Tag Archives: Python

Building a Data Pipeline in Python – Part 2 of N – Data Exploration

Initial data acquisition and data analysis

In order to get an idea of what our data looks like, we need to look at it! The Jupyter Notebook, embedded below, will show steps to load your data into Python and find some basic statistics to use them to identify potentially issues with new data that arrives.

This process is simply the exploratory step, we will build part of the pipeline in the next step. It’s imporant to have notebooks involved once in a while in order to make sure we know what we’re looking at.

Keep in mind, this is the first look at the data and we’re checking out some very basic testing. These tests will become more robust and meaningful as we continue to build out this pipeline.

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ETL – Building a Data Pipeline With Python – Introduction – Part 1 of N

ETL (Extract, Transform, Load) is not always the favorite part of a data scientist’s job but it’s an absolute necessity in the real world. If you don’t understand this process, you will have a basic grasp on it by the time you’re done with these lessons. I will be covering:

  • Data exploration
    • Understanding your data
    • Looking for red flags
    • Utilizing both statistics and data visualization
  • Checking your data for issues
    • Identifying things outside of the “normal” range
    • Deciding what to do with NaN or missing values
    • Discovering data with the wrong data type
  • How to clean and transform your data
    • Utilize the pandas library
    • Utilize pyjanitor
    • Getting data into tidy format
  • Dealing with your database
    • Determining whether or not you actually need a database
    • Choosing the right database
      • Deciding between relational and NoSQL
    • Basic schema design and normalization
    • Using an ORM – SQLAlchemy to insert data
  • Building a data pipeline
    • Separate your ETL into parts
    • Utilize luigi to keep you on track
    • Error montitoring

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100 Days of Code – What Does it Look Like at Day 11

Stoltzmaniac Fans – It’s time for a #100DaysOfCode update.

I have completed 11 days of the challenge. Let me tell you, it has been a blast and I have already learned a lot. In this post I’ll walk you through what I’ve done thus far. Here is a link to the code on my GitHub repository.  

As you may recall from my previous post I set out to create a flask application to host data science projects for the Meetup group that I organize (Fort Collins Data Science Meetup). My goal is to provide people with an outlet to run code online where they will get the benefits of having a server and a dynamic UI. This will improve the group’s collaboration and Git skills along with allowing people to showcase their work without having to build infrastructure. In case you’re wondering, I built this using Docker Compose, Flask, NGINX, PostgreSQL, and MongoDB.

In order to keep from boring myself to sleep while writing this, I’m going to keep it short and to the point. You might be asking, “what does this application look like?” That’s a great question. It’s a normal website where people contribute Python scripts to do some sort of data processing or analysis. For example, here’s a word cloud generator where the user inserts a Twitter handle with a link to a logo of some sort and then a word cloud is created from all of the most recent tweets! Here is @realdonaldtrump as the Republican elephant and @barackobama as the Democrat donkey.

 

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Exploratory Analysis – When to Choose R, Python, Tableau or a Combination

Not all data analysis tools are created equal.

Recently, I started looking into data sets to compete in Go Code Colorado (check it out if you live in CO). The problem with such diversity in data sets is finding a way to quickly visualize the data and do exploratory analysis. While tools like Tableau make data visualization extremely easy, the data isn’t always properly formatted to be easily consumed. Here’s are a few tips to help speed up your exploratory data analysis!

We’ll use data from two sources to aid with this example:

Picking the right tool

Always be able to answer the following before choosing a tool:

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George Washington as a Constitutional Word Cloud

Is George Washington better looking on the dollar bill or represented by a word cloud built with the text of The Constitution of the USA?

A colleague recently asked me that exact question. If you want to be taken seriously in the data science world, you better be able to answer something like this!

I decided that it would be fun to show off a Python package by Andreas Mueller called word_cloud (here) to make a fun image with the text of the Constitution and an image of one of the Founding Fathers.

I must warn you, word clouds are like pie charts people like the way they look but clouds don’t provide much information. That said, this package is really neat because it allows you to easily turn text into images utilizing masks, colors, and numpy!

I’ll keep this post short, what you want to do is simple:

  1. Select an image which you would like to mimic in both color and shape
  2. Read your image into Python using numpy
  3. Read your text into Python using open() and read()
  4. Make your word cloud!

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