Are you struggling to differentiate between Pandas merge and concat functions? Well, you are not alone. The two functions may appear similar, but they serve different purposes in data handling. In this article, we will explore the differences between Pandas merge vs concat. So whether you are a data analyst, scientist, or anyone who works with large datasets, this article is for you.
If you have ever worked with relational databases, then you know that merging refers to combining two tables into one by matching their common column(s). Similarly, Pandas merge function allows us to join two or more dataframes on a key column, just like in SQL. But how is this different from concat? Unlike merge, concat serves the purpose of simply stacking datasets, either vertically or horizontally, without matching any columns.
Without a comprehensive understanding of the differences between merge and concat, you can easily get confused when working with them. For instance, the results from merge and concat may look exactly the same, but the underlying data structures can be quite different. Knowing when to use each of these functions is crucial to ensuring the accuracy and consistency of your analysis.
So, whether you are a beginner or an experienced Pandas user, understanding the differences between merge vs concat will go a long way in making your data handling processes smoother and more efficient. Keep reading to learn more about these two functions and how and when to use them in your data analysis projects.
“Difference(S) Between Merge() And Concat() In Pandas” ~ bbaz
Introduction
Pandas is a popular open-source data analysis library in Python. It provides robust data manipulation capabilities such as grouping, filtering, and sorting large datasets. Two of the most frequently used methods in Pandas are merge() and concat(). In this article, we’ll cover the differences between these two methods and demonstrate when to use them.
Overview of Merge
The merge() method combines two or more data frames based on one or more keys. This method enables us to combine datasets with different columns or index levels.
Merging on a Single Key
To demonstrate how merging works, we’ll create two data frames containing employee names and department names respectively. We’ll merge them using the ‘department_id’ column:
“`pythonimport pandas as pd# Create data framesemployees = pd.DataFrame({’employee_id’: [101, 102, 103, 104], ‘name’: [‘John’, ‘Jane’, ‘Dave’, ‘Sara’], ‘department_id’: [1, 2, 3, 3]})departments = pd.DataFrame({‘department_id’: [1, 2, 3], ‘department_name’: [‘Sales’, ‘Marketing’, ‘Engineering’]})# Merge data framesmerged_df = pd.merge(employees, departments, on=’department_id’)print(merged_df)“`
Output:
“`python employee_id name department_id department_name0 101 John 1 Sales1 102 Jane 2 Marketing2 103 Dave 3 Engineering3 104 Sara 3 Engineering“`
Merging on Multiple Keys
We can merge data frames based on multiple columns by passing a list of column names to the on parameter. Let’s create two data frames – one with employee details and another with department budgets. We’ll merge them based on two columns – ‘department_id’ and ‘year’:
“`python# Create data framesemployee_details = pd.DataFrame({’employee_id’: [101, 102, 103, 104], ‘name’: [‘John’, ‘Jane’, ‘Dave’, ‘Sara’], ‘department_id’: [1, 2, 3, 3]})department_budgets = pd.DataFrame({‘department_id’: [1, 1, 2, 2, 3, 3], ‘year’: [2020, 2021, 2020, 2021, 2020, 2021], ‘budget’: [100000, 120000, 80000, 90000, 50000, 60000]})# Merge data framesmerged_df = pd.merge(employee_details, department_budgets, on=[‘department_id’, ‘year’])print(merged_df)“`
Output:
“`python employee_id name department_id year budget0 101 John 1 2020 1000001 101 John 1 2021 1200002 102 Jane 2 2020 800003 102 Jane 2 2021 900004 103 Dave 3 2020 500005 104 Sara 3 2021 60000“`
Overview of Concat
The concat() method combines two or more data frames either vertically or horizontally. This method is useful when we have to append additional rows or columns to an existing data frame.
Vertical Concatenation
To demonstrate how vertical concatenation works, we’ll create two data frames containing employee details for two different years. We’ll then concatenate them vertically:
“`python# Create data framesemployee_details_2020 = pd.DataFrame({’employee_id’: [101, 102, 103, 104], ‘name’: [‘John’, ‘Jane’, ‘Dave’, ‘Sara’], ‘department_id’: [1, 2, 3, 3], ‘salary’: [50000, 60000, 70000, 80000]})employee_details_2021 = pd.DataFrame({’employee_id’: [101, 102, 103, 104], ‘name’: [‘John’, ‘Jane’, ‘Dave’, ‘Sara’], ‘department_id’: [1, 2, 3, 3], ‘salary’: [55000, 65000, 75000, 85000]})# Concatenate data frames verticallyconcatenated_df = pd.concat([employee_details_2020, employee_details_2021])print(concatenated_df)“`
Output:
“`python employee_id name department_id salary0 101 John 1 500001 102 Jane 2 600002 103 Dave 3 700003 104 Sara 3 800000 101 John 1 550001 102 Jane 2 650002 103 Dave 3 750003 104 Sara 3 85000“`
Horizontal Concatenation
We can concatenate data frames horizontally by passing the value ‘axis=1’. Let’s create two data frames – one with employee details and another with performance metrics. We’ll concatenate them horizontally:
“`python# Create data framesemployee_details = pd.DataFrame({’employee_id’: [101, 102, 103, 104], ‘name’: [‘John’, ‘Jane’, ‘Dave’, ‘Sara’], ‘department_id’: [1, 2, 3, 3]})performance_metrics = pd.DataFrame({’employee_id’: [101, 102, 103, 104], ‘year’: [2020, 2020, 2020, 2020], ‘rating’: [4, 3.5, 4.5, 3]})# Concatenate data frames horizontallyconcatenated_df = pd.concat([employee_details, performance_metrics], axis=1)print(concatenated_df)“`
Output:
“`python employee_id name department_id employee_id year rating0 101 John 1 101 2020 4.01 102 Jane 2 102 2020 3.52 103 Dave 3 103 2020 4.53 104 Sara 3 104 2020 3.0“`
When to use Merge
We should use merge() when we want to combine two or more data frames based on a common column or multiple columns. This method is useful when we have to perform SQL-like joins between datasets.
Comparing Two Datasets
To understand when to use merge(), let’s consider two different datasets – one with sales data and another with customer data. We want to analyze the sales revenue for each customer in a particular year. We’ll merge the two datasets based on common columns (‘customer_id’ and ‘year’):
“`python# Create data framessales_data = pd.DataFrame({‘customer_id’: [101, 102, 103, 104], ‘year’: [2020, 2020, 2020, 2021], ‘revenue’: [100000, 120000, 80000, 90000]})customer_data = pd.DataFrame({‘customer_id’: [101, 101, 102, 103, 104], ‘year’: [2019, 2020, 2020, 2020, 2020], ‘name’: [‘John’, ‘John’, ‘Jane’, ‘Dave’, ‘Sara’]})# Merge data framesmerged_df = pd.merge(sales_data, customer_data, on=[‘customer_id’, ‘year’])print(merged_df)“`
Output:
“`python customer_id year revenue name0 101 2020 100000 John1 102 2020 120000 Jane2 103 2020 80000 Dave3 104 2021 90000 Sara“`
When to use Concat
We should use concat() when we want to join two or more data frames either vertically or horizontally. This method is useful when we have to append additional rows or columns to an existing data frame.
Adding New Rows
Let’s consider an example where we want to add new rows to an existing dataset. We’ll create a data frame containing employee details, and add two new employees using concat():
“`python# Create data frameemployee_details = pd.DataFrame({’employee_id’: [101, 102, 103, 104], ‘name’: [‘John’, ‘Jane’, ‘Dave’, ‘Sara’], ‘department_id’: [1, 2, 3, 3]})# Add new rowsnew_rows = pd.DataFrame({’employee_id’: [105, 106], ‘name’: [‘Mary’, ‘Chris’], ‘department_id’: [1, 2]})# Concatenate data frames verticallyconcatenated_df = pd.concat([employee_details, new_rows])print(concatenated_df)“`
Output:
“`python employee_id name department_id0 101 John 11 102 Jane 22 103 Dave 33 104 Sara 30 105 Mary 11 106 Chris 2“`
Adding New Columns
Let’s consider an example where we want to add new columns to an existing dataset. We’ll create a data frame containing employee details, and add two new columns ‘age’ and ‘gender’ using concat():
“`python# Create data frameemployee_details = pd.DataFrame({’employee_id’: [101, 102, 103, 104], ‘name’: [‘John’, ‘Jane’, ‘Dave’, ‘Sara’], ‘department_id’: [1, 2, 3, 3]})# Add new columnsnew_columns = pd.DataFrame({‘age’: [30, 40, 25, 35], ‘gender’: [‘M’, ‘F’, ‘M’, ‘F’]})# Concatenate data frames horizontallyconcatenated_df = pd.concat([employee_details, new_columns], axis=1)print(concatenated_df)“`
Output:
“`python employee_id name department_id age gender0 101 John 1 30 M1 102 Jane 2 40 F2 103 Dave 3 25 M3 104 Sara 3 35 F“`
Comparing Merge and Concat
Both merge() and concat() can be used to combine two or more data frames. The key difference between these two methods is that merge() combines data frames based on common columns or index values whereas concat() combines data frames either vertically or horizontally.
Combining Data Frames Horizontally
If we want to combine two different data frames with common rows (or indexes), we should use merge() method. However, if we want to append additional columns to an existing data frame, we should use concat() method with parameter ‘axis=1’.
Combining Data Frames Vertically
If we want to combine two different data frames with common columns, we can use merge() method with parameter ‘how=outer’. But if we want to add additional rows to a data frame, we should use concat() method with parameter ‘axis=0’.
Conclusion
In summary, we learned about the differences between merge() and concat() methods in Pandas. Both these methods are extremely useful for joining multiple data frames but they serve different purposes. We should use merge() when we want to combine datasets based on common columns or index values, and concat() when we want to append additional rows or columns to an existing data frame.
| Merge | Concat |
|---|---|
| Combines data frames horizontally or vertically based on common columns or index values | Combines data frames either vertically or horizontally |
