Pandas DataFrame merge is the process of combining two DataFrames into a single DataFrame based on a common column or columns. This can be useful for combining data from different sources or for performing data analysis on multiple data sets.
Using the `merge()` method
The `merge()` method takes two DataFrames as input and returns a new DataFrame that is the merged result of the two input DataFrames. The `merge()` method can be used to perform a variety of merge types, including inner joins, outer joins, left joins, and right joins.
Here is an example of how to use the `merge()` method to merge two DataFrames:
import pandas as pd
df1 = pd.DataFrame({'name': ['Alice', 'Bob', 'Carol'], 'age': [25, 30, 35]})
df2 = pd.DataFrame({'name': ['Alice', 'David', 'Eve'], 'occupation': ['Software Engineer', 'Data Scientist', 'Product Manager']})
merged_df = df1.merge(df2, on='name')
print(merged_df)
Using the `join()` method
The `join()` method takes two DataFrames as input and returns a new DataFrame that is the joined result of the two input DataFrames. The `join()` method can only be used to perform inner joins.
Here is an example of how to use the `join()` method to merge two DataFrames:
import pandas as pd
df1 = pd.DataFrame({'name': ['Alice', 'Bob', 'Carol'], 'age': [25, 30, 35]})
df2 = pd.DataFrame({'name': ['Alice', 'David', 'Eve'], 'occupation': ['Software Engineer', 'Data Scientist', 'Product Manager']})
merged_df = df1.join(df2, on='name')
print(merged_df)
Which method you use to merge DataFrames in Pandas depends on your specific needs. If you need to perform a variety of merge types, the `merge()` method is more flexible. If you only need to perform inner joins, the `join()` method is more efficient.