Pandas is a powerful data manipulation library in Python and comes with a number of useful built-in features and properties to work with tabular (2D) data. In this tutorial, we will look at how to get the size of a dataframe in pandas with the help of some examples.

## What is size of a dataframe in Pandas?

The size of a pandas dataframe refers to the number of items (or cells) in the dataframe. Since a dataframe is a two-dimensional data structure, its size is the number of rows * number of columns. For example, the size a dataframe with 4 rows and 3 columns is 12.

## How to get the size of a dataframe in Pandas?

You can use the pandas dataframe `size`

property to get the size of a dataframe. The following is the syntax –

# get dataframe size df.size

It returns an integer representing the objects (items) in the dataframe. That is, the number of rows * the number of columns.

You can access a pandas Serie’s `size`

property which return the count of values (length) in the series.

## Examples

Let’s now look at some examples of using the above syntax on a dataframe in Pandas.

First, we will create a sample dataframe that we will be using throughout this tutorial.

import numpy as np import pandas as pd # employee data data = { "Name": ["Jim", "Dwight", "Angela", "Tobi"], "Age": [26, 28, 27, 32] } # create pandas dataframe df = pd.DataFrame(data) # display the dataframe df

Output:

**Data Science Programs By Skill Level**

**Introductory** ⭐

- Harvard University Data Science: Learn R Basics for Data Science
- Standford University Data Science: Introduction to Machine Learning
- UC Davis Data Science: Learn SQL Basics for Data Science
- IBM Data Science: Professional Certificate in Data Science
- IBM Data Analysis: Professional Certificate in Data Analytics
- Google Data Analysis: Professional Certificate in Data Analytics
- IBM Data Science: Professional Certificate in Python Data Science
- IBM Data Engineering Fundamentals: Python Basics for Data Science

**Intermediate ⭐⭐⭐**

- Harvard University Learning Python for Data Science: Introduction to Data Science with Python
- Harvard University Computer Science Courses: Using Python for Research
- IBM Python Data Science: Visualizing Data with Python
- DeepLearning.AI Data Science and Machine Learning: Deep Learning Specialization

**Advanced ⭐⭐⭐⭐⭐**

- UC San Diego Data Science: Python for Data Science
- UC San Diego Data Science: Probability and Statistics in Data Science using Python
- Google Data Analysis: Professional Certificate in Advanced Data Analytics
- MIT Statistics and Data Science: Machine Learning with Python - from Linear Models to Deep Learning
- MIT Statistics and Data Science: MicroMasters® Program in Statistics and Data Science

**🔎 Find Data Science Programs 👨💻 111,889 already enrolled**

Disclaimer: Data Science Parichay is reader supported. When you purchase a course through a link on this site, we may earn a small commission at no additional cost to you. Earned commissions help support this website and its team of writers.

Here, we created a dataframe having the “Name” and “Age” data of some employees in an office. You can see that the above dataframe has 4 rows and 2 columns.

### Example 1 – Size of a pandas dataframe using `size`

property

Let’s get the size of the dataframe created above using its `size`

property.

# get dataframe size print(df.size)

Output:

8

We get the size of the above dataframe as 8 (which is 4*2, rows*colums).

Note that the `size`

property returns the size of a dataframe irrespective of values (whether they are `NaN`

or non-`NaN`

).

Let’s recreate the employee dataframe with the same number of rows and columns but this time having some values as `NaN`

and then compute its size.

# employee data with some NaN values data = { "Name": ["Jim", "Dwight", "Angela", "Tobi"], "Age": [26, np.nan, np.nan, 32] } # create pandas dataframe with some Nan values df = pd.DataFrame(data) # get dataframe size print(df.size)

Output:

8

We get the size of the dataframe as 8 (same as what we got above).

### Example 2 – Size of a pandas dataframe using its `shape`

property

Alternatively, you can also calculate the size of a dataframe in pandas by accessing its `.shape`

property which return a tuple of (number of rows, number of columns). Multiply the two values together to the size.

# get dataframe shape print("Shape: ", df.shape) # get dataframe size print("Size: ", df.shape[0]*df.shape[1])

Output:

Shape: (4, 2) Size: 8

You can see that we get the shape of the dataframe `df`

as `(4, 2)`

and multiplying them together gives us the size of the dataframe.

## Length of a dataframe in Pandas

Note that the size of a dataframe is different from its length. The size of a dataframe in pandas is the total number of objects (or cells) in the dataframe whereas the length of a dataframe is the total number of rows in the dataframe.

You can use the built-in `len()`

function in Python or the first value in the tuple returned by the `shape`

property to get a dataframe’s length.

# get dataframe length using len() function print(len(df)) # get dataframe length using .shape property print(df.shape[0])

Output:

4 4

We get the length of the dataframe `df`

as 4 (whereas we got its size as 8).

## Pop Quiz

The following are some quick questions to test your understanding of the topics mentioned in this tutorial –

What does the size of a dataframe refer to?

The size of a dataframe in pandas refers to the number of items (or cells) in the dataframe. It is equal to the number of rows * the number of columns.

How to get the size of a pandas dataframe?

Use the pandas dataframe `size`

property to get the size of a dataframe in Pandas.

What is the difference between the length and size of a dataframe in pandas?

The length of a dataframe refers to the number of rows in the dataframe whereas its size represents the number of elements (or cells) in the dataframe which is the number of rows * the number of columns.

You might also be interested in –

- Pandas – Check if a DataFrame is Empty
- Get the number of rows in a Pandas DataFrame
- Understanding Joins in Pandas
- Reset Index in Pandas – With Examples
- Pandas – Rename Column Names

**Subscribe to our newsletter for more informative guides and tutorials. ****We do not spam and you can opt out any time.**