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）

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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 np
x = 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 inline
import matplotlib.pyplot as plt
plt.style.use("seaborn-whitegrid")
import numpy as np

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

[<matplotlib.lines.Line2D at 0x18846169780>]

• 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))

[<matplotlib.lines.Line2D at 0x1884615f9e8>]

【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)

?plt.axis # 可以查询其中的功能

Object `plt.axis # 可以查询其中的功能` not found.

• 对数坐标

x = np.logspace(0, 5, 100)
plt.plot(x, np.log(x))
plt.xscale("log")

• 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))

([<matplotlib.axis.XTick at 0x18846412828>,
  <matplotlib.axis.XTick at 0x18847665898>,
  <matplotlib.axis.XTick at 0x18847665630>,
  <matplotlib.axis.XTick at 0x18847498978>,
  <matplotlib.axis.XTick at 0x18847498390>,
  <matplotlib.axis.XTick at 0x18847497d68>,
  <matplotlib.axis.XTick at 0x18847497748>,
  <matplotlib.axis.XTick at 0x18847497438>,
  <matplotlib.axis.XTick at 0x1884745f438>,
  <matplotlib.axis.XTick at 0x1884745fd68>,
  <matplotlib.axis.XTick at 0x18845fcf4a8>,
  <matplotlib.axis.XTick at 0x18845fcf320>],
 <a list of 12 Text xticklabel objects>)

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))

([<matplotlib.axis.YTick at 0x188474f0860>,
  <matplotlib.axis.YTick at 0x188474f0518>,
  <matplotlib.axis.YTick at 0x18847505a58>,
  <matplotlib.axis.YTick at 0x188460caac8>,
  <matplotlib.axis.YTick at 0x1884615c940>,
  <matplotlib.axis.YTick at 0x1884615cdd8>,
  <matplotlib.axis.YTick at 0x1884615c470>,
  <matplotlib.axis.YTick at 0x1884620c390>,
  <matplotlib.axis.YTick at 0x1884611f898>,
  <matplotlib.axis.YTick at 0x188461197f0>,
  <matplotlib.axis.YTick at 0x18846083f98>],
 <a list of 11 Text yticklabel objects>)

• 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()

<matplotlib.legend.Legend at 0x1884749f908>

• 修饰图例

import matplotlib.pyplot as plt 
import numpy as np
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.ylim(-1.5, 2)
plt.legend(loc="upper center", frameon=True, fontsize=15) # frameon=True增加图例的边框

<matplotlib.legend.Legend at 0x19126b53b80>

【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

<matplotlib.collections.PathCollection at 0x188461eb4a8>

【2】颜色配置

x = np.linspace(0, 10, 100)
y = x**2
plt.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

<matplotlib.colorbar.Colorbar at 0x18848d392e8>

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")

<matplotlib.collections.PathCollection at 0x18847b48748>

【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()

<matplotlib.colorbar.Colorbar at 0x18848f2be10>

【例】随机漫步

from random import choice

class 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([])

([], <a list of 0 Text yticklabel objects>)

​

​

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()

<matplotlib.legend.Legend at 0x2052db25cc0>

【3】并列柱形图

x = np.arange(15)
y1 = x+1
y2 = 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()

<matplotlib.legend.Legend at 0x2052daf35f8>

【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")

<matplotlib.collections.PathCollection at 0x2052d9f63c8>

【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)

[<matplotlib.lines.Line2D at 0x2052d6fae80>]

13.1.5 直方图

【1】普通频次直方图

mu, sigma = 100, 15
x = 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]),
 <a list of 50 Patch objects>)

【2】概率密度

mu, sigma = 100, 15
x = 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, 15
x = 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 norm
mu, 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, 15
x = 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))

([<matplotlib.axis.XTick at 0x2052fae23c8>,
  <matplotlib.axis.XTick at 0x2052ff1fa20>,
  <matplotlib.axis.XTick at 0x2052fb493c8>,
  <matplotlib.axis.XTick at 0x2052e9b5a20>,
  <matplotlib.axis.XTick at 0x2052e9b5e80>,
  <matplotlib.axis.XTick at 0x2052e9b5978>,
  <matplotlib.axis.XTick at 0x2052e9cc668>,
  <matplotlib.axis.XTick at 0x2052e9ccba8>,
  <matplotlib.axis.XTick at 0x2052e9ccdd8>,
  <matplotlib.axis.XTick at 0x2052fac5668>,
  <matplotlib.axis.XTick at 0x2052fac5ba8>,
  <matplotlib.axis.XTick at 0x2052fac5dd8>,
  <matplotlib.axis.XTick at 0x2052fad9668>],
 <a list of 13 Text xticklabel objects>)

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0.5
y = np.sin(x) + dy*np.random.randn(50)

plt.errorbar(x, y , yerr=dy, fmt="+b")

<ErrorbarContainer object of 3 artists>

【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()

<matplotlib.legend.Legend at 0x20531035630>

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 ** 2

fig = 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 ** 2

fig = 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mplot3d

ax = 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

<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x2052fd1e5f8>

【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

<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x20531baa5c0>

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d

t = 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")

<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x1c540cf1cc0>

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 sns

x = 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")

<matplotlib.legend.Legend at 0x20533d825f8>

【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 sns

plt.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)

<matplotlib.axes._subplots.AxesSubplot at 0x20533e92048>

sns.barplot?

【3】以鸢尾花数据集为例

iris = sns.load_dataset("iris")

iris.head()

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	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa

</div>

sns.pairplot(data=iris, hue="species")

<seaborn.axisgrid.PairGrid at 0x205340655f8>

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()

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	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

</div>

df.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x20534763f28>

df = pd.DataFrame()
df.plot?

【2】柱形图

df2 = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])
df2

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	a	b	c	d
0	0.587600	0.098736	0.444757	0.877475
1	0.580062	0.451519	0.212318	0.429673
2	0.415307	0.784083	0.891205	0.756287
3	0.190053	0.350987	0.662549	0.729193
4	0.485602	0.109974	0.891554	0.473492
5	0.331884	0.128957	0.204303	0.363420
6	0.962750	0.431226	0.917682	0.972713
7	0.483410	0.486592	0.439235	0.875210
8	0.054337	0.985812	0.469016	0.894712
9	0.730905	0.237166	0.043195	0.600445

</div>

• 多组数据竖图

df2.plot.bar()

<matplotlib.axes._subplots.AxesSubplot at 0x20534f1cb00>

• 多组数据累加竖图

df2.plot.bar(stacked=True) # 累加的柱形图

<matplotlib.axes._subplots.AxesSubplot at 0x20534f22208>

• 多组数据累加横图

df2.plot.barh(stacked=True) # 变为barh

<matplotlib.axes._subplots.AxesSubplot at 0x2053509d048>

【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()

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	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

</div>

• 普通直方图

df4.plot.hist(bins=50)

<matplotlib.axes._subplots.AxesSubplot at 0x20538383b38>

• 累加直方图

df4['A'].plot.hist(cumulative=True)

<matplotlib.axes._subplots.AxesSubplot at 0x2053533bbe0>

• 概率密度图

df4['A'].plot(kind="kde")

<matplotlib.axes._subplots.AxesSubplot at 0x205352c4e48>

• 差分

df = pd.DataFrame(np.random.randn(1000, 4).cumsum(axis=0),
                  columns=list("ABCD"),
                  index=np.arange(1000))
df.head()

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	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

</div>

df.diff().hist(bins=50, color="r")

array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002053942A6A0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x000002053957FE48>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000205395A4780>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x00000205395D4128>]],
      dtype=object)

df = pd.DataFrame()
df.hist?

【4】散点图

housing = pd.read_csv("housing.csv")
housing.head()

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	longitude	latitude	housing_median_age	total_rooms	total_bedrooms	population	households	median_income	median_house_value	ocean_proximity
0	-122.23	37.88	41.0	880.0	129.0	322.0	126.0	8.3252	452600.0	NEAR BAY
1	-122.22	37.86	21.0	7099.0	1106.0	2401.0	1138.0	8.3014	358500.0	NEAR BAY
2	-122.24	37.85	52.0	1467.0	190.0	496.0	177.0	7.2574	352100.0	NEAR BAY
3	-122.25	37.85	52.0	1274.0	235.0	558.0	219.0	5.6431	341300.0	NEAR BAY
4	-122.25	37.85	52.0	1627.0	280.0	565.0	259.0	3.8462	342200.0	NEAR BAY

</div>

"""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.

<matplotlib.axes._subplots.AxesSubplot at 0x2053a45a9b0>

【5】多子图

df = pd.DataFrame(np.random.randn(1000, 4).cumsum(axis=0),
                  columns=list("ABCD"),
                  index=np.arange(1000))
df.head()

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	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

</div>

• 默认情形

df.plot(subplots=True, figsize=(6, 16))

array([<matplotlib.axes._subplots.AxesSubplot object at 0x0000020539BF46D8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x0000020539C11898>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x0000020539C3D0B8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x0000020539C60908>],
      dtype=object)

• 设定图形安排

df.plot(subplots=True, layout=(2, 2), figsize=(16, 6), sharex=False)

array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002053D9C2898>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x000002053D9F5668>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002053D68BF98>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x000002053D6B7940>]],
      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​​