分组

import matplotlib.pyplot as plt
import numpy as np
import polars as pl
from matplotlib import patches
from scipy.spatial import ConvexHull

Bar

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

shape: (5, 11)

manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class
str	str	f64	i64	i64	str	str	i64	i64	str	str
"audi"	"a4"	1.8	1999	4	"auto(l5)"	"f"	18	29	"p"	"compact"
"audi"	"a4"	1.8	1999	4	"manual(m5)"	"f"	21	29	"p"	"compact"
"audi"	"a4"	2.0	2008	4	"manual(m6)"	"f"	20	31	"p"	"compact"
"audi"	"a4"	2.0	2008	4	"auto(av)"	"f"	21	30	"p"	"compact"
"audi"	"a4"	2.8	1999	6	"auto(l5)"	"f"	16	26	"p"	"compact"

mpg_group = (
    mpg[:, ["cty", "manufacturer"]].group_by("manufacturer").mean().sort(by="cty")
)
mpg_group.head()

shape: (5, 2)

manufacturer	cty
str	f64
"lincoln"	11.333333
"land rover"	11.5
"dodge"	13.135135
"mercury"	13.25
"jeep"	13.5

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

x = mpg_group["manufacturer"].str.to_uppercase()
y = mpg_group["cty"]

ax.bar(x=x, height=y)

for i, cty in enumerate(y):
    ax.text(i, cty + 0.5, round(cty, 1), horizontalalignment="center")

p1 = patches.Rectangle(
    (0.57, -0.005),
    width=0.33,
    height=0.13,
    alpha=0.1,
    facecolor="green",
    transform=fig.transFigure,
)
p2 = patches.Rectangle(
    (0.124, -0.005),
    width=0.446,
    height=0.13,
    alpha=0.1,
    facecolor="red",
    transform=fig.transFigure,
)
fig.add_artist(p1)
fig.add_artist(p2)

plt.setp(ax.get_xticklabels(), rotation=60, horizontalalignment="right")
ax.set(ylim=(0, 30), ylabel="Miles Per Gallon", title="Bar Chart for Highway Mileage")

[(0.0, 30.0),
 Text(0, 0.5, 'Miles Per Gallon'),
 Text(0.5, 1.0, 'Bar Chart for Highway Mileage')]

Dendrogram

from scipy.cluster import hierarchy

arrests = pl.read_csv("data/us_arrests.csv")
arrests.head()

shape: (5, 5)

Murder	Assault	UrbanPop	Rape	State
f64	i64	i64	f64	str
13.2	236	58	21.2	"Alabama"
10.0	263	48	44.5	"Alaska"
8.1	294	80	31.0	"Arizona"
8.8	190	50	19.5	"Arkansas"
9.0	276	91	40.6	"California"

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

dend = hierarchy.dendrogram(
    hierarchy.linkage(
        arrests[["Murder", "Assault", "UrbanPop", "Rape"]], method="ward"
    ),
    labels=arrests["State"].to_numpy(),
    color_threshold=100,
)

ax.set(title="USArrests Dendograms")
plt.show()

Encircling

midwest = pl.read_csv("data/midwest_filter.csv")
midwest.with_columns(
    popdensity=midwest["poptotal"] / 100, state=midwest["state"].cast(pl.Categorical)
)
midwest.head()

shape: (5, 29)

PID	county	state	area	poptotal	popdensity	popwhite	popblack	popamerindian	popasian	popother	percwhite	percblack	percamerindan	percasian	percother	popadults	perchsd	percollege	percprof	poppovertyknown	percpovertyknown	percbelowpoverty	percchildbelowpovert	percadultpoverty	percelderlypoverty	inmetro	category	dot_size
i64	str	str	f64	i64	f64	i64	i64	i64	i64	i64	f64	f64	f64	f64	f64	i64	f64	f64	f64	i64	f64	f64	f64	f64	f64	i64	str	f64
561	"ADAMS"	"IL"	0.052	66090	1270.96154	63917	1702	98	249	124	96.712059	2.575276	0.148283	0.376759	0.187623	43298	75.107395	19.631392	4.355859	63628	96.274777	13.151443	18.011717	11.009776	12.443812	0	"AAR"	250.944411
562	"ALEXANDER"	"IL"	0.014	10626	759.0	7054	3496	19	48	9	66.38434	32.900433	0.1788067	0.451722	0.084698	6724	59.726353	11.243308	2.870315	10529	99.087145	32.244278	45.826514	27.385647	25.228976	0	"LHR"	185.78126
563	"BOND"	"IL"	0.022	14991	681.409091	14477	429	35	16	34	96.571276	2.861717	0.233473	0.106731	0.226803	9669	69.334988	17.033819	4.488572	14235	94.956974	12.068844	14.036061	10.85209	12.69741	0	"AAR"	175.905385
564	"BOONE"	"IL"	0.017	30806	1812.11765	29344	127	46	150	1139	95.254171	0.412257	0.149322	0.486918	3.697332	19272	75.472188	17.278954	4.1978	30337	98.477569	7.209019	11.179536	5.536013	6.217047	1	"ALU"	319.823487
565	"BROWN"	"IL"	0.018	5836	324.222222	5264	547	14	5	6	90.198766	9.372858	0.23989	0.085675	0.10281	3979	68.861523	14.475999	3.36768	4815	82.50514	13.520249	13.022889	11.143211	19.2	0	"AAR"	130.442161

midwest_select = midwest.filter(pl.col("state") == "IN")
midwest_select.head()

shape: (5, 29)

PID	county	state	area	poptotal	popdensity	popwhite	popblack	popamerindian	popasian	popother	percwhite	percblack	percamerindan	percasian	percother	popadults	perchsd	percollege	percprof	poppovertyknown	percpovertyknown	percbelowpoverty	percchildbelowpovert	percadultpoverty	percelderlypoverty	inmetro	category	dot_size
i64	str	str	f64	i64	f64	i64	i64	i64	i64	i64	f64	f64	f64	f64	f64	i64	f64	f64	f64	i64	f64	f64	f64	f64	f64	i64	str	f64
663	"ADAMS"	"IN"	0.021	31095	1480.71429	30530	36	42	60	427	98.182988	0.115774	0.13507	0.192957	1.373211	18119	74.397042	16.11568	4.862299	30490	98.05435	11.636602	17.194524	9.101888	8.714027	1	"AAU"	277.642023
665	"BARTHOLOMEW"	"IN"	0.022	63657	2893.5	61774	1005	97	610	171	97.041959	1.578774	0.152379	0.958261	0.268627	41218	76.903295	22.846815	6.844097	62784	98.628588	8.545171	10.736855	6.99242	10.811943	0	"AAR"	457.463283
666	"BENTON"	"IN"	0.024	9441	393.375	9389	6	16	1	29	99.449211	0.063553	0.169474	0.0105921	0.307171	6053	77.085743	13.464398	4.014538	9300	98.506514	8.043011	8.349218	6.842329	10.502283	0	"AAR"	139.24402
667	"BLACKFORD"	"IN"	0.01	14067	1406.7	13978	7	44	16	22	99.367314	0.049762	0.3127888	0.113741	0.1563944	9259	72.988444	12.981963	4.428124	13903	98.834151	9.853988	12.323745	8.332247	10.9375	0	"AAR"	268.221385
668	"BOONE"	"IN"	0.024	38147	1589.45833	37814	83	90	94	66	99.127061	0.217579	0.235929	0.246415	0.173015	24915	82.468393	27.830624	8.813967	37402	98.047029	6.296455	8.021754	5.239599	7.089425	1	"HLU"	291.48311

_, ax = plt.subplots(figsize=(10, 8))

categories = np.unique(midwest["category"])
colors = [plt.cm.tab10(i / float(len(categories) - 1)) for i in range(len(categories))]

for i, category in enumerate(categories):
    plt.scatter(
        "area",
        "poptotal",
        data=midwest.filter(pl.col("category") == category),
        s="dot_size",
        color=colors[i],
        label=str(category),
        edgecolors="black",
        linewidths=0.5,
    )


# Encircling
def encircle(x, y, ax=None, **kw):
    ax = ax or plt.gca()
    p = np.stack([x, y], axis=1)
    hull = ConvexHull(p)
    poly = patches.Polygon(xy=p[hull.vertices, :], closed=True, **kw)
    ax.add_patch(poly)


ax.set(
    xlim=(0.0, 0.1),
    ylim=(0, 90000),
    xlabel="Area",
    ylabel="Population",
    title="Bubble Plot with Encircling",
)

x = midwest_select["area"]
y = midwest_select["poptotal"]

# Draw polygon surrounding vertices
encircle(x, y, ec="k", fc="gold", alpha=0.1, ax=ax)
encircle(x, y, ec="firebrick", fc="none", lw=1.5, ax=ax)
ax.legend()

<matplotlib.legend.Legend at 0x7fd0c4c674d0>