This Python script generates a customizable word cloud from a predefined list of terms, allowing the user to select a background color and a color palette, randomize term weights, and save the resulting visualization and ranked term list. The output is tailored to fit the LinkedIn banner size.
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Background Color Selection: Users can choose between a light or dark background for the word cloud, enhancing visibility based on preference.
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Customizable Color Palette with Descriptions: The script offers a selection of predefined color palettes, each described to help users decide the best fit for their visualization.
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Interactive User Input: Prompts the user to select a background color and color palette from the console, handling any input errors gracefully.
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Automated Randomization: Adjusts the frequency of terms within a specified range to introduce variability and reflect the relative importance of each term.
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Visualization: Displays the word cloud using matplotlib and saves it as a PNG image formatted specifically for LinkedIn banners.
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Output Saving: Saves the ranked list of terms and their weights to a text file,
common_words.txt. -
File Size Management: Ensures the output PNG file size is under 3MB, adjusting the image quality if necessary.
As of the current date, the LinkedIn banner size used in this script is 1584x396 pixels, ensuring that the generated word cloud fits perfectly as a LinkedIn profile banner.
To run this script, ensure you have the following Python libraries installed:
matplotlib: For generating and displaying the word cloud image.wordcloud: For creating the word cloud from frequencies.PIL(fromPillow): For image processing and file size adjustment.tqdm: For displaying a progress bar during file size adjustment.os: For checking file size.
You can install the necessary libraries using pip:
pip install matplotlib wordcloud Pillow tqdm-
Run the Script: Execute the script in a Python environment. The script first asks you to choose a background color and then displays available color palettes with descriptions.
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Select a Background Color and Color Palette: Enter the number corresponding to your chosen background color and color palette when prompted.
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View and Save Outputs: The script will display the word cloud in the chosen color scheme and save it as
linkedin_banner.png. Additionally, it will save the sorted terms and their weights tocommon_words.txt. -
File Size Adjustment: The script ensures that the output image file size is under 3MB, adjusting the quality as necessary.
A dictionary common_terms holds key terms and their initial weights. These weights represent the frequency or importance of each term in the word cloud.
The script uses the random library to adjust each term's weight within a logical range, adding variability to the visualization.
Users first choose between a light or dark background. A dictionary color_palettes maps user-friendly names to matplotlib colormap strings and includes short descriptions. The script prompts the user to select one of these palettes.
The WordCloud class from the wordcloud library generates the word cloud with the specified dimensions (1584x396 pixels), background color, and colormap. The word cloud is then displayed using matplotlib.
The word cloud image is saved as linkedin_banner.png, fitting the LinkedIn banner size. The terms with their adjusted weights are saved in common_words.txt.
The script ensures the output image file size is under 3MB. If the initial file size exceeds this limit, the image quality is adjusted downwards in increments until the file size is within the acceptable range.
To customize the script further, you can modify the common_terms dictionary to include different terms and weights or add more color palettes to the color_palettes dictionary.
linkedin-banner-wordcloud-generator © 2024 by Philip Bachas-Daunert is licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International