How to Count NaN in pandas DataFrame in Python (2 Examples)
In this tutorial you’ll learn how to count the number of NaN values in a pandas DataFrame in Python.
Preparing the Examples
import pandas as pd # Import pandas library in Python |
import pandas as pd # Import pandas library in Python
my_df = pd.DataFrame({'A':[1, 1, 1, float('NaN'), 1], # Construct example DataFrame 'B':[2, float('NaN'), 2, float('NaN'), 2], 'C':[float('NaN'), float('NaN'), 3, 3, float('NaN')]}) print(my_df) # Display example DataFrame in console # A B C # 0 1.0 2.0 NaN # 1 1.0 NaN NaN # 2 1.0 2.0 3.0 # 3 NaN NaN 3.0 # 4 1.0 2.0 NaN |
my_df = pd.DataFrame({'A':[1, 1, 1, float('NaN'), 1], # Construct example DataFrame 'B':[2, float('NaN'), 2, float('NaN'), 2], 'C':[float('NaN'), float('NaN'), 3, 3, float('NaN')]}) print(my_df) # Display example DataFrame in console # A B C # 0 1.0 2.0 NaN # 1 1.0 NaN NaN # 2 1.0 2.0 3.0 # 3 NaN NaN 3.0 # 4 1.0 2.0 NaN
Example 1: Number of NaN Values by Column
print(my_df.isna().sum()) # Count NaNs # A 1 # B 2 # C 3 # dtype: int64 |
print(my_df.isna().sum()) # Count NaNs # A 1 # B 2 # C 3 # dtype: int64
Example 2: Number of NaN Values by Row
print(my_df.isnull().sum(axis = 1)) # Count NaNs # 0 1 # 1 2 # 2 0 # 3 2 # 4 1 # dtype: int64 |
print(my_df.isnull().sum(axis = 1)) # Count NaNs # 0 1 # 1 2 # 2 0 # 3 2 # 4 1 # dtype: int64