This article will teach you how to combine two data frames using a Python program with a simple, understandable code.
In the Python programming languages, we are using the Pandas library, which is a popular open-source Python library to combine two data frames. By using it, we can load, clean, analyze, and visualize the data
Combining two data frames in Python
Firstly, we will discuss merging two data frames using the merge method. Combining Data Frames in a common field is called joining. Displaying the values in the columns that contain the common values is called joining the Data Frame
Merging Data Frames in Pandas
Here, we are creating two data frames that contain two columns named age and name. In the merge method, there are three types of joins involved.
- Right Join
- Inner Join
- Left Join
Note: The process of joining methods is similar to the SQL database.
Example 1: Right Join
The left column displays the common values between right and left columns.
import pandas as pd
df1=pd.DataFrame({'name':['suresh','john','sarathi','ramesh','joseph'],'age':[32,36,33,34,31]})
df2=pd.DataFrame({'name':['sathish','john','dinesh','ramesh','Suresh'],'age':[37,36,29,34,32]})
result=df1.merge(df2,how='right',on='age')
Output: name_x age name_y 0 NaN 37 sathish 1 john 36 john 2 NaN 29 dinesh 3 ramesh 34 ramesh 4 suresh 32 suresh
Example 2: Inner Join
In the inner join method, only the common values are displayed in the data frame
import pandas as pd
df1=pd.DataFrame({'name':['suresh','john','sarathi','ramesh','joseph'],'age':[32,36,33,34,31]})
df2=pd.DataFame({'name':['sathish','john','dinesh','ramesh','suresh'],'age':[37,36,29,34,32]})
result=df1.merge(df2,how='inner',left_on='age',right_on='age')
Output: name_x age name_y 0 suresh 32 suresh 1 john 36 john 2 ramesh 34 ramesh
Example 3: Left Join
The right column displays the common values between right and left columns.
import pandas as pd
df1=pd.DataFrame({'name':['suresh','john','sarathi','ramesh','joseph'],'age':[32,36,33,34,31]})
df2=pd.DataFrame({'name':['sathish','john','dinesh','ramesh','suresh'],'age':[37,36,29,34,32]})
result=df1.merge(df2,how='left',on='age')
Output: name_x age name_y 0 suresh 32 suresh 1 john 36 john 2 sarathi 33 NaN 3 ramesh 34 ramesh 4 joseph 31 NaN
Concatenating Data Frames using Pandas
The concatenate method is a bit different from the merge method, which we have discussed above. To combine two data frames into single, we can use this concatenating function.
Here, we are creating two data frames that contain two columns named Employee id and Place.
Example 1: Concatenating two data frames into single
import pandas as pd
df1=pd.DataFrame({'Employee id':['A12',A13,'A14','A15'],'Place':[23,27,32,35]})
df2=pd.DataFrame(['Employee id':['B21','B22','B23','B24'],'Place':[32,38,28,27]})
dataframes=[df1,df2]
result=pd.concat(dataframes).reset_index(drop=True)
Output: Employee id Place 0 A12 23 1 A13 27 2 A14 32 3 A15 35 4 B21 32 5 B22 38 6 B23 28 7 B24 27
Example 2: If you want to Concat horizontally
import pandas as pd
df1=pd.DataFrame({'Employee id':['A12','A13','A14','A15'],'Place':[23,27,32,35]})
df2=pd.DataFrame({'Employee id':['B21','B22','B23','B24'],'Place':[26,33,28,27]})
dataframes=[df1,df2]
result=pd.concat([df1,df2],axis=1)
Output: Employee id Place Employee id Place 0 A12 23 B21 26 1 A13 27 B22 33 2 A14 32 B23 28 3 A15 35 B24 27
Conclusion
In this article, you have learned the process of creating a simple python program to combine two data frames in pandas, this is the basic python program you can modify with your specifications. Thank you for learning with us. Have a great journey.