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

```python
import pandas as pd
```

Here's an example of calculating the **mean** and **median** for the `'work_year'` column in the dataset named `df`.

Feel free to change the columns and observe the results:

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 the important statistical values, we need to apply methods that handle the measurements:
```
.mean()
.median()
```

This course gives you a basic knowledge of statistics. It is intended for students with basic knowledge of Python syntax as they will be working with NumPy and pandas modules. During the course, you will become familiar with the basic concepts and then gradually move on to more complex concepts and tasks.

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.

Learning Statistics with Python

Statistics with pandas

Learning Statistics with Python

Statistics with pandas