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Hands-On NumPy Practice Exercises

1. Array Creation and Attributes

Scenario:

You are working with satellite sensor data. Each sensor collects readings for temperature, humidity, and pressure every hour.

Tasks:

  • Create a 2D NumPy array of shape (24, 3), representing one day (24 hours) of readings for three variables.
  • Fill the array with random integer values: temperature (15-35), humidity (30-80), pressure (950-1050).
  • Print the array and display its attributes: ndim, shape, size, dtype, itemsize, and nbytes.
import numpy as np

# Generate synthetic data
data = np.column_stack([
    np.random.randint(15, 36, 24),      # Temperature
    np.random.randint(30, 81, 24),      # Humidity
    np.random.randint(950, 1051, 24)    # Pressure
])
# Print attributes

2. Indexing, Slicing, and Reshaping

Scenario:

You have a grayscale image represented as an 8x8 NumPy array (pixel values 0-255).

Tasks:

  • Create the array as described.
  • Extract the top-left 4x4 corner using slicing.
  • Flip the image vertically using slicing.
  • Reshape the image into a 1D array and then back to 8x8.
# Create synthetic image data
image = np.random.randint(0, 256, (8, 8))

# Top-left corner


# Flip vertically


# Reshape

3. Concatenation and Splitting

Scenario:

You are combining weather data from three stations (each a 5x3 array for five days: temp, humidity, wind).

Tasks:

  • Generate three arrays with random data.
  • Concatenate them vertically to create a 15x3 array.
  • Split the combined array back into three separate arrays.
station1 = np.random.randint(15, 25, (5, 3))
station2 = np.random.randint(18, 28, (5, 3))
station3 = np.random.randint(20, 30, (5, 3))

4. Mathematical and Statistical Functions

Scenario:

You have daily heights (in cm) of 10 basketball players for a month (30 days).

Tasks:

  • Generate a (30, 10) array of heights (simulate with normal distribution, mean=200, std=10).
  • Compute mean, standard deviation, min, max, median, and 25th/75th percentiles for each player.
heights = np.random.normal(200, 10, (30, 10))

5. Broadcasting

Scenario:

You have temperature readings for 5 cities over 7 days (5x7 array). You want to center each city's data by subtracting the weekly mean.

Tasks:

  • Generate the array.
  • Subtract the mean temperature for each city (row) from its values using broadcasting.
temps = np.random.randint(15, 35, (5, 7))

6. Boolean Indexing and Logical Operations

Scenario:

You have rainfall data (in mm) for 365 days.

Tasks:

  • Generate a 1D array of rainfall values (0-20 mm).
  • Count the number of dry days (0 mm), rainy days (>0 mm), and heavy rain days (>10 mm).
  • Find days where rainfall was between 1 and 5 mm.
rainfall = np.random.randint(0, 21, 365)

7. Advanced: Real-World Data Analysis

Scenario:

You are a financial analyst working with stock prices for 5 companies over 252 trading days.

Tasks:

  • Generate a (5, 252) array of stock prices (simulate with random walk).
  • Calculate daily returns (use np.diff).
  • Find the day with the highest return for each company.
prices = 100 + np.cumsum(np.random.randn(5, 252), axis=1)

8. Sorting, Searching, and Argsort

Scenario:

You have test scores for 10 students.

Tasks:

  • Generate a 1D array of 10 random scores (0-100).
  • Sort the scores.
  • Find the indices that would sort the array (argsort).
  • Find the top 3 scores and their student indices.
scores = np.random.randint(0, 101, 10)