Data Table Summary: Lending Club

Data Table Summary: Lending Club

This example displays a plain-text summary of loan data from the first quarter of 2018, sourced from the Lending Club website. It shows the total row count, column count, and file size in kilobytes, followed by the first ten rows formatted as JSON. The code uses D3 v5’s `csv` and `csvFormat` functions to load and measure the data, then appends `<pre>` elements to the body. The black background and green monospace text mimic a terminal interface, emphasizing the raw, tabular nature of the summary.

Data Table Summary: Lending Club

This example displays a plain-text summary of the Lending Club dataset, showing the row count, column count, and file size in kilobytes, followed by the first ten rows formatted as indented JSON. The code loads the data with `d3.csv`, formats it back to CSV using `csvFormat` to calculate its size, and renders the output by appending `<pre>` elements to the body with D3’s `select` and `append`. The styling uses a black background and green monospace text. The data covers loans issued in Q1 2018, including loan amounts, terms, home ownership, issue month, and state.

Data Table Summary: Lending Club

Data Table Summary: Lending Club

This example displays a plain-text summary of a Lending Club dataset containing loans under $5000 from the first quarter of 2018. It shows the total row count, column count, and file size in kilobytes, followed by the first ten rows formatted as JSON. Using D3 v5’s `select`, `csv`, and `csvFormat` functions, the code loads `data.csv` and appends the summary to the page, styled with black background and green monospace text. The data originates from the Lending Club website.

Data Table Summary

This example summarizes a CSV dataset by displaying its row count, column count, and file size in kilobytes, followed by the first ten rows as formatted JSON. Two `pre` elements present this text output, styled with green monospace text on a black background. The data originates from the Lending Club loan dataset, though the code uses a simpler ten-country population sample. It loads the file with `d3.csv`, computes the size via `d3.csvFormat`, and renders the output using D3 selections with `select` and `append`.

82% match

Data Table Summary: Credit Lending

This example displays a plain-text summary of a credit lending dataset, showing the row count, column count, file size, and the first ten records as formatted JSON. It uses D3's `select`, `csv`, and `csvFormat` APIs to load and process the data, then appends the output as `<pre>` elements with a green monospace aesthetic. The data originates from the 2018 United Nations World Population Prospects, despite the title referencing credit lending.

81% match

Data Table Summary: Credit Lending

This example summarizes a CSV dataset of the most populous countries in 2018, sourced from the United Nations World Population Prospects. It displays the total row count, column count, and file size in kilobytes, followed by a JSON-formatted preview of the first ten rows. The visualization uses d3.csv to load the data and d3.csvFormat to compute file size, rendering the summary as styled text elements in a black-and-green terminal aesthetic. Data is presented as simple text output rather than a chart.

74% match