import pandas as pd
# DataFrame 1
df1 = pd.DataFrame({'id': ['1000', '1001', '1002', '1003'],
'company': ['Devsheet', 'SpaceX', 'Tesla', 'Open AI']})
# DataFrame 2
df2 = pd.DataFrame({'location': ['India', 'US', 'US', 'US'],
'product': ['Website', 'Rocket', 'Car', 'ML']})
result = pd.concat([df1, df2], axis=1)
print(result)
Output
id company location product
0 1000 Devsheet India Website
1 1001 SpaceX US Rocket
2 1002 Tesla US Car
3 1003 Open AI US ML
We are joining two DataFrames df1 and df2 here using the pandas.concat() function. We are making a list from the DataFrames df1 and df2 and passing the list to concat() function.
We are merging DataFrames by columns means adding new columns that are new in the second DataFrame.
To merge DataFrame by columns we pass axis=1 in the concat() function. This will add new columns from the first and second DataFrame to the new DataFrame.
import pandas as pd
# DataFrame 1
df1 = pd.DataFrame({'company': ['Devsheet', 'SpaceX', 'Tesla', 'Open AI']})
# DataFrame 2
df2 = pd.DataFrame({'location': ['India', 'US', 'US', 'US']})
result = pd.concat([df1, df2], axis=1)
print(result)
Output
company location
0 Devsheet India
1 SpaceX US
2 Tesla US
3 Open AI US
You can merge DataFrames by rows by joining rows of Columns with the same names. Below is an example that can be helpful to understand.
import pandas as pd
# DataFrame 1
df1 = pd.DataFrame({
'id': [100, 101, 102, 103],
'company': ['Devsheet', 'SpaceX', 'Tesla', 'Open AI'],
})
# DataFrame 2
df2 = pd.DataFrame({
'id': [104, 105, 106],
'company': ['Microsoft', 'Apple', 'Amazon']
})
result = pd.concat([df1, df2])
print(result)
Output
id company
0 100 Devsheet
1 101 SpaceX
2 102 Tesla
3 103 Open AI
0 104 Microsoft
1 105 Apple
2 106 Amazon
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