Summary  
This chapter demonstrates how to import NumPy and apply its mean() and median() functions to compute central tendency measures on numeric data in a DataFrame column.  

General domain of usage  
Data analysis

You're already familiar with the mean and median. Now it's time to calculate them using Python. Start by importing the `numpy` library using the `np` alias:

```python
import numpy as np
```

Use the `.mean()` and `.median()` functions to compute the mean and median values. Here's a code example, feel free to modify the columns and observe the results:

# Import library
import numpy as np
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)

# 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)

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.

Calculating Mean and Median Values with Python