Summary
This chapter demonstrates how to compute key statistical measures (mean and median) for DataFrame columns using pandas’ built-in .mean() and .median() methods and printing the results.

General domain of usage
data analysis

The Pandas library already has three built-in functions for calculating the **mean** and **median**. To import `pandas` using the `pd` alias, 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)

To calculate key statistical values, use methods designed for handling measurements, such as:  
```python
.mean()
.median()
```

Build a strong foundation in statistics using Python. Learn essential statistical concepts and apply them through NumPy and pandas. Move from basic measures like mean and variance to hypothesis testing, confidence intervals, and data-driven insights with hands-on practice.

Discover core statistical principles, including data types, measures of central tendency, and key differences between samples and populations.

Learn to calculate and interpret mean, median, and mode using Python. Practice these operations with pandas to analyze real datasets.

Understand how variance and standard deviation measure data spread. Learn to compute both manually and using Python tools.

Explore how covariance and correlation describe relationships between variables. Practice calculating and comparing both metrics in Python.

Master confidence intervals to estimate population parameters. Use NumPy, pandas, and visualization libraries to calculate and interpret intervals with real data.

Learn the fundamentals of hypothesis testing and the t-test. Understand how to design, perform, and interpret tests using Python to support data-driven decisions.

Statistics with pandas