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# Sample Datasets
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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.
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## Import Patterns
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```python
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# Import specific datasets
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from plotnine.data import mtcars, diamonds, economics
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# Import all datasets (not recommended for production)
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from plotnine.data import *
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# Access via module reference
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import plotnine.data as data
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df = data.mtcars
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```
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## Capabilities
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### Automotive Data
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```python { .api }
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# Motor Trend Car Road Tests - 32 automobiles (1973-74 models)
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mtcars: pandas.DataFrame
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# Columns: mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb
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# Fuel economy data from 1999 and 2008 for 38 popular car models  
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mpg: pandas.DataFrame
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# Columns: manufacturer, model, displ, year, cyl, trans, drv, cty, hwy, fl, class
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```
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**Usage Example:**
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```python
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from plotnine import ggplot, aes, geom_point
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from plotnine.data import mtcars
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# Scatter plot of weight vs mpg
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plot = (ggplot(mtcars, aes(x='wt', y='mpg')) + 
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        geom_point())
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```
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### Jewelry and Precious Stones
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```python { .api }
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# Prices and attributes of ~54,000 diamonds
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diamonds: pandas.DataFrame  
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# Columns: price, carat, cut, color, clarity, x, y, z, depth, table
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# cut: Factor with levels Fair, Good, Very Good, Premium, Ideal
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# color: Factor with levels D, E, F, G, H, I, J (D=best, J=worst)
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# clarity: Factor with levels I1, SI2, SI1, VS2, VS1, VVS2, VVS1, IF (I1=worst, IF=best)
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```
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### Economic Data
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```python { .api }
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# US economic time series from FRED database
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economics: pandas.DataFrame
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# Columns: date, psavert, pce, unemploy, uempmed, pop
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# US economic data in long format for easier visualization
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economics_long: pandas.DataFrame
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# Same data as economics but in tidy/long format
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```
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### Biological Data
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```python { .api }
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# Palmer Penguins - 3 species from Antarctica
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penguins: pandas.DataFrame
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# Columns: species, island, bill_length_mm, bill_depth_mm, flipper_length_mm, body_mass_g, sex, year
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# species: Factor with levels Adelie, Chinstrap, Gentoo
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# Updated mammals sleep dataset  
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msleep: pandas.DataFrame
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# Columns: name, genus, vore, order, conservation, sleep_total, sleep_rem, sleep_cycle, awake, brainwt, bodywt
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```
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### Geographic and Demographic Data
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```python { .api }
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# Midwest demographics by county
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midwest: pandas.DataFrame  
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# Columns: PID, county, state, area, poptotal, popdensity, popwhite, popblack, etc.
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# Texas housing market data from TAMU real estate center
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txhousing: pandas.DataFrame
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# Columns: city, year, month, sales, volume, median, listings, inventory, date
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```
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### Natural Phenomena
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```python { .api }
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# Old Faithful Geyser eruption data
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faithful: pandas.DataFrame
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# Columns: eruptions, waiting
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# Old Faithful data with density estimates (grid format)
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faithfuld: pandas.DataFrame  
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# Columns: eruptions, waiting, density
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# Lake Huron water levels 1875-1972
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huron: pandas.DataFrame
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# Columns: year, level, decade
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# Vector field of seal movements
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seals: pandas.DataFrame
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# Columns: lat, long, delta_long, delta_lat
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```
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### Food Production and Web Data
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```python { .api }
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# US meat production by month (millions of lbs)
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meat: pandas.DataFrame
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# Columns: date, beef, veal, pork, lamb_and_mutton, broilers, other_chicken, turkey
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# Website pageview data 
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pageviews: pandas.DataFrame
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# Columns: date, pageviews
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```
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### Political Data
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```python { .api }
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# Terms of 11 US presidents from Eisenhower to Obama
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presidential: pandas.DataFrame
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# Columns: name, start, end, party
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```
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### Statistical Datasets
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```python { .api }
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# Anscombe's Quartet - 4 datasets with identical statistical properties
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anscombe_quartet: pandas.DataFrame
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# Columns: dataset, x, y
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# Colors in Luv color space
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luv_colours: pandas.DataFrame
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# Columns: L, u, v, col
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```
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## Common Usage Patterns
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### Quick Data Exploration
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```python
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from plotnine import ggplot, aes, geom_histogram, geom_point, facet_wrap
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from plotnine.data import diamonds, penguins
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# Explore diamond prices
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price_dist = (ggplot(diamonds, aes(x='price')) + 
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             geom_histogram(bins=30) +
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             facet_wrap('cut'))
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# Penguin species comparison  
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penguin_plot = (ggplot(penguins, aes(x='bill_length_mm', y='bill_depth_mm', color='species')) +
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               geom_point())
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```
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### Time Series Analysis
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```python
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from plotnine import ggplot, aes, geom_line
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from plotnine.data import economics
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# Economic trends over time
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econ_plot = (ggplot(economics, aes(x='date', y='unemploy')) +
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            geom_line())
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```
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### Statistical Examples
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```python
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from plotnine import ggplot, aes, geom_point, stat_smooth
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from plotnine.data import mtcars
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# Regression analysis
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regression_plot = (ggplot(mtcars, aes(x='wt', y='mpg')) +  
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                  geom_point() +
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                  stat_smooth(method='lm'))
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```
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## Dataset Categories
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| Category | Datasets | Use Cases |
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|----------|----------|-----------|
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| **Automotive** | mtcars, mpg | Regression, clustering, factor analysis |
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| **Economics** | economics, economics_long, txhousing | Time series, trend analysis |
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| **Biology** | penguins, msleep, faithful | Species comparison, behavioral analysis |
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| **Geography** | midwest, seals, huron | Spatial analysis, movement patterns |
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| **Retail** | diamonds | Price modeling, categorical analysis |
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| **Food** | meat | Production trends, seasonal patterns |
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| **Politics** | presidential | Timeline analysis, categorical data |
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| **Statistics** | anscombe_quartet, luv_colours | Statistical education, color analysis |
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All datasets are provided as pandas DataFrames with appropriate data types, including categorical variables where relevant for optimal plotting performance.