Remove Rows with Empty Cells from pandas DataFrame in Python (2 Examples)
In this article, I’ll explain how to delete rows with empty cells from a pandas DataFrame in the Python programming language.
Setting up the Examples
import pandas as pd # Import pandas library to Python |
import pandas as pd # Import pandas library to Python
my_df = pd.DataFrame({'A':[5, '',' ', 5, ' ', 5, 5], # Construct example DataFrame in Python 'B':['w', 'x','y', 'z', '', 'x', 'y']}) print(my_df) # Display example DataFrame in console # A B # 0 5 w # 1 x # 2 y # 3 5 z # 4 # 5 5 x # 6 5 y |
my_df = pd.DataFrame({'A':[5, '',' ', 5, ' ', 5, 5], # Construct example DataFrame in Python 'B':['w', 'x','y', 'z', '', 'x', 'y']}) print(my_df) # Display example DataFrame in console # A B # 0 5 w # 1 x # 2 y # 3 5 z # 4 # 5 5 x # 6 5 y
Example 1: Replacing Empty Cells by NaN in pandas DataFrame in Python
my_df = my_df.replace(r'^s*$', float('NaN'), regex = True) # Exchanging blanks by NaN print(my_df) # Displaying updated DataFrame # A B # 0 5.0 w # 1 NaN x # 2 NaN y # 3 5.0 z # 4 NaN NaN # 5 5.0 x # 6 5.0 y |
my_df = my_df.replace(r'^s*$', float('NaN'), regex = True) # Exchanging blanks by NaN print(my_df) # Displaying updated DataFrame # A B # 0 5.0 w # 1 NaN x # 2 NaN y # 3 5.0 z # 4 NaN NaN # 5 5.0 x # 6 5.0 y
Example 2: Deleting Rows with NaN Values in pandas DataFrame
my_df.dropna(inplace = True) # Dropping rows with NaN print(my_df) # Displaying updated DataFrame # A B # 0 5.0 w # 3 5.0 z # 5 5.0 x # 6 5.0 y |
my_df.dropna(inplace = True) # Dropping rows with NaN print(my_df) # Displaying updated DataFrame # A B # 0 5.0 w # 3 5.0 z # 5 5.0 x # 6 5.0 y