Concatenating data in Pandas

Data Science, AI and ML
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Concatenating data in Pandas

Concatenation combines one or more different DataFrames into one. The concat() function of Pandas for combining DataFrames across rows or columns. Consider the following DataFrames:

import pandas as pd

record1 = [[‘John’,14,82.5],[‘Maria’,12,90.0],[‘Tom’,13,77.0]]
df1 = pd.DataFrame(record1,columns=[‘Name’,’Age’,’Marks’],index=[0, 1, 2])
print(df1)

record2 = [[‘Ben’,12,65.5],[‘Amy’,12,71.0],[‘Tina’,14,63.5]]
df2 = pd.DataFrame(record2,columns=[‘Name’,’Age’,’Marks’],index=[3, 4, 5])
print(df2)

record3 = [[‘Adam’,15,87.0],[‘Carla’,14,73.0]]
df3 = pd.DataFrame(record3,columns=[‘Name’,’Age’,’Marks’],index=[6, 7])
print(df3)

The three DataFrames are:

Name Age Marks
0 John 14 82.5
1 Maria 12 90.0
2 Tom 13 77.0

Name Age Marks
3 Ben 12 65.5
4 Amy 12 71.0
5 Tina 14 63.5

Name Age Marks
6 Adam 15 87.0
7 Carla 14 73.0

Now, to concatenate them into one, we use:

df = pd.concat([df1,df2,df3])

Name Age Marks
0 John 14 82.5
1 Maria 12 90.0
2 Tom 13 77.0
3 Ben 12 65.5
4 Amy 12 71.0
5 Tina 14 63.5
6 Adam 15 87.0
7 Carla 14 73.0

Using concat() , we can also concatenate along the columns. We just need to change the parameter “ axis=1 ”. The type of join can also be specified. For example:

import pandas as pd

record1 = [[‘S1′,’John’,14], [‘S2′,’Maria’,12], [‘S3′,’Tom’,13], [‘S4′,’Adam’,15]]
df1 = pd.DataFrame(record1,columns=[‘S_Id’,’Name’,’Age’])
print(df1)

record2 = [[‘S1’,14,82.5], [‘S2’,13,90.0], [‘S3’,14,77.0], [‘S4’,15,87.0]]
df2 = pd.DataFrame(record2,columns=[‘S_Id’,’Age’,’Marks’])
print(df2)

df = pd.concat([df1,df2],axis=1)
print(df)

The two DataFrames are:

S_Id Name Age
0 S1 John 14
1 S2 Maria 12
2 S3 Tom 13
3 S4 Adam 15

S_Id Age Marks
0 S1 14 82.5
1 S2 13 90.0
2 S3 14 77.0
3 S4 15 87.0

And the concatenated one is:

S_Id Name Age S_Id Age Marks
0 S1 John 14 S1 14 82.5
1 S2 Maria 12 S2 13 90.0
2 S3 Tom 13 S3 14 77.0
3 S4 Adam 15 S4 15 87.0

Summary

This article focused on Concatenating data in Pandas.