Pandas 1.0.0 is the Python’s primary library for data analysis and manipulation.
Although at first sight this latest version is not much different for the user than the previous release starting with a 0: 0.25.3, there are plenty of enhanced features that boost performance and lay a better foundation in the long run. They represent 1.0.0 as a stable version of pandas with a strengthened API, which has also been cleaned of many prior version deprecations. Here are the most notable improvements that come with 1.0.0.
- The dedicated string and Boolean data types These features are still “experimental”, which means that further improvements are expected to happen in the near future. So, as of yet, pandas will not automatically assign “string” or “bool” to your data.
- The select data types method is much quicker now! It relies on vectorization instead of iterating over a loop. So, you can run select data types, string to pull all string values, or select data types, “bool” to retrieve the Boolean data from a DataFrame, provided that you have set them as such beforehand.
- We now can enjoy the pandas, NA scalar that denotes missing values. Using pandas, NA is a new concept in the scientific ecosystem of Python, and its goal is to provide an indicator for missing values that can be used consistently and successfully across data types.
- A method that will convert the data types of columns containing such null values has been introduced – convert data types.
- The well-known info has been improved. It is much more readable and this does help you to explore your data in a quicker and more efficient way.
- Now we also have the “to markdown” – this new method allows you to display a Series or DataFrame object as a markdown table.