tessl/pypi-plotnine

A Grammar of Graphics for Python providing a declarative approach to data visualization similar to R's ggplot2

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Overview

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Files

Sample Datasets

Name: tessl/pypi-plotnine
Author: tessl

Plotnine includes a comprehensive collection of datasets commonly used for data visualization examples, tutorials, and exploration. These datasets provide real-world data across various domains including economics, biology, automotive, and demographics.

Import Patterns

# Import specific datasets
from plotnine.data import mtcars, diamonds, economics

# Import all datasets (not recommended for production)
from plotnine.data import *

# Access via module reference
import plotnine.data as data
df = data.mtcars

Capabilities

Automotive Data

# Motor Trend Car Road Tests - 32 automobiles (1973-74 models)
mtcars: pandas.DataFrame
# Columns: mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb

# Fuel economy data from 1999 and 2008 for 38 popular car models  
mpg: pandas.DataFrame
# Columns: manufacturer, model, displ, year, cyl, trans, drv, cty, hwy, fl, class

Usage Example:

from plotnine import ggplot, aes, geom_point
from plotnine.data import mtcars

# Scatter plot of weight vs mpg
plot = (ggplot(mtcars, aes(x='wt', y='mpg')) + 
        geom_point())

Jewelry and Precious Stones

# Prices and attributes of ~54,000 diamonds
diamonds: pandas.DataFrame  
# Columns: price, carat, cut, color, clarity, x, y, z, depth, table
# cut: Factor with levels Fair, Good, Very Good, Premium, Ideal
# color: Factor with levels D, E, F, G, H, I, J (D=best, J=worst)
# clarity: Factor with levels I1, SI2, SI1, VS2, VS1, VVS2, VVS1, IF (I1=worst, IF=best)

Economic Data

# US economic time series from FRED database
economics: pandas.DataFrame
# Columns: date, psavert, pce, unemploy, uempmed, pop

# US economic data in long format for easier visualization
economics_long: pandas.DataFrame
# Same data as economics but in tidy/long format

Biological Data

# Palmer Penguins - 3 species from Antarctica
penguins: pandas.DataFrame
# Columns: species, island, bill_length_mm, bill_depth_mm, flipper_length_mm, body_mass_g, sex, year
# species: Factor with levels Adelie, Chinstrap, Gentoo

# Updated mammals sleep dataset  
msleep: pandas.DataFrame
# Columns: name, genus, vore, order, conservation, sleep_total, sleep_rem, sleep_cycle, awake, brainwt, bodywt

Geographic and Demographic Data

# Midwest demographics by county
midwest: pandas.DataFrame  
# Columns: PID, county, state, area, poptotal, popdensity, popwhite, popblack, etc.

# Texas housing market data from TAMU real estate center
txhousing: pandas.DataFrame
# Columns: city, year, month, sales, volume, median, listings, inventory, date

Natural Phenomena

# Old Faithful Geyser eruption data
faithful: pandas.DataFrame
# Columns: eruptions, waiting

# Old Faithful data with density estimates (grid format)
faithfuld: pandas.DataFrame  
# Columns: eruptions, waiting, density

# Lake Huron water levels 1875-1972
huron: pandas.DataFrame
# Columns: year, level, decade

# Vector field of seal movements
seals: pandas.DataFrame
# Columns: lat, long, delta_long, delta_lat

Food Production and Web Data

# US meat production by month (millions of lbs)
meat: pandas.DataFrame
# Columns: date, beef, veal, pork, lamb_and_mutton, broilers, other_chicken, turkey

# Website pageview data 
pageviews: pandas.DataFrame
# Columns: date, pageviews

Political Data

# Terms of 11 US presidents from Eisenhower to Obama
presidential: pandas.DataFrame
# Columns: name, start, end, party

Statistical Datasets

# Anscombe's Quartet - 4 datasets with identical statistical properties
anscombe_quartet: pandas.DataFrame
# Columns: dataset, x, y

# Colors in Luv color space
luv_colours: pandas.DataFrame
# Columns: L, u, v, col

Common Usage Patterns

Quick Data Exploration

from plotnine import ggplot, aes, geom_histogram, geom_point, facet_wrap
from plotnine.data import diamonds, penguins

# Explore diamond prices
price_dist = (ggplot(diamonds, aes(x='price')) + 
             geom_histogram(bins=30) +
             facet_wrap('cut'))

# Penguin species comparison  
penguin_plot = (ggplot(penguins, aes(x='bill_length_mm', y='bill_depth_mm', color='species')) +
               geom_point())

Time Series Analysis

from plotnine import ggplot, aes, geom_line
from plotnine.data import economics

# Economic trends over time
econ_plot = (ggplot(economics, aes(x='date', y='unemploy')) +
            geom_line())

Statistical Examples

from plotnine import ggplot, aes, geom_point, stat_smooth
from plotnine.data import mtcars

# Regression analysis
regression_plot = (ggplot(mtcars, aes(x='wt', y='mpg')) +  
                  geom_point() +
                  stat_smooth(method='lm'))

Dataset Categories

Category	Datasets	Use Cases
Automotive	mtcars, mpg	Regression, clustering, factor analysis
Economics	economics, economics_long, txhousing	Time series, trend analysis
Biology	penguins, msleep, faithful	Species comparison, behavioral analysis
Geography	midwest, seals, huron	Spatial analysis, movement patterns
Retail	diamonds	Price modeling, categorical analysis
Food	meat	Production trends, seasonal patterns
Politics	presidential	Timeline analysis, categorical data
Statistics	anscombe_quartet, luv_colours	Statistical education, color analysis

All datasets are provided as pandas DataFrames with appropriate data types, including categorical variables where relevant for optimal plotting performance.

Install with Tessl CLI