登录查看更多内容

Last updated on 2024年5月30日

How do you handle large datasets in Python without sacrificing performance?

由人工智能和领英社区提供技术支持

Handling large datasets in Python can be a daunting task, especially when performance is a priority. You might have encountered situations where your scripts become sluggish or even crash due to memory overload. This is a common challenge in data engineering, where efficiently processing and analyzing vast amounts of data is crucial. The key is to use the right tools and techniques to manage the data without compromising speed. In this article, you'll learn some strategies to handle large datasets in Python that will keep your data pipelines running smoothly and efficiently.

此文章中的业界达人

由社区从 6 条内容中精选。了解更多

Astikar Vivek Kumar

Linkedin Top Data Engineering Voice | @Google @Microsoft Certified | Magma M Scholar | @Data Maverick | Building the…
Suresh Bisoyi

Senior Data Engineer | Snowflakes | Infogix SME | 2x AWS | Business Intelligence | Informatica | GCP | PySpark | Kafka
Pulkit Shrivastava

Experian | McKinsey | Hoonartek

1 Optimize Memory

When dealing with large datasets, memory optimization is your first line of defense against performance issues. Python's built-in data structures are not always the most memory-efficient, especially for large-scale data. To counter this, consider using specialized libraries like pandas for data manipulation. pandas offers data structures like DataFrame, which is optimized for performance and can handle large datasets more efficiently than Python's native dictionaries or lists. Additionally, be mindful of data types; for example, using category data types for categorical variables can significantly reduce memory usage.

添加您的观点

Astikar Vivek Kumar

Linkedin Top Data Engineering Voice | @Google @Microsoft Certified | Magma M Scholar | @Data Maverick | Building the Future with AI
举报内容
To handle giant data in Python without slowing things down using - parallel processing - Memory optimization Lets try to understand with a pizza example :- - Chunk it: Read the data in smaller bites instead of all at once. Imagine eating a giant pizza slice by slice, not all at once! - Work in parts: Process each chunk of data instead of loading everything in memory. Like making mini pizzas from a giant one, work on smaller pieces! - Parallelize : Use libraries like Dask to spread the work across multiple cores. Imagine having friends help you eat the pizza - faster together! #Happy_Learning

已翻译

赞
Suresh Bisoyi

Senior Data Engineer | Snowflakes | Infogix SME | 2x AWS | Business Intelligence | Informatica | GCP | PySpark | Kafka
举报内容
To handle large datasets in Python efficiently, use libraries like Pandas with techniques such as: - Optimizing data types to reduce memory usage, like downcasting numeric columns.Use smaller data types like `uint8` for integers if possible. - Chunking: Process data in chunks with `pd.read_csv(chunksize=your_size)` to avoid loading the entire dataset into memory. - Lazy evaluation: Use `DataFrame.iterrows()` or `DataFrame.itertuples()` for row-wise operations without full memory load. - Parallel processing: Consider Dask for parallel computation on large, distributed datasets. - Selective loading: Load only necessary columns with `usecols` parameter. - Dask library: Utilize Dask for parallel computing and out-of-core dataframes.

已翻译

赞
Pulkit Shrivastava

Experian | McKinsey | Hoonartek
举报内容
Use Efficient Data Structures: - NumPy Arrays: NumPy arrays are more memory-efficient and faster than Python lists for numerical data. - Pandas DataFrames: Use the `Pandas` library for handling tabular data. Pandas is optimized for performance and offers various methods for efficient data manipulation.

已翻译

赞
Tushar Patil

Associate Data Engineer | Certified Professional | PySpark & SQL Expert | ADF | ADLS | Databricks | SQL Azure | Medallion Architecture | Driving insights through ETL pipelines & Big Data Analytics
举报内容
To handle large datasets in Python without sacrificing performance, we use efficient libraries like Pandas and NumPy. Pandas and NumPy provide optimized data structures and functions for data manipulation. We use generators and iterators to process data in chunks, reducing memory usage. For file I/O, we prefer using binary formats like Parquet or HDF5 instead of CSVs, as they are faster and more efficient. We leverage multiprocessing or multithreading to parallelize tasks. Additionally, we use memory profiling tools to identify and optimize memory-intensive parts of our code. By employing these strategies, we can effectively manage large datasets in Python while maintaining performance.

已翻译

赞

2 Streamline Data

Streamlining data involves loading and processing it in chunks rather than all at once, which can be a game-changer for performance. Libraries like pandas allow you to read data in segments using the chunksize parameter in functions like read_csv() . This way, you only keep a portion of the data in memory at any given time, reducing the likelihood of memory errors. Also, consider filtering out irrelevant columns or rows early in the data processing pipeline to minimize the amount of data you're working with.

添加您的观点

3 Employ Dask

For larger-than-memory datasets, consider using Dask, a parallel computing library that integrates seamlessly with pandas . Dask allows you to work with large datasets by breaking them down into manageable chunks and processing them in parallel, leveraging multiple cores of your CPU. It provides a DataFrame abstraction similar to pandas , but operates on larger datasets without running into memory constraints. Dask can distribute computation across clusters, making it highly scalable and efficient for big data tasks.

添加您的观点

4 Utilize SQL Databases

Sometimes, it's more efficient to process large datasets using a SQL database rather than in-memory with Python. SQL databases are designed to handle large volumes of data and can execute complex queries efficiently. By storing your dataset in a database and using Python to send SQL queries, you can offload much of the heavy lifting to the database engine. This approach can significantly improve performance, especially for tasks like filtering, grouping, and aggregating large datasets.

添加您的观点

5 Leverage Caching

Caching is a technique where you store the results of expensive computations so that you can reuse them without recalculating. In Python, you can implement caching by using memoization decorators or by manually saving intermediate results to disk. When working with large datasets, caching intermediate results after data transformations can save a considerable amount of time, especially if the transformations are computationally intensive and you need to run them multiple times.

添加您的观点

6 Parallel Processing

Parallel processing is the practice of executing multiple operations simultaneously, which can significantly speed up data processing tasks. Python's concurrent.futures module or third-party libraries like joblib can help you run your data processing functions in parallel. This is particularly effective when you have a multi-core processor, as each core can handle a portion of the dataset independently. Always ensure that your code is thread-safe and that the operations you're parallelizing are CPU-bound to get the best performance gains.

添加您的观点

Maria Cecília Rom?o Santos

Full-stack Software Engineer
举报内容
é crucial estar ciente das limita??es impostas pelo Global Interpreter Lock (GIL) do Python. Uma abordagem comum é usar o paralelismo baseado em processos em vez de threads. A biblioteca multiprocessing do Python é uma excelente alternativa, pois cada processo tem seu próprio GIL, permitindo execu??o real em paralelo. Outra considera??o importante é a sobrecarga de comunica??o entre processos ou threads. Dividir os dados e distribuir as tarefas deve ser feito de maneira a minimizar a necessidade de comunica??o entre as diferentes partes, para evitar que o tempo ganho no processamento paralelo seja perdido na coordena??o e transferência de dados.

已翻译

赞

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

添加您的观点

Sean Moch

Data Scientist, Problem Solver, Engineer
举报内容
Use pyarrow, pandas 3 can also now use this if it is detected. This makes handling and querying larger datasets more efficient. Also use latest m2 x4 ssd for data storage speed.

已翻译

赞

Data Engineering

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

查看全部

How do you handle large datasets in Python without sacrificing performance?

1

2

3

4

5

6

7

1 Optimize Memory

2 Streamline Data

3 Employ Dask

4 Utilize SQL Databases

5 Leverage Caching

6 Parallel Processing

7 Here’s what else to consider

Data Engineering

给文章评分

感谢您的反馈

更多Data Engineering相关文章

更多相关阅读内容

How do you handle large datasets in Python without sacrificing performance?

1

2

3

4

5

6

7

1 Optimize Memory

2 Streamline Data

3 Employ Dask

4 Utilize SQL Databases

5 Leverage Caching

6 Parallel Processing

7 Here’s what else to consider

Data Engineering

给文章评分

感谢您的反馈

查看其他技能