.Here are 3 More Cool Tools for PYTHON Fans:
1. Behold – The power of IDE in the palm of your hand…
Behold is a Visual Studio Code extension that brings IDE capabilities to Atom Editor or any other editor. It offers Intellisense, debugging and project support for Python code as well as many other features such as integration with matplotlib for plotting and MySQL management. It’s true that VSCode already has some Python plugins, but I usually prefer using Behold because it integrates better with MATLAB than those which come with the base installation. For example, if you use scikit-learn or TensorFlow and you try to run them from Command Prompt (or Terminal in the case of Linux), you’ll see an error message because there’s no compiler installed. That’s when you can install Behold and it will take care of installing compilers for you.
Behold – Visual Studio Code extension
2. Freqtrade – Awesome tool that helps you do statistical analysis on stock trade data in Python
I love working with stock market data in Python, especially in visualization libraries like pandas, plotly and seaborn. There are many cool things that one could do using visualizations, but today I’m here to introduce to another awesome library called free trade. It is a powerful statistical analysis package which helps users summarizes stock trade data. With free trade, it is very easy to calculate descriptive statistics, do hypothesis testing, compare multiple groups of data and understand the value of your portfolio. With free trade you can calculate things like daily, weekly or monthly mean returns for each stock in your portfolio. It is also possible to compare relative performance between stocks using t-tests or cross-sectional regressions.
It provides a unique interface where you can easily switch between “analysis mode” and “visualization mode”. So what it means is that if you have your trade data already loaded into Pandas Dataframe then you don’t need to do anything else other than pressing a button and it will output nice looking tabular information along with statistical analysis results. However, if you happen not to have any trade data yet you can simply press another button and it will output a nice looking chart/graph using matplotlib library.
So closing words, Freqtrade is really powerful, easy to use tool for doing statistical analysis on stock market trade data.
Freqtrade – Tool for Doing Statistical Analysis on Stock Market Data
3. NumPy 1.15 + MKL 2019 Update – The new version of numpy uses the highly optimized Intel Math Kernel Library (MKL)
The one other important update that came out recently is MKL2019 which makes Python super fast again. Some notable features are: Faster Binary Ufunc via AVX512BW Enabled Auto Parallelization Improved Recursive Quicksort Iterators Improved Scatter/gather performance with AVX512_VBMI Enabled
If you are at expert level in Python and know about the benefits of MKL, then I would recommend using MKL2019 because it will halve your running time. However, if you are new to Python or don’t have any idea how to set up MKL on your computer then I would recommend staying on the previous version.
MKL is an advanced library for high-performance computing that includes routines optimized specifically for Intel processors. There’s also one plugin called Speed Up My Code which automatically detects the best optimizer for your system configuration and tries various optimizations on your Python code to find out which one gives maximum speed up. So it’s great tool for testing whether MKL is working for you or not.
MKL – Highly Optimized Routine for High-Performance Computing In Python
4. bqplot – MATLAB inspired plotting tool in Jupyter Notebook
bqplot is a MATLAB inspired plotting library for IPython notebook, which provides an interactive environment with support for Excel like data selection and modification tools, fully customizable plots using HTML/CSS, a full suite of text rendering capabilities, support for many input types (including pandas DataFrames ), etc… It has following features: Modern GUI based on Qt5 Customizable Plots with CSS support (using Plotly) Line Charts Pie Charts Scatterplots Step Plots Utilities to make plots look more MATLAB like HTML rendering capability (PNG, SVG and PDF) Support for many input types (including pandas DataFrames )
I personally like bqplot because it follows the same philosophy that of pandas. It is an interactive tool which lets you get into your data without worrying about writing any functions or loops. I also feel this is a more modern and polished version of iPython notebook’s plotting capabilities.
Some time prior, I composed this article, and from that point forward, VS Code has extended a great deal. New augmentations are currently accessible on the store, and some others I found, a few ideas of individual perusers, while others were burrowing and examining all alone.
Here are more tools for PYTHON
As you might know, I love the Jetbrains group of items, so PyCharm and WebStorm are my go-to for IDEs and working with projects, however I’ve been utilizing VS Code significantly more of late for altering speedy documents or in any event, working in little tasks.
Testing: Run and troubleshoot tests through the Test Explorer with unittest, pytest, or nose
Jupyter Notebooks: Create and alter Jupyter Notebooks, add and run code cells, render plots, picture factors through the variable traveler, imagine dataframes with the information watcher, and then some
Conditions: Automatically enact and switch between virtualenv, venv, pipenv, conda, and pyenv conditions
Modes of Python
Refactoring: Restructure your Python code with variable extraction, strategy extraction, and import arranging
I feel more great now with the editorial manager, and a portion of the areas where it was behind PyCharm are currently improving all alone or with the assistance of modules. Specifically, there’s a new module I was as of late acquainted with, which changed the manner in which I use VS Code and the amount more agreeable I feel coding in it. Be that as it may, I would rather not kick off, so kindly completion the article to discover more with regards to it.
We should find my cherished VS Code expansions for Python, and if it’s not too much trouble, let note that they are not all together. They are largely great!
Assumption with the python
Assuming that you work with Python, you really want this expansion. I realize VS Code upholds Python out of the crate, however this expansion takes it to an unheard of level. It is to such an extent that VS Code will propose you introduce the expansion when you open a Python document.
The Python Test Explorer extension allows you to run your Python unittest or Pytest tests with the Test Explorer UI. This small and handy tool will enable you to test your code from VS Code’s comfort. With an excellent user interface and debugging capabilities.
We know the importance of unit testing so having a tool. It like this on your IDE or code editor is a must-have.
The augmentation is formally upheld by Microsoft, a similar organization behind VS Code. So it is actually an easy decision, yet how precisely will it help you? How about we investigate the main elements:
Recording is really fundamental, however it is a dreary errand, and at times we pursue faster routes. We come up short on devices to make it more straightforward or more effective. Python Docstring Generator reduces engineers’ undertaking via auto-making docstrings, and however it might sound minor, it is an efficient device. Maybe PyCharm is ruining me, however I was so accustomed.That observing this augmentation was no joking matter for me.
Steps regarding the python
IntelliSense: Edit your code with auto-finish, code route, sentence structure checking, and then some
Linting: Get extra code investigation with Pylint, Flake8, and that’s only the tip of the iceberg
Code designing: Format your code with dark, autopep or yapf
Investigating: Debug your Python scripts, web applications, remote or multi-strung cycles
Testing: Run and troubleshoot tests through the Test Explorer with unittest, pytest, or nose
Jupyter Notebooks: Create and alter Jupyter Notebooks, add and run code cells, render plots, picture factors. The variable traveler, imagine dataframes with the information watcher, and then some
Conditions: Automatically enact and switch between virtualenv, venv, pipenv, conda, and pyenv conditions
Refactoring: Restructure your Python code with variable extraction, strategy extraction, and import arranging
PYTHON
The best thing about this extension is that it follows all standard formats of docstring and that is cool. Moreover, this docstring generator supports args, kwargs, decorators, errors, and parameter types with multiline commenting features.
Just see it in action and be amazed:
Conclusion:
Here, in this blog post we talked about some of the most useful and advanced tools for data science. If you like to add any such tool that is missing in this list then please mentions. It down in comment section below; I will definitely consider sharing that with our readers next time.
