Gist 0350c21c054d3bc0f23d8ddd8c857ed1

100%

Gist 0350c21c054d3bc0f23d8ddd8c857ed1

✓ Published0🌍 Public

Last edited May 3, 2016

Created on May 2, 2016

This example shows a reusable Python function that converts lists of numeric values between different units of measurement using the Pint library. It defines `convert`, which takes an array, a source unit, and a target unit, then applies Pint’s `Quantity` constructor and `.to()` method to return a list of converted quantities. The code relies on `pint.UnitRegistry` to access unit definitions, as demonstrated in the docstring where inches are converted to meters. The function is designed for interactive use, such as in IPython, to avoid repeating unit conversion logic.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist 70c868110a00f3ba70900612538f1fce

This example demonstrates a unit-validation function that checks whether a string represents a valid Pint unit. It defines `is_str_valid_pint_unit` to attempt parsing the input with the Pint `UnitRegistry` and returns `False` when an `UndefinedUnitError` occurs. The code prints `True` for the sample unit `'degC'`, showing how the registry recognizes common units. The example uses the Pint library’s `ureg()` call to perform the validation.

Aalpha-beta-soup

65% match

Loading thumbnail…

Gist 3b6728f4276fe7beb14795f830abbebe

This Ruby script converts a list of weight values from pounds to kilograms and prints the converted values in a formatted table. It uses the `FormatWeight` module to process an array of hashes containing names and weight data. The code calculates and displays the maximum and minimum weight entries after conversion, relying on the built-in `Float::INFINITY` constant to initialize the search for the lightest object. The program outputs both the full list of converted weights and the identified extremes without using any external visualization libraries.

KKiwilicious

53% match

Loading thumbnail…

Gist 16f26cdfd47fc8110f64ba1f4e292ad2

This example demonstrates basic integer division and exponentiation in Python. It assigns the values 11 and 5 to variables x and y, then computes the floor division result (2) and prints it, with a comment noting that 2.2 would be the floating-point result. The code also calculates and prints 2 raised to the power of 3, yielding 8. The visualization relies on standard Python print statements rather than any external data-visualization libraries, serving as a simple scripted arithmetic demonstration.

MMistyBlunch

51% match

Loading thumbnail…

Gist 51fecbcad3c5a744b1a96ad079f2c210

This example demonstrates the behavior of D3’s `scaleLinear` function, showing how a numeric input domain of [0, 1] maps to an output range of [0, 100]. It prints the scaled results for values 0.5, 0.75, 0, 1, and 2, revealing that the mapping is continuous and extends beyond the specified domain. The code imports the scale from `d3-scale` (via the `d3` namespace) and logs each conversion to the console, making the linear transformation’s direct proportionality clear.

RRonolibert

50% match

Loading thumbnail…

Gist 07c637765f8aad3f648d483be9e42cd8

This example demonstrates basic Python syntax and output within a Jupyter notebook, showing how simple print statements and expression evaluations behave. It highlights the classic floating-point precision issue where `0.1 + 0.2` does not equal `0.3`, and contrasts that with an epsilon-based comparison that returns `True`. The code also illustrates string handling with triple-quoted multiline strings and the difference between echoing a value and using `print()`. No external visualization libraries are used; the focus is purely on language fundamentals.

FFrieseWoudloper

49% match

Loading thumbnail…

Gist 9b046ace330f16e0c7dfd93c5e979e10

This example shows a numeric comparison game where the user inputs an integer, and the script prints whether it is less than, greater than, or equal to the fixed value 10. It then demonstrates a loop that either adds or subtracts based on the input’s relationship to 10. The code uses Python’s `raw_input` for interactive console input, a `while` loop with a `try`/`except` block to handle invalid entries, and `range()` with `print` statements to output series of numbers. The logic is entirely text-based, with no visualization or graphical rendering libraries involved.

MMTClass

49% match

Loading thumbnail…

Gist 740405b6405a6644fd16d3a50588de51

This example shows a step-by-step transformation of the classic wine dataset, progressively filtering and selecting columns to isolate alcohol content, color intensity, and hue before randomly sampling five rows. The code chains pandas operations—including `rename`, `assign`, `query`, and `sort_values`—through a custom `csnap` helper that snapshots the intermediate state after each step. It uses NumPy’s `np.where` to create a binary `color_filter` column based on hue and color intensity thresholds, then resets the index and samples five rows with `sample(5)`. The visualization highlights how pipelined data wrangling can be inspected at each stage.

Aadiamaan92

49% match

Loading thumbnail…

[复杂的四舍五入] 例如对大数在十位、百位上四舍五入 #number

This example demonstrates a visual note on Python’s built-in `round` function, showing both standard decimal rounding and rounding to larger place values (tens, hundreds, thousands). The code first illustrates that `round` follows banker’s rounding at the .5 boundary, where 1.5 and 2.5 both yield 2, and then showcases negative `ndigits` arguments to round an integer like 1627731 down to 1627730, 1627700, or up to 1628000. The source is a Python script (likely run in an interactive shell) that uses only the standard library’s `round` API, with no external data or plotting library involved.

SShawn91

48% match