Normally merge
When we join a dataset using pd.merge() function with type ‘inner’, the output will have prefix and suffix attached to the identical columns on two data frames, as shown in the output.
Python3
# import python pandas packageimport pandas as pd# import the numpy packageimport numpy as np# Create sample dataframe data1 and data2data1 = pd.DataFrame(np.random.randint(1000, size=(1000, 3)), columns=['EMI', 'Salary', 'Debt'])data2 = pd.DataFrame(np.random.randint(1000, size=(1000, 3)), columns=['Salary', 'Debt', 'Bonus'])# Merge the DataFramesmerged = pd.merge(data1, data2, how='inner', left_index=True, right_index=True)print(merged) |
Output:
Prevent duplicated columns when joining two Pandas DataFrames
Column duplication usually occurs when the two data frames have columns with the same name and when the columns are not used in the JOIN statement. In this article, let us discuss the three different methods in which we can prevent duplication of columns when joining two data frames.
Syntax: pandas.merge(left, right, how=’inner’, on=None, left_on=None, right_on=None)
Explanation:
- left – Dataframe which has to be joined from left
- right – Dataframe which has to be joined from the right
- how – specifies the type of join. left, right, outer, inner, cross
- on – Column names to join the two dataframes.
- left_on – Column names to join on in the left DataFrame.
- right_on – Column names to join on in the right DataFrame.
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