For this post, I want to describe a text analytics and visualization technique using a basic keyword extraction mechanism using nothing but a word counter to find the top 3 keywords from a corpus of articles that I’ve created from my blog at http://ericbrown.com. To create this corpus, I downloaded all of my blog posts […]

# Author: Python Data

## Text Analytics with Python – A book review

This is a book review of Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from your Data by Dipanjan Sarkar One of my go-to books for natural language processing with Python has been Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit by Steven Bird, Ewan Klein, and Edward Loper. This […]

## Python and AWS Lambda – A match made in heaven

In recent months, I’ve begun moving some of my analytics functions to the cloud. Specifically, I’ve been moving them many of my python scripts and API’s to AWS’ Lambda platform using the Zappa framework. In this post, I’ll share some basic information about Python and AWS Lambda…hopefully it will get everyone out there thinking about new ways […]

## Stock market forecasting with prophet

In a previous post, I used stock market data to show how prophet detects changepoints in a signal (http://pythondata.com/forecasting-time-series-data-prophet-trend-changepoints/). After publishing that article, I’ve received a few questions asking how well (or poorly) prophet can forecast the stock market so I wanted to provide a quick write-up to look at stock market forecasting with prophet. This […]

## Forecasting Time Series data with Prophet – Trend Changepoints

In these posts, I’ve been looking at using Prophet to forecast time series data at a monthly level using sales revenue data. In this post, I want to look at a very interesting aspect of Prophet (and time series analysis) that most people overlook – that of trend changepoints. This is the fourth in a […]

## Forecasting Time Series data with Prophet – Part 3

This is the third in a series of posts about using Prophet to forecast time series data. The other parts can be found here: Forecasting Time Series data with Prophet – Part 1 Forecasting Time Series data with Prophet – Part 2 In those previous posts, I looked at forecasting monthly sales data 24 months […]

## Forecasting Time Series data with Prophet – Jupyter Notebook

In previous posts, I described how I use Prophet to forecast time series data. There were some questions in the comments about the code not working, so I wanted to publish a new post with a link to a Jupyter Notebook that will hopefully provide a full, correct working example. The original posts are: Forecasting […]

## Forecasting Time-Series data with Prophet – Part 2

In Forecasting Time-Series data with Prophet – Part 1, I introduced Facebook’s Prophet library for time-series forecasting. In this article, I wanted to take some time to share how I work with the data after the forecasts. Specifically, I wanted to share some tips on how I visualize the Prophet forecasts using matplotlib rather than […]

## Visualizing data – overlaying charts in python

Visualizing data is vital to analyzing data. If you can’t see your data – and see it in multiple ways – you’ll have a hard time analyzing that data. There are quite a few ways to visualize data and, thankfully, with pandas, matplotlib and/or seaborn, you can make some pretty powerful visualizations during analysis. One of […]

## Forecasting Time-Series data with Prophet – Part 1

This is part 1 of a series where I look at using Prophet for Time-Series forecasting in Python A lot of what I do in my data analytics work is understanding time series data, modeling that data and trying to forecast what might come next in that data. Over the years I’ve used many different […]

## Collecting / Storing Tweets with Python and MySQL

A few days ago, I published Collecting / Storing Tweets with Python and MongoDB. In that post, I describe the steps needed to collect and store tweets gathered via the Twitter Streaming API. I received a comment on that post asking how to store data into MySQL instead of MongoDB. Here’s what you’d need to do […]

## Jupyter with Vagrant

I’ve written about using vagrant for 99.9% of my python work on here before (see here and here for examples). In addition to vagrant, I use jupyter notebooks on 99.9% of the work that I do, so I figured I’d spend a little time describing how I use jupyter with vagrant. First off, you’ll […]

## Stockstats – Python module for various stock market indicators

I’m always working with stock market data and stock market indicators. During this work, there’s times that I need to calculate things like Relative Strength Index (RSI), Average True Range (ATR), Commodity Channel Index (CCI) and other various indicators and stats. My go-to for this type of work is TA-Lib and the python wrapper for […]

## Vagrant on Windows

There are many different ways to install python and work with python on Windows. You can install Canopy or Anaconda to have an entire python ecosystem self-contained or you can install python directly onto your machine and configure all the bits and bytes yourself. My current recommendation is to use Vagrant on Windows combined with Virtualbox to […]

## pandas Cheat Sheet (via yhat)

The folks over at yhat just released a cheat sheet for pandas. You can download the cheat sheet in PDF for here. There’s a couple important functions that I use all the time missing from their cheat sheet (actually….there are a lot of things missing, but its a great starter cheat sheet). A few things […]