ndarray as the core data structure in NumPy - Mini Project: Build & Apply

ndarray as the core data structure

📖 Scenario: You are working with temperature data collected from three cities over four days. The data is organized in a table format, where each row represents a city and each column represents a day.

🎯 Goal: You will create a NumPy ndarray to hold this temperature data, set up a variable for the number of days, extract temperatures for a specific city, and finally print the extracted data.

📋 What You'll Learn

Use NumPy to create an ndarray with exact temperature values

Create a variable to store the number of days

Extract temperatures for the city 'CityB' using ndarray indexing

Print the extracted temperatures

💡 Why This Matters

🌍 Real World

Scientists and analysts often use ndarrays to store and analyze data collected over time or across different locations.

💼 Career

Understanding how to use ndarrays is essential for data scientists and analysts working with numerical data in Python.

1

Create the temperature data ndarray

Import NumPy as np and create a 2D ndarray called temperatures with these exact values: first row [22, 21, 23, 22], second row [19, 20, 21, 20], third row [25, 26, 27, 26].

NumPy

import numpy as np
# Create the ndarray 'temperatures' with the exact values
# Your code here

Hint

Use np.array() to create the ndarray with the exact nested list of values.

2

Set the number of days variable

Create a variable called num_days and set it to the number of columns in the temperatures ndarray.

NumPy

import numpy as np

temperatures = np.array([[22, 21, 23, 22],
                         [19, 20, 21, 20],
                         [25, 26, 27, 26]])

# Create 'num_days' as the number of columns in 'temperatures'
# Your code here

Hint

Use the shape attribute of the ndarray to get the number of columns.

3

Extract temperatures for CityB

Create a variable called cityb_temps that extracts the second row (index 1) from the temperatures ndarray.

NumPy

import numpy as np

temperatures = np.array([[22, 21, 23, 22],
                         [19, 20, 21, 20],
                         [25, 26, 27, 26]])

num_days = temperatures.shape[1]

# Extract the temperatures for CityB (second row) into 'cityb_temps'
# Your code here

Hint

Use indexing with [1] to get the second row from the ndarray.

4

Print the extracted temperatures

Print the variable cityb_temps to display the temperatures for CityB.

NumPy

import numpy as np

temperatures = np.array([[22, 21, 23, 22],
                         [19, 20, 21, 20],
                         [25, 26, 27, 26]])

num_days = temperatures.shape[1]

cityb_temps = temperatures[1]

# Print the temperatures for CityB
# Your code here

Hint

Use print(cityb_temps) to display the array.

Practice

1.

What is the main purpose of a numpy ndarray?

easy

A. To display images on screen

B. To create text documents

C. To store numbers in one or more dimensions for fast math operations

D. To connect to databases

Solution

1. Step 1: Understand what ndarray stores

   ndarray is designed to hold numbers arranged in rows and columns or more dimensions.

2. Step 2: Recognize its use for math

   It allows fast math operations on many numbers at once, unlike regular Python lists.

3. Final Answer:

   To store numbers in one or more dimensions for fast math operations -> Option C

4. Quick Check:

   ndarray = fast number storage [OK]

Hint: ndarray = fast multi-dimensional number storage [OK]

Common Mistakes:

• Thinking ndarray stores text or images
• Confusing ndarray with database tools
• Assuming ndarray is for display only

2.

Which of the following is the correct way to create a 1D ndarray from a Python list [1, 2, 3]?

import numpy as np
# Which line is correct?

easy

A. arr = np.array([1, 2, 3])

B. arr = np.ndarray([1, 2, 3])

C. arr = np.array(1, 2, 3)

D. arr = np.array{1, 2, 3}

Solution

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

   np.array() takes a list or nested lists inside parentheses.

2. Step 2: Check each option

   arr = np.array([1, 2, 3]) uses correct syntax with square brackets inside parentheses. Others have wrong function calls or braces.

3. Final Answer:

   arr = np.array([1, 2, 3]) -> Option A

4. Quick Check:

   np.array(list) syntax = correct [OK]

Hint: Use np.array() with list inside parentheses [OK]

Common Mistakes:

• Using np.ndarray() directly without parameters
• Passing multiple arguments instead of one list
• Using curly braces {} instead of []

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. (4,)

B. (2, 2)

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: Check print output

   arr.shape returns a tuple with dimensions, so output is (2, 2).

3. Final Answer:

   (2, 2) -> Option B

4. Quick Check:

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

Hint: Shape shows rows and columns as tuple [OK]

Common Mistakes:

• Confusing shape with total elements
• Expecting a list instead of tuple
• Mixing up rows and columns count

4.

Find the error in this code snippet:

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

medium

A. SyntaxError due to wrong print statement

B. No error, prints 3

C. TypeError because arr is not a list

D. IndexError because index 3 is out of bounds

Solution

1. Step 1: Check array length and indexing

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

2. Step 2: Understand error type

   Accessing arr[3] causes IndexError because index is invalid.

3. Final Answer:

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

4. Quick Check:

   Index out of range = IndexError [OK]

Hint: Check array length before indexing [OK]

Common Mistakes:

• Thinking arr[3] returns last element
• Confusing IndexError with SyntaxError
• Assuming numpy arrays allow out-of-range access

5.

You have a nested list [[1, 2], [3, 4], [5, 6]]. How do you create a numpy ndarray and then get the sum of all elements?

import numpy as np
nested_list = [[1, 2], [3, 4], [5, 6]]
# Fill in the blanks
arr = np.array(nested_list)
total = _____
print(total)

hard

A. arr.sum()

B. sum(arr)

C. arr.add()

D. np.sum(arr, axis=1)

Solution

1. Step 1: Create ndarray from nested list

   np.array(nested_list) creates a 2D array with shape (3, 2).

2. Step 2: Use ndarray method to sum all elements

   arr.sum() returns sum of all elements in the array.

3. Final Answer:

   arr.sum() -> Option A

4. Quick Check:

   Use arr.sum() for total sum [OK]

Hint: Use arr.sum() to sum all elements [OK]

Common Mistakes:

• Using Python sum() which sums rows, not elements
• Using arr.add() which does not exist
• Using np.sum with axis=1 sums rows, not total