Pandas support several methods to join two DataFrames similar to SQL joins to combine columns. In this article, I will explain how to join two DataFrames using merge(), join(), and concat() methods. Each of these methods provides different ways to join DataFrames. merge() is the most used approach to join two DataFrames by columns and index. join() is primarily used to combine on index and concat() is used to append DataFrame rows but it can also be used to join.
Key Points –
- Use the
pd.merge()function in Pandas to join two DataFrames based on one or more common columns. - Specify the columns to join on using the
onparameter, or useleft_onandright_onparameters if the column names are different in the two DataFrames. - Control the type of join (inner, outer, left, or right) using the
howparameter. - Choose the appropriate join type (e.g., inner, outer, left, right) based on the desired outcome and the nature of the data being merged.
- Handle duplicate column names resulting from the join using the
suffixesparameter to distinguish them.
Quick Examples of Pandas Join Two DataFrames
Below are some quick examples of pandas joining two DataFrames.
# Below are the quick examples
# Pandas join two DataFrames by index
df3=df1.join(df2, lsuffix="_left", rsuffix="_right")
# Pandas.merge() by column
df3=pd.merge(df1,df2)
# DataFrame.merge() by column
df3=df1.merge(df2)
# Merge DataFrames by column
df3=pd.merge(df1,df2, on='Courses')
# When column names are different
df3=pd.merge(df1,df2, left_on='Courses', right_on='Courses')
# By using concat()
df3=pd.concat([df1,df2],axis=1,join='inner')
First, let’s create a DataFrame that I can use to demonstrate with examples.
# Create DataFrames
import pandas as pd
technologies = {
'Courses':["Spark","PySpark","Python","pandas"],
'Fee' :[20000,25000,22000,30000],
'Duration':['30days','40days','35days','50days'],
}
index_labels=['r1','r2','r3','r4']
df1 = pd.DataFrame(technologies,index=index_labels)
technologies2 = {
'Courses':["Spark","Java","Python","Go"],
'Discount':[2000,2300,1200,2000]
}
index_labels2=['r1','r6','r3','r5']
df2 = pd.DataFrame(technologies2,index=index_labels2)
print(df1)
print(df2)
Yields below output
Pandas join() Two DataFrames
Panads.DataFrame.join() method can be used to combine two DataFrames on row indices. This by default does the left join and provides a way to specify the different join types. It supports left, inner, right, and outer join types. It also supports different params, refer to pandas join() for syntax, usage, and more examples.
By default, it uses left join on the row index. This is unlike merge() where it does inner join on common columns.
# Pandas join two DataFrames
df3=df1.join(df2, lsuffix="_left", rsuffix="_right")
print(df3)
Yields below output.
# Output:
Courses_left Fee Duration Courses_right Discount
r1 Spark 20000 30days Spark 2000.0
r2 PySpark 25000 40days NaN NaN
r3 Python 22000 35days Python 1200.0
r4 pandas 30000 50days NaN NaN
Pandas merge() Two DataFrames
In this section, I will explain the usage of pandas DataFrames using merge() method. This method is the most efficient way to join DataFrames on columns. It also supports joining on the index but an efficient way would be to use join().
Using merge() you can merge by columns, by index, merging on multiple columns, and different join types. By default, it joins on all common columns that exist on both DataFrames and performs an inner join.
# Pandas.merge()
df3=pd.merge(df1,df2)
# DataFrame.merge()
df3=df1.merge(df2)
Yields below output.
# Output:
Courses Fee Duration Discount
0 Spark 20000 30days 2000
1 Python 22000 35days 1200
You can also specify the column names explicitly.
# Merge DataFrames by Columns
df3=pd.merge(df1,df2, on='Courses')
In case if you wanted to combine column names that are different on two pandas DataFrames.
# When column names are different
df3=pd.merge(df1,df2, left_on='Courses', right_on='Courses')
print(df3)
merge() also supports different params, refer to pandas merge() to learn syntax, usage with examples.
pandas concat() Two DataFrames
pandas concat() method is the least used to join two DataFrames. It is mainly used to append DataFrames Rows. however, it can also be used to join pandas DataFrames.
# By using concat()
df3=pd.concat([df1,df2],axis=1,join='inner')
print(df3)
Yields below output.
# Output:
Courses Fee Duration Discount
0 Spark 20000 30days 2000
1 Python 22000 35days 1200
Complete Examples of Pandas Join two DataFrames
import pandas as pd
technologies = {
'Courses':["Spark","PySpark","Python","pandas"],
'Fee' :[20000,25000,22000,30000],
'Duration':['30days','40days','35days','50days'],
}
index_labels=['r1','r2','r3','r4']
df1 = pd.DataFrame(technologies,index=index_labels)
technologies2 = {
'Courses':["Spark","Java","Python","Go"],
'Discount':[2000,2300,1200,2000]
}
index_labels2=['r1','r6','r3','r5']
df2 = pd.DataFrame(technologies2,index=index_labels2)
print(df1)
print(df2)
# Pandas join two DataFrames
df3=df1.join(df2, lsuffix="_left", rsuffix="_right")
print(df3)
# Pandas.merge()
df3=pd.merge(df1,df2)
# DataFrame.merge()
df3=df1.merge(df2)
# Merge DataFrames by Columns
df3=pd.merge(df1,df2, on='Courses')
# When column names are different
df3=pd.merge(df1,df2, left_on='Courses', right_on='Courses')
print(df3)
# By using concat()
df3=pd.concat([df1,df2],axis=1,join='inner')
print(df3)
Frequently Asked Questions on Join Pandas Two DataFrames
You can use pd.merge(), df.join(), or df.concat() functions in Pandas to join two DataFrames. The choice of method depends upon the type of join you want to perform.
The common types of joins are:
Inner Join: It returns only the matching rows from both DataFrames.
Left Join: It returns all rows from the left DataFrame and matching rows from the right DataFrame.
Right Join: It returns all rows from the right DataFrame and matching rows from the left DataFrame.
Outer Join: It returns all rows from both DataFrames, filling in missing values with NaN.
To perform an inner join use the pd.merge() method, specifying the two DataFrames and the common column(s) on which to join.
In Pandas, merging and joining essentially perform the same operation of combining two DataFrames based on common columns. However, “merge” is the preferred term and function (pd.merge()), while “join” typically refers to specific types of merges, such as SQL-style joins (df.join()).
You can merge DataFrames based on multiple columns by passing a list of column names to the on parameter in the merge() function. Alternatively, you can use the left_on and right_on parameters for DataFrames with different column names.
If the DataFrames have different column names but represent the same data, you can use the left_on right_on parameters in the merge() function to specify the columns to join on explicitly.
Conclusion
In this article, you have learned joining two DataFrames using join(), merge(), and concat() methods with explanations and examples.
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