How Do You Create a DataFrame Data Type?


Angela Bailey

In this tutorial, we will learn how to create a DataFrame data type in Python. DataFrame is a two-dimensional data structure that stores data in tabular form, similar to a spreadsheet or SQL table. It is a powerful tool for data analysis and manipulation.

Creating a DataFrame

To create a DataFrame, we first need to import the pandas library. If you don’t have it installed, you can install it using the command: pip install pandas.

Once you have pandas installed, you can use the following code to create a DataFrame:

import pandas as pd

data = {'Name': ['John', 'Emma', 'Michael'],
        'Age': [25, 28, 32],
        'City': ['New York', 'London', 'Paris']}

df = pd.DataFrame(data)

The above code creates a dictionary called data, which contains the column names as keys and lists of values as values. Each key-value pair represents a column of the DataFrame. In this example, we have three columns: Name, Age, and City.

We then pass this dictionary to the DataFrame() function of pandas and assign it to the variable df. This creates our DataFrame.

Displaying the DataFrame

To display the contents of the DataFrame, simply print it:


This will output:

      Name  Age       City
0     John   25   New York
1     Emma   28     London
2  Michael   32      Paris

Accessing Data in the DataFrame

You can access individual columns by using the column name as an index:

0       John
1       Emma
2    Michael
Name: Name, dtype: object

You can also access multiple columns by passing a list of column names:

print(df[['Name', 'City']])
      Name       City
0     John   New York
1     Emma     London
2  Michael      Paris

Adding and Removing Columns

To add a new column to the DataFrame, you can simply assign values to it:

df['Salary'] = [50000, 60000, 70000]

This will add a new column named ‘Salary’ with the specified values:

      Name  Age       City  Salary
0     John   25   New York   50000
1     Emma   28     London   60000
2  Michael   32      Paris   70000

To remove a column, you can use the drop() function:

df = df.drop('Age', axis=1)

This will remove the ‘Age’ column from the DataFrame:

      Name       City  Salary
0     John   New York   50000
1     Emma     London   60000
2  Michael      Paris   70000

Saving the DataFrame to a CSV File

If you want to save your DataFrame as a CSV file, you can use the to_csv() function:

df.to_csv('data.csv', index=False)

This will save the DataFrame as a CSV file named ‘data.csv’ in the current directory. The index=False parameter specifies that we don’t want to include the row numbers in the CSV file.


In this tutorial, we have learned how to create a DataFrame data type in Python using the pandas library. We covered creating a DataFrame from a dictionary, displaying its contents, accessing data within the DataFrame, adding and removing columns, and saving the DataFrame to a CSV file. DataFrames are an essential tool for data analysis and manipulation, and understanding how to create and work with them is crucial for any data scientist or analyst.

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