EasyGraph-py 1.0.1

A simple python module to create all types of graphs.

EasyGraph 📊

EasyGraph is your go-to Python package for creating beautiful, intuitive, and now interactive graphs with just a few lines of code. Dive into data visualization with various chart types including line charts, bar charts, scatter plots, and more. With the new interactive functionality, users can now hover over data points to get detailed information, pan, and zoom to explore their graphs in a much more engaging manner.

Features 🚀

• Interactive Charts and Graphs: All your favorite charts and graphs are now interactive, offering dynamic insights with functionalities like hover, pan, and zoom.
• Variety of Chart Types: Supports a wide range of chart types for versatile data visualization.
• Zero to Hero: Start creating insightful graphs with no prior experience.
• Plug and Play: Easy to install and get started with.
• Flexible: Customize your charts with a wide range of parameters.
• Theming Support: Adapt the visuals of your charts with seaborn and matplotlib themes.

Installation 💻

Get it fresh from PyPI:

pip install EasyGraph-py

Usage 🛠

Here’s a sneak peek at how easy it is to use EasyGraph:

from easy_graph import bar_chart, line_chart, set_theme
import pandas as pd

data = {
    'Month': ['January', 'February', 'March', 'April'],
    'Sales': [100, 200, 150, 250]
}
df = pd.DataFrame(data)

# Set a theme for your charts (optional)
set_theme('darkgrid')

# Enable interactive mode (new feature!)
interactive=True # False by default

bar_chart(df, x_col='Month', y_col='Sales', title='Monthly Sales', xlabel='Month', ylabel='Sales', interactive=True)

# Boom! A bar chart!
bar_chart(df, 'Month', 'Sales', title='Monthly Sales', xlabel='Month', ylabel='Sales')

# Kaboom! A line chart!
line_chart(df, 'Month', 'Sales', title='Monthly Sales', xlabel='Month', ylabel='Sales')

Documentation 📖

Interactive Functionality 🔍

The latest update brings interactive functionality to all the chart and graph types in EasyGraph. Now, while visualizing data using any of the chart types, users can hover over individual data points to view detailed information. This not only makes the data analysis process more insightful but also offers a dynamic user experience. Make sure to explore functionalities like panning and zooming to dive deeper into your visualizations!

set_theme(theme=None)

Sets the theme for your plots globally. Parameters

theme (str, optional): The name of the theme. Supports all seaborn and matplotlib themes. Defaults to seaborn's "whitegrid".

bar_chart(data, x_col, y_col, title='Bar Chart', xlabel='X-axis', ylabel='Y-axis')

Generates a bar chart from a pandas DataFrame.

Parameters:

data: The pandas DataFrame containing your data.
x_col: The column to use for the x-axis.
y_col: The column to use for the y-axis.
title: The title of your graph.
xlabel: The label for the x-axis.
ylabel: The label for the y-axis.

line_chart(data, x_col, y_col, title='Line Chart', xlabel='X-axis', ylabel='Y-axis')

Generates a line chart from a pandas DataFrame.

Parameters:

data: The pandas DataFrame containing your data.
x_col: The column to use for the x-axis.
y_col: The column to use for the y-axis.
title: The title of your graph.
xlabel: The label for the x-axis.
ylabel: The label for the y-axis.

scatter_plot(data, x_col, y_col, title='Scatter Plot', xlabel='X-axis', ylabel='Y-axis', theme=None)

Creates a scatter plot to visualize the relationship between two variables. Parameters

Parameters:

data: The pandas DataFrame containing your data.
x_col: The column to use for the x-axis.
y_col: The column to use for the y-axis.
title: The title of your graph.
xlabel: The label for the x-axis.
ylabel: The label for the y-axis.

histogram(data, col, bins=10, title='Histogram', xlabel='X-axis', ylabel='Frequency', theme=None)

Generates a histogram to visualize the distribution of a single variable. Parameters

Parameters:

data: The pandas DataFrame containing your data.
x_col: The column to use for the x-axis.
y_col: The column to use for the y-axis.
title: The title of your graph.
xlabel: The label for the x-axis.
ylabel: The label for the y-axis.

boxplot(data, col, title='Boxplot', ylabel='Y-axis', theme=None)

Creates a box plot to depict groups of numerical data through their quartiles. Parameters

Parameters:

data: The pandas DataFrame containing your data.
x_col: The column to use for the x-axis.
y_col: The column to use for the y-axis.
title: The title of your graph.
xlabel: The label for the x-axis.
ylabel: The label for the y-axis.

pie_chart(data, col, labels_col, title='Pie Chart', theme=None)

Generates a pie chart to illustrate numerical proportions in a dataset. Parameters

Parameters:

data: The pandas DataFrame containing your data.
x_col: The column to use for the x-axis.
y_col: The column to use for the y-axis.
title: The title of your graph.
xlabel: The label for the x-axis.
ylabel: The label for the y-axis.

stacked_bar_chart(data, x_col, y_cols, title='Stacked Bar Chart', xlabel='X-axis', ylabel='Y-axis', theme=None)

Creates a stacked bar chart to visualize the total amount that is subdivided into sub-groups. Parameters

Parameters:

data: The pandas DataFrame containing your data.
x_col: The column to use for the x-axis.
y_col: The column to use for the y-axis.
title: The title of your graph.
xlabel: The label for the x-axis.
ylabel: The label for the y-axis.

area_chart(data, x_col, y_col, title='Area Chart', xlabel='X-axis', ylabel='Y-axis', theme=None)

Generates an area chart to represent quantities through area filled under lines. Parameters

Parameters:

data: The pandas DataFrame containing your data.
x_col: The column to use for the x-axis.
y_col: The column to use for the y-axis.
title: The title of your graph.
xlabel: The label for the x-axis.
ylabel: The label for the y-axis.

hexbin_plot(data, x_col, y_col, gridsize=50, title='Hexbin Plot', xlabel='X-axis', ylabel='Y-axis', theme=None)

Creates a hexbin plot to represent the relationship between two numerical variables when you have a lot of data points. Parameters

Parameters:

data: The pandas DataFrame containing your data.
x_col: The column to use for the x-axis.
y_col: The column to use for the y-axis.
title: The title of your graph.
xlabel: The label for the x-axis.
ylabel: The label for the y-axis.

violin_plot(data, col, title='Violin Plot', ylabel='Y-axis', theme=None)

Generates a violin plot which combines a boxplot with a kernel density plot to visualize the distribution of numerical data. Parameters

Parameters:

data: The pandas DataFrame containing your data.
x_col: The column to use for the x-axis.
y_col: The column to use for the y-axis.
title: The title of your graph.
xlabel: The label for the x-axis.
ylabel: The label for the y-axis.

correlation_matrix(data, title='Correlation Matrix', theme=None)

Creates a heatmap of the correlation matrix to visualize the relationship between every pair of variables in your dataset. Parameters

Parameters:

data: The pandas DataFrame containing your data.
x_col: The column to use for the x-axis.
y_col: The column to use for the y-axis.
title: The title of your graph.
xlabel: The label for the x-axis.
ylabel: The label for the y-axis.

pair_plot(data, title='Pair Plot', theme=None)

Generates a pair plot to visualize the relationships between all pairs of variables in your dataset. Parameters

Parameters:

data: The pandas DataFrame containing your data.
x_col: The column to use for the x-axis.
y_col: The column to use for the y-axis.
title: The title of your graph.
xlabel: The label for the x-axis.
ylabel: The label for the y-axis.

Contribution 🤝

Feel free to fork, open a pull request, or submit issues. Let's make EasyGraph the easiest graphing package in the Python ecosystem together! License 📄

MIT ##Kudos 💖

Big shoutout to all cool devs and data scientists out there! Happy graphing!