np.std() and np.var() for spread in NumPy - Mini Project: Build & Apply

Understanding np.std() and np.var() for Spread

📖 Scenario: You are a data analyst working with daily temperatures recorded over a week. You want to understand how much the temperatures vary each day.

🎯 Goal: Calculate the variance and standard deviation of the temperature data using np.var() and np.std() to measure the spread of the temperatures.

📋 What You'll Learn

Create a numpy array called temperatures with the exact values: 22, 24, 19, 23, 25, 20, 21

Create a variable called ddof and set it to 0

Calculate the variance of temperatures using np.var() with ddof and store it in variance

Calculate the standard deviation of temperatures using np.std() with ddof and store it in std_dev

Print the values of variance and std_dev

💡 Why This Matters

🌍 Real World

Understanding data spread helps in weather forecasting, quality control, and risk assessment by showing how much data varies.

💼 Career

Data scientists and analysts use variance and standard deviation to summarize data variability and make informed decisions.

1

Create the temperature data array

Create a numpy array called temperatures with these exact values: 22, 24, 19, 23, 25, 20, 21

NumPy

import numpy as np
# Create the numpy array temperatures with the given values
# Your code here

Need a hint?

Use np.array() to create the array with the exact numbers inside square brackets.

2

Set the degrees of freedom variable

Create a variable called ddof and set it to 0

NumPy

import numpy as np

temperatures = np.array([22, 24, 19, 23, 25, 20, 21])
# Create the variable ddof and set it to 0
# Your code here

Need a hint?

Just assign 0 to the variable ddof.

3

Calculate variance and standard deviation

Calculate the variance of temperatures using np.var() with ddof and store it in variance. Then calculate the standard deviation using np.std() with ddof and store it in std_dev.

NumPy

import numpy as np

temperatures = np.array([22, 24, 19, 23, 25, 20, 21])
ddof = 0
# Calculate variance using np.var() with ddof
# Calculate standard deviation using np.std() with ddof
# Your code here

Need a hint?

Use np.var(temperatures, ddof=ddof) and np.std(temperatures, ddof=ddof) to calculate the spread.

4

Print the variance and standard deviation

Print the values of variance and std_dev on separate lines.

NumPy

import numpy as np

temperatures = np.array([22, 24, 19, 23, 25, 20, 21])
ddof = 0

variance = np.var(temperatures, ddof=ddof)
std_dev = np.std(temperatures, ddof=ddof)
# Print variance and std_dev
# Your code here

Need a hint?

Use two print() statements, one for variance and one for std_dev.