数据可视化基础专题（十三）：Matplotlib 基础（五）常用图表（三）环形图、热力图、直方图

数据可视化基础专题（十三）：Matplotlib 基础（五）常用图表（三）环形图、热力图、直方图 环形图

环形图其实是另一种饼图，使用的还是上面的 pie() 这个方法，这里只需要设置一下参数 wedgeprops 即可。

例子一：

import matplotlib.pyplot as plt

# 中文和负号的正常显示

plt.rcParams['font.sans-serif']=['SimHei']

plt.rcParams['axes.unicode_minus'] = False

# 数据

edu = [0.2515,0.3724,0.3336,0.0368,0.0057]

labels = ['中专','大专','本科','硕士','其他']

# 让本科学历离圆心远一点

explode = [0,0,0.1,0,0]

# 将横、纵坐标轴标准化处理，保证饼图是一个正圆，否则为椭圆

plt.axes(aspect='equal')

# 自定义颜色

colors=['#9999ff','#ff9999','#7777aa','#2442aa','#dd5555'] # 自定义颜色

# 绘制饼图

plt.pie(x=edu,  # 绘图数据

    explode = explode,  # 突出显示大专人群

    labels = labels,  # 添加教育水平标签

    colors = colors,  # 设置饼图的自定义填充色

    autopct = '%.1f%%',  # 设置百分比的格式，这里保留一位小数

    wedgeprops = {'width': 0.3, 'edgecolor':'green'}

    )

# 添加图标题

plt.title('xxx 公司员工教育水平分布')

# 保存图形

plt.savefig('pie_demo1.png')

这个示例仅仅在前面示例的基础上增加了一个参数 wedgeprops 的设置，我们看下结果：

热力图

plt.imshow(x, cmap)

import numpy as np

import matplotlib.pyplot as plt

x = np.random.rand(10, 10)

plt.imshow(x, cmap=plt.cm.hot)

# 显示右边颜色条

plt.colorbar()

plt.savefig('imshow_demo.png')

例子二

import numpy as np

import matplotlib.cm as cm

import matplotlib.pyplot as plt

import matplotlib.cbook as cbook

from matplotlib.path import Path

from matplotlib.patches import PathPatch

delta = 0.025

x = y = np.arange(-3.0, 3.0, delta)

X, Y = np.meshgrid(x, y)

Z1 = np.exp(-X**2 - Y**2)

Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)

Z = (Z1 - Z2) * 2

fig, ax = plt.subplots()

im = ax.imshow(Z, interpolation='bilinear', cmap=cm.RdYlGn,

               origin='lower', extent=[-3, 3, -3, 3],

               vmax=abs(Z).max(), vmin=-abs(Z).max())

plt.show()

例子3

import matplotlib.pyplot as plt

import numpy as np

def func3(x, y):

    return (1 - x / 2 + x**5 + y**3) * np.exp(-(x**2 + y**2))

# make these smaller to increase the resolution

dx, dy = 0.05, 0.05

x = np.arange(-3.0, 3.0, dx)

y = np.arange(-3.0, 3.0, dy)

X, Y = np.meshgrid(x, y)

# when layering multiple images, the images need to have the same

# extent.  This does not mean they need to have the same shape, but

# they both need to render to the same coordinate system determined by

# xmin, xmax, ymin, ymax.  Note if you use different interpolations

# for the images their apparent extent could be different due to

# interpolation edge effects

extent = np.min(x), np.max(x), np.min(y), np.max(y)

fig = plt.figure(frameon=False)

Z1 = np.add.outer(range(8), range(8)) % 2  # chessboard

im1 = plt.imshow(Z1, cmap=plt.cm.gray, interpolation='nearest',

                 extent=extent)

Z2 = func3(X, Y)

im2 = plt.imshow(Z2, cmap=plt.cm.viridis, alpha=.9, interpolation='bilinear',

                 extent=extent)

plt.show()

直方图

例子1

import matplotlib.pyplot as plt

import numpy as np

from matplotlib import colors

from matplotlib.ticker import PercentFormatter

# Fixing random state for reproducibility

np.random.seed(19680801)

N_points = 100000

n_bins = 20

# Generate a normal distribution, center at x=0 and y=5

x = np.random.randn(N_points)

y = .4 * x + np.random.randn(100000) + 5

fig, axs = plt.subplots(1, 2, sharey=True, tight_layout=True)

# We can set the number of bins with the `bins` kwarg

axs[0].hist(x, bins=n_bins)

axs[1].hist(y, bins=n_bins)

例子2

fig, axs = plt.subplots(1, 2, tight_layout=True)

# N is the count in each bin, bins is the lower-limit of the bin

N, bins, patches = axs[0].hist(x, bins=n_bins)

# We'll color code by height, but you could use any scalar

fracs = N / N.max()

# we need to normalize the data to 0..1 for the full range of the colormap

norm = colors.Normalize(fracs.min(), fracs.max())

# Now, we'll loop through our objects and set the color of each accordingly

for thisfrac, thispatch in zip(fracs, patches):

    color = plt.cm.viridis(norm(thisfrac))

    thispatch.set_facecolor(color)

# We can also normalize our inputs by the total number of counts

axs[1].hist(x, bins=n_bins, density=True)

# Now we format the y-axis to display percentage

axs[1].yaxis.set_major_formatter(PercentFormatter(xmax=1))