NumPy with SciPy - Mini Project: Build & Apply

Analyzing Data with NumPy and SciPy

📖 Scenario: You work as a data analyst for a small company. You have collected some data points representing the heights (in cm) of a group of people. You want to analyze this data to find the average height and understand how spread out the heights are. You will use NumPy to handle the data and SciPy to calculate the standard deviation.

🎯 Goal: Build a simple Python program that uses NumPy to store height data and SciPy to calculate the standard deviation. You will then print the average height and the standard deviation.

📋 What You'll Learn

Create a NumPy array with the exact height values given.

Create a variable to hold the average height using NumPy.

Use SciPy's stats module to calculate the standard deviation of the heights.

Print the average height and the standard deviation.

💡 Why This Matters

🌍 Real World

Data scientists often need to summarize and understand data using statistics like mean and standard deviation to make decisions.

💼 Career

Knowing how to use NumPy and SciPy for basic statistics is essential for data analysis roles in many industries.

1

Create a NumPy array with height data

Import NumPy as np and create a NumPy array called heights with these exact values: 160, 165, 170, 175, 180.

NumPy

import numpy as np
# Create the NumPy array called heights with the given values
# Your code here

Need a hint?

Use np.array([...]) to create the array with the exact numbers.

2

Calculate the average height using NumPy

Create a variable called average_height and set it to the mean of the heights array using NumPy's mean function.

NumPy

import numpy as np
heights = np.array([160, 165, 170, 175, 180])
# Calculate the average height using np.mean
# Your code here

Need a hint?

Use np.mean(heights) to get the average.

3

Calculate the standard deviation using SciPy

Import stats from scipy. Then create a variable called std_dev and set it to the standard deviation of heights using stats.tstd.

NumPy

import numpy as np
heights = np.array([160, 165, 170, 175, 180])
average_height = np.mean(heights)
# Import stats from scipy and calculate standard deviation using stats.tstd
# Your code here

Need a hint?

Use from scipy import stats and then stats.tstd(heights) to get the standard deviation.

4

Print the average height and standard deviation

Write two print statements: one to print average_height with the text "Average height:" and another to print std_dev with the text "Standard deviation:".

NumPy

import numpy as np
heights = np.array([160, 165, 170, 175, 180])
average_height = np.mean(heights)
from scipy import stats
std_dev = stats.tstd(heights)
# Print the average height and standard deviation
# Your code here

Need a hint?

Use print("Average height:", average_height) and print("Standard deviation:", std_dev).