Adventures in Machine Learning

Joining or Merging DataFrames? How to Choose the Right Method in Python

Join() vs

Merge() Functions: How to Combine DataFrames with Python

Python is a powerful programming language that offers a wide range of libraries and tools for data analysis and manipulation. One of the most popular libraries for working with data in Python is Pandas.

Pandas provides several functions that allow you to manipulate and combine data in various ways. Two of the most commonly used functions for combining DataFrames are join() and merge().

In this article, we will explore the differences between these two functions and provide examples of how they can be used.

Join() Function

The join() function is used to combine two DataFrames based on their index. In other words, join() combines two DataFrames by aligning their rows based on their index values.

If two rows have the same index value, they will be combined into a single row in the result DataFrame. Here’s the syntax for the join() function:

result = df1.join(df2)

In this syntax, df1 and df2 are the two DataFrames that you want to join.

The result variable contains the combined DataFrame after the join operation. Example 1: Using the

Join() Function

Let’s create two DataFrames to demonstrate how the join() function works.

Here’s the code:

import pandas as pd

# create first DataFrame

df1 = pd.DataFrame({‘A’: [‘A0’, ‘A1’, ‘A2’, ‘A3’],

‘B’: [‘B0’, ‘B1’, ‘B2’, ‘B3’],

‘C’: [‘C0’, ‘C1’, ‘C2’, ‘C3’],

‘D’: [‘D0’, ‘D1’, ‘D2’, ‘D3’]},

index=[0, 1, 2, 3])

# create second DataFrame

df2 = pd.DataFrame({‘E’: [‘E0’, ‘E1’, ‘E2’, ‘E3’],

‘F’: [‘F0’, ‘F1’, ‘F2’, ‘F3’],

‘G’: [‘G0’, ‘G1’, ‘G2’, ‘G3’],

‘H’: [‘H0’, ‘H1’, ‘H2’, ‘H3’]},

index=[1, 2, 3, 4])

If you print out these two DataFrames, you’ll see that they have different index values:

print(df1)

Output:

A B C D

0 A0 B0 C0 D0

1 A1 B1 C1 D1

2 A2 B2 C2 D2

3 A3 B3 C3 D3

print(df2)

Output:

E F G H

1 E0 F0 G0 H0

2 E1 F1 G1 H1

3 E2 F2 G2 H2

4 E3 F3 G3 H3

If we now use the join() function to combine these two DataFrames, Pandas will only combine the rows that have the same index values. Here’s the code:

result = df1.join(df2)

print(result)

Output:

A B C D E F G H

0 A0 B0 C0 D0 NaN NaN NaN NaN

1 A1 B1 C1 D1 E0 F0 G0 H0

2 A2 B2 C2 D2 E1 F1 G1 H1

3 A3 B3 C3 D3 E2 F2 G2 H2

As you can see in the output, the result DataFrame only contains the rows with index values 1, 2, and 3, which are present in both DataFrames. The rows with index values 0 and 4 are missing in the result DataFrame, as they are not present in both DataFrames.

Merge() Function

The merge() function is used to combine two DataFrames based on a specified column. In other words, merge() combines two DataFrames by aligning their rows based on the values in a specified column.

If two rows have the same value in the specified column, they will be combined into a single row in the result DataFrame. Here’s the syntax for the merge() function:

result = pd.merge(df1, df2, on=’key’)

In this syntax, df1 and df2 are the two DataFrames that you want to merge, and ‘key’ is the name of the column that you want to use for the merge operation.

The result variable contains the combined DataFrame after the merge operation. Example 2: Using the

Merge() Function

Let’s create two DataFrames to demonstrate how the merge() function works.

Here’s the code:

# create first DataFrame

df1 = pd.DataFrame({‘key’: [‘K0’, ‘K1’, ‘K2’, ‘K3’],

‘A’: [‘A0’, ‘A1’, ‘A2’, ‘A3’],

‘B’: [‘B0’, ‘B1’, ‘B2’, ‘B3’]})

# create second DataFrame

df2 = pd.DataFrame({‘key’: [‘K1’, ‘K2’, ‘K3’, ‘K4’],

‘C’: [‘C1’, ‘C2’, ‘C3’, ‘C4’],

‘D’: [‘D1’, ‘D2’, ‘D3’, ‘D4’]})

If you print out these two DataFrames, you’ll see that they have the same column ‘key’:

print(df1)

Output:

key A B

0 K0 A0 B0

1 K1 A1 B1

2 K2 A2 B2

3 K3 A3 B3

print(df2)

Output:

key C D

0 K1 C1 D1

1 K2 C2 D2

2 K3 C3 D3

3 K4 C4 D4

If we now use the merge() function to combine these two DataFrames based on the ‘key’ column, Pandas will combine the rows that have the same value in the ‘key’ column. Here’s the code:

result = pd.merge(df1, df2, on=’key’)

print(result)

Output:

key A B C D

0 K1 A1 B1 C1 D1

1 K2 A2 B2 C2 D2

2 K3 A3 B3 C3 D3

As you can see in the output, the result DataFrame only contains the rows with ‘key’ values K1, K2, and K3, which are present in both DataFrames. The row with ‘key’ value K0 is missing in the result DataFrame, as it is not present in the second DataFrame.

Similarly, the row with ‘key’ value K4 is missing in the result DataFrame, as it is not present in the first DataFrame.

Conclusion

In this article, we have explored two commonly used functions for combining DataFrames in Pandas: join() and merge(). The join() function is used to combine two DataFrames based on their index, while the merge() function is used to combine two DataFrames based on a specified column.

By understanding the differences between these two functions, you can choose the appropriate function for your data analysis needs. Whether you’re working with large datasets or small datasets, Pandas provides a powerful set of tools to help you manipulate and combine data with ease.

Example 2: Using the

Merge() Function

Let’s take a deeper dive into how to use the merge() function to combine two DataFrames based on a specified column. We’ll walk through the steps of creating two DataFrames, viewing their contents, and then using the merge() function to combine them.

Creating Two DataFrames

To create two DataFrames, we’ll use the pd.DataFrame() function in pandas. Here’s an example of how to create the first DataFrame:

import pandas as pd

df1 = pd.DataFrame({‘name’: [‘Alice’, ‘Bob’, ‘Charlie’, ‘David’],

‘age’: [25, 35, 45, 55],

‘gender’: [‘F’, ‘M’, ‘M’, ‘M’]})

This will create a DataFrame with columns for name, age, and gender, and four rows with data for each column. To create the second DataFrame, we can use a similar approach:

df2 = pd.DataFrame({‘name’: [‘Alice’, ‘David’, ‘Edward’, ‘Frank’],

‘company’: [‘A’, ‘B’, ‘C’, ‘D’],

‘salary’: [75000, 85000, 95000, 105000]})

This will create a DataFrame with columns for name, company, and salary, and four rows with data for each column.

Viewing Two DataFrames

To view the contents of these DataFrames, we can simply print them out using the print() function:

print(df1)

print(df2)

This will display the contents of both DataFrames in the console, so we can make sure that they contain the data we expect.

Combining Two DataFrames using Merge()

Now that we have our two DataFrames, we can use the merge() function to combine them based on the ‘name’ column. Here’s an example of how to use the merge() function:

result = pd.merge(df1, df2, on=’name’)

In this example, we’re using the pd.merge() function to combine df1 and df2 based on the ‘name’ column.

We’re storing the result in a new variable called ‘result’ so that we can view the combined DataFrame later. When we print out the ‘result’ variable, we’ll see that the merge operation has combined the two DataFrames based on their shared name values:

print(result)

This will display the following table in the console:

name age gender company salary

0 Alice 25 F A 75000

1 David 55 M B 85000

As you can see, the resulting DataFrame contains only the rows that had matching name values in both DataFrames. The other rows were excluded from the merge operation, as there was no matching value in the other DataFrame.

Additional Resources

To learn more about the join() and merge() functions in pandas, you can consult the complete online documentation for these functions. The pandas documentation provides a detailed explanation of the syntax, parameters, and examples of each function, so you can learn how to use them effectively in your data analysis projects.

In addition to these functions, pandas provides a wide range of other tools and functions for data analysis and manipulation. If you’re interested in learning more about pandas, there are many tutorials available online that cover other common functions and techniques in this powerful library.

Whether you’re a beginner or an experienced data analyst, there is always something new to learn in pandas. In conclusion, joining and merging DataFrames are essential functions in pandas for manipulating and combining data.

The join() function combines DataFrames by aligning their rows based on their index values, while the merge() function combines DataFrames based on a specified column. Creating two DataFrames with the pd.DataFrame() function and using print() to view their contents before combining them is the ideal way of going about it.

The pandas documentation gives a comprehensive guide to join() and merge() functions for those seeking in-depth knowledge. Understanding these functions is crucial for data analysts who work with big data.

Therefore, it is essential to learn and master these functions to make your data manipulation more efficient and effective.

Popular Posts