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title: python pandas merge: selecting columns tutorial
introduction:
pandas is a powerful data manipulation library in python, widely used for working with structured data. one common operation is merging dataframes, where you combine two or more datasets based on a common column. in this tutorial, we'll focus on merging dataframes in pandas and explore how to select specific columns during the merge process.
requirements:
make sure you have pandas installed in your python environment. if not, you can install it using the following command:
now, let's dive into the tutorial.
start by importing the pandas library and creating two sample dataframes for demonstration purposes:
now, let's merge these dataframes based on the 'id' column while selecting specific columns to include in the result. we'll use the merge() function and the on parameter to specify the common column:
by default, this merge includes all columns from both dataframes. to select specific columns, use the suffixes parameter to differentiate columns with the same name and the left and right parameters to specify which columns to include:
now, let's display the original dataframes and the merged dataframes to observe the results:
in this tutorial, you learned how to merge pandas dataframes based on a common column while selecting specific columns for the resulting dataframe. the merge() function is a powerful tool for combining datasets, and by choosing columns wisely, you can tailor the merged dataframe to your specific needs.
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