Matplotlib库的应用

0508

Matplotlib库

数据可视化是数据分析的一个重要工具，掌声有请Matplotlib

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
import matplotlib.pyplot as plt
plt.style.use("seaborn-whitegrid")
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']
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
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)   
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
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)
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
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]
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)
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
  • 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
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
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
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)
<matplotlib.legend.Legend at 0x188476d2a20>

【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)
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")  
plt.colorbar()
<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))                 
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

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")
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)

plt.subplot(grid[0, 0])
plt.plot(x, f(x))

plt.subplot(grid[0, 1:])
plt.plot(x, f(x), "r--", lw=2)

plt.subplot(grid[1, :])
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")
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')
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
x = mu + sigma * np.random.randn(10000)

_, bins, __ = plt.hist(x, 50, density=True)
y = norm.pdf(bins, mu, sigma)
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")
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
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

ax = plt.axes(projection="3d")
zline = np.linspace(0, 15, 1000)
xline = np.sin(zline)
yline = np.cos(zline)
ax.plot3D(xline, yline ,zline)

zdata = 15*np.random.random(100)
xdata = np.sin(zdata)
ydata = np.cos(zdata)
ax.scatter3D(xdata, ydata ,zdata, c=zdata, cmap="spring")
<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)
Z = f(X, Y)

ax = plt.axes(projection="3d")
ax.plot_surface(X, Y, Z, cmap="viridis")
<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()
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()
sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
05.13.51.40.2setosa
14.93.01.40.2setosa
24.73.21.30.2setosa
34.63.11.50.2setosa
45.03.61.40.2setosa
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()
ABCD
0-1.3114430.970917-1.635011-0.204779
1-1.6185020.810056-1.1192461.239689
2-3.5587871.431716-0.8162011.155611
3-5.377557-0.3127440.6509220.352176
4-3.9170451.1810971.5724060.965921
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
abcd
00.5876000.0987360.4447570.877475
10.5800620.4515190.2123180.429673
20.4153070.7840830.8912050.756287
30.1900530.3509870.6625490.729193
40.4856020.1099740.8915540.473492
50.3318840.1289570.2043030.363420
60.9627500.4312260.9176820.972713
70.4834100.4865920.4392350.875210
80.0543370.9858120.4690160.894712
90.7309050.2371660.0431950.600445
  • 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)
<matplotlib.axes._subplots.AxesSubplot at 0x20534f22208>
  • 多组数据累加横图
df2.plot.barh(stacked=True)
<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()
ABC
0-4.2504241.0432681.356106
1-2.393362-0.8916203.787906
2-4.4112250.4363811.242749
3-3.465659-0.8459661.540347
4-3.6068501.6434043.689431
  • 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()
ABCD
0-0.277843-0.310656-0.782999-0.049032
10.644248-0.505115-0.3638420.399116
2-0.614141-1.227740-0.787415-0.117485
3-0.055964-2.376631-0.814320-0.716179
40.058613-2.355537-2.1742910.351918
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()
longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximity
0-122.2337.8841.0880.0129.0322.0126.08.3252452600.0NEAR BAY
1-122.2237.8621.07099.01106.02401.01138.08.3014358500.0NEAR BAY
2-122.2437.8552.01467.0190.0496.0177.07.2574352100.0NEAR BAY
3-122.2537.8552.01274.0235.0558.0219.05.6431341300.0NEAR BAY
4-122.2537.8552.01627.0280.0565.0259.03.8462342200.0NEAR 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.





<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()
ABCD
0-0.1345100.364371-0.831193-0.796903
10.1301021.003402-0.622822-1.640771
20.0668730.1261740.180913-2.928643
3-1.686890-0.0507400.312582-2.379455
40.655660-0.390920-1.144121-2.625653
  • 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

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