排序

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from plotnine import *

Lollipop

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_group = mpg.loc[:, ["cty", "manufacturer"]].groupby("manufacturer").mean("city")
mpg_group.head()

	cty
manufacturer
audi	17.611111
chevrolet	15.000000
dodge	13.135135
ford	14.000000
honda	24.444444

mpg_group = mpg_group.sort_values("cty")
mpg_group = mpg_group.reset_index()
mpg_group.head()

	manufacturer	cty
0	lincoln	11.333333
1	land rover	11.500000
2	dodge	13.135135
3	mercury	13.250000
4	jeep	13.500000

(
    ggplot(mpg_group, aes(x="manufacturer", y="cty", label="cty"))
    + geom_point(size=3, color="firebrick")
    + geom_segment(
        aes(x="manufacturer", xend="manufacturer", y=0, yend="cty"), color="firebrick"
    )
    + geom_text(color="black", size=4, nudge_y=1, format_string="{:.2f}")
    + labs(title="Lollipop Chart", subtitle="Manufacturer Vs Avg. Mileage")
    + scale_x_discrete(limits=mpg_group.manufacturer)
    + theme(axis_text_x=element_text(angle=45, vjust=1))
    + ylim(0, 30)
)

Slope

gdp = pd.read_csv("data/gdp_per_cap.csv")
gdp.head()

	continent	1952	1957
0	Africa	1252.572466	1385.236062
1	Americas	4079.062552	4616.043733
2	Asia	5195.484004	4003.132940
3	Europe	5661.057435	6963.012816
4	Oceania	10298.085650	11598.522455

gdp_new = gdp.melt(
    id_vars=["continent"],
    value_vars=["1952", "1957"],
    var_name="time",
    value_name="total",
    ignore_index=False,
)
gdp_new["continent"] = gdp_new["continent"].astype("category")
gdp_new

	continent	time	total
0	Africa	1952	1252.572466
1	Americas	1952	4079.062552
2	Asia	1952	5195.484004
3	Europe	1952	5661.057435
4	Oceania	1952	10298.085650
0	Africa	1957	1385.236062
1	Americas	1957	4616.043733
2	Asia	1957	4003.132940
3	Europe	1957	6963.012816
4	Oceania	1957	11598.522455

_, ax = plt.subplots(figsize=(10, 5))

gdp_new.groupby("continent").plot.line(x="time", y="total", ax=ax)

for i in [0, 1]:
    ax.vlines(
        x=i,
        ymin=gdp_new["total"].min(),
        ymax=gdp_new["total"].max(),
        colors="k",
        linestyle="dotted",
    )

ax.set(
    xlabel="Time",
    ylabel="Mean GDP Per Capita",
    title="Slopechart: Comparing GDP Per Capita between 1952 vs 1957\n",
    xticks=[0, 1],
    xticklabels=["1952", "1957"],
)

ax.spines[["right", "top", "left"]].set_visible(False)

ax.legend(
    gdp_new["continent"].unique(),
    loc="center",
    bbox_to_anchor=(0.5, 1),
    ncol=len(gdp_new["continent"].unique()),
    fontsize="small",
)
plt.show()

/tmp/ipykernel_2025/1178036945.py:3: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

gdp["class"] = np.where(gdp["1957"] - gdp["1952"] < 0, "red", "green")

p = (
    ggplot(gdp)
    + geom_segment(aes(x=1, xend=2, y="1952", yend="1957", color="class"), size=0.75)
    + geom_vline(xintercept=1, linetype="dashed", size=0.1)
    + geom_vline(xintercept=2, linetype="dashed", size=0.1)
    + scale_color_manual(
        labels=["Up", "Down"], values={"green": "#00ba38", "red": "#f8766d"}
    )
    + labs(x="", y="Mean GdpPerCap")
    + xlim(0.5, 2.5)
    + theme(
        panel_background=element_blank(),
        panel_grid=element_blank(),
        axis_ticks=element_blank(),
        # axis_text_x=element_blank(),
        panel_border=element_blank(),
        legend_position="none",
    )
)

p