dotplotlib 0.1.3

A basic extension library for creating strip plots or dot charts w/ matplotlib + seaboard

Great things come in tiny packages. A bare minimum extension library with the sole aim of providing a dot plot (aka strip plot/dot chart)

• Designed to work with matplotlib and seaborn.
• Provides a simple yet powerful interface for visualizing data distributions, frequencies and categories.

installation

pip install dotplotlib

basic usage

dotplotlib can be used to generate dot charts with minimal code. Here are some basic examples

Example 1: Simple Dot Chart

.dotchart returns x and y lists that can be inputted straight into matplotlib or seaborn scatterplots.

from dotplotlib import dotchart
import matplotlib.pyplot as plt

# Data preparation
data = {'size': [1, 2, 3, 4, 5, 6]}

# Generate dot chart data
x, y = dotchart(data['size'])

# Plot
plt.scatter(x, y)
plt.show()

Example 2: Dot Chart with Color Mapping

Returns an extra c list that should be passed into the c= parameter if using matplotlib or hue= if using seaborn.

from dotplotlib import dotchart
import matplotlib.pyplot as plt

# Data preparation
data = {'size': [1, 2, 3, 4, 5, 6], 'rating': [3, 2, 5, 4, 3, 6]}

# Generate dot chart data with color mapping
x, y, c = dotchart(data['size'], color_by=data['rating'])

# Plot with color mapping
plt.scatter(x, y, c=c, cmap='viridis')
plt.colorbar()
plt.xlabel('Size')
plt.ylabel('Number')
plt.title('Mushroom Size Count Colored by Rating')
plt.show()

Example 3: Using make_dotchart for Simplified Plotting

Instead of just giving you x, y data to make the plot yourself, make_dotplot() actually generates the plot.

from dotplotlib import make_dotchart

# Data preparation
test_df = {'size': [1, 2, 3, 4, 5, 6], 'rating': [3, 2, 5, 4, 3, 6]}

# Create a dot chart with additional customization
make_dotchart(test_df['size'], color_by=test_df['rating'], dot_size=40, theme='gnuplot2')

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preset themes

custom:lavender

cmap

Any cmap value supported by matplotlib (see here) will work when passed into theme='viridis'.

viridis:

gnuplot:

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features

• generate strip plots/dot charts by exploiting matplotlib/seaborn scatterplots
• supports any cmap color profile
• the data can be automatically sorted for better visualization, especially when using color mapping.
• accepts both list and pandas.Series as input data.
• set custom labels, titles, and dot sizes for your charts.

credit