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Predictive Data Analysis with Python

Solution: Summing and Swapping

Explore how to calculate the sum of minimum and maximum values across rows and columns in a Pandas DataFrame. Learn how to use NumPy functions to extract these values, add new columns to store the sums, and swap values correctly using deep copies to avoid reference issues. This lesson strengthens your data manipulation skills for predictive data analysis.

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Solution #

Python 3.5
def Sum_Swap(df):
    
    minm_r = np.min(df, axis = 1) # Get minimum elements from all rows
    
    maxm_r = np.max(df, axis = 1) # Get maximum elements from all rows
    
    df['row_sum'] = minm_r + maxm_r # Add the min & max values and assign them to new column
    minm_c = np.min(df, axis = 0) # Get minimum elements from all columns
    
    maxm_c = np.max(df, axis = 0) # Get maximum elements from all columns
    
    df.loc['col_sum'] = minm_c + maxm_c # Add the min & max values and assign them to new row
    a, b = df['row_sum'].copy(), df.loc['col_sum'].copy() # Store values of row and column in temparory variables
    
    df['row_sum'], df.loc['col_sum'] = b, a # Interchange the values
    
    return df
# Test Code
df = pd.DataFrame(np.random.randint(1, 100, 25).reshape(5, 5))
    
df_res = Sum_Swap(df.copy())
print(df_res)

Explanation

A function Sum_Swap is declared with df passed to it as a parameter.

  • On line 3, the np.min function is applied on the DataFrame to retrieve all the minimum values from each row. The axis=1 indicates that operation should only be performed on the rows. A Series is returned containing the minimum values for each ...