Hi.
In this video we will learn how to style under data frames in Jupiter Notebook.
Here, in this short example,
I would like to show you how is it possible to style the data frames in the
notebook? Uh, we can do it, uh, via the style accessor object.
We can, for example,
change number formatting to just show two decimal places or
make color bar charts in some of the column. Or we can, for example,
put a gradient color as a background where low values are in some
color and high values in other color.
We see here that the lowest values are white and the highest are
dark blue.
There are really many options on how to start the data frames in notebooks. Uh,
feel free to explore it more.
Okay, this is the end of this module. I hope you enjoyed it. Uh,
you see that pandas is a very big library and a number one library to use
for manipulating and analyzing data in Python.
It'll require practice to be comfortable working with it, but with this module,
you should go to very solid foundations of the most common methods you will use
on daily basis.
We covered the base and methods used to get, uh,
necessary information about data frame and theories,
and we learned about pandas data types index and multi-index.
We have learned also the most common data frame operations.
We covered also the apply method.
We also learned the ways how to merge data frames,
how to summarize data using pivot table, cross up and group.
And we also had a short example about styling the data frames in the notebook.
Next,
we'll start our new module focused on data visualization in Jupyter
Notebooks.