Combining fancy and slice indexing in NumPy - Mini Project: Build & Apply

Combining fancy and slice indexing

📖 Scenario: Imagine you have a small dataset of daily temperatures recorded over a week for different cities. You want to select specific days and cities to analyze the temperature trends.

🎯 Goal: You will create a NumPy array with temperature data, set up an index list for cities, use fancy and slice indexing together to select data, and finally print the selected temperatures.

📋 What You'll Learn

Create a 2D NumPy array called temps with shape (7, 4) representing 7 days and 4 cities.

Create a list called city_indices containing the city indices 0 and 2.

Use fancy indexing with city_indices and slice indexing for days 2 to 5 (inclusive start, exclusive end) on temps.

Print the resulting selected temperatures.

💡 Why This Matters

🌍 Real World

Selecting specific rows and columns from data is common in data science when analyzing subsets of data, like temperatures for certain days and cities.

💼 Career

Data scientists often use fancy and slice indexing in NumPy to efficiently manipulate and analyze large datasets.

1

Create the temperature data array

Create a NumPy array called temps with these exact values representing temperatures for 7 days (rows) and 4 cities (columns): [[30, 32, 31, 29], [31, 33, 30, 28], [29, 31, 32, 30], [28, 30, 29, 27], [27, 29, 28, 26], [26, 28, 27, 25], [25, 27, 26, 24]].

NumPy

import numpy as np
# Create the temps array with the exact values
# Your code here

Need a hint?

Use np.array([...]) with the exact nested list of temperatures.

2

Create the city indices list

Create a list called city_indices containing the integers 0 and 2 to select the first and third cities.

NumPy

import numpy as np
temps = np.array([[30, 32, 31, 29], [31, 33, 30, 28], [29, 31, 32, 30], [28, 30, 29, 27], [27, 29, 28, 26], [26, 28, 27, 25], [25, 27, 26, 24]])
# Create city_indices list with 0 and 2
# Your code here

Need a hint?

Use a list with the exact values [0, 2].

3

Select temperatures using fancy and slice indexing

Create a variable called selected_temps that selects rows from day 2 to day 5 (use slice 2:6) and columns using the city_indices list from the temps array.

NumPy

import numpy as np
temps = np.array([[30, 32, 31, 29], [31, 33, 30, 28], [29, 31, 32, 30], [28, 30, 29, 27], [27, 29, 28, 26], [26, 28, 27, 25], [25, 27, 26, 24]])
city_indices = [0, 2]
# Use fancy and slice indexing to select temperatures
# Your code here

Need a hint?

Use temps[2:6, city_indices] to combine slice and fancy indexing.

4

Print the selected temperatures

Print the variable selected_temps to display the selected temperature data.

NumPy

import numpy as np
temps = np.array([[30, 32, 31, 29], [31, 33, 30, 28], [29, 31, 32, 30], [28, 30, 29, 27], [27, 29, 28, 26], [26, 28, 27, 25], [25, 27, 26, 24]])
city_indices = [0, 2]
selected_temps = temps[2:6, city_indices]
# Print the selected_temps variable
# Your code here

Need a hint?

Use print(selected_temps) to show the result.