Anaconda & Jupyter
There are many tools to analyze data, R, Excel and Python. In this article I want to speak about 2 Python tools: Anaconda and Jupyter.
Anaconda is a Python distribution that includes nearly 200 packages, which comprises NumPy, SciPy, Pandas, Jupyter, Matplotlib, Scikit-learn. It’s a cross-platform distribution (Windows, Linux, and Mac OS X) that can be installed on machines with other existing Python distributions and versions. Its base version is free.
The open-source Anaconda Distribution is the easiest way to perform Python/R data science and machine learning. Anaconda enables individual data scientists to analyze data with scalability and performance with NumPy, Pandas and visualize results with Matplotlib.
Jupyter is a tool that allows data scientists to record their complete analysis process, much in the same way other scientists use a lab notebook to record tests, progress, results, and conclusions.
The Jupyter product was originally developed as part of the IPython project. The IPython project was used to provide interactive online access to Python. Over time it became useful to interact with other data analysis tools, such as R, in the same manner.
Jupyter can simply be described as a tool for interactive tasks operable by a console or by a web-based notebook, which offers special commands that help developers to better understand and build the code that is being currently written.
Contrary to an IDE—which is built around the idea of writing a script, running it afterwards, and finally evaluating its results—Jupyter lets you write your code in chunks, named cells, run each of them sequentially, and evaluate the results of each one separately, examining both textual and graphic outputs.
Jupyter works on your favorite browser (which could be Explorer, Firefox, or Chrome, for instance) and, when started, presents a cell waiting for code to be written in. Each block of code enclosed in a cell can be run and its results are reported in the space just after the cell.
Jupyter magic commands
As a special tool for interactive tasks, Jupyter offers special commands that help to better understand the code that you are currently writing. Below some examples of magic comands:
You can get the full list of Magic Commands typing %quickref on Jupyter.
Cool !