plotnine.geom_line

geom_line(
    mapping=None,
    data=None,
    *,
    stat="identity",
    position="identity",
    na_rm=False,
    inherit_aes=True,
    show_legend=None,
    raster=False,
    lineend="butt",
    linejoin="round",
    arrow=None,
    **kwargs
)

Connected points

Parameters

mapping : aes = None

Aesthetic mappings created with aes. If specified and inherit_aes=True, it is combined with the default mapping for the plot. You must supply mapping if there is no plot mapping.

Aesthetic	Default value
x
y
alpha	`1`
color	`'black'`
group
linetype	`'solid'`
size	`0.5`

The bold aesthetics are required.

data : DataFrame = None

The data to be displayed in this layer. If None, the data from from the ggplot() call is used. If specified, it overrides the data from the ggplot() call.

stat : str | stat = "identity"

The statistical transformation to use on the data for this layer. If it is a string, it must be the registered and known to Plotnine.

position : str | position = "identity"

Position adjustment. If it is a string, it must be registered and known to Plotnine.

na_rm : bool = False

If False, removes missing values with a warning. If True silently removes missing values.

inherit_aes : bool = True

If False, overrides the default aesthetics.

show_legend : bool | dict = None

Whether this layer should be included in the legends. None the default, includes any aesthetics that are mapped. If a bool, False never includes and True always includes. A dict can be used to exclude specific aesthetis of the layer from showing in the legend. e.g show_legend={'color': False}, any other aesthetic are included by default.

raster : bool = False

If True, draw onto this layer a raster (bitmap) object even ifthe final image is in vector format.

**kwargs : Any = {}

Aesthetics or parameters used by the stat.

Examples

from plotnine import (
    ggplot,
    aes,
    geom_line,
    facet_wrap,
    labs,
    scale_x_datetime,
    element_text,
    theme_538
)
from plotnine.data import meat

Line plots

geom_line() connects the dots, and is useful for time series data.

meat.head()

	date	beef	veal	pork	lamb_and_mutton	broilers	other_chicken	turkey
0	1944-01-01	751.0	85.0	1280.0	89.0	NaN	NaN	NaN
1	1944-02-01	713.0	77.0	1169.0	72.0	NaN	NaN	NaN
2	1944-03-01	741.0	90.0	1128.0	75.0	NaN	NaN	NaN
3	1944-04-01	650.0	89.0	978.0	66.0	NaN	NaN	NaN
4	1944-05-01	681.0	106.0	1029.0	78.0	NaN	NaN	NaN

Make it tidy.

meat_long = meat.melt(
    id_vars="date",
    value_vars=["beef", "veal", "pork", "lamb_and_mutton", "broilers", "turkey"],
    var_name="animal",
    value_name="weight"
).dropna()

meat_long.head()

	date	animal	weight
0	1944-01-01	beef	751.0
1	1944-02-01	beef	713.0
2	1944-03-01	beef	741.0
3	1944-04-01	beef	650.0
4	1944-05-01	beef	681.0

First try

p = (
    ggplot(meat_long, aes(x="date", y="weight"))
    + geom_line()
)
p

It looks crowded because each there is more than one monthly entry at each x-point. We can get a single trend line by getting a monthly aggregate of the weights.

meat_long_monthly_agg = meat_long.groupby("date").agg({"weight": "sum"}).reset_index()
meat_long_monthly_agg

	date	weight
0	1944-01-01	2205.0
1	1944-02-01	2031.0
2	1944-03-01	2034.0
3	1944-04-01	1783.0
4	1944-05-01	1894.0
...	...	...
955	2023-08-01	9319.1
956	2023-09-01	8586.1
957	2023-10-01	9452.5
958	2023-11-01	8951.1
959	2023-12-01	8555.1

960 rows × 2 columns

A Single Trend Line

(
    ggplot(meat_long_monthly_agg, aes(x="date", y="weight"))
    + geom_line()
)

Add some style

# Gallery, lines
(
    ggplot(meat_long_monthly_agg, aes(x="date", y="weight"))
    + geom_line()
    # Styling
    + scale_x_datetime(date_breaks="10 years", date_labels="%Y")
    + theme_538()
)

Or we can group by the animals to get a trend line for each animal

Multiple Trend Lines

(
    ggplot(meat_long, aes(x="date", y="weight", group="animal"))
    + geom_line()
    # Styling
    + scale_x_datetime(date_breaks="10 years", date_labels="%Y")
    + theme_538()
)

Make each group be a different color.

# Gallery, lines
(
    ggplot(meat_long, aes(x="date", y="weight", color="animal"))
    + geom_line()
    # Styling
    + scale_x_datetime(date_breaks="10 years", date_labels="%Y")
    + theme_538()
)

A Trend Line Per Facet

Plot each group on a separate panel. The legend is no longer required and we adjust to the smaller panels by reducing the size of the line, size of the text and the number of grid lines.

# Gallery, lines
def titled(strip_title):
    return " ".join(s.title() if s != "and" else s for s in strip_title.split("_"))

(
    ggplot(meat_long, aes("date", "weight", color="animal"))
    + geom_line(size=.5, show_legend=False)
    + facet_wrap("animal", labeller=titled)
    + scale_x_datetime(date_breaks="20 years", date_labels="%Y")
    + labs(
        x="Date",
        y="Weight (million pounds)",
        title="Meat Production"
    )
    + theme_538(base_size=9)
)

Source: Line plots