Matplotlib可视化--有序条形图（笔记）

该例子来自于机器学习网站MachineLearning Plus上的博文：Python可视化50图

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

准备数据

df_raw = pd.read_csv('https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv')

df_raw

	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
...	...	...	...	...	...	...	...	...	...	...	...
229	volkswagen	passat	2.0	2008	4	auto(s6)	f	19	28	p	midsize
230	volkswagen	passat	2.0	2008	4	manual(m6)	f	21	29	p	midsize
231	volkswagen	passat	2.8	1999	6	auto(l5)	f	16	26	p	midsize
232	volkswagen	passat	2.8	1999	6	manual(m5)	f	18	26	p	midsize
233	volkswagen	passat	3.6	2008	6	auto(s6)	f	17	26	p	midsize

234 rows × 11 columns

name = ["汽车制造商","型号名称","发动机排量(L)","制造年份","气缸数量","手动/自动"
              ,"驱动类型","城市里程/加仑","公路里程/加仑","汽油种类","车辆种类"]

条形图中x轴是汽车的型号，y轴是manufacturer（型号名称）的平均cty（城市里程/加仑），x轴和y轴的取值过程如下：

df = df_raw[['cty','manufacturer']].groupby('manufacturer').mean()

df.head()

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

df.sort_values('cty', inplace=True)  # 如果为True，则就地执行操作

df.head()

manufacturer	cty
lincoln	11.333333
land rover	11.500000
dodge	13.135135
mercury	13.250000
jeep	13.500000

df.reset_index(inplace=True)

df.head()

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

画图

fig, ax = plt.subplots(figsize=(16,10), facecolor='white', dpi=80)
ax.vlines(x=df.index, ymin=0, ymax=df.cty, color='firebrick', alpha=0.7, linewidth=20);

源码整合

# Draw plot
import matplotlib.patches as patches

fig, ax = plt.subplots(figsize=(16,10), facecolor='white', dpi= 80)
ax.vlines(x=df.index, ymin=0, ymax=df.cty, color='firebrick', alpha=0.7, linewidth=20)
'''
画条形图
x:           横坐标
ymin:        纵坐标的起点
ymax:        纵坐标的终点
color:       颜色
alpha:       饱和度
linewidth:   图的宽度
'''

# Annotate Text 文本注释
for i, cty in enumerate(df.cty):
    ax.text(i, cty+0.5, round(cty, 1), horizontalalignment='center')

# Title, Label, Ticks and Ylim
ax.set_title('城市里程柱状图', fontdict={'size':22})
ax.set(ylabel='城市公里/加仑', ylim=(0, 30))
plt.xticks(df.index, df.manufacturer.str.upper(), rotation=60, horizontalalignment='right', fontsize=12)

# Add patches to color the X axis labels 添加补丁以使X轴标签着色
p1 = patches.Rectangle((.57, -0.005), width=.33, height=.13, alpha=.1, facecolor='green', transform=fig.transFigure)
p2 = patches.Rectangle((.124, -0.005), width=.446, height=.13, alpha=.1, facecolor='red', transform=fig.transFigure)
'''
matplotlib.patches.Rectangle(xy, width, height, angle=0.0, **kwargs)
xy: 2元组 矩形左下角xy坐标
width:矩形的宽度
height:矩形的高度
angle: float, 可选，矩形相对于x轴逆时针旋转角度，默认0
fill: bool, 可选，是否填充矩形
transform=fig.transFigure #确保矩形显示在图像最上方，如果我们对fig作画，不会被ax挡住
'''

fig.add_artist(p1)
fig.add_artist(p2)
plt.show()

代码中ylable、xticks、text、title对应条形图中的部分如图：