Create New pandas DataFrame from Existing Data in Python (2 Examples)

In this Python tutorial you’ll learn how to construct a new pandas DataFrame based on an existing data set.

The article looks as follows:

1) Exemplifying Data & Libraries

2) Example 1: Create Copy of Entire pandas DataFrame

3) Example 2: Extract Specific Columns & Create New pandas DataFrame

4) Video & Further Resources

5) Subscribe to the Statistics Globe Newsletter

6) Thank you!

So without further additions, let’s jump right to the examples!

Exemplifying Data & Libraries

In order to use the commands and functions of the pandas library, we first need to load pandas:

import pandas as pd                        # Load pandas

I’ll also have to create some example data:

data = pd.DataFrame({'x1':range(7, 12),    # Create example DataFrame
                     'x2':['a', 'b', 'c', 'd', 'e'],
                     'x3':range(6, 1, - 1),
                     'x4':[5, 9, 2, 8, 7]})
print(data)                                # Print example DataFrame

table 1 DataFrame create new pandas dataframe from existing python

As you can see based on Table 1, the example data is a DataFrame having five rows and four columns.

Example 1: Create Copy of Entire pandas DataFrame

Example 1 illustrates how to create a duplicate of a pandas DataFrame.

For this task, we can use the copy() function as shown below:

data_new1 = data.copy()                    # Create copy
print(data_new1)                           # Print copy

table 2 DataFrame create new pandas dataframe from existing python

After running the previous Python programming code the new pandas DataFrame called data_new1 illustrated in Table 2 has been created.

As you can see, this DataFrame contains exactly the same variables and rows as our input data set.

Example 2: Extract Specific Columns & Create New pandas DataFrame

In this example, I’ll demonstrate how to extract particular columns of a pandas DataFrame and save it in a new pandas DataFrame.

Once again, we can use the copy function. However, this time we also have to specify the columns that we want to select within double square brackets:

data_new2 = data[['x2', 'x4']].copy()      # Extract selected columns
print(data_new2)                           # Print DataFrame subset

table 3 DataFrame create new pandas dataframe from existing python

After running the previous Python programming syntax the pandas DataFrame subset shown in Table 3 has been created.

Video & Further Resources

Do you need more info on the Python code of this tutorial? Then you might have a look at the following video on my YouTube channel. I demonstrate the Python code of this page in the video:

In addition, you might have a look at some of the other articles on statisticsglobe.com. You can find a selection of tutorials below.

On this page you have learned how to create a new pandas DataFrame based on an existing DataFrame in the Python programming language. In case you have further questions or comments, let me know in the comments.