Gist feea61c798fdb955f16b5ba785974e1a

100%

Gist feea61c798fdb955f16b5ba785974e1a

✓ Published0🌍 Public

Last edited May 10, 2016

Created on May 10, 2016

This example highlights differences between two pandas DataFrames by key. It displays the two records side-by-side, transposing them into a single table where each column represents a field. Any column containing differing values between the two rows is visually marked with a yellow background. The implementation uses the pandas `DataFrame.style.apply` method to apply a custom `highlight_diff` function that checks for uniqueness across the row pairs, returning conditional formatting styles for the table.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist 379b1fa0356b03ac6d9319f157a229a8

This example uses pandas and seaborn to explore a dataset of user study schedules and referral scores, first loading the CSV into a DataFrame and previewing its structure. The visualization reveals the relationship between whether users follow a study schedule, their note-taking behavior, and their referral scores. Using matplotlib and seaborn with a white background and deep palette, the code generates statistical plots to compare referral scores across binary study-schedule and note-taking groups, highlighting patterns in user engagement. The analysis relies on the pandas DataFrame operations and seaborn’s plotting functions to summarize the data, though the specific chart type is not shown in the provided cells.

HHawkinsJM

56% match

Loading thumbnail…

Gist a193c0f8df23b74a6ac3ff2ac88e7124

This example displays a pandas DataFrame table listing 14 unique superpowers alongside their corresponding heroes, such as Kool-Aid Man’s Thirstokinesis and Dr. Manhattan’s Molecular Dissipation. The code constructs the table from a Python dictionary using `pd.DataFrame.from_dict`, rendering the data as an HTML table with styled headers and row indices. The output is a static, text-based visualization of the one-to-one mapping between each distinct ability and its associated character, with no charts or graphical elements involved.

AAsutosh989

54% 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

52% match

Loading thumbnail…

Selectively colouring cells in gt::gt()

This example uses the R package `gt()` to display a data table where individual cells are selectively colored grey, and then a second table where cells are filled with colors from a predefined palette based on a comparison. The code first identifies cells containing "<" characters using `grepl` and applies a uniform grey fill via `tab_style` and `cell_fill`. A more complex workflow melts data frames with `data.table::melt`, merges them with guideline thresholds, and determines which numeric results exceed those thresholds, assigning a color from `RColorBrewer` to each qualifying cell. The `mapply` function iterates over row and column indices to apply the styling individually, producing a table where colored cells highlight values that meet specific criteria. The data is created inline as sample data frames, and the rendering approach relies on the `gt` package's HTML table output.

JJustinCally

52% 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

52% match

Loading thumbnail…

Gist 670c7537c7467ba60116977d68f891af

This example explores superhero abilities by visualizing the presence or absence of various superpowers using a tabular, boolean matrix representation. Each row corresponds to a specific hero character (e.g., 3-D Man, A-Bomb, Abin Sur), while each column represents a distinct superpower or capability, such as Agility, Flight, or Web Creation. The visualization employs a color-coded table, likely rendered interactively within a Jupyter Notebook using the pandas DataFrame and its built-in styling API to implement conditional formatting. This provides an accessible, grid-based overview of which characters possess which powers, facilitating quick comparisons across the dataset.

AAsutosh989

50% match

Loading thumbnail…

Full Difference Chart Example

This chart visualizes the difference in daily Leanpub book sales between two titles, “Science vs Style,” using an area graph with color-coded regions (orange for above, green for below) to highlight performance gaps. It renders via D3.js (version 3.5.6) with SVG, employing time and linear scales, area and line interpolations, and clip paths to delineate the positive and negative differences. Data from `downloads.csv` is parsed, grouped by date, and transformed to compute daily deltas, with axes and a legend providing context. The visualization is an example from the book *D3 Tips and Tricks* by d3noob.

Dd3noob

50% match

Loading thumbnail…

Gist a25625bb83ff3d640f628b2437480ca3

This example presents a tabular view of superhero character attributes from a dataset, displaying fields such as name, gender, eye color, race, height, publisher, and alignment. The notebook uses Python and the pandas library to load and inspect the data, as evidenced by the DataFrame output and the `superhero_info.head()` call. The rendered HTML table shows the first five records, with entries from Marvel Comics, DC Comics, and Dark Horse Comics, including characters like A-Bomb and Abomination, along with their physical and biographical traits.

AAsutosh989

50% match