Combining data from various sources is crucial for data professionals to extract valuable insights. In this course, Merging Data from Different Sources in Python, you’ll learn the techniques to merge and concatenate diverse data sets seamlessly using pandas. First, you’ll delve into concatenation with pandas’ concat() and append() functions. Next, you’ll explore different types of joins, such as one-to-one, many-to-one, and many-to-many, using the pd.merge() function. Finally, you’ll learn how to handle non-matching column names, merge with indices, and resolve overlapping column names using advanced merging strategies. When you finish this course, you’ll have the skills and knowledge needed to effectively combine data from diverse sources in Python, facilitating more comprehensive data analysis. Software required: Python 3.x and pandas library.
Author Name: Rudi Bruchez
Author Description:
Rudi Bruchez is a freelance consultant and trainer based in Paris, France. He has more than 15 years of experience with SQL Server and started to venture into NoSQL territories. He worked first as a developer and started as a DBA in 2001, in Switzerland at MSC (Mediterranean Shipping Company). He moved to France in 2005 and is working freelance since 2006. He provides consulting, administration, audits and training. As SQL Server evolves into a more complex solution, he tries to make sure that d… more

Table of Contents

• Course Overview
  1min
• Introduction to Data Merging and Concatenation
  17mins
• Implementing Joins with pd.merge()
  27mins