What is the difference between join and merge in Pandas?

For better understanding, let us first create a DataFrame,

# Importing pandas package
import pandas as pd

# Creating a Dictionary
dict1 = {
    'Name':['Amit Sharma','Bhairav Pandey','Chirag Bharadwaj','Divyansh Chaturvedi','Esha Dubey'],
    'Age':[20,20,23,19,18]
}

dict2 = {
    'Name':['Jatin Prajapati','Rahul Shakya','Gaurav Dixit','Pooja Sharma','Mukesh Jha'],
    'Age':[21,20,21,19,23]
}

# Creating a DataFrame
df1 = pd.DataFrame(dict1)
df2 = pd.DataFrame(dict2)

# Display DataFrame
print("DataFrame1:\n",df1,"\n")
print("DataFrame2:\n",df2,"\n")

Output:

Now we will apply merge and join separately on these DataFrames to understand the functional difference.

1) join() Method

# Using join method
df_join = df1.join(df2, lsuffix='_')

# Display method
print(df_join)

Output:

Here, the join method combines the two DataFrames on the basis of their indexes, and we can observe from the above example, that the second DataFrame is simply added to the first DataFrame with properly aligned rows. Also, since our column names are same for both the DataFrames, we have assigned a left suffix to the first DataFrame to distinguish the two DataFrames and to prevent from overlapping.

2) merge() Method

# Using merge method
df_merged = df1.merge(df2, on='Age', how='outer')

# Display result
print(df_merged)

Output: