PYTHON — Merging Grade Dataframes in Python
For a successful technology, reality must take precedence over public relations, for nature cannot be fooled. — Richard Feynman
PYTHON — Avoid using if name == main in Python
To merge two dataframes in Python using the pandas library, you can use the merge() function. In this tutorial, we will demonstrate how to merge dataframes using pandas and handle missing data.
First, let’s consider two dataframes: the roster dataframe and the homework exam grades dataframe. Both of these dataframes have indices that point to the same data, uniquely identifying each student. We will merge these dataframes on their indices.
import pandas as pd
# Merge the roster and homework exam grades dataframes on their indices
final_df = pd.merge(roster, hw_exam_grades, left_index=True, right_index=True)The default behavior of the merge() function is to find a common column on both dataframes and use it as the key to merge the rows. If the dataframes don't have a common column, you must specify the key to use for merging. If the dataframes have the same index, you can specify the left and right indices as the keys for merging.
# Merge using the indices as keys
final_df = pd.merge(roster, hw_exam_grades, left_index=True, right_index=True)You can also use the .join() method to join two dataframes on their indices, resulting in the same outcome as the merge() function when using indices as keys.
# Join the roster dataframe with the hw_exam_grades dataframe on their indices
final_df = roster.join(hw_exam_grades)Now, let’s merge the final dataframe with the quiz grades dataframe.
# Merge the final dataframe with the quiz grades dataframe using the email address as the key
final_df = pd.merge(final_df, quiz_grades, left_on="Email Address", right_index=True)After merging dataframes, it’s common to encounter missing values, represented as NaN in pandas. To handle missing values, you can use the .fillna() method to replace them with a specified value, such as 0.
# Fill missing values with 0
final_df = final_df.fillna(0)By following these steps, you can effectively merge dataframes using pandas, handle missing values, and prepare the merged dataframe for further data analysis and calculations.
