a list of column names in the desired order.
2. Use the loc method to select all rows and columns in the new order.

For example, if you have a DataFrame with the columns ‘Name’, ‘Age’, and ‘Salary,’ and you want to change the order to ‘Salary’, ‘Name’, and ‘Age,’ you can use the following code:

df = df.loc[:, ['Salary','Name','Age']]

2. Change the Order by Adding a New First Column

The second method you can use to change the order of columns in a Pandas DataFrame is by adding a new column to the beginning. This is useful if you want to add new information to your DataFrame and make it the first column.

2.1 Steps to Change the Order by Adding a New First Column

1. Create a new column with the desired data.
2. Use the insert method to insert the new column into the DataFrame at the beginning.

For example, if you have a DataFrame with the columns ‘Name’, ‘Age’, and ‘Salary,’ and you want to add a new column ‘Gender’ at the beginning, you can use the following code:

df.insert(0, 'Gender', ['M', 'F', 'M'])

3. Change the Order by Adding a New Last Column

The third method you can use to change the order of columns in a Pandas DataFrame is by adding a new column to the end. This is useful if you want to add new information to your DataFrame and make it the last column.

3.1 Steps to Change the Order by Adding a New Last Column

1. Create a new column with the desired data.
2. Use the assign method to append the new column to the DataFrame.

For example, if you have a DataFrame with the columns ‘Name’, ‘Age’, and ‘Salary,’ and you want to add a new column ‘Department’ at the end, you can use the following code:

df = df.assign(Department=['Sales', 'Marketing', 'IT'])

4. Example 1: Changing the Order of Columns by Name

Suppose you have a DataFrame of employees with the columns ‘Name’, ‘Age’, and ‘Salary.’ You want to change the order of the columns to ‘Salary’, ‘Name’, and ‘Age.’ Here are the steps to do it:

4.1 Step 1: Create a list of column names in the desired order.

new_order = ['Salary', 'Name', 'Age']

4.2 Step 2: Use the loc method to select all rows and columns in the new order.

df = df.loc[:, new_order]

And there you have it! You have successfully changed the order of columns in your Pandas DataFrame.

5. Example 2: Changing the Order by Adding New First Column

Adding a new column to the beginning of a DataFrame can be useful in many situations.

For instance, if you have a DataFrame with hundreds of columns, you may need to restructure it by adding a new column at the beginning. This will make it easier to identify certain data points and to perform simple operations such as sorting and filtering.

In this example, we will discuss how to add a new first column to a DataFrame. Steps to change the order of columns by inserting a new column in the first position:

5.1 Step 1: Create a new column.

Before we can insert a new column, we need to create a new column. There are different ways to create a new column, but we will show you one simple way using the assign() function in Pandas.

For this example, let’s create a new column called ‘ID’ that contains unique identifiers for each row in the DataFrame.

df = df.assign(ID=range(1, len(df) + 1))

Here, we are using the assign() function to create a new column called ‘ID,’ and we are assigning it a range of values starting from 1 to the length of the DataFrame plus 1.

This means that each row in the DataFrame will have a unique ID starting from 1.

5.2 Step 2: Add the new column to the beginning of the DataFrame.

After creating the new column, we can insert it at the beginning of the DataFrame. We can do this using the insert() function in Pandas.

The insert() function takes three arguments: the index location where the new column should be inserted, the name of the new column, and the data for the new column.

df.insert(0, 'ID', range(1, len(df) + 1))

Here, we are using the insert() function to insert the new column ‘ID’ at index location 0, which means that it will be the first column in the DataFrame.

We are also assigning it the same values that we used in the assign() function earlier.

6. Example 3: Changing the Order by Adding New Last Column

Adding a new column to the end of a DataFrame can also be useful in many situations.

It can help you append new data to your DataFrame or add additional information that you forgot to include earlier. In this example, we will discuss how to add a new last column to a DataFrame.

7. Steps to change the order of columns by inserting a new column in the last position:

7.1 Step 1: Create a new column.

As with the previous example, we first need to create a new column before we can insert it into the DataFrame.

For this example, let’s create a new column called ‘Performance rating’ that contains performance ratings for each employee.

df = df.assign(performance_rating=[3.5, 4.2, 2.9])

Here, we are using the assign() function again to create a new column called ‘performance_rating,’ and we are assigning it a list of performance ratings for each employee.

7.2 Step 2: Add the new column to the end of the DataFrame.

After creating the new column, we can append it to the end of the DataFrame using the assign() function again.

The assign() function appends the new column to the right-hand side of the DataFrame by default.

df = df.assign(performance_rating=[3.5, 4.2, 2.9])

Here, we are using the assign() function again to append the new column ‘performance_rating’ to the end of the DataFrame.

8. Conclusion

Adding new columns to a DataFrame is a helpful feature in Pandas that allows you to restructure your data to better suit your analytical needs. With these easy-to-follow steps, you can quickly add new columns to the beginning or end of a DataFrame, making your data more accessible and simpler to analyze and manage.

In this article, we looked at three methods to change the order of columns in a Pandas DataFrame. We discussed how to change the order of columns by name, adding a new first column, and adding a new last column.

These methods can help you organize your data and make it easier to analyze and manage. Adding new columns to a DataFrame is a helpful feature in Pandas that allows you to restructure your data to better suit your analytical needs.

By following these easy-to-follow steps, you can quickly add new columns, making your data more accessible and simpler to analyze and manage.