Hey, welcome to day three.
Yesterday, we built this API.
That's the home page of the API and that's one API URL example there.
So that returns a dictionary.
But this dictionary full now is just some fake data.
So we are returning that dictionary and this line.
Basically we simply get the station and the data from the user via the URL.
So here station ten, that's the date.
And then we simply return that dictionary with that value for station and that value for date.
The temperature is also fake.
So these are all fake data.
What we want to do today is we want to get the temperature out of the actual dataset.
So I'll give you the dataset in the next video and we're going to load the dataset in Python and extract
the temperature for any particular station for any particular date.
Now, before we do that though, we need to explore the data via Python.
We need to understand what these data are about and how do we do that.
We do that by using PANDAS and Jupyter.
You already know what panders is.
And Jupiter.
Jupiter is like a code ed like culture.
But it's quite different.
It's designed specifically to code for data analysis purposes.
So in other words, Jupiter is designed to work with pandas.
Actually, I don't want to go into further detail about Jupiter.
You will understand what it is about exactly in the next video where I introduce it to Jupiter.
I'll show you how to use it, and then we will analyze the weather data file that we have with Jupiter
and pandas.
So I'll see you in the next video.