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This article mainly explains "what are the Python data conversion tools for ETL". The content of the article is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought to study and learn what are the Python data conversion tools for ETL.

Pandas

Website: https://pandas.pydata.org/

Overview

Pandas certainly doesn't need an introduction, but I'll give it an introduction anyway.

Pandas adds the concept of DataFrame to Python and is widely used to analyze and clean up datasets in the data science community. It is very useful as an ETL transformation tool because it makes it very easy and intuitive to manipulate data.

Advantages

Widely used in data processing

Simple and intuitive syntax

Good integration with other Python tools, including visualization libraries

Support common data formats (read from SQL databases, CSV files, etc.)

Shortcoming

Because it loads all data into memory, it cannot be extended and may be a wrong choice for very large (larger than memory) datasets

Further reading

10 minutes Pandas

Data processing of Pandas Machine Learning

Dask

Website: https://dask.org/

Overview

According to their website, "Dask is a flexible library for Python parallel computing."

In essence, Dask extends generic interfaces such as Pandas for use in distributed environments-for example, Dask DataFrame mimics.

Advantages

Scalability-Dask can be run on the local computer and extended to the cluster

Ability to handle out-of-memory datasets

Using the same features can improve performance even on the same hardware (due to parallel computing)

Can be switched from Pandas with minimal code changes

Designed to integrate with other Python libraries

Shortcoming

In addition to parallelism, there are other ways to improve the performance of Pandas (usually more significantly)

If the amount of calculation you do is very small, it won't do you any good.

Some functions are not implemented in Dask DataFrame

Further reading

Dask document

Why should every data scientist use Dask

Modin

Website: https://github.com/modin-project/modin

Overview

Modin is similar to Dask in that it attempts to improve the efficiency of Pandas by using parallelism and enabling distributed DataFrames. Unlike Dask, Modin is based on Ray (Task parallel execution Framework).

The main advantage of Modin over Dask is that Modin can automatically handle the distribution of data across the computer core (without configuration).

Advantages

Scalability-Ray offers more than Modin

The exact same functionality (even on the same hardware) can improve performance

You can switch from Pandas with minimal code changes (change import statements)

Provide all Pandas features-more "embedded" solutions than Dask

Shortcoming

In addition to parallelism, there are other ways to improve the performance of Pandas (usually more significantly)

If the amount of calculation you do is very small, it won't do you any good.

Further reading

Modin document

What's the difference between Dask and Modin?

Petl

Website: https://petl.readthedocs.io/en/stable/

Overview

Petl contains many of the features of pandas, but is designed specifically for ETL, so there is a lack of additional functionality, such as one for analysis. Petl has tools for all three parts of ETL, but this article focuses only on data transformation.

Although petl provides the ability to convert tables, other tools, such as pandas, seem to be more widely used for transformations and documented documents, so petl is less attractive.

Advantages

Minimize the use of system memory so that it can scale to millions of lines

Useful for migrating between SQL databases

Lightweight and efficient

Shortcoming

By greatly reducing the use of system memory, the execution of petl becomes slower-not recommended in applications where performance is important

Less use of other solutions in this list for data processing

Further reading

Use Petl to quickly understand data transformation and migration

Petl conversion document

PySpark

Website: http://spark.apache.org/

Overview

Spark is designed to process and analyze big data and provides API in multiple languages. The main advantage of using Spark is that Spark DataFrames uses distributed memory and takes advantage of delayed execution, so they can use clustering to handle larger datasets, while tools such as Pandas cannot.

If the data to be processed is very large, and the speed and size of data manipulation is large, Spark is an ideal choice for ETL.

Advantages

Scalability and support for larger datasets

As far as syntax is concerned, Spark DataFrames is very similar to Pandas

Query using SQL syntax through Spark SQL

Compatible with other popular ETL tools, including Pandas (you can actually convert Spark DataFrame to Pandas DataFrame so that you can use a variety of other libraries)

Compatible with Jupyter laptop

Built-in support for SQL, streaming and graphics processing

Shortcoming

Requires a distributed file system, such as S3

The use of data formats such as CSV limits delayed execution and requires data conversion to other formats such as Parquet

Lack of direct support for data visualization tools such as Matplotlib and Seaborn, both of which are well supported by Pandas

Further reading

Apache Spark in Python: a beginner's Guide

Introduction to PySpark

PySpark documents (especially syntax)

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