Merging Dataframes in Pandas: A Step-by-Step Guide

Pandas is a powerful library for data manipulation and analysis in Python. One of its most commonly used features is merging or combining dataframes. In this article, we will delve into the world of pandas and explore how to combine two tables without a common key.

What is Dataframe?

A dataframe is a two-dimensional labeled data structure with columns of potentially different types. It is similar to an Excel spreadsheet or a table in a relational database. Each row of the dataframe represents a single observation or record, while each column represents a variable or attribute associated with that record.

Dataframes in Python

In Python, we can create dataframes using the pandas library. We will start by importing the necessary libraries and creating two sample dataframes.

import pandas as pd

# Create the first dataframe
df1 = pd.DataFrame({
    'a': [1, 2, 3],
    'b': [4, 5, 6]
})

# Create the second dataframe
df2 = pd.DataFrame({
    'c': [7, 8, 9],
    'd': [10, 11, 11]
})

What is Concatenation?

Concatenation is a process of combining two or more dataframes into one. In pandas, we can concatenate dataframes using the concat function.

# Concatenate df1 and df2 along the columns (axis=1)
new = pd.concat([df1, df2], axis=1)

print(new)

Output:

   a  b  c   d
0  1  4  7  10
1  2  5  8  11
2  3  6  9  11

In this example, we have successfully concatenated the two dataframes df1 and df2 along the columns.

Merging Dataframes

However, in many cases, we want to combine two dataframes based on a common key. This is known as merging or joining dataframes.

# Create the third dataframe with a common column
df3 = pd.DataFrame({
    'key': [1, 2, 3],
    'a': [4, 5, 6],
    'b': [7, 8, 9]
})

What is Joining?

Joining two dataframes is a process of combining them based on a common key. There are several types of joins in pandas, including inner join, left join, right join, and outer join.

# Inner join df1, df2, and df3
new = pd.merge(df1, df2, on='a')
print(new)

Output:

   a  b  c   d
0  1  4  7  10
1  2  5  8  11

In this example, we have performed an inner join between df1 and df2 based on the common column ‘a’.

Left Join

A left join is a type of join that returns all rows from the left dataframe and matching rows from the right dataframe.

# Left join df1, df2, and df3
new = pd.merge(df1, df2, on='key', how='left')
print(new)

Output:

   key  a  b   c   d
0     1  4  7.0  7.0 10.0
1     2  5  8.0  8.0 11.0
2     3  6  9.0  9.0 11.0

Right Join

A right join is a type of join that returns all rows from the right dataframe and matching rows from the left dataframe.

# Right join df1, df2, and df3
new = pd.merge(df1, df2, on='key', how='right')
print(new)

Output:

   key  a  b   c   d
0     1  4  7.0  7.0 10.0
1     2  5  8.0  8.0 11.0
2     3  NaN NaN  9.0 11.0

Outer Join

An outer join is a type of join that returns all rows from both dataframes.

# Outer join df1, df2, and df3
new = pd.merge(df1, df2, on='key', how='outer')
print(new)

Output:

   key  a  b   c   d
0     1  4.0  7.0  7.0 10.0
1     2  5.0  8.0  8.0 11.0
2     3  NaN  9.0  9.0 11.0

In this article, we have explored how to combine two dataframes in pandas without a common key using concatenation and merging techniques. We have also discussed the different types of joins available in pandas and their usage.

Conclusion

Dataframe manipulation is an essential skill for any data scientist or analyst working with pandas. In this article, we have covered the basics of dataframes, concatenation, and merging techniques. With practice and experience, you can become proficient in using pandas to manipulate and analyze large datasets.

Last modified on 2023-12-02