bae0n-utils 0.1.2

Utility functions to be used in Python

bae0n_utils

Collection of utility functions to be used in Python.

ColoredText(r, g, b, text)

ex: print(ColoredText(255, 0, 0, 'Hello World'))

Displays colored text in the console.

params:

• r - Value from 0-255 for Red
• g - Value from 0-255 for Green
• b - Value from 0-255 for Blue
• text - Text to display with color

Returns: Modified text string, now with the given color.

FitCellsToWindow

ex: FitCellsToWindow()

Fits Jupyter cells to window size.

ActivateCellDoneSound

ex: ActivateCellDoneSound(url='https://bigsoundbank.com/UPLOAD/mp3/0116.mp3')

Plays a sound effect when a cell is complete. Currently kind of buggy with a failed cell.

params:

• url (optional) - url of the sound you want to play. Accepts .wav and .mp3

ActivateCellFailSound

ex: ActivateCellFailSound()

Plays a sound effect if the cell execution fails.

ClearDir

ex: ClearDir('./images')

Removes all files from a directory.

params:

• path - The source directory of the images to turn into a gif. Must include preceding ./, should not include ending /
• safe_del - Prompts user input for confirmation if set to True. Default Value: True

MakeGif

ex: MakeGif('./data', './', 'test', 100, 'jpg')

Turns a directory of images into a gif.

params:

• source_dir - The source directory of the images to turn into a gif. Must include preceding ./
• out_dir - The directory to save the gif to. Must include preceding ./
• gif_name - The name of the gif. Do not include filetype.
• duration - Number of frames in the gif...I think.
• file_type - File extension for the images. Do not include preceding .

CorrMatrixAnalysis

Displays in depth analysis of the correlation between features. Currently only addresses correlation of dependent feature to independent features, but will be updated soon.

params:

• df - The dataframe to analyze.
• dep_feature - The dependent feature.

example call:

df = pd.read_csv('Iris.csv')

CorrMatrixAnalysis(df, 'species')

Example output:

Features With High Correlation to diagnosis:
  -0.79  - concave points_worst
  -0.78  - perimeter_worst
  -0.78  - concave points_mean
  -0.78  - radius_worst
  -0.74  - perimeter_mean
  -0.73  - area_worst
  -0.73  - radius_mean
  -0.71  - area_mean

  Features With Moderate Correlation to diagnosis:
  -0.70  - concavity_mean
  -0.66  - concavity_worst
  -0.60  - compactness_mean
  -0.59  - compactness_worst
  -0.57  - radius_se
  -0.56  - perimeter_se
  -0.55  - area_se

  Features With No Correlation to diagnosis:
  -0.29  - compactness_se
  -0.25  - concavity_se
  -0.08  - fractal_dimension_se
    0.07  - smoothness_se
  -0.04  - id
    0.01  - fractal_dimension_mean
    0.01  - texture_se
    0.01  - symmetry_se

Many thanks to my muse for the constant inspiration, great ideas, and support <3