Gist dfbc4b41961e5b098fffce6af3828f57

100%

Gist dfbc4b41961e5b098fffce6af3828f57

✓ Published0🌍 Public

Last edited Dec 29, 2021

Created on Dec 29, 2021

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.

AI-generated description

Similar vizzes

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

91% 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

78% match

Loading thumbnail…

Gist 2c4db01f16e82a0746be7819b4d925d5

This example visualizes influencer engagement by computing an engagement rate from comment and like counts relative to follower totals, using pandas to add and inspect the new column. It then extracts URLs from post captions with a regex pattern, storing them in a new “Links” column to identify which posts contain external links. The code relies on pandas for data manipulation and the `str.extract` method for pattern matching, while the head and describe functions provide a quick statistical summary. The visualization focuses on showing how engagement varies with link presence, though no charting library is used in the provided snippet.

AArizonaTay

76% match

Loading thumbnail…

Gist 7083cd8264b6aa48ce4bbfaf44546f07

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.

AArizonaTay

76% 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

74% match

Loading thumbnail…

Gist 0d66893f89c8190de7c21268dfeef1d1

The visualization explores sponsored Instagram posts, showing how engagement rates and posting frequency vary by hour of the day. Two line charts, built with Plotly Express and Plotly Graph Objects, reveal a peak in average engagement at 4pm and the highest number of posts at noon. A separate horizontal bar chart, also using Plotly, ranks the top 20 brand collaborators by counting how often each sponsor appears in the filtered dataset, which only includes posts marked as "Sponsored." The code groups timestamps by hour and processes sponsor tags by stripping "@" symbols to tally frequencies.

AArizonaTay

71% match

Loading thumbnail…

Gist ba350b90fcee35acc38ff961c4eec95a

Two horizontal bar charts display the percentage of sponsored posts by industry and by country, using Plotly Express’s `px.bar` after grouping data with pandas `groupby` and `apply`. Each chart sorts its top 20 categories descending by sponsored share, with the industry chart titled “Sponsored Post(%) by Industry” and the country chart “Sponsored Post(%) by Country.” The code computes sponsored counts per category from the `inf_df` DataFrame, merges sponsored and non-sponsored totals, then calculates percentages for visualization. The source data appears to originate from a dataset of influencer posts, with plots rendered as static, interactive HTML figures.

AArizonaTay

71% match

Loading thumbnail…

Gist 905df75f1305bdbeb7b97d77c8fe96d2

A bar chart ranks the top 20 brands by the number of influencer collaborations, using a horizontal count of sponsored posts per sponsor. The visualization shows which brands appear most frequently in the influencer’s content, with higher counts indicating stronger collaboration presence. The code builds a Pandas DataFrame by extracting sponsor handles from a pre-existing `sponsored_df`, then groups and sorts the data before plotting with Plotly Express’s `px.bar`. Engagement rates are recorded alongside each sponsor, though the final chart displays only the frequency counts, not the engagement values.

AArizonaTay

70% match