NumPy array foundation review in SciPy - Mini Project: Build & Apply

NumPy array foundation review

📖 Scenario: You are working with temperature data collected from sensors in a greenhouse. The data is stored as a list of daily temperature readings. You want to use NumPy arrays to handle this data efficiently.

🎯 Goal: Build a simple program that creates a NumPy array from a list of temperatures, sets a threshold temperature, filters the array to find days warmer than the threshold, and prints those warmer temperatures.

📋 What You'll Learn

Create a NumPy array with exact temperature values

Create a threshold variable with a specific value

Use NumPy array filtering to select temperatures above the threshold

Print the filtered warmer temperatures

💡 Why This Matters

🌍 Real World

Scientists and engineers often use NumPy arrays to efficiently handle and analyze sensor data like temperatures.

💼 Career

Data analysts and data scientists use array filtering to quickly find important data points and make decisions based on thresholds.

1

Create a NumPy array of temperatures

Import the numpy library as np. Then create a NumPy array called temps with these exact temperature values: 22.5, 25.0, 19.8, 23.1, 26.7, 21.0.

SciPy

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

Hint

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

2

Set a temperature threshold

Create a variable called threshold and set it to the value 23.0.

SciPy

import numpy as np

temps = np.array([22.5, 25.0, 19.8, 23.1, 26.7, 21.0])
# Create a variable called threshold and set it to 23.0
# Your code here

Hint

Just assign the number 23.0 to the variable threshold.

3

Filter temperatures above the threshold

Create a new NumPy array called warm_days that contains only the temperatures from temps that are greater than threshold. Use NumPy array filtering.

SciPy

import numpy as np

temps = np.array([22.5, 25.0, 19.8, 23.1, 26.7, 21.0])

threshold = 23.0
# Create warm_days array with temps greater than threshold
# Your code here

Hint

Use the condition temps > threshold inside square brackets to filter.

4

Print the warmer temperatures

Print the warm_days array to display the temperatures warmer than the threshold.

SciPy

import numpy as np

temps = np.array([22.5, 25.0, 19.8, 23.1, 26.7, 21.0])

threshold = 23.0

warm_days = temps[temps > threshold]
# Print the warm_days array
# Your code here

Hint

Use print(warm_days) to show the filtered temperatures.

Practice

1. What is a key advantage of using a NumPy array over a regular Python list for storing numbers?

easy

A. NumPy arrays automatically sort the data

B. NumPy arrays can store different data types in the same array

C. NumPy arrays use less memory and allow faster math operations

D. NumPy arrays are slower but more flexible than lists

Solution

1. Step 1: Understand memory and speed benefits

   NumPy arrays store data in a compact way and use optimized code for math.

2. Step 2: Compare with Python lists

   Python lists store references and are slower for math operations.

3. Final Answer:

   NumPy arrays use less memory and allow faster math operations -> Option C

4. Quick Check:

   NumPy arrays = efficient storage + fast math [OK]

Hint: NumPy arrays are built for fast math and memory efficiency [OK]

Common Mistakes:

• Thinking NumPy arrays can store mixed data types like lists
• Assuming NumPy arrays sort data automatically
• Believing NumPy arrays are slower than lists

2. Which of the following is the correct way to create a 1-dimensional NumPy array with elements 1, 2, and 3?

easy

A. np.array(1, 2, 3)

B. np.array([1, 2, 3])

C. np.array{1, 2, 3}

D. np.array([1; 2; 3])

Solution

1. Step 1: Recall correct syntax for np.array

   NumPy arrays are created by passing a list inside parentheses: np.array([elements]).

2. Step 2: Check each option

   np.array([1, 2, 3]) uses correct syntax with square brackets inside parentheses. Others have syntax errors.

3. Final Answer:

   np.array([1, 2, 3]) -> Option B

4. Quick Check:

   np.array needs a list inside parentheses [OK]

Hint: Use square brackets inside parentheses to create arrays [OK]

Common Mistakes:

• Omitting square brackets around elements
• Using curly braces instead of square brackets
• Separating elements with semicolons inside list

3. What will be the output of the following code?

import numpy as np
arr = np.array([[1, 2], [3, 4]])
print(arr.shape)

medium

A. (2, 2)

B. (4,)

C. (1, 4)

D. (2,)

Solution

1. Step 1: Understand the array shape

   The array has 2 rows and 2 columns, so shape is (2, 2).

2. Step 2: Confirm shape output

   Printing arr.shape returns a tuple with dimensions (2, 2).

3. Final Answer:

   (2, 2) -> Option A

4. Quick Check:

   2 rows and 2 columns = (2, 2) [OK]

Hint: Shape shows (rows, columns) for 2D arrays [OK]

Common Mistakes:

• Confusing total elements with shape
• Expecting a flat tuple like (4,)
• Misreading rows and columns order

4. Identify the error in this code snippet:

import numpy as np
arr = np.array([1, 2, 3])
print(arr[3])

medium

A. IndexError because index 3 is out of bounds

B. SyntaxError due to wrong array creation

C. TypeError because arr is not subscriptable

D. No error, prints 3

Solution

1. Step 1: Check array length and indexing

   Array has elements at indices 0, 1, 2. Index 3 is out of range.

2. Step 2: Understand error type

   Accessing arr[3] causes IndexError for out-of-bounds access.

3. Final Answer:

   IndexError because index 3 is out of bounds -> Option A

4. Quick Check:

   Index 3 invalid for length 3 array [OK]

Hint: Array indices start at 0; max index is length-1 [OK]

Common Mistakes:

• Thinking index 3 accesses the last element
• Confusing syntax error with runtime error
• Assuming arr[3] prints 3

5. Given a 2D NumPy array arr = np.array([[1, 2, 3], [4, 5, 6]]), which code correctly extracts the second column as a 1D array?

hard

A. arr[:, 2]

B. arr[1, :]

C. arr[1]

D. arr[:, 1]

Solution

1. Step 1: Understand array slicing for columns

   To get a column, select all rows (:) and the column index.

2. Step 2: Identify correct column index

   Second column has index 1 (zero-based indexing).

3. Step 3: Check options

   arr[:, 1] uses arr[:, 1] which selects all rows, column 1 correctly.

4. Final Answer:

   arr[:, 1] -> Option D

5. Quick Check:

   All rows, column 1 = arr[:, 1] [OK]

Hint: Use arr[:, col_index] to get a column [OK]

Common Mistakes:

• Using arr[1, :] which selects a row, not a column
• Using arr[1] which selects second row
• Using wrong column index