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

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

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

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

Hint

Use print(selected_temps) to show the result.

Practice

1. What does combining fancy indexing and slice indexing in NumPy allow you to do?
arr[[0, 2], 1:4] selects:

easy

A. Rows 0 to 2, columns 1 and 4

B. Rows 1 to 4, columns 0 and 2

C. Rows 0 and 2, columns 1 to 3

D. Rows 1 and 4, columns 0 to 2

Solution

1. Step 1: Understand fancy indexing for rows

   The fancy index [0, 2] selects rows 0 and 2 specifically.

2. Step 2: Understand slice indexing for columns

   The slice 1:4 selects columns starting at 1 up to but not including 4, so columns 1, 2, and 3.

3. Final Answer:

   Rows 0 and 2, columns 1 to 3 -> Option C

4. Quick Check:

   Fancy rows + slice columns = selected subarray [OK]

Hint: Fancy picks rows, slice picks continuous columns [OK]

Common Mistakes:

• Confusing rows and columns in indexing
• Forgetting slice end is exclusive
• Mixing up fancy and slice roles

2. Which of the following is the correct syntax to select rows 1 and 3 and columns 2 to 4 from a NumPy array arr?

easy

A. arr[[1, 3], 2:5]

B. arr[1:3, 2:5]

C. arr[1, 3, 2:5]

D. arr[[1, 3], [2:5]]

Solution

1. Step 1: Identify fancy indexing for rows

   To select rows 1 and 3, use fancy indexing with a list: [1, 3].

2. Step 2: Use slice for columns

   To select columns 2 to 4, use slice 2:5 (end exclusive).

3. Final Answer:

   arr[[1, 3], 2:5] -> Option A

4. Quick Check:

   Fancy rows + slice columns syntax correct [OK]

Hint: Fancy indexing uses lists, slice uses colon syntax [OK]

Common Mistakes:

• Using commas inside fancy index list
• Using slice inside list brackets
• Incorrect slice end index

3. Given the array:

import numpy as np
arr = np.array([[10, 20, 30, 40],
                [50, 60, 70, 80],
                [90, 100, 110, 120],
                [130, 140, 150, 160]])
print(arr[[1, 3], 1:3])

What is the output?

medium

A. [[ 70 80]
[150 160]]

B. [[ 60 70]
[140 150]]

C. [[ 50 60]
[130 140]]

D. [[ 60 70 80]
[140 150 160]]

Solution

1. Step 1: Select rows using fancy indexing

   Rows 1 and 3 are:
   [50, 60, 70, 80]
   [130, 140, 150, 160]

2. Step 2: Select columns 1 to 2 (slice 1:3)

   Columns 1 and 2 for these rows are:
   Row 1: 60, 70
   Row 3: 140, 150

3. Final Answer:

   [[ 60 70] [140 150]] -> Option B

4. Quick Check:

   Fancy rows + slice columns output matches [OK]

Hint: Pick rows first, then slice columns [OK]

Common Mistakes:

• Selecting wrong rows or columns
• Including column 3 by mistake
• Confusing row and column indices

4. What is wrong with this code snippet?

import numpy as np
arr = np.arange(16).reshape(4,4)
result = arr[1:3, [0, 2]]
print(result)

medium

A. It prints rows 1 and 2 with columns 0 and 2 correctly

B. It raises an error because slice and fancy indexing cannot be combined

C. It prints rows 0 and 2 with columns 1 and 3 incorrectly

D. It raises an error because the fancy index list is invalid

Solution

1. Step 1: Understand slice and fancy indexing combination

   Slice 1:3 selects rows 1 and 2. Fancy index [0, 2] selects columns 0 and 2.

2. Step 2: Check if code runs and output

   This combination is valid in NumPy and prints the selected subarray without error.

3. Final Answer:

   It prints rows 1 and 2 with columns 0 and 2 correctly -> Option A

4. Quick Check:

   Slice rows + fancy columns works fine [OK]

Hint: Slice and fancy indexing can combine on different axes [OK]

Common Mistakes:

• Thinking slice and fancy indexing cannot mix
• Confusing which axis uses slice or fancy
• Expecting an error when code is valid

5. You have a 5x5 NumPy array arr. You want to select rows 0, 2, and 3, but only columns 1 to 3 inclusive. Which code correctly does this?

hard

A. arr[1:4, [0, 2, 4]]

B. arr[0:5:2, 1:4]

C. arr[[0, 2, 4], [1, 2, 3]]

D. arr[[0, 2, 3], 1:4]

Solution

1. Step 1: Identify fancy indexing for rows

   Rows 0, 2, and 3 are selected using fancy indexing: [0, 2, 3].

2. Step 2: Use slice for columns 1 to 3 inclusive

   Slice 1:4 selects columns 1, 2, and 3 (end index exclusive).

3. Final Answer:

   arr[[0, 2, 3], 1:4] -> Option D

4. Quick Check:

   Fancy rows + slice columns selects correct subarray [OK]

Hint: Fancy list for rows, slice for continuous columns [OK]

Common Mistakes:

• Using slice for rows instead of fancy list
• Using fancy list for columns incorrectly
• Off-by-one error in slice end index