python数据处理7

#==============================================================================

# 第七章使用正确的图表理解数据

# 7.1 简介

#==============================================================================

# 7.2 理解对数图

#==============================================================================

from matplotlib import pyplot as plt

import numpy as np

'''

根据经验,以下情况使用对数标度：

1.当要展示的数据的值跨越好几个量级时

2.当要展示的数据有朝向大值(一些数据点比其他数据大很多)的倾斜度。

3.当要展示变化率(增长率),而不是值的变化

'''

x = np.linspace(1, 10)

y = [10 ** el for el in x] #

z = [2 * el for el in x]

fig = plt.figure(figsize=(10, 8))

ax1 = fig.add_subplot(2, 2, 1)

ax1.plot(x, y, color='blue')

ax1.set_yscale('log') #对数标度

ax1.set_title(r'Logarithmic plot of $ {10}^{x} $ ')

ax1.set_ylabel(r'$ {y} = {10}^{x} $')

plt.grid(b=True, which='both', axis='both')

ax2 = fig.add_subplot(2, 2, 2)

ax2.plot(x, y, color='red')

ax2.set_yscale('linear') #线性标度

ax2.set_title(r'Linear plot of $ {10}^{x} $ ')

ax2.set_ylabel(r'$ {y} = {10}^{x} $')

plt.grid(b=True, which='both', axis='both')

ax3 = fig.add_subplot(2, 2, 3)

ax3.plot(x, z, color='green')

ax3.set_yscale('log') #对数标度

ax3.set_title(r'Logarithmic plot of $ {2}*{x} $ ')

ax3.set_ylabel(r'$ {y} = {2}*{x} $')

plt.grid(b=True, which='both', axis='both')

ax4 = fig.add_subplot(2, 2, 4)

ax4.plot(x, z, color='magenta')

ax4.set_yscale('linear') #线性标度

ax4.set_title(r'Linear plot of $ {2}*{x} $ ')

ax4.set_ylabel(r'$ {y} = {2}*{x} $')

plt.grid(b=True, which='both', axis='both')

plt.show()

#==============================================================================

# 7.3 理解频谱图

#==============================================================================

安装libsndfile1系统库来读/写音频文件

sudu apt-get install libasound1-dev

sudu apt-get install libasound2-dev

pip install scikits.audiolab

import os

from math import floor, log

from scikits.audiolab import Sndfile

import numpy as np

from matplotlib import pyplot as plt

soundfile = Sndfile("test.wav")

samplerate = soundfile.samplerate

start_sec = 0

stop_sec = 5

start_frame = start_sec * soundfile.samplerate

stop_frame = stop_sec * soundfile.samplerate

soundfile.seek(start_frame)

delta_frames = stop_frame - start_frame

sample = soundfile.read_frames(delta_frames)

map = 'CMRmap'

fig = plt.figure(figsize=(10, 6), )

ax = fig.add_subplot(111)

NFFT = 128

noverlap = 65

pxx, freq, t, cax = ax.specgram(sample, Fs=soundfile.samplerate,

NFFT=NFFT, noverlap=noverlap,

cmap=plt.get_cmap(map))

plt.colorbar(cax)

plt.xlabel("Times [sec]")

plt.ylabel("Frequency [Hz]")

plt.show()

-----------------------------------------------

import numpy

import matplotlib.pyplot as plt

def _get_mask(t, t1, t2, lvl_pos, lvl_neg):

if t1 >= t2:

raise ValueError("t1 must be less than t2")

return numpy.where(numpy.logical_and(t > t1, t < t2), lvl_pos, lvl_neg)

def generate_signal(t):

sin1 = numpy.sin(2 * numpy.pi * 100 * t)

sin2 = 2 * numpy.sin(2 * numpy.pi * 200 * t)

# add interval of high pitched signal

masks = _get_mask(t, 2, 4, 1.0, 0.0) + \

_get_mask(t, 14, 15, 1.0, 0.0)

sin2 = sin2 * masks

noise = 0.02 * numpy.random.randn(len(t))

final_signal = sin1 + sin2 + noise

return final_signal

if __name__ == '__main__':

step = 0.001

sampling_freq=1000

t = numpy.arange(0.0, 20.0, step)

y = generate_signal(t)

# we can visualize this now

# in time

ax1 = plt.subplot(211)

plt.plot(t, y)

# and in frequency

plt.subplot(212)

plt.specgram(y, NFFT=1024, noverlap=900,

Fs=sampling_freq, cmap=plt.cm.gist_heat)

plt.show()

#==============================================================================

# 7.4 创建火柴杆图

#==============================================================================

import matplotlib.pyplot as plt

import numpy as np

x = np.linspace(0, 20, 50)

y = np.sin(x + 1) + np.cos(x ** 2)

bottom = -0.1 # 基线位置

# True,hold current axes for further plotting

# False,opposite. clear and use new figure/plot

hold = False #把所有当前图形放在当前坐标轴上

label = "delta" #设置火柴杠图图例

'''

markerline:保存了表示火柴杆本身的线条引用,仅渲染了标记,不包括连接标记的线条

stemlines:保存了表示stemlines原点的水平线条的引用。

baseline:表示茎线的集合

'''

markerline, stemlines, baseline = plt.stem(x, y, bottom=bottom,label=label, hold=hold)

plt.setp(markerline, color='red', marker='o')

plt.setp(stemlines, color='blue', linestyle=':')

plt.setp(baseline, color='grey', linewidth=2, linestyle='-')

# draw a legend

plt.legend()

plt.show()

#==============================================================================

# 7.5 绘制矢量场流线图

#==============================================================================

import matplotlib.pyplot as plt

import numpy as np

Y, X = np.mgrid[0:5:100j, 0:5:100j]

U = X # np.sin(X)

V = Y # np.sin(Y)

from pprint import pprint

print "X"

pprint(X)

print "Y"

pprint(Y)

plt.streamplot(X, Y, U, V)

plt.show()

#==============================================================================

# 7.6 使用颜色表

#==============================================================================

数据没有与红色/绿色有很强的关联时,要尽可能的避免使用这两种颜色。

颜色表：

Sequential:表示同一颜色从低饱和度到高饱和度的两个色调的单色颜色表。

Diverging:表示中间值,是颜色的中值,然后颜色范围在高和低两个方向上变化到两个不同的色调。

例如中值为0,能清晰的显示负值和正值之间的区别。

Qualitative:对于数据没有固定的顺序,并且想要让不同种类的数据之间能轻易的区分开的情况,可选用该颜色表。

Cyclic:当数据可以围绕端点值显示的时候,比较方便，例如一天的时间，风向，相位角。

matplotlib预定义的颜色表:autumn、bone、cool、copper、flag、gray、hot、hsv、jet、pink、prism、sprint、summer、winter、spectral

Yorick科学可视化包:gist_earth、gist_heat、gist_ncar、gist_rainbow、gist_stern

ColorBrewer颜色表

Diverging:中间亮度最高,向两端递减

Sequential:亮度单调地递减

Qualitative:不同种类的颜色用来区分不同的数据类别

其他：

brg：表示一个发散型的蓝、红、绿颜色表

bwr：发散型蓝、白、红颜色表

seismic：发散蓝、白、红

coolwarm：对于3D阴影,色盲和颜色排序非常有用

rainbow：发散亮度的紫、蓝、绿、黄、橙、红光谱

terrain：地图标记的颜色(蓝、绿、黄、棕、白)

颜色表名_r表示反转。

import matplotlib as mpl

import matplotlib.pyplot as plt

import numpy as np

red_yellow_green = ['#d73027', '#f46d43', '#fdae61',

'#fee08b', '#ffffbf', '#d9ef8b',

'#a6d96a', '#66bd63', '#1a9850']

fig,ax = plt.subplots(1)

for i in range(9):

y = np.random.normal(size=1000).cumsum()

x = np.arange(1000)

ax.scatter(x, y, label=str(i), linewidth=0.1, edgecolors='grey', facecolor=red_yellow_green[i])

ax.legend()

plt.show()

#==============================================================================

# 7.7 使用散点图和直方图

#==============================================================================

散点图

-----------------------------------------------------------

import matplotlib.pyplot as plt

import numpy as np

# import the data

from ch07_search_data import DATA

data = DATA, data1 = np.random.random(365)

assert len(data) == len(data1)

fig = plt.figure()

ax1 = fig.add_subplot(221)

ax1.scatter(data, data1, alpha=0.5)

ax1.set_title('No correlation')

ax1.grid(True)

ax2 = fig.add_subplot(222)

ax2.scatter(data1, data1, alpha=0.5)

ax2.set_title('Ideal positive correlation')

ax2.grid(True)

ax3 = fig.add_subplot(223)

ax3.scatter(data1, data1*-1, alpha=0.5)

ax3.set_title('Ideal negative correlation')

ax3.grid(True)

ax4 = fig.add_subplot(224)

ax4.scatter(data1, data1+data, alpha=0.5)

ax4.set_title('Non ideal positive correlation')

ax4.grid(True)

plt.tight_layout()

plt.show()

直方图

-----------------------------------------------------------

import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.axes_grid1 import make_axes_locatable

def scatterhist(x, y, figsize=(8,8)):

"""

Create simple scatter & histograms of data x, y inside given plot

@param figsize: Figure size to create figure

@type figsize: Tuple of two floats representing size in inches

@param x: X axis data set

@type x: np.array

@param y: Y axis data set

@type y: np.array

"""

_, scatter_axes = plt.subplots(figsize=figsize)

# the scatter plot:

scatter_axes.scatter(x, y, alpha=0.5)

scatter_axes.set_aspect(1.)

divider = make_axes_locatable(scatter_axes)

axes_hist_x = divider.append_axes(position="top", sharex=scatter_axes, size=1, pad=0.1)

axes_hist_y = divider.append_axes(position="right", sharey=scatter_axes, size=1, pad=0.1)

binwidth = 0.25# compute bins accordingly

# global max value in both data sets

xymax = np.max([np.max(np.fabs(x)), np.max(np.fabs(y))])

# number of bins

bincap = int(xymax / binwidth) * binwidth

bins = np.arange(-bincap, bincap, binwidth)

nx, binsx, _ = axes_hist_x.hist(x, bins=bins, histtype='stepfilled',

orientation='vertical')

ny, binsy, _ = axes_hist_y.hist(y, bins=bins, histtype='stepfilled',

orientation='horizontal')

tickstep = 50

ticksmax = np.max([np.max(nx), np.max(ny)])

xyticks = np.arange(0, ticksmax + tickstep, tickstep)

# hide x and y ticklabels on histograms

for tl in axes_hist_x.get_xticklabels():

tl.set_visible(False)

axes_hist_x.set_yticks(xyticks)

for tl in axes_hist_y.get_yticklabels():

tl.set_visible(False)

axes_hist_y.set_xticks(xyticks)

plt.show()

if __name__ == '__main__':

# import the data

from ch07_search_data import DATA

d = DATA

# Now let's generate random data for the same period

d1 = np.random.random(365)

assert len(d) == len(d1)

# try with the random data

# d = np.random.randn(1000)

# d1 = np.random.randn(1000)

scatterhist(d, d1)

#==============================================================================

# 7.8 绘制两个变量之间的互相图形

#==============================================================================

绘两个数据集之间的相互关系

import matplotlib.pyplot as plt

import numpy as np

# import the data

from ch07_search_data import DATA

data = DATA

total = sum(data)

av = total / len(data)

z = [i - av for i in data]

av1 = sum(data1) / len(data1)

z1 = [i - av1 for i in data1]

data1 = np.random.random(365)

assert len(data) == len(data1)

fig = plt.figure()

ax1 = fig.add_subplot(311)

ax1.plot(data)

ax1.set_xlabel('Google Trends data for "flowers"')

ax2 = fig.add_subplot(312)

ax2.plot(data1)

ax2.set_xlabel('Random data')

ax3 = fig.add_subplot(313)

ax3.set_xlabel('Cross correlation of random data')

ax3.xcorr(z, z1, usevlines=True, maxlags=None, normed=True, lw=2)

ax3.grid(True)

plt.ylim(-1,1)

plt.tight_layout()

plt.show()

#==============================================================================

# 7.9 自相关的重要性

#==============================================================================

import matplotlib.pyplot as plt

import numpy as np

from ch07_search_data import DATA as data

total = sum(data)

av = total / len(data)

z = [i - av for i in data]

fig = plt.figure()

plt.title('Comparing autocorrelations')

ax1 = fig.add_subplot(221)

ax1.plot(data)

ax1.set_xlabel('Google Trends data for "flowers"')

ax2 = fig.add_subplot(222)

ax2.acorr(z, usevlines=True, maxlags=None, normed=True, lw=2)

ax2.grid(True)

ax2.set_xlabel('Autocorrelation')

data1 = np.random.random(365)

assert len(data) == len(data1)

total = sum(data1)

av = total / len(data1)

z = [i - av for i in data1]

ax3 = fig.add_subplot(223)

ax3.plot(data1)

ax3.set_xlabel('Random data')

ax4 = fig.add_subplot(224)

ax4.set_xlabel('Autocorrelation of random data')

ax4.acorr( z, usevlines=True, maxlags=None, normed=True, lw=2)

ax4.grid(True)

plt.show()

#==============================================================================

# 第八章更多matplotlib

# 8.2 绘制风杠barbs

#==============================================================================

import matplotlib.pyplot as plt

import numpy as np

'''

barbcolor:风杠中除旗标颜色

flagcolor:风杠中旗标颜色

facecolor:默认,前两者将覆盖这个

spacing:旗标/风杠属性间的间距

height:箭杆到旗标或者风杠顶部的距离

width:旗标的宽度

emptybarb:定义最小值的圆圈的半径

'''

V = [0, -5, -10, -15, -30, -40, -50, -60, -100]

U = np.zeros(len(V))

y = np.ones(len(V))

x = [0, 5, 10, 15, 30, 40, 50, 60, 100]

plt.barbs(x, y, U, V, length=9)

plt.xticks(x)

plt.ylim(0.98, 1.05)

plt.show()

----------------------------------------------

import matplotlib.pyplot as plt

import numpy as np

x = np.linspace(-20, 20, 8)

y = np.linspace( 0, 20, 8)

# make 2D coordinates

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

U, V = X + 25, Y - 35

# plot the barbs

plt.subplot(1,2,1)

plt.barbs(X, Y, U, V, flagcolor='green', alpha=0.75)

plt.grid(True, color='gray')

# compare that with quiver / arrows

plt.subplot(1,2,2)

plt.quiver(X, Y, U, V, facecolor='red', alpha=0.75)

# misc settings

plt.grid(True, color='grey')

plt.show()

#==============================================================================

# 8.3 绘制箱线图

#==============================================================================

import matplotlib.pyplot as plt

PROCESSES = {

"A": [12, 15, 23, 24, 30, 31, 33, 36, 50, 73],

"B": [6, 22, 26, 33, 35, 47, 54, 55, 62, 63],

"C": [2, 3, 6, 8, 13, 14, 19, 23, 60, 69],

"D": [1, 22, 36, 37, 45, 47, 48, 51, 52, 69],

}

DATA = PROCESSES.values()

LABELS = PROCESSES.keys()

plt.boxplot(DATA, widths=0.3)

# set ticklabel to process name

plt.gca().xaxis.set_ticklabels(LABELS)

# some makeup (removing chartjunk)

for spine in plt.gca().spines.values():

spine.set_visible(False)

plt.gca().xaxis.set_ticks_position('none')

plt.gca().yaxis.set_ticks_position('left')

plt.gca().grid(axis='y', color='gray')

# set axes labels

plt.ylabel("Errors observed over defined period.")

plt.xlabel("Process observed over defined period.")

plt.show()

#==============================================================================

# 8.4 绘制甘特图

#==============================================================================

from datetime import datetime

import sys

import numpy as np

import matplotlib.pyplot as plt

import matplotlib.font_manager as font_manager

import matplotlib.dates as mdates

import logging

class Gantt(object):

# from http://colorbrewer2.org/

RdYlGr = ['#d73027', '#f46d43', '#fdae61',

'#fee08b', '#ffffbf', '#d9ef8b',

'#a6d96a', '#66bd63', '#1a9850']

POS_START = 1.0

POS_STEP = 0.5

def __init__(self, tasks):

self._fig = plt.figure()

self._ax = self._fig.add_axes([0.1, 0.1, .75, .5])

self.tasks = tasks[::-1]

def _format_date(self, date_string):

try:

date = datetime.strptime(date_string, '%Y-%m-%d %H:%M:%S')

except ValueError as err:

logging.error("String '{0}' can not be converted to datetime object: {1}"

.format(date_string, err))

sys.exit(-1)

mpl_date = mdates.date2num(date)

return mpl_date

def _plot_bars(self):

i = 0

for task in self.tasks:

start = self._format_date(task['start'])

end = self._format_date(task['end'])

bottom = (i * Gantt.POS_STEP) + Gantt.POS_START

width = end - start

self._ax.barh(bottom, width, left=start, height=0.3,

align='center', label=task['label'],

color = Gantt.RdYlGr[i])

i += 1

def _configure_yaxis(self):'''y axis'''

task_labels = [t['label'] for t in self.tasks]

pos = self._positions(len(task_labels))

ylocs = self._ax.set_yticks(pos)

ylabels = self._ax.set_yticklabels(task_labels)

plt.setp(ylabels, size='medium')

def _configure_xaxis(self):''''x axis'''

self._ax.xaxis_date()

# format date to ticks on every 7 days

rule = mdates.rrulewrapper(mdates.DAILY, interval=7)

loc = mdates.RRuleLocator(rule)

formatter = mdates.DateFormatter("%d %b")

self._ax.xaxis.set_major_locator(loc)

self._ax.xaxis.set_major_formatter(formatter)

xlabels = self._ax.get_xticklabels()

plt.setp(xlabels, rotation=30, fontsize=9)

def _configure_figure(self):

self._configure_xaxis()

self._configure_yaxis()

self._ax.grid(True, color='gray')

self._set_legend()

self._fig.autofmt_xdate()

def _set_legend(self):

font = font_manager.FontProperties(size='small')

self._ax.legend(loc='upper right', prop=font)

def _positions(self, count):

end = count * Gantt.POS_STEP + Gantt.POS_START

pos = np.arange(Gantt.POS_START, end, Gantt.POS_STEP)

return pos

def show(self):

self._plot_bars()

self._configure_figure()

plt.show()

if __name__ == '__main__':

TEST_DATA = (

{ 'label': 'Research', 'start':'2013-10-01 12:00:00', 'end': '2013-10-02 18:00:00'}, # @IgnorePep8

{ 'label': 'Compilation', 'start':'2013-10-02 09:00:00', 'end': '2013-10-02 12:00:00'}, # @IgnorePep8

{ 'label': 'Meeting #1', 'start':'2013-10-03 12:00:00', 'end': '2013-10-03 18:00:00'}, # @IgnorePep8

{ 'label': 'Design', 'start':'2013-10-04 09:00:00', 'end': '2013-10-10 13:00:00'}, # @IgnorePep8

{ 'label': 'Meeting #2', 'start':'2013-10-11 09:00:00', 'end': '2013-10-11 13:00:00'}, # @IgnorePep8

{ 'label': 'Implementation', 'start':'2013-10-12 09:00:00', 'end': '2013-10-22 13:00:00'}, # @IgnorePep8

{ 'label': 'Demo', 'start':'2013-10-23 09:00:00', 'end': '2013-10-23 13:00:00'}, # @IgnorePep8

)

gantt = Gantt(TEST_DATA)

gantt.show()

#==============================================================================

# 8.5 绘制误差条

#==============================================================================

import matplotlib.pyplot as plt

import numpy as np

import scipy.stats as sc

TEST_DATA = np.array([[1,2,3,2,1,2,3,4,2,3,2,1,2,3,4,4,3,2,3,2,3,2,1],

[5,6,5,4,5,6,7,7,6,7,7,2,8,7,6,5,5,6,7,7,7,6,5],

[9,8,7,8,8,7,4,6,6,5,4,3,2,2,2,3,3,4,5,5,5,6,1],

[3,2,3,2,2,2,2,3,3,3,3,4,4,4,4,5,6,6,7,8,9,8,5],

])

y = np.mean(TEST_DATA, axis=1, dtype=np.float64)

#计算出95%的置信区间

ci95 = np.abs(y - 1.96 * sc.sem(TEST_DATA, axis=1))

tries = np.arange(0, len(y), 1.0)# each set is one try

plt.grid(True, alpha=0.5)

plt.gca().set_xlabel('Observation #')

plt.gca().set_ylabel('Mean (+- 95% CI)')

plt.title("Observations with corresponding 95% CI as error bar.")

plt.bar(tries, y, align='center', alpha=0.2)

plt.errorbar(tries, y, yerr=ci95, fmt=None)

plt.show()

#==============================================================================

# 8.6 使用文本和字体属性

#==============================================================================

import matplotlib.pyplot as plt

from matplotlib.font_manager import FontProperties

#字体类型

families = ['serif', 'sans-serif', 'cursive', 'fantasy', 'monospace']

#字体大小

sizes = ['xx-small', 'x-small', 'small', 'medium', 'large',

'x-large', 'xx-large']

#字体风格

styles = ['normal', 'italic', 'oblique']

#字体粗细

weights = ['light', 'normal', 'medium', 'semibold', 'bold', 'heavy', 'black']

#字体的变体形式

variants = ['normal', 'small-caps']

fig = plt.figure(figsize=(9,17))

ax = fig.add_subplot(111)

ax.set_xlim(0,9)

ax.set_ylim(0,17)

# VAR: FAMILY, SIZE

y = 0

size = sizes[0]

style = styles[0]

weight = weights[0]

variant = variants[0]

for family in families:

x = 0

y = y + .5

for size in sizes:

y = y + .4

sample = family + " " + size

ax.text(x, y, sample,

family=family,

size=size,

style=style,

weight=weight,

variant=variant)

# VAR: STYLE, WEIGHT

y = 0

family = families[0]

size = sizes[4]

variant = variants[0]

for weight in weights:

x = 5

y = y + .5

for style in styles:

y = y + .4

print x, y

sample = weight + " " + style

ax.text(x, y, sample,

family=family,

size=size,

style=style,

weight=weight,

variant=variant)

ax.set_axis_off()

plt.show()

#==============================================================================

# 8.7 使用LaTeX渲染文本

#==============================================================================

import numpy as np

import matplotlib.pyplot as plt

# Example data

t = np.arange(0.0, 1.0 + 0.01, 0.01)

s = np.cos(4 * np.pi * t) * np.sin(np.pi*t/4) + 2

plt.rc('text', usetex=True)

plt.rc('font',**{'family':'sans-serif','sans-serif':['Helvetica'], 'size':16})

plt.plot(t, s, alpha=0.25)

# first, the equation for 's'

plt.annotate(r'$\cos(4 \times \pi \times {t}) \times \sin(\pi \times \frac {t} 4) + 2$', xy=(.9,2.2), xytext=(.5, 2.6), color='red', arrowprops={'arrowstyle':'->'})

# some math alphabet

plt.text(.01, 2.7, r'$\alpha, \beta, \gamma, \Gamma, \pi, \Pi, \phi, \varphi, \Phi$')

# some equation

plt.text(.01, 2.5, r'some equations $\frac{n!}{k!(n-k)!} = {n \choose k}$')

# more equations

plt.text(.01, 2.3, r'EQ1 $\lim_{x \to \infty} \exp(-x) = 0$')

# some ranges...

plt.text(.01, 2.1, r'Ranges: $( a ), [ b ], \{ c \}, | d |, \| e \|, \langle f \rangle, \lfloor g \rfloor, \lceil h \rceil$')

# you can multiply apples and oranges

plt.text(.01, 1.9, r'Text: $50 apples \times 100 oranges = lots of juice$')

plt.text(.01, 1.7, r'More text formatting: $50 \textrm{ apples} \times 100 \textbf{ apples} = \textit{lots of juice}$')

plt.text(.01, 1.5, r'Some indexing: $\beta = (\beta_1,\beta_2,\dotsc,\beta_n)$')

# we can also write on labels

plt.xlabel(r'\textbf{time} (s)')

plt.ylabel(r'\textit{y values} (W)')

# and write titles using LaTeX

plt.title(r"\TeX\ is Number "

r"$\displaystyle\sum_{n=1}^\infty\frac{-e^{i\pi}}{2^n}$!",

fontsize=16, color='gray')

# Make room for the ridiculously large title.

plt.subplots_adjust(top=0.8)

plt.savefig('tex_demo')

plt.show()

#==============================================================================

# 8.8 理解pyplot和OO API的不同

#==============================================================================

import matplotlib.pyplot as plt

from matplotlib.path import Path

import matplotlib.patches as patches

# add figure and axes

fig = plt.figure()

ax = fig.add_subplot(111)

coords = [

(1., 0.), # start position

(0., 1.),

(0., 2.), # left side

(1., 3.),

(2., 3.),

(3., 2.), # top right corner

(3., 1.), # right side

(2., 0.),

(0., 0.), # ignored

]

line_cmds = [Path.MOVETO,

Path.LINETO,

Path.CLOSEPOLY,

]

# construct path

path = Path(coords, line_cmds)

# construct path patch

patch = patches.PathPatch(path, lw=1,

facecolor='#A1D99B', edgecolor='#31A354')

# add it to *ax* axes

ax.add_patch(patch)

ax.text(1.1, 1.4, 'Python', fontsize=24)

ax.set_xlim(-1, 4)

ax.set_ylim(-1, 4)

plt.show()

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python数据处理7

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