### Setup Development Environment with Pixi

Source: https://github.com/holoviz/hvplot/blob/main/doc/developer_guide.md

Run this command to create a development environment, install hvPlot in editable mode, download test datasets, and install pre-commit hooks. This command may take a minute or two to complete.

```bash
pixi run setup-dev
```

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

### Install hvPlot from source

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/installation.md

Install hvPlot from its GitHub repository by cloning the repository and running the pip install command in the root directory.

```bash
pip install -e .
```

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

### Install Pre-commit Hooks Manually

Source: https://github.com/holoviz/hvplot/blob/main/doc/developer_guide.md

Manually installs pre-commit hooks if not using pixi for installation.

```bash
pre-commit install
```

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

### Install hvPlot with uv

Source: https://github.com/holoviz/hvplot/blob/main/doc/tutorials/getting_started.ipynb

Use uv, a fast Python package installer, to install hvplot.

```bash
uv pip install hvplot
```

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

### Run Example Tests

Source: https://github.com/holoviz/hvplot/blob/main/doc/developer_guide.md

Executes all Jupyter Notebook example tests using the 'test-example' pixi task.

```bash
pixi run test-example
```

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

### Install Pre-commit Hooks

Source: https://github.com/holoviz/hvplot/blob/main/doc/developer_guide.md

Installs pre-commit hooks for linting and formatting the source code.

```bash
pixi run lint-install
```

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

### Install hvPlot pre-releases using uv

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/installation.md

Use this command to install pre-release versions (alpha, beta, RC) of hvPlot from PyPI using uv.

```bash
uv pip install hvplot --prerelease allow
```

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

### Install hvPlot using uv

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/installation.md

Use this command to install the latest stable version of hvPlot from PyPI using uv.

```bash
uv pip install hvplot
```

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

### Install hvSampledata with pip

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api/index.md

Install the hvsampledata package using pip if hvPlot was installed with minimal dependencies.

```bash
pip install hvsampledata
```

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

### Xarray Example

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api/manual/hvplot.hvPlot.line.ipynb

Provides an example of plotting a line from an xarray Dataset, specifically showing the evolution of air temperature at a fixed location.

```APIDOC
## hvplot.hvPlot.line with Xarray

### Description
Plots time-series data from an xarray Dataset.

### Method Signature
`hvplot.hvPlot.line(y=None, ...)`

### Parameters
*   **y** (str, optional): Variable name from the xarray Dataset to plot on the y-axis.

### Request Example
```python
import hvplot.xarray

ds = hvplot.sampledata.air_temperature("xarray").sel(lon=285.,lat=40.)
ds.hvplot.line(y="air")
```
```

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

### Install hvPlot pre-releases using pip

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/installation.md

Use this command to install pre-release versions (alpha, beta, RC) of hvPlot from PyPI using pip.

```bash
pip install hvplot --pre
```

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

### Install hvPlot from conda-forge

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/installation.md

Install hvPlot from the community-maintained conda-forge channel using conda.

```bash
conda install conda-forge::hvplot
```

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

### Get hvPlot help via command line

Source: https://github.com/holoviz/hvplot/blob/main/doc/user_guide/Introduction.ipynb

Outside an interactive environment, use `hvplot.help()` to get information about plot kinds. For detailed options, refer to the plotting options documentation.

```python
hvplot.help('line')
```

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

### Install hvPlot with Pip

Source: https://github.com/holoviz/hvplot/blob/main/README.md

Use this command to install hvPlot using the Pip package manager.

```bash
pip install hvplot
```

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

### Install in Specific Test Environment

Source: https://github.com/holoviz/hvplot/blob/main/doc/developer_guide.md

Installs hvPlot in a specific pixi test environment, e.g., 'test-core'.

```bash
pixi run -e test-core install
```

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

### Install hvPlot from defaults channel

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/installation.md

Install hvPlot from the Anaconda defaults channel using conda. Note: This channel may have licensing restrictions.

```bash
conda install defaults::hvplot
```

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

### Get Plotting Help

Source: https://github.com/holoviz/hvplot/blob/main/README.md

Use hvplot.help() to retrieve documentation for specific plot kinds, such as 'scatter'. In notebook environments, standard help() and ? syntax also work.

```python
hvplot.help(kind='scatter')
```

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

### Xarray example

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api/manual/hvplot.hvPlot.hexbin.ipynb

Creates a hexbin plot from an xarray DataArray, aggregating temperature readings and using a logarithmic color scale.

```APIDOC
## Xarray example

### Description
Creates a hexbin plot from an xarray DataArray, aggregating temperature readings and using a logarithmic color scale.

### Usage
```python
import hvplot.xarray  # noqa
import numpy as np

da = hvplot.sampledata.air_temperature("xarray").sel(time="2014-02-25 12:00")

da.hvplot.hexbin(
    x="lon", y="lat", cmap="inferno",
    bgcolor="#020210", data_aspect=1, frame_width=400,
    C="air", reduce_function=np.mean, clabel="Air temperature (K)",
    logz=True, gridsize=30,
)
```
```

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

### KDE from Long-Form Data by Category

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api/manual/hvplot.hvPlot.kde.ipynb

Compares distributions across categories using the `by` keyword. This example plots the distribution of bill lengths for each penguin species.

```python
import hvplot.pandas # noqa

df = hvplot.sampledata.penguins("pandas")

df.hvplot.kde(y="bill_length_mm", by="species")
```

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

### Xarray Example: Air Temperature Evolution

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api/manual/hvplot.hvPlot.line.ipynb

Plots the evolution of air temperature from an xarray Dataset at a specific location. Requires hvplot.xarray to be imported.

```python
import hvplot.xarray

ds = hvplot.sampledata.air_temperature("xarray").sel(lon=285.,lat=40.)

ds.hvplot.line(y="air")
```

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

### Load xarray air temperature dataset

Source: https://github.com/holoviz/hvplot/blob/main/doc/user_guide/Gridded_Data.ipynb

Imports necessary libraries and loads the sample air temperature dataset from xarray. This sets up the data for subsequent plotting examples.

```python
import xarray as xr
import hvplot.xarray  # noqa

air_ds = xr.tutorial.open_dataset('air_temperature').load()
air = air_ds.air
air_ds
```

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

### Create hvPlot environment

Source: https://github.com/holoviz/hvplot/blob/main/doc/tutorials/getting_started.ipynb

Create a conda environment with all necessary dependencies to run hvPlot guides locally. This includes hvplot and its core dependencies like HoloViews, Datashader, and Pandas.

```bash
conda create -n hvplot-env -c conda-forge --override-channels hvplot geoviews datashader xarray pandas geopandas dask networkx intake intake-xarray intake-parquet s3fs scipy spatialpandas pooch rasterio fiona plotly matplotlib hvsampledata jupyterlab
```

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

### Build Documentation

Source: https://github.com/holoviz/hvplot/blob/main/doc/developer_guide.md

Builds the hvPlot documentation using the 'docs-build' pixi task.

```bash
pixi run docs-build
```

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

### Install hvSampledata with conda

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api/index.md

Install the hvsampledata package using conda if hvPlot was installed with minimal dependencies.

```bash
conda install conda-forge::hvsampledata
```

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

### Launch Jupyter Lab with Development Environment

Source: https://github.com/holoviz/hvplot/blob/main/doc/developer_guide.md

Launches Jupyter Lab using the default pixi development environment.

```bash
pixi run lab
```

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

### Generating Similar Plot with HoloViews

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api_compatibility/pandas/Pandas_API.ipynb

This example shows how to generate a similar plot to Pandas' multi-column grouped histograms using HoloViews directly, as this specific functionality is not yet supported in hvPlot.

```python
import holoviews as hv
from holoviews.operation import histogram

ds = hv.Dataset(data, kdims=['a', 'b'], vdims=['c', 'd'])
dsg = ds.groupby(['a', 'b'])
(
    hv.NdLayout(histogram(dsg, dimension='c'))
    * hv.NdLayout(histogram(dsg, dimension='d'))
).opts(
    hv.opts.Histogram(height=100, width=800)
).cols(1)
```

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

### Chaining Interactive Methods for Complex Plots

Source: https://github.com/holoviz/hvplot/blob/main/doc/user_guide/Interactive.ipynb

Chain multiple interactive methods and widgets to control different aspects of the data pipeline. This example combines selection, quantile calculation, and plotting.

```python
q = pnw.FloatSlider(name='quantile', start=0, end=1)

(ds.air.interactive(loc='left')
 .sel(time=pnw.DiscreteSlider)
 .quantile(q=q, dim='lon')
 .hvplot(aspect=1))
```

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

### List Available Tile Sources

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/plotting_options/geographic.ipynb

Prints the available tile sources from HoloViews and GeoViews. Requires importing the respective libraries.

```python
import geoviews as gv
import holoviews as hv

print("HoloViews tiles:", *hv.element.tiles.tile_sources, sep=" ", end="\n\n")
print("GeoViews tiles:", *gv.tile_sources.tile_sources, sep=" ")
```

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

### Install hvPlot with Conda

Source: https://github.com/holoviz/hvplot/blob/main/README.md

Use this command to install hvPlot using the Conda package manager.

```bash
conda install hvplot
```

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

### Instantiate Explorer with Options

Source: https://github.com/holoviz/hvplot/blob/main/doc/user_guide/Explorer.ipynb

Instantiates the hvPlot Explorer and pre-customizes the plot using options such as title and width.

```python
data.hvplot.explorer(title='Penguins', width=200)
```

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

### Install hvPlot pre-releases from pyviz/label/dev

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/installation.md

Install pre-release versions of hvPlot from the pyviz development channel using conda.

```bash
conda install pyviz/label/dev::hvplot
```

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

### Create and Display Explorer Instance

Source: https://github.com/holoviz/hvplot/blob/main/doc/user_guide/Explorer.ipynb

Loads the penguins dataset and creates an hvPlot Explorer instance from it. The explorer object is then displayed.

```python
from bokeh.sampledata.penguins import data as df

df.head(2)
```

```python
hvexplorer = df.hvplot.explorer()
hvexplorer
```

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

### Control Smoothing with Bandwidth

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api/manual/hvplot.hvPlot.kde.ipynb

Demonstrates how to control the smoothness of a KDE plot using the `bandwidth` argument. Smaller values increase detail. Defaults to Scott's rule of thumb if not set.

```python
import hvplot.pandas # noqa

df = hvplot.sampledata.earthquakes("pandas")

df.hvplot.kde(
    y='depth', bandwidth=0.1,
    width=300, title='bandwidth=0.1'
) +
df.hvplot.kde(
    y='depth', bandwidth=0.5,
    width=300, shared_axes=False, title='bandwidth=0.5'
)
```

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

### Control Smoothing with `bandwidth`

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api/manual/hvplot.hvPlot.kde.ipynb

Demonstrates how to control the smoothness of the KDE plot using the `bandwidth` argument. Smaller values result in more detail, while not setting it uses Scott's rule of thumb.

```APIDOC
## Control smoothing with `bandwidth`

You can control the smoothness of the estimate using the `bandwidth` argument that accepts a positive numerical value. Smaller values yield more detail. When not set, the bandwidth is internally computed using Scott's rule of thumb.

```python
import hvplot.pandas # noqa

df = hvplot.sampledata.earthquakes("pandas")

df.hvplot.kde(
    y='depth', bandwidth=0.1,
    width=300, title='bandwidth=0.1'
)
+
df.hvplot.kde(
    y='depth', bandwidth=0.5,
    width=300, shared_axes=False, title='bandwidth=0.5'
)
```
```

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

### Load Sample Data with Pandas

Source: https://github.com/holoviz/hvplot/blob/main/doc/user_guide/Plotting.ipynb

Load the apple_stocks sample dataset as a pandas DataFrame. This is useful for demonstrating plotting capabilities.

```python
from hvplot.sampledata import apple_stocks, nyc_taxi_remote, penguins

apple = apple_stocks('pandas')
print(type(apple))
```

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

### Load Sample Data

Source: https://github.com/holoviz/hvplot/blob/main/doc/user_guide/Plotting_Extensions.ipynb

Loads the apple_stocks dataset into a pandas DataFrame for plotting.

```python
from hvplot.sampledata import apple_stocks

df = apple_stocks('pandas')
```

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

### Activate Development Shell

Source: https://github.com/holoviz/hvplot/blob/main/doc/developer_guide.md

Activates the development shell, equivalent to sourcing a virtual environment.

```bash
pixi shell
```

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

### Image vs. Quadmesh for Irregular Grids

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api/manual/hvplot.hvPlot.image.ipynb

Explains the difference between `Image` and `Quadmesh` elements and demonstrates how to handle potentially irregular grids by adjusting `rtol` or using `quadmesh`.

```APIDOC
## Image vs. Quadmesh

The `hvplot.image` method, which constructs a HoloViews `Image` element, validates input data for regular sampling. If the data is not sampled with sufficient precision, an exception may suggest using the `Quadmesh` element instead. To address this for `Image` elements, you can increase the allowed deviation in sample spacing by globally setting `hv.config.image_rtol`. Otherwise, consider using the `quadmesh` method.

```python
import hvplot.xarray  # noqa

ds = hvplot.sampledata.air_temperature("xarray").sel(time="2014-02-25 12:00")
# Emulate irregular grid
ds.coords["lat"] = ds.coords["lat"]**2

ds.hvplot.image(x="lon", y="lat", z="air", cmap="blues", width=400, height=400)
```
```

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

### Save Plot to PNG

Source: https://github.com/holoviz/hvplot/blob/main/doc/user_guide/Viewing.ipynb

Export a HoloViews plot as a PNG image. This requires Selenium and PhantomJS to be installed.

```python
from pathlib import Path

Path('test.html').unlink(missing_ok=True)
Path('test.png').unlink(missing_ok=True)
```

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

### Load Sample xarray Dataset

Source: https://github.com/holoviz/hvplot/blob/main/doc/user_guide/Interactive.ipynb

Loads the 'air_temperature' dataset from xarray's tutorial data.

```python
ds = xr.tutorial.load_dataset('air_temperature')
ds
```

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

### Simple Explorer with Pandas DataFrame

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api/manual/hvplot.hvPlot.explorer.ipynb

Demonstrates how to create a basic interactive explorer for a pandas DataFrame using hvplot. This example initializes the explorer and sets initial parameters for plotting, such as x-axis, y-axis, and grouping variables.

```python
import hvplot.pandas

df = hvplot.sampledata.penguins("pandas")

hvexplorer = df.hvplot.explorer()
# Start: only required for building the site
hvexplorer.param.update(x='bill_length_mm', y_multi=['bill_depth_mm'], by=['species'])
hvexplorer.labels.title = 'Penguins Scatter'
# End
hvexplorer
```

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

### Create Geographic Plot with GeoViews

Source: https://github.com/holoviz/hvplot/blob/main/doc/tutorials/getting_started.ipynb

Generates a quadmesh plot with advanced mapping features using GeoViews. Requires GeoViews to be installed.

```python
import cartopy.crs as crs

air_ds.hvplot.quadmesh(
    'lon', 'lat', projection=crs.Orthographic(-90, 30), project=True,
    global_extent=True, cmap='viridis', coastline=True
)
```

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

### Import hvplot for Pandas DataFrames

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api/index.md

Install the hvplot namespace on a Pandas DataFrame to enable plotting. This registers an accessor for convenient plotting.

```python
import pandas as pd
import hvplot.pandas  # noqa
df = pd.DataFrame()

df.hvplot.scatter()
# or
df.hvplot(kind='scatter')
```

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

### hvPlot Scatter Plot Example

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api_compatibility/pandas/index.ipynb

Generates a scatter plot using hvPlot. This returns a HoloViews object, which wraps the dataset and represents a plot.

```python
plot = df.hvplot.scatter('bill_length_mm', 'bill_depth_mm', hover_cols=['species'])
```

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

### Initialize hvplot with Fugue

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/data_libraries.ipynb

Import the hvplot extension for Fugue and use the `fugue_sql` function to define a FugueSQL query that outputs a line plot. This enables plotting directly from FugueSQL.

```python
import hvplot.fugue  # noqa
import fugue

fugue.api.fugue_sql(
    """
    OUTPUT df_pandas USING hvplot:line(
        height=150,
    )
    """
)
```

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

### Xarray Timeseries Data Loading

Source: https://github.com/holoviz/hvplot/blob/main/doc/user_guide/Timeseries_Data.ipynb

Loads the 'air_temperature' dataset from xarray's tutorial data, demonstrating the setup for using hvplot with xarray.

```python
import xarray as xr
import hvplot.xarray  # noqa

air_ds = xr.tutorial.open_dataset('air_temperature').load()
air_ds
```

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

### Create Interactive Xarray Data Pipeline with Widgets

Source: https://github.com/holoviz/hvplot/blob/main/doc/index.md

Demonstrates creating an interactive data pipeline from an xarray DataArray using `.interactive()`. It allows adjusting a time index with a slider and calculates the difference between a selected time step and the overall mean.

```python
import hvplot.xarray
import panel as pn
import xarray as xr

w_time = pn.widgets.IntSlider(name='time', start=0, end=10)

da = xr.tutorial.open_dataset('air_temperature').air
da.interactive.isel(time=w_time).mean().item() - da.mean().item()
```

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

### Plotting points with custom projection

Source: https://github.com/holoviz/hvplot/blob/main/doc/user_guide/Geographic_Data.ipynb

Plots geographic points with coastline and a specified orthographic projection centered over North America. Requires GeoViews to be installed.

```python
airports.hvplot.points(
    'Longitude', 'Latitude', color='red', alpha=0.2,
    coastline=True, projection=ccrs.Orthographic(-90, 30)
)
```

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

### Line Dash Styles (Bokeh)

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api/manual/hvplot.hvPlot.line.ipynb

Demonstrates various line dash styles including named options and custom dash patterns for the Bokeh backend.

```python
import hvplot.pandas
import pandas as pd

df = pd.DataFrame({"x": [0, 1, 2, 3], "y": [0, 1, 4, 9]})

plot_opts = dict(x="x", y="y", width=300, height=200)
(
    df.hvplot.line(line_dash="dashed", title="dashed", **plot_opts) +
    df.hvplot.line(line_dash="dotted", title="dotted", **plot_opts) +
    df.hvplot.line(line_dash="dotdash", title="dotdash", **plot_opts) +
    df.hvplot.line(line_dash="dashdot", title="dashdot", **plot_opts) +
    df.hvplot.line(line_dash=(20, 10), title="Dash pattern (20, 10)", **plot_opts) +
    df.hvplot.line(line_dash=(20, 10, 5), title="Dash pattern (20, 10, 5)", **plot_opts)
).cols(2)
```

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

### Customize groupby widget location

Source: https://github.com/holoviz/hvplot/blob/main/doc/user_guide/Widgets.ipynb

Changes the position of the automatically generated groupby widget using the 'widget_location' option. This example places the widget at the top-left.

```python
flowers.hvplot.bivariate(x='sepal_width', y='sepal_length', width=600, 
                         groupby='species', widget_location='left_top')
```

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

### Generate Sample Vector Field Data

Source: https://github.com/holoviz/hvplot/blob/main/doc/gallery/big-data/large_vectorfield_downsampled.ipynb

Creates a sample dataset with 4,000,000 points for demonstrating vector field visualization.

```python
def sample_data(shape=(20, 30)):
    x = np.linspace(311.9, 391.1, shape[1])
    y = np.linspace(-23.6, 24.8, shape[0])
    x2d, y2d = np.meshgrid(x, y)
    u = 10 * (2 * np.cos(2 * np.deg2rad(x2d) + 3 * np.deg2rad(y2d + 30)) ** 2)
    v = 20 * np.cos(6 * np.deg2rad(x2d))
    return x, y, u, v

xs, ys, U, V = sample_data(shape=(2000, 2000))
mag = np.sqrt(U**2 + V**2)
angle = (np.pi/2.) - np.arctan2(U/mag, V/mag)
ds = xr.Dataset({
    'mag': xr.DataArray(mag, dims=('y', 'x'), coords={'y': ys, 'x': xs}),
    'angle': xr.DataArray(angle, dims=('y', 'x'), coords={'y': ys, 'x': xs})
})
ds
```

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

### Customizing Tick Formatters in hvPlot

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api_compatibility/pandas/Pandas_API.ipynb

Example of setting custom y-axis formatters using format strings and Bokeh TickFormatters, and x-axis formatters with a DatetimeTickFormatter.

```python
from bokeh.models.formatters import DatetimeTickFormatter

formatter = DatetimeTickFormatter(months='%b %Y')
df['A'].plot(yformatter='$%.2f', xformatter=formatter)
```

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

### Run Pre-commit on All Files Manually

Source: https://github.com/holoviz/hvplot/blob/main/doc/developer_guide.md

Manually runs pre-commit on all files if not using pixi for execution.

```bash
pre-commit run --all-files
```

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

### Customizing Axis Labels with Matplotlib Backend

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api_compatibility/pandas/Pandas_API.ipynb

Example of setting custom labels for the x and y axes using 'xlabel' and 'ylabel' arguments with the matplotlib backend.

```python
df.plot(xlabel="new x", ylabel="new y", backend='matplotlib');
```

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

### Box Plot using DataFrame.boxplot()

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api_compatibility/pandas/Pandas_API.ipynb

Generates a box plot using the `DataFrame.boxplot()` method, which is an alternative interface to `DataFrame.plot.box()`. This example uses the matplotlib backend.

```python
df = pd.DataFrame(np.random.rand(10, 5))
df.boxplot(backend='matplotlib')
```

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

### Build Interactive Pipeline with Widgets

Source: https://github.com/holoviz/hvplot/blob/main/doc/tutorials/getting_started.ipynb

Create an interactive object using .interactive() and integrate the Panel widgets into the data pipeline. This setup allows for dynamic updates of the pipeline's output as widget values change.

```python
airi = air.interactive()
baseline = airi.sel(lat=w_latitude).mean().item()
pipeline = (
    airi
    .sel(lat=w_latitude)
    .to_dataframe()
    .drop(columns='lat')
    .groupby('time').mean()
    .rolling(w_rolling_window).mean()
    - baseline
)
pipeline.describe()
```

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

### Load Sample Data with Dask

Source: https://github.com/holoviz/hvplot/blob/main/doc/user_guide/Plotting.ipynb

Load the nyc_taxi_remote sample dataset as a dask DataFrame. This demonstrates handling larger datasets with dask.

```python
taxi = nyc_taxi_remote('dask', lazy=True)
print(type(taxi))
```

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

### Interactive Bokeh Plots with .hvplot

Source: https://github.com/holoviz/hvplot/blob/main/doc/user_guide/Interactive.ipynb

Utilize the `.hvplot` method for fully interactive Bokeh-based plots. This example uses a `FloatSlider` to control the quantile calculation.

```python
slider = pnw.FloatSlider(name='quantile', start=0, end=1)

ds.air.interactive.quantile(slider, dim='time').hvplot(data_aspect=1)
```

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

### Plotting Multi-Column Grouped Histograms with Pandas

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api_compatibility/pandas/Pandas_API.ipynb

In Pandas, the `by` keyword can also be specified in `DataFrame.plot.hist()`. This example demonstrates plotting histograms grouped by multiple columns.

```python
data = pd.DataFrame(
    {
        "a": np.random.choice(["x", "y", "z"], 1000),
        "b": np.random.choice(["e", "f", "g"], 1000),
        "c": np.random.randn(1000),
        "d": np.random.randn(1000) - 1,
    },
)
data.plot.hist(by=["a", "b"], figsize=(10, 5), backend='matplotlib');
```

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

### Initialize hvPlot Explorer

Source: https://github.com/holoviz/hvplot/blob/main/doc/tutorials/getting_started.ipynb

Instantiate the hvPlot explorer with pre-defined parameters for data exploration. This provides a graphical interface to select and customize plots.

```python
explorer = df_penguins.hvplot.explorer(x='bill_length_mm', y='bill_depth_mm', by=['species'])
explorer
```

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

### Instantiate Explorer via Method

Source: https://github.com/holoviz/hvplot/blob/main/doc/user_guide/Explorer.ipynb

Instantiates the hvPlot Explorer using the `.explorer()` method available on the `.hvplot` namespace. This method was added in version 0.9.0.

```python
data.hvplot.explorer()
```

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

### Plotting Histogram of Differences with hvPlot

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api_compatibility/pandas/Pandas_API.ipynb

This example shows plotting the histogram of the differences between consecutive elements in a column using hvPlot's default behavior.

```python
df['A'].diff().hist()
```

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

### Load Sample Datasets in Python

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api/index.md

Import hvPlot extensions and load sample datasets into pandas, xarray, rioxarray, and geopandas data structures.

```python
import hvplot.pandas  # noqa
import hvplot.xarray  # noqa

penguins = hvplot.sampledata.penguins("pandas")
air_temp = hvplot.sampledata.air_temperature("xarray")
landsat = hvplot.sampledata.landsat_rgb("rioxarray")
us_states = hvplot.sampledata.us_states("geopandas")
```

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

### Display unique toolbar in a layout

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/plotting_options/interactivity.ipynb

In a layout of plots, a single toolbar is displayed above all plots. This example shows a scatter plot combined with a line plot.

```python
import hvplot.pandas
import pandas as pd

df = pd.DataFrame({"y": [1, 4, 2]})

df.hvplot.scatter(width=300, height=150) + df.hvplot.line(width=300, height=150)
```

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

### Load and display air temperature dataset

Source: https://github.com/holoviz/hvplot/blob/main/doc/user_guide/Geographic_Data.ipynb

Loads the air temperature dataset from xarray's tutorial data.

```python
air_ds = xr.tutorial.open_dataset('air_temperature').load()
air_ds
```

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

### Load Sample Data with Pandas

Source: https://github.com/holoviz/hvplot/blob/main/doc/user_guide/Plotting_with_Plotly.ipynb

Load the 'apple_stocks' dataset into a pandas DataFrame using the hvplot sample data module. This is useful for time-series plotting.

```python
from hvplot.sampledata import apple_stocks, nyc_taxi_remote, penguins

apple = apple_stocks('pandas')
print(type(apple))
apple.head()
```

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

### Timeseries Bar Plot with hvplot (Pandas)

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api/manual/hvplot.hvPlot.bar.ipynb

Create a bar plot where the x-axis represents a timeseries. This example uses pandas DataFrames and requires hvplot.pandas.

```python
import hvplot.pandas

df = hvplot.sampledata.stocks("pandas").set_index("date").resample("1ME").mean() - 1

df.hvplot.bar(y="Netflix")
```

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

### Get Explorer Settings

Source: https://github.com/holoviz/hvplot/blob/main/doc/user_guide/Explorer.ipynb

Retrieves a dictionary of the explorer's current customized settings. These settings can be used to re-create the plot using the `.hvplot()` accessor.

```python
settings = hvexplorer.settings()
settings
```

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

### Grouping by Categories

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api/manual/hvplot.hvPlot.line.ipynb

Illustrates how to use the `by` option to visually distinguish categories, automatically coloring lines based on the specified column and returning a HoloViews NdOverlay.

```APIDOC
## hvplot.hvPlot.line with Grouping

### Description
Groups and colors lines based on categories in a specified column.

### Method Signature
`hvplot.hvPlot.line(by=None, ...)`

### Parameters
*   **by** (str, optional): Column name to group and color lines by.

### Note
The `color` option cannot be used to vectorize coloring each individual line when `by` is used.

### Request Example
```python
import hvplot.pandas

df = hvplot.sampledata.stocks("pandas").set_index("date").melt(ignore_index=False, var_name="stock")
df.hvplot.line(by="stock")
```
```

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

### Flip X and Y Axes

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/plotting_options/axis.ipynb

Use `flip_xaxis` and `flip_yaxis` options to invert the direction of the respective axes. This can change the starting point and direction of value progression.

```python
import hvplot.pandas  # noqa
import pandas as pd

df = pd.DataFrame({
    "x": list(range(3)), "y": list(range(3)),
    "v": ["a", "b", "c"], "s": list(range(100, 700, 200)),
})

plot_opts = dict(
    x="x", y="y", color="v", s="s", legend=False,
    aspect=1, frame_width=200, shared_axes=False,
)
(   df.hvplot.scatter(flip_xaxis=False, flip_yaxis=False, title="None", **plot_opts) +
    df.hvplot.scatter(flip_xaxis=True, flip_yaxis=False, title="flip_xaxis", **plot_opts) +
    df.hvplot.scatter(flip_xaxis=False, flip_yaxis=True, title="flip_yaxis", **plot_opts) +
    df.hvplot.scatter(flip_xaxis=True, flip_yaxis=True, title="flip_xaxis + flip_yaxis", **plot_opts)
).cols(2)
```

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

### Control Marker Style with Bokeh and Matplotlib Markers

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api/manual/hvplot.hvPlot.scatter.ipynb

Demonstrates controlling marker styles using the `marker` option, showcasing both Bokeh-specific and Matplotlib-compatible markers. Requires importing bokeh and holoviews.

```python
import bokeh as bk
import holoviews as hv
import hvplot.pandas
import itertools
import pandas as pd

bokeh_orig_markers = list(bk.core.enums.MarkerType)
hv_bk_mpl_compat_markers = list(hv.plotting.bokeh.styles.markers)
print('Bokeh original markers:')
print(*map(repr, bokeh_orig_markers), sep=', ', end='\n\n')
print('Matplotlib-compatible markers for Bokeh:')
print(*map(repr, hv_bk_mpl_compat_markers), sep=', ')

df = pd.DataFrame(list(itertools.product(range(6), range(6))), columns=['x', 'y'])
df['marker_col'] = bokeh_orig_markers + [''] * (len(df) - len(bokeh_orig_markers))

df.hvplot.scatter(
    x='x', y='y', marker='marker_col', s=150, title='Bokeh-specific markers'
) *
df.assign(y=df.y+0.2).hvplot.labels(
    x='x', y='y', text='marker_col', text_color='black',
    text_baseline='bottom', text_font_size='9pt', padding=0.2
)
```

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

### Plot Xarray Image

Source: https://github.com/holoviz/hvplot/blob/main/doc/tutorials/getting_started.ipynb

Create an image plot from an Xarray Dataset using hvplot. This example demonstrates setting plot dimensions and disabling dynamic updates.

```python
air_ds.hvplot.image(data_aspect=1, frame_width=400, dynamic=False)
```

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

### Build hvPlot Packages

Source: https://github.com/holoviz/hvplot/blob/main/doc/developer_guide.md

Execute these commands to build both a Python package for PyPI and a Conda package for Anaconda.

```bash
pixi run build-pip
```

```bash
pixi run build-conda
```

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

### Create a Grouped Scatter Plot

Source: https://github.com/holoviz/hvplot/blob/main/doc/user_guide/Plotting.ipynb

Generate a scatter plot grouped by 'passenger_count'. This example samples the data and filters for specific passenger counts to ensure readability.

```python
taxi_subset = taxi[taxi.passenger_count.isin([1, 2])].sample(frac=0.001).compute()
taxi_subset.hvplot(x='pickup_x', y='pickup_y', by='passenger_count', kind='scatter', alpha=0.6)
```

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

### Plotting Step Charts

Source: https://github.com/holoviz/hvplot/blob/main/doc/user_guide/Plotting_with_Plotly.ipynb

Create a step chart, similar to a line chart but visualizing discrete steps. The 'where' keyword controls the stepping point, with options 'pre', 'mid' (default), and 'post'.

```python
apple_subset.hvplot.step(x='date', y=['high', 'low'])
```

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

### Plotting Interactive Data with Matplotlib

Source: https://github.com/holoviz/hvplot/blob/main/doc/user_guide/Interactive.ipynb

Feed the output of chained .interactive method calls into a plot. This example uses a DiscreteSlider to select time and then plots the data.

```python
ds.air.interactive.sel(time=pnw.DiscreteSlider).plot()
```

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

### Customizing Step Interpolation with 'where'

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api/manual/hvplot.hvPlot.step.ipynb

Demonstrates how to control the step transition point using the 'where' keyword ('pre', 'mid', 'post'). Multiple step plots and a scatter plot are overlaid.

```python
import hvplot.pandas  # noqa
import pandas as pd

df = pd.DataFrame({"y": [0, 1, 2]})

df.hvplot.step(where="pre", color="blue", label="pre", alpha=.5) *
df.hvplot.step(color="red", label="mid (default)", alpha=.5) *
df.hvplot.step(where="post", color="green", label="post", alpha=.5) *
df.hvplot.scatter(color="black", padding=0.1)
```

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

### Specifying Colormap in hvPlot

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api_compatibility/pandas/index.ipynb

hvPlot allows specifying a colormap locally using the `cmap` argument. This example explicitly sets the colormap to `viridis` for a scatter plot.

```python
df.hvplot.scatter('bill_length_mm', 'flipper_length_mm', c=df['body_mass_g'], cmap='viridis')
```

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

### General Plot Styling with Matplotlib Backend

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api_compatibility/pandas/Pandas_API.ipynb

Demonstrates basic line plot styling using the 'k--' style argument with the matplotlib backend.

```python
ts.plot(style='k--', label='Series', backend='matplotlib');
```

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

### Default Categorical Colormap in Pandas

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api_compatibility/pandas/index.ipynb

Pandas uses a grayscale colormap for categorical data by default. This example plots scatter points colored by a categorical column.

```python
categories = list('ABCDEFGHIJLKMNOPQRST')
dfc = pd.DataFrame({
    'x': np.random.rand(len(categories)),
    'y': np.random.rand(len(categories)),
    'category': categories,
})
dfc['category'] = dfc['category'].astype('category')
dfc.plot.scatter('x', 'y', c='category');
```

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

### Pandas Scatter Plot Example

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api_compatibility/pandas/index.ipynb

Generates a scatter plot using Pandas' plotting API. The `ax` argument, used for overlaying plots, is not supported in hvPlot.

```python
plot = df.plot.scatter('bill_length_mm', 'bill_depth_mm', figsize=(4, 3))
```

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

### Using String and Datashader Reductions for Aggregators

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/plotting_options/resampling.ipynb

Demonstrates using both string-based aggregators ('var') and Datashader reduction objects (ds.min('s')) with rasterize. This highlights how different aggregators can visualize different aspects of the data, such as variance or minimum values.

```python
import hvplot.pandas
import datashader as ds

df = hvplot.sampledata.synthetic_clusters("pandas")

plot_opts = dict(x='x', y='y', data_aspect=1, frame_height=250)
df.hvplot.points(
    rasterize=True, aggregator='var', color='s',
    clabel='var(s)', **plot_opts
) +
df.hvplot.points(
    rasterize=True, aggregator=ds.min('s'), clabel='min(s)',
    **plot_opts
)
```

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

### Create and cumulate time series data

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api_compatibility/pandas/Pandas_API.ipynb

Create a pandas Series with a date range index and cumulative random data. This data is used for plotting examples.

```python
ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
ts = ts.cumsum()
```

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

### Giant Component Setup

Source: https://github.com/holoviz/hvplot/blob/main/doc/user_guide/NetworkX.ipynb

Sets up parameters for analyzing the giant connected component in a binomial random graph. Includes fallback layout if pygraphviz or pydot are not found.

```python
#    Copyright (C) 2006-2018
#    Aric Hagberg <hagberg@lanl.gov>
#    Dan Schult <dschult@colgate.edu>
#    Pieter Swart <swart@lanl.gov>
#    All rights reserved.
#    BSD license.

# Adapted by Philipp Rudiger <prudiger@anaconda.com>

import math

try:
    import pygraphviz  # noqa
    from networkx.drawing.nx_agraph import graphviz_layout
    layout = graphviz_layout
except ImportError:
    try:
        import pydot  # noqa
        from networkx.drawing.nx_pydot import graphviz_layout
        layout = graphviz_layout
    except ImportError:
        print("PyGraphviz and pydot not found;\n"\
              "drawing with spring layout;\n"\
              "will be slow.")
        layout = nx.spring_layout

n = 150  # 150 nodes
# p value at which giant component (of size log(n) nodes) is expected
p_giant = 1.0 / (n - 1)
# p value at which graph is expected to become completely connected
p_conn = math.log(n) / float(n)
```

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

### Configure Intersphinx Mapping

Source: https://github.com/holoviz/hvplot/blob/main/doc/developer_guide.md

Extend `intersphinx_mapping` in `conf.py` to enable linking to external documentation. The key is used to reference the site in links.

```python
intersphinx_mapping = {
    'holoviews': ('https://holoviews.org/', None),
}
```

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

### Generate random time series data

Source: https://github.com/holoviz/hvplot/blob/main/doc/ref/api_compatibility/pandas/Pandas_API.ipynb

Generate a time series of random numbers and set the matplotlib inline backend. This is a setup step for creating plots.

```python
%matplotlib inline
np.random.seed(123456)
```