csvplotter 0.0.2

Plotter using .csv filesWritten by pedrohenriquecoimbra

Plotter Module

A Python module to generate plots from a CSV configuration file using seaborn. This module allows you to create custom plots by reading plot configurations directly from a CSV file and automatically generating the plots according to the specified settings.

Features

• CSV-driven Plot Generation: Read plot configurations from a CSV file and generate plots accordingly.
• Customizable Plot Settings: Control plot features such as axis labels, titles, themes, color palettes, and more.
• Multiple Plot Sizes: Choose from predefined aspect ratios (small, medium, big, wide).
• Save Plots: Optionally save plots as PNG, SVG, or PDF with custom resolutions.
• Flexible Theme: Select from light or dark themes.
• Seaborn-powered: Built on top of seaborn, making it easy to work with beautiful, statistical plots.

Installation

To use this module, you need Python 3.x and the following libraries:

• seaborn
• matplotlib
• pandas

Install via pip:

pip install seaborn matplotlib pandas

Usage

1. Importing the Module:

from plotter import Plotter
import pandas as pd

2. Prepare Your Data:

Ensure you have a DataFrame ready with the data to plot. For example:

data = pd.DataFrame({
    'time': [1, 2, 3, 4, 5],
    'value': [10, 20, 15, 30, 25],
    'category': ['A', 'B', 'A', 'B', 'A']
})

3. Prepare Your Configuration CSV File:

Create a CSV file (plot_config.csv) with the following columns (or your own as needed):

x_var	y_var	x_label	y_label	hue	style	size	theme	palette	aspect	xlim	ylim	title
time	value	Time	Value	category			light	deep	wide	0,35	0,40	Sample Plot 1
time	value	Time	Value	category			dark	pastel	small	0,40	0,50	Sample Plot 2

4. Generate Plots from CSV:

Call the plot_from_csv function to read the configuration and generate plots:

plotter = Plotter(data)
plotter.plot_from_csv('plot_config.csv', data, save_folder="plots")

Parameters:

• csv_file: Path to the CSV file containing plot configurations.
• data: DataFrame containing the data to plot.
• save_folder (optional): Folder where the plots will be saved. If not specified, plots will be shown without saving.
• dpi (optional): Resolution for saving plots (default is 300).

5. Plot Customization:

You can customize the plot appearance in the CSV:

• x_var and y_var: Column names for the X and Y axes.
• x_label and y_label: Labels for the X and Y axes.
• hue: Variable to use for color encoding.
• style: Variable to use for line style.
• size: Variable to use for size encoding.
• theme: Select between 'light' or 'dark' for plot background theme.
• palette: Choose color palette (e.g., 'deep', 'pastel', 'muted').
• aspect: Set plot aspect ratio ('small', 'medium', 'big', 'wide').
• xlim and ylim: Set axis limits as tuples.
• title: Add a title to the plot.

6. Example Code:

import pandas as pd
from plotter import Plotter

# Sample Data
data = pd.DataFrame({
    'time': [1, 2, 3, 4, 5],
    'value': [10, 20, 15, 30, 25],
    'category': ['A', 'B', 'A', 'B', 'A']
})

# Create the Plotter instance
plotter = Plotter(data)

# Plot the configurations from the CSV file
plotter.plot_from_csv('plot_config.csv', data, save_folder="plots")

7. Saving the Plot:

If you specify a folder in save_folder, the plots will be saved with the name plot_1.png, plot_2.png, etc. You can also specify custom file formats (e.g., PNG, SVG) and DPI.

Example CSV (plot_config.csv):

x_var,y_var,x_label,y_label,hue,style,size,theme,palette,aspect,xlim,ylim,title
time,value,Time,Value,category,,medium,light,deep,wide,0,35,Sample Plot 1
time,value,Time,Value,category,,big,dark,pastel,small,0,40,Sample Plot 2

Advanced Usage

You can extend the Plotter class to include more specific functionalities such as:

• Tooltips or zooming features (for future versions).
• Error handling for missing data or invalid configurations.

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Contributing

Feel free to open issues, create pull requests, or suggest improvements. This module is open for contributions and enhancements.

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License

This project is licensed under the MIT License - see the LICENSE file for details.

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You can modify and expand this template as your project evolves! It should give users a clear guide on how to get started with your module.