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**|**name**|**est_diameter_min**|**est_diameter_max**|**absolute_magnitude**|**hazardous**|
|---|---|---|---|---|---|---|
|**0**|2162635|162635 (2000 SS164)|1.198271|2.679415|16.73|False|
|**1**|2277475|277475 (2005 WK4)|0.265800|0.594347|20.00|True|
|**2**|2512244|512244 (2015 YE18)|0.722030|1.614507|17.83|False|
|**3**|3596030|(2012 BV13|0.096506|0.215794|22.20|False|
|**4**|3667127|(2014 GE35)|0.255009|0.570217|20.09|True|

- `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[]` function, 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[]` function. 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 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. 

	id	name	est_diameter_min	est_diameter_max	absolute_magnitude	hazardous
0	2162635	162635 (2000 SS164)	1.198271	2.679415	16.73	False
1	2277475	277475 (2005 WK4)	0.265800	0.594347	20.00	True
2	2512244	512244 (2015 YE18)	0.722030	1.614507	17.83	False
3	3596030	(2012 BV13	0.096506	0.215794	22.20	False
4	3667127	(2014 GE35)	0.255009	0.570217	20.09	True

Select a few correct answers