scatter plot

abutils provides functionality for creating highly customizable scatter plots. The scatter plot function supports a wide range of features including categorical and continuous color scaling, point highlighting, and legend customization. It is designed to work seamlessly with data in various formats, including pandas DataFrames and abutils Sequence objects.

plotting function	description
abutils.plots.scatter()	creates scatter plots with extensive customization options

examples

basic scatter plot

Create a simple scatter plot from x and y coordinates:

import abutils
import numpy as np

# create sample data
x = np.random.normal(0, 1, 100)
y = x + np.random.normal(0, 0.5, 100)

# create a basic scatter plot
ax = abutils.plots.scatter(
    x=x,
    y=y,
    color='steelblue',
    size=50,
    alpha=0.7,
    xlabel='X Variable',
    ylabel='Y Variable',
    figsize=[8, 8]
)

scatter plot with categorical hue

Color points based on a categorical variable:

import abutils
import numpy as np
import pandas as pd

# create data with categories
n_points = 150
data = pd.DataFrame({
    'x': np.concatenate([np.random.normal(-1, 0.5, n_points//3),
                        np.random.normal(0, 0.5, n_points//3),
                        np.random.normal(1, 0.5, n_points//3)]),
    'y': np.concatenate([np.random.normal(1, 0.5, n_points//3),
                        np.random.normal(0, 0.5, n_points//3),
                        np.random.normal(-1, 0.5, n_points//3)]),
    'group': np.repeat(['Group A', 'Group B', 'Group C'], n_points//3)
})

# create a scatter plot with categorical hue
ax = abutils.plots.scatter(
    x='x',
    y='y',
    hue='group',
    data=data,
    palette={'Group A': 'crimson', 'Group B': 'navy', 'Group C': 'forestgreen'},
    size=80,
    alpha=0.7,
    xlabel='X Coordinate',
    ylabel='Y Coordinate',
    equal_axes=True,
    figsize=[10, 8]
)

scatter plot with continuous hue and color bar

Color points based on a continuous variable with a custom colormap:

import abutils
import numpy as np
import pandas as pd

# create data with a continuous variable
x = np.random.uniform(-3, 3, 200)
y = np.random.uniform(-3, 3, 200)

# calculate distance from origin as the hue value
distance = np.sqrt(x**2 + y**2)

# create a scatter plot with continuous hue
ax = abutils.plots.scatter(
    x=x,
    y=y,
    hue=distance,
    cmap='plasma',  # custom colormap
    size=70,
    alpha=0.8,
    xlabel='X Coordinate',
    ylabel='Y Coordinate',
    cbar_title='Distance from Origin',
    cbar_title_fontsize=14,
    equal_axes=True,
    figsize=[10, 8]
)

scatter plot with highlighted points

Highlight specific points of interest in the scatter plot:

import abutils
import numpy as np
import pandas as pd

# create random data
np.random.seed(42)
n_points = 100
x = np.random.normal(0, 1, n_points)
y = np.random.normal(0, 1, n_points)

# identify points to highlight (those far from the origin)
distance = np.sqrt(x**2 + y**2)
highlight_indices = np.where(distance > 2.0)[0]

# create a scatter plot with highlighted points
ax = abutils.plots.scatter(
    x=x,
    y=y,
    highlight_index=highlight_indices,
    highlight_marker='*',
    highlight_size=200,
    highlight_color='red',
    highlight_name='Outliers',
    color='darkblue',
    size=50,
    alpha=0.6,
    xlabel='X Value',
    ylabel='Y Value',
    equal_axes=True,
    figsize=[10, 8]
)

scatter plot with customized legend

Create a scatter plot with a custom-positioned legend:

import abutils
import numpy as np
import pandas as pd

# create data with categorical and continuous variables
n_points = 200
categories = ['Low', 'Medium', 'High']
data = pd.DataFrame({
    'x': np.random.normal(0, 1, n_points),
    'y': np.random.normal(0, 1, n_points),
    'category': np.random.choice(categories, n_points),
    'value': np.random.uniform(0, 10, n_points)
})

# create a scatter plot with custom legend
ax = abutils.plots.scatter(
    x='x',
    y='y',
    hue='category',
    data=data,
    size=80,
    alpha=0.7,
    palette={'Low': 'blue', 'Medium': 'green', 'High': 'red'},
    legend_on_data=True,  # place legend on the data
    legend_marker_alpha=1.0,  # make legend markers fully opaque
    legend_fontsize=14,
    legend_fontweight='bold',
    xlabel='X Variable',
    ylabel='Y Variable',
    figsize=[12, 10]
)

api

abutils.plots.scatter()