相关

import pandas as pd
from plotnine import *

Regression Plot

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

	manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class
0	audi	a4	1.8	1999	4	auto(l5)	f	18	29	p	compact
1	audi	a4	1.8	1999	4	manual(m5)	f	21	29	p	compact
2	audi	a4	2.0	2008	4	manual(m6)	f	20	31	p	compact
3	audi	a4	2.0	2008	4	auto(av)	f	21	30	p	compact
4	audi	a4	2.8	1999	6	auto(l5)	f	16	26	p	compact

mpg_select = mpg.query("cyl in [4, 8]")

(
    ggplot(mpg_select, aes(x="displ", y="hwy", color="cyl", fill="cyl"))
    + geom_point(size=2)
    + geom_smooth(method="lm", size=1)
    + facet_wrap("cyl")
    + lims(x=(0, 8), y=(0, 50))
    + labs(title="Scatterplot with line of best fit grouped by number of cylinders")
    + theme(plot_title=element_text(hjust=0.5), legend_position="none")
    + scale_color_gradient(low="#E69F00", high="#56B4E9")
    + scale_fill_gradient(low="#E69F00", high="#56B4E9")
)

Jittering

g = ggplot(mpg, aes(x="cty", y="hwy"))

(
    g
    + geom_jitter(height=0.5, size=2, color="#06527f")
    + labs(title="Jittered Points")
    + theme(plot_title=element_text(hjust=0.5))
)

(
    g
    + geom_count(color="red")
    + labs(title="Counts Plot")
    + theme(plot_title=element_text(hjust=0.5), legend_position="none")
)

FacetGrid

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

	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb	fast	cars
0	4.582576	6	160.0	110	3.90	2.620	16.46	0	1	4	4	1	Mazda RX4
1	4.582576	6	160.0	110	3.90	2.875	17.02	0	1	4	4	1	Mazda RX4 Wag
2	4.774935	4	108.0	93	3.85	2.320	18.61	1	1	4	1	1	Datsun 710
3	4.626013	6	258.0	110	3.08	3.215	19.44	1	0	3	1	1	Hornet 4 Drive
4	4.324350	8	360.0	175	3.15	3.440	17.02	0	0	3	2	1	Hornet Sportabout

(
    ggplot(mtcars, aes("mpg", "wt"))
    + geom_point(color="#06527f")
    + facet_wrap(["am", "vs"])
)

Heatmap

corr_matrix = mtcars.corr(numeric_only=True)
corr_matrix.head()

	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb	fast
mpg	1.000000	-0.858539	-0.867536	-0.787309	0.680312	-0.883453	0.420317	0.669260	0.593153	0.487226	-0.553703	0.730748
cyl	-0.858539	1.000000	0.902033	0.832447	-0.699938	0.782496	-0.591242	-0.810812	-0.522607	-0.492687	0.526988	-0.695182
disp	-0.867536	0.902033	1.000000	0.790949	-0.710214	0.887980	-0.433698	-0.710416	-0.591227	-0.555569	0.394977	-0.732073
hp	-0.787309	0.832447	0.790949	1.000000	-0.448759	0.658748	-0.708223	-0.723097	-0.243204	-0.125704	0.749812	-0.751422
drat	0.680312	-0.699938	-0.710214	-0.448759	1.000000	-0.712441	0.091205	0.440278	0.712711	0.699610	-0.090790	0.400430

corr_matrix2 = (
    corr_matrix.reset_index()
    .melt(id_vars="index")
    .sort_values("value", ascending=False)
)
corr_matrix2.head()

	index	variable	value
0	mpg	mpg	1.0
13	cyl	cyl	1.0
130	carb	carb	1.0
78	qsec	qsec	1.0
52	drat	drat	1.0

sort_seq = corr_matrix2.variable.unique()

(
    ggplot(corr_matrix2, aes("index", "variable", fill="value"))
    + geom_tile(aes(width=0.95, height=0.95))
    + scale_fill_gradient(low="#0c71b0", high="#ff0000")
    + geom_text(aes(label="value"), size=8, format_string="{:.2f}", color="white")
    + labs(title="Correlogram of mtcars", x="", y="")
    + scale_x_discrete(limits=sort_seq[::-1])
    + scale_y_discrete(limits=sort_seq)
    + coord_fixed()
)

Marginal Plot

g1 = ggplot(mpg) + geom_point(aes("displ", "hwy", color="manufacturer"))
g2 = ggplot(mpg) + geom_boxplot(aes("displ", "hwy"))
g3 = ggplot(mpg) + geom_boxplot(aes("hwy", "displ")) + coord_flip()
g1