Gist 7083cd8264b6aa48ce4bbfaf44546f07

100%

Gist 7083cd8264b6aa48ce4bbfaf44546f07

✓ Published0🌍 Public

Last edited Dec 27, 2021

Created on Dec 27, 2021

This example builds a profile-scraping pipeline for social media data, turning raw CSV input into a structured DataFrame with fields for user names, follower counts, post dates, likes, comments, captions, and industry tags. The code reads `raw_data.csv` via `pandas.read_csv` and then initializes an empty DataFrame with predefined column names, preparing it for subsequent population. The visualization focuses on the data-preparation stage rather than graphical output, demonstrating how pandas handles tabular ingestion and schema definition. No rendering library like d3 or Three.js appears here; instead, the emphasis is on clean data structuring for later analysis or charting.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist dfbc4b41961e5b098fffce6af3828f57

This visualization scrapes Instagram profile data to compare follower counts, post engagement, and captions across influencers in different industries. Using Selenium to automate Chrome and BeautifulSoup to parse page JSON, the script extracts post dates, like counts, comments, and partnership flags from each profile’s embedded `_sharedData` blob. It then compiles the results into a pandas DataFrame and exports them to CSV. The example highlights the workflow of collecting social-media metrics at scale, with the final chart likely plotting follower growth or engagement trends per industry.

AArizonaTay

76% match

Loading thumbnail…

Gist fa05e9e0684e37e537c08085e5900435

This example shows a data-cleaning pipeline for an Instagram influencer dataset, highlighting the number of unique users and missing values across key columns. The code uses pandas to inspect and transform the data, filling missing industry labels with "General," dropping rows without captions, removing duplicate entries, and converting the post date column to datetime format for time-series indexing. The visualization relies on pandas' built-in data manipulation functions, such as `fillna`, `drop_duplicates`, and `set_index`, rather than a plotting library. The final output would be a cleaned dataframe ready for trend analysis, though no chart is generated in the provided script.

AArizonaTay

75% match

Loading thumbnail…

Gist a99d287be62c6d73536521c0e5e74e58

This example scrapes Instagram profile and post data to track follower counts, likes, comments, captions, and post dates for selected users, associating each entry with an industry topic. The script uses Selenium to automate login and search, BeautifulSoup to parse the page source, and the Instagram GraphQL data embedded in `window._sharedData` to extract metrics. It writes the collected records to a CSV via pandas, pausing with random sleeps to avoid detection. The visualization highlights how social-media engagement varies across profiles and industries over time.

AArizonaTay

74% match

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.

GGeorgeMcIntire

69% match

Loading thumbnail…

Gist 55fc864ec9cea1107845d3ed7a3dab8b

This example demonstrates a text-cleaning pipeline for caption data, showing how raw captions are converted into tokenized, filtered lists for downstream analysis. The visualization itself is not rendered; instead, the focus is on the data preprocessing logic. The code uses `pandas` with `apply` to run natural language processing via `spaCy` (`nlp`), extracting tokens from the `Caption` column. It then applies a custom `tokens` function that removes stop words and non-alphanumeric characters using `re.match`, while preserving tokens containing "@" symbols. The result is a new `clean_caption` column, highlighting a common step in text mining workflows.

AArizonaTay

66% match

Loading thumbnail…

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

66% match

Loading thumbnail…

Social Media Analysis Loading Data

This example demonstrates the initial data-loading stage of a social media analysis dashboard. It fetches a CSV file from GitHub, filters out responses from participants who did not consent or are under 18, and restructures the raw survey columns into consolidated fields using a custom `combine` function. The raw data is displayed as JSON in a `<pre>` element. The process uses the `fetch` API, `csvParse`, and `select` from D3 v7, with state management handled through a custom `setState` callback.

Fflanagancarlie@gmail.com

65% match

Loading thumbnail…

Gist 4aa3c41126f0bd7d7e9c92f4c7e7be4d

The visualization shows how influencer posts align with a predefined organizational corpus by mapping text overlap through a force-directed network layout. It loads influencer data and a static corpus list from CSV files, then uses Python’s pandas for data ingestion and likely computes similarity metrics. The rendering leverages a custom interactive graph—likely built with D3.js’s `d3.forceSimulation`—where nodes represent individual influencers and corpus terms, with edges weighted by textual matching. The code explicitly references `influencers_raw` and `orgcorpus_raw` to drive the analysis, while the visual output emphasizes clusters of semantic association. The approach relies on raw CSV parsing and a simulation engine, but no external charting library is imported in the shown snippet.

AArizonaTay

65% match