I need to quench my thirst for knowledge and learn something new just for the sake of it.
I’ve already learned:
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Equations and pivot tables in Excel
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Vector graphics in Inkscape
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Music mixing in rekordbox
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Personal VPN on a raspberry pi 4 with OpenVPN
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LAMP stack web hosting
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Streaming & video capture with OBS
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Manual & automatic backups with FreeFileSync
Things I’m open to:
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FOSS (even beta) or free-as-in-beer software
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A high or low learning curve
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Tools for niche fields that I’d otherwise have no reason to learn
Python, and you kind of want to take the long way around.
Get it up and running in its own virtual environment. On Linux and Windows this’ll just be a directory its in, and once you’ve activated that install via a terminal command everything stays self contained. It has a package manager called “pip” that will handle package and dependency management. Use pip to install spyder, pandas, matplotlib, numpy, scipy, and openpyxl. If you install anaconda this is what it’s doing under the hood, but there are licensing hiccups with using their package repos.
Anyway, however you get up and going, numpy is fast numerical storage, scipy is a lot of scientific algorithms, pandas is a data analysis library that rides on top of numpy, and openpyxl is an interface to excel files from python. Pandas will get you one line CSV file reads and writes and more complex manipulation of Excel spreadsheets. Openpyxl gets you cell by cell manipulation of a spreadsheet. Spyder is a development environment.
It can do much, much more.
I asked an LLM for a small python example with pandas and matplotlib. Load it into spyder and run it and see what happens (tip, go into the settings/preferences, IPython Console, Plotting, and change the Graphics Backend to “Qt” to get the plots in their own window).
Python Example
import numpy as np import pandas as pd import matplotlib.pyplot as plt def generate_damped_signal(): # 1. Setup Parameters fs = 50 # 50Hz Sampling frequency t_max = 6.0 # Run for 6 seconds to clearly visualize it hitting 0 at 5s freq = 2.0 # Oscillation frequency in Hz # 2. Generate Independent Time Axis # 50 samples per second from 0 to t_max time = np.arange(0, t_max, 1/fs) # 3. Generate Dependent Damped Signal Axis # Using an exponential decay constant of 1.0 ensures that at t=5 seconds, # e^(-5) drops down to ~0.006, effectively decaying the signal to zero. amplitude = np.exp(-time) * np.cos(2 * np.pi * freq * time) # 4. Create Pandas DataFrame df = pd.DataFrame({ 'Time_Seconds': time, 'Signal_Amplitude': amplitude }) # 5. Export DataFrame to CSV File csv_filename = "damped_signal.csv" df.to_csv(csv_filename, index=False) print(f"Successfully generated DataFrame and saved to '{csv_filename}'") # 6. Plot the Data Using Matplotlib plt.figure(figsize=(10, 5)) plt.plot(df['Time_Seconds'], df['Signal_Amplitude'], label='Damped Signal', color='cyan', linewidth=2) # Visual Anchors for the 5-second decay mark plt.axvline(x=5.0, color='red', linestyle='--', alpha=0.7, label='5-Second Decay Target') plt.axhline(y=0.0, color='gray', linestyle='-', alpha=0.5) # Labeling and Grid Configuration plt.title('Damped Signal Decay Over Time (50Hz Sampling Rate)') plt.xlabel('Time (Independent Axis - Seconds)') plt.ylabel('Signal (Dependent Axis - Amplitude)') plt.grid(True, linestyle=':', alpha=0.6) plt.legend() # Display the Plot Window plt.show() if __name__ == "__main__": generate_damped_signal()