Pandas Check If DataFrame is Empty | Examples

Naveen Nelamali

3 years ago

By using pandas.DataFrame.empty attribute you can check if DataFrame is empty or not. We refer DataFrame as empty when it has zero rows. This pandas DataFrame empty attribute returns a Boolean value; value True when DataFrame is empty and False when it is empty.

We are often required to check whether panda DataFrame is empty for several reasons and wanted to ignore performing certain operations/actions when it is empty. In this article, I will cover few examples of how to check if DataFrame is empty or not with examples.

Key Points –

Pandas DataFrame has an empty attribute that returns True if the DataFrame is empty, i.e., it has no rows or columns.
Check the size property of the DataFrame, which represents the total number of elements. An empty DataFrame will have a size of zero.
The shape attribute returns a tuple representing the dimensions of the DataFrame. If either dimension is zero, the DataFrame is considered empty.
Apply the len() function to the DataFrame, which returns the number of rows. An empty DataFrame will have a length of zero.
Use boolean logic with DataFrame methods: Utilize boolean logic with DataFrame methods such as any() or all() along specific axes (rows or columns) to check if any elements are present, indicating non-emptiness.

Notes:

If your DataFrame contains only NaN/None values then it is not considered empty so df.empty attribute returns False.
Note that len(DataFrame.index) gives you better performance compared with len(DataFrame) and DataFrame.empty attribute.

1. Quick Examples of Pandas Check If DataFrame is Empty

If you are in a hurry, below are some quick examples of pandas check if DataFrame is empty.


# Check using empty attribute
print(df_empty.empty) ==> Prints True

# Check DataFrame Empty using shape()
print(df_empty.shape[0] == 0) ==> Prints True

# Check DataFrame Empty using size()
print(df_empty.size == 0) ==> Prints True

# Using len() Function
print(len(df_empty) == 0) ==> Prints True

# Using len() & index
print(len(df_empty.index) == 0) ==> Prints True

2. Create Sample DataFrame to Check If it is Empty

In order to check if DataFrame is empty, let’s create a DataFrame with empty using pandas.DataFrame() constructor and another DataFrame with few rows.


import pandas as pd
import numpy as np

# This creates empty DataFrame
df_empty=pd.DataFrame()

# DataFrame with few rows
df_non_empty=pd.DataFrame(['A','B','C'])

3. Check DataFrame has Rows Using empty Attribute

df_empty.empty return boolean value True as this DataFrame has no rows and df_non_empty.empty return False as it has 3 rows. In a real-time application, you would do something like below using if or similar conditional statements.


# Check DataFrame is empty
if(df_empty.empty == True):
    print('Empty DataFrame')
    # Do some thing when DF is empty
else:
    print('Non Empty DataFrame')  
    # Proceed with regular operation

Yields below output.


# Output:
Empty DataFrame

4. Using DataFrame.shape() & DataFrame.size to check If it is Empty

pandas.DataFrame.shape() method returns the number of rows and number of columns as a tuple, you can use this to check if pandas DataFrame is empty. DataFrame.shape[0] return number of rows. If you have no rows then it gives you 0 and comparing it with 0 gives you True.

Similarly, you can also try size() method. size() returns a count of all cells (rows * columns).


# Check DataFrame Empty using shape()
print(df_empty.shape[0] == 0) == Prints True

# Check DataFrame Empty using size()
print(df_empty.size == 0) == Prints True

5. Check Whether DataFrame is empty Using Length & Index

len(df) function gives a number of rows in DataFrame hence, you can use this to check whether DataFrame is empty.


# Using len() Function
print(len(df_empty) == 0) ==> Prints True

But the best way to check if DataFrame is empty is using its Index. When there are no rows present, Dataframe.index returns an empty Series, and getting the length of it’s gets you 0.


# Using len() & index
print(len(df_empty.index) == 0) ==> Prints True

Note that len(DataFrame.index) gives you better performance compared with len(DataFrame) and DataFrame.empty attribute. For details check stackoverflow.com.

6. Checking Empty DataFrame If it has All NaN or None Values

If you have a pandas DataFrame with all NaN/None values, checking if it is an empty return False. In order to get this as empty first, you need to drop all None/NaN values using dropna() method. Below I have provided examples.


import numpy as np
# DataFrame with all NaN values
df_all_nan = pd.DataFrame([None,np.nan,np.NaN])

print(df_all_nan.empty) ==> Returns False

print(df_all_nan.dropna().empty) ==> Returns True

7. Complete Example


import pandas as pd
import numpy as np

# This creates empty DataFrame
df_empty=pd.DataFrame()

# DataFrame with few rows
df_non_empty = pd.DataFrame(['A','B','C'])

# Anotehr DataFrame with all NaN values
df_all_nan = pd.DataFrame([np.nan,np.nan,np.NaN])

# Check DataFrame is Empty using empty attribute
print(df_all_nan.empty)
print(df_empty.empty)

# Check using empty attribute & if condition
if(df_empty.empty == True):
    print('Empty DataFrame')
    # Do some thing when DF is empty
else:
    print('Non Empty DataFrame')  
    # Proceed with regular operation

# Check DF Empty using len() & index
print(len(df_empty)==0)
print(len(df_empty.index) ==0)

# Check using share() & size
print(df_empty.size == 0)
print(df_empty.shape[0] == 0)

# Check Empty if all values are None & NaN
print(df_all_nan.empty)
print(df_all_nan.dropna().empty)

Frequently Asked Questions on Pandas Check If DataFrame is Empty

How can I determine if a Pandas DataFrame is empty?

Use the empty attribute of the DataFrame. It returns True if the DataFrame is empty and False otherwise.

What does the size property signify when checking for an empty DataFrame?

The size property represents the total number of elements in the DataFrame. If the size is zero, the DataFrame is considered empty.

Can a DataFrame be considered empty if it contains only missing values?

A DataFrame with only missing values is not considered empty. The empty attribute and other methods focus on the presence of rows or columns, regardless of the values they contain.

What is the recommended approach for checking DataFrame emptiness?

Using the empty attribute is often recommended due to its clarity and simplicity. It directly provides a boolean result indicating whether the DataFrame is empty.

Are there alternative methods to check if a DataFrame is empty?

You can use the shape attribute and verify if either dimension is zero. Additionally, applying the len() function to the DataFrame and checking if the length is zero provides another approach.

Is there a performance difference between these methods?

While there might be subtle performance variations, the differences are generally negligible. The empty attribute is often preferred for its simplicity, but choosing a method depends on the specific use case and personal preference.

Conclusion

In this article, you have learned how to check whether a pandas DataFrame is empty using an empty attribute, size, share, and len methods. If your DataFrame contains only NaN/None values then it is not considered empty so df.empty attribute returns False. Note that len(DataFrame.index) gives you better performance compared with len(DataFrame) and DataFrame.empty attribute.

References

https://www.w3schools.com/python/pandas/default.asp