In previous lessons, you learned how to handle datasets stored in compressed formats and manage large numerical datasets using NumPy arrays. Building on that foundation, today's lesson will teach you how to read and process data in batches from multiple CSV files. This is important because working with data in smaller chunks, or batches, can make your code more efficient and faster when dealing with large datasets.

Our focus in this lesson will be on a practical scenario where a dataset containing car information is spread across multiple files. You will learn to read, process, and analyze this data to extract meaningful insights, such as determining the car with the lowest price.

In this lesson, we'll work with a set of CSV files containing car data. Here's what a typical record might look like:

• Model: Ford Mustang
• Transmission: Automatic
• Year: 2020
• Price: 25000.00
• Distance Traveled (km): 50000
• Color: Red

These files are divided into multiple parts to allow batch processing, and understanding their structure is crucial as you learn to read and process them efficiently.

Now, let's delve into reading these CSV files in batches. We'll build our solution step-by-step.

First, we need to specify the filenames for our CSV files and prepare a data structure to hold the combined data.

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1import csv
2
3# Filenames to read
4filenames = ['data_part1.csv', 'data_part2.csv', 'data_part3.csv']
5
6# List to store all car data
7car_data = []

Here, we initialize a list of filenames and create an empty list car_data to store all the car data read from the files.

The csv.DictReader class in Python's csv module is a helpful tool for reading CSV files into dictionaries, allowing for easier manipulation of data. When using csv.DictReader, each row in the CSV file is read as a dictionary, with the CSV headers as the dictionary keys and the corresponding data fields as values. This format facilitates accessing individual fields by name, simplifying data handling.

For example, consider a CSV file with the following content:

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1model,transmission,year,price,distance_traveled,color
2Ford Mustang,Automatic,2020,25000.00,50000,Red

Reading this file with csv.DictReader produces:

Python
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1{
2    'model': 'Ford Mustang',
3    'transmission': 'Automatic',
4    'year': '2020',
5    'price': '25000.00',
6    'distance_traveled': '50000',
7    'color': 'Red'
8}

This feature is particularly useful when working with CSV data, as it allows for straightforward access to individual elements by their column names, improving code clarity and maintainability.

Now, we'll loop through each filename, read the data, and append it to our car_data list.

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1for filename in filenames:
2    with open(filename, newline='') as csv_file:
3        reader = csv.DictReader(csv_file)
4        for row in reader:
5            # Convert price from string to float for comparison
6            row['price'] = float(row['price'])
7            car_data.append(row)

In this snippet:

• We use a for loop to iterate over our list of filenames.
• For each file, we open it using with open(filename), ensuring it is closed properly after processing.
• csv.DictReader(csv_file) reads each line into a dictionary, which makes accessing fields by name straightforward.
• We convert the price field from a string to a float for numerical comparison purposes and then store the entire row in car_data.

With all data combined in car_data, the next step is identifying the car with the lowest price.

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1# Find the car with the lowest price
2lowest_cost_car = min(car_data, key=lambda car: car['price'])
3print(f"Model: {lowest_cost_car['model']}")
4print(f"Price: ${lowest_cost_car['price']:.2f}")

Here:

• We use Python’s min() function with a key argument to find the car with the lowest price in car_data.
• A lambda function is used to specify that the function should look at the price field while evaluating each dictionary in the list.
• We then print the model and price of the car with the lowest price, providing a clear output.

While the previous solution loads all data into memory, an alternative approach involves streaming data, processing each record as it's read. This is beneficial for systems with limited memory or when working with extremely large datasets. Below, you'll find the implementation of this streaming approach:

Python
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1import csv
2
3# Filenames to read
4filenames = ['data_part1.csv', 'data_part2.csv', 'data_part3.csv']
5
6# Initialize variables to keep track of the lowest price car
7lowest_cost_car = None
8lowest_price = float('inf')
9
10# Process each file one by one
11for filename in filenames:
12    with open(filename, newline='') as csv_file:
13        reader = csv.DictReader(csv_file)
14        for row in reader:
15            # Convert price from string to float for comparison
16            price = float(row['price'])
17            if price < lowest_price:
18                lowest_price = price
19                lowest_cost_car = row
20
21# Output the car with the lowest price
22if lowest_cost_car:
23    print(f"Model: {lowest_cost_car['model']}")
24    print(f"Price: ${lowest_cost_car['price']:.2f}")

In this implementation:

• We maintain two variables: lowest_cost_car to store the data of the car with the lowest price and lowest_price initialized to infinity for comparison purposes.
• As we stream through each CSV file, we convert the price field and compare it with lowest_price.
• If the current record's price is lower, we update lowest_price and store the entire row in lowest_cost_car.
• This approach processes each line individually and does not accumulate the data, effectively reducing the application's memory footprint.

In this lesson, you learned how to:

• Read data in batches from multiple CSV files using Python's csv.DictReader.
• Process that data efficiently and convert data types when necessary.
• Identify specific insights, such as the car with the lowest price, by using the min() function with a key argument.

Now, you're ready to apply these skills with practice exercises designed to reinforce your understanding. These exercises will challenge you to read and analyze data from similar datasets efficiently. Continuous practice is key to mastering these data handling techniques.