Pandas library already has three built-in functions for calculating **mean** and **median**. To import pandas, use such syntax:

```python
import pandas as pd
```

Look at the example of calculating the **mean** and **median** of the `'work_year'`, dataset `df`.

Try to change the columns and look at the result:

import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/update/ds_salaries_statistics', index_col = 0)

# Calculating the mean value
mean = df['work_year'].mean()
# Calculating the median value
median = df['work_year'].median()

print('The mean value is', mean)
print('The median value is', median)

You see, to calculate all important statistical values, we need to apply functions maintaining the measurements:
```
.mean()
.median()
```


This section will help us deal with the first real statistical case: finding confidence intervals. It requires knowledge of NumPy, pandas, Matplotlib, and Seaborn library to calculate math formulas and build visualization! To encourage you to pass this section, I want to point out that you will run across a small amount of theory but a significant amount of practice!

An inseparable part of a data analyst's life is conducting hypothesis testing. After completing this section, you will understand the idea behind testing in statistics and will be able to conduct a t-test using Python.