Python's pandas: Multiple Ways to Add or Update DataFrame Values
Python's pandas library offers several methods to add external values to a dataframe. A new column can be added using a dictionary, while specific values can be updated or replaced using index values.
The first method involves using a dictionary with keys matching one column of the dataframe. This allows adding new values to the dataframe, creating a new column without repeating key values. A similar approach can be used to update existing values in the dataframe, using index values to target specific columns.
The second method allows for more flexibility, enabling users to add or replace some values in the dataframe with external data. This can be particularly useful when dealing with large datasets, as it allows for targeted updates without the need to reload the entire dataframe.
The book 'Python-pandas', published in 2018, provides a comprehensive guide to these methods and more. Authored by Wes McKinney, the book is a valuable resource for anyone working with data in Python.
Python's pandas library provides multiple methods to add or update values in a dataframe using external data. These methods, detailed in Wes McKinney's book 'Python-pandas', offer flexibility and efficiency when working with data.
