GeorgeMcIntire

@GeorgeMcIntire·10 public vizzes

Stacked Bar Chart w/ CSV- V4- Fully Commented

This stacked bar chart visualizes the distribution of eight age groups across five ethnic categories, with each bar segment colored by age range. The chart loads data from a CSV file using the `d3.csv` method, then applies `d3.stack` to compute series coordinates and `d3.scaleBand`, `d3.scaleLinear`, and `d3.scaleOrdinal` for the x-axis, y-axis, and color encoding, respectively. Rendered as SVG, the chart sorts categories by total population and uses a categorical color scheme.

Oct 4, 2018

Loading thumbnail…

Gist d003ece0b6ca171be8e095e0bc90af7e

This notebook example demonstrates how to programmatically scrape, parse, and structure a corpus of New York Times articles into a pandas DataFrame. The code loads a pre-saved pickle file of 50 article links, iterates through each URL using the `newspaper` library's `Article` class, and extracts the article’s text, title, publication date, keywords, and summary. It then stores the results in a list of dictionaries and converts that list into a structured DataFrame. The script includes error handling for unparseable articles, inserting `NaN` values, and prints the total execution time, which took roughly 38.5 seconds for this run. The displayed table shows the resulting columns: date, keywords, link, summary, text, and title.

Oct 9, 2017

Loading thumbnail…

Gist e71cb443307994e24e63465bac636d3e

This example demonstrates how to extract and summarize the content of a New York Times opinion article about regulating artificial intelligence. The notebook uses the `newspaper` library to download and parse the article, returning its title, publication date, and initial text. It then calls the `nlp()` method to generate a list of keywords and a multi-sentence summary, showing a straightforward pipeline from a URL to condensed article insights. The code relies on `Article` from `newspaper` and standard pandas imports.

Oct 9, 2017

Loading thumbnail…

Gist d3be7947ba494f0cc785ad4ff966f6d5

This example demonstrates a workflow for collecting and structuring Twitter data through the streaming API. It uses the `tweepy` library to authenticate and open a live stream, filtering tweets by specified terms or hashtags, and saving each raw tweet as a JSON line to a file. The code then reads that file back, parses each tweet, and extracts selected fields into a nested list, including the text, user handle, favorite and retweet counts, timestamp, follower count, and language. Finally, it organizes the data into a structured pandas DataFrame with corresponding column names, preparing it for further analysis or visualization.

Jun 6, 2017

Loading thumbnail…

Gist b87ac9ed9766b78a72565a895d3a660f

This example shows a scatterplot-style visualization of audio features for tracks from a Spotify playlist, with each point representing a song’s metadata retrieved via the Spotify Web API. The code pulls playlist tracks using `spotipy`’s `user_playlist` method, extracts track IDs, and calls `audio_features` to gather numeric acoustics attributes. Those features are then loaded into a pandas `DataFrame`, which serves as the dataset for the plotted chart. The visualization relies on Spotify’s API response structure and pandas for tabular organization, but does not include any specific plotting library call in the provided code.

May 25, 2017

Loading thumbnail…

Gist ae6b977c060cc0ef01506b020aceeba5

This example maps the listening history of a Pandora user by scraping song attributes from Pandora’s song pages. It shows a network where each node represents a song and edges connect songs sharing common attributes, revealing clusters of similar musical traits. The scraper uses Python’s `requests` and `re` to fetch and parse HTML, building a Pandas DataFrame of binary attribute presence, then visualizes the graph with d3’s force simulation in JavaScript.

Apr 4, 2017

Loading thumbnail…

How to use The Guardian's API to download article data for content analysis (in Python 3.x)

This example demonstrates how to perform content analysis on Guardian news articles by programmatically downloading article data through The Guardian’s open API. It shows the entire workflow, from constructing API queries with parameters like `from-date`, `to-date`, and `show-fields`, to parsing the returned JSON metadata and article bodies. The Python script uses the `requests` library to fetch paginated results into day-sized chunks, saving the raw JSON files locally for further analysis. The example also highlights the API’s response structure, including fields such as `wordcount`, `byline`, and full article HTML.

Mar 7, 2016

Loading thumbnail…

Portugal banking

A horizontal bar chart shows the population of Portuguese regions, sorted by region name, with blue bars animating from zero to their final lengths. The chart uses animated text tweens to count up to each region's population value during the transition. Built with D3.js v3, the visualization loads data from a CSV file and uses d3.transition with ease curves and d3.interpolate for the numeric text animation, while ordinal scales position the region labels.

Feb 25, 2016

Loading thumbnail…

Portugal banking

A vertical bar chart displays the population of Portuguese regions, with each region listed along the y-axis and a horizontal bar showing its number of people. The chart loads data from a CSV file and animates both the bars and their labels over two seconds. Using d3.v3, the code employs a linear scale for bar widths, an ordinal scale for region positions, and `transition.attr` for width changes, while `transition.tween` with `d3.interpolate` animates the text labels. The rendering uses SVG elements with a light blue fill for the bars.

Feb 25, 2016

Loading thumbnail…

How Long Will The Warriors' Streak Last? (cf.: http://www.buzzfeed.com/jsvine/how-long-will-the-golden-state-warriors-win-streak-last)

This interactive visualization estimates the Golden State Warriors’ odds of matching or beating the NBA’s 33-game win-streak record, using per-game probabilities from FiveThirtyEight’s CARM-Elo projections. It shows cumulative and per-game chances of extending the streak over the season, with the longest regular-season streak held by the ’71–’72 Lakers as context. Built with Vue, the visualization fetches and parses data from FiveThirtyEight’s JavaScript bundle using Python libraries, including pandas, requests, and seaborn, to compute and display the odds.

Dec 9, 2015