Learn the basics of Exploratory Data Analysis (EDA) in Python with Pandas, Matplotlib and NumPy, such as sampling, feature engineering, correlation, etc.

As you will know by now, the Python data manipulation library Pandas is used for data manipulation; For those who are just starting out, this might imply that this package can only be handy when preprocessing data, but much less is true: Pandas is also great to explore your data and to store it after you’re done preprocessing the data.

Additionally, for those who have been following DataCamp’s Python tutorials or that have already been introduced to the basics of SciPy, NumPy, Matplotlib and Pandas, it might be a good idea to recap some of the knowledge that you have built up.

Today’s tutorial will actually introduce you to some ways to explore your data efficiently with all the above packages so that you can start modeling your data: