Articles on Python

Market basket analysis determines what products are purchased together. An approach using Python and Pandas to perform this retail analysis.

If you use pandas in your data analysis, you can now use modin to speed it up. In this post, I do a test to find the savings from modin and pandas.

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

A quick tip on Comparing two pandas dataframes and getting the differences in python pandas.

Using LIME (Local Interpretable Model-agnostic Explanations) in Python to provide visual explanations of your classification and regression models.

Using basic Text Analytics and Visualization techniques, keywords can be automatically extracted from text and relationships can be visualized.

Text Analytics with Python by Dipanjan Sarkar provides an overview of how to use Python to perform text analytics / natural language processing.

Using Python and AWS Lambda, I've been able to offload a number of python scripts (set up as API endpoints on AWS) to allow more flexibility and save money.

In this article I look at stock market forecasting with prophet and compare a few errors measures to see how well prophet can forecast the market.

Prophet does a very good job of detecting trend changepoints. In this post, I provide an example where I look at the capabilities of Prophet.

This third installment of Forecasting time series data with prophet provides an example using holidays to try to improve the model.

An overview of using Prophet for forecasting time series data with a link to jupyter notebooks.

In this article, I provide a few tips to make a bit more realistic and useful visualizations from Facebook's Prophet for forecasting time-series library.

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

Forecasting time-series data with Prophet. Prophet is a fairly new library for python and R to help with forecasting time-series data.

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

I use jupyter with vagrant to do 99.% of all my python development. This post explains how easy it is to set up jupyter with vagrant.

Stockstats currently has about 26 stats and stock market indicators included. Definitely not as robust as TA-Lib, but it does have the basics.

Running vagrant on windows allows python developers to run *nix in in a VM for python development, which might help with some Windows python issues.

yhat just released a pandas cheat sheet. I also provided some tip on functions that I use all the time with pandas.

A quick tutorial and code to show how to get the 'next' row of data for a pandas dataframe.

Using dask, you can easily work with large data sets including large CSV files without loading the data into memory via out-of-core computations.

When working wth large CSV files in Python, you can sometimes run into memory issue. Using pandas and sqllite can help you work around these limitations.

Need help installing python on OSX? This post provides some information and a walk-through of manually installing python on your mac.

Need help Installing python on Windows? This post can help you get started. In this post I show how to install Canopy on Windows.

Why Python became my go-to tool for data analytics over R and Excel, plus the modules that make it possible.