Examine the Dataset | Mean, Median and Mode with Python
Learning Statistics with Python

## Examine the Dataset

In this section, we will analyze a sample of IT specialists' salaries. Let's first take a look at the first five observations of the dataset:

 work_year experience_level job_title salary salary_currency salary_in_usd company_location company_size 0 2020 MI Data Scientist 70000 EUR 79833 DE L 1 2020 SE Machine Learning Scientist 260000 USD 260000 JP S 2 2020 SE Big Data Engineer 85000 GBP 109024 GB M 3 2020 MI Product Data Analyst 20000 USD 20000 HN S 4 2020 SE Machine Learning Engineer 150000 USD 150000 US L
• `work_year` - this year, the salary was paid.
• `experience_level` - the experience level: EN is Entery-level, MI is Mid-level, SE-Senior-level, EX is Executive level.
• `job_title` - the name of a job.
• `salary` - the value of the salary.
• `salary_currency` - the currency of the salary.
• `salary_in_usd` - the value of the salary in USD.
• `company_location` - the location of the company.
• `company_size` - the size of the company: S-Small, M-Medium, L-Large.

Now, let's review the data types in statistics, and afterward, you'll match each column name with its respective type.

#### Match the column name with its type.

work_year
experience_level

salary_currency

salary_in_usd

company_size

Click or drag`n`drop items and fill in the blanks

Everything was clear?

Section 2. Chapter 1

Course Content

Learning Statistics with Python

## Examine the Dataset

In this section, we will analyze a sample of IT specialists' salaries. Let's first take a look at the first five observations of the dataset:

 work_year experience_level job_title salary salary_currency salary_in_usd company_location company_size 0 2020 MI Data Scientist 70000 EUR 79833 DE L 1 2020 SE Machine Learning Scientist 260000 USD 260000 JP S 2 2020 SE Big Data Engineer 85000 GBP 109024 GB M 3 2020 MI Product Data Analyst 20000 USD 20000 HN S 4 2020 SE Machine Learning Engineer 150000 USD 150000 US L
• `work_year` - this year, the salary was paid.
• `experience_level` - the experience level: EN is Entery-level, MI is Mid-level, SE-Senior-level, EX is Executive level.
• `job_title` - the name of a job.
• `salary` - the value of the salary.
• `salary_currency` - the currency of the salary.
• `salary_in_usd` - the value of the salary in USD.
• `company_location` - the location of the company.
• `company_size` - the size of the company: S-Small, M-Medium, L-Large.

Now, let's review the data types in statistics, and afterward, you'll match each column name with its respective type.

#### Match the column name with its type.

work_year
experience_level

salary_currency

salary_in_usd

company_size

Click or drag`n`drop items and fill in the blanks

Everything was clear?

Section 2. Chapter 1