Summary  
The chapter demonstrates how to use boolean indexing with the `.loc[]` accessor to filter DataFrame rows by specifying conditions on column values.  

General domain of usage  
Astronomy data analysis

In this section, we will learn how to extract data using specific conditions, but first I want you to examine the data set we will use. It includes data on asteroids: 

- `id` - Unique identifier for each asteroid;
- `name` - Name given by NASA;
- `est_diameter_min` - Minimum estimated diameter in kilometers;
- `est_diameter_max` - Maximum estimated diameter in kilometers;
- `absolute_magnitude` - Describes how light the object is;
- `hazardous` - Boolean feature that shows whether asteroid is harmful or not.

You are already familiar with the `.loc[]` attribute, but here we will expand its possibilities.

One of the most useful tools is to set conditions on a column to extract specific values. So, you just put the condition of the column inside the `.loc[]` attribute. Look at the several conditions and outputs.

- `data.loc[data['est_diameter_max'] < 0.005].head()` - extracts the first five rows where the column values `'est_diameter_max'` are less then `0.005`;
- `data.loc[data['absolute_magnitude'] >= 30].head()` - extracts the first five rows where the column values `'absolute_magnitude'` are greater than or equal to `30`.

Your task is to choose all correct forms of setting conditions on the column `'est_diameter_min'`. Please look; this column is numerical, so think about the conditions we can use with numbers.

This course contains a lot of useful functions for a future data analyst. You will learn different ways of extracting data and even set conditions on it. After it, you will be familiar with the methods of grouping data. Also, you will learn how to preprocess data. Each section has its data set so that the course will be gripping.

This section will teach you how to output specific columns by their titles or indices. Also, you will get acquainted with the ways you can select rows  by indices.

Here, you will learn how to extract data that has specific conditions. Also, you will learn how to combine them and even create your own.

In this section, you will expand your knowledge on setting different data conditions. You will learn to check if your data is in a defined list of values or between two values. You will also learn how to find the largest and smallest values.

This section is one of the most fascinating of the course. Here, you will learn how to group data in different ways. It will help you work as a data analyst to find out information on specific data groups.

This section is one of the most significant for a data analyst because if the data contains missing data values in the incorrect format, it will be impossible to work with. Thus, you will learn how to deal with such inappropriate values here. 

Setting Condition