**Calculating Variance with NumPy**

In **NumPy** you need to put the sequence of values (in our case, the column of the dataset) into the function `np.var()` like `np.var(df['work_year'])`.  

**Calculating Variance with pandas**

In **pandas** you need to apply function `.var()` to the sequence of values (in our case the column of the dataset) like `df['work_year'].var()`.

In two cases, the result is almost the same. The differences caused by different denumerators: N in NumPy, and N-1 in pandas. Check it now!



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 variance using the function from the NumPy library
var_1 = np.var(df['salary_in_usd'])
# Calculate the variance using the function from the pandas library
var_2 = df['salary_in_usd'].var()

print('The variace using NumPy library is', var_1)
print('The variace using pandas library is', var_2)

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.