W209 Async 11.3 with pivot_table transform

Gist 2daccab1b2fa33a23871

This visualization displays electrode voltage data from an experiment, with multiple electrode readings plotted over time and separated by side (Right/Left). The notebook uses pandas to organize the data, matplotlib for plotting, and mpld3’s `enable_notebook` to render the figure interactively in a Jupyter environment. A custom `ClickInfo` plugin registers a mousedown handler on the plotted points, triggering an alert with the point index. Seaborn is imported for styling, and the code includes a table of electrode measurements for reference.

68% match

Ddeepshikathakur111@gmail.com

ice-9

A multi-page Vue dashboard presents workout data through scatter plots, stacked bar charts, and line charts. The visualizations explore workout duration, calories burned, and intensity levels. Charts are embedded as static PNG images generated with Python and Matplotlib, while the interface uses HTML, CSS, and JavaScript to structure navigation between pages. The layout includes descriptive text accompanying each chart to explain the data encodings used.

64% match

Gist 8e62843a6ffa1dfd7293

This visualization shows how brain tissue volumes (CSF, WM, GM, and total brain) change across multiple time points and scanner types, with each subject’s measurements pivoted into a comparative table. The code uses pandas for data wrangling, including `pivot_table` and column aggregation, and relies on `seaborn` and matplotlib’s `%pylab inline` for plotting. Data comes from SIENAX report files parsed into a DataFrame, then normalized to cubic centimeters. The final table displays time point and scanner metadata alongside the computed tissue volumes.

Gist 42b954f71c1668a26572

This example visualizes longitudinal Freesurfer brain imaging data, showing how volumes of cerebrospinal fluid, white matter, gray matter, and whole brain, along with mean cortical thickness, change across time points and scanner types. The code loads CSV tables into pandas DataFrames, aggregates specific anatomical regions into tissue categories, and merges hemisphere thickness values. It uses matplotlib, seaborn, and statsmodels for plotting and statistical modeling, with the data prepared through pandas operations before visualization.

TD2

This visualization presents a multi-panel dashboard for exploring sales data from a CSV file. It displays several distinct plots, likely including bar charts and other summaries, within a grid layout. The implementation uses D3 v5 to load the data and render SVG graphics, leveraging scales and axes for plotting. The page is built with Vue and uses Moment.js for any date parsing, with tooltips providing interactivity for detailed values.

AAdrien

Gist 439dddffaa8ff758dc07

The visualization compares brain structure volumes across subjects before and after a software upgrade, using pandas to load, merge, and reshape CSV data from cortical and subcortical parcellation files. It calculates coefficients of variation per subject and region, then computes percentage volume changes relative to a reference measurement, isolating specific regions of interest for display. The code relies on pandas operations such as `pd.melt`, `pivot_table`, and `groupby` with lambda functions to structure the data for plotting.

Gist f052b3898971d32fb695

This example uses Python’s matplotlib within a Jupyter notebook to create subplots, leveraging NumPy and matplotlib’s interactive mode. The visualization demonstrates the construction of multiple panels in a single figure, with code likely generating sample plots for comparison or exploration. The notebook runs `%pylab inline` to enable inline plotting and produce rendered subplot images as output.

JJonasHeylen

62% match

Gist d8aa62e81e12e25d45a3

This visualization presents statistical calculations for brain imaging data, comparing reliability metrics across different analysis methods. The notebook uses Python with rpy2 to bridge R and Python, calling the `psych` package for intraclass correlation coefficients and `statsmodels` for additional statistical modeling. It generates plots with matplotlib and seaborn, applying custom chart-cleaning functions to remove unnecessary axes and gridlines. The code also leverages pandas for data manipulation and NumPy for z-score normalization, with output rendered inline through IPython’s `%pylab` magic.