I ran across a neat little library called Modin recently that claims to run pandas faster. The one line sentence that they use to describe the project is: Speed up your Pandas workflows by changing a single line of code Interesting…and important if true. Using modin only requires importing modin instead of pandas and thats […]
Author: Python Data
Quick Tip: Consuming Google Search results to use for web scraping
While working on a project recently, I needed to grab some google search results for specific search phrases and then scrape the content from the page results. For example, when searching for a Sony 16-35mm f2.8 GM lens on google, I wanted to grab some content (reviews, text, etc) from the results. While this isn’t hard […]
Quick Tip: Comparing two pandas dataframes and getting the differences
There are times when working with different pandas dataframes that you might need to get the data that is ‘different’ between the two dataframes (i.e.,g Comparing two pandas dataframes and getting the differences). This seems like a straightforward issue, but apparently its still a popular ‘question’ for many people and is my most popular question […]
Local Interpretable Model-agnostic Explanations – LIME in Python
When working with classification and/or regression techniques, its always good to have the ability to ‘explain’ what your model is doing. Using Local Interpretable Model-agnostic Explanations (LIME), you now have the ability to quickly provide visual explanations of your model(s). Its quite easy to throw numbers or content into an algorithm and get a result […]
Text Analytics and Visualization
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 […]
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 […]