Combining data from different sources in a Python course. In this course, you will learn techniques for merging and combining different data sets using the Pandas library in Python. This skill is essential for data scientists looking to extract valuable information from a variety of sources. In this course you will learn the following:
- Data Concatenation: Combine data from different sources using the concat() and add() functions in Pandas.
- Different types of merge: Test different types of merge like one to one, many to one and many to many using pd.merge() function.
- Advanced merge strategies: Learn how to deal with mismatched column names, merge 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 different sources in Python, allowing you to perform more comprehensive data analysis.
What you will learn in the “Combining data from different sources in Python” course:
- Integrating data from different sources using the Pandas library
- The different types of data integration are one-to-one, many-to-one, and many-to-many.
- Advanced merge strategies for column name mismatches, index merges, and eliminating overlapping column names.
- How to use the pd.merge() function to merge data sets
- How to use the concat() and append() functions to append data
- How to Solve Common Data Integration Problems