Tips and Tricks To Boost Up Data Analysis With Python. There are numerous tips and tricks in the world of Python that can help you speed up your tasks in data science, improve your code, and also help you to write code more efficiently.
If you're learning Python, you've likely heard about sci-kit-learn, NumPy and Pandas. And these are all important libraries to learn. But there is more to them than you might initially realize.
There are numerous tips and tricks in the world of Python that can help you speed up your tasks in data science, improve your code, and also help you to write code more efficiently.
So I decided to compile some of the most valuable data analysis tips in this article for you.
The primary role or purpose of profiling is to get a clear understanding of the data. And this is what the Python package, Pandas Profiling, does. This method is straightforward and fast in performing data analysis of dataframes in Pandas.
The exploratory data analysis process includes the Pandas df.info()functions and df.describe() as the first steps. But you only get a basic data overview, which might not be very helpful if you're dealing with a large data set.
Pandas’s profiling function also extends the dataframe of Pandas with the df.profile_report(), which helps you quickly analyze data. It displays plenty of information in just one line of code, which also happens to be an HTML report that's interactive.
For a set of data, Pandas profiling computes these statistics:
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