Masked arrays concept in NumPy - Mini Project: Build & Apply

Masked arrays concept

📖 Scenario: Imagine you have a list of temperatures recorded over a week. Some sensors failed and gave wrong readings, which you want to ignore in your analysis.

🎯 Goal: You will create a masked array to hide the wrong temperature readings and then calculate the average temperature ignoring those bad values.

📋 What You'll Learn

Create a numpy array with exact temperature values

Create a mask array to mark wrong readings

Create a masked array using numpy.ma module

Calculate the mean of the masked array ignoring masked values

Print the masked array and the calculated mean

💡 Why This Matters

🌍 Real World

Sensors and data collection often produce invalid or missing data. Masked arrays help ignore these bad values during analysis.

💼 Career

Data scientists and analysts use masked arrays to clean and analyze real-world datasets with missing or corrupted data.

1

Create the temperature data array

Create a numpy array called temperatures with these exact values: [22.5, 21.0, -999.0, 23.0, 22.0, -999.0, 24.5]. The value -999.0 represents wrong sensor readings.

NumPy

# 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 values.

2

Create the mask for wrong readings

Create a boolean numpy array called mask that is True where temperatures equals -999.0 and False elsewhere.

NumPy

import numpy as np

temperatures = np.array([22.5, 21.0, -999.0, 23.0, 22.0, -999.0, 24.5])
# Create the mask array where temperatures == -999.0
# Your code here

Need a hint?

Use a comparison like temperatures == -999.0 to create the mask.

3

Create the masked array

Use np.ma.masked_array to create a masked array called masked_temps from temperatures using the mask array.

NumPy

import numpy as np

temperatures = np.array([22.5, 21.0, -999.0, 23.0, 22.0, -999.0, 24.5])

mask = temperatures == -999.0
# Create masked_temps using np.ma.masked_array with temperatures and mask
# Your code here

Need a hint?

Use np.ma.masked_array(data, mask=mask) to create the masked array.

4

Print the masked array and calculate mean

Print the masked_temps array. Then calculate the mean of masked_temps using its .mean() method and print the result.

NumPy

import numpy as np

temperatures = np.array([22.5, 21.0, -999.0, 23.0, 22.0, -999.0, 24.5])

mask = temperatures == -999.0

masked_temps = np.ma.masked_array(temperatures, mask=mask)
# Print masked_temps
# Calculate mean of masked_temps and print it
# Your code here

Need a hint?

Use print(masked_temps) and print(masked_temps.mean()).