Why You Should Use Python For Your Next Project

1

SciPy

Python package manager (pip) consists of over 300,000 packages. You can use libraries like ​​SciPy, which contains various modules for optimization, linear algebra, integration, and statistics, or Plotly, which you can use for scientific-quality graphing. The scope of problems that Python libraries address is vast. Rarely will you struggle to find the right library that will help you solve the problem.

#Data Science And Machine Learning #Python Tools #Software Libraries 16 social mentions

2

Plotly

Python package manager (pip) consists of over 300,000 packages. You can use libraries like ​​SciPy, which contains various modules for optimization, linear algebra, integration, and statistics, or Plotly, which you can use for scientific-quality graphing. The scope of problems that Python libraries address is vast. Rarely will you struggle to find the right library that will help you solve the problem.

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3

Pandas

If you are interested in tech, you probably noticed that people in the field like to call data the new oil. That would make data scientists, data engineers, and other data personas the oil magnates of the future, and they in turn rely on Python. These people spend most of their time preparing and working with data with the help of tools like Pandas, SciPy, TensorFlow, etc.

#Data Science And Machine Learning #Data Science Tools #Python Tools 196 social mentions

4

NumPy

Still, it is worth mentioning that some libraries like NumPy, which is a library for scientific computing with Python, have some parts written in C, making it fast. It is always better to do the heavy computation in low-level languages such as C or C++, but their syntax and complexity might not be suitable for large projects. Therefore, some parts of Python libraries are written in C/C++ to make them faster and much more straightforward for data scientists, data analysts, and others. If you are looking for high performance in your code, consider Cython, which gives you C-like performance with code mostly written in Python.

#Data Science And Machine Learning #Data Science Tools #Python Tools 107 social mentions

5

Jupyter

Notebooks, such as Jupyter or Google Colab, allow you to create and share documents that contain code, equations, and for example, visualizations. What is super useful is that these documents are human-readable and allow you to execute code to see the results immediately. These documents are a powerful tool for scientists and data analysts.

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6

FastAPI

Python is a high-level, dynamically typed programming language that focuses on rapid and robust development. It can be used in many areas, from web scraping to writing algorithms and data structures. Thanks to its versatility and simplicity, it has become a trendy language for data scientists in the last few years. Also, web developers are increasingly using Python thanks to frameworks such as Django, Flask, or FastAPI. World’s largest companies, such as Google, Facebook, Microsoft, and Spotify rely on Python every day, ensuring a lively community and a plethora of libraries for you to use for years to come.

#Developer Tools #API Tools #Python Programming 232 social mentions

7

Cython

Still, it is worth mentioning that some libraries like NumPy, which is a library for scientific computing with Python, have some parts written in C, making it fast. It is always better to do the heavy computation in low-level languages such as C or C++, but their syntax and complexity might not be suitable for large projects. Therefore, some parts of Python libraries are written in C/C++ to make them faster and much more straightforward for data scientists, data analysts, and others. If you are looking for high performance in your code, consider Cython, which gives you C-like performance with code mostly written in Python.

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8

Colaboratory

Notebooks, such as Jupyter or Google Colab, allow you to create and share documents that contain code, equations, and for example, visualizations. What is super useful is that these documents are human-readable and allow you to execute code to see the results immediately. These documents are a powerful tool for scientists and data analysts.

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9

BeeWare

Python is great as a server-side language, but it is not very suitable for mobile application development. Android and iOS applications were simply not meant for Python developers. Libraries like Kivy and Beeware are helping make Python more mobile friendly in recent years, but they cannot compete with Kotlin, Java, or Swift in mobile development.

#Rapid Application Development #IDE #Development Tools 28 social mentions