占比

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

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

Simple Pie Chart

data = {
    "Scale": [
        ">300,000 tons per year",
        "100,000~300,000 tons per year",
        "<100,000 tons per year",
    ],
    "Proportion": ["0.2623", "0.2459", "0.4918"],
}

df = pd.DataFrame(data)  # noqa: PD901
df.head()

	Scale	Proportion
0	>300,000 tons per year	0.2623
1	100,000~300,000 tons per year	0.2459
2	<100,000 tons per year	0.4918

plt.pie(df.Proportion, colors=["#bd4634", "#7fbd34bd", "#348ABD"], autopct="%.2f%%")

plt.legend(
    df.Scale, title="Scale", bbox_to_anchor=(0.5, -0.45, 0.4, 0.5), frameon=False
)

<matplotlib.legend.Legend at 0x7fea6f9a4750>

Pie Chart with Convex

mpg_group = mpg.groupby("class").size().reset_index(name="counts")

_, ax = plt.subplots(figsize=(12, 7), subplot_kw={"aspect": "equal"})

data = mpg_group["counts"]
categories = mpg_group["class"]
explode = [0, 0, 0, 0, 0, 0.1, 0]


def func(pct, allvals) -> str:
    absolute = int(pct / 100.0 * np.sum(allvals))
    return f"{pct:.1f}% ({absolute})"


wedges, texts, autotexts = ax.pie(
    data,
    autopct=lambda pct: func(pct, data),
    textprops={"color": "w"},
    colors=plt.cm.Dark2.colors,
    startangle=140,
    explode=explode,
)

ax.legend(
    wedges,
    categories,
    title="Vehicle Class",
    loc="center left",
    bbox_to_anchor=(1, 0, 0.5, 1),
)
plt.setp(autotexts, size=10, weight=700)
ax.set(title="Class of Vehicles: Pie Chart")
plt.show()

Pie Chart with Wedges

colors = ["#f94144", "#f8961e", "#f9c74f", "#90be6d", "#2b1ac9", "#2994e0", "#94428d"]
wedgeprops = {
    "edgecolor": "k",
    "linewidth": 2,
    "linestyle": "solid",
    "antialiased": True,
}

categories = mpg_group["class"]
data = mpg_group["counts"]

_, ax = plt.subplots(figsize=(6, 6), subplot_kw={"aspect": "equal"})

wedges, texts, autotexts = ax.pie(
    data,
    colors=colors,
    autopct="%1.1f%%",
    startangle=60,
    pctdistance=0.75,
    wedgeprops=wedgeprops,
    textprops={"fontsize": 10, "color": "white"},
)

ax.set(title="Class of Vehicles: Pie Chart")
bbox_props = {"boxstyle": "square, pad=0.3", "fc": "w", "ec": "w", "lw": 0.5}

kw = {
    "arrowprops": {"arrowstyle": "-"},
    "bbox": bbox_props,
    "zorder": 0,
    "va": "center",
}

for k, p in enumerate(wedges):
    ang = (p.theta2 - p.theta1) / 2.0 + p.theta1
    y = np.sin(np.deg2rad(ang))
    x = np.cos(np.deg2rad(ang))
    horizontalalignment = {-1: "right", 1: "left"}[int(np.sign(x))]
    connectionstyle = f"angle, angleA=0, angleB={ang}"
    kw["arrowprops"].update({"connectionstyle": connectionstyle})
    ax.annotate(
        categories[k],
        xy=(x, y),
        xytext=(1.2 * np.sign(x), 1.2 * y),
        horizontalalignment=horizontalalignment,
        **kw,
    )

plt.show()