Python Programming-Environment


Python is one of the famous programming languages and it is so common for Machine Learning.

It is a multi-purpose language that has been leveraged with the aim of device programming, object-oriented programming, machine learning and so forth.

In this post, I will introduce some of the common Python IDE programming environment for the aim of machine learning and also how to install some important packages.

Python IDE

There are different IDEs that you can use. According to DataCamp website, there is 5 top environment for writing code, among them, I start to work with two of them.

Spyder: the environment is so similar to Rstuido, so if you switching from R to Python, it is a really good choice. It will be supported in Windows and MacOS. and it can be download from Anaconda website.


Rodeo, Is another Python IDE that is so similar to RStudio. you can download it from

PyCharm, is another IDE for writing Python codes. Azure ML workbench current versions support this IDE.


Jupyter, is another IDE for writing Python code, Jupyter can be accessed via browser or via a desktop version.



Python Shell, However, when you download the Python a simple IDE as Python Shell also will be downloaded that is much easier to use. This IDE is not user-friendly as the other one.


How to start

To work with Python, there is a need to get familiar with the basic language. The first step is to install some required packages.

Hence, you need to install packages such as:

  • scipy
  • numpy
  • matplotlib
  • pandas
  • sklearn

To install these packages, you need to run the below code on the Rodeo.

In the next posts, I will explained how to write simple Python code.

Your First Machine Learning Project in Python Step-By-Step

Leila Etaati
Dr. Leila Etaati is Principal Data Scientist, BI Consultant, and Speaker. She has over 10 years’ experience working with databases and software systems. She was involved in many large-scale projects for big sized companies. Leila has PhD of Information System department, University of Auckland, MS and BS in computer science. Leila is Microsoft Data Platform MVP.

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