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Python | Pandas DataFrame.empty

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  • Last Updated : 30 Sep, 2022
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Pandas DataFrame is a two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Arithmetic operations align on both row and column labels.

It can be thought of as a dict-like container for Series objects. This is the primary data structure of the Pandas. Pandas DataFrame.empty attribute checks if the dataframe is empty or not. It returns True if the dataframe is empty else it returns False in Python.

In this article we will see How to Check if Pandas DataFrame is Empty.

Pandas – Check if DataFrame is Empty

Syntax: DataFrame.empty 

Parameter: None

 Returns: Boolean Type

Creating a DataFrame

Python3




# importing pandas as pd
import pandas as pd
 
# Creating the DataFrame
df = pd.DataFrame({'Weight':[45, 88, 56, 15, 71],
                   'Name':['Sam', 'Andrea', 'Alex', 'Robin', 'Kia'],
                   'Age':[14, 25, 55, 8, 21]})
 
# Create the index
index_ = ['Row_1', 'Row_2', 'Row_3', 'Row_4', 'Row_5']
 
# Set the index
df.index = index_
 
# Print the DataFrame
print(df)

Output : 

 

Use DataFrame.empty attribute

Example 1: 

Here we will use DataFrame.empty attribute to check if the given dataframe is empty or not.

Python3




# check if there is any element
# in the given dataframe or not
result = df.empty
 
# Print the result
print(result)

Output :

 False

Note: As we can see in the output, the DataFrame.empty attribute has returned False indicating that the given dataframe is not empty.

Example 2: 

Now we will use DataFrame.empty attribute to check if the given dataframe is empty or not. 

Python3




# importing pandas as pd
import pandas as pd
 
# Creating an empty DataFrame
df = pd.DataFrame(index = ['Row_1', 'Row_2',
                           'Row_3', 'Row_4',
                           'Row_5'])
 
# Print the DataFrame
print(df)
 
# check if there is any element
# in the given dataframe or not
result = df.empty
 
# Print the result
print(result)

Output :

 

 True 

Note: As we can see in the output, the DataFrame.empty attribute has returned True indicating that the given dataframe is empty.

Using dataframe.shape[0]

Here we are using the dataframe’s shape that gives us the count of number of rows and number of columns.

Python3




import pandas as pd
import numpy as np
 
# Creating an empty DataFrame
df = pd.DataFrame({'A' : [np.nan]})
 
print(df.dropna().shape[0] == 0)

Output :

 True 

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