How to Create a Mapper for Two CSV Files in Python?

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To create a mapper for two CSV files in Python, you can use the Pandas library. Pandas provides functions to read data from CSV files and merge two DataFrames based on a common column. Here’s an example:

Assume we have two CSV files: file1.csv and file2.csv. Both files have a common column id and we want to create a mapper between the two files based on this column.

file1.csv:

id,name,age
1,John,25
2,Emily,28
3,David,32
4,Susan,26

file2.csv:

id,city,country
1,New York,USA
2,Toronto,Canada
3,Mexico City,Mexico
5,London,UK

We want to merge these two files based on the id column, and create a new CSV file mapper.csv with the following columns:

id,name,age,city,country
1,John,25,New York,USA
2,Emily,28,Toronto,Canada
3,David,32,Mexico City,Mexico
4,Susan,26,,,
5,,,London,UK

Here’s how you can create this mapper using Python and Pandas:

import pandas as pd

# read the two CSV files
df1 = pd.read_csv('file1.csv')
df2 = pd.read_csv('file2.csv')

# merge the two DataFrames based on the 'id' column
mapper = pd.merge(df1, df2, on='id', how='outer')

# write the mapper to a new CSV file
mapper.to_csv('mapper.csv', index=False)

In this example, we first import the Pandas library. We then use the pd.read_csv function to read the two CSV files into two separate DataFrames, df1 and df2.

We then use the pd.merge function to merge the two DataFrames based on the id column, using an outer join. The how='outer' parameter ensures that all rows from both DataFrames are included in the merged DataFrame, even if there is no matching id value in one of the files.

Finally, we use the to_csv function to write the merged DataFrame to a new CSV file called mapper.csv, without including the row index.

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