Gist ba350b90fcee35acc38ff961c4eec95a

100%

Gist ba350b90fcee35acc38ff961c4eec95a

✓ Published0🌍 Public

Last edited Dec 27, 2021

Created on Dec 27, 2021

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.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist 7324dc1c477b5214c422752e1390c042

Two horizontal bar charts display the percentage of sponsored posts by industry and by country, ranking the top twenty categories in descending order. The first chart highlights industries with the highest sponsorship rates, while the second compares countries, both using Plotly Express’s `px.bar` function. The code computes percentages by grouping the dataset with `groupby`, counting "Yes" and "No" values for the `Sponsored` column, merging results, and calculating proportions before sorting. Each chart is titled with centered layout via `update_layout(title_x=0.5)`, and the source data originates from a pandas DataFrame derived from the gist’s CSV.

AArizonaTay

94% 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

83% match

Loading thumbnail…

Gist 95ca610ee079b0bdb562a98f57c792f4

The visualization displays the top 20 brands that collaborate with influencers, ranked by the frequency of their appearances in a dataset. It highlights which sponsors are most commonly associated with influencer posts, providing a clear view of brand partnership patterns. The chart is a horizontal bar plot generated using Plotly Express, with the `px.bar` function called on a sorted dataframe. The code processes the data by iterating through sponsored entries, cleaning sponsor names, and aggregating counts via `groupby` and `reset_index`. The final plot is styled with a title and centered axis labels, relying on Pandas for data manipulation.

AArizonaTay

81% match

Loading thumbnail…

Gist f911ff730b6f8a7d90f293521061801c

Two line charts from the same dataset show hourly trends for sponsored posts: the first displays average engagement rate by time of day, peaking at 4pm and 11pm, while the second shows the count of posts, with a maximum at 10am. Both use Plotly’s `go.Figure` and `go.Scatter` to render the lines, with x-axis labels formatted as `%H:%M:%S` from the index’s floor to the hour. Annotations via `add_annotation` highlight these peaks. The data comes from a pandas DataFrame filtered by `Sponsored == 'Yes'`, grouped by hourly floor.

AArizonaTay

78% 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

77% match

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

71% match

Loading thumbnail…

Gist 8f9cf917c41ea1240a5ea6b149d602dd

This visualization shows how the geographic spread of influencer locations relates to the frequency of their associated topics. It first loads influencer data from a CSV and a separate corpus of organization names, then renders the results using D3’s force-directed simulation to position nodes by influence, with geographic context provided by a d3.geoPath projection. The underlying text analysis relies on reading raw text files, while the visual encoding uses Canvas’s getImageData to sample pixel-level density for heatmapping, and transition.attrTween animates node movement as forces settle. The data source is explicitly read via pandas from the two files, and the rendering approach combines raster canvas operations with SVG overlays for labels and links.

AArizonaTay

67% match

Loading thumbnail…

Gist 751982c1e21147b124a9b6d5fde96a4e

The visualization examines sponsored content in a social media dataset by processing raw captions through a custom pipeline. It uses Python’s `re` module to strip emojis and punctuation, then applies spaCy’s named entity recognition to extract organizations and products into a "Sponsor" list, while also capturing tags via string matching. The code builds a DataFrame with `pandas`, adding a binary "Sponsored" column based on whether either field is non-empty. It highlights the detection of promotional mentions by combining entity-based and rule-based tagging, ultimately displaying cleaned captions alongside the derived sponsorship metadata.

AArizonaTay

67% match