plotnine.geom_tile

geom_tile(
    mapping=None,
    data=None,
    *,
    stat="identity",
    position="identity",
    na_rm=False,
    inherit_aes=True,
    show_legend=None,
    raster=False,
    **kwargs
)

Rectangles specified using a center 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	`None`
fill	`'#333333'`
group
linetype	`'solid'`
size	`0.1`

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

import pandas as pd
import numpy as np

from plotnine import (
    ggplot,
    aes,
    geom_tile,
    geom_text,
    scale_y_reverse,
    scale_y_discrete,
    scale_fill_brewer,
    scale_color_manual,
    coord_equal,
    theme,
    theme_void,
    element_blank,
    element_rect,
    element_text,
)

Periodic Table

Graphing of highly organised tabular information

Read the data.

elements = pd.read_csv("data/elements.csv")
elements.head()

	atomic number	symbol	name	atomic mass	CPK	electronic configuration	electronegativity	atomic radius	ion radius	van der Waals radius	...	EA	standard state	bonding type	melting point	boiling point	density	metal	year discovered	group	period
0	1	H	Hydrogen	1.00794	#FFFFFF	1s1	2.20	37.0	NaN	120.0	...	-73.0	gas	diatomic	14.0	20.0	0.00009	nonmetal	1766	1	1
1	2	He	Helium	4.002602	#D9FFFF	1s2	NaN	32.0	NaN	140.0	...	0.0	gas	atomic	NaN	4.0	0.00000	noble gas	1868	18	1
2	3	Li	Lithium	6.941	#CC80FF	[He] 2s1	0.98	134.0	76 (+1)	182.0	...	-60.0	solid	metallic	454.0	1615.0	0.54000	alkali metal	1817	1	2
3	4	Be	Beryllium	9.012182	#C2FF00	[He] 2s2	1.57	90.0	45 (+2)	NaN	...	0.0	solid	metallic	1560.0	2743.0	1.85000	alkaline earth metal	1798	2	2
4	5	B	Boron	10.811	#FFB5B5	[He] 2s2 2p1	2.04	82.0	27 (+3)	NaN	...	-27.0	solid	covalent network	2348.0	4273.0	2.46000	metalloid	1807	13	2

5 rows × 21 columns

Alter the data types of the information that will be plotted. This makes it convenient to work with.

elements["group"] = [-1 if g == "-" else int(g) for g in elements.group]
elements["bonding type"] = elements["bonding type"].astype("category")
elements["metal"] = elements["metal"].astype("category")
elements["atomic_number"] = elements["atomic number"].astype(str)

The periodic table has two tables, a top and bottom. The elements in the top have groups, and those in the bottom have no groups. We make separate dataframes for both – they have different alignments.

top = elements.query("group != -1").copy()
bottom = elements.query("group == -1").copy()

The top table is nice and well behaving. The x location of the elements indicate the group and the y locations the period.

top["x"] = top.group
top["y"] = top.period

The bottom table has 2 rows, with the atomic number increasing to the right. We create an x based on the atomic number and add a horizontal shift. As the dataframe is ordered by atomic number, the operation is easier. The bottom elements are labelled with a “period”. We add a vertical shift to give us a good y location that gives the appearance of two tables.

nrows = 2
hshift = 3.5
vshift = 3
bottom["x"] = np.tile(np.arange(len(bottom) // nrows), nrows) + hshift
bottom["y"] = bottom.period + vshift

We will be plotting using tiles and we want to have some space between the tiles. We have set the x and y locations above to take up a unit of space. To get a good effect, the tile dimensions should be less than 1.

tile_width = 0.95
tile_height = 0.95

First peak

(
    ggplot(aes("x", "y"))
    + geom_tile(top, aes(width=tile_width, height=tile_height))
    + geom_tile(bottom, aes(width=tile_width, height=tile_height))
)

The table upside down. We could have been more careful when creating the y locations since the periods are drawn in descending order. But, we can fix that with a reverse scale.

(
    ggplot(aes("x", "y"))
    + geom_tile(top, aes(width=tile_width, height=tile_height))
    + geom_tile(bottom, aes(width=tile_width, height=tile_height))
    + scale_y_reverse()  # new
)

Let us apply some color to it.

(
    ggplot(aes("x", "y"))
    + aes(fill="metal")  # new
    + geom_tile(top, aes(width=tile_width, height=tile_height))
    + geom_tile(bottom, aes(width=tile_width, height=tile_height))
    + scale_y_reverse()
)

Now for some trick

Goal: To add text to the tiles

There are four pieces of text that we shall add to the tiles, that is 4 geom_text additions. As we have two tables, that comes to 8 geom_text additions. When any geom is added to a ggplot object, behind the scenes a layer is created and added. We can create a group of layers that can be added to a ggplot object in one go using a list.

We use a function that accepts a dataframe, and returns a list of geoms.

def inner_text(data):
    layers = [
        geom_text(
            data,
            aes(label="atomic_number"),
            nudge_x=-0.40,
            nudge_y=0.40,
            ha="left",
            va="top",
            fontweight="normal",
            size=6,
        ),
        geom_text(data, aes(label="symbol"), nudge_y=0.1, size=9),
        geom_text(
            data, aes(label="name"), nudge_y=-0.125, fontweight="normal", size=4.5
        ),
        geom_text(
            data, aes(label="atomic mass"), nudge_y=-0.3, fontweight="normal", size=4.5
        ),
    ]
    return layers

(
    ggplot(aes("x", "y"))
    + aes(fill="metal")
    + geom_tile(top, aes(width=tile_width, height=tile_height))
    + geom_tile(bottom, aes(width=tile_width, height=tile_height))
    + inner_text(top)  # new
    + inner_text(bottom)  # new
    + scale_y_reverse()
)

It is crowded in there and the tiles do not have equal dimentions. Use the theme create a larger figure. coord_equal give us equal units along the axes, this makes the tiles square.

(
    ggplot(aes("x", "y"))
    + aes(fill="metal")
    + geom_tile(top, aes(width=tile_width, height=tile_height))
    + geom_tile(bottom, aes(width=tile_width, height=tile_height))
    + inner_text(top)
    + inner_text(bottom)
    + scale_y_reverse()
    + coord_equal(expand=False)  # new
    + theme(figure_size=(12, 6))  # new
)

It is has all the information we want, except one for complication. Elements Lu and Lr also belong in the bottom table. One way to show this duality is to have tiles with two colors split horizontally.

The colors are determined by the metal field, and we know the x and y locations. We create a dataframe with this information to create a half-tile. A half-tile is centered at the quarter mark.

split_df = pd.DataFrame(
    {
        "x": 3 - tile_width / 4,
        "y": [6, 7],
        "metal": pd.Categorical(["lanthanoid", "actinoid"]),
    }
)

(
    ggplot(aes("x", "y"))
    + aes(fill="metal")
    + geom_tile(top, aes(width=tile_width, height=tile_height))
    + geom_tile(split_df, aes(width=tile_width / 2, height=tile_height))  # new
    + geom_tile(bottom, aes(width=tile_width, height=tile_height))
    + inner_text(top)
    + inner_text(bottom)
    + scale_y_reverse()
    + coord_equal(expand=False)
    + theme(figure_size=(12, 6))
)

Change the fill color for a different look and use a theme that clears out all the clutter.

from plotnine import guides
(
    ggplot(aes("x", "y"))
    + aes(fill="metal")
    + geom_tile(top, aes(width=tile_width, height=tile_height))
    + geom_tile(split_df, aes(width=tile_width / 2, height=tile_height))
    + geom_tile(bottom, aes(width=tile_width, height=tile_height))
    + inner_text(top)
    + inner_text(bottom)
    + scale_y_reverse()
    + scale_fill_brewer(type="qual", palette=3)  # new
    + coord_equal(expand=False)
    + theme_void()  # new
    + theme(
        figure_size=(12, 6),
        plot_margin_left=0.01,
        plot_margin_right=0.01,
        plot_background=element_rect(fill="white"),
    )  # new
)

Add the group number along the top most row of each column, and period number along the left side of the top table.

For the period number, we set the breaks on the y scale.

# The location of the group number is the top most (and therefore smallest period)
# element with the group
groupdf = top.groupby("group").agg(y=("period", "min")).reset_index()

Finally,

# Gallery, tiles

(
    ggplot(aes("x", "y"))
    + aes(fill="metal")
    + geom_tile(top, aes(width=tile_width, height=tile_height))
    + geom_tile(split_df, aes(width=tile_width / 2, height=tile_height))
    + geom_tile(bottom, aes(width=tile_width, height=tile_height))
    + inner_text(top)
    + inner_text(bottom)
    + geom_text(
        groupdf,
        aes("group", "y", label="group"),
        color="gray",
        nudge_y=0.525,
        va="bottom",
        fontweight="normal",
        size=9,
        inherit_aes=False,
    )  # new
    + scale_y_reverse(breaks=range(1, 8), limits=(0, 10.5))  # modified
    + scale_fill_brewer(type="qual", palette=3)
    + coord_equal(expand=False)
    + theme_void()
    + theme(
        figure_size=(12, 6),
        plot_margin_left=0.01,
        plot_margin_right=0.01,
        plot_background=element_rect(fill="white"),
        axis_text_y=element_text(margin={"r": 5}, color="gray", size=9),  # new
    )
)

What we could have done different:

After we set the x and y positions in th the top and bottom dataframes, we could have concatenated them back together. Then, that Layers trick would not save us much.

Pro tip: Save the plot as a pdf.

Annotated Heatmap

Conditinous data recorded at discrete time intervals over many cycles

Read data

flights = pd.read_csv("data/flights.csv")
months = flights["month"].unique()  # Months ordered January, ..., December
flights["month"] = pd.Categorical(flights["month"], categories=months)
flights.head()

	year	month	passengers
0	1949	January	112
1	1949	February	118
2	1949	March	132
3	1949	April	129
4	1949	May	121

# We use 'factor(year)' -- a discrete -- instead of 'year' so that all the years
# are displayed along the x-axis.
# The .95s create spacing between the tiles.

(
    ggplot(flights, aes("factor(year)", "month", fill="passengers"))
    + geom_tile(aes(width=0.95, height=0.95))
    + geom_text(aes(label="passengers"), size=9)
)

That looks like what we want, but it could do with a few tweaks. First the contrast between the tiles and the text is not good for the lower passenger numbers. We use pd.cut to partition the number of passengers into two discrete groups.

flights["p_group"] = pd.cut(
    flights["passengers"], (0, 300, 1000), labels=("low", "high")
)
flights.head()

	year	month	passengers	p_group
0	1949	January	112	low
1	1949	February	118	low
2	1949	March	132	low
3	1949	April	129	low
4	1949	May	121	low

(
    ggplot(flights, aes("factor(year)", "month", fill="passengers"))
    + geom_tile(aes(width=0.95, height=0.95))
    + geom_text(aes(label="passengers", color="p_group"), size=9, show_legend=False)  # modified
    + scale_color_manual(["white", "black"])  # new
)

Last tweaks, put January at the top and remove the axis ticks and plot background.

# Gallery, tiles

(
    ggplot(flights, aes("factor(year)", "month", fill="passengers"))
    + geom_tile(aes(width=0.95, height=0.95))
    + geom_text(aes(label="passengers", color="p_group"), size=9, show_legend=False)
    + scale_color_manual(["white", "black"])  # new
    + scale_y_discrete(limits=months[::-1])  # new
    + theme(  # new
        axis_ticks=element_blank(),
        panel_background=element_rect(fill="white"),
    )
)

You can get similar results if you replace

 + geom_tile(aes(width=.95, height=.95))
 + geom_text(aes(label='passengers', color='p_group'), size=9, show_legend=False)

with

+ geom_label(aes(label='passengers', color='p_group'), size=9, show_legend=False)

Credit: This example is a recreation of this seaborn example.

Source: Periodic Table

Parameters

See Also

Examples

Periodic Table

Now for some trick

Annotated Heatmap