Here are some keypoints of what I did and discovered the last few days:

Here is a very good book of its kind https://www.edwardtufte.com/tufte/books_vdqi

From the description “*The classic book on statistical graphics, charts, tables. Theory and practice in the design of data graphics, 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis*”.

Seaborn uses a technique called kernel density estimation , or KDE for short, to create a smoothed line chart over the histogram.

When we use the `concat()`

function to combine dataframes with the same shape and index, we can think of the function as “gluing” dataframes together.

Unlike the `concat`

function, the `merge`

function only combines dataframes horizontally (axis=1) and can only combine two dataframes at a time.

An __inner join__ returns only the *intersection* of the keys, or the elements that appear in both dataframes with a common key.

__Outer join__: includes all data from both dataframes

__Left join__: includes all of the rows from the “left” dataframe along with any rows from the “right” dataframe with a common key; the result retains all columns from both of the original dataframes.

I also started a project on kaggle, the dataset “Red wine quality” which I think it will help me understand and apply some of the concepts of linear regression and classification methods. I also want to read again the concepts of regression in the book “Introduction to statistical learning” to better understand them through practical applications.