### Install upsetplot using pip

Source: https://github.com/jnothman/upsetplot/blob/master/README.rst

Installs the upsetplot library using pip. Ensure you have Python and pip configured.

```bash
pip install upsetplot
```

--------------------------------

### Generate and Plot Example Data

Source: https://github.com/jnothman/upsetplot/blob/master/doc/index.md

Generates sample data using `generate_counts` and then plots it using the `plot` function. Requires matplotlib to display the plot.

```python
from upsetplot import generate_counts
example = generate_counts()
print(example)
```

```python
from upsetplot import plot
plot(example)
from matplotlib import pyplot
pyplot.show()
```

--------------------------------

### Import upsetplot library

Source: https://github.com/jnothman/upsetplot/blob/master/README.rst

Imports the upsetplot library in a Python environment after installation. This confirms the installation was successful.

```python
import upsetplot
```

--------------------------------

### Create Data from Memberships

Source: https://github.com/jnothman/upsetplot/blob/master/doc/index.md

Reconstructs the example data structure from a list of category memberships for each data point and their corresponding counts. This is an alternative to `generate_counts`.

```python
from upsetplot import from_memberships
example = from_memberships(
    [[],
     ['cat2'],
     ['cat1'],
     ['cat1', 'cat2'],
     ['cat0'],
     ['cat0', 'cat2'],
     ['cat0', 'cat1'],
     ['cat0', 'cat1', 'cat2'],
     ],
     data=[56, 283, 1279, 5882, 24, 90, 429, 1957]
)
print(example)
```

--------------------------------

### Create UpSet Plot from Memberships

Source: https://github.com/jnothman/upsetplot/blob/master/README.rst

Reconstructs the example data format for UpSetPlot using category memberships for each data point. This is an alternative to `generate_counts`.

```python
from upsetplot import from_memberships

example = from_memberships(
    [[],
     ['cat2'],
     ['cat1'],
     ['cat1', 'cat2'],
     ['cat0'],
     ['cat0', 'cat2'],
     ['cat0', 'cat1'],
     ['cat0', 'cat1', 'cat2'],
     ],
     data=[56, 283, 1279, 5882, 24, 90, 429, 1957]
)
print(example)
```

--------------------------------

### Generate Example Sample Values for UpSetPlot

Source: https://github.com/jnothman/upsetplot/blob/master/doc/formats.ipynb

Generates a pandas Series with a 3-level MultiIndex, where each row represents an observation with associated floating-point values. Use this when you have raw data points and want to aggregate them.

```python
from upsetplot import generate_samples

example_values = generate_samples().value
example_values
```

--------------------------------

### Generate Example Counts for UpSetPlot

Source: https://github.com/jnothman/upsetplot/blob/master/doc/formats.ipynb

Generates a pandas Series with a 3-level MultiIndex representing subset memberships and their counts. Use this as input for plotting when you have aggregate counts.

```python
from upsetplot import generate_counts

example_counts = generate_counts()
example_counts
```

--------------------------------

### Generate Example Counts for UpSetPlot

Source: https://github.com/jnothman/upsetplot/blob/master/README.rst

Generates a pandas Series with counts for subset sizes, used as input for plotting. The index represents intersections of named categories.

```python
from upsetplot import generate_counts
example = generate_counts()
print(example)
```

--------------------------------

### Prepare Data with from_indicators

Source: https://context7.com/jnothman/upsetplot/llms.txt

Demonstrates creating indicator-based data structures for UpSet plots using column names, callables, or missingness.

```python
# Using column names from a DataFrame
df = pd.DataFrame({
    "value": [5, 4, 6, 4],
    "cat1": [True, False, True, False],
    "cat2": [False, True, False, False],
    "cat3": [True, True, False, False]
})
result = from_indicators(["cat1", "cat3"], data=df)

# Using a callable to identify indicator columns
result = from_indicators(lambda data: data.select_dtypes(bool), data=df)

# Using missingness as an indicator (useful for missing data analysis)
missing_data = pd.DataFrame({
    "val1": [pd.NA, 0.7, pd.NA, 0.9],
    "val2": ["male", pd.NA, "female", "female"],
    "val3": [pd.NA, pd.NA, 23000, 78000]
})
result = from_indicators(pd.isna, data=missing_data)
```

--------------------------------

### Importing from Indicators

Source: https://github.com/jnothman/upsetplot/blob/master/doc/formats.ipynb

This is a placeholder import statement. Use `from_indicators` when your data is already in a boolean format, where each column represents a set.

```python
from upsetplot import from_indicators
```

--------------------------------

### Generate sample DataFrame

Source: https://github.com/jnothman/upsetplot/blob/master/doc/formats.ipynb

Creates a sample DataFrame for use with UpSetPlot.

```python
from upsetplot import generate_samples

example_samples_df = generate_samples()
example_samples_df.head()
```

--------------------------------

### Build Data with Associated Information from Contents

Source: https://context7.com/jnothman/upsetplot/llms.txt

Builds data from category contents and associates it with additional information, such as metadata for each member.

```python
# With associated data
info = pd.DataFrame({
    'color': ['red', 'blue', 'green', 'yellow', 'purple']
}, index=['a', 'b', 'c', 'd', 'e'])

result = from_contents(contents, data=info)
print(result)
```

--------------------------------

### Create Plots with plot()

Source: https://context7.com/jnothman/upsetplot/llms.txt

Convenience function for quick visualization with various sorting, filtering, and formatting options.

```python
from upsetplot import plot, generate_counts
from matplotlib import pyplot as plt

example = generate_counts()

# Basic plot with default settings
plot(example)
plt.suptitle("Basic UpSet Plot")
plt.show()

# With sorting by cardinality instead of degree
plot(example, sort_by="cardinality")
plt.suptitle("Sorted by cardinality")
plt.show()

# Show counts and percentages on bars
plot(example, show_counts="{:,}", show_percentages=True)
plt.suptitle("With counts and percentages")
plt.show()

# Vertical orientation
plot(example, orientation="vertical", show_counts="{:d}")
plt.suptitle("Vertical orientation")
plt.show()

# Custom figure size
fig = plt.figure(figsize=(12, 4))
plot(example, fig=fig, element_size=None)
plt.show()

# Filtering by subset size
plot(example, min_subset_size=100, max_subset_size=2000)
plt.suptitle("Filtered by size")
plt.show()

# Filtering by degree (number of intersecting categories)
plot(example, min_degree=1, max_degree=2)
plt.suptitle("Degree 1 or 2 only")
plt.show()
```

--------------------------------

### Access Specific Subset Data from Samples

Source: https://context7.com/jnothman/upsetplot/llms.txt

Demonstrates how to access specific subset data from the generated samples DataFrame, filtering by category membership.

```python
# Access specific subset data
print(samples.loc[(True, True, True)])  # Samples in all three categories
```

--------------------------------

### Generate Sample Data with UpSetPlot

Source: https://context7.com/jnothman/upsetplot/llms.txt

Generates artificial samples assigned to set intersections, returning a DataFrame with a 'value' column and boolean index levels. Useful for raw sample data.

```python
from upsetplot import generate_samples

# Generate sample data with 3 categories
samples = generate_samples(seed=0, n_samples=10000, n_categories=3)
print(samples.head())
```

--------------------------------

### Build Data from Category Contents with UpSetPlot

Source: https://context7.com/jnothman/upsetplot/llms.txt

Builds data from category listings where keys are category names and values are sets of identifiers. Useful when data is organized as 'which items belong to each category'.

```python
from upsetplot import from_contents
import pandas as pd

# Basic usage: dictionary of category -> member IDs
contents = {
    'cat1': ['a', 'b', 'c'],
    'cat2': ['b', 'd'],
    'cat3': ['e']
}
data = from_contents(contents)
print(data)
```

--------------------------------

### Load Data with Associated Values from Memberships

Source: https://context7.com/jnothman/upsetplot/llms.txt

Loads data from category memberships and associates it with provided data values. Useful when each membership has associated numerical data.

```python
# With associated data values
values = from_memberships(
    [['A', 'B'], ['B', 'C'], ['A'], ['C']],
    data=np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
)
print(values)
```

--------------------------------

### Convert memberships to UpSet format

Source: https://github.com/jnothman/upsetplot/blob/master/doc/formats.ipynb

Converts a mapping of items to their memberships into a format suitable for UpSetPlot using from_memberships.

```python
from upsetplot import from_memberships

animal_memberships = {
    "Cat": "Mammal",
    "Dog": "Mammal,Domesticated",
    "Horse": "Mammal,Herbivore,Domesticated",
    "Sheep": "Mammal,Herbivore,Domesticated",
    "Pig": "Mammal,Domesticated",
    "Cattle": "Mammal,Herbivore,Domesticated",
    "Rhinoceros": "Mammal,Herbivore",
    "Moose": "Mammal,Herbivore",
    "Chicken": "Domesticated",
    "Duck": "Domesticated",
}

# Turn this into a list of lists:
animal_membership_lists = [
    categories.split(",") for categories in animal_memberships.values()
]

animals = from_memberships(animal_membership_lists)
animals
```

--------------------------------

### Load Data from Category Memberships with UpSetPlot

Source: https://context7.com/jnothman/upsetplot/llms.txt

Loads data where each sample has a collection of category names indicating its set memberships. The output is suitable for UpSet or plot functions.

```python
from upsetplot import from_memberships
import numpy as np

# Basic usage: list of category memberships
data = from_memberships([
    ['cat1', 'cat3'],      # Item belongs to cat1 and cat3
    ['cat2', 'cat3'],      # Item belongs to cat2 and cat3
    ['cat1'],              # Item belongs only to cat1
    []                     # Item belongs to no categories
])
print(data)
```

--------------------------------

### Creating Genre Indicator DataFrame

Source: https://github.com/jnothman/upsetplot/blob/master/doc/formats.ipynb

Convert a DataFrame with comma-separated genres into a boolean indicator DataFrame. Each column represents a genre, and `True` indicates the presence of that genre for a given row. This format is suitable for `from_indicators`.

```python
import pandas as pd
from upsetplot import from_indicators

genre_indicators = pd.DataFrame(
    [{cat: True for cat in cats} for cats in movies.Genre.str.split(",").values]
).fillna(False)
genre_indicators
```

--------------------------------

### Style Subsets with style_subsets

Source: https://context7.com/jnothman/upsetplot/llms.txt

Updates visual attributes of subsets based on category presence, size, or degree.

```python
from upsetplot import UpSet, generate_counts
from matplotlib import pyplot as plt

example = generate_counts()

# Style by category presence
upset = UpSet(example)
upset.style_subsets(present=["cat1", "cat2"], facecolor="blue", label="Both cat1 & cat2")
upset.plot()
plt.suptitle("Highlight subsets with both cat1 and cat2")
plt.show()

# Style by category absence
upset = UpSet(example)
upset.style_subsets(present="cat2", absent="cat1", edgecolor="red", linewidth=2)
upset.plot()
plt.suptitle("Border for subsets with cat2 but not cat1")
plt.show()

# Style by subset size
upset = UpSet(example)
upset.style_subsets(min_subset_size=1000, facecolor="lightblue", hatch="xx", label="Large (>1000)")
upset.plot()
plt.suptitle("Hatched subsets with size > 1000")
plt.show()

# Style by degree (color gradient)
upset = UpSet(example)
upset.style_subsets(min_degree=1, facecolor="blue")
upset.style_subsets(min_degree=2, facecolor="purple")
upset.style_subsets(min_degree=3, facecolor="red")
upset.plot()
plt.suptitle("Color by intersection degree")
plt.show()

# Multiple overlapping styles
upset = UpSet(example, facecolor="gray")
upset.style_subsets(present="cat0", label="Contains cat0", facecolor="blue")
upset.style_subsets(present="cat1", label="Contains cat1", hatch="xx", edgecolor="black")
upset.style_subsets(present="cat2", label="Contains cat2", edgecolor="red")
upset.plot()
plt.show()
```

--------------------------------

### Convert dictionary of contents to UpSet format

Source: https://github.com/jnothman/upsetplot/blob/master/doc/formats.ipynb

Constructs a DataFrame from a dictionary of category members using from_contents.

```python
from upsetplot import from_contents

animals = from_contents(
    {"mammal": mammals, "herbivore": herbivores, "domesticated": domesticated}
)
animals
```

--------------------------------

### Load Data from Boolean Indicators with UpSetPlot

Source: https://context7.com/jnothman/upsetplot/llms.txt

Loads category membership indicated by a boolean indicator matrix. Supports column names, DataFrames, or callable functions to derive indicators from data.

```python
from upsetplot import from_indicators
import pandas as pd

# Using a dictionary of boolean indicators
indicators = {
    "cat1": [True, False, True, False],
    "cat2": [False, True, False, False],
    "cat3": [True, True, False, False]
}
data = from_indicators(indicators)
print(data)
```

--------------------------------

### Generate Default Counts with UpSetPlot

Source: https://context7.com/jnothman/upsetplot/llms.txt

Generates default counts for set intersections using 3 categories and 10000 samples. Useful for testing and demonstrations.

```python
from upsetplot import generate_counts

# Generate default counts with 3 categories and 10000 samples
example = generate_counts()
print(example)
```

--------------------------------

### Customize Plots with UpSet Class

Source: https://context7.com/jnothman/upsetplot/llms.txt

Provides full control over styling, layout, and plot elements for complex visualizations.

```python
from upsetplot import UpSet, generate_counts
from matplotlib import pyplot as plt

example = generate_counts()

# Basic UpSet plot
upset = UpSet(example)
axes = upset.plot()
plt.show()

# With custom sorting and display options
upset = UpSet(
    example,
    orientation="horizontal",
    sort_by="cardinality",
    sort_categories_by="cardinality",
    show_counts=True,
    show_percentages="{:.1%}",
    element_size=32,
    intersection_plot_elements=6,
    totals_plot_elements=3
)
upset.plot()
plt.show()

# Customize colors
upset = UpSet(
    example,
    facecolor="darkblue",
    other_dots_color=0.3,
    shading_color="lightgray"
)
upset.plot()
plt.show()

# Disable totals plot or intersection bars
upset = UpSet(example, totals_plot_elements=0)  # No totals
upset = UpSet(example, intersection_plot_elements=0)  # No intersection bars
```

--------------------------------

### Apply Matplotlib themes and custom colors

Source: https://context7.com/jnothman/upsetplot/llms.txt

Adapts UpSet plots to Matplotlib styles and allows manual color configuration for elements like faces, dots, and shading.

```python
from upsetplot import plot, generate_counts
from matplotlib import pyplot as plt

example = generate_counts()

# Using matplotlib stylesheets
with plt.style.context("dark_background"):
    plot(example, show_counts=True)
    plt.suptitle("Dark background theme")
    plt.show()

with plt.style.context("Solarize_Light2"):
    plot(example)
    plt.suptitle("Solarize theme")
    plt.show()

# Manual color control on dark background
with plt.style.context("dark_background"):
    plot(
        example,
        show_counts=True,
        facecolor="red",
        other_dots_color=0.4,  # 40% opacity of facecolor
        shading_color=0.2      # 20% opacity of facecolor
    )
    plt.suptitle("Custom colors on dark background")
    plt.show()
```

--------------------------------

### Style Categories with style_categories

Source: https://context7.com/jnothman/upsetplot/llms.txt

Updates the visual style of category total bars and row shading.

```python
from upsetplot import UpSet, generate_counts
from matplotlib import pyplot as plt

example = generate_counts()

# Style category shading
upset = UpSet(example)
upset.style_categories("cat2", shading_edgecolor="darkgreen", shading_linewidth=1)
upset.style_categories("cat1", shading_facecolor="lavender")
upset.plot()
plt.suptitle("Custom category shading")
plt.show()
```

--------------------------------

### Add stacked bar charts to UpSet

Source: https://context7.com/jnothman/upsetplot/llms.txt

Visualizes the distribution of a categorical variable within each intersection using stacked bars, supporting custom color mappings.

```python
from upsetplot import UpSet
from matplotlib import pyplot as plt, cm
import pandas as pd

# Load Titanic data
TITANIC_URL = "https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv"
df = pd.read_csv(TITANIC_URL)

# Create binary indicators for survival and first class
df = df.set_index(df.Survived == 1).set_index(df.Pclass == 1, append=True)
df.index.names = ["Survived", "FirstClass"]

# UpSet with stacked bars by gender
upset = UpSet(df, intersection_plot_elements=0)  # Disable default bar chart
upset.add_stacked_bars(
    by="Sex",
    colors=cm.Pastel1,
    title="Count by gender",
    elements=10
)
upset.plot()
plt.suptitle("Gender distribution for survival/class combinations")
plt.show()

# With custom color mapping
upset = UpSet(df, intersection_plot_elements=0)
upset.add_stacked_bars(
    by="Sex",
    colors={"male": "steelblue", "female": "coral"},
    title="Passengers by sex"
)
upset.plot()
plt.show()
```

--------------------------------

### Create DataFrame with Genre Indicators

Source: https://github.com/jnothman/upsetplot/blob/master/doc/formats.ipynb

Concatenates the original movies DataFrame with genre indicator columns. This prepares the data for creating upset plots based on genre intersections.

```python
movies_with_indicators = pd.concat([movies, genre_indicators], axis=1)
movies_with_indicators
```

--------------------------------

### Define categorical lists

Source: https://github.com/jnothman/upsetplot/blob/master/doc/formats.ipynb

Defines lists of items belonging to specific categories for conversion.

```python
mammals = ["Cat", "Dog", "Horse", "Sheep", "Pig", "Cattle", "Rhinoceros", "Moose"]
herbivores = ["Horse", "Sheep", "Cattle", "Moose", "Rhinoceros"]
domesticated = ["Dog", "Chicken", "Horse", "Sheep", "Pig", "Cattle", "Duck"]
(mammals, herbivores, domesticated)
```

--------------------------------

### Customize Count Generation in UpSetPlot

Source: https://context7.com/jnothman/upsetplot/llms.txt

Customizes the generation of artificial counts by specifying the random seed, number of samples, and number of categories.

```python
# Customize generation parameters
counts = generate_counts(seed=42, n_samples=5000, n_categories=4)
```

--------------------------------

### Data Loading Functions

Source: https://context7.com/jnothman/upsetplot/llms.txt

Functions for loading data into a format suitable for UpSet plots from various input types.

```APIDOC
## from_memberships

### Description
Loads data where each sample has a collection of category names indicating its set memberships. The output is suitable for passing directly to UpSet or plot functions.

### Method
`from_memberships`

### Parameters
#### Arguments
- **memberships** (list of lists or similar) - Required - A list where each inner list contains category names for a sample.
- **data** (numpy.ndarray or similar) - Optional - Associated data values for each sample.

### Request Example
```python
from upsetplot import from_memberships
import numpy as np

# Basic usage: list of category memberships
data = from_memberships([
    ['cat1', 'cat3'],      # Item belongs to cat1 and cat3
    ['cat2', 'cat3'],      # Item belongs to cat2 and cat3
    ['cat1'],              # Item belongs only to cat1
    []                     # Item belongs to no categories
])
print(data)

# With associated data values
values = from_memberships(
    [['A', 'B'], ['B', 'C'], ['A'], ['C']],
    data=np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
)
print(values)
```

### Response
#### Success Response
- **pandas.Series or pandas.DataFrame** - Data structured for UpSet plots, with a MultiIndex representing set memberships.
```

```APIDOC
## from_contents

### Description
Builds data from category listings where keys are category names and values are sets of identifiers. Useful when your data is organized as "which items belong to each category" rather than "which categories each item belongs to."

### Method
`from_contents`

### Parameters
#### Arguments
- **contents** (dict) - Required - A dictionary where keys are category names and values are lists or sets of member identifiers.
- **data** (pandas.DataFrame or similar) - Optional - DataFrame containing associated data for each member identifier.

### Request Example
```python
from upsetplot import from_contents
import pandas as pd

# Basic usage: dictionary of category -> member IDs
contents = {
    'cat1': ['a', 'b', 'c'],
    'cat2': ['b', 'd'],
    'cat3': ['e']
}
data = from_contents(contents)
print(data)

# With associated data
info = pd.DataFrame({
    'color': ['red', 'blue', 'green', 'yellow', 'purple']
}, index=['a', 'b', 'c', 'd', 'e'])

result = from_contents(contents, data=info)
print(result)
```

### Response
#### Success Response
- **pandas.Series or pandas.DataFrame** - Data structured for UpSet plots, with a MultiIndex representing set memberships and columns for associated data if provided.
```

```APIDOC
## from_indicators

### Description
Loads category membership indicated by a boolean indicator matrix. Supports column names, DataFrames, or callable functions to derive indicators from data.

### Method
`from_indicators`

### Parameters
#### Arguments
- **indicators** (dict, pandas.DataFrame, or callable) - Required - Data representing boolean membership in categories.

### Request Example
```python
from upsetplot import from_indicators
import pandas as pd

# Using a dictionary of boolean indicators
indicators = {
    "cat1": [True, False, True, False],
    "cat2": [False, True, False, False],
    "cat3": [True, True, False, False]
}
data = from_indicators(indicators)
print(data)
```

### Response
#### Success Response
- **pandas.Series or pandas.DataFrame** - Data structured for UpSet plots, with a MultiIndex representing set memberships.
```

--------------------------------

### Load CSV data into a Pandas DataFrame

Source: https://github.com/jnothman/upsetplot/blob/master/doc/formats.ipynb

Loads a CSV file into a Pandas DataFrame for further processing. Ensure the file path is correct.

```python
import pandas as pd

movies = pd.read_csv(
    "https://raw.githubusercontent.com/peetck/IMDB-Top1000-Movies/master/IMDB-Movie-Data.csv"
)
movies.head()
```

--------------------------------

### Add categorical plots to UpSet

Source: https://context7.com/jnothman/upsetplot/llms.txt

Integrates seaborn-style categorical plots like strip, box, or violin plots into UpSet intersections to visualize continuous variable distributions.

```python
from upsetplot import UpSet
from matplotlib import pyplot as plt
import pandas as pd
from sklearn.datasets import load_diabetes

# Load sample data
diabetes = load_diabetes()
df = pd.DataFrame(diabetes.data, columns=diabetes.feature_names)
df["target"] = diabetes.target

# Create boolean indicators for above-median values
df["high_bmi"] = df["bmi"] > df["bmi"].median()
df["high_bp"] = df["bp"] > df["bp"].median()
df["high_s5"] = df["s5"] > df["s5"].median()

# Set indicators as index
df = df.set_index(["high_bmi", "high_bp", "high_s5"])

# Create UpSet with catplot
upset = UpSet(df, subset_size="count", intersection_plot_elements=3)
upset.add_catplot(value="target", kind="strip", color="blue")
upset.add_catplot(value="bmi", kind="box", color="green")
upset.plot()
plt.suptitle("Distribution of target and BMI across subsets")
plt.show()

# Vertical orientation with violin plots
upset = UpSet(df, subset_size="count", orientation="vertical")
upset.add_catplot(value="target", kind="violin", color="purple")
upset.plot()
plt.show()
```

--------------------------------

### Data Generation Functions

Source: https://context7.com/jnothman/upsetplot/llms.txt

Functions for generating artificial data for UpSet plots, useful for testing and demonstrations.

```APIDOC
## generate_counts

### Description
Generates artificial counts corresponding to set intersections, useful for testing and demonstrations. Returns a pandas Series with a MultiIndex where each level is a boolean indicator for category membership.

### Method
`generate_counts`

### Parameters
#### Keyword Parameters
- **seed** (int) - Optional - Seed for random number generation.
- **n_samples** (int) - Optional - Number of samples to generate.
- **n_categories** (int) - Optional - Number of categories to generate.

### Request Example
```python
from upsetplot import generate_counts

# Generate default counts with 3 categories and 10000 samples
example = generate_counts()
print(example)

# Customize generation parameters
counts = generate_counts(seed=42, n_samples=5000, n_categories=4)
```

### Response
#### Success Response
- **pandas.Series** - A Series with a MultiIndex representing set intersections and their counts.
```

```APIDOC
## generate_samples

### Description
Generates artificial samples assigned to set intersections, returning a DataFrame with a 'value' column and boolean index levels. This is useful when you need the raw sample data rather than aggregated counts.

### Method
`generate_samples`

### Parameters
#### Keyword Parameters
- **seed** (int) - Optional - Seed for random number generation.
- **n_samples** (int) - Optional - Number of samples to generate.
- **n_categories** (int) - Optional - Number of categories to generate.

### Request Example
```python
from upsetplot import generate_samples

# Generate sample data with 3 categories
samples = generate_samples(seed=0, n_samples=10000, n_categories=3)
print(samples.head())

# Access specific subset data
print(samples.loc[(True, True, True)])  # Samples in all three categories
```

### Response
#### Success Response
- **pandas.DataFrame** - A DataFrame with a MultiIndex representing set intersections and a 'value' column for sample data.
```

--------------------------------

### Plot UpSet Diagram

Source: https://github.com/jnothman/upsetplot/blob/master/README.rst

Plots an UpSet diagram from a pandas Series containing subset counts. Requires matplotlib to display the plot.

```python
from upsetplot import plot
from matplotlib import pyplot

# Assuming 'example' is a pandas Series generated by generate_counts() or similar
# plot(example)  # doctest: +SKIP
# pyplot.show()  # doctest: +SKIP
```

--------------------------------

### Generate UpSet Plot from Genre Indicators

Source: https://github.com/jnothman/upsetplot/blob/master/doc/formats.ipynb

Creates an UpSet plot by specifying a list of genre column names directly from a DataFrame that includes these indicators. This is a convenient way to visualize intersections of multiple categories.

```python
UpSet(
    from_indicators(
        ["Drama", "Action", "Comedy", "Adventure"], data=movies_with_indicators
    )
)
```

--------------------------------

### query

Source: https://context7.com/jnothman/upsetplot/llms.txt

Transforms, filters, and aggregates categorised data. Returns a QueryResult object containing filtered data, subset sizes, category totals, and the grand total.

```APIDOC
## query

### Description
Transforms, filters, and aggregates categorised data. Returns a QueryResult object containing filtered data, subset sizes, category totals, and the grand total.

### Parameters
#### Query Parameters
- **present** (str) - Optional - Categories that must be present.
- **absent** (str) - Optional - Categories that must be absent.
- **min_subset_size** (int/str) - Optional - Minimum size constraint.
- **max_subset_size** (int/str) - Optional - Maximum size constraint.
- **min_degree** (int) - Optional - Minimum number of categories in intersection.
- **max_degree** (int) - Optional - Maximum number of categories in intersection.
- **max_subset_rank** (int) - Optional - Limit to top N subsets by size.
- **sort_by** (str) - Optional - Sorting criteria for subsets.
- **sort_categories_by** (str) - Optional - Sorting criteria for categories.
- **include_empty_subsets** (bool) - Optional - Whether to include empty subsets.
```

--------------------------------

### Plot UpSet Plot from Sample Values (Sum Weights)

Source: https://github.com/jnothman/upsetplot/blob/master/doc/formats.ipynb

Plots an UpSet plot from a pandas Series of sample values, weighting each subset's size by the sum of its corresponding series values. Use `subset_size='sum'` and `show_counts=True` to visualize weighted subset sizes with counts displayed.

```python
from upsetplot import UpSet

ax_dict = UpSet(example_values, subset_size="sum", show_counts=True).plot()
```

--------------------------------

### Plot converted contents

Source: https://github.com/jnothman/upsetplot/blob/master/doc/formats.ipynb

Plots the UpSet diagram for the converted categorical data.

```python
from upsetplot import UpSet

ax_dict = UpSet(animals, subset_size="count").plot()
```

--------------------------------

### Customize UpSetPlot Figure and Axes

Source: https://context7.com/jnothman/upsetplot/llms.txt

Access individual plot components using the dictionary returned by the plot method. This allows for fine-grained control over labels, titles, and layout adjustments.

```python
from upsetplot import UpSet, generate_counts
from matplotlib import pyplot as plt

example = generate_counts()

fig = plt.figure(figsize=(12, 6))
upset = UpSet(example, show_counts=True)
axes_dict = upset.plot(fig=fig)

# Access individual axes
# axes_dict contains: 'matrix', 'intersections', 'totals', 'shading'
axes_dict['intersections'].set_ylabel("Custom Y Label")
axes_dict['matrix'].set_title("Matrix Title")

plt.suptitle("Customized UpSet Plot")
plt.tight_layout()
plt.show()
```

--------------------------------

### Save Plot to File

Source: https://github.com/jnothman/upsetplot/blob/master/doc/index.md

Saves the currently generated matplotlib plot to a file in PDF or PNG format. Ensure the directory exists.

```python
pyplot.savefig("/path/to/myplot.pdf")
```

```python
pyplot.savefig("/path/to/myplot.png")
```

--------------------------------

### Style category total bars

Source: https://context7.com/jnothman/upsetplot/llms.txt

Customizes the appearance of category bars in an UpSet plot using facecolor, hatch, and edgecolor parameters.

```python
upset = UpSet(example)
upset.style_categories(
    ["cat2", "cat1"],
    bar_facecolor="aqua",
    bar_hatch="xx",
    bar_edgecolor="black"
)
upset.plot()
plt.suptitle("Custom category bar styling")
plt.show()
```

--------------------------------

### Plotting Upset Plot from Genre Data

Source: https://github.com/jnothman/upsetplot/blob/master/doc/formats.ipynb

Use this to plot an upset plot from a DataFrame where one column contains comma-separated genres. Set `min_subset_size` to filter out small intersections and `show_counts` to display the number of elements in each subset.

```python
from upsetplot import UpSet

UpSet(movies_by_genre, min_subset_size=15, show_counts=True).plot()
```

--------------------------------

### UpSet.add_stacked_bars

Source: https://context7.com/jnothman/upsetplot/llms.txt

Adds stacked bar charts showing the distribution of a categorical variable within each intersection.

```APIDOC
## UpSet.add_stacked_bars

### Description
Adds stacked bar charts showing the distribution of a categorical variable within each intersection.

### Parameters
#### Request Body
- **by** (str) - Required - The categorical variable to group by.
- **colors** (dict or colormap) - Optional - Color mapping for the categories.
- **title** (str) - Optional - Title for the plot element.
- **elements** (int) - Optional - Number of elements to display.
```

--------------------------------

### Plot subset counts from DataFrame

Source: https://github.com/jnothman/upsetplot/blob/master/doc/formats.ipynb

Plots the number of observations in each unique subset using the count method.

```python
from upsetplot import UpSet

ax_dict = UpSet(example_samples_df, subset_size="count").plot()
```

--------------------------------

### Save UpSet Plot to File

Source: https://github.com/jnothman/upsetplot/blob/master/README.rst

Saves the currently displayed matplotlib plot to a file in various formats like PDF or PNG. Ensure a plot is active before calling this.

```python
from matplotlib import pyplot

# Assuming a plot has been generated
# pyplot.savefig("/path/to/myplot.pdf")  # doctest: +SKIP
# pyplot.savefig("/path/to/myplot.png")  # doctest: +SKIP
```

--------------------------------

### Create Upset plot from category memberships

Source: https://github.com/jnothman/upsetplot/blob/master/doc/formats.ipynb

Converts a column of comma-separated categories into a DataFrame suitable for upset plots. This function is useful when category membership is indicated in DataFrame columns.

```python
from upsetplot import from_memberships

movies_by_genre = from_memberships(movies.Genre.str.split(","), data=movies)
movies_by_genre
```

--------------------------------

### Query categorized data

Source: https://context7.com/jnothman/upsetplot/llms.txt

Filters and aggregates data based on category presence, subset size, degree, or rank, returning a QueryResult object.

```python
from upsetplot import query, generate_samples

data = generate_samples(n_samples=1000)

# Filter by category presence
result = query(data, present="cat1", absent="cat0")
print(f"Total matching samples: {result.total}")
print(f"Subset sizes:\n{result.subset_sizes}")
print(f"Category totals:\n{result.category_totals}")

# Filter by size constraints
result = query(data, min_subset_size=50, max_subset_size=200)
print(result.subset_sizes)

# Filter by size as percentage
result = query(data, min_subset_size="5%", max_subset_size="50%")

# Filter by degree
result = query(data, min_degree=2, max_degree=2)  # Exactly 2 categories

# Limit to top N subsets by size
result = query(data, max_subset_rank=5)

# Custom sorting
result = query(data, sort_by="cardinality", sort_categories_by="-cardinality")

# Access individual subset DataFrames
result = query(data)
for categories, subset_df in result.subsets.items():
    print(f"Categories {categories}: {len(subset_df)} samples")

# Include empty subsets (all possible combinations)
result = query(data, include_empty_subsets=True)
```

--------------------------------

### Create Upset Plot from Boolean Columns

Source: https://github.com/jnothman/upsetplot/blob/master/doc/formats.ipynb

Use `pd.select_dtypes(bool)` to filter for boolean columns and then create an Upset plot. Requires the `upsetplot` library and `pandas`.

```python
from upsetplot import UpSet, from_indicators
import pandas as pd

# Assuming movies_with_indicators is a pandas DataFrame with boolean columns
# Example DataFrame creation (replace with your actual data)
movies_with_indicators = pd.DataFrame({
    'col1': [True, False, True, True, False],
    'col2': [False, True, True, False, True],
    'col3': [True, True, False, True, True]
})

UpSet(
    from_indicators(lambda df: df.select_dtypes(bool), data=movies_with_indicators),
    min_subset_size=15,
    show_counts=True,
)
```

--------------------------------

### Plot sum of variables from DataFrame

Source: https://github.com/jnothman/upsetplot/blob/master/doc/formats.ipynb

Plots the sum of a specific variable across unique subsets.

```python
from upsetplot import UpSet

ax_dict = UpSet(example_samples_df, sum_over="index", subset_size="sum").plot()
```

--------------------------------

### Plot UpSet Plot from Sample Values (Count Subsets)

Source: https://github.com/jnothman/upsetplot/blob/master/doc/formats.ipynb

Plots an UpSet plot from a pandas Series of sample values. It counts the number of observations in each unique subset and visualizes these counts. Use `subset_size='count'` when your Series contains individual data points.

```python
from upsetplot import UpSet

ax_dict = UpSet(example_values, subset_size="count").plot()
```

--------------------------------

### Generate Upset Plot for Movie Genres

Source: https://github.com/jnothman/upsetplot/blob/master/doc/formats.ipynb

This snippet generates an upset plot from a DataFrame containing movie genre data. Ensure the DataFrame is correctly formatted before passing it to the UpSet function.

```python
UpSet(movies_by_genre)
```

--------------------------------

### UpSet.add_catplot

Source: https://context7.com/jnothman/upsetplot/llms.txt

Adds a seaborn categorical plot (strip, box, violin, etc.) over subsets to show distributions of continuous variables within each intersection.

```APIDOC
## UpSet.add_catplot

### Description
Adds a seaborn categorical plot (strip, box, violin, etc.) over subsets to show distributions of continuous variables within each intersection.

### Parameters
#### Request Body
- **value** (str) - Required - The column name to plot.
- **kind** (str) - Required - The type of seaborn plot (e.g., "strip", "box", "violin").
- **color** (str) - Optional - The color of the plot elements.
```

--------------------------------

### Plot UpSet Plot from Counts Series

Source: https://github.com/jnothman/upsetplot/blob/master/doc/formats.ipynb

Plots an UpSet plot using a pandas Series that contains pre-calculated subset counts. The Series must have a MultiIndex indicating set memberships.

```python
from upsetplot import UpSet

ax_dict = UpSet(example_counts).plot()
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.