The first function is from **NumPy**, and the second method is from **pandas**. Take a look at the example of calculating the standard deviation for the `work_year`:

import pandas as pd
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 standard deviation using the function from the NumPy library
std_1 = np.std(df['salary_in_usd'])
# Calculate the standard deviation using the function from the pandas library
std_2 = df['salary_in_usd'].std()

print('The standard deviation using NumPy library is', round(std_1, 2))
print('The standard deviation using pandas library is', round(std_2, 2))

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

Standard Deviation with Python

Learning Statistics with Python

Standard Deviation with Python