Merging Data from Different Sources in Python course. In this course, you will learn techniques for merging and combining diverse datasets using the Pandas library in Python. This skill is essential for data professionals looking to extract valuable insights from a variety of sources. In this course, you will learn the following:
- Data Concatenation: Combine data from different sources using the concat() and append() functions in Pandas.
- Different merge types: Check different merge types like one-to-one, many-to-one and many-to-many using pd.merge() function.
- Advanced merge strategies: Learn how to work with mismatched column names, merge with indexes, and resolve overlapping column names using advanced merge strategies.
After completing this course, you will have the skills and knowledge to effectively combine data from various sources in Python, allowing you to perform more comprehensive data analysis.
What you will learn in Merging Data from Different Sources in Python course:
- Integrating data from different sources using the Pandas library
- Different types of data integration: one-to-one, many-to-one and many-to-many
- Advanced merge strategies for mismatching column names, merging with indexes, and resolving overlapping column names
- How to use the pd.merge() function to merge datasets
- How to use concat() and append() functions to append data
- How to solve common data integration problems
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