排序#
import matplotlib.pyplot as plt
import numpy as np
import polars as pl
from plotnine import *
Lollipop#
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").agg(pl.col("cty").mean())
)
mpg_group.head()
shape: (5, 2)
| manufacturer | cty |
|---|---|
| str | f64 |
| "honda" | 24.444444 |
| "jeep" | 13.5 |
| "toyota" | 18.529412 |
| "ford" | 14.0 |
| "subaru" | 19.285714 |
mpg_group = mpg_group.sort(by="cty").with_row_index()
mpg_group.head()
shape: (5, 3)
| index | manufacturer | cty |
|---|---|---|
| u32 | str | f64 |
| 0 | "lincoln" | 11.333333 |
| 1 | "land rover" | 11.5 |
| 2 | "dodge" | 13.135135 |
| 3 | "mercury" | 13.25 |
| 4 | "jeep" | 13.5 |
(
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 = pl.read_csv("data/gdp_per_cap.csv")
gdp.head()
shape: (5, 3)
| continent | 1952 | 1957 |
|---|---|---|
| str | f64 | f64 |
| "Africa" | 1252.572466 | 1385.236062 |
| "Americas" | 4079.062552 | 4616.043733 |
| "Asia" | 5195.484004 | 4003.13294 |
| "Europe" | 5661.057435 | 6963.012816 |
| "Oceania" | 10298.08565 | 11598.522455 |
gdp_new = gdp.unpivot(
on=["1952", "1957"], index="continent", variable_name="time", value_name="total"
).with_columns(continent=pl.col("continent").cast(pl.Categorical))
gdp_new
shape: (10, 3)
| continent | time | total |
|---|---|---|
| cat | str | f64 |
| "Africa" | "1952" | 1252.572466 |
| "Americas" | "1952" | 4079.062552 |
| "Asia" | "1952" | 5195.484004 |
| "Europe" | "1952" | 5661.057435 |
| "Oceania" | "1952" | 10298.08565 |
| "Africa" | "1957" | 1385.236062 |
| "Americas" | "1957" | 4616.043733 |
| "Asia" | "1957" | 4003.13294 |
| "Europe" | "1957" | 6963.012816 |
| "Oceania" | "1957" | 11598.522455 |
_, ax = plt.subplots(figsize=(10, 5))
for cont in gdp_new["continent"]:
gdp_newi = gdp_new.filter(pl.col("continent") == cont)
ax.plot(gdp_newi["time"], gdp_newi["total"], label=cont)
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.legend(
gdp_new["continent"].unique(),
loc="center",
bbox_to_anchor=(0.5, 1),
ncol=len(gdp_new["continent"].unique()),
fontsize="small",
)
ax.spines[["right", "top", "left"]].set_visible(False)
gdp = gdp.with_columns(Class=np.where(gdp["1957"] - gdp["1952"] < 0, "red", "green"))
gdp
shape: (5, 4)
| continent | 1952 | 1957 | Class |
|---|---|---|---|
| str | f64 | f64 | str |
| "Africa" | 1252.572466 | 1385.236062 | "green" |
| "Americas" | 4079.062552 | 4616.043733 | "green" |
| "Asia" | 5195.484004 | 4003.13294 | "red" |
| "Europe" | 5661.057435 | 6963.012816 | "green" |
| "Oceania" | 10298.08565 | 11598.522455 | "green" |
(
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",
)
)
