Gist 7014a4039307a88d7d10e9e4adce0549

Gist 541f344f0b112c3ace68d676a41682ee

This example fetches a dataset of Indian Prime Minister Narendra Modi’s speeches from Kaggle using the `kaggle_api_extended.KaggleApi` class, specifically calling `dataset_download_files` to download and unzip the “modi-speeches” dataset. The code then reads the downloaded CSV file into a pandas DataFrame via `pd.read_csv` and extracts the first speech title from the `title` column. The visualization implicitly relies on the Kaggle API and pandas to retrieve and display the dataset’s content, but no charting or rendering library is shown beyond these data-access steps.

70% match

Gist 7ef9c17d99cb5a83e7312992e4ec683d

This visualization maps the geographic and thematic reach of Narendra Modi’s speeches, sourced from a Kaggle dataset of text speeches starting in 2018. It displays an interactive choropleth of India, with state boundaries drawn using `d3.geoPath` and a `d3.forceSimulation` layout that positions speech-related keywords as nodes. Hovering over a state reveals its name, while colored circles represent speech themes, with sizes scaled to frequency. The code uses D3.js for SVG rendering and transitions, and it loads the dataset from a JSON file, updating the visualization dynamically.

60% match

Gist 58c79c81e9a950a0cdafc7d2d0275742

This example shows a dynamic, browser-automated web-scraping workflow that captures rendered page content. It uses Selenium’s `webdriver` to launch a Chrome browser, wait for elements via `WebDriverWait` and `expected_conditions`, and then extracts page HTML with BeautifulSoup. The script programmatically sends keyboard inputs via `Keys`, and also employs the `requests` library for direct HTTP calls. It parses the returned data with `json`, timestamps actions with `datetime`, and structures results into a pandas DataFrame. The process includes randomized delays (`random` and `time`) to mimic human browsing behavior, though no explicit visualization or rendering library is used beyond the raw captured markup.

AArizonaTay

56% match

Gist Scraping Script Prototype

This prototype demonstrates a data migration workflow that iterates through every file within each GitHub Gist associated with a given username. The visualization shows the raw JSON structure of a Gist, including its metadata and file contents. The application uses the Octokit library to fetch Gist data from the GitHub API and displays example file entries such as `App.js`, `data.csv`, and `package.json`. This proof of concept is built with React and includes placeholder functions for generating identifiers and converting timestamps, though the actual migration logic is not yet implemented.

CCurran Kelleher

Gist d5f9fd4d851d95e8815ba77e1364eb5d

This example demonstrates how to update a dataset of speeches by appending new entries and committing the changes to a data repository. It shows the process of merging delta records with existing speech data using pandas, then saving the combined result to a CSV file. The code uses `pd.concat` to combine the DataFrames, `to_csv` to persist the updated file, and the `api.dataset_create_version` method from the data platform’s API to create a new version with a timestamped note. This workflow highlights automated data maintenance and version control for evolving datasets.

Gist 80444441f83babe9e45d8a3166c837ce

This example demonstrates a small automated data-update workflow that refreshes a dataset by running a Python script. It shows how environment variables supply credentials and user identification, which are then passed to an asynchronous update function. The code uses Python’s `asyncio` library to manage the asynchronous task, while `os.environ` retrieves the required secrets from the system environment. The visualization itself is not rendered here; instead, the example focuses on the programmatic data-fetching and update logic, with the actual charting or rendering handled separately by an unspecified front-end tool.

CConorAspell

Fetch Delta using Direct API Calls

This example shows how to incrementally fetch newly published speech titles from the Narendra Modi website using direct API calls. It paginates through the site’s `loadspeeche` endpoint, parsing the returned HTML with `lxml`’s `etree` and XPath queries. The script stops fetching pages once it encounters a title matching the previously saved latest speech, avoiding redundant downloads. It then calls `get_article_data(tree)` on new entries, demonstrating a simple delta-extraction pattern for tracking content updates over time.

Gist d2f2bc6be1716820160ed5183ea07eaa

This example shows a browser-based text analytics pipeline that scrapes and processes web content before visualizing topic models. The code first uses Selenium with Chrome WebDriver to scrape data, then applies NLP libraries including NLTK, spaCy, TextBlob, and gensim’s LdaModel for topic modeling and coherence scoring. The final visualization employs Plotly’s `plotly.express` and `plotly.graph_objs` to render interactive charts, with `pyo.init_notebook_mode()` enabling notebook display. The rendering approach is evident from the use of `go` traces and `px` figures, though the specific chart type is not shown in the code snippet. The data source is dynamic web pages, as indicated by the Selenium scraping logic.

AArizonaTay