Both methods produce similar results, with slight differences due to the use of different denominators: N in numpy (population variance) and N-1 in pandas (sample variance).


              123456789101112
            
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)

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 3

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Course Content

Learning Statistics with Python

1. Basic Concepts

Sample vs Population Types of Statistics Types of Data Mean Value Median Value Median Value of the Even Number of Values Mean or Median Mode Value Descriptive Statistics Quiz

2. Mean, Median and Mode with Python

Examine the Dataset Calculating Mean and Median Values with Python Statistics with pandas Calculate the Mean and Median Salary

3. Variance and Standard Deviation

Population Variance Sample Variance Calculate Variance with Python Standard Deviation Standard Deviation with Python Calculating Variance and Standard Deviation

4. Covariance vs Correlation

Covariance Correlation Covariance and Correlation Quiz Calculate Covariance and Correlation

5. Confidence Interval

6. Statistical Testing

What is t-test Hypotheses t-test Mathematically One-Tailed And Two-Tailed Test t-test Assumptions Performing a t-test in Python Conduct a t-test Paired t-test

Calculate Variance with Python

Calculating Variance with NumPy

In numpy, pass the sequence of values (such as a column from the dataset) into the np.var() function, for example: np.var(df['work_year']).

Calculating Variance with pandas

In pandas, apply the .var() method directly to the column, like this: df['work_year'].var().

Both methods produce similar results, with slight differences due to the use of different denominators: N in numpy (population variance) and N-1 in pandas (sample variance).


              123456789101112
            
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)

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 3