Best Python Libraries for Data Processing

Data processing services are available in various encodings, including CSV, XML, HTML, SQL, and JSON. Each situation requires a unique treatment format. There are many programming languages. Python is frequently recommended as a viable alternative for machine learning applications due to its implementation of major libraries and advanced technologies. Machine learning relies on data processing, and the success of the model is highly dependent on the ability to read and transform the data into the format required for the task at hand. Let’s look at the different Python libraries in terms of the data types they provide.

Below, we’ve covered the Python libraries used to process different types of data:

Tabular data

Most big data is available in table form, with rows referring to records and columns corresponding to entities. Pandas in Python can handle this type data very well. The advent of tabular data has evolved into a comprehensive library that can handle both series and tabular data.

Text data

First, it’s worth noting Python’s extensive built-in word processing capabilities. However, many natural language processing techniques, such as tokenization and lemmatization, can be performed using NLTK. As well as, Spacious is a good choice for advanced natural language processing and optimized pipelines.

Audio and music data

Audio processing is enabled through libraries such as librosa and essentia. mido And pretty midday are good choices for symbolic music, such as MIDI. Finally, the music21 is a sophisticated library for musicological analysis.


Pillow is an image processing library in Python. Opencv is a computer vision library that can process video or camera data. Due to its wide range of supported formats, imageio can give image data to python script.

Python, in particular, is a popular data processing language for a variety of reasons, including:

  • Prototyping and experimenting with code is incredibly simple. Processing data, especially from less than clean sources, requires a lot of tweaking, back and forth, and a struggle to capture all the options.
  • Python3 greatly improved multilingual support by making every string in the system UTF-8, which allows processing of data encoded in different character sets by different languages.
  • The standard library is quite powerful and contains essential modules that provide native support for common file types such as CSV files, zip files, and databases.
  • The third-party Python library is huge and contains a host of excellent modules that allow it to augment a program’s capabilities. There are also modules for analyzing geospatial data, building CLIs, GUIs, data analysis, and everything in between.
  • Jupyter Notebooks lets you run code and receive immediate feedback. Python is fairly agnostic about the required development environment, allowing it to work with anything from a simple text editor to more complex alternatives such as Visual Studio.


In general, Python and R programming are two widely used data processing languages. JavaScript, like Python, has a thriving ecosystem. Julia is also present. Almost all modern languages ​​are capable of analyzing data. However, the capacity varies depending on the lens. While R has the best statistical analysis features of any package, Python meets the needs of the vast majority of analysts and is rapidly gaining popularity. It’s best to start with Excel, SQL, and basic programming concepts, then move on to a more popular language and master it. After that, take a step back and apply the principles to real situations. To sum up, familiarize yourself with R if conceptual understanding and application are crucial during this time. If large-scale data analysis is required, it is recommended to familiarize yourself with Python’s Big Data features.

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