Get Column & Row Means of pandas DataFrame in Python (2 Examples)
In this tutorial you’ll learn how to compute the column and row means of 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':[1, 2, 4, 5, 2, 7], # Construct example DataFrame 'B':[5, 1, 8, 1, 9, 2], 'C':[1, 2, 1, 2, 1, 2]}) print(my_df) # Display example DataFrame in console # A B C # 0 1 5 1 # 1 2 1 2 # 2 4 8 1 # 3 5 1 2 # 4 2 9 1 # 5 7 2 2 |
my_df = pd.DataFrame({'A':[1, 2, 4, 5, 2, 7], # Construct example DataFrame 'B':[5, 1, 8, 1, 9, 2], 'C':[1, 2, 1, 2, 1, 2]}) print(my_df) # Display example DataFrame in console # A B C # 0 1 5 1 # 1 2 1 2 # 2 4 8 1 # 3 5 1 2 # 4 2 9 1 # 5 7 2 2
Example 1: Computing Column Means for pandas DataFrame
print(my_df.mean()) # Column means # A 3.500000 # B 4.333333 # C 1.500000 # dtype: float64 |
print(my_df.mean()) # Column means # A 3.500000 # B 4.333333 # C 1.500000 # dtype: float64
Example 2: Computing Row Means for pandas DataFrame
print(my_df.mean(axis = 1)) # Row means # 0 2.333333 # 1 1.666667 # 2 4.333333 # 3 2.666667 # 4 4.000000 # 5 3.666667 # dtype: float64 |
print(my_df.mean(axis = 1)) # Row means # 0 2.333333 # 1 1.666667 # 2 4.333333 # 3 2.666667 # 4 4.000000 # 5 3.666667 # dtype: float64