matlab绘图
在matlab中，使用基本的绘图函数可以绘制出各种曲线图。matlab绘图简单易用，容易上手，能快速绘制出漂亮的图片。
matlab绘图基础：MATLAB 绘图 | 易安的小窝
matplotlib
matplotlib是受MATLAB的启发构建的。调用方法基本相同。
导入包
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
matplotlib.rc("font",family='times new roman')若图片中有中文字体，需设置为中文
matplotlib.rc("font",family='KaiTi')绘图时需要使用plt.show()显示出图像
可以通过设置%matplotlib inline默认显示图像
%matplotlib inline1. 基本二维绘图
1.1 绘制折线图
plt.figure(figsize=[3, 3]) # 设置图窗大小
plt.plot([1,3,6,7,12]) # 绘图outputs:
[<matplotlib.lines.Line2D at 0x2579751a430>]
1.2 绘制散点图
plt.figure(figsize=[4, 4])
plt.plot([1,3,5,7,9], [1,3,6,7,12], 'ro')outputs:
[<matplotlib.lines.Line2D at 0x2579736a310>]
其中plt.plot([1,3,5,7,9], [1,3,6,7,12], 'ro')中三个参数：
- [1,3,5,7,9] 横坐标
[1,3,6,7,12] 纵坐标
‘ro’: r表示颜色为红色，o表示点用o表示
绘图自定义样式：
1.3 绘制多组数据
plt.figure(figsize=[4, 4])
plt.plot([1,3,5,7,9], [1,3,6,7,12], 'ro')
plt.plot([2,4,6,8,10], [4,7,12,15,20], 'g-')outputs:
[<matplotlib.lines.Line2D at 0x25797540a30>]
1.4 绘制图例信息
查看当前plt的参数
plt.rcParams.keys()KeysView(RcParams({'_internal.classic_mode': False,
...
'font.cursive': ['Apple Chancery',
'Textile',
'Zapf Chancery',
'Sand',
'Script MT',
'Felipa',
'Comic Neue',
'Comic Sans MS',
'cursive'],
'font.family': ['Times New Roman'],
'font.fantasy': ['Chicago',
'Charcoal',
'Impact',
'Western',
'Humor Sans',
'xkcd',
'fantasy'],
'font.monospace': ['DejaVu Sans Mono',
'Bitstream Vera Sans Mono',
'Computer Modern Typewriter',
'Andale Mono',
'Nimbus Mono L',
'Courier New',
'Courier',
'Fixed',
'Terminal',
'monospace'],
'font.sans-serif': ['KaiTi'],
'font.serif': ['DejaVu Serif',
'Bitstream Vera Serif',
'Computer Modern Roman',
'New Century Schoolbook',
'Century Schoolbook L',
'Utopia',
'ITC Bookman',
'Bookman',
'Nimbus Roman No9 L',
'Times New Roman',
'Times',
'Palatino',
'Charter',
'serif'],
'font.size': 10.0,
...
'ytick.right': False}))from matplotlib.font_manager import FontProperties
plt.figure(figsize=[3,3])
kaiti = FontProperties("KaiTi") # 楷体字体
plt.rcParams['axes.unicode_minus'] = False # 显示负号
plt.plot([-1,-2,3,4,5], [1,2,3,6,10], 'g*',label="label1") # 绘图
plt.plot([1,3,4,-6,7], [5,1,3,7,9], 'ro',label="label2") # 绘图
plt.title('标题', fontproperties=kaiti) # 中文字体
plt.xlabel('X')
plt.ylabel('Y')
plt.legend(loc='best')
plt.show() 
1.5 子图绘制
from numpy.random import randint
kaiti = FontProperties("KaiTi", size=20) # 楷体字体
plt.figure(figsize=[16,4])
plt.suptitle("子图绘制", fontproperties=kaiti)
for t in range(4):
plt.subplot(1, 4, t+1)
plt.plot(randint(0, 10, 5), randint(0, 10, 5), 'g*',label="label1") # 绘图
plt.plot(randint(0, 10, 5), randint(0, 10, 5), 'ro',label="label2") # 绘图
plt.title('标题', fontproperties=kaiti) # 中文字体
plt.xlabel('X')
plt.ylabel('Y')
plt.legend(loc='best')
plt.show()
子图绘制布局
plt.figure()
plt.subplot(2,4,1)
plt.gca().set(ylim=(-2.0, 1), xlim=(0, 1))
plt.subplot(1,2,2)
plt.gca().set(ylim=(-2.0, 1), xlim=(0, 1))
plt.subplot(4,4,2)
plt.gca().set(ylim=(-2.0, 1), xlim=(0, 1))
plt.subplot(2,2,3)
plt.gca().set(ylim=(-2.0, 1), xlim=(0, 1))
plt.subplot(4,8,11)
plt.gca().set(ylim=(-2.0, 1), xlim=(0, 1))
plt.subplot(4,8,12)
plt.gca().set(ylim=(-2.0, 1), xlim=(0, 1))[(-2.0, 1.0), (0.0, 1.0)]
3. 使用axes绘图
plt绘图和axes绘图的区别：
- plt绘图：先生成了一个Figure画布，然后在这个画布上隐式生成一个画图区域进行画图。
- axes绘图：同时生成了Figure和axes两个对象，然后用axes对象在其区域内进行绘图。
figure相当于画布，axes相当于画布中的某个区域。
二者绘制的图像是一样的，但是对图像的零部件进行绘制时使用axes绘图更加方便。
3.1 axes绘图区域
fig = plt.figure()
ax1 = plt.subplot2grid((3,3), (0,0), colspan=2, rowspan=2)
ax1 = plt.subplot2grid((3,3), (0,2), rowspan=3)
ax2 = plt.subplot2grid((3,3), (2,0))
ax3 = plt.subplot2grid((3,3), (2,1))
3.2 axes绘图
kaiti = FontProperties("KaiTi", size=12) # 楷体字体
x = np.arange(-2*np.pi, 2*np.pi, 0.01)
y1 = np.sin(x)
y2 = np.cos(x)
fig = plt.figure(figsize=(10, 5))
ax1, ax2 = fig.subplots(1, 2)
ax1.plot(x, y1, label='label1')
ax1.plot(x, y2, label='label2')
ax1.legend(loc='best')
ax1.set_xlim(-2 * np.pi - 1, 2 * np.pi + 3)
ax1.set_xticks([-2 * np.pi, -np.pi, 0, np.pi, 2 * np.pi])
ax1.set_xticklabels(['-pi/2', '-pi', '0', 'pi', 'pi/2'])
ax1.set_title('标题', fontproperties=kaiti)
ax1.set_xlabel('x轴', fontproperties=kaiti)
ax1.set_ylabel('y轴', fontproperties=kaiti)
ax1.axhline(y=0, c='black')
ax1.axvline(x=0, c='black')
ax2.matshow(np.random.randint(0, 255, (12, 12)), vmin=0, vmax=255, cmap='gray')
fig.suptitle("Axes绘图示例", fontproperties=kaiti)
plt.show()
4. 函数绘图
4.1 三角函数
plt.figure(figsize=[16, 4])
plt.subplot(1, 4, 1)
x = np.array([1,2,3,4,5])
y = np.sin(x)
plt.plot(x, y)
plt.subplot(1, 4, 2)
x = np.array([1,2,3,4,5])
y = np.cos(x)
plt.plot(x, y)
plt.subplot(1, 4, 3)
x = np.array([1,2,3,4,5])
y = np.arctan(x)
plt.plot(x, y)
plt.subplot(1, 4, 4)
t=np.arange(0, 2*np.pi, np.pi/50)
x=np.sin(t);
y=np.cos(t);
plt.plot(x, y)outputs:
[<matplotlib.lines.Line2D at 0x2579a44dfa0>]
4.2 柱状图直方图饼图阶梯图
data = pd.read_csv("data/Eueo2012.csv")dataoutputs:
| Team | Goals | Shots on target | Shots off target | Shooting Accuracy | % Goals-to-shots | Total shots (inc. Blocked) | Hit Woodwork | Penalty goals | Penalties not scored | ... | Saves made | Saves-to-shots ratio | Fouls Won | Fouls Conceded | Offsides | Yellow Cards | Red Cards | Subs on | Subs off | Players Used | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Croatia | 4 | 13 | 12 | 51.9% | 16.0% | 32 | 0 | 0 | 0 | ... | 13 | 81.3% | 41 | 62 | 2 | 9 | 0 | 9 | 9 | 16 |
| 1 | Czech Republic | 4 | 13 | 18 | 41.9% | 12.9% | 39 | 0 | 0 | 0 | ... | 9 | 60.1% | 53 | 73 | 8 | 7 | 0 | 11 | 11 | 19 |
| 2 | Denmark | 4 | 10 | 10 | 50.0% | 20.0% | 27 | 1 | 0 | 0 | ... | 10 | 66.7% | 25 | 38 | 8 | 4 | 0 | 7 | 7 | 15 |
| 3 | England | 5 | 11 | 18 | 50.0% | 17.2% | 40 | 0 | 0 | 0 | ... | 22 | 88.1% | 43 | 45 | 6 | 5 | 0 | 11 | 11 | 16 |
| 4 | France | 3 | 22 | 24 | 37.9% | 6.5% | 65 | 1 | 0 | 0 | ... | 6 | 54.6% | 36 | 51 | 5 | 6 | 0 | 11 | 11 | 19 |
| 5 | Germany | 10 | 32 | 32 | 47.8% | 15.6% | 80 | 2 | 1 | 0 | ... | 10 | 62.6% | 63 | 49 | 12 | 4 | 0 | 15 | 15 | 17 |
| 6 | Greece | 5 | 8 | 18 | 30.7% | 19.2% | 32 | 1 | 1 | 1 | ... | 13 | 65.1% | 67 | 48 | 12 | 9 | 1 | 12 | 12 | 20 |
| 7 | Italy | 6 | 34 | 45 | 43.0% | 7.5% | 110 | 2 | 0 | 0 | ... | 20 | 74.1% | 101 | 89 | 16 | 16 | 0 | 18 | 18 | 19 |
| 8 | Netherlands | 2 | 12 | 36 | 25.0% | 4.1% | 60 | 2 | 0 | 0 | ... | 12 | 70.6% | 35 | 30 | 3 | 5 | 0 | 7 | 7 | 15 |
| 9 | Poland | 2 | 15 | 23 | 39.4% | 5.2% | 48 | 0 | 0 | 0 | ... | 6 | 66.7% | 48 | 56 | 3 | 7 | 1 | 7 | 7 | 17 |
| 10 | Portugal | 6 | 22 | 42 | 34.3% | 9.3% | 82 | 6 | 0 | 0 | ... | 10 | 71.5% | 73 | 90 | 10 | 12 | 0 | 14 | 14 | 16 |
| 11 | Republic of Ireland | 1 | 7 | 12 | 36.8% | 5.2% | 28 | 0 | 0 | 0 | ... | 17 | 65.4% | 43 | 51 | 11 | 6 | 1 | 10 | 10 | 17 |
| 12 | Russia | 5 | 9 | 31 | 22.5% | 12.5% | 59 | 2 | 0 | 0 | ... | 10 | 77.0% | 34 | 43 | 4 | 6 | 0 | 7 | 7 | 16 |
| 13 | Spain | 12 | 42 | 33 | 55.9% | 16.0% | 100 | 0 | 1 | 0 | ... | 15 | 93.8% | 102 | 83 | 19 | 11 | 0 | 17 | 17 | 18 |
| 14 | Sweden | 5 | 17 | 19 | 47.2% | 13.8% | 39 | 3 | 0 | 0 | ... | 8 | 61.6% | 35 | 51 | 7 | 7 | 0 | 9 | 9 | 18 |
| 15 | Ukraine | 2 | 7 | 26 | 21.2% | 6.0% | 38 | 0 | 0 | 0 | ... | 13 | 76.5% | 48 | 31 | 4 | 5 | 0 | 9 | 9 | 18 |
16 rows × 35 columns
plt.figure(figsize=[16, 4])
x = data["Goals"]
y = data["Shots on target"]
plt.subplot(1, 4, 1)
plt.bar(x,y, width = 0.3,color = 'red') # 柱状图
x = data["Goals"]
plt.subplot(1, 4, 2)
plt.hist(x,bins=10, rwidth=0.8,histtype='barstacked',color='r') # 柱状图
x = data["% Goals-to-shots"].str.strip("%").astype(float) / 100 # %转数字
label = data["Team"] #定义饼图的标签
plt.subplot(1, 4, 3)
plt.pie(x, labels=label,autopct='%.3f%%')
x = [1,2,3,4,5]
y = [3,5,7,9,13]
plt.subplot(1, 4, 4)
plt.step(x, y)outputs:
[<matplotlib.lines.Line2D at 0x257a50507c0>]
5.特殊图形
5.1 气泡图
midwest = pd.read_csv("data/midwest_filter.csv")
kaiti = FontProperties("KaiTi", size=24) # 楷体字体
fig = plt.figure(figsize=(14, 7), dpi= 80, facecolor='w', edgecolor='k')
plt.scatter('area', 'poptotal', data=midwest,
s='dot_size', c='popdensity',
cmap='Reds', edgecolors='black', linewidths=.5)
plt.title("气泡图 PopTotal vs Area", fontproperties=kaiti)
plt.xlabel('面积', fontsize=18, fontproperties=kaiti)
plt.ylabel('人口总数', fontsize=18, fontproperties=kaiti)
plt.colorbar()
plt.show()
5.2 气泡彩图
midwest = pd.read_csv("data/midwest_filter.csv")
kaiti = FontProperties("KaiTi", size=24) # 楷体字体
fig = plt.figure(figsize=(14, 9), dpi= 80, facecolor='w', edgecolor='k')
colors = plt.cm.tab20.colors
categories = np.unique(midwest['category'])
for i, category in enumerate(categories):
plt.scatter('area', 'poptotal', data=midwest.loc[midwest.category==category, :],
s='dot_size', label=str(category), edgecolors='black', linewidths=.5)
for dot_size in [100, 300, 1000]:
plt.scatter([], [], c='k', alpha=0.5, s=dot_size, label=str(dot_size) + ' TotalPop')
plt.legend(loc='upper right', scatterpoints=1,
frameon=False, labelspacing=2,
title='Saravana Stores', fontsize=8)
plt.title("气泡彩图 PopTotal vs Area", fontproperties=kaiti)
plt.xlabel('面积', fontsize=18, fontproperties=kaiti)
plt.ylabel('人口总数', fontsize=18, fontproperties=kaiti)
plt.show()
5.3 时间序列
plt.figure()
ax = plt.subplot2grid((2,4), (0,0), colspan=8)
x = np.linspace(-0.75, 1., 100)
dates = pd.date_range('2018-01-01', periods = len(x))
ax.plot(dates, x + np.random.randn(len(x)))
ax.set_xticks(dates[::30])
ax.set_xticklabels(dates.strftime('%Y-%m-%d')[::30])
ax.set_title("Time Series")outputs:
Text(0.5, 1.0, 'Time Series')
