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How to Merge Pandas DataFrames with the Same Columns and One Varying Column

What will you learn?

In this tutorial, you will master the art of merging two Pandas dataframes that share identical columns except for one varying column.

Introduction to the Problem and Solution

When dealing with multiple datasets in Python using Pandas, there arises a common need to merge them based on shared columns. Imagine having two dataframes with mostly similar columns but differing in one column. This is where the merge function in Pandas comes to the rescue. By specifying the common column along with the varying column, you can seamlessly merge these dataframes and consolidate all relevant information into a single dataframe effortlessly.

Code

# Importing necessary library
import pandas as pd

# Creating sample dataframes df1 and df2
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': ['X', 'Y', 'Z'], 'C': [10, 20, 30]})
df2 = pd.DataFrame({'A': [4, 5], 'B': ['P', 'Q'], 'C': [40, 50]})

# Merging the dataframes on column 'C' which varies between them
merged_df = pd.merge(df1, df2, on='C')

# Displaying the merged dataframe
print(merged_df)

# Copyright PHD

Explanation

In this code snippet: – We first import the pandas library as pd. – Two sample DataFrames df1 and df2 are created with some dummy data. – The pd.merge() function is used where we specify both DataFrames along with the common column (‘C’) that will be used for merging. – The resulting DataFrame (merged_df) contains all columns from both input DataFrames where they share a common value in column C.

    How does the merge() function work in Pandas?

    The merge() function in Pandas combines two DataFrames by linking rows using one or more keys. It performs database-like join operations.

    What happens if there are duplicate key values when merging?

    If there are duplicate key values during merging using merge(), it will produce a Cartesian product of rows matching those key values.

    Can we merge DataFrames based on multiple columns?

    Yes. You can pass a list of multiple columns as arguments to the on parameter of .merge() method when you want to use more than one key for merging DataFrames.

    What is the difference between inner join and outer join while merging DataFrames?

    An inner join returns only those records where key values exist in both participating tables whereas an outer join returns all records when there is a match in either left or right table.

    Is it possible to specify different suffixes for overlapping column names after merging?

    Yes. You can use parameters like suffixes=(‘_x’, ‘_y’) within .merge() method call to distinguish overlapping column names from each DataFrame being merged.

    Conclusion

    Mastering how to merge Pandas DataFrames based on common columns empowers you to efficiently combine datasets into a unified structure ready for further analysis or processing tasks. Understanding different types of joins enhances your ability to manipulate extensive datasets effectively within Python’s robust ecosystem.

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