Working with large files efficiently in NumPy - Mini Project: Build & Apply

Working with large files efficiently

📖 Scenario: You work as a data analyst for a weather station. You receive daily temperature data files that are very large. Loading the entire file at once can be slow and use too much memory. You want to learn how to read and process these large files efficiently using numpy.

🎯 Goal: Learn how to load a large file in smaller parts (chunks) using numpy and calculate the average temperature from the entire file without loading it all at once.

📋 What You'll Learn

Use numpy to load data

Read the file in chunks to save memory

Calculate the average temperature from all chunks

💡 Why This Matters

🌍 Real World

Reading large data files in chunks helps avoid memory overload and speeds up processing in real-world data analysis tasks.

💼 Career

Data scientists and analysts often work with large datasets that cannot fit into memory. Knowing how to process data in chunks is a valuable skill.

1

Create a large temperature data file

Create a numpy array called temperatures with 100000 values starting from 10.0 to 40.0 (inclusive) using np.linspace. Then save this array to a file named temp_data.txt using np.savetxt.

NumPy

import numpy as np
# Create the temperatures array and save it to 'temp_data.txt'
# Your code here

Need a hint?

Use np.linspace(10.0, 40.0, 100000) to create the array and np.savetxt to save it.

2

Set chunk size for reading the file

Create a variable called chunk_size and set it to 10000. This will be the number of temperature values you read at a time from the file.

NumPy

import numpy as np

# Create the temperatures array with 100000 values from 10.0 to 40.0
temperatures = np.linspace(10.0, 40.0, 100000)

# Save the array to 'temp_data.txt'
np.savetxt('temp_data.txt', temperatures)

# Set the chunk size for reading
# Your code here

Need a hint?

Set chunk_size to 10000 to read 10000 values at a time.

3

Read the file in chunks and calculate total sum and count

Open the file 'temp_data.txt' for reading. Use a while loop to read chunk_size lines at a time. Convert each chunk to a numpy array of floats. Keep track of the total sum of all temperatures in a variable called total_sum and the total count of values in a variable called total_count. Stop reading when no more data is left.

NumPy

import numpy as np

# Create the temperatures array with 100000 values from 10.0 to 40.0
temperatures = np.linspace(10.0, 40.0, 100000)

# Save the array to 'temp_data.txt'
np.savetxt('temp_data.txt', temperatures)

# Set the chunk size for reading
chunk_size = 10000

# Initialize total_sum and total_count
# Open the file and read in chunks
# Your code here

Need a hint?

Use a while True loop and read chunk_size lines each time. Convert lines to floats with np.array(..., dtype=float). Add sums and counts to total_sum and total_count.

4

Calculate and print the average temperature

Calculate the average temperature by dividing total_sum by total_count. Print the average temperature rounded to 2 decimal places using print(f"Average temperature: {average:.2f}").

NumPy

import numpy as np

# Create the temperatures array with 100000 values from 10.0 to 40.0
temperatures = np.linspace(10.0, 40.0, 100000)

# Save the array to 'temp_data.txt'
np.savetxt('temp_data.txt', temperatures)

# Set the chunk size for reading
chunk_size = 10000

# Initialize total_sum and total_count
total_sum = 0.0
total_count = 0

# Open the file and read in chunks
with open('temp_data.txt', 'r') as file:
    while True:
        lines = [file.readline() for _ in range(chunk_size)]
        lines = [line.strip() for line in lines if line.strip() != '']
        if not lines:
            break
        chunk = np.array(lines, dtype=float)
        total_sum += chunk.sum()
        total_count += chunk.size

# Calculate and print the average temperature
# Your code here

Need a hint?

Divide total_sum by total_count and print with 2 decimals using an f-string.