Pandas allows you to set specific conditions on the dataset without an if/else statement and loops. Look at the syntax:
- `DataFrame.loc[DataFrame['job_title'] == 'Data Scientist'].count()`

So inside the `.loc[]` function, you just put the condition you need. Here you compare if `'job_title'` is equal to `'Data Scientist'`. 

It compares each value of `'job_title'` to the `'Data Scientist'` string. 

The ways of creating such conditions are the same as for `if` statements; you can use `>`, `<`, `>=`, `=<`, `==` with the same meaning.

This course will teach you the necessary skills to manage simple analytical tasks. You are going to learn Python and its libraries, such as pandas and NumPy. Besides, you will deal with real-life analytical tasks such as calculating vitally important marketing metrics.

Here, we will face challenges related to data types in python. In this section, we will learn the Python syntax necessary for each person who wants to work with this language. After the section, we will undoubtedly be able to cope with simple Python tasks.

This section will teach you simple Python functions. We will be able to work with if/else statements and loops. Such skills are necessary for everyone who wants to start programming and doing fascinating Python tasks.

This section is the most complicated than the previous one, but it is gripping. Here, we will manage the most commonly used pandas function to help we do essential operations with data. Also, we will learn the basics of the NumPy library.

This section allows you to become a real analyst. Let's go!

.loc Function