You already know the mean and median, but now let's calculate them using Python. To do it, we need to import the **NumPy** library with the `np` alias:

**Calculating the Mean and the Median with Python**

We start with importing library:

```python
import numpy as np
```

We will use functions `.mean()` and `.median()` to calculate the mean and median value correspondingly. The example of code is here:

Try to change the columns and look at the result:


# Import library
import numpy as np

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)

# Calculate the mean value
mean = np.mean(df['salary_in_usd'])
# Calculate the median value
median = np.median(df['salary_in_usd'])

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