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    Python Project: Process IMDB Data Easily

    Importance of Data Processing as a Python Developer

    By learning to process IMDB data in Python you will learn the core skill of data processing.

    Data processing is a critical aspect of modern computing and is essential for businesses, organizations, and individuals who need to work with large amounts of data.

    Python is a popular language for data processing due to its ease of use, large and active community, and extensive libraries and frameworks.

    Here are some of the key reasons why data processing is important and why Python is a great language for data processing:

    1. Data processing helps organizations make better decisions. In today’s data-driven world, businesses and organizations rely on data to make informed decisions. Data processing allows them to analyze, manipulate, and transform large amounts of data to extract insights and make better decisions.
    2. Python is a powerful language for data processing. Python’s simplicity, flexibility, and extensive libraries make it an ideal language for data processing tasks. Libraries like NumPy, Pandas, and Matplotlib provide powerful tools for data manipulation, analysis, and visualization.
    3. Data processing can improve efficiency. By automating data processing tasks, organizations can improve efficiency and reduce the time and effort required for manual data processing.
    4. Python is easy to learn and use. Python is known for its simplicity and readability, making it easy for beginners to learn and use. This makes it an ideal language for data processing tasks, even for those without extensive programming experience.
    5. Data processing can uncover insights and trends. Data processing allows businesses and organizations to analyze large amounts of data to uncover insights, patterns, and trends that can inform future decisions.
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    Project Description

    This project will teach you how to make data processing on IMDB data in Python.

    We will consider a small subset of the IMDB movie database.

    The file consists of about 5,000 rows of data and is found here.

    In this simple project, we will filter the rows and only keep some of the metadata.

    This is a classical task in the data processing.

    The job is to process the 5,000 rows and create a new csv file with less metadata and only for movies rated above imdb_score 7.

    Project Design

    While this seems to be an easy task, it is always important to make a few thoughts before starting to code.

    One goal is to break the code down into smaller steps that can be implemented isolated and have minimal functionality to it. This will keep the code easier to understand and maintain.

    One way to break it down is as follows.

    1. Read all the data
    2. Prepare all the data
    3. Process all the data
    4. Write the processed data

    The idea is not to try to do it all at once, but to keep the code in these steps.

    Step 1 Read all the data

    The IMDB data is kept in CSV files and we will use Python to process it.

    We work with CSV data and we want to read it into a list of dictionaries.

    import csv
    filename = 'https://raw.githubusercontent.com/LearnPythonWithRune/Python-Projects/main/files/movie_metadata.csv'
    with open(filename) as f:
        csv_reader = csv.DictReader(f)
        records = list(csv_reader)
    

    Now we have all the data in the list records.

    Step 2 Prepare all the data

    You will notice, that this will keep all values in the dictionaries as strings.

    We need to convert the imdb_score to floats in order to make the comparision.

    This can be done as follows using the type conversion function.

    for record in records:
        record['imdb_score'] = float(record['imdb_score'])
    

    Now the entry is converted to float and we can compare it as floats.

    Step 3 Process the data

    As we only need to keep some records, a great way is to create a new lists to keep them.

    This makes the code easier to understand and maintain.

    processed_records = []
    for record in records:
        if record['imdb_score'] > 7:
            new_record = {
                'movie_title': record['movie_title'],
                'imdb_score': record['imdb_score']
            }
            processed_records.append(new_record)
    

    Now we have all the data we need to write.

    Step 4 Write the processed data

    Again, remembering how to work with CSV files.

    You need to tell the fieldnames and write the header.

    But all the records can be written at once.

    with open('best_movies.csv', 'w') as f:
        csv_writer = csv.DictWriter(f, fieldnames=['movie_title', 'imdb_score'])
        csv_writer.writeheader()
        csv_writer.writerows(processed_records)
    

    While this is a simple project, it demonstrates the importance of keeping the code in steps that makes everything easy to understand and maintain.

    Better do less in each step than thinking about performance and speed.

    Want more Python projects?

    This is part of 19 Python Projects and you can learn about text processing an important skill with Python.

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