Week 3A: More Interactive Data Viz

Section 401
Sep 18, 2023

Housekeeping

Homework #1 due on Monday, 9/25
Homework #2 will be assigned same day
Choose a dataset to visualize and explore
- OpenDataPhilly or one your choosing
- Email me if you want to analyze one that’s not on OpenDataPhilly

Agenda for Week #3

Two parts:

Part 1: More interactive data visualization: the HoloViz ecosystem
Part 2: Getting started with geospatial data analysis in Python

Part 1: More interactive data viz

Recap: The Python data viz landscape

What have we learned so far

Matplotlib

The classic, most flexible library
Can handle geographic data well
Overly verbose syntax, syntax is not declarative

Pandas

Quick, built-in interface
Not as many features as other libraries

Seaborn

Best for visualizing complex relationships between variables
Improves matplotlib’s syntax: more declarative

Altair

Easy, declarative syntax
Lots of interactive features
Complex visualizations with minimal amounts of code

We’ll learn one more today…

HoloViz: A set of coordinated visualization libraries in Python

The motivation behind HoloViz mirrors the goals of this course

Proper data visualization is crucial throughout all of the steps of the data science pipeline: data wrangling, modeling, and storytelling

Today: `hvPlot`, `Holoviews`, `Geoviews`

Later in the course: `Datashader`, `Param`, `Panel`

HoloViz: A quick overview

Bokeh: creating interactive visualizations using Javascript using Python
HoloViews: a declarative, high-level library for creating bokeh visualizations

Note: The relationship between Bokeh and Holoviews is similar to Altair and Vega

A significant pro

GeoViews builds on HoloViews to add support for geographic data

The major cons

All are relatively new
Bokeh is the most well-tested
HoloViews, GeoViews, hvPlot are being actively developed but are very promising

How does `hvPlot` fit in?

High-level visualization library designed to help you quickly create interactive charts during your data wrangling

Main uses: - Quickly generate interactive plots from your data - Seamlessly handles pandas and geopandas data - Relies on Holoviews and Geoviews under the hood

It provides an interface just like the pandas plot() function, but much more useful.

Example: let’s return to the measles dataset

# Our usual imports
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt

%matplotlib inline

# Let's load the measles data from week 2

url = "https://raw.githubusercontent.com/MUSA-550-Fall-2023/week-2/main/data/measles_incidence.csv"
measles_data_raw = pd.read_csv(url, skiprows=2, na_values="-")

measles_data_raw.head()

	YEAR	WEEK	ALABAMA	ALASKA	ARIZONA	ARKANSAS	CALIFORNIA	COLORADO	CONNECTICUT	DELAWARE	...	SOUTH DAKOTA	TENNESSEE	TEXAS	UTAH	VERMONT	VIRGINIA	WASHINGTON	WEST VIRGINIA	WISCONSIN	WYOMING
0	1928	1	3.67	NaN	1.90	4.11	1.38	8.38	4.50	8.58	...	5.69	22.03	1.18	0.4	0.28	NaN	14.83	3.36	1.54	0.91
1	1928	2	6.25	NaN	6.40	9.91	1.80	6.02	9.00	7.30	...	6.57	16.96	0.63	NaN	0.56	NaN	17.34	4.19	0.96	NaN
2	1928	3	7.95	NaN	4.50	11.15	1.31	2.86	8.81	15.88	...	2.04	24.66	0.62	0.2	1.12	NaN	15.67	4.19	4.79	1.36
3	1928	4	12.58	NaN	1.90	13.75	1.87	13.71	10.40	4.29	...	2.19	18.86	0.37	0.2	6.70	NaN	12.77	4.66	1.64	3.64
4	1928	5	8.03	NaN	0.47	20.79	2.38	5.13	16.80	5.58	...	3.94	20.05	1.57	0.4	6.70	NaN	18.83	7.37	2.91	0.91

5 rows × 53 columns

Convert from wide to long formats…

measles_data = measles_data_raw.melt(
    id_vars=["YEAR", "WEEK"], value_name="incidence", var_name="state"
)

measles_data.head()

	YEAR	WEEK	state	incidence
0	1928	1	ALABAMA	3.67
1	1928	2	ALABAMA	6.25
2	1928	3	ALABAMA	7.95
3	1928	4	ALABAMA	12.58
4	1928	5	ALABAMA	8.03

Reminder: plotting with `pandas`

The default .plot() doesn’t know which variables to plot.

fig, ax = plt.subplots(figsize=(10, 6))
measles_data.plot(ax=ax)

<Axes: >

But we can group by the year, and plot the national average each year

by_year = measles_data.groupby("YEAR", as_index=False)["incidence"].sum()
by_year.head()

	YEAR	incidence
0	1928	16924.34
1	1929	12060.96
2	1930	14575.11
3	1931	15427.67
4	1932	14481.11

fig, ax = plt.subplots(figsize=(10, 6))

# Plot the annual average by year
by_year.plot(x='YEAR', y='incidence', ax=ax)

# Add the vaccine year and label
ax.axvline(x=1963, c='k', linewidth=2)
ax.text(1963, 27000, " Vaccine introduced", ha='left', fontsize=18);

Adding interactivity with `hvplot`

Use the .hvplot() function to create interactive plots.

# This will add the .hvplot() function to your DataFrame!
# Import holoviews too
import holoviews as hv
import hvplot.pandas

# Load bokeh
hv.extension("bokeh")

img = by_year.hvplot(x='YEAR', y='incidence', kind="line")

img

In this case, .hvplot() creates a Holoviews Curve object.

Not unlike altair Chart objects, it’s an object that knows how to translate from your DataFrame data to a visualization.

print(img)

:Curve   [YEAR]   (incidence)

Many different chart types are available…

by_year.hvplot(x="YEAR", y="incidence", kind="scatter")

by_year.hvplot(x='YEAR', y='incidence', kind="bar", rot=90, width=1000)

Just like in altair, we can also layer chart elements together

Use the * operator to layer together chart elements.

Note: the same thing can be accomplished in altair, but with the + operator.

# The line chart of incidence vs year
incidence = by_year.hvplot(x='YEAR', y='incidence', kind="line")

# Vertical line + label for vaccine year
vline = hv.VLine(1963).opts(color="black")
label = hv.Text(1963, 27000, " Vaccine introduced", halign="left")

final_chart = incidence * vline * label
final_chart

We can group charts by a specific column, with automatic widget selectors

This is some powerful magic.

Let’s calculate the annual measles incidence for each year and state:

by_state = measles_data.groupby(["YEAR", "state"], as_index=False)["incidence"].sum()

by_state.head()

	YEAR	state	incidence
0	1928	ALABAMA	334.99
1	1928	ALASKA	0.00
2	1928	ARIZONA	200.75
3	1928	ARKANSAS	481.77
4	1928	CALIFORNIA	69.22

Now, tell hvplot to plot produce a chart of incidence over time for each state:

by_state_chart = by_state.hvplot(
    x="YEAR", y="incidence", groupby="state", width=400, kind="line"
)

by_state_chart

We can select out individual charts from the set of grouped objects

Use the dict-like selection syntax: [key]

PA = by_state_chart["PENNSYLVANIA"].relabel("PA") # .relabel() is optional — it just changes the title
NJ = by_state_chart["NEW JERSEY"].relabel("NJ")

Combine charts as subplots with the `+` operator

combined = PA + NJ

combined

print(combined)

:Layout
   .Curve.PA :Curve   [YEAR]   (incidence)
   .Curve.NJ :Curve   [YEAR]   (incidence)

The charts are side-by-side by default. You can also specify the number of rows/columns explicitly.

# one column
combined.cols(1)

We can also show overlay lines on the same plot

First, select a subset of states we want to highlight using the .isin() function:

states = ["NEW YORK", "NEW JERSEY", "CALIFORNIA", "PENNSYLVANIA"]
sub_states = by_state.loc[ by_state['state'].isin(states) ]

sub_states.head(n=10)

	YEAR	state	incidence
4	1928	CALIFORNIA	69.22
30	1928	NEW JERSEY	797.14
32	1928	NEW YORK	649.97
38	1928	PENNSYLVANIA	583.95
55	1929	CALIFORNIA	72.80
81	1929	NEW JERSEY	181.86
83	1929	NEW YORK	249.09
89	1929	PENNSYLVANIA	489.56
106	1930	CALIFORNIA	760.24
132	1930	NEW JERSEY	602.87

Now, use the by keyword to show multiple plots on the same axes:

sub_state_chart = sub_states.hvplot(
    x="YEAR",  # year on x axis
    y="incidence",  # total incidence on y axis
    by="state",  # NEW: multiple states on same axes
    kind="line",
)

sub_state_chart * vline

We can also show faceted plots

We can explicitly map variables to rows/columns of our visualization. This is similar to the functionality we saw in altair, when we used the alt.Chart().facet(column='state') syntax

Below, we specify the state column should be mapped to each column of the chart:

img = sub_states.reset_index().hvplot(
    x="YEAR",  # year on x axis
    y="incidence",  # total incidence on y axis
    col="state",  # NEW: map the "state" value to each column in the chart
    kind="line",
    rot=90,
    frame_width=200,
)

img * vline

Note

Functions for each kind of chart type are available too. Try tab complete on df.hvplot. to see the options. You can use these functions directly or use pass the kind='chart type' keyword to the .hvplot() function.

# Tab complete
by_state.hvplot.scatter?

Signature:
by_state.hvplot.scatter(
    x=None,
    y=None,
    *,
    alpha,
    angle,
    cmap,
    color,
    fill_alpha,
    fill_color,
    hover_alpha,
    hover_color,
    hover_fill_alpha,
    hover_fill_color,
    hover_line_alpha,
    hover_line_cap,
    hover_line_color,
    hover_line_dash,
    hover_line_join,
    hover_line_width,
    line_alpha,
    line_cap,
    line_color,
    line_dash,
    line_join,
    line_width,
    marker,
    muted,
    muted_alpha,
    muted_color,
    muted_fill_alpha,
    muted_fill_color,
    muted_line_alpha,
    muted_line_cap,
    muted_line_color,
    muted_line_dash,
    muted_line_join,
    muted_line_width,
    nonselection_alpha,
    nonselection_color,
    nonselection_fill_alpha,
    nonselection_fill_color,
    nonselection_line_alpha,
    nonselection_line_cap,
    nonselection_line_color,
    nonselection_line_dash,
    nonselection_line_join,
    nonselection_line_width,
    palette,
    selection_alpha,
    selection_color,
    selection_fill_alpha,
    selection_fill_color,
    selection_line_alpha,
    selection_line_cap,
    selection_line_color,
    selection_line_dash,
    selection_line_join,
    selection_line_width,
    size,
    visible,
    s,
    c,
    scale,
    logz,
    width,
    height,
    shared_axes,
    grid,
    legend,
    rot,
    xlim,
    ylim,
    xticks,
    yticks,
    colorbar,
    invert,
    title,
    logx,
    logy,
    loglog,
    xaxis,
    yaxis,
    xformatter,
    yformatter,
    xlabel,
    ylabel,
    clabel,
    padding,
    responsive,
    max_height,
    max_width,
    min_height,
    min_width,
    frame_height,
    frame_width,
    aspect,
    data_aspect,
    fontscale,
    datashade,
    rasterize,
    x_sampling,
    y_sampling,
    aggregator,
    **kwargs,
)
Docstring:
The `scatter` plot visualizes your points as markers in 2D space. You can visualize
one more dimension by using colors.
The `scatter` plot is a good first way to plot data with non continuous axes.
Reference: https://hvplot.holoviz.org/reference/pandas/scatter.html
Parameters
----------
x : string, optional
    Field name(s) to draw x-positions from. If not specified, the index is
    used. Can refer to continous and categorical data.
y : string or list, optional
    Field name(s) to draw y-positions from. If not specified, all numerical
    fields are used.
marker : string, optional
    The marker shape specified above can be any supported by matplotlib, e.g. s, d, o etc.
    See https://matplotlib.org/stable/api/markers_api.html.
c : string, optional
    A color or a Field name to draw the color of the marker from
s : int, optional, also available as 'size'
    The size of the marker
by : string, optional
    A single field or list of fields to group by. All the subgroups are visualized.
groupby: string, list, optional
    A single field or list of fields to group and filter by. Adds one or more widgets to
    select the subgroup(s) to visualize.
scale: number, optional
    Scaling factor to apply to point scaling.
logz : bool
    Whether to apply log scaling to the z-axis. Default is False.
color : str or array-like, optional.
    The color for each of the series. Possible values are:
    A single color string referred to by name, RGB or RGBA code, for instance 'red' or
    '#a98d19.
    A sequence of color strings referred to by name, RGB or RGBA code, which will be used
    for each series recursively. For instance ['green','yellow'] each field’s line will be
    filled in green or yellow, alternatively. If there is only a single series to be
    plotted, then only the first color from the color list will be used.
**kwds : optional
    Additional keywords arguments are documented in `hvplot.help('scatter')`.
Returns
-------
A Holoviews object. You can `print` the object to study its composition and run
.. code-block::
    import holoviews as hv
    hv.help(the_holoviews_object)
to learn more about its parameters and options.
Example
-------
.. code-block::
    import hvplot.pandas
    import pandas as pd
    df = pd.DataFrame(
        {
            "actual": [100, 150, 125, 140, 145, 135, 123],
            "forecast": [90, 160, 125, 150, 141, 141, 120],
            "numerical": [1.1, 1.9, 3.2, 3.8, 4.3, 5.0, 5.5],
            "date": pd.date_range("2022-01-03", "2022-01-09"),
            "string": ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"],
        },
    )
    scatter = df.hvplot.scatter(
        x="numerical",
        y=["actual", "forecast"],
        ylabel="value",
        legend="bottom",
        height=500,
        color=["#f16a6f", "#1e85f7"],
        size=100,
    )
    scatter
You can overlay the `scatter` markers on for example a `line` plot
.. code-block::
    line = df.hvplot.line(
        x="numerical", y=["actual", "forecast"], color=["#f16a6f", "#1e85f7"], line_width=5
    )
    scatter * line
References
----------
- Bokeh: https://docs.bokeh.org/en/latest/docs/user_guide/plotting.html#scatter-markers
- HoloViews: https://holoviews.org/reference/elements/matplotlib/Scatter.html
- Pandas: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.scatter.html
- Plotly: https://plotly.com/python/line-and-scatter/
- Matplotlib:  https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html
- Seaborn: https://seaborn.pydata.org/generated/seaborn.scatterplot.html
- Wiki: https://en.wikipedia.org/wiki/Scatter_plot
Generic options
---------------
clim: tuple
    Lower and upper bound of the color scale
cnorm (default='linear'): str
    Color scaling which must be one of 'linear', 'log' or 'eq_hist'
colorbar (default=False): boolean
    Enables a colorbar
fontscale: number
    Scales the size of all fonts by the same amount, e.g. fontscale=1.5
    enlarges all fonts (title, xticks, labels etc.) by 50%
fontsize: number or dict
    Set title, label and legend text to the same fontsize. Finer control
    by using a dict: {'title': '15pt', 'ylabel': '5px', 'ticks': 20}
flip_xaxis/flip_yaxis: boolean
    Whether to flip the axis left to right or up and down respectively
grid (default=False): boolean
    Whether to show a grid
hover : boolean
    Whether to show hover tooltips, default is True unless datashade is
    True in which case hover is False by default
hover_cols (default=[]): list or str
    Additional columns to add to the hover tool or 'all' which will
    includes all columns (including indexes if use_index is True).
invert (default=False): boolean
    Swaps x- and y-axis
frame_width/frame_height: int
    The width and height of the data area of the plot
legend (default=True): boolean or str
    Whether to show a legend, or a legend position
    ('top', 'bottom', 'left', 'right')
logx/logy (default=False): boolean
    Enables logarithmic x- and y-axis respectively
logz (default=False): boolean
    Enables logarithmic colormapping
loglog (default=False): boolean
    Enables logarithmic x- and y-axis
max_width/max_height: int
    The maximum width and height of the plot for responsive modes
min_width/min_height: int
    The minimum width and height of the plot for responsive modes
padding: number or tuple
    Fraction by which to increase auto-ranged extents to make
    datapoints more visible around borders. Supports tuples to
    specify different amount of padding for x- and y-axis and
    tuples of tuples to specify different amounts of padding for
    upper and lower bounds.
rescale_discrete_levels (default=True): boolean
    If `cnorm='eq_hist'` and there are only a few discrete values,
    then `rescale_discrete_levels=True` (the default) decreases
    the lower limit of the autoranged span so that the values are
    rendering towards the (more visible) top of the `cmap` range,
    thus avoiding washout of the lower values.  Has no effect if
    `cnorm!=`eq_hist`.
responsive: boolean
    Whether the plot should responsively resize depending on the
    size of the browser. Responsive mode will only work if at
    least one dimension of the plot is left undefined, e.g. when
    width and height or width and aspect are set the plot is set
    to a fixed size, ignoring any responsive option.
rot: number
    Rotates the axis ticks along the x-axis by the specified
    number of degrees.
shared_axes (default=True): boolean
    Whether to link axes between plots
transforms (default={}): dict
    A dictionary of HoloViews dim transforms to apply before plotting
title (default=''): str
    Title for the plot
tools (default=[]): list
    List of tool instances or strings (e.g. ['tap', 'box_select'])
xaxis/yaxis: str or None
    Whether to show the x/y-axis and whether to place it at the
    'top'/'bottom' and 'left'/'right' respectively.
xformatter/yformatter (default=None): str or TickFormatter
    Formatter for the x-axis and y-axis (accepts printf formatter,
    e.g. '%.3f', and bokeh TickFormatter)
xlabel/ylabel/clabel (default=None): str
    Axis labels for the x-axis, y-axis, and colorbar
xlim/ylim (default=None): tuple or list
    Plot limits of the x- and y-axis
xticks/yticks (default=None): int or list
    Ticks along x- and y-axis specified as an integer, list of
    ticks positions, or list of tuples of the tick positions and labels
width (default=700)/height (default=300): int
    The width and height of the plot in pixels
attr_labels (default=None): bool
    Whether to use an xarray object's attributes as labels, defaults to
    None to allow best effort without throwing a warning. Set to True
    to see warning if the attrs can't be found, set to False to disable
    the behavior.
sort_date (default=True): bool
    Whether to sort the x-axis by date before plotting
symmetric (default=None): bool
    Whether the data are symmetric around zero. If left unset, the data
    will be checked for symmetry as long as the size is less than
    ``check_symmetric_max``.
check_symmetric_max (default=1000000):
    Size above which to stop checking for symmetry by default on the data.
Datashader options
------------------
aggregator (default=None):
    Aggregator to use when applying rasterize or datashade operation
    (valid options include 'mean', 'count', 'min', 'max' and more, and
    datashader reduction objects)
dynamic (default=True):
    Whether to return a dynamic plot which sends updates on widget and
    zoom/pan events or whether all the data should be embedded
    (warning: for large groupby operations embedded data can become
    very large if dynamic=False)
datashade (default=False):
    Whether to apply rasterization and shading (colormapping) using
    the Datashader library, returning an RGB object instead of
    individual points
dynspread (default=False):
    For plots generated with datashade=True or rasterize=True,
    automatically increase the point size when the data is sparse
    so that individual points become more visible
rasterize (default=False):
    Whether to apply rasterization using the Datashader library,
    returning an aggregated Image (to be colormapped by the
    plotting backend) instead of individual points
x_sampling/y_sampling (default=None):
    Specifies the smallest allowed sampling interval along the x/y axis.
Geographic options
------------------
coastline (default=False):
    Whether to display a coastline on top of the plot, setting
    coastline='10m'/'50m'/'110m' specifies a specific scale.
crs (default=None):
    Coordinate reference system of the data specified as Cartopy
    CRS object, proj.4 string or EPSG code.
features (default=None): dict or list
    A list of features or a dictionary of features and the scale
    at which to render it. Available features include 'borders',
    'coastline', 'lakes', 'land', 'ocean', 'rivers' and 'states'.
    Available scales include '10m'/'50m'/'110m'.
geo (default=False):
    Whether the plot should be treated as geographic (and assume
    PlateCarree, i.e. lat/lon coordinates).
global_extent (default=False):
    Whether to expand the plot extent to span the whole globe.
project (default=False):
    Whether to project the data before plotting (adds initial
    overhead but avoids projecting data when plot is dynamically
    updated).
projection (default=None): str or Cartopy CRS
    Coordinate reference system of the plot specified as Cartopy
    CRS object or class name.
tiles (default=False):
    Whether to overlay the plot on a tile source. Tiles sources
    can be selected by name or a tiles object or class can be passed,
    the default is 'Wikipedia'.
Style options
-------------
alpha
angle
cmap
color
fill_alpha
fill_color
hover_alpha
hover_color
hover_fill_alpha
hover_fill_color
hover_line_alpha
hover_line_cap
hover_line_color
hover_line_dash
hover_line_join
hover_line_width
line_alpha
line_cap
line_color
line_dash
line_join
line_width
marker
muted
muted_alpha
muted_color
muted_fill_alpha
muted_fill_color
muted_line_alpha
muted_line_cap
muted_line_color
muted_line_dash
muted_line_join
muted_line_width
nonselection_alpha
nonselection_color
nonselection_fill_alpha
nonselection_fill_color
nonselection_line_alpha
nonselection_line_cap
nonselection_line_color
nonselection_line_dash
nonselection_line_join
nonselection_line_width
palette
selection_alpha
selection_color
selection_fill_alpha
selection_fill_color
selection_line_alpha
selection_line_cap
selection_line_color
selection_line_dash
selection_line_join
selection_line_width
size
visible
File:      ~/mambaforge/envs/musa-550-fall-2023/lib/python3.10/site-packages/hvplot/plotting/core.py
Type:      method

For example, we could plot a bar chart for these four states

Let’s select a subset by both year and state:

# Two selections
sel_year = by_state["YEAR"].isin(range(1960, 1970))
sel_state = by_state["state"].isin(states)

# Use the boolean AND operator: &
sel = sel_year & sel_state

# Grouped bar chart for the desired states and years
by_state.loc[sel].hvplot.bar(x="YEAR", y="incidence", by="state", rot=90)

Change bar() to line() and we get the same thing as before.

by_state.loc[sel].hvplot.line(x="YEAR", y="incidence", by="state", rot=90)

Customizing charts

See the help message for explicit hvplot functions:

by_state.hvplot.line?

Signature:
by_state.hvplot.line(
    x=None,
    y=None,
    *,
    alpha,
    color,
    hover_alpha,
    hover_color,
    hover_line_alpha,
    hover_line_cap,
    hover_line_color,
    hover_line_dash,
    hover_line_join,
    hover_line_width,
    line_alpha,
    line_cap,
    line_color,
    line_dash,
    line_join,
    line_width,
    muted,
    muted_alpha,
    muted_color,
    muted_line_alpha,
    muted_line_cap,
    muted_line_color,
    muted_line_dash,
    muted_line_join,
    muted_line_width,
    nonselection_alpha,
    nonselection_color,
    nonselection_line_alpha,
    nonselection_line_cap,
    nonselection_line_color,
    nonselection_line_dash,
    nonselection_line_join,
    nonselection_line_width,
    selection_alpha,
    selection_color,
    selection_line_alpha,
    selection_line_cap,
    selection_line_color,
    selection_line_dash,
    selection_line_join,
    selection_line_width,
    visible,
    width,
    height,
    shared_axes,
    grid,
    legend,
    rot,
    xlim,
    ylim,
    xticks,
    yticks,
    colorbar,
    invert,
    title,
    logx,
    logy,
    loglog,
    xaxis,
    yaxis,
    xformatter,
    yformatter,
    xlabel,
    ylabel,
    clabel,
    padding,
    responsive,
    max_height,
    max_width,
    min_height,
    min_width,
    frame_height,
    frame_width,
    aspect,
    data_aspect,
    fontscale,
    datashade,
    rasterize,
    x_sampling,
    y_sampling,
    aggregator,
    **kwargs,
)
Docstring:
The `line` plot connects the points with a continous curve.
Reference: https://hvplot.holoviz.org/reference/pandas/line.html
Parameters
----------
x : string, optional
    Field name(s) to draw x-positions from. If not specified, the index is
    used. Can refer to continous and categorical data.
y : string or list, optional
    Field name(s) to draw y-positions from. If not specified, all numerical
    fields are used.
by : string, optional
    A single column or list of columns to group by. All the subgroups are visualized.
groupby: string, list, optional
    A single field or list of fields to group and filter by. Adds one or more widgets to
    select the subgroup(s) to visualize.
color : str or array-like, optional.
    The color for each of the series. Possible values are:
    A single color string referred to by name, RGB or RGBA code, for instance 'red' or
    '#a98d19.
    A sequence of color strings referred to by name, RGB or RGBA code, which will be used
    for each series recursively. For instance ['green','yellow'] each field’s line will be
    filled in green or yellow, alternatively. If there is only a single series to be
    plotted, then only the first color from the color list will be used.
**kwds : optional
    Additional keywords arguments are documented in `hvplot.help('line')`.
Returns
-------
A Holoviews object. You can `print` the object to study its composition and run
.. code-block::
    import holoviews as hv
    hv.help(the_holoviews_object)
to learn more about its parameters and options.
Examples
--------
.. code-block::
    import hvplot.pandas
    import pandas as pd
    df = pd.DataFrame(
        {
            "actual": [100, 150, 125, 140, 145, 135, 123],
            "forecast": [90, 160, 125, 150, 141, 141, 120],
            "numerical": [1.1, 1.9, 3.2, 3.8, 4.3, 5.0, 5.5],
            "date": pd.date_range("2022-01-03", "2022-01-09"),
            "string": ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"],
        },
    )
    line = df.hvplot.line(
        x="numerical",
        y=["actual", "forecast"],
        ylabel="value",
        legend="bottom",
        height=500,
        color=["steelblue", "teal"],
        alpha=0.7,
        line_width=5,
    )
    line
You can can add *markers* to a `line` plot by overlaying with a `scatter` plot.
.. code-block::
    markers = df.hvplot.scatter(
        x="numerical", y=["actual", "forecast"], color=["steelblue", "teal"], size=50
    )
    line * markers
Please note that you can pass widgets or reactive functions as arguments instead of
literal values, c.f. https://hvplot.holoviz.org/user_guide/Widgets.html.
References
----------
- Bokeh: https://docs.bokeh.org/en/latest/docs/reference/models/glyphs/line.html
- HoloViews: https://holoviews.org/reference/elements/bokeh/Curve.html
- Pandas: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.line.html
- Plotly: https://plotly.com/python/line-charts/
- Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html
- Seaborn: https://seaborn.pydata.org/generated/seaborn.lineplot.html
- Wiki: https://en.wikipedia.org/wiki/Line_chart
Generic options
---------------
clim: tuple
    Lower and upper bound of the color scale
cnorm (default='linear'): str
    Color scaling which must be one of 'linear', 'log' or 'eq_hist'
colorbar (default=False): boolean
    Enables a colorbar
fontscale: number
    Scales the size of all fonts by the same amount, e.g. fontscale=1.5
    enlarges all fonts (title, xticks, labels etc.) by 50%
fontsize: number or dict
    Set title, label and legend text to the same fontsize. Finer control
    by using a dict: {'title': '15pt', 'ylabel': '5px', 'ticks': 20}
flip_xaxis/flip_yaxis: boolean
    Whether to flip the axis left to right or up and down respectively
grid (default=False): boolean
    Whether to show a grid
hover : boolean
    Whether to show hover tooltips, default is True unless datashade is
    True in which case hover is False by default
hover_cols (default=[]): list or str
    Additional columns to add to the hover tool or 'all' which will
    includes all columns (including indexes if use_index is True).
invert (default=False): boolean
    Swaps x- and y-axis
frame_width/frame_height: int
    The width and height of the data area of the plot
legend (default=True): boolean or str
    Whether to show a legend, or a legend position
    ('top', 'bottom', 'left', 'right')
logx/logy (default=False): boolean
    Enables logarithmic x- and y-axis respectively
logz (default=False): boolean
    Enables logarithmic colormapping
loglog (default=False): boolean
    Enables logarithmic x- and y-axis
max_width/max_height: int
    The maximum width and height of the plot for responsive modes
min_width/min_height: int
    The minimum width and height of the plot for responsive modes
padding: number or tuple
    Fraction by which to increase auto-ranged extents to make
    datapoints more visible around borders. Supports tuples to
    specify different amount of padding for x- and y-axis and
    tuples of tuples to specify different amounts of padding for
    upper and lower bounds.
rescale_discrete_levels (default=True): boolean
    If `cnorm='eq_hist'` and there are only a few discrete values,
    then `rescale_discrete_levels=True` (the default) decreases
    the lower limit of the autoranged span so that the values are
    rendering towards the (more visible) top of the `cmap` range,
    thus avoiding washout of the lower values.  Has no effect if
    `cnorm!=`eq_hist`.
responsive: boolean
    Whether the plot should responsively resize depending on the
    size of the browser. Responsive mode will only work if at
    least one dimension of the plot is left undefined, e.g. when
    width and height or width and aspect are set the plot is set
    to a fixed size, ignoring any responsive option.
rot: number
    Rotates the axis ticks along the x-axis by the specified
    number of degrees.
shared_axes (default=True): boolean
    Whether to link axes between plots
transforms (default={}): dict
    A dictionary of HoloViews dim transforms to apply before plotting
title (default=''): str
    Title for the plot
tools (default=[]): list
    List of tool instances or strings (e.g. ['tap', 'box_select'])
xaxis/yaxis: str or None
    Whether to show the x/y-axis and whether to place it at the
    'top'/'bottom' and 'left'/'right' respectively.
xformatter/yformatter (default=None): str or TickFormatter
    Formatter for the x-axis and y-axis (accepts printf formatter,
    e.g. '%.3f', and bokeh TickFormatter)
xlabel/ylabel/clabel (default=None): str
    Axis labels for the x-axis, y-axis, and colorbar
xlim/ylim (default=None): tuple or list
    Plot limits of the x- and y-axis
xticks/yticks (default=None): int or list
    Ticks along x- and y-axis specified as an integer, list of
    ticks positions, or list of tuples of the tick positions and labels
width (default=700)/height (default=300): int
    The width and height of the plot in pixels
attr_labels (default=None): bool
    Whether to use an xarray object's attributes as labels, defaults to
    None to allow best effort without throwing a warning. Set to True
    to see warning if the attrs can't be found, set to False to disable
    the behavior.
sort_date (default=True): bool
    Whether to sort the x-axis by date before plotting
symmetric (default=None): bool
    Whether the data are symmetric around zero. If left unset, the data
    will be checked for symmetry as long as the size is less than
    ``check_symmetric_max``.
check_symmetric_max (default=1000000):
    Size above which to stop checking for symmetry by default on the data.
Datashader options
------------------
aggregator (default=None):
    Aggregator to use when applying rasterize or datashade operation
    (valid options include 'mean', 'count', 'min', 'max' and more, and
    datashader reduction objects)
dynamic (default=True):
    Whether to return a dynamic plot which sends updates on widget and
    zoom/pan events or whether all the data should be embedded
    (warning: for large groupby operations embedded data can become
    very large if dynamic=False)
datashade (default=False):
    Whether to apply rasterization and shading (colormapping) using
    the Datashader library, returning an RGB object instead of
    individual points
dynspread (default=False):
    For plots generated with datashade=True or rasterize=True,
    automatically increase the point size when the data is sparse
    so that individual points become more visible
rasterize (default=False):
    Whether to apply rasterization using the Datashader library,
    returning an aggregated Image (to be colormapped by the
    plotting backend) instead of individual points
x_sampling/y_sampling (default=None):
    Specifies the smallest allowed sampling interval along the x/y axis.
Geographic options
------------------
coastline (default=False):
    Whether to display a coastline on top of the plot, setting
    coastline='10m'/'50m'/'110m' specifies a specific scale.
crs (default=None):
    Coordinate reference system of the data specified as Cartopy
    CRS object, proj.4 string or EPSG code.
features (default=None): dict or list
    A list of features or a dictionary of features and the scale
    at which to render it. Available features include 'borders',
    'coastline', 'lakes', 'land', 'ocean', 'rivers' and 'states'.
    Available scales include '10m'/'50m'/'110m'.
geo (default=False):
    Whether the plot should be treated as geographic (and assume
    PlateCarree, i.e. lat/lon coordinates).
global_extent (default=False):
    Whether to expand the plot extent to span the whole globe.
project (default=False):
    Whether to project the data before plotting (adds initial
    overhead but avoids projecting data when plot is dynamically
    updated).
projection (default=None): str or Cartopy CRS
    Coordinate reference system of the plot specified as Cartopy
    CRS object or class name.
tiles (default=False):
    Whether to overlay the plot on a tile source. Tiles sources
    can be selected by name or a tiles object or class can be passed,
    the default is 'Wikipedia'.
Style options
-------------
alpha
color
hover_alpha
hover_color
hover_line_alpha
hover_line_cap
hover_line_color
hover_line_dash
hover_line_join
hover_line_width
line_alpha
line_cap
line_color
line_dash
line_join
line_width
muted
muted_alpha
muted_color
muted_line_alpha
muted_line_cap
muted_line_color
muted_line_dash
muted_line_join
muted_line_width
nonselection_alpha
nonselection_color
nonselection_line_alpha
nonselection_line_cap
nonselection_line_color
nonselection_line_dash
nonselection_line_join
nonselection_line_width
selection_alpha
selection_color
selection_line_alpha
selection_line_cap
selection_line_color
selection_line_dash
selection_line_join
selection_line_width
visible
File:      ~/mambaforge/envs/musa-550-fall-2023/lib/python3.10/site-packages/hvplot/plotting/core.py
Type:      method

Can we reproduce the WSJ measles heatmap?

Last week, we reproduced the WSJ measles heatmap using altair….can we do it with hvplot too?

Take a look at the help function for the .hvplot.heatmap() function:

measles_data.hvplot.heatmap?

Signature:
measles_data.hvplot.heatmap(
    x=None,
    y=None,
    C=None,
    colorbar=True,
    *,
    alpha,
    annular_alpha,
    annular_color,
    annular_fill_alpha,
    annular_fill_color,
    annular_hover_alpha,
    annular_hover_color,
    annular_hover_fill_alpha,
    annular_hover_fill_color,
    annular_hover_line_alpha,
    annular_hover_line_cap,
    annular_hover_line_color,
    annular_hover_line_dash,
    annular_hover_line_join,
    annular_hover_line_width,
    annular_line_alpha,
    annular_line_cap,
    annular_line_color,
    annular_line_dash,
    annular_line_join,
    annular_line_width,
    annular_muted,
    annular_muted_alpha,
    annular_muted_color,
    annular_muted_fill_alpha,
    annular_muted_fill_color,
    annular_muted_line_alpha,
    annular_muted_line_cap,
    annular_muted_line_color,
    annular_muted_line_dash,
    annular_muted_line_join,
    annular_muted_line_width,
    annular_nonselection_alpha,
    annular_nonselection_color,
    annular_nonselection_fill_alpha,
    annular_nonselection_fill_color,
    annular_nonselection_line_alpha,
    annular_nonselection_line_cap,
    annular_nonselection_line_color,
    annular_nonselection_line_dash,
    annular_nonselection_line_join,
    annular_nonselection_line_width,
    annular_selection_alpha,
    annular_selection_color,
    annular_selection_fill_alpha,
    annular_selection_fill_color,
    annular_selection_line_alpha,
    annular_selection_line_cap,
    annular_selection_line_color,
    annular_selection_line_dash,
    annular_selection_line_join,
    annular_selection_line_width,
    annular_visible,
    cmap,
    color,
    dilate,
    fill_alpha,
    fill_color,
    hover_alpha,
    hover_color,
    hover_fill_alpha,
    hover_fill_color,
    hover_line_alpha,
    hover_line_cap,
    hover_line_color,
    hover_line_dash,
    hover_line_join,
    hover_line_width,
    line_alpha,
    line_cap,
    line_color,
    line_dash,
    line_join,
    line_width,
    muted,
    muted_alpha,
    muted_color,
    muted_fill_alpha,
    muted_fill_color,
    muted_line_alpha,
    muted_line_cap,
    muted_line_color,
    muted_line_dash,
    muted_line_join,
    muted_line_width,
    nonselection_alpha,
    nonselection_color,
    nonselection_fill_alpha,
    nonselection_fill_color,
    nonselection_line_alpha,
    nonselection_line_cap,
    nonselection_line_color,
    nonselection_line_dash,
    nonselection_line_join,
    nonselection_line_width,
    selection_alpha,
    selection_color,
    selection_fill_alpha,
    selection_fill_color,
    selection_line_alpha,
    selection_line_cap,
    selection_line_color,
    selection_line_dash,
    selection_line_join,
    selection_line_width,
    ticks_text_align,
    ticks_text_alpha,
    ticks_text_baseline,
    ticks_text_color,
    ticks_text_font,
    ticks_text_font_size,
    ticks_text_font_style,
    visible,
    xmarks_alpha,
    xmarks_color,
    xmarks_hover_alpha,
    xmarks_hover_color,
    xmarks_hover_line_alpha,
    xmarks_hover_line_cap,
    xmarks_hover_line_color,
    xmarks_hover_line_dash,
    xmarks_hover_line_join,
    xmarks_hover_line_width,
    xmarks_line_alpha,
    xmarks_line_cap,
    xmarks_line_color,
    xmarks_line_dash,
    xmarks_line_join,
    xmarks_line_width,
    xmarks_muted,
    xmarks_muted_alpha,
    xmarks_muted_color,
    xmarks_muted_line_alpha,
    xmarks_muted_line_cap,
    xmarks_muted_line_color,
    xmarks_muted_line_dash,
    xmarks_muted_line_join,
    xmarks_muted_line_width,
    xmarks_nonselection_alpha,
    xmarks_nonselection_color,
    xmarks_nonselection_line_alpha,
    xmarks_nonselection_line_cap,
    xmarks_nonselection_line_color,
    xmarks_nonselection_line_dash,
    xmarks_nonselection_line_join,
    xmarks_nonselection_line_width,
    xmarks_selection_alpha,
    xmarks_selection_color,
    xmarks_selection_line_alpha,
    xmarks_selection_line_cap,
    xmarks_selection_line_color,
    xmarks_selection_line_dash,
    xmarks_selection_line_join,
    xmarks_selection_line_width,
    xmarks_visible,
    ymarks_alpha,
    ymarks_color,
    ymarks_hover_alpha,
    ymarks_hover_color,
    ymarks_hover_line_alpha,
    ymarks_hover_line_cap,
    ymarks_hover_line_color,
    ymarks_hover_line_dash,
    ymarks_hover_line_join,
    ymarks_hover_line_width,
    ymarks_line_alpha,
    ymarks_line_cap,
    ymarks_line_color,
    ymarks_line_dash,
    ymarks_line_join,
    ymarks_line_width,
    ymarks_muted,
    ymarks_muted_alpha,
    ymarks_muted_color,
    ymarks_muted_line_alpha,
    ymarks_muted_line_cap,
    ymarks_muted_line_color,
    ymarks_muted_line_dash,
    ymarks_muted_line_join,
    ymarks_muted_line_width,
    ymarks_nonselection_alpha,
    ymarks_nonselection_color,
    ymarks_nonselection_line_alpha,
    ymarks_nonselection_line_cap,
    ymarks_nonselection_line_color,
    ymarks_nonselection_line_dash,
    ymarks_nonselection_line_join,
    ymarks_nonselection_line_width,
    ymarks_selection_alpha,
    ymarks_selection_color,
    ymarks_selection_line_alpha,
    ymarks_selection_line_cap,
    ymarks_selection_line_color,
    ymarks_selection_line_dash,
    ymarks_selection_line_join,
    ymarks_selection_line_width,
    ymarks_visible,
    reduce_function,
    logz,
    width,
    height,
    shared_axes,
    grid,
    legend,
    rot,
    xlim,
    ylim,
    xticks,
    yticks,
    invert,
    title,
    logx,
    logy,
    loglog,
    xaxis,
    yaxis,
    xformatter,
    yformatter,
    xlabel,
    ylabel,
    clabel,
    padding,
    responsive,
    max_height,
    max_width,
    min_height,
    min_width,
    frame_height,
    frame_width,
    aspect,
    data_aspect,
    fontscale,
    datashade,
    rasterize,
    x_sampling,
    y_sampling,
    aggregator,
    **kwargs,
)
Docstring:
`heatmap` visualises tabular data indexed by two key dimensions as a grid of colored values.
This allows spotting correlations in multivariate data and provides a high-level overview
of how the two variables are plotted.
Reference: https://hvplot.holoviz.org/reference/pandas/heatmap.html
Parameters
----------
x : string, optional
    Field name to draw x coordinates from. If not specified, the index is used. Can refer
    to continous and categorical data.
y : string
    Field name to draw y-positions from. Can refer to continous and categorical data.
C : string, optional
    Field to draw heatmap color from. If not specified a simple count will be used.
colorbar: boolean, optional
    Whether to display a colorbar. Default is True.
logz : bool
    Whether to apply log scaling to the z-axis. Default is False.
reduce_function : function, optional
    Function to compute statistics for heatmap, for example `np.mean`.
**kwds : optional
    Additional keywords arguments are documented in `hvplot.help('heatmap')`.
Returns
-------
A Holoviews object. You can `print` the object to study its composition and run
.. code-block::
    import holoviews as hv
    hv.help(the_holoviews_object)
to learn more about its parameters and options.
Example
-------
.. code-block::
    import hvplot.pandas
    import numpy as np
    from bokeh.sampledata import sea_surface_temperature as sst
    df = sst.sea_surface_temperature
    df.hvplot.heatmap(
        x="time.month", y="time.day", C="temperature", reduce_function=np.mean,
        height=500, width=500, colorbar=False, cmap="blues"
    )
References
----------
- Bokeh: https://docs.bokeh.org/en/latest/docs/gallery/categorical_heatmap.html
- HoloViews: https://holoviews.org/reference/elements/bokeh/HeatMap.html
- Matplotlib: https://matplotlib.org/stable/gallery/images_contours_and_fields/image_annotated_heatmap.html
- Plotly: https://plotly.com/python/heatmaps/
- Wiki: https://en.wikipedia.org/wiki/Heat_map
Generic options
---------------
clim: tuple
    Lower and upper bound of the color scale
cnorm (default='linear'): str
    Color scaling which must be one of 'linear', 'log' or 'eq_hist'
colorbar (default=False): boolean
    Enables a colorbar
fontscale: number
    Scales the size of all fonts by the same amount, e.g. fontscale=1.5
    enlarges all fonts (title, xticks, labels etc.) by 50%
fontsize: number or dict
    Set title, label and legend text to the same fontsize. Finer control
    by using a dict: {'title': '15pt', 'ylabel': '5px', 'ticks': 20}
flip_xaxis/flip_yaxis: boolean
    Whether to flip the axis left to right or up and down respectively
grid (default=False): boolean
    Whether to show a grid
hover : boolean
    Whether to show hover tooltips, default is True unless datashade is
    True in which case hover is False by default
hover_cols (default=[]): list or str
    Additional columns to add to the hover tool or 'all' which will
    includes all columns (including indexes if use_index is True).
invert (default=False): boolean
    Swaps x- and y-axis
frame_width/frame_height: int
    The width and height of the data area of the plot
legend (default=True): boolean or str
    Whether to show a legend, or a legend position
    ('top', 'bottom', 'left', 'right')
logx/logy (default=False): boolean
    Enables logarithmic x- and y-axis respectively
logz (default=False): boolean
    Enables logarithmic colormapping
loglog (default=False): boolean
    Enables logarithmic x- and y-axis
max_width/max_height: int
    The maximum width and height of the plot for responsive modes
min_width/min_height: int
    The minimum width and height of the plot for responsive modes
padding: number or tuple
    Fraction by which to increase auto-ranged extents to make
    datapoints more visible around borders. Supports tuples to
    specify different amount of padding for x- and y-axis and
    tuples of tuples to specify different amounts of padding for
    upper and lower bounds.
rescale_discrete_levels (default=True): boolean
    If `cnorm='eq_hist'` and there are only a few discrete values,
    then `rescale_discrete_levels=True` (the default) decreases
    the lower limit of the autoranged span so that the values are
    rendering towards the (more visible) top of the `cmap` range,
    thus avoiding washout of the lower values.  Has no effect if
    `cnorm!=`eq_hist`.
responsive: boolean
    Whether the plot should responsively resize depending on the
    size of the browser. Responsive mode will only work if at
    least one dimension of the plot is left undefined, e.g. when
    width and height or width and aspect are set the plot is set
    to a fixed size, ignoring any responsive option.
rot: number
    Rotates the axis ticks along the x-axis by the specified
    number of degrees.
shared_axes (default=True): boolean
    Whether to link axes between plots
transforms (default={}): dict
    A dictionary of HoloViews dim transforms to apply before plotting
title (default=''): str
    Title for the plot
tools (default=[]): list
    List of tool instances or strings (e.g. ['tap', 'box_select'])
xaxis/yaxis: str or None
    Whether to show the x/y-axis and whether to place it at the
    'top'/'bottom' and 'left'/'right' respectively.
xformatter/yformatter (default=None): str or TickFormatter
    Formatter for the x-axis and y-axis (accepts printf formatter,
    e.g. '%.3f', and bokeh TickFormatter)
xlabel/ylabel/clabel (default=None): str
    Axis labels for the x-axis, y-axis, and colorbar
xlim/ylim (default=None): tuple or list
    Plot limits of the x- and y-axis
xticks/yticks (default=None): int or list
    Ticks along x- and y-axis specified as an integer, list of
    ticks positions, or list of tuples of the tick positions and labels
width (default=700)/height (default=300): int
    The width and height of the plot in pixels
attr_labels (default=None): bool
    Whether to use an xarray object's attributes as labels, defaults to
    None to allow best effort without throwing a warning. Set to True
    to see warning if the attrs can't be found, set to False to disable
    the behavior.
sort_date (default=True): bool
    Whether to sort the x-axis by date before plotting
symmetric (default=None): bool
    Whether the data are symmetric around zero. If left unset, the data
    will be checked for symmetry as long as the size is less than
    ``check_symmetric_max``.
check_symmetric_max (default=1000000):
    Size above which to stop checking for symmetry by default on the data.
Datashader options
------------------
aggregator (default=None):
    Aggregator to use when applying rasterize or datashade operation
    (valid options include 'mean', 'count', 'min', 'max' and more, and
    datashader reduction objects)
dynamic (default=True):
    Whether to return a dynamic plot which sends updates on widget and
    zoom/pan events or whether all the data should be embedded
    (warning: for large groupby operations embedded data can become
    very large if dynamic=False)
datashade (default=False):
    Whether to apply rasterization and shading (colormapping) using
    the Datashader library, returning an RGB object instead of
    individual points
dynspread (default=False):
    For plots generated with datashade=True or rasterize=True,
    automatically increase the point size when the data is sparse
    so that individual points become more visible
rasterize (default=False):
    Whether to apply rasterization using the Datashader library,
    returning an aggregated Image (to be colormapped by the
    plotting backend) instead of individual points
x_sampling/y_sampling (default=None):
    Specifies the smallest allowed sampling interval along the x/y axis.
Geographic options
------------------
coastline (default=False):
    Whether to display a coastline on top of the plot, setting
    coastline='10m'/'50m'/'110m' specifies a specific scale.
crs (default=None):
    Coordinate reference system of the data specified as Cartopy
    CRS object, proj.4 string or EPSG code.
features (default=None): dict or list
    A list of features or a dictionary of features and the scale
    at which to render it. Available features include 'borders',
    'coastline', 'lakes', 'land', 'ocean', 'rivers' and 'states'.
    Available scales include '10m'/'50m'/'110m'.
geo (default=False):
    Whether the plot should be treated as geographic (and assume
    PlateCarree, i.e. lat/lon coordinates).
global_extent (default=False):
    Whether to expand the plot extent to span the whole globe.
project (default=False):
    Whether to project the data before plotting (adds initial
    overhead but avoids projecting data when plot is dynamically
    updated).
projection (default=None): str or Cartopy CRS
    Coordinate reference system of the plot specified as Cartopy
    CRS object or class name.
tiles (default=False):
    Whether to overlay the plot on a tile source. Tiles sources
    can be selected by name or a tiles object or class can be passed,
    the default is 'Wikipedia'.
Style options
-------------
alpha
annular_alpha
annular_color
annular_fill_alpha
annular_fill_color
annular_hover_alpha
annular_hover_color
annular_hover_fill_alpha
annular_hover_fill_color
annular_hover_line_alpha
annular_hover_line_cap
annular_hover_line_color
annular_hover_line_dash
annular_hover_line_join
annular_hover_line_width
annular_line_alpha
annular_line_cap
annular_line_color
annular_line_dash
annular_line_join
annular_line_width
annular_muted
annular_muted_alpha
annular_muted_color
annular_muted_fill_alpha
annular_muted_fill_color
annular_muted_line_alpha
annular_muted_line_cap
annular_muted_line_color
annular_muted_line_dash
annular_muted_line_join
annular_muted_line_width
annular_nonselection_alpha
annular_nonselection_color
annular_nonselection_fill_alpha
annular_nonselection_fill_color
annular_nonselection_line_alpha
annular_nonselection_line_cap
annular_nonselection_line_color
annular_nonselection_line_dash
annular_nonselection_line_join
annular_nonselection_line_width
annular_selection_alpha
annular_selection_color
annular_selection_fill_alpha
annular_selection_fill_color
annular_selection_line_alpha
annular_selection_line_cap
annular_selection_line_color
annular_selection_line_dash
annular_selection_line_join
annular_selection_line_width
annular_visible
cmap
color
dilate
fill_alpha
fill_color
hover_alpha
hover_color
hover_fill_alpha
hover_fill_color
hover_line_alpha
hover_line_cap
hover_line_color
hover_line_dash
hover_line_join
hover_line_width
line_alpha
line_cap
line_color
line_dash
line_join
line_width
muted
muted_alpha
muted_color
muted_fill_alpha
muted_fill_color
muted_line_alpha
muted_line_cap
muted_line_color
muted_line_dash
muted_line_join
muted_line_width
nonselection_alpha
nonselection_color
nonselection_fill_alpha
nonselection_fill_color
nonselection_line_alpha
nonselection_line_cap
nonselection_line_color
nonselection_line_dash
nonselection_line_join
nonselection_line_width
selection_alpha
selection_color
selection_fill_alpha
selection_fill_color
selection_line_alpha
selection_line_cap
selection_line_color
selection_line_dash
selection_line_join
selection_line_width
ticks_text_align
ticks_text_alpha
ticks_text_baseline
ticks_text_color
ticks_text_font
ticks_text_font_size
ticks_text_font_style
visible
xmarks_alpha
xmarks_color
xmarks_hover_alpha
xmarks_hover_color
xmarks_hover_line_alpha
xmarks_hover_line_cap
xmarks_hover_line_color
xmarks_hover_line_dash
xmarks_hover_line_join
xmarks_hover_line_width
xmarks_line_alpha
xmarks_line_cap
xmarks_line_color
xmarks_line_dash
xmarks_line_join
xmarks_line_width
xmarks_muted
xmarks_muted_alpha
xmarks_muted_color
xmarks_muted_line_alpha
xmarks_muted_line_cap
xmarks_muted_line_color
xmarks_muted_line_dash
xmarks_muted_line_join
xmarks_muted_line_width
xmarks_nonselection_alpha
xmarks_nonselection_color
xmarks_nonselection_line_alpha
xmarks_nonselection_line_cap
xmarks_nonselection_line_color
xmarks_nonselection_line_dash
xmarks_nonselection_line_join
xmarks_nonselection_line_width
xmarks_selection_alpha
xmarks_selection_color
xmarks_selection_line_alpha
xmarks_selection_line_cap
xmarks_selection_line_color
xmarks_selection_line_dash
xmarks_selection_line_join
xmarks_selection_line_width
xmarks_visible
ymarks_alpha
ymarks_color
ymarks_hover_alpha
ymarks_hover_color
ymarks_hover_line_alpha
ymarks_hover_line_cap
ymarks_hover_line_color
ymarks_hover_line_dash
ymarks_hover_line_join
ymarks_hover_line_width
ymarks_line_alpha
ymarks_line_cap
ymarks_line_color
ymarks_line_dash
ymarks_line_join
ymarks_line_width
ymarks_muted
ymarks_muted_alpha
ymarks_muted_color
ymarks_muted_line_alpha
ymarks_muted_line_cap
ymarks_muted_line_color
ymarks_muted_line_dash
ymarks_muted_line_join
ymarks_muted_line_width
ymarks_nonselection_alpha
ymarks_nonselection_color
ymarks_nonselection_line_alpha
ymarks_nonselection_line_cap
ymarks_nonselection_line_color
ymarks_nonselection_line_dash
ymarks_nonselection_line_join
ymarks_nonselection_line_width
ymarks_selection_alpha
ymarks_selection_color
ymarks_selection_line_alpha
ymarks_selection_line_cap
ymarks_selection_line_color
ymarks_selection_line_dash
ymarks_selection_line_join
ymarks_selection_line_width
ymarks_visible
File:      ~/mambaforge/envs/musa-550-fall-2023/lib/python3.10/site-packages/hvplot/plotting/core.py
Type:      method

Two methods:

We want to plot ‘YEAR’ on the x axis, ‘state’ on the y axis, and specify ‘incidence’ as the values begin plotted in each heatmap bin.

You can use the by_state data frame which has already summed over weeks for each state
Use the original, tidy data (measles_data) with columns for state, week, year, and incidence
- You will need to use the reduce_function keyword to sum over weeks

Method #1

Use our aggregated dataframe: by_state

by_state.head()

	YEAR	state	incidence
0	1928	ALABAMA	334.99
1	1928	ALASKA	0.00
2	1928	ARIZONA	200.75
3	1928	ARKANSAS	481.77
4	1928	CALIFORNIA	69.22

# METHOD #1: just plot the incidence
heatmap = by_state.hvplot.heatmap(
    x="YEAR",  # x-axis
    y="state",  # y-axis
    C="incidence",  # color in each heatmap box
    cmap="viridis",  # color map to use
    height=500,
    width=800,
    flip_yaxis=True,
    rot=90,
)

# Turn off tools
heatmap = heatmap.opts(default_tools=[])

# Change the axes labels
heatmap = heatmap.redim(
    state="State",
    YEAR="Year",
)

heatmap

Method #2

Pass in the original data and have hvplot do the aggregation for us, using the reduce_function keyword:

measles_data.head()

	YEAR	WEEK	state	incidence
0	1928	1	ALABAMA	3.67
1	1928	2	ALABAMA	6.25
2	1928	3	ALABAMA	7.95
3	1928	4	ALABAMA	12.58
4	1928	5	ALABAMA	8.03

## METHOD 2: hvplot does the aggregation
heatmap = measles_data.hvplot.heatmap(
    x="YEAR",
    y="state",
    C="incidence",
    cmap="viridis",
    reduce_function=np.sum,  # NEW: Aggregate and sum incidence
    height=500,
    width=800,
    flip_yaxis=True,
    rot=90,
)

# Turn off tools
heatmap = heatmap.opts(default_tools=[])

# Relabel axes
heatmap = heatmap.redim(state="State", YEAR="Year")

heatmap

Just like altair: save the file as html

import hvplot

hvplot.save(heatmap, "measles.html")

# load the html file and display it
from IPython.display import HTML

HTML("measles.html")

measles

Two more useful features:

Scatter matrix plots
Explorer mode

1. Scatter matrix plots

Visualizing relationships between variables, as we have seen in seaborn and altair

Let’s load the penguins data set from week 2

url = "https://raw.githubusercontent.com/MUSA-550-Fall-2023/week-2/main/data/penguins.csv"
penguins = pd.read_csv(url)

penguins.head()

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	male	2007
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	female	2007
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	female	2007
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN	2007
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	female	2007

Use the hvplot.scatter_matrix() function:

penguins.hvplot.scatter?

Signature:
penguins.hvplot.scatter(
    x=None,
    y=None,
    *,
    alpha,
    angle,
    cmap,
    color,
    fill_alpha,
    fill_color,
    hover_alpha,
    hover_color,
    hover_fill_alpha,
    hover_fill_color,
    hover_line_alpha,
    hover_line_cap,
    hover_line_color,
    hover_line_dash,
    hover_line_join,
    hover_line_width,
    line_alpha,
    line_cap,
    line_color,
    line_dash,
    line_join,
    line_width,
    marker,
    muted,
    muted_alpha,
    muted_color,
    muted_fill_alpha,
    muted_fill_color,
    muted_line_alpha,
    muted_line_cap,
    muted_line_color,
    muted_line_dash,
    muted_line_join,
    muted_line_width,
    nonselection_alpha,
    nonselection_color,
    nonselection_fill_alpha,
    nonselection_fill_color,
    nonselection_line_alpha,
    nonselection_line_cap,
    nonselection_line_color,
    nonselection_line_dash,
    nonselection_line_join,
    nonselection_line_width,
    palette,
    selection_alpha,
    selection_color,
    selection_fill_alpha,
    selection_fill_color,
    selection_line_alpha,
    selection_line_cap,
    selection_line_color,
    selection_line_dash,
    selection_line_join,
    selection_line_width,
    size,
    visible,
    s,
    c,
    scale,
    logz,
    width,
    height,
    shared_axes,
    grid,
    legend,
    rot,
    xlim,
    ylim,
    xticks,
    yticks,
    colorbar,
    invert,
    title,
    logx,
    logy,
    loglog,
    xaxis,
    yaxis,
    xformatter,
    yformatter,
    xlabel,
    ylabel,
    clabel,
    padding,
    responsive,
    max_height,
    max_width,
    min_height,
    min_width,
    frame_height,
    frame_width,
    aspect,
    data_aspect,
    fontscale,
    datashade,
    rasterize,
    x_sampling,
    y_sampling,
    aggregator,
    **kwargs,
)
Docstring:
The `scatter` plot visualizes your points as markers in 2D space. You can visualize
one more dimension by using colors.
The `scatter` plot is a good first way to plot data with non continuous axes.
Reference: https://hvplot.holoviz.org/reference/pandas/scatter.html
Parameters
----------
x : string, optional
    Field name(s) to draw x-positions from. If not specified, the index is
    used. Can refer to continous and categorical data.
y : string or list, optional
    Field name(s) to draw y-positions from. If not specified, all numerical
    fields are used.
marker : string, optional
    The marker shape specified above can be any supported by matplotlib, e.g. s, d, o etc.
    See https://matplotlib.org/stable/api/markers_api.html.
c : string, optional
    A color or a Field name to draw the color of the marker from
s : int, optional, also available as 'size'
    The size of the marker
by : string, optional
    A single field or list of fields to group by. All the subgroups are visualized.
groupby: string, list, optional
    A single field or list of fields to group and filter by. Adds one or more widgets to
    select the subgroup(s) to visualize.
scale: number, optional
    Scaling factor to apply to point scaling.
logz : bool
    Whether to apply log scaling to the z-axis. Default is False.
color : str or array-like, optional.
    The color for each of the series. Possible values are:
    A single color string referred to by name, RGB or RGBA code, for instance 'red' or
    '#a98d19.
    A sequence of color strings referred to by name, RGB or RGBA code, which will be used
    for each series recursively. For instance ['green','yellow'] each field’s line will be
    filled in green or yellow, alternatively. If there is only a single series to be
    plotted, then only the first color from the color list will be used.
**kwds : optional
    Additional keywords arguments are documented in `hvplot.help('scatter')`.
Returns
-------
A Holoviews object. You can `print` the object to study its composition and run
.. code-block::
    import holoviews as hv
    hv.help(the_holoviews_object)
to learn more about its parameters and options.
Example
-------
.. code-block::
    import hvplot.pandas
    import pandas as pd
    df = pd.DataFrame(
        {
            "actual": [100, 150, 125, 140, 145, 135, 123],
            "forecast": [90, 160, 125, 150, 141, 141, 120],
            "numerical": [1.1, 1.9, 3.2, 3.8, 4.3, 5.0, 5.5],
            "date": pd.date_range("2022-01-03", "2022-01-09"),
            "string": ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"],
        },
    )
    scatter = df.hvplot.scatter(
        x="numerical",
        y=["actual", "forecast"],
        ylabel="value",
        legend="bottom",
        height=500,
        color=["#f16a6f", "#1e85f7"],
        size=100,
    )
    scatter
You can overlay the `scatter` markers on for example a `line` plot
.. code-block::
    line = df.hvplot.line(
        x="numerical", y=["actual", "forecast"], color=["#f16a6f", "#1e85f7"], line_width=5
    )
    scatter * line
References
----------
- Bokeh: https://docs.bokeh.org/en/latest/docs/user_guide/plotting.html#scatter-markers
- HoloViews: https://holoviews.org/reference/elements/matplotlib/Scatter.html
- Pandas: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.scatter.html
- Plotly: https://plotly.com/python/line-and-scatter/
- Matplotlib:  https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html
- Seaborn: https://seaborn.pydata.org/generated/seaborn.scatterplot.html
- Wiki: https://en.wikipedia.org/wiki/Scatter_plot
Generic options
---------------
clim: tuple
    Lower and upper bound of the color scale
cnorm (default='linear'): str
    Color scaling which must be one of 'linear', 'log' or 'eq_hist'
colorbar (default=False): boolean
    Enables a colorbar
fontscale: number
    Scales the size of all fonts by the same amount, e.g. fontscale=1.5
    enlarges all fonts (title, xticks, labels etc.) by 50%
fontsize: number or dict
    Set title, label and legend text to the same fontsize. Finer control
    by using a dict: {'title': '15pt', 'ylabel': '5px', 'ticks': 20}
flip_xaxis/flip_yaxis: boolean
    Whether to flip the axis left to right or up and down respectively
grid (default=False): boolean
    Whether to show a grid
hover : boolean
    Whether to show hover tooltips, default is True unless datashade is
    True in which case hover is False by default
hover_cols (default=[]): list or str
    Additional columns to add to the hover tool or 'all' which will
    includes all columns (including indexes if use_index is True).
invert (default=False): boolean
    Swaps x- and y-axis
frame_width/frame_height: int
    The width and height of the data area of the plot
legend (default=True): boolean or str
    Whether to show a legend, or a legend position
    ('top', 'bottom', 'left', 'right')
logx/logy (default=False): boolean
    Enables logarithmic x- and y-axis respectively
logz (default=False): boolean
    Enables logarithmic colormapping
loglog (default=False): boolean
    Enables logarithmic x- and y-axis
max_width/max_height: int
    The maximum width and height of the plot for responsive modes
min_width/min_height: int
    The minimum width and height of the plot for responsive modes
padding: number or tuple
    Fraction by which to increase auto-ranged extents to make
    datapoints more visible around borders. Supports tuples to
    specify different amount of padding for x- and y-axis and
    tuples of tuples to specify different amounts of padding for
    upper and lower bounds.
rescale_discrete_levels (default=True): boolean
    If `cnorm='eq_hist'` and there are only a few discrete values,
    then `rescale_discrete_levels=True` (the default) decreases
    the lower limit of the autoranged span so that the values are
    rendering towards the (more visible) top of the `cmap` range,
    thus avoiding washout of the lower values.  Has no effect if
    `cnorm!=`eq_hist`.
responsive: boolean
    Whether the plot should responsively resize depending on the
    size of the browser. Responsive mode will only work if at
    least one dimension of the plot is left undefined, e.g. when
    width and height or width and aspect are set the plot is set
    to a fixed size, ignoring any responsive option.
rot: number
    Rotates the axis ticks along the x-axis by the specified
    number of degrees.
shared_axes (default=True): boolean
    Whether to link axes between plots
transforms (default={}): dict
    A dictionary of HoloViews dim transforms to apply before plotting
title (default=''): str
    Title for the plot
tools (default=[]): list
    List of tool instances or strings (e.g. ['tap', 'box_select'])
xaxis/yaxis: str or None
    Whether to show the x/y-axis and whether to place it at the
    'top'/'bottom' and 'left'/'right' respectively.
xformatter/yformatter (default=None): str or TickFormatter
    Formatter for the x-axis and y-axis (accepts printf formatter,
    e.g. '%.3f', and bokeh TickFormatter)
xlabel/ylabel/clabel (default=None): str
    Axis labels for the x-axis, y-axis, and colorbar
xlim/ylim (default=None): tuple or list
    Plot limits of the x- and y-axis
xticks/yticks (default=None): int or list
    Ticks along x- and y-axis specified as an integer, list of
    ticks positions, or list of tuples of the tick positions and labels
width (default=700)/height (default=300): int
    The width and height of the plot in pixels
attr_labels (default=None): bool
    Whether to use an xarray object's attributes as labels, defaults to
    None to allow best effort without throwing a warning. Set to True
    to see warning if the attrs can't be found, set to False to disable
    the behavior.
sort_date (default=True): bool
    Whether to sort the x-axis by date before plotting
symmetric (default=None): bool
    Whether the data are symmetric around zero. If left unset, the data
    will be checked for symmetry as long as the size is less than
    ``check_symmetric_max``.
check_symmetric_max (default=1000000):
    Size above which to stop checking for symmetry by default on the data.
Datashader options
------------------
aggregator (default=None):
    Aggregator to use when applying rasterize or datashade operation
    (valid options include 'mean', 'count', 'min', 'max' and more, and
    datashader reduction objects)
dynamic (default=True):
    Whether to return a dynamic plot which sends updates on widget and
    zoom/pan events or whether all the data should be embedded
    (warning: for large groupby operations embedded data can become
    very large if dynamic=False)
datashade (default=False):
    Whether to apply rasterization and shading (colormapping) using
    the Datashader library, returning an RGB object instead of
    individual points
dynspread (default=False):
    For plots generated with datashade=True or rasterize=True,
    automatically increase the point size when the data is sparse
    so that individual points become more visible
rasterize (default=False):
    Whether to apply rasterization using the Datashader library,
    returning an aggregated Image (to be colormapped by the
    plotting backend) instead of individual points
x_sampling/y_sampling (default=None):
    Specifies the smallest allowed sampling interval along the x/y axis.
Geographic options
------------------
coastline (default=False):
    Whether to display a coastline on top of the plot, setting
    coastline='10m'/'50m'/'110m' specifies a specific scale.
crs (default=None):
    Coordinate reference system of the data specified as Cartopy
    CRS object, proj.4 string or EPSG code.
features (default=None): dict or list
    A list of features or a dictionary of features and the scale
    at which to render it. Available features include 'borders',
    'coastline', 'lakes', 'land', 'ocean', 'rivers' and 'states'.
    Available scales include '10m'/'50m'/'110m'.
geo (default=False):
    Whether the plot should be treated as geographic (and assume
    PlateCarree, i.e. lat/lon coordinates).
global_extent (default=False):
    Whether to expand the plot extent to span the whole globe.
project (default=False):
    Whether to project the data before plotting (adds initial
    overhead but avoids projecting data when plot is dynamically
    updated).
projection (default=None): str or Cartopy CRS
    Coordinate reference system of the plot specified as Cartopy
    CRS object or class name.
tiles (default=False):
    Whether to overlay the plot on a tile source. Tiles sources
    can be selected by name or a tiles object or class can be passed,
    the default is 'Wikipedia'.
Style options
-------------
alpha
angle
cmap
color
fill_alpha
fill_color
hover_alpha
hover_color
hover_fill_alpha
hover_fill_color
hover_line_alpha
hover_line_cap
hover_line_color
hover_line_dash
hover_line_join
hover_line_width
line_alpha
line_cap
line_color
line_dash
line_join
line_width
marker
muted
muted_alpha
muted_color
muted_fill_alpha
muted_fill_color
muted_line_alpha
muted_line_cap
muted_line_color
muted_line_dash
muted_line_join
muted_line_width
nonselection_alpha
nonselection_color
nonselection_fill_alpha
nonselection_fill_color
nonselection_line_alpha
nonselection_line_cap
nonselection_line_color
nonselection_line_dash
nonselection_line_join
nonselection_line_width
palette
selection_alpha
selection_color
selection_fill_alpha
selection_fill_color
selection_line_alpha
selection_line_cap
selection_line_color
selection_line_dash
selection_line_join
selection_line_width
size
visible
File:      ~/mambaforge/envs/musa-550-fall-2023/lib/python3.10/site-packages/hvplot/plotting/core.py
Type:      method

columns = ["flipper_length_mm", "bill_length_mm", "body_mass_g", "species"]
hvplot.scatter_matrix(penguins[columns], c="species")

Note the “box select” and “lasso” features on the tool bar for interactions

2. Explorer mode

An interactive interface that allows you to easily generate customized plots, which makes it easy to explore both your data and hvPlot’s options, parameters, etc.
New feature just released!

penguins.head()

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	male	2007
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	female	2007
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	female	2007
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN	2007
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	female	2007

hvexplorer = hvplot.explorer(penguins)
hvexplorer

Get the code for your customized plot!

You can export the current state of the explorer by running hvexplorer.plot_code()

hvexplorer.plot_code()

"df.hvplot(x='index')"

penguins.hvplot(
    by=["species"],
    colorbar=True,
    kind="scatter",
    title="Bill Depth vs. Bill Length by Species",
    x="bill_depth_mm",
    xlabel="Bill Depth (mm)",
    y=["bill_length_mm"],
    ylabel="Bill Length (mm)",
)

Recap: altair vs hvplot

Both use a declarative syntax (altair more so than hvplot)
Users of ggplot might be more familiar with altair’s syntax
hvplot integrates directly into pandas dataframes via the .hvplot() function
Both have support for cross-filtering and interactions
Both can be incorporated into web-based dashboard via HTML (later in course)
hvplot has better support for large data (later in course)

It’s largely up to you which one you feel is easier to use

Documentation references

Hvplot user guide
HoloViz tutorial: introduction to the HoloViz ecosystem
HoloViews user guide and gallery

Some very cool examples available in the galleries

That’s it!

We’ll get started with geopandas and geospatial data analysis next time
See you on Wednesday!

Housekeeping

Agenda for Week #3

Part 1: More interactive data viz

Recap: The Python data viz landscape

What have we learned so far

Matplotlib

Pandas

Seaborn

Altair

We’ll learn one more today…

HoloViz: A set of coordinated visualization libraries in Python

The motivation behind HoloViz mirrors the goals of this course

Today: hvPlot, Holoviews, Geoviews

Later in the course: Datashader, Param, Panel

HoloViz: A quick overview

A significant pro

The major cons

How does hvPlot fit in?

Example: let’s return to the measles dataset

Convert from wide to long formats…

Reminder: plotting with pandas

Adding interactivity with hvplot

Many different chart types are available…

Just like in altair, we can also layer chart elements together

We can group charts by a specific column, with automatic widget selectors

We can select out individual charts from the set of grouped objects

Combine charts as subplots with the + operator

We can also show overlay lines on the same plot

We can also show faceted plots

For example, we could plot a bar chart for these four states

Customizing charts

Can we reproduce the WSJ measles heatmap?

Two methods:

Method #1

Method #2

Just like altair: save the file as html

Two more useful features:

1. Scatter matrix plots

2. Explorer mode

Recap: altair vs hvplot

Documentation references

That’s it!

Today: `hvPlot`, `Holoviews`, `Geoviews`

Later in the course: `Datashader`, `Param`, `Panel`

How does `hvPlot` fit in?

Reminder: plotting with `pandas`

Adding interactivity with `hvplot`

Combine charts as subplots with the `+` operator