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Hey, welcome to this new section, and in

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this section, you'll learn how to use

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pandas, which is a very important Python

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library, and you really don't want to

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miss that. So what is pandas? Well,

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pandas is a library providing data

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structures and data analysis tools

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within Python. Or if the word tools

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confuses you, then you can say pandas is

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a library providing data structures and

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data analysis code. So basically, pandas

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allows you to load data from different

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sources into Python, and then use Python

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code to analyze those data and produce

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results, which can be in the form of

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tables, text, and also visualization

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with the help of visualization

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libraries, such as bokeh. bokeh,

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which is covered later in the course. So

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for now, we'll focus on data without

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visualizing them, and pandas is great

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for that. So practically, how do we use

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pandas? Well, you learn how to open text

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files using Python built in file

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handling methods earlier in the course.

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Now, what we open from text files was

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just plain text. But what if you want to

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load text files with data constructed

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off rows and columns. So things get a

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bit complicated. But here is where

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pandas comes into play. So you can

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probably do that using build-in Python

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methods that we have learned in the

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course. But to be more efficient and to

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be much more efficient, you need to

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have a high level library such as

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pandas, which is able to recognize such

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data structures automatically. So I use

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Pandas for loading data from data mining

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activities, such as web scraping. So

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you scrap data from a website with

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Python, and then store those data in

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pandas dataframes. So you use pandas to

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provide data structures for you in

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Python. I use Pandas for loading data

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from Excel files, and also use pandas

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for analyzing those data instead of

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using Excel. Excel can be good for

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analyzing a small table of data that

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fits in your screen, in your computer

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screen. But for data larger than

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that, so you really want to use code.

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So you write Python code once and then

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you reuse it with other data as well, and

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you don't want to do selections,

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and dragging, and many other cumbersome

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operations that you normally do in

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a graphical base program, such as Excel.

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So code is the way to go if you want to

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be efficient with data. and Python is great

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for that with pandas. So you really want

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to get a good hang of pandas, and you

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will learn that in this section, and

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also practice it with real world

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applications that we will be building as

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you progress through the course. Let's

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go ahead and dive into some code for now

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in the next lectures. See you.