Python基础（十二） | 还不会python绘图？两万字博文教你Matplotlib库（超详细总结）

⭐本专栏旨在对Python的基础语法进行详解，精炼地总结语法中的重点，详解难点，面向零基础及入门的学习者，通过专栏的学习可以熟练掌握python编程，同时为后续的数据分析，机器学习及深度学习的代码能力打下坚实的基础。

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文章目录

• • 13.0 环境配置
  • 13.1 Matplotlib库
  • • 13.1.1 折线图
    • • • marker设置坐标点
        • markersize 设置坐标点大小
        • 颜色跟风格设置的简写 color_linestyles = [“g-“, “b–“, “k-.”, “r:”]
        • 颜色_风格_线性 设置的简写 color_marker_linestyles = [“g*-“, “b+–“, “ko-.”, “rs:”]
      • 【2】调整坐标轴
      • 【3】设置图形标签
    • 13.1.2 散点图
    • 13.1.3 柱形图
    • 13.1.4 多子图
    • 13.1.5 直方图
    • 13.1.6 误差图
    • 13.1.7 面向对象的风格简介
    • 13.1.8 三维图形简介
  • 13.2 Seaborn库-文艺青年的最爱
  • 13.3 Pandas 中的绘图函数概览

13.0 环境配置

【1】 要不要plt.show()

• ipython中可用魔术方法 %matplotlib inline

• pycharm 中必须使用plt.show()

%matplotlib inline # 配置，可以再ipython中生成就显示，而不需要多余plt.show来完成。import matplotlib.pyplot as plt plt.style.use("seaborn-whitegrid") # 用来永久地改变风格，与下文with临时改变进行对比

x = [1, 2, 3, 4]y = [1, 4, 9, 16]plt.plot(x, y)plt.ylabel("squares")# plt.show()   

Text(0, 0.5, 'squares')

【2】设置样式

plt.style.available[:5]

['bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight']

临时地改变风格，采用with这个上下文管理器。

with plt.style.context("seaborn-white"):    plt.plot(x, y)

【3】将图像保存为文件

import numpy as npx = np.linspace(0, 10 ,100)plt.plot(x, np.exp(x))plt.savefig("my_figure.png")

13.1 Matplotlib库

13.1.1 折线图

%matplotlib inlineimport matplotlib.pyplot as pltplt.style.use("seaborn-whitegrid")import numpy as np

x = np.linspace(0, 2*np.pi, 100)plt.plot(x, np.sin(x))

[]

• 绘制多条曲线

x = np.linspace(0, 2*np.pi, 100)plt.plot(x, np.cos(x))plt.plot(x, np.sin(x))

[]

【1】调整线条颜色和风格

• 调整线条颜色

offsets = np.linspace(0, np.pi, 5)colors = ["blue", "g", "r", "yellow", "pink"]for offset, color in zip(offsets, colors):    plt.plot(x, np.sin(x-offset), color=color)         # color可缩写为c

• 调整线条风格

x = np.linspace(0, 10, 11)offsets = list(range(8))linestyles = ["solid", "dashed", "dashdot", "dotted", "-", "--", "-.", ":"]for offset, linestyle in zip(offsets, linestyles):    plt.plot(x, x+offset, linestyle=linestyle)        # linestyle可简写为ls

• 调整线宽

x = np.linspace(0, 10, 11)offsets = list(range(0, 12, 3))linewidths = (i*2 for i in range(1,5))for offset, linewidth in zip(offsets, linewidths):    plt.plot(x, x+offset, linewidth=linewidth)                 # linewidth可简写为lw

• 调整数据点标记

marker设置坐标点

x = np.linspace(0, 10, 11)offsets = list(range(0, 12, 3))markers = ["*", "+", "o", "s"]for offset, marker in zip(offsets, markers):    plt.plot(x, x+offset, marker=marker)   

markersize 设置坐标点大小

x = np.linspace(0, 10, 11)offsets = list(range(0, 12, 3))markers = ["*", "+", "o", "s"]for offset, marker in zip(offsets, markers):    plt.plot(x, x+offset, marker=marker, markersize=10)      # markersize可简写为ms

颜色跟风格设置的简写 color_linestyles = [“g-”, “b–”, “k-.”, “r:”]

x = np.linspace(0, 10, 11)offsets = list(range(0, 8, 2))color_linestyles = ["g-", "b--", "k-.", "r:"]for offset, color_linestyle in zip(offsets, color_linestyles):    plt.plot(x, x+offset, color_linestyle)

颜色_风格_线性 设置的简写 color_marker_linestyles = [“g*-”, “b±-”, “ko-.”, “rs:”]

x = np.linspace(0, 10, 11)offsets = list(range(0, 8, 2))color_marker_linestyles = ["g*-", "b+--", "ko-.", "rs:"]for offset, color_marker_linestyle in zip(offsets, color_marker_linestyles):    plt.plot(x, x+offset, color_marker_linestyle)

​

​

其他用法及颜色缩写、数据点标记缩写等请查看官方文档，如下：

https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot

【2】调整坐标轴

• xlim, ylim # 限制x，y轴

x = np.linspace(0, 2*np.pi, 100)plt.plot(x, np.sin(x))plt.xlim(-1, 7)plt.ylim(-1.5, 1.5)

(-1.5, 1.5)

• axis

x = np.linspace(0, 2*np.pi, 100)plt.plot(x, np.sin(x))plt.axis([-2, 8, -2, 2])

[-2, 8, -2, 2]

tight 会紧凑一点

x = np.linspace(0, 2*np.pi, 100)plt.plot(x, np.sin(x))plt.axis("tight")

(0.0, 6.283185307179586, -0.9998741276738751, 0.9998741276738751)

equal 会松一点

x = np.linspace(0, 2*np.pi, 100)plt.plot(x, np.sin(x))plt.axis("equal")

(0.0, 7.0, -1.0, 1.0)

" />
 
调整坐标轴刻度
 
plt.xticks(np.arange(0, 12, step=1))
 
x = np.linspace(0, 10, 100)plt.plot(x, x**2)plt.xticks(np.arange(0, 12, step=1))
([,  ,  ,  ,  ,  ,  ,  ,  ,  ,  ,  ], )
x = np.linspace(0, 10, 100)plt.plot(x, x**2)plt.xticks(np.arange(0, 12, step=1), fontsize=15)plt.yticks(np.arange(0, 110, step=10))
([,  ,  ,  ,  ,  ,  ,  ,  ,  ,  ], )
调整刻度样式
plt.tick_params(axis=“both”, labelsize=15)
x = np.linspace(0, 10, 100)plt.plot(x, x**2)plt.tick_params(axis="both", labelsize=15)
【3】设置图形标签
x = np.linspace(0, 2*np.pi, 100)plt.plot(x, np.sin(x))plt.title("A Sine Curve", fontsize=20)plt.xlabel("x", fontsize=15)plt.ylabel("sin(x)", fontsize=15)
Text(0, 0.5, 'sin(x)')
【4】设置图例
默认
x = np.linspace(0, 2*np.pi, 100)plt.plot(x, np.sin(x), "b-", label="Sin")plt.plot(x, np.cos(x), "r--", label="Cos")plt.legend()
修饰图例
import matplotlib.pyplot as plt import numpy as npx = np.linspace(0, 2*np.pi, 100)plt.plot(x, np.sin(x), "b-", label="Sin")plt.plot(x, np.cos(x), "r--", label="Cos")plt.ylim(-1.5, 2)plt.legend(loc="upper center", frameon=True, fontsize=15) # frameon=True增加图例的边框
【5】添加文字和箭头
添加文字
x = np.linspace(0, 2*np.pi, 100)plt.plot(x, np.sin(x), "b-")plt.text(3.5, 0.5, "y=sin(x)", fontsize=15) # 前两个为文字的坐标，后面是内容和字号
Text(3.5, 0.5, 'y=sin(x)')
添加箭头
x = np.linspace(0, 2*np.pi, 100)plt.plot(x, np.sin(x), "b-")plt.annotate('local min', xy=(1.5*np.pi, -1), xytext=(4.5, 0),             arrowprops=dict(facecolor='black', shrink=0.1),             )
Text(4.5, 0, 'local min')
13.1.2 散点图
【1】简单散点图
x = np.linspace(0, 2*np.pi, 20)plt.scatter(x, np.sin(x), marker="o", s=30, c="r")    # s 大小  c 颜色
【2】颜色配置
x = np.linspace(0, 10, 100)y = x**2plt.scatter(x, y, c=y, cmap="inferno")  # 让c随着y的值变化在cmap中进行映射plt.colorbar() # 输出颜色条
颜色配置参考官方文档
https://matplotlib.org/examples/color/colormaps_reference.html
【3】根据数据控制点的大小
x, y, colors, size = (np.random.rand(100) for i in range(4))plt.scatter(x, y, c=colors, s=1000*size, cmap="viridis")
【4】透明度
x, y, colors, size = (np.random.rand(100) for i in range(4))plt.scatter(x, y, c=colors, s=1000*size, cmap="viridis", alpha=0.3)plt.colorbar()
【例】随机漫步
from random import choiceclass RandomWalk():    """一个生产随机漫步的类"""    def __init__(self, num_points=5000):        self.num_points = num_points        self.x_values = [0]        self.y_values = [0]        def fill_walk(self):        while len(self.x_values) < self.num_points:            x_direction = choice([1, -1])            x_distance = choice([0, 1, 2, 3, 4])            x_step = x_direction * x_distance                        y_direction = choice([1, -1])            y_distance = choice([0, 1, 2, 3, 4])            y_step = y_direction * y_distance                                if x_step == 0 or y_step == 0:                continue            next_x = self.x_values[-1] + x_step            next_y = self.y_values[-1] + y_step            self.x_values.append(next_x)            self.y_values.append(next_y)
rw = RandomWalk(10000)rw.fill_walk()point_numbers = list(range(rw.num_points))plt.figure(figsize=(12, 6))        # 设置画布大小        plt.scatter(rw.x_values, rw.y_values, c=point_numbers, cmap="inferno", s=1)plt.colorbar()plt.scatter(0, 0, c="green", s=100)plt.scatter(rw.x_values[-1], rw.y_values[-1], c="red", s=100)plt.xticks([])plt.yticks([])
([], )
​
 
 ​
13.1.3 柱形图
【1】简单柱形图
x = np.arange(1, 6)plt.bar(x, 2*x, align="center", width=0.5, alpha=0.5, color='yellow', edgecolor='red')plt.tick_params(axis="both", labelsize=13)
x = np.arange(1, 6)plt.bar(x, 2*x, align="center", width=0.5, alpha=0.5, color='yellow', edgecolor='red')plt.xticks(x, ('G1', 'G2', 'G3', 'G4', 'G5'))plt.tick_params(axis="both", labelsize=13) 
x = ('G1', 'G2', 'G3', 'G4', 'G5')y = 2 * np.arange(1, 6)plt.bar(x, y, align="center", width=0.5, alpha=0.5, color='yellow', edgecolor='red')plt.tick_params(axis="both", labelsize=13) 
x = ["G"+str(i) for i in range(5)]y = 1/(1+np.exp(-np.arange(5)))colors = ['red', 'yellow', 'blue', 'green', 'gray']plt.bar(x, y, align="center", width=0.5, alpha=0.5, color=colors)plt.tick_params(axis="both", labelsize=13)
【2】累加柱形图
x = np.arange(5)y1 = np.random.randint(20, 30, size=5)y2 = np.random.randint(20, 30, size=5)plt.bar(x, y1, width=0.5, label="man")plt.bar(x, y2, width=0.5, bottom=y1, label="women")plt.legend()
【3】并列柱形图
x = np.arange(15)y1 = x+1y2 = y1+np.random.random(15)plt.bar(x, y1, width=0.3, label="man")plt.bar(x+0.3, y2, width=0.3, label="women")plt.legend()
【4】横向柱形图barh
x = ['G1', 'G2', 'G3', 'G4', 'G5']y = 2 * np.arange(1, 6)plt.barh(x, y, align="center", height=0.5, alpha=0.8, color="blue", edgecolor="red") # 注意这里将bar改为barh，宽度用height设置plt.tick_params(axis="both", labelsize=13)
13.1.4 多子图
【1】简单多子图
def f(t):    return np.exp(-t) * np.cos(2*np.pi*t)t1 = np.arange(0.0, 5.0, 0.1)t2 = np.arange(0.0, 5.0, 0.02)plt.subplot(211)plt.plot(t1, f(t1), "bo-", markerfacecolor="r", markersize=5)plt.title("A tale of 2 subplots")plt.ylabel("Damped oscillation")plt.subplot(212)plt.plot(t2, np.cos(2*np.pi*t2), "r--")plt.xlabel("time (s)")plt.ylabel("Undamped")
Text(0, 0.5, 'Undamped')
【2】多行多列子图
x = np.random.random(10)y = np.random.random(10)plt.subplots_adjust(hspace=0.5, wspace=0.3)plt.subplot(321)plt.scatter(x, y, s=80, c="b", marker=">")plt.subplot(322)plt.scatter(x, y, s=80, c="g", marker="*")plt.subplot(323)plt.scatter(x, y, s=80, c="r", marker="s")plt.subplot(324)plt.scatter(x, y, s=80, c="c", marker="p")plt.subplot(325)plt.scatter(x, y, s=80, c="m", marker="+")plt.subplot(326)plt.scatter(x, y, s=80, c="y", marker="H")
【3】不规则多子图
def f(x):    return np.exp(-x) * np.cos(2*np.pi*x)x = np.arange(0.0, 3.0, 0.01)grid = plt.GridSpec(2, 3, wspace=0.4, hspace=0.3) # 两行三列的网格plt.subplot(grid[0, 0]) # 第一行第一列位置plt.plot(x, f(x))plt.subplot(grid[0, 1:]) # 第一行后两列的位置plt.plot(x, f(x), "r--", lw=2)plt.subplot(grid[1, :]) # 第二行所有位置plt.plot(x, f(x), "g-.", lw=3)
[]
13.1.5 直方图
【1】普通频次直方图
mu, sigma = 100, 15x = mu + sigma * np.random.randn(10000)plt.hist(x, bins=50, facecolor='g', alpha=0.75)
(array([  1.,   0.,   0.,   5.,   3.,   5.,   1.,  10.,  15.,  19.,  37.,         55.,  81.,  94., 125., 164., 216., 258., 320., 342., 401., 474.,        483., 590., 553., 551., 611., 567., 515., 558., 470., 457., 402.,        347., 261., 227., 206., 153., 128.,  93.,  79.,  41.,  22.,  17.,         21.,   9.,   2.,   8.,   1.,   2.]), array([ 40.58148736,  42.82962161,  45.07775586,  47.32589011,         49.57402436,  51.82215862,  54.07029287,  56.31842712,         58.56656137,  60.81469562,  63.06282988,  65.31096413,         67.55909838,  69.80723263,  72.05536689,  74.30350114,         76.55163539,  78.79976964,  81.04790389,  83.29603815,         85.5441724 ,  87.79230665,  90.0404409 ,  92.28857515,         94.53670941,  96.78484366,  99.03297791, 101.28111216,        103.52924641, 105.77738067, 108.02551492, 110.27364917,        112.52178342, 114.76991767, 117.01805193, 119.26618618,        121.51432043, 123.76245468, 126.01058893, 128.25872319,        130.50685744, 132.75499169, 135.00312594, 137.25126019,        139.49939445, 141.7475287 , 143.99566295, 146.2437972 ,        148.49193145, 150.74006571, 152.98819996]), )
【2】概率密度
mu, sigma = 100, 15x = mu + sigma * np.random.randn(10000)plt.hist(x, 50, density=True, color="r")# 概率密度图plt.xlabel('Smarts')plt.ylabel('Probability')plt.title('Histogram of IQ')plt.text(60, .025, r'$\mu=100,\ \sigma=15$')plt.xlim(40, 160)plt.ylim(0, 0.03)
(0, 0.03)
mu, sigma = 100, 15x = mu + sigma * np.random.randn(10000)plt.hist(x, bins=50, density=True, color="r", histtype='step') #不填充，只获得边缘plt.xlabel('Smarts')plt.ylabel('Probability')plt.title('Histogram of IQ')plt.text(60, .025, r'$\mu=100,\ \sigma=15$')plt.xlim(40, 160)plt.ylim(0, 0.03)
(0, 0.03)
from scipy.stats import normmu, sigma = 100, 15 # 想获得真正高斯分布的概率密度图x = mu + sigma * np.random.randn(10000)# 先获得bins，即分配的区间_, bins, __ = plt.hist(x, 50, density=True)y = norm.pdf(bins, mu, sigma) # 通过norm模块计算符合的概率密度plt.plot(bins, y, 'r--', lw=3)  plt.xlabel('Smarts')plt.ylabel('Probability')plt.title('Histogram of IQ')plt.text(60, .025, r'$\mu=100,\ \sigma=15$')plt.xlim(40, 160)plt.ylim(0, 0.03)
(0, 0.03)
【3】累计概率分布
mu, sigma = 100, 15x = mu + sigma * np.random.randn(10000)plt.hist(x, 50, density=True, cumulative=True, color="r") # 将累计cumulative设置为true即可plt.xlabel('Smarts')plt.ylabel('Cum_Probability')plt.title('Histogram of IQ')plt.text(60, 0.8, r'$\mu=100,\ \sigma=15$')plt.xlim(50, 165)plt.ylim(0, 1.1)
(0, 1.1)
【例】模拟投两个骰子
class Die():    "模拟一个骰子的类"        def __init__(self, num_sides=6):        self.num_sides = num_sides        def roll(self):        return np.random.randint(1, self.num_sides+1)
重复投一个骰子
die = Die()results = []for i in range(60000):    result = die.roll()    results.append(result)    plt.hist(results, bins=6, range=(0.75, 6.75), align="mid", width=0.5)plt.xlim(0 ,7)
(0, 7)
重复投两个骰子
die1 = Die()die2 = Die()results = []for i in range(60000):    result = die1.roll()+die2.roll()    results.append(result)    plt.hist(results, bins=11, range=(1.75, 12.75), align="mid", width=0.5)plt.xlim(1 ,13)plt.xticks(np.arange(1, 14))
([,  ,  ,  ,  ,  ,  ,  ,  ,  ,  ,  ,  ], )
13.1.6 误差图
【1】基本误差图
x = np.linspace(0, 10 ,50)dy = 0.5 # 每个点的y值误差设置为0.5y = np.sin(x) + dy*np.random.randn(50)plt.errorbar(x, y , yerr=dy, fmt="+b")
【2】柱形图误差图
menMeans = (20, 35, 30, 35, 27)womenMeans = (25, 32, 34, 20, 25)menStd = (2, 3, 4, 1, 2)womenStd = (3, 5, 2, 3, 3)ind = ['G1', 'G2', 'G3', 'G4', 'G5'] width = 0.35       p1 = plt.bar(ind, menMeans, width=width, label="Men", yerr=menStd)p2 = plt.bar(ind, womenMeans, width=width, bottom=menMeans, label="Men", yerr=womenStd)plt.ylabel('Scores')plt.title('Scores by group and gender')plt.yticks(np.arange(0, 81, 10))plt.legend()
13.1.7 面向对象的风格简介
【例1】 普通图
x = np.linspace(0, 5, 10)y = x ** 2fig = plt.figure(figsize=(8,4), dpi=80)        # 图像axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])      # 轴 left, bottom, width, height (range 0 to 1)axes.plot(x, y, 'r')axes.set_xlabel('x')axes.set_ylabel('y')axes.set_title('title')
Text(0.5, 1.0, 'title')
【2】画中画
x = np.linspace(0, 5, 10)y = x ** 2fig = plt.figure()ax1 = fig.add_axes([0.1, 0.1, 0.8, 0.8]) ax2 = fig.add_axes([0.2, 0.5, 0.4, 0.3]) ax1.plot(x, y, 'r')ax1.set_xlabel('x')ax1.set_ylabel('y')ax1.set_title('title')ax2.plot(y, x, 'g')ax2.set_xlabel('y')ax2.set_ylabel('x')ax2.set_title('insert title')
Text(0.5, 1.0, 'insert title')
【3】 多子图
def f(t):    return np.exp(-t) * np.cos(2*np.pi*t)t1 = np.arange(0.0, 3.0, 0.01)fig= plt.figure()fig.subplots_adjust(hspace=0.4, wspace=0.4)ax1 = plt.subplot(2, 2, 1)ax1.plot(t1, f(t1))ax1.set_title("Upper left")ax2 = plt.subplot(2, 2, 2)ax2.plot(t1, f(t1))ax2.set_title("Upper right")ax3 = plt.subplot(2, 1, 2)ax3.plot(t1, f(t1))ax3.set_title("Lower")
Text(0.5, 1.0, 'Lower')
13.1.8 三维图形简介
【1】三维数据点与线
from mpl_toolkits import mplot3d # 注意要导入mplot3dax = plt.axes(projection="3d")zline = np.linspace(0, 15, 1000)xline = np.sin(zline)yline = np.cos(zline)ax.plot3D(xline, yline ,zline)# 线的绘制zdata = 15*np.random.random(100)xdata = np.sin(zdata)ydata = np.cos(zdata)ax.scatter3D(xdata, ydata ,zdata, c=zdata, cmap="spring") # 点的绘制
【2】三维数据曲面图
def f(x, y):    return np.sin(np.sqrt(x**2 + y**2))x = np.linspace(-6, 6, 30)y = np.linspace(-6, 6, 30)X, Y = np.meshgrid(x, y) # 网格化Z = f(X, Y)ax = plt.axes(projection="3d")ax.plot_surface(X, Y, Z, cmap="viridis") # 设置颜色映射
import numpy as npimport matplotlib.pyplot as pltfrom mpl_toolkits import mplot3dt = np.linspace(0, 2*np.pi, 1000)X = np.sin(t)Y = np.cos(t)Z = np.arange(t.size)[:, np.newaxis]ax = plt.axes(projection="3d")ax.plot_surface(X, Y, Z, cmap="viridis")
13.2 Seaborn库-文艺青年的最爱
【1】Seaborn 与 Matplotlib
Seaborn 是一个基于 matplotlib 且数据结构与 pandas 统一的统计图制作库
x = np.linspace(0, 10, 500)y = np.cumsum(np.random.randn(500, 6), axis=0)with plt.style.context("classic"):    plt.plot(x, y)    plt.legend("ABCDEF", ncol=2, loc="upper left")   
import seaborn as snsx = np.linspace(0, 10, 500)y = np.cumsum(np.random.randn(500, 6), axis=0)sns.set()# 改变了格式plt.figure(figsize=(10, 6))plt.plot(x, y)plt.legend("ABCDEF", ncol=2, loc="upper left")
【2】柱形图的对比
x = ['G1', 'G2', 'G3', 'G4', 'G5']y = 2 * np.arange(1, 6)plt.figure(figsize=(8, 4))plt.barh(x, y, align="center", height=0.5, alpha=0.8, color="blue")plt.tick_params(axis="both", labelsize=13)
import seaborn as snsplt.figure(figsize=(8, 4))x = ['G5', 'G4', 'G3', 'G2', 'G1']y = 2 * np.arange(5, 0, -1)#sns.barplot(y, x)sns.barplot(y, x, linewidth=5)
sns.barplot" />sepal_lengthsepal_widthpetal_lengthpetal_widthspecies05.13.51.40.2setosa14.93.01.40.2setosa24.73.21.30.2setosa34.63.11.50.2setosa45.03.61.40.2setosa 
sns.pairplot(data=iris, hue="species")
13.3 Pandas 中的绘图函数概览
import pandas as pd
【1】线形图
df = pd.DataFrame(np.random.randn(1000, 4).cumsum(axis=0),                  columns=list("ABCD"),                  index=np.arange(1000))df.head()
A
B
C
D
0
-1.311443
0.970917
-1.635011
-0.204779
1
-1.618502
0.810056
-1.119246
1.239689
2
-3.558787
1.431716
-0.816201
1.155611
3
-5.377557
-0.312744
0.650922
0.352176
4
-3.917045
1.181097
1.572406
0.965921
df.plot()
df = pd.DataFrame()df.plot" />abcd00.5876000.0987360.4447570.87747510.5800620.4515190.2123180.42967320.4153070.7840830.8912050.75628730.1900530.3509870.6625490.72919340.4856020.1099740.8915540.47349250.3318840.1289570.2043030.36342060.9627500.4312260.9176820.97271370.4834100.4865920.4392350.87521080.0543370.9858120.4690160.89471290.7309050.2371660.0431950.600445 
多组数据竖图
 
df2.plot.bar()
多组数据累加竖图
df2.plot.bar(stacked=True) # 累加的柱形图
多组数据累加横图
df2.plot.barh(stacked=True) # 变为barh
【3】直方图和密度图
df4 = pd.DataFrame({"A": np.random.randn(1000) - 3, "B": np.random.randn(1000),                     "C": np.random.randn(1000) + 3})df4.head()
A
B
C
0
-4.250424
1.043268
1.356106
1
-2.393362
-0.891620
3.787906
2
-4.411225
0.436381
1.242749
3
-3.465659
-0.845966
1.540347
4
-3.606850
1.643404
3.689431
普通直方图
df4.plot.hist(bins=50)
累加直方图
df4['A'].plot.hist(cumulative=True)
概率密度图
df4['A'].plot(kind="kde")
差分
df = pd.DataFrame(np.random.randn(1000, 4).cumsum(axis=0),                  columns=list("ABCD"),                  index=np.arange(1000))df.head()
A
B
C
D
0
-0.277843
-0.310656
-0.782999
-0.049032
1
0.644248
-0.505115
-0.363842
0.399116
2
-0.614141
-1.227740
-0.787415
-0.117485
3
-0.055964
-2.376631
-0.814320
-0.716179
4
0.058613
-2.355537
-2.174291
0.351918
df.diff().hist(bins=50, color="r")
array([[,        ],       [,        ]],      dtype=object)
df = pd.DataFrame()df.hist" />longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximity0-122.2337.8841.0880.0129.0322.0126.08.3252452600.0NEAR BAY1-122.2237.8621.07099.01106.02401.01138.08.3014358500.0NEAR BAY2-122.2437.8552.01467.0190.0496.0177.07.2574352100.0NEAR BAY3-122.2537.8552.01274.0235.0558.0219.05.6431341300.0NEAR BAY4-122.2537.8552.01627.0280.0565.0259.03.8462342200.0NEAR BAY 
"""基于地理数据的人口、房价可视化"""# 圆的半价大小代表每个区域人口数量(s),颜色代表价格(c),用预定义的jet表进行可视化with sns.axes_style("white"):    housing.plot(kind="scatter", x="longitude", y="latitude", alpha=0.6,                 s=housing["population"]/100, label="population",                 c="median_house_value", cmap="jet", colorbar=True, figsize=(12, 8))plt.legend()plt.axis([-125, -113.5, 32, 43])
[-125, -113.5, 32, 43]
housing.plot(kind="scatter", x="median_income", y="median_house_value", alpha=0.8)
'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.
【5】多子图
df = pd.DataFrame(np.random.randn(1000, 4).cumsum(axis=0),                  columns=list("ABCD"),                  index=np.arange(1000))df.head()
A
B
C
D
0
-0.134510
0.364371
-0.831193
-0.796903
1
0.130102
1.003402
-0.622822
-1.640771
2
0.066873
0.126174
0.180913
-2.928643
3
-1.686890
-0.050740
0.312582
-2.379455
4
0.655660
-0.390920
-1.144121
-2.625653
默认情形
df.plot(subplots=True, figsize=(6, 16))
array([,       ,       ,       ],      dtype=object)
设定图形安排
df.plot(subplots=True, layout=(2, 2), figsize=(16, 6), sharex=False)
array([[,        ],       [,        ]],      dtype=object)
其他内容请参考Pandas中文文档
https://www.pypandas.cn/docs/user_guide/visualization.html#plot-formatting