Unlock the power of pandas and master data merging in Python! Discover join types, merge key specs, and advanced techniques to solve real-world problems.
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.
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.
