Posted on

My Journey With Python (Part I)

I’ve been working with Java for about 14 years now, and the last two years I’ve also worked with Node.js and Golang to build some middleware AI platforms at my startup. Nevertheless, I often push myself in learning other technologies as well. I’ve been learning Python these past few weeks. I was inspired by its concise syntax and the prominent feature that supports almost anything. You can use Python for any GUI and/or web development, operating system shell, data processing and analysis (data science), etc. Someone said that you can build the world with Python. Mmmmm, sounds interesting.

Since I believe that the best way to grasp the knowledge is to write what I’ve learned, I’m gonna try here to write everything I know so far about Python. I have read different books and websites, so here I’m trying to summarize within six parts everything I gotten from these different resources. Hopefully, you'll find this article useful, especially for those of you who are trying to learn Python in a crash course.

Posted on

Comparison of Data Analysis Tools: Excel, R, Python, and BI Tools

The era of data analysis has already arrived. From the state, government, and enterprises to individuals, big data and data analysis have become trends that everyone is familiar with. But you may not have the professional knowledge of data analysis and programming, or you have learned a lot about the theory of data analysis, but you still can't practice it. Here, I will compare the four tools that are most popular with data analysts, Excel, R, Python, and BI, as the basis for getting started with data analysis.

Data analytics tools

1. Excel

1.1 Usage Scenarios

  • Data processing work under general office requirements.
  • Data management and storage of small and medium-sized companies.
  • Simple statistical analysis for students or teachers (such as analysis of variance, regression analysis, etc.).
  • Combine Word and PowerPoint to create data analysis reports.
  • Assistant tool of data analysts.
  • Production of charts for some business magazines and newspapers (data visualization).

1.2 Advantages

  • It's easy to get started with Excel.
  • The learning resources are very rich.
  • You can do a lot of things with Excel: modeling, visualization, reports, dynamic charts, etc.
  • It can help you understand the meaning of many operations before further learning other tools (such as Python and R).

1.3 Disadvantages

  • To fully master Excel, you need to learn VBA, so the difficulty is still very high.
  • When the amount of data is large, there will be a situation of stuttering.
  • The Excel data file itself can hold only 1.08 million rows without the aid of other tools, and it's not suitable for processing large-scale data sets.
  • The built-in statistical analysis is too simple and has little practical value.
  • Unlike Python, R, and other open source software, there is a charge for the genuine Excel.

2. R

2.1 Usage Scenarios

The functions of R cover almost any area where data is needed. As far as our general data analysis or academic data analysis work is concerned, the things that R can do mainly include the following aspects.

Posted on

10 Programming Languages You Should Know in 2019

Who says your cat can't help you learn new programming languages?

A couple of days ago, I was reading an interesting article on HackerNews, which argued that you should learn numerous programming languages even if you won't immediately use them, and I have to say that I agree. Since each programming language is good for something specific but not so great for others, it makes sense to know more than one language so you can choose the right tool for the job.

But which languages should you learn? Which languages will give you the biggest bang for your buck?

Posted on

PyMongo Tutorial: Testing MongoDB Failover in Your Python App

Python is a powerful and flexible programming language used by millions of developers around the world to build their applications. It comes as no surprise that Python developers commonly leverage MongoDB hosting, the most popular NoSQL database, for their deployments due to its flexible nature and lack of schema requirements.

So, what’s the best way to use MongoDB with Python? PyMongo is a Python distribution containing tools for working with MongoDB, and the recommended Python MongoDB driver. It is a fairly mature driver that supports most of the common operations with the database, and you can check out this tutorial for an introduction to the PyMongo driver.

Posted on

Learn TensorFlow: Linear Regression

Introduction to Linear Regression

An important algorithm of supervised learning is linear regression. In this article, I am going to re-use the following notations that I have referred from [1] (in the References section).

  • xi denotes the “input” variables, also called input features
  • yi denotes the “ouput” or target variable that we are trying to predict
  • A pair (xi, yi) is called a training example
  • A list of m training examples {xi, yi; i = 1,…,m} is called a training set
  • The superscript “i” in the notation is an index into the training set
  • X denotes the space of input values and Y denotes the space of output values. In this article, I am going to assume that X = Y = R
  • A function h: X -> Y, where h(x) is a good predictor for the corresponding value of y, is called a hypothesis or a model

When the target variable that we are trying to predict is continuous, we call the learning problem a regression problem. When y takes on only a small number of discrete values, we call it a classification problem.

Posted on

Enriching GeoJSON Data to Render a Map of Smart City IoT Sensors

The City of San Diego deployed the world’s largest smart city platform where thousands of streetlights around the city have been equipped with IoT sensors. These sensors collect metadata that can be fetched from a set of public access web services providing traffic, pedestrian flow, parking, and environment data.

It can be hard to get your head around how immense this network is until you look at it on a map. Let’s look at how to do that, first fetching and scrubbing the data with Python and then using JavaScript, Tangram, and HERE XYZ to visualize the results.

Posted on

A Tutorial Introduction to Privacy-Centric Deep Learning in PyTorch

Secure multi-party computation and homomorphic encryption add computational overhead, but the results are well worth it! Data privacy and model parameter security are mutually protected with clever encryption schemes.

The Importance of Data

As machine learning continues to impact our society in massive ways it’s often the models that make the headlines, but ask any data scientist or machine learning engineer about what they spend the majority of their time on, and the answer is likely to be the data.

Posted on

Useful Programming Libraries to Create Your Own Cryptocurrency Wallet

Since 2009, and especially over the past few years, cryptocurrency development has become a skill in high demand. Whether you are looking to create a new currency or contribute to an existing project, creating a new user-friendly wallet is a necessary part of the process. This includes creating a front-end and a back-end to the client.

For inspiration, you should take a look at the source code of some of the top cryptocurrency wallets. If you wish to compete with what’s on the market, features like offline cold storage, paper wallet generation, or asset management would be well received.

Posted on

A Bootiful Podcast: Data Sovereignty, Microservices, Cloud Foundry, and More

Hi Spring fans! In this installment of a Bootiful Podcast, I, Josh Long, welcome Josh Mckenty - or “better Josh,” as I affectionately call him - to the show to discuss Pivotal, Cloud Foundry, Python, microservices, and data sovereignty, among other things.


Posted on

Typing Resistance

I assure you, this is a post about programming, it’ll just take a few paragraphs to get there.

There’s a biological mechanism known as resistance, and it plays out in many different systems. For example, as you habitually drink more alcohol, you gain a tolerance, which prevents you from getting drunk as easily. This can be called alcohol resistance. When you habitually run high levels of insulin, your body becomes less sensitive to insulin, making you insulin resistant. When you go into a loud room, your ears adjust down the sound, making you noise resistant. And when you change enough dirty diapers, you become smell resistant.

Posted on

Zato: A Successful Python 3 Migration Story

Now that Python 3 support is available as a preview for developers, this post summarizes the effort that went into making sure that Zato works smoothly using both Python 2.7 and 3.x.

In fact, the works required were remarkably straightforward and trouble-free, and the article discusses the thought process behind it, some of the techniques applied, and tools used.

Posted on

Tornado Web Server on Elastic Beanstalk

Using Tornado Web server for Python on Elastic Beanstalk isn't quite straightforward. There isn't much information on the internet, either. The objective of this article is to be a guide and reference.

Tornado Web Server uses non-blocking IO to support thousands of connections in parallel. It is different from most Python web frameworks. It is not based on WSGI, and it is typically run with only one thread per process. Tornado is integrated with the standard library asyncio module and shares the same event loop (by default since Tornado 5.0). In general, libraries designed for use with asyncio can be mixed freely with Tornado.