Pandas: Checking whether a DataFrame is empty (5 ways)

Updated: February 19, 2024 By: Guest Contributor Post a comment


Working with data in Python often leads us to use the Pandas library, a powerful tool for data manipulation and analysis. In many scenarios, it’s essential to determine whether a DataFrame is empty before performing operations on it. This can help to avoid errors and ensure that the code behaves as expected. In this tutorial, we will explore various methods to check if a DataFrame is empty in Pandas, starting from basic techniques to more advanced ones.

Understanding DataFrames

Before we dive into the specifics of checking if a DataFrame is empty, let’s understand what a DataFrame is. Pandas DataFrame is a two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). It can be thought of as a dict-like container for Series objects.

Method 1: Using empty Attribute

The simplest way to check if a DataFrame is empty is by using the empty attribute. This returns a boolean value indicating whether the DataFrame is empty or not.

import pandas as pd

df_empty = pd.DataFrame()
# Output: True

df = pd.DataFrame({
    'A': [1, 2],
    'B': [3, 4],
    'C': [5, 6]
# Output: False

Method 2: Checking DataFrame Shape

Another simple method to determine if a DataFrame is empty is to check its shape. The shape attribute of a DataFrame returns a tuple representing the dimensionality of the DataFrame. An empty DataFrame will have a shape of (0, 0).

import pandas as pd

df = pd.DataFrame()
# Output: (0, 0)
The result considered in context of empty DataFrames means there are no rows and no columns.

Method 3: Using len() Function

Another approach is to use the len() function to check the length of the DataFrame’s index. If the length is 0, the DataFrame is considered empty.

import pandas as pd

df = pd.DataFrame()
# Output: 0

Advanced Techniques

While the methods mentioned above are straightforward, there are cases where you might want to apply more sophisticated checks, especially when dealing with DataFrames loaded from external sources where the structure might not be immediately clear or might be inconsistent.

Method 4: Checking for Non-NaN Values

In some cases, a DataFrame might not technically be empty (having rows and columns) but could be filled entirely with NaN values, making it effectively useless for most analysis purposes. In such situations, you can use the dropna() method to remove rows with NaN values and then check if the resultant DataFrame is empty.

import pandas as pd

df = pd.DataFrame({
    'A': [None, None],
    'B': [np.nan, np.nan]
df_clean = df.dropna()
# Output: True

Method 5: Custom Function to Check for Data Presence

For more tailored scenarios where you need to run specific checks on a DataFrame, writing a custom function might be the best approach. For example, you could write a function that checks not only if the DataFrame is empty but also if it meets certain data-quality criteria, such as having a minimum number of numeric values or specific columns.

def check_dataframe(df):
    if df.empty:
        return 'DataFrame is empty'
    elif len(df.dropna()) < LowThreshold:
        return 'DataFrame has insufficient data'
    # Additional checks can be placed here
        return 'DataFrame is valid'


Determining whether a DataFrame is empty is a crucial step in data analysis and can help prevent errors and inefficiencies in your code. By utilizing the simple and advanced techniques covered in this tutorial, you can effectively manage and analyze your data with confidence. Whether you’re using the empty attribute, checking the DataFrame’s shape, or writing custom validation functions, you now have the tools to ensure your DataFrames are ready for analysis.