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Issue - 165

Worthy Read

Various topics of retrospective on Python 3. What is working, what failed, what is still a work in progress as the language evolves?

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This post is filled with examples ranging from a simple Dockerfile to multistage production builds for Python apps.

Let’s review the many ways to do the most common operations over dataframe columns using pandas.

You already know the longer it takes to detect a problem, the more expensive it is to resolve. Your testing needs to happen earlier in the development pipeline while taking into account all aspects of privacy, security and monitoring.

Here at Kalo, we’ve been exploring ways of upgrading some of our API’s and found that concrete examples for developers looking to create GraphQL servers Python were lacking, so we decided to build a small project as an experiment to compare the two paradigms and share the knowledge by writing about it.

Reinforcement learning is an area of machine learning dealing with delayed reward.
machine learning

Data Visualization is a big part of a data scientist’s jobs. In the early stages of a project, you’ll often be doing an Exploratory Data Analysis (EDA) to gain some insights into your data. Creating visualizations really helps make things clearer and easier to understand, especially with larger, high dimensional datasets. Towards the end of your project, it’s important to be able to present your final results in a clear, concise, and compelling manner that your audience, whom are often non-technical clients, can understand.

Once, I was talking with my colleague about outdoor activities, and he told me that he is a fishing enthusiast. It didn’t bring up my attention at first since I am not a fishing guy. However, he propose a idea to use Google Map to search for all those fishing sites due to the hard time of playing around with the official fishing website. The idea is brilliant and it attract me as well. In the end, that’s how we start our first project at Weclouddata.

Reddit’s r/algotrading seems to have a constant number of posts which revolve about the ideas: How can I trade with Python (using frameworks or not) and Interactive Brokers? Let’s try to see how to achieve it with backtrader.
algo trading

Apple released a few weeks ago, Turicreate, an open source framework to create easily model for CoreML. In this tutorial, you don’t need Apple device. This recommender can be used only with python. And later used on Apple device after using converter tool.
machine learning

An open-source framework for real-time anomaly detection using Python, Elasticsearch and Kibana.

How to use simple Python libraries and built-in capabilities to scrape the web for movie information and store them in a local SQLite database.

How to pass data between Google Sheets and Jupyter Notebooks.

Part of being a developer includes setting up your computer so that you can develop the applications you want to write.

Multiple libraries are available to perform basic EDA but I am going to use pandas and matplotlib for this post. Pandas for data manipulation and matplotlib, well, for plotting graphs. Jupyter Nootbooks to write code and other findings. Jupyter notebooks is kind of diary for data analysis and scientists, a web based platform where you can mix Python, html and Markdown to explain your data insights.

This talk introduces the open source Python database module turbodbc. It uses standard ODBC drivers to connect with virtually any database and is a viable (and often faster) alternative to “native” Python drivers.
database access


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Aerie: a regex-like engine nested word grammars.

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best practice, zero config python logging.

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Disorganized collection of useful functions for working with audio and images, especially in the context of machine learning.