Seaborn

https://seaborn.pydata.org
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Seaborn is a Python data visualization library based on matplotlib that provides a high-level...
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### Quickstart: Load and plot example dataset

Source: https://seaborn.pydata.org/installing.html

Load the 'penguins' dataset and create a pairplot to test your Seaborn installation. This requires seaborn and matplotlib.

```python
import seaborn as sns
df = sns.load_dataset("penguins")
sns.pairplot(df, hue="species")
```

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

### Load and inspect the 'tips' dataset

Source: https://seaborn.pydata.org/generated/seaborn.relplot.html

Loads the 'tips' dataset using seaborn and displays the first few rows to understand its structure. This is a common starting point for examples using this dataset.

```python
import seaborn as sns
tips = sns.load_dataset("tips")
tips.head()
```

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

### Load and Inspect Tips Dataset

Source: https://seaborn.pydata.org/generated/seaborn.scatterplot.html

Loads the 'tips' dataset and displays the first few rows to understand its structure. This is a common starting point for Seaborn examples.

```python
tips = sns.load_dataset("tips")
tips.head()
```

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

### Load Example Dataset

Source: https://seaborn.pydata.org/tutorial/introduction.html

Load an example dataset into a Pandas DataFrame using `sns.load_dataset()`. This function provides easy access to sample data for examples.

```python
# Load an example dataset
tips = sns.load_dataset("tips")


```

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

### Basic Seaborn Plotting Example

Source: https://seaborn.pydata.org/tutorial/introduction.html

This example demonstrates the fundamental steps of using Seaborn: importing the library, setting a theme, loading a dataset, and creating a relational plot.

```python
# Import seaborn
import seaborn as sns

# Apply the default theme
sns.set_theme()

# Load an example dataset
tips = sns.load_dataset("tips")

# Create a visualization
sns.relplot(
    data=tips,
    x="total_bill", y="tip", col="time",
    hue="smoker", style="smoker", size="size",
)


```

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

### Basic clustermap usage

Source: https://seaborn.pydata.org/generated/seaborn.clustermap.html

This example demonstrates the basic usage of the clustermap function with a sample dataset.

```APIDOC
## clustermap

### Description
Plots a clustered heatmap.

### Method
`clustermap`

### Parameters
- **data** (DataFrame or array-like) - The data to plot.
- **z_score** (int or None) - If 0, center and scale along the columns. If 1, center and scale along the rows. If None, do not scale.
- **cmap** (string) - The colormap to use for the heatmap.
- **center** (float or None) - The value at which to center the colormap when plotting.

### Example
```python
import seaborn as sns
import matplotlib.pyplot as plt

iris = sns.load_dataset("iris")
sns.clustermap(iris, z_score=0, cmap="vlag", center=0)
plt.show()
```
```

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

### Install Seaborn with pip

Source: https://seaborn.pydata.org/installing.html

Use this command to install the latest stable release of Seaborn from PyPI. This will also install mandatory dependencies.

```bash
pip install seaborn
```

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

### Install Seaborn with optional dependencies

Source: https://seaborn.pydata.org/installing.html

Install Seaborn along with optional dependencies like statsmodels for advanced regression plots.

```bash
pip install seaborn[stats]
```

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

### seaborn.load_dataset

Source: https://seaborn.pydata.org/generated/seaborn.load_dataset.html

Loads an example dataset from the online repository. Requires internet access. Datasets are useful for documentation and reproducible examples. Some datasets may have preprocessing applied for categorical variable ordering. Use get_dataset_names() to see available datasets.

```APIDOC
## seaborn.load_dataset

### Description
Load an example dataset from the online repository (requires internet). This function provides quick access to a small number of example datasets that are useful for documenting seaborn or generating reproducible examples for bug reports. It is not necessary for normal usage. Note that some of the datasets have a small amount of preprocessing applied to define a proper ordering for categorical variables. Use `get_dataset_names()` to see a list of available datasets.

### Parameters

* **name** (str) - Name of the dataset (`{name}.csv` on https://github.com/mwaskom/seaborn-data).
* **cache** (boolean, optional) - If True, try to load from the local cache first, and save to the cache if a download is required.
* **data_home** (string, optional) - The directory in which to cache data; see `get_data_home()`.
* **kws** (keys and values, optional) - Additional keyword arguments are passed to passed through to `pandas.read_csv()`.

### Returns

* **df** (pandas.DataFrame) - Tabular data, possibly with some preprocessing applied.
```

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

### Get Dataset Names

Source: https://seaborn.pydata.org/generated/seaborn.get_dataset_names.html

Retrieves a list of available example datasets in seaborn. This function requires an active internet connection.

```APIDOC
## get_dataset_names()

### Description
Report available example datasets, useful for reporting issues. Requires an internet connection.

### Method
`get_dataset_names()`

### Parameters
This function does not accept any parameters.

### Returns
- list: A list of strings, where each string is the name of an available dataset.
```

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

### Customize cubehelix palette with start and rotation

Source: https://seaborn.pydata.org/tutorial/color_palettes.html

Adjust the `start` and `rot` parameters to control the hue and intensity variations in the cubehelix palette.

```python
sns.cubehelix_palette(start=.5, rot=-.5, as_cmap=True)
```

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

### Stack Transform Example

Source: https://seaborn.pydata.org/generated/seaborn.objects.Stack.html

Demonstrates how to use the Stack transform with Bar marks to count occurrences within categories.

```APIDOC
## Stack Transform

### Description
Displaces overlapping bar or area marks along the value axis to eliminate overlap.

### Usage
```python
so.Stack()
```

### Example
```python
so.Plot(titanic, x="class", color="sex").add(so.Bar(), so.Count(), so.Stack())
```
```

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

### Get Light Sequential Gradient

Source: https://seaborn.pydata.org/generated/seaborn.color_palette.html

Return a light sequential gradient.

```python
sns.color_palette("light:#5A9", as_cmap=True)
```

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

### get_dataset_names

Source: https://seaborn.pydata.org/api.html

Report available example datasets, useful for reporting issues. This function lists the names of datasets that can be loaded.

```APIDOC
## get_dataset_names

### Description
Report available example datasets, useful for reporting issues.

### Method
(Not specified, typically a function call in a Python SDK)

### Endpoint
(Not applicable for SDK functions)

### Parameters
(Parameters not explicitly detailed in the source text)

### Request Example
(Not applicable for SDK functions)

### Response
(Not applicable for SDK functions)
```

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

### load_dataset

Source: https://seaborn.pydata.org/api.html

Load an example dataset from the online repository (requires internet). This function downloads and loads sample datasets for use with Seaborn.

```APIDOC
## load_dataset

### Description
Load an example dataset from the online repository (requires internet).

### Method
(Not specified, typically a function call in a Python SDK)

### Endpoint
(Not applicable for SDK functions)

### Parameters
(Parameters not explicitly detailed in the source text)

### Request Example
(Not applicable for SDK functions)

### Response
(Not applicable for SDK functions)
```

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

### Import Seaborn Library

Source: https://seaborn.pydata.org/tutorial/introduction.html

Import the seaborn library using the conventional alias 'sns'. This is the only import needed for basic examples.

```python
# Import seaborn
import seaborn as sns


```

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

### light_palette

Source: https://seaborn.pydata.org/api.html

Make a sequential palette that blends from light to `color`. This function creates a sequential palette starting from a light color.

```APIDOC
## light_palette

### Description
Make a sequential palette that blends from light to `color`.

### Method
(Not specified, typically a function call in a Python SDK)

### Endpoint
(Not applicable for SDK functions)

### Parameters
(Parameters not explicitly detailed in the source text)

### Request Example
(Not applicable for SDK functions)

### Response
(Not applicable for SDK functions)
```

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

### Setting Marker Property with String Codes

Source: https://seaborn.pydata.org/tutorial/properties.html

Markers can be specified using simple string codes. This example uses a circle marker.

```python
alt.MarkDef(marker='circle')
```

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

### Get Palette Colors as Hex Codes

Source: https://seaborn.pydata.org/generated/seaborn.color_palette.html

See the underlying color values of a palette as hex codes. This example uses the 'pastel6' palette.

```python
print(sns.color_palette("pastel6").as_hex())
```

```text
['#a1c9f4', '#8de5a1', '#ff9f9b', '#d0bbff', '#fffea3', '#b9f2f0']
```

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

### Evaluating KDE at Datapoints

Source: https://seaborn.pydata.org/generated/seaborn.objects.KDE.html

Shows how to evaluate the KDE directly at the original datapoints by setting `gridsize=None`. This can be useful for specific analyses where a continuous grid is not necessary.

```python
p = so.Plot(penguins, x="flipper_length_mm")
p.add(so.Dots(), so.KDE(gridsize=None))
```

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

### Initialize FacetGrid with data

Source: https://seaborn.pydata.org/generated/seaborn.FacetGrid.__init__.html

Load a dataset and initialize a FacetGrid object. This step sets up the grid structure but does not produce any plots.

```python
tips = sns.load_dataset("tips")
sns.FacetGrid(tips)
```

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

### Cubehelix Color System

Source: https://seaborn.pydata.org/tutorial/properties.html

The cubehelix color system can be used for continuous scales, offering control over start, rotation, and saturation. This example demonstrates its usage.

```python
alt.Color('value:Q', scale=alt.Scale(scheme='ch:start=.2,rot=-.4'))
```

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

### Categorical Scatterplot with Swarmplot

Source: https://seaborn.pydata.org/examples/scatterplot_categorical.html

Use `swarmplot` to draw a categorical scatterplot. This example loads the penguins dataset and visualizes body mass by sex, differentiating points by species. Ensure the `seaborn` library is installed and imported.

```python
import seaborn as sns
sns.set_theme(style="whitegrid", palette="muted")

# Load the penguins dataset
df = sns.load_dataset("penguins")

# Draw a categorical scatterplot to show each observation
ax = sns.swarmplot(data=df, x="body_mass_g", y="sex", hue="species")
ax.set(ylabel="")

```

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

### Initialize PairGrid with default settings

Source: https://seaborn.pydata.org/generated/seaborn.PairGrid.__init__.html

Calling the constructor sets up a blank grid of subplots with each row and one column corresponding to a numeric variable in the dataset.

```python
penguins = sns.load_dataset("penguins")
g = sns.PairGrid(penguins)
```

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

### choose_light_palette

Source: https://seaborn.pydata.org/api.html

Launch an interactive widget to create a light sequential palette. This function opens a widget for creating light sequential palettes.

```APIDOC
## choose_light_palette

### Description
Launch an interactive widget to create a light sequential palette.

### Method
(Not specified, typically a function call in a Python SDK)

### Endpoint
(Not applicable for SDK functions)

### Parameters
(Parameters not explicitly detailed in the source text)

### Request Example
(Not applicable for SDK functions)

### Response
(Not applicable for SDK functions)
```

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

### Install Seaborn with conda

Source: https://seaborn.pydata.org/installing.html

Install Seaborn from the Anaconda distribution using the conda package manager.

```bash
conda install seaborn
```

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

### choose_cubehelix_palette

Source: https://seaborn.pydata.org/api.html

Launch an interactive widget to create a sequential cubehelix palette. This function opens a widget for creating cubehelix palettes.

```APIDOC
## choose_cubehelix_palette

### Description
Launch an interactive widget to create a sequential cubehelix palette.

### Method
(Not specified, typically a function call in a Python SDK)

### Endpoint
(Not applicable for SDK functions)

### Parameters
(Parameters not explicitly detailed in the source text)

### Request Example
(Not applicable for SDK functions)

### Response
(Not applicable for SDK functions)
```

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

### Install Seaborn from conda-forge

Source: https://seaborn.pydata.org/installing.html

Install Seaborn from the conda-forge channel for potentially newer releases than the main Anaconda repository.

```bash
conda install seaborn -c conda-forge
```

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

### Configure PairGrid Aesthetics and Palette

Source: https://seaborn.pydata.org/tutorial/axis_grids.html

Initialize a PairGrid with the 'tips' dataset, coloring by 'size' using the 'GnBu_d' palette. Maps scatterplots with custom size and edge color, and adds a legend.

```python
g = sns.PairGrid(tips, hue="size", palette="GnBu_d")
g.map(plt.scatter, s=50, edgecolor="white")
g.add_legend()
```

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

### Change the starting hue of the helix

Source: https://seaborn.pydata.org/generated/seaborn.cubehelix_palette.html

Adjust the `start` parameter to modify the hue value at the beginning of the color helix.

```python
sns.cubehelix_palette(start=2)
```

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

### Basic KDE Plot

Source: https://seaborn.pydata.org/generated/seaborn.objects.KDE.html

This example shows how to add a Kernel Density Estimate (KDE) to a plot using the Area mark. It visualizes the density distribution of the 'flipper_length_mm' data.

```python
p = so.Plot(penguins, x="flipper_length_mm")
p.add(so.Area(), so.KDE())
```

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

### Initializing JointGrid with Customizations

Source: https://seaborn.pydata.org/generated/seaborn.JointGrid.__init__.html

Demonstrates how to initialize a `JointGrid` object, controlling the display of marginal ticks and setting axis limits.

```APIDOC
## seaborn.JointGrid.__init__

### Description
Initializes a `JointGrid` object, which is a figure-level object that can hold a `JointGrid` plot.

### Parameters

#### Keyword Arguments
- **marginal_ticks** (bool, optional) - If True, display ticks on the marginal axes. Defaults to False.
- **xlim** (tuple of (lower, upper), optional) - Set the limits for the x-axis.
- **ylim** (tuple of (lower, upper), optional) - Set the limits for the y-axis.

### Usage Examples

#### Enabling marginal ticks:
```python
sns.JointGrid(marginal_ticks=True)
```

#### Setting axis limits:
```python
sns.JointGrid(xlim=(-2, 5), ylim=(0, 10))
```
```

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

### Install Seaborn in Jupyter Notebook

Source: https://seaborn.pydata.org/faq.html

Use this command within a Jupyter notebook to ensure Seaborn is installed in the same environment as the notebook kernel.

```python
%pip install <package>
```

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

### Adjusting KDE Smoothing Bandwidth

Source: https://seaborn.pydata.org/generated/seaborn.objects.KDE.html

Demonstrates how to adjust the smoothing bandwidth of the KDE using the `bw_adjust` parameter. Increasing this value results in a smoother curve, while decreasing it reveals more details.

```python
p = so.Plot(penguins, x="flipper_length_mm")
p.add(so.Area(), so.KDE(bw_adjust=0.25))
```

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

### Change Hue Start Point in HLS Palette

Source: https://seaborn.pydata.org/generated/seaborn.hls_palette.html

Modify the starting point for hue sampling by adjusting the `h` parameter, which should be between 0 and 1.

```python
sns.hls_palette(h=.5)
```

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

### Apply light palette using color_palette()

Source: https://seaborn.pydata.org/tutorial/color_palettes.html

Use `color_palette()` with a 'light:' prefix to create a light sequential palette based on a single color.

```python
sns.color_palette("light:b", as_cmap=True)
```

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

### Initialize FacetGrid

Source: https://seaborn.pydata.org/generated/seaborn.FacetGrid.html

Initialize the grid with a long-form data object. This sets up the structure but does not plot anything.

```python
import seaborn as sns
tips = sns.load_dataset("tips")
sns.FacetGrid(tips)
```

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

### Generate HUSL Palette with Different Hue Start

Source: https://seaborn.pydata.org/generated/seaborn.husl_palette.html

Change the starting point for hue sampling by providing a float value between 0 and 1 for the `h` parameter.

```python
sns.husl_palette(h=.5)
```

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

### apply

Source: https://seaborn.pydata.org/generated/seaborn.PairGrid.html

Pass the grid to a user-supplied function and return self.

```APIDOC
## apply

### Description
Pass the grid to a user-supplied function and return self.

### Method
`apply`

### Parameters
- **func**: callable
- **args**: positional arguments
- **kwargs**: keyword arguments

### Example
```python
import seaborn as sns
import matplotlib.pyplot as plt

def set_title(ax, title):
    ax.set_title(title)

penguins = sns.load_dataset("penguins")
g = sns.PairGrid(penguins)
g.map_diag(sns.histplot)
g.map_offdiag(sns.scatterplot)
g.apply(set_title, title="Pair Plot")
plt.show()
```
```

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

### Generate a discrete palette with default parameters

Source: https://seaborn.pydata.org/generated/seaborn.cubehelix_palette.html

Use the default settings to create a discrete color palette.

```python
sns.cubehelix_palette()
```

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

### Import necessary libraries and set theme

Source: https://seaborn.pydata.org/tutorial/relational.html

Import numpy, pandas, matplotlib, and seaborn. Set the Seaborn theme to 'darkgrid'.

```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_theme(style="darkgrid")

```

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

### FacetGrid.__init__()

Source: https://seaborn.pydata.org/generated/seaborn.FacetGrid.html

Initializes a FacetGrid object, which is used to plot data across a factorial grid of Axes.

```APIDOC
## FacetGrid.__init__()

### Description
Initializes a FacetGrid object. This object can be used to plot data across a factorial grid of Axes, making it easy to visualize relationships within subsets of your data.

### Method
__init__

### Parameters
This method does not explicitly list parameters in the provided source. Refer to the Seaborn documentation for detailed parameter information.
```

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

### Get Customized Cubehelix Palette

Source: https://seaborn.pydata.org/generated/seaborn.color_palette.html

Return a customized cubehelix color palette.

```python
sns.color_palette("ch:s=.25,rot=-.25", as_cmap=True)
```

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

### Get Blend Gradient Between Two Endpoints

Source: https://seaborn.pydata.org/generated/seaborn.color_palette.html

Return a blend gradient between two endpoints.

```python
sns.color_palette("blend:#7AB,#EDA", as_cmap=True)
```

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

### PairGrid with Histplot and Scatterplot

Source: https://seaborn.pydata.org/generated/seaborn.PairGrid.__init__.html

This example demonstrates how to initialize a PairGrid, map a histogram to the diagonal axes with stacked elements, and a scatterplot to the off-diagonal axes. A legend is then added to distinguish between different categories.

```python
g = sns.PairGrid(penguins, hue="species")
g.map_diag(sns.histplot, multiple="stack", element="step")
g.map_offdiag(sns.scatterplot)
g.add_legend()

```

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

### Adjusting KDE Grid Resolution

Source: https://seaborn.pydata.org/generated/seaborn.objects.KDE.html

Demonstrates how to control the resolution of the grid where the KDE is evaluated using the `gridsize` parameter. A higher `gridsize` increases resolution.

```python
p = so.Plot(penguins, x="flipper_length_mm")
p.add(so.Dots(), so.KDE(gridsize=100))
```

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

### Get Reversed Dark Sequential Gradient

Source: https://seaborn.pydata.org/generated/seaborn.color_palette.html

Return a reversed dark sequential gradient.

```python
sns.color_palette("dark:#5A9_r", as_cmap=True)
```

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

### KDE Grouped by Variable

Source: https://seaborn.pydata.org/generated/seaborn.objects.KDE.html

Illustrates how to group KDE estimations by another variable, in this case, 'species'. This allows for comparing density distributions across different categories.

```python
p = so.Plot(penguins, x="flipper_length_mm")
p.add(so.Area(), so.KDE(), color="species")
```

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

### Get Color Brewer Palette

Source: https://seaborn.pydata.org/generated/seaborn.color_palette.html

Return all unique colors in a categorical Color Brewer palette.

```python
sns.color_palette("Set2")
```

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

### Basic FacetGrid Initialization

Source: https://seaborn.pydata.org/generated/seaborn.FacetGrid.__init__.html

Initialize a FacetGrid with a dataset and specify variables for columns and rows to create subplots.

```APIDOC
## sns.FacetGrid(data, col=None, row=None, ...)

### Description
Initialize a FacetGrid object to map plotting functions over subsets of a dataset.

### Parameters
- **data** (DataFrame or dict) - Tidy-plotted data or a dict of arrays.
- **col** (string, optional) - Variable in `data` to map onto the columns of the grid.
- **row** (string, optional) - Variable in `data` to map onto the rows of the grid.

### Example
```python
sns.FacetGrid(tips, col="time", row="sex")
```
```

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

### Create a light palette from a color name

Source: https://seaborn.pydata.org/generated/seaborn.light_palette.html

Use this to generate a default sequential palette blending from light to 'seagreen'.

```python
sns.light_palette("seagreen")
```

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

### Color Palette Specification

Source: https://seaborn.pydata.org/tutorial/properties.html

Color scales can be parameterized by the name of a palette. This example uses the 'viridis' palette.

```python
alt.Color('value:Q', scale=alt.Scale(palette='viridis'))
```

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

### Basic Joint Plot

Source: https://seaborn.pydata.org/generated/seaborn.jointplot.html

A basic example of `jointplot` showing the relationship between two variables with marginal histograms.

```APIDOC
## sns.jointplot

### Description
Plots the relationship between two variables with marginal distributions.

### Parameters
- **data** (DataFrame or array-like) - Dataset for plotting.
- **x** (string or int) - Variable name for the x-axis.
- **y** (string or int) - Variable name for the y-axis.
- **height** (int, optional) - Size of the figure in inches.
- **ratio** (int, optional) - Ratio of joint axes height to marginal axes height.
- **marginal_ticks** (bool, optional) - If True, show marginal ticks.

### Request Example
```python
import seaborn as sns
import pandas as pd

# Assuming 'penguins' DataFrame is available
# penguins = sns.load_dataset("penguins")

sns.jointplot(data=penguins, x="bill_length_mm", y="bill_depth_mm", height=5, ratio=2, marginal_ticks=True)
```

### Response
Returns a `JointGrid` object which can be used for further customization.
```

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

### Get Seaborn Categorical Palette by Name

Source: https://seaborn.pydata.org/generated/seaborn.color_palette.html

Reference other variants on the seaborn categorical color palette by name.

```python
sns.color_palette("pastel")
```

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

### Show density with thinner lines and alpha blending

Source: https://seaborn.pydata.org/generated/seaborn.rugplot.html

This example demonstrates how to use `lw` (linewidth) and `alpha` parameters with a large dataset to visualize density with very thin, semi-transparent lines, effectively showing the distribution without overwhelming the plot.

```python
diamonds = sns.load_dataset("diamonds")
sns.scatterplot(data=diamonds, x="carat", y="price", s=5)
sns.rugplot(data=diamonds, x="carat", y="price", lw=1, alpha=.005)
```

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

### Interpolated Color Scale

Source: https://seaborn.pydata.org/tutorial/properties.html

Continuous scales can be parameterized by a tuple of colors to interpolate between. This example interpolates between blue and red.

```python
alt.Color('value:Q', scale=alt.Scale(range=['blue', 'red']))
```

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

### Paths Mark Initialization

Source: https://seaborn.pydata.org/generated/seaborn.objects.Paths.html

Initializes the Paths mark with customizable properties.

```APIDOC
## Paths()

### Description
A faster but less-flexible mark for drawing many paths.

### Properties
- `_artist_kws` (factory): Keyword arguments passed to the underlying artist.
- `_color` (str, optional): The color of the paths. Defaults to 'C0'.
- `_alpha` (float, optional): The transparency of the paths. Defaults to 1.
- `_linewidth` (float, optional): The width of the paths. Defaults to rcParams['lines.linewidth'].
- `_linestyle` (str, optional): The style of the paths. Defaults to rcParams['lines.linestyle'].
```

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

### Customizing Lines Mark with Properties

Source: https://seaborn.pydata.org/generated/seaborn.objects.Lines.html

Illustrates how to customize the appearance of lines using properties like `linewidth` and `color`, and how to combine multiple `Lines` marks with different styles.

```APIDOC
## Customizing Lines Mark with Properties

### Description
This example demonstrates customizing the `so.Lines()` mark with specific aesthetic properties such as `linewidth` and `color`. It also shows how to layer multiple `Lines` marks with different configurations on the same plot, potentially for comparison or highlighting.

### Method
`add(so.Lines(linewidth=..., color=...))

### Parameters
- **linewidth** (float) - Controls the thickness of the lines.
- **color** (str) - Sets the color of the lines.

### Request Example
```python
(so.Plot(
    x=seaice["Date"].dt.day_of_year,
    y=seaice["Extent"],
    color=seaice["Date"].dt.year
)
.facet(seaice["Date"].dt.year.round(-1))
.add(so.Lines(linewidth=.5, color="#bbca"), col=None)
.add(so.Lines(linewidth=1))
.scale(color="ch:rot=-.2,light=.7")
.layout(size=(8, 4))
.label(title="{}s".format)
)
```

### Response
This operation generates a faceted plot where each facet displays lines with customized styles, potentially showing trends over different years or groups.
```

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

### get_data_home

Source: https://seaborn.pydata.org/api.html

Return a path to the cache directory for example datasets. This function provides the location where datasets are stored locally.

```APIDOC
## get_data_home

### Description
Return a path to the cache directory for example datasets.

### Method
(Not specified, typically a function call in a Python SDK)

### Endpoint
(Not applicable for SDK functions)

### Parameters
(Parameters not explicitly detailed in the source text)

### Request Example
(Not applicable for SDK functions)

### Response
(Not applicable for SDK functions)
```

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

### Configuring ticks and labels with scale objects

Source: https://seaborn.pydata.org/generated/seaborn.objects.Plot.scale.html

Illustrates advanced configuration of tick locations and label formatting using scale objects and their methods.

```APIDOC
## Plot.scale()

### Description
Specify mappings from data units to visual properties.

### Method
`scale(self, _** scales)`

### Parameters
* `_** scales`: Keyword arguments where keys are plot variables (e.g., `x`, `y`, `color`) and values specify the scale mapping.

### Example
```python
(
    p1.add(so.Dots(), color="price")
    .scale(
        x=so.Continuous(trans="sqrt").tick(every=.5),
        y=so.Continuous().label(like="${x:g}"),
        color=so.Continuous("ch:.2").tick(upto=4).label(unit=""),
    )
    .label(y="")
)
```
```

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

### Using scale() with palette names

Source: https://seaborn.pydata.org/generated/seaborn.objects.Plot.scale.html

Shows how to set a color scale using a named palette like "crest".

```APIDOC
## Plot.scale()

### Description
Specify mappings from data units to visual properties.

### Method
`scale(self, _** scales)`

### Parameters
* `_** scales`: Keyword arguments where keys are plot variables (e.g., `x`, `y`, `color`) and values specify the scale mapping.

### Example
```python
p1.add(so.Dots(), color="clarity").scale(color="crest")
```
```

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

### Boxenplot with linear width method

Source: https://seaborn.pydata.org/generated/seaborn.boxenplot.html

This example illustrates how to control the width of each box in a boxenplot by setting the 'width_method' to 'linear'.

```python
sns.boxenplot(data=diamonds, x="price", y="clarity", width_method="linear")
```

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

### Get Perceptually Uniform Palette as Colormap

Source: https://seaborn.pydata.org/generated/seaborn.color_palette.html

Return one of the perceptually-uniform palettes included in seaborn as a continuous colormap.

```python
sns.color_palette("flare", as_cmap=True)
```

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

### Get Diverging Color Brewer Palette as Colormap

Source: https://seaborn.pydata.org/generated/seaborn.color_palette.html

Return a diverging Color Brewer palette as a continuous colormap.

```python
sns.color_palette("Spectral", as_cmap=True)
```

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

### Using scale() with Continuous scale object

Source: https://seaborn.pydata.org/generated/seaborn.objects.Plot.scale.html

Shows how to use the `so.Continuous` scale object for detailed control over transform, domain, and range.

```APIDOC
## Plot.scale()

### Description
Specify mappings from data units to visual properties.

### Method
`scale(self, _** scales)`

### Parameters
* `_** scales`: Keyword arguments where keys are plot variables (e.g., `x`, `y`, `color`) and values specify the scale mapping.

### Example
```python
(
    p1.add(so.Dots(), color="carat")
    .scale(color=so.Continuous((".4", "#68d"), norm=(1, 3), trans="sqrt"))
)
```
```

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

### Get Current Color Cycle

Source: https://seaborn.pydata.org/generated/seaborn.color_palette.html

Calling color_palette() with no arguments returns all colors from the current default color cycle.

```python
sns.color_palette()
```

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

### plotting_context

Source: https://seaborn.pydata.org/api.html

Get the parameters that control the scaling of plot elements. This function retrieves the current plotting context parameters.

```APIDOC
## plotting_context

### Description
Get the parameters that control the scaling of plot elements.

### Method
(Not specified, typically a function call in a Python SDK)

### Endpoint
(Not applicable for SDK functions)

### Parameters
(Parameters not explicitly detailed in the source text)

### Request Example
(Not applicable for SDK functions)

### Response
(Not applicable for SDK functions)
```

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

### Band Class Initialization

Source: https://seaborn.pydata.org/generated/seaborn.objects.Band.html

Initialize a Band mark with various aesthetic properties to control its appearance.

```APIDOC
## seaborn.objects.Band

### Description
A fill mark representing an interval between values.

This mark defines the following properties:
color, alpha, fill, edgecolor, edgealpha, edgewidth, edgestyle

### Parameters
* `_artist_kws` (_dict_) - Artist keyword arguments.
* `_color` (_str_) - Default: 'C0'. The color of the mark.
* `_alpha` (_float_) - Default: 0.2. The transparency of the mark.
* `_fill` (_bool_) - Default: True. Whether to fill the band.
* `_edgecolor` (_str_) - Default: depend:color. The color of the band's edge.
* `_edgealpha` (_float_) - Default: 1. The transparency of the band's edge.
* `_edgewidth` (_float_) - Default: 0. The width of the band's edge.
* `_edgestyle` (_str_) - Default: '-'. The style of the band's edge.

### See Also
`Area`: A fill mark drawn from a baseline to data values.

### Examples
```python
p = so.Plot(seaice, x="Day", ymin="1980", ymax="2019")
p.add(so.Band())
```

By default it draws a faint ribbon with no edges, but edges can be added:

```python
p.add(so.Band(alpha=.5, edgewidth=2))
```

The defaults are optimized for the main expected usecase, where the mark is combined with a line to show an errorbar interval:

```python
(
    so.Plot(fmri, x="timepoint", y="signal", color="event")
    .add(so.Band(), so.Est())
    .add(so.Line(), so.Agg())
)
```

When min/max values are not explicitly assigned or added in a transform, the band will cover the full extent of the data:

```python
(
    so.Plot(fmri, x="timepoint", y="signal", color="event")
    .add(so.Line(linewidth=.5), group="subject")
    .add(so.Band())
)
```
```

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

### Using the 'hue' semantic

Source: https://seaborn.pydata.org/generated/seaborn.JointGrid.__init__.html

Initialize a JointGrid with a `hue` variable to map semantic information to color. This allows for visualizing subgroups within the data.

```python
g = sns.JointGrid(data=penguins, x="bill_length_mm", y="bill_depth_mm", hue="species")
g.plot(sns.scatterplot, sns.histplot)
```

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

### axes_style

Source: https://seaborn.pydata.org/api.html

Get the parameters that control the general style of the plots. This function retrieves the current plotting style parameters.

```APIDOC
## axes_style

### Description
Get the parameters that control the general style of the plots.

### Method
(Not specified, typically a function call in a Python SDK)

### Endpoint
(Not applicable for SDK functions)

### Parameters
(Parameters not explicitly detailed in the source text)

### Request Example
(Not applicable for SDK functions)

### Response
(Not applicable for SDK functions)
```

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

### Applying scale() before limit()

Source: https://seaborn.pydata.org/generated/seaborn.objects.Plot.scale.html

Demonstrates the order of operations where a scale transform is applied before data limits are set.

```APIDOC
## Plot.scale()

### Description
Specify mappings from data units to visual properties.

### Method
`scale(self, _** scales)`

### Parameters
* `_** scales`: Keyword arguments where keys are plot variables (e.g., `x`, `y`, `color`) and values specify the scale mapping.

### Example
```python
(
    p1.add(so.Dots(color=".7"))
    .add(so.Line(), so.PolyFit(order=2))
    .scale(y="log")
    .limit(y=(250, 25000))
)
```
```

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

### Basic Lines Plot

Source: https://seaborn.pydata.org/generated/seaborn.objects.Lines.html

Draws a connecting line between sorted observations using the Lines mark. This is a fundamental usage example.

```python
so.Plot(seaice, "Date", "Extent").add(so.Lines())
```

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

### Using scale() with a tuple for range

Source: https://seaborn.pydata.org/generated/seaborn.objects.Plot.scale.html

Illustrates setting the output range for a point size scale using a tuple of minimum and maximum values.

```APIDOC
## Plot.scale()

### Description
Specify mappings from data units to visual properties.

### Method
`scale(self, _** scales)`

### Parameters
* `_** scales`: Keyword arguments where keys are plot variables (e.g., `x`, `y`, `color`) and values specify the scale mapping.

### Example
```python
p1.add(so.Dots(), pointsize="carat").scale(pointsize=(2, 10))
```
```

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

### Median Estimate with Est

Source: https://seaborn.pydata.org/generated/seaborn.objects.Est.html

Select other estimators by name if they are pandas methods. This example shows how to compute the median instead of the mean.

```python
p.add(so.Range(), so.Est("median"))
```

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

### choose_dark_palette

Source: https://seaborn.pydata.org/api.html

Launch an interactive widget to create a dark sequential palette. This function opens a widget for creating dark sequential palettes.

```APIDOC
## choose_dark_palette

### Description
Launch an interactive widget to create a dark sequential palette.

### Method
(Not specified, typically a function call in a Python SDK)

### Endpoint
(Not applicable for SDK functions)

### Parameters
(Parameters not explicitly detailed in the source text)

### Request Example
(Not applicable for SDK functions)

### Response
(Not applicable for SDK functions)
```

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

### Generate Sequential Color Brewer palette (multi-hue)

Source: https://seaborn.pydata.org/tutorial/color_palettes.html

Apply a sequential palette from the Color Brewer library that features multiple hues, like 'YlOrBr'.

```python
sns.color_palette("YlOrBr", as_cmap=True)
```

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

### Draw a single horizontal boxenplot

Source: https://seaborn.pydata.org/generated/seaborn.boxenplot.html

This example demonstrates how to create a basic horizontal boxenplot by assigning data directly to the x-axis.

```python
sns.boxenplot(x=diamonds["price"])
```

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

### KDE with Stack Transform

Source: https://seaborn.pydata.org/generated/seaborn.objects.KDE.html

Shows how the KDE stat can be combined with the `Stack` transform. This is possible when `common_grid=True`, allowing for stacked density plots.

```python
p = so.Plot(penguins, x="flipper_length_mm")
p.add(so.Area(), so.KDE(), so.Stack(), color="sex")
```

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

### Change luminance at start and end points

Source: https://seaborn.pydata.org/generated/seaborn.cubehelix_palette.html

Control the intensity of the darkest and lightest colors using the `dark` and `light` parameters.

```python
sns.cubehelix_palette(dark=.25, light=.75)
```

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

### Basic Plot Initialization

Source: https://seaborn.pydata.org/generated/seaborn.objects.Plot.theme.html

Initializes a Plot object with data and mappings, then displays it. This serves as a baseline for theme customization.

```python
import seaborn.objects as so
p = (
    so.Plot(anscombe, "x", "y", color="dataset")
    .facet("dataset", wrap=2)
    .add(so.Line(), so.PolyFit(order=1))
    .add(so.Dot())
)
p
```

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

### dark_palette

Source: https://seaborn.pydata.org/api.html

Make a sequential palette that blends from dark to `color`. This function creates a sequential palette starting from a dark color.

```APIDOC
## dark_palette

### Description
Make a sequential palette that blends from dark to `color`.

### Method
(Not specified, typically a function call in a Python SDK)

### Endpoint
(Not applicable for SDK functions)

### Parameters
(Parameters not explicitly detailed in the source text)

### Request Example
(Not applicable for SDK functions)

### Response
(Not applicable for SDK functions)
```

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

### Get Perceptually Uniform Palette as Discrete Palette

Source: https://seaborn.pydata.org/generated/seaborn.color_palette.html

Return one of the perceptually-uniform palettes included in seaborn as a discrete palette.

```python
sns.color_palette("flare")
```

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

### seaborn.distplot()

Source: https://seaborn.pydata.org/generated/seaborn.distplot.html

This function is deprecated and will be removed in seaborn v0.14.0. It has been replaced by `histplot()` and `displot()`. Use the provided link for a guide to updating.

```APIDOC
## seaborn.distplot()

### Description
This function is deprecated and will be removed in seaborn v0.14.0. It has been replaced by `histplot()` and `displot()`, two functions with a modern API and many more capabilities.

For a guide to updating, please see this notebook: https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

### Parameters
- `_a` (None): 
- `_bins` (None): 
- `_hist` (True): 
- `_kde` (True): 
- `_rug` (False): 
- `_fit` (None): 
- `_hist_kws` (None): 
- `_kde_kws` (None): 
- `_rug_kws` (None): 
- `_fit_kws` (None): 
- `_color` (None): 
- `_vertical` (False): 
- `_norm_hist` (False): 
- `_axlabel` (None): 
- `_label` (None): 
- `_ax` (None): 
- `_x` (None): 
```
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