Line Chart¶
This section showcases the line chart. It contains examples of how to create line charts using the datachart.charts.LineChart function.
Looking for a specific customization? Jump straight to the quick reference, which maps common tasks to the parameter or style attribute that does the job.
As mentioned above, the line charts are created using the LineChart function found in the datachart.charts module. Let's import it:
from datachart.charts import LineChart
Line Chart Input Attributes¶
The LineChart function accepts keyword arguments for chart configuration. The main argument is data, which contains the data points. For a single line chart, data is a list of dictionaries. For multiple line charts, data is a list of lists.
LineChart(
data=[{ # A list of line data points (or list of lists for multiple charts)
"x": Union[int, float], # The x-axis value
"y": Union[int, float], # The y-axis value
"yerr": Optional[Union[int, float]] # The y-axis error value (to plot the confidence interval)
}],
style={ # The style of the line (optional)
"plot_line_color": Optional[str], # The color of the line (hex color code)
"plot_line_style": Optional[LINE_STYLE], # The line style (solid, dashed, etc.)
"plot_line_marker": Optional[LINE_MARKER], # The marker style of the line (circle, square, etc.)
"plot_line_width": Optional[float], # The width of the line
"plot_line_alpha": Optional[float], # The alpha of the line (how visible the line is)
"plot_line_drawstyle": Optional[LINE_DRAW_STYLE], # The drawstyle of the line (step, steps-mid, etc.)
"plot_line_zorder": Optional[int], # The zorder of the line
"plot_area_color": Optional[str], # The color of the area under the line / confidence band
"plot_area_alpha": Optional[float], # The alpha of the area
"plot_area_hatch": Optional[HATCH_STYLE], # The hatch style of the area
},
subtitle=Optional[str], # The subtitle of the chart (or list for multiple charts)
emphasis=Optional[str], # "highlight" or "background" (or list for multiple charts)
title=Optional[str], # The title of the chart
xlabel=Optional[str], # The x-axis label
ylabel=Optional[str], # The y-axis label
figsize=Optional[Tuple[float, float]], # The figure size in inches
show_grid=Optional[str], # Which grid lines to show ("both", "x", "y")
aspect_ratio=Optional[str], # The aspect ratio of the axes ("auto", "equal")
show_legend=Optional[bool], # Whether to show the legend
show_area=Optional[bool], # Whether to fill the area under the line
show_yerr=Optional[bool], # Whether to show the confidence interval (from "yerr")
subplots=Optional[bool], # Whether to draw each chart in its own subplot
max_cols=Optional[int], # Maximum number of subplots per row
sharex=Optional[bool], # Whether subplots share the x-axis
sharey=Optional[bool], # Whether subplots share the y-axis
scalex=Optional[str], # The x-axis scale ("linear", "log", "symlog", "asinh")
scaley=Optional[str], # The y-axis scale ("linear", "log", "symlog", "asinh")
xmin=Optional[Union[int, float]], # The x-axis range
xmax=Optional[Union[int, float]],
ymin=Optional[Union[int, float]], # The y-axis range
ymax=Optional[Union[int, float]],
xticks=Optional[List[Union[int, float]]], # the x-axis ticks
xticklabels=Optional[List[str]], # the x-axis tick labels (must be same length as xticks)
xtickrotate=Optional[int], # the x-axis tick labels rotation
yticks=Optional[List[Union[int, float]]], # the y-axis ticks
yticklabels=Optional[List[str]], # the y-axis tick labels (must be same length as yticks)
ytickrotate=Optional[int], # the y-axis tick labels rotation
vlines=Optional[Union[dict, List[dict]]], # the vertical lines
hlines=Optional[Union[dict, List[dict]]], # the horizontal lines
x=Optional[str], # the key holding the x-axis value (default: "x")
y=Optional[str], # the key holding the y-axis value (default: "y")
yerr=Optional[str], # the key holding the y-axis error value (default: "yerr")
)
For more details, see the datachart.charts.LineChart function.
Basics¶
The examples in this guide share one dataset: the average monthly temperature (in °C) of three European cities, based on their 1991–2020 climate normals. The data is hard-coded in a hidden cell; temperature_ljubljana holds the twelve monthly values of Ljubljana, and temperature_by_city holds one series per city — Ljubljana, Reykjavik and Lisbon — with the year-to-year standard deviation of each monthly mean as yerr. MONTHS holds the month names used as tick labels.
Each data point is a dictionary with an x value (here the month number) and a y value:
temperature_ljubljana[:3]
[{'x': 1, 'y': 0.3}, {'x': 2, 'y': 1.9}, {'x': 3, 'y': 6.2}]
Basic example. Only the data argument is required to draw the line chart.
LineChart(
# add the data to the chart
data=temperature_ljubljana
).show()
Customizing the Line Chart¶
Every customization is either a keyword argument of LineChart or a plot_line_* / plot_area_* attribute of its style dictionary. The table maps common tasks to the one you need and links to the subsection that shows it.
| I want to… | Use | See |
|---|---|---|
| add a title and axis labels | title, xlabel, ylabel |
Title, axis labels and ticks |
| set custom tick positions and labels | xticks, xticklabels, yticks, yticklabels |
Title, axis labels and ticks |
| rotate the tick labels | xtickrotate, ytickrotate |
Title, axis labels and ticks |
| fix the axis range | xmin, xmax, ymin, ymax |
Title, axis labels and ticks |
| resize the figure | figsize |
Figure size and grid |
| show grid lines | show_grid |
Figure size and grid |
| fix the aspect ratio of the axes | aspect_ratio |
Figure size and grid |
| change the line color | style={"plot_line_color": ...} |
Line style |
| dash or dot the line | style={"plot_line_style": ...} |
Line style |
| mark the data points | style={"plot_line_marker": ...} |
Line style |
| draw the line as steps | style={"plot_line_drawstyle": ...} |
Line style |
| change the line width or transparency | style={"plot_line_width": ..., "plot_line_alpha": ...} |
Line style |
| fill the area under the line | show_area, style={"plot_area_color": ..., "plot_area_alpha": ...} |
Area under the line |
| highlight one series, mute the rest | emphasis |
Emphasis |
| mark a threshold or an event | hlines, vlines |
Reference lines |
| compare several series in one chart | data as a list of lists, subtitle, show_legend |
Multiple Line Charts |
| draw each series in its own subplot | subplots, sharex, sharey, max_cols |
Subplots |
| draw a confidence interval | yerr in data, show_yerr |
Confidence interval |
| use a logarithmic axis | scaley, scalex |
Axis scales |
| plot data with other key names | x, y, yerr |
Custom data keys |
| save the chart to a file | save_figure |
Saving the Chart as an Image |
The full list of style attributes is in the datachart.typings.LineStyleAttrs and datachart.typings.AreaStyleAttrs types; the full list of parameters is in the datachart.charts.LineChart reference.
Title, axis labels and ticks¶
To add the chart title and axis labels, add the title, xlabel and ylabel attributes. The tick positions and their labels can be set with xticks and xticklabels (or yticks and yticklabels) — here the month numbers on the x-axis are replaced by month names. Tick labels can be rotated with xtickrotate (or ytickrotate), and the axis range can be fixed with xmin, xmax, ymin and ymax.
LineChart(
data=temperature_ljubljana,
# add the title
title="Average monthly temperature in Ljubljana",
# add the x and y axis labels
xlabel="Month",
ylabel="Temperature (°C)",
# show the month names instead of the month numbers
xticks=MONTH_TICKS,
xticklabels=MONTHS,
# rotate the x-axis tick labels
xtickrotate=45,
# fix the y-axis range
ymin=-5,
ymax=25,
).show()
Figure size and grid¶
To change the figure size, add the figsize attribute. The figsize attribute can be a tuple (width, height), values are in inches. The datachart package provides a datachart.constants.FIG_SIZE constant, which contains some of the predefined figure sizes.
To add the grid, add the show_grid attribute. The possible options are:
| Option | Description |
|---|---|
"both" |
shows both the x-axis and the y-axis gridlines. |
"x" |
shows only the x-axis grid lines. |
"y" |
shows only the y-axis grid lines. |
Again, datachart provides a datachart.constants.SHOW_GRID constant, which contains the supported options.
Related is the aspect_ratio attribute, which fixes the aspect ratio of the axes rather than of the figure: "auto" (the default) lets the axes fill the figure, "equal" keeps one data unit the same length on both axes. The supported values are in the datachart.constants.ASPECT_RATIO constant; the ROC curve example below uses it.
from datachart.constants import FIG_SIZE, SHOW_GRID
LineChart(
data=temperature_ljubljana,
title="Average monthly temperature in Ljubljana",
xlabel="Month",
ylabel="Temperature (°C)",
xticks=MONTH_TICKS,
xticklabels=MONTHS,
# add to determine the figure size
figsize=FIG_SIZE.FULL_SHORT,
# add to show the grid lines
show_grid=SHOW_GRID.BOTH,
).show()
Line style¶
To change the line style, add the style attribute with the corresponding attributes. The supported attributes are shown in the datachart.typings.LineStyleAttrs type, which contains the following attributes:
| Attribute | Description |
|---|---|
"plot_line_color" |
The color of the line (hex color code). |
"plot_line_alpha" |
The alpha of the line (how visible the line is). |
"plot_line_width" |
The width of the line. |
"plot_line_style" |
The line style (solid, dashed, etc.). |
"plot_line_marker" |
The marker style of the line (circle, square, etc.). |
"plot_line_drawstyle" |
The drawstyle of the line (step, steps-mid, etc.). |
"plot_line_zorder" |
The zorder of the line. |
"plot_xticks_label_rotate" |
The rotation of the x-axis tick labels. |
"plot_yticks_label_rotate" |
The rotation of the y-axis tick labels. |
Again, to help with the style settings, the datachart.constants module contains the following constants:
| Constant | Description |
|---|---|
| datachart.constants.LINE_STYLE | The line style (solid, dashed, etc.) |
| datachart.constants.LINE_MARKER | The marker style of the line (circle, square, etc.) |
| datachart.constants.LINE_DRAW_STYLE | The drawstyle of the line (step, steps-mid, etc.) |
The example below changes the color, width, dash pattern and marker of the line in one go. Any attribute you leave out keeps the value of the active theme.
from datachart.constants import LINE_STYLE, LINE_MARKER, LINE_DRAW_STYLE
LineChart(
data=temperature_ljubljana,
# define the style of the line
style={
"plot_line_color": "#e76f51",
"plot_line_width": 2,
"plot_line_style": LINE_STYLE.DASHED,
"plot_line_marker": LINE_MARKER.CIRCLE,
},
title="Average monthly temperature in Ljubljana",
xlabel="Month",
ylabel="Temperature (°C)",
xticks=MONTH_TICKS,
xticklabels=MONTHS,
figsize=FIG_SIZE.FULL_SHORT,
show_grid=SHOW_GRID.BOTH,
).show()
Monthly averages are one value per month rather than a continuous curve. The plot_line_drawstyle attribute draws the line as steps instead — LINE_DRAW_STYLE.STEPS_MID centers each step on its data point.
LineChart(
data=temperature_ljubljana,
style={
# draw the line as steps centered on the data points
"plot_line_drawstyle": LINE_DRAW_STYLE.STEPS_MID,
"plot_line_marker": LINE_MARKER.POINT,
},
title="Average monthly temperature in Ljubljana",
xlabel="Month",
ylabel="Temperature (°C)",
xticks=MONTH_TICKS,
xticklabels=MONTHS,
figsize=FIG_SIZE.FULL_SHORT,
show_grid=SHOW_GRID.BOTH,
).show()
Area under the line¶
To fill the area between the line and the bottom of the axes, add the show_area attribute. The fill takes the color of the line at a lower alpha; the plot_area_color, plot_area_alpha and plot_area_hatch style attributes from the datachart.typings.AreaStyleAttrs type override that. With the step draw style the fill follows the steps.
LineChart(
data=temperature_ljubljana,
style={
"plot_line_drawstyle": LINE_DRAW_STYLE.STEPS_MID,
# make the fill a bit stronger than the theme default
"plot_area_alpha": 0.35,
},
title="Average monthly temperature in Ljubljana",
xlabel="Month",
ylabel="Temperature (°C)",
xticks=MONTH_TICKS,
xticklabels=MONTHS,
figsize=FIG_SIZE.FULL_SHORT,
show_grid=SHOW_GRID.BOTH,
# fill the area under the line
show_area=True,
).show()
Emphasis¶
When a chart carries several series, the story is often about one of them. The emphasis attribute expresses that directly: "highlight" thickens a line and brings it to the front, "background" mutes a line (the theme's muted color at a lower alpha, thinner and drawn behind the others), and None leaves a line unchanged. For multiple charts, emphasis is a list aligned with data, just like subtitle and style. Only emphasized-or-unset series appear in the legend — background lines drop out of it. The role strings are also available as the datachart.constants.EMPHASIS constants.
The example highlights Ljubljana against the other two cities. See the Highlighting guide for how emphasis works across all chart types and themes.
LineChart(
data=temperature_by_city,
subtitle=CITIES,
# highlight Ljubljana, mute the other cities
emphasis=["highlight", "background", "background"],
title="Average monthly temperature",
xlabel="Month",
ylabel="Temperature (°C)",
xticks=MONTH_TICKS,
xticklabels=MONTHS,
figsize=FIG_SIZE.FULL_SHORT,
show_grid=SHOW_GRID.BOTH,
show_legend=True,
).show()
Reference lines¶
Reference lines mark a threshold or an event on the chart.
Horizontal lines. Use the hlines argument with the datachart.typings.HLinePlotAttrs typing, which is either a dict or a List[dict] where each dictionary contains some of the following attributes:
{
"y": Union[int, float], # The y-axis value
"xmin": Optional[Union[int, float]], # The minimum x-axis value
"xmax": Optional[Union[int, float]], # The maximum x-axis value
"style": { # The style of the line (optional)
"plot_hline_color": Optional[str], # The color of the line (hex color code)
"plot_hline_style": Optional[LineStyle], # The line style (solid, dashed, etc.)
"plot_hline_width": Optional[float], # The width of the line
"plot_hline_alpha": Optional[float], # The alpha of the line (how visible the line is)
},
"label": Optional[str], # The label of the line (shown in the legend)
}
Vertical lines. Use the vlines argument with the datachart.typings.VLinePlotAttrs typing, which has the same shape with x, ymin, ymax and plot_vline_* style attributes. The x value is in data coordinates, so a line can sit anywhere along the axis — here between two months.
The example marks the freezing point with a dashed horizontal line and the summer solstice (21 June) with a vertical line. The line labels appear in the legend.
LineChart(
data=temperature_ljubljana,
subtitle="Ljubljana",
# add a horizontal line at the freezing point
hlines={
"y": 0,
"label": "freezing point",
"style": {
"plot_hline_color": "#1d3557",
"plot_hline_style": LINE_STYLE.DASHED,
"plot_hline_width": 1.5,
},
},
# add a vertical line at the summer solstice
vlines={
"x": 6.7,
"label": "summer solstice",
"style": {
"plot_vline_color": "#e9a03b",
"plot_vline_style": LINE_STYLE.DOTTED,
"plot_vline_width": 1.5,
},
},
title="Average monthly temperature in Ljubljana",
xlabel="Month",
ylabel="Temperature (°C)",
xticks=MONTH_TICKS,
xticklabels=MONTHS,
figsize=FIG_SIZE.FULL_SHORT,
show_grid=SHOW_GRID.BOTH,
show_legend=True,
).show()
Multiple Line Charts¶
To create multiple line charts, pass a list of lists to the data argument. Each inner list represents the data for one line. Per-chart attributes like subtitle, style and emphasis can be passed as lists, where each element corresponds to a chart.
Multiple charts pattern
For multiple charts, data becomes a list of lists, and per-chart attributes like subtitle and style become lists where each element applies to the corresponding chart.
The temperature_by_city dataset is such a list of lists, one series per city. A single style dictionary applies to every line; a list of dictionaries styles each line separately (None keeps the theme style for that line).
LineChart(
# use a list of lists to define multiple lines
data=temperature_by_city,
# style can be a list (one per chart) or a single dict (applies to all)
style=[
{"plot_line_marker": LINE_MARKER.CIRCLE},
{"plot_line_marker": LINE_MARKER.SQUARE},
None, # keep the theme style for the third line
],
title="Average monthly temperature",
xlabel="Month",
ylabel="Temperature (°C)",
xticks=MONTH_TICKS,
xticklabels=MONTHS,
figsize=FIG_SIZE.FULL_SHORT,
show_grid=SHOW_GRID.BOTH,
).show()
Sub-chart subtitles¶
We can name each chart by passing a list of subtitles to the subtitle argument. In addition, to help with discerning which chart is which, use the show_legend argument to show the legend of the charts.
LineChart(
data=temperature_by_city,
# add a subtitle to each line
subtitle=CITIES,
title="Average monthly temperature",
xlabel="Month",
ylabel="Temperature (°C)",
xticks=MONTH_TICKS,
xticklabels=MONTHS,
figsize=FIG_SIZE.FULL_SHORT,
show_grid=SHOW_GRID.BOTH,
# show the legend
show_legend=True,
).show()
Subplots¶
To draw each chart in its own subplot, add the subplots attribute. The chart's subtitle are then added at the top of each subplot, while the title, xlabel and ylabel are positioned to be global for all charts. The max_cols attribute limits the number of subplots per row.
LineChart(
data=temperature_by_city,
subtitle=CITIES,
title="Average monthly temperature",
xlabel="Month",
ylabel="Temperature (°C)",
xticks=MONTH_TICKS,
xticklabels=MONTHS,
figsize=FIG_SIZE.FULL_MEDIUM,
show_grid=SHOW_GRID.BOTH,
# show each chart in its own subplot
subplots=True,
# at most two subplots per row
max_cols=2,
).show()
Sharing the x-axis and/or y-axis across subplots¶
To share the x-axis and/or y-axis across subplots, add the sharex and/or sharey attributes, which are boolean values that specify whether to share the axis across all subplots. With a shared y-axis, the cities become directly comparable — Reykjavik's flat curve no longer fills its subplot.
LineChart(
data=temperature_by_city,
subtitle=CITIES,
title="Average monthly temperature",
xlabel="Month",
ylabel="Temperature (°C)",
xticks=MONTH_TICKS,
xticklabels=MONTHS,
figsize=FIG_SIZE.FULL_MEDIUM,
show_grid=SHOW_GRID.BOTH,
subplots=True,
max_cols=2,
# share the x-axis across subplots
sharex=True,
# share the y-axis across subplots
sharey=True,
).show()
Area under the lines¶
Specifying the show_area attribute fills the area under each line. In a single chart the fills overlap, so the attribute is at its best with subplots.
LineChart(
data=temperature_by_city,
subtitle=CITIES,
title="Average monthly temperature",
xlabel="Month",
ylabel="Temperature (°C)",
xticks=MONTH_TICKS,
xticklabels=MONTHS,
figsize=FIG_SIZE.FULL_MEDIUM,
show_grid=SHOW_GRID.BOTH,
subplots=True,
max_cols=2,
sharex=True,
sharey=True,
# fill the area under the line in all subplots
show_area=True,
).show()
Confidence interval¶
If a line chart has a confidence interval, it can be added by adding the yerr attribute to the chart's data attribute. Afterwards, the show_yerr attribute can be set to True to draw the band between y - yerr and y + yerr. The temperature_by_city data points carry the year-to-year standard deviation of each monthly mean as yerr. The band is styled with the same plot_area_* attributes as the area under the line.
LineChart(
data=temperature_by_city,
subtitle=CITIES,
title="Average monthly temperature",
xlabel="Month",
ylabel="Temperature (°C)",
xticks=MONTH_TICKS,
xticklabels=MONTHS,
figsize=FIG_SIZE.FULL_SHORT,
show_grid=SHOW_GRID.BOTH,
show_legend=True,
# draw the confidence interval using the error values
show_yerr=True,
).show()
Additional Features¶
Axis scales¶
The user can change the axis scale using the scalex and scaley attributes. The supported scale options are:
| Options | Description |
|---|---|
"linear" |
The linear scale. |
"log" |
The log scale. |
"symlog" |
The symmetric log scale. |
"asinh" |
The asinh scale. |
Again, to help with the options settings, the datachart.constants module contains the following constants:
| Constant | Description |
|---|---|
| datachart.constants.SCALE | The axis options. |
A logarithmic scale pays off when the values span several orders of magnitude. The hidden cell below defines transistors, the transistor count of a representative microprocessor per year from the Intel 4004 (1971) to the Apple M1 Ultra (2022) — Moore's law in sixteen data points, rounded from the manufacturers' figures.
from datachart.constants import SCALE
On a linear scale the first forty years collapse onto the x-axis; on a log scale the exponential growth becomes the straight line it is famous for.
for scale in [SCALE.LINEAR, SCALE.LOG]:
figure = LineChart(
data=transistors,
style={"plot_line_marker": LINE_MARKER.CIRCLE},
title=f"Transistors per microprocessor on the '{scale}' scale",
xlabel="Year",
ylabel="Transistors",
figsize=FIG_SIZE.FULL_SHORT,
show_grid=SHOW_GRID.BOTH,
# set the scale of the y axis
scaley=scale,
)
figure.show()
Custom data keys¶
By default, the data items are dictionaries with the keys x, y and, optionally, yerr. Data that comes from elsewhere rarely uses those names, and renaming every key just to plot it is a chore. Instead, tell LineChart which keys to read with the x, y and yerr arguments. The readings list below stores the Ljubljana temperatures under month and temperature, with the deviation under spread.
readings = [
{"month": month, "temperature": temp, "spread": std}
for month, temp, std in zip(MONTH_TICKS, TEMPERATURE["Ljubljana"], TEMPERATURE_STD["Ljubljana"])
]
readings[:3]
[{'month': 1, 'temperature': 0.3, 'spread': 2.1},
{'month': 2, 'temperature': 1.9, 'spread': 2.4},
{'month': 3, 'temperature': 6.2, 'spread': 1.8}]
figure = LineChart(
data=readings,
# specify which keys hold the x, y and error values
x="month",
y="temperature",
yerr="spread",
title="Average monthly temperature in Ljubljana",
xlabel="Month",
ylabel="Temperature (°C)",
xticks=MONTH_TICKS,
xticklabels=MONTHS,
figsize=FIG_SIZE.FULL_SHORT,
show_grid=SHOW_GRID.BOTH,
show_yerr=True,
)
figure.show()
Saving the Chart as an Image¶
To save the chart as an image, use the datachart.utils.save_figure function.
from datachart.utils import save_figure
save_figure(figure, "./fig_line_chart.png", dpi=300)
The figure should be saved in the current working directory.
Real-World Examples¶
The following examples put the features above to work on real or realistic data. Each one states what its data is and where it comes from; the data itself lives in a hidden cell.
Example 1: ROC Curve (Custom Data Keys and Equal Aspect Ratio)¶
roc_curves holds the receiver operating characteristic of two illustrative binary classifiers: each point is the false positive rate (fp) and true positive rate (tp) at one decision threshold, so the keys are mapped with the x and y arguments. A ROC curve is read against the diagonal, so the subplots share an equal aspect ratio (aspect_ratio) and the area under each curve — the AUC — is filled with a hatch pattern.
from datachart.constants import ASPECT_RATIO, HATCH_STYLE
LineChart(
data=roc_curves,
subtitle=list(ROC_POINTS),
# the points are stored as "fp" and "tp", instead of "x" and "y"
x="fp",
y="tp",
# hatch the area under each curve (a single style applies to every chart)
style={"plot_area_hatch": HATCH_STYLE.DIAGONAL},
title="ROC curve",
xlabel="False positive rate",
ylabel="True positive rate",
figsize=FIG_SIZE.FULL_SHORT,
show_grid=SHOW_GRID.BOTH,
xmin=0,
xmax=1,
ymin=0,
ymax=1,
show_area=True,
subplots=True,
sharex=True,
sharey=True,
# keep one unit the same length on both axes
aspect_ratio=ASPECT_RATIO.EQUAL,
).show()
Example 2: Training Loss (Confidence Interval on a Log Scale)¶
training_loss holds the validation loss of three illustrative training methods, evaluated every five steps over 200 steps and averaged over several runs; spread is the standard deviation across the runs. The loss decays exponentially toward a floor, so the y-axis uses a log scale to keep the late-training differences readable, and show_yerr draws the run-to-run spread as a band around each mean.
LineChart(
data=training_loss,
subtitle=list(LOSS_CURVES),
# the points are stored as "step", "loss" and "spread"
x="step",
y="loss",
yerr="spread",
title="Validation loss during training",
xlabel="Training step",
ylabel="Validation loss",
figsize=FIG_SIZE.FULL_SHORT,
show_grid=SHOW_GRID.Y,
show_legend=True,
# draw the run-to-run spread as a band
show_yerr=True,
# depict the y-axis as a log scale
scaley=SCALE.LOG,
).show()
Example 3: One Index Among Many (Emphasis)¶
sector_indices holds the illustrative performance of five stock market sector indices over three years, sampled quarterly and rebased to 100 at the end of 2022. The question is how the technology sector did against the market, so emphasis highlights it and mutes the other four. Muted indices drop out of the legend automatically.
LineChart(
data=sector_indices,
subtitle=list(SECTOR_INDEX),
# highlight Technology, mute the other sectors
emphasis=["highlight", "background", "background", "background", "background"],
title="Sector indices, rebased to 100",
xlabel="Quarter",
ylabel="Index level",
# the x values are quarter offsets; label them with the quarter names
xticks=list(range(len(QUARTERS))),
xticklabels=QUARTERS,
xtickrotate=45,
figsize=FIG_SIZE.FULL_SHORT,
show_grid=SHOW_GRID.Y,
show_legend=True,
).show()
Example 4: Website Traffic (Reference Lines)¶
weekly_visitors holds the illustrative weekly unique visitors of a website over sixteen weeks. A marketing campaign launched in week 7, and the hosting plan is sized for 60,000 weekly visitors. A vertical line marks the launch and a horizontal line the capacity, so the chart answers both "did the campaign work" and "when do we need to upgrade" at a glance.
LineChart(
data=weekly_visitors,
subtitle="unique visitors",
style={"plot_line_marker": LINE_MARKER.CIRCLE},
# mark the campaign launch
vlines={
"x": CAMPAIGN_WEEK,
"label": "campaign launch",
"style": {
"plot_vline_color": "#2a9d8f",
"plot_vline_style": LINE_STYLE.DASHED,
"plot_vline_width": 1.5,
},
},
# mark the hosting capacity
hlines={
"y": CAPACITY,
"label": "hosting capacity",
"style": {
"plot_hline_color": "#c1121f",
"plot_hline_style": LINE_STYLE.DOTTED,
"plot_hline_width": 1.5,
},
},
title="Weekly website visitors",
xlabel="Week",
ylabel="Visitors (thousands)",
figsize=FIG_SIZE.FULL_SHORT,
show_grid=SHOW_GRID.Y,
show_legend=True,
ymin=0,
).show()
