Utility functions to be used in Python
Project description
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
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