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

Need a 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

Need a 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

Need a 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

Need a hint?

Use print(cityb_temps) to display the array.