DataFrame-level Visualization Examples

Basic examples of visualization methods.

Used Libraries

The following methods selectively use these libraries:

• pandas

• numpy

• plotly

• scipy

• pingouin

import plotly.io as pio
pio.renderers.default = "notebook"

Bar Chart

bar() - Basic bar chart showing average values by category.

from frameon import load_dataset, FrameOn as fo

titanic = fo(load_dataset('titanic'))
fig = titanic.viz.bar(
    x='class',
    y='fare',
    color='sex',
    facet_col='embarked',
    agg_func='mean',
    text_auto='.0f',
    title='Average Fare by Class, Sex and Embarkation Port',
    width=900
)
fig.show()

Line Chart

line() - Time series line chart showing trends over time.

from frameon import load_dataset, FrameOn as fo

superstore = fo(load_dataset('superstore'))
fig = superstore.viz.line(
    x='Order Date',
    y='Sales',
    color='Category',
    agg_func='mean',
    freq='ME',
    title='Average Sales Over Time by Category'
)
fig.show()

Area Chart

area() - Stacked area chart showing composition over time.

from frameon import load_dataset, FrameOn as fo

superstore = fo(load_dataset('superstore'))
fig = superstore.viz.area(
    x='Order Date',
    y='Sales',
    color='Category',
    agg_func='mean',
    freq='ME',
    title='Sales Composition Over Time by Category'
)
fig.show()

Box Plot

box() - Shows distribution of values across categories.

from frameon import load_dataset, FrameOn as fo

superstore = fo(load_dataset('superstore'))
fig = superstore.viz.box(
    x='Category',
    y='Sales',
    color='Segment',
    show_dual=True,
    upper_quantile=0.95,
    show_legend_title=True,
    title='Sales Distribution by Category and Segmen (Right: to 0.95 Quantile)'
)
fig.show()

Heatmap

heatmap() - Heatmap showing relationships between two categorical variables.

from frameon import load_dataset, FrameOn as fo

titanic = fo(load_dataset('titanic'))
fig = titanic.viz.heatmap(
    x='class',
    y='alive',
    z='age',
    agg_func='mean',
    do_pivot=True,
    text_auto='.2f',
    title='Average Age by Passenger Class and Survival Status',
    width=600,
    height=400
)
fig.show()

Pairplot

pairplot() - Matrix of scatterplots showing relationships between numeric variables.

from frameon import load_dataset, FrameOn as fo

iris = fo(load_dataset('iris'))
fig = iris.viz.pairplot(
    pairs=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'],
    color_mode='category',
    color_column='species',
    renderer=None,
    width=900
)
fig.show()

Colored by kernel density estimation.

from frameon import load_dataset, FrameOn as fo

iris = fo(load_dataset('iris'))
fig = iris.viz.pairplot(
    pairs=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'],
    color_mode='kde',
    renderer=None,
    width=900
)
fig.show()

Pie/Bar Combo Chart

pie_bar() - Creates pie/bar combo charts

from frameon import load_dataset, FrameOn as fo

diamonds = fo(load_dataset('diamonds'))
fig = diamonds.viz.pie_bar(
    y='cut',
    x='price',
    agg_func='mean',
    text_auto='.0f',
    pull=0.1,
    category_orders={'cut': 'descending'},
    title='Average Diamond Price by Cut Quality'
)
fig.show()

Histogram

histogram() - Shows distribution of a numeric variable.

Base mode display simple histogram.

from frameon import load_dataset, FrameOn as fo

titanic = fo(load_dataset('titanic'))
fig = titanic.viz.histogram(
    x='age',
    color='alive',
    title='Passenger Age Distribution by Survival Status'
)
fig.show()

Displays a dual plot with the original histogram on the left and a trimmed histogram (based on quantiles) on the right.

from frameon import load_dataset, FrameOn as fo

superstore = fo(load_dataset('superstore'))
fig = superstore.viz.histogram(
    x='Sales',
    color='Category',
    mode='dual_hist_trim',
    upper_quantile=0.95,
    nbins=20,
    title='Sales Distribution by Category (Right: to 0.95 Quantile)'
)
fig.show()

Displays a dual plot with a boxplot on the left and a trimmed histogram (based on quantiles) on the right.

from frameon import load_dataset, FrameOn as fo

superstore = fo(load_dataset('superstore'))
fig = superstore.viz.histogram(
    x='Sales',
    color='Region',
    show_hist=False,
    show_kde=True,
    mode='dual_box_trim',
    upper_quantile=0.95,
    nbins=20,
    show_legend_title=True,
    title='Sales Distribution by Region (Right: to 0.95 Quantile)'
)
fig.show()

Categorical Comparison

cat_compare() - Compares categorical distributions

from frameon import load_dataset, FrameOn as fo

titanic = fo(load_dataset('titanic'))
titanic.viz.cat_compare(
    cat1='class',
    cat2='embarked',
    text_auto=True
)

Word Cloud

wordcloud() - Visualizes word frequencies from text data.

Click and drag to zoom into specific areas.

from frameon import load_dataset, FrameOn as fo

reviews = fo(load_dataset('reviews'))
reviews.viz.wordcloud(
    text_column='Text',
)

Parallel Categories

parallel_categories() - Shows relationships between multiple categorical variables.

from frameon import load_dataset, FrameOn as fo

titanic = fo(load_dataset('titanic'))
fig = titanic.viz.parallel_categories(
    dimensions=['class', 'sex', 'alive'],
    title='Titanic Passenger Characteristics Flow',
    width=900
)
fig.show()

Period Change

period_change() - Shows changes between time periods.

from frameon import load_dataset, FrameOn as fo

superstore = fo(load_dataset('superstore'))
fig = superstore.viz.period_change(
    date_col='Order Date',
    metric_col='Sales',
    agg_func='sum',
    period='mom',
    title='Month-over-Month Sales Change'
)
fig.show()