使用Python一步一步地来进行数据分析总结

原文链接:Step by step approach to perform data analysis using Python
译文链接:使用Python一步一步地来进行数据分析--By Michael翔

你已经决定来学习Python,但是你之前没有编程经验。因此,你常常对从哪儿着手而感到困惑,这么多Python的知识需要去学习。以下这些是那些开始使用Python数据分析的初学者的普遍遇到的问题:

  • 需要多久来学习Python?

  • 我需要学习Python到什么程度才能来进行数据分析呢?

  • 学习Python最好的书或者课程有哪些呢?

  • 为了处理数据集,我应该成为一个Python的编程专家吗?

当开始学习一项新技术时,这些都是可以理解的困惑,这是《在20小时内学会任何东西》的作者所说的。不要害怕,我将会告诉你怎样快速上手,而不必成为一个Python编程“忍者”。

不要犯我之前犯过的错

在开始使用Python之前,我对用Python进行数据分析有一个误解:我必须不得不对Python编程特别精通。因此,我参加了Udacity的Python编程入门课程,完成了code academy上的Python教程,同时阅读了若干本Python编程书籍。就这样持续了3个月(平均每天3个小时),我那会儿通过完成小的软件项目来学习Python。敲代码是快乐的事儿,但是我的目标不是去成为一个Python开发人员,而是要使用Python数据分析。之后,我意识到,我花了很多时间来学习用Python进行软件开发,而不是数据分析。

在几个小时的深思熟虑之后,我发现,我需要学习5个Python库来有效地解决一系列的数据分析问题。然后,我开始一个接一个的学习这些库。

在我看来,精通用Python开发好的软件才能够高效地进行数据分析,这观点是没有必要的。

忽略给大众的资源

有许多优秀的Python书籍和在线课程,然而我不并不推荐它们中的一些,因为,有些是给大众准备的而不是给那些用来数据分析的人准备的。同样也有许多书是“用Python科学编程”的,但它们是面向各种数学为导向的主题的,而不是成为为了数据分析和统计。不要浪费浪费你的时间去阅读那些为大众准备的Python书籍。

在进一步继续之前,首先设置好你的编程环境,然后学习怎么使用IPython notebook

学习途径

code academy开始学起,完成上面的所有练习。每天投入3个小时,你应该在20天内完成它们。Code academy涵盖了Python基本概念。但是,它不像Udacity那样以项目为导向;没关系,因为你的目标是从事数据科学,而不是使用Python开发软件。

当完成了code academy练习之后,看看这个Ipython notebook:

Python必备教程(在总结部分我已经提供了下载链接)。

它包括了code academy中没有提到的一些概念。你能在1到2小时内学完这个教程。

现在,你知道足够的基础知识来学习Python库了。

Numpy

首先,开始学习Numpy吧,因为它是利用Python科学计算的基础包。对Numpy好的掌握将会帮助你有效地使用其他工具例如Pandas。

我已经准备好了IPython笔记,这包含了Numpy的一些基本概念。这个教程包含了Numpy中最频繁使用的操作,例如,N维数组,索引,数组切片,整数索引,数组转换,通用函数,使用数组处理数据,常用的统计方法,等等。

Numpy Basics Tutorial

Index Numpy 遇到Numpy陌生函数,查询用法,推荐!

Pandas

Pandas包含了高级的数据结构和操作工具,它们使得Python数据分析更加快速和容易。

教程包含了series, data frams,从一个axis删除数据,缺失数据处理,等等。

Pandas Basics Tutorial

Index Pandas 遇到陌生函数,查询用法,推荐!

pandas教程-百度经验

Matplotlib

这是一个分为四部分的Matplolib教程。

1st 部分:

第一部分介绍了Matplotlib基本功能,基本figure类型。

Simple Plotting example

In [113]:
%matplotlib inline
import matplotlib.pyplot as plt #importing matplot lib library
import numpy as np
x = range(100)
#print x, print and check what is x
y =[val**2 for val in x]
#print y
plt.plot(x,y) #plotting x and y
Out[113]:
[<matplotlib.lines.Line2D at 0x7857bb0>]
 
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" alt="" />
 
fig, axes = plt.subplots(nrows=1, ncols=2)

for ax in axes:
ax.plot(x, y, 'r')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_title('title') fig.tight_layout()
 
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" alt="" />
fig, ax = plt.subplots()

ax.plot(x, x**2, label="y = x**2")
ax.plot(x, x**3, label="y = x**3")
ax.legend(loc=2); # upper left corner
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_title('title');
 
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" alt="" />
fig, axes = plt.subplots(1, 2, figsize=(10,4))

axes[0].plot(x, x**2, x, np.exp(x))
axes[0].set_title("Normal scale") axes[1].plot(x, x**2, x, np.exp(x))
axes[1].set_yscale("log")
axes[1].set_title("Logarithmic scale (y)");
 
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" alt="" />
n = np.array([0,1,2,3,4,5])
In [47]:
fig, axes = plt.subplots(1, 4, figsize=(12,3))

axes[0].scatter(xx, xx + 0.25*np.random.randn(len(xx)))
axes[0].set_title("scatter") axes[1].step(n, n**2, lw=2)
axes[1].set_title("step") axes[2].bar(n, n**2, align="center", width=0.5, alpha=0.5)
axes[2].set_title("bar") axes[3].fill_between(x, x**2, x**3, color="green", alpha=0.5);
axes[3].set_title("fill_between");
 
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" alt="" />
 

Using Numpy

In [17]:
x = np.linspace(0, 2*np.pi, 100)
y =np.sin(x)
plt.plot(x,y)
Out[17]:
[<matplotlib.lines.Line2D at 0x579aef0>]
 
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" alt="" />
In [24]:
x= np.linspace(-3,2, 200)
Y = x ** 2 - 2 * x + 1.
plt.plot(x,Y)
Out[24]:
[<matplotlib.lines.Line2D at 0x6ffb310>]
 
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" alt="" />
In [32]:
# plotting multiple plots
x =np.linspace(0, 2 * np.pi, 100)
y = np.sin(x)
z = np.cos(x)
plt.plot(x,y)
plt.plot(x,z)
plt.show() # Matplot lib picks different colors for different plot.
 
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" alt="" />
In [35]:
cd C:\Users\tk\Desktop\Matplot
 
C:\Users\tk\Desktop\Matplot
In [39]:
data = np.loadtxt('numpy.txt')
plt.plot(data[:,0], data[:,1]) # plotting column 1 vs column 2
# The text in the numpy.txt should look like this
# 0 0
# 1 1
# 2 4
# 4 16
# 5 25
# 6 36
Out[39]:
[<matplotlib.lines.Line2D at 0x740f090>]
 
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" alt="" />
In [56]:
data1 = np.loadtxt('scipy.txt') # load the file
print data1.T for val in data1.T: #loop over each and every value in data1.T
plt.plot(data1[:,0], val) #data1[:,0] is the first row in data1.T # data in scipy.txt looks like this:
# 0 0 6
# 1 1 5
# 2 4 4
# 4 16 3
# 5 25 2
# 6 36 1
 
[[  0.   1.   2.   4.   5.   6.]
[ 0. 1. 4. 16. 25. 36.]
[ 6. 5. 4. 3. 2. 1.]]
 
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" alt="" />
 

Scatter Plots and Bar Graphs

In [64]:
sct = np.random.rand(20, 2)
print sct
plt.scatter(sct[:,0], sct[:,1]) # I am plotting a scatter plot.
 
[[ 0.51454542  0.61859101]
[ 0.45115993 0.69774873]
[ 0.29051205 0.28594808]
[ 0.73240446 0.41905186]
[ 0.23869394 0.5238878 ]
[ 0.38422814 0.31108919]
[ 0.52218967 0.56526379]
[ 0.60760426 0.80247073]
[ 0.37239096 0.51279078]
[ 0.45864677 0.28952167]
[ 0.8325996 0.28479446]
[ 0.14609382 0.8275477 ]
[ 0.86338279 0.87428696]
[ 0.55481585 0.24481165]
[ 0.99553336 0.79511137]
[ 0.55025277 0.67267026]
[ 0.39052024 0.65924857]
[ 0.66868207 0.25186664]
[ 0.64066313 0.74589812]
[ 0.20587731 0.64977807]]
Out[64]:
<matplotlib.collections.PathCollection at 0x78a7110>
 
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" alt="" />
In [65]:
ghj =[5, 10 ,15, 20, 25]
it =[ 1, 2, 3, 4, 5]
plt.bar(ghj, it) # simple bar graph
Out[65]:
<Container object of 5 artists>
 
aaarticlea/png;base64,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" alt="" />
In [74]:
ghj =[5, 10 ,15, 20, 25]
it =[ 1, 2, 3, 4, 5]
plt.bar(ghj, it, width =5)# you can change the thickness of a bar, by default the bar will have a thickness of 0.8 units
Out[74]:
<Container object of 5 artists>
 
aaarticlea/png;base64,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" alt="" />
In [75]:
ghj =[5, 10 ,15, 20, 25]
it =[ 1, 2, 3, 4, 5]
plt.barh(ghj, it) # barh is a horizontal bar graph
Out[75]:
<Container object of 5 artists>
 
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Multiple bar charts

In [95]:
new_list = [[5., 25., 50., 20.], [4., 23., 51., 17.], [6., 22., 52., 19.]]
x = np.arange(4)
plt.bar(x + 0.00, new_list[0], color ='b', width =0.25)
plt.bar(x + 0.25, new_list[1], color ='r', width =0.25)
plt.bar(x + 0.50, new_list[2], color ='g', width =0.25) #plt.show()
 
aaarticlea/png;base64,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*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" alt="" />
In [100]:
#Stacked Bar charts
p = [5., 30., 45., 22.]
q = [5., 25., 50., 20.]
x =range(4)
plt.bar(x, p, color ='b')
plt.bar(x, q, color ='y', bottom =p)
Out[100]:
<Container object of 4 artists>
 
aaarticlea/png;base64,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" alt="" />
In [35]:
# plotting more than 2 values
A = np.array([5., 30., 45., 22.])
B = np.array([5., 25., 50., 20.])
C = np.array([1., 2., 1., 1.])
X = np.arange(4)
plt.bar(X, A, color = 'b')
plt.bar(X, B, color = 'g', bottom = A)
plt.bar(X, C, color = 'r', bottom = A + B) # for the third argument, I use A+B
plt.show()
 
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In [94]:
black_money = np.array([5., 30., 45., 22.])
white_money = np.array([5., 25., 50., 20.])
z = np.arange(4)
plt.barh(z, black_money, color ='g')
plt.barh(z, -white_money, color ='r')# - notation is needed for generating, back to back charts
Out[94]:
<Container object of 4 artists>
 
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" alt="" />
 

Other Plots

In [114]:
#Pie charts
y = [5, 25, 45, 65]
plt.pie(y)
Out[114]:
([<matplotlib.patches.Wedge at 0x7a19d50>,
<matplotlib.patches.Wedge at 0x7a252b0>,
<matplotlib.patches.Wedge at 0x7a257b0>,
<matplotlib.patches.Wedge at 0x7a25cb0>],
[<matplotlib.text.Text at 0x7a25070>,
<matplotlib.text.Text at 0x7a25550>,
<matplotlib.text.Text at 0x7a25a50>,
<matplotlib.text.Text at 0x7a25f50>])
 
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" alt="" />
In [115]:
#Histograms
d = np.random.randn(100)
plt.hist(d, bins = 20)
Out[115]:
(array([  2.,   3.,   2.,   1.,   2.,   6.,   5.,   7.,  10.,  12.,   9.,
12., 11., 5., 6., 4., 1., 0., 1., 1.]),
array([-2.9389701 , -2.64475645, -2.35054281, -2.05632916, -1.76211551,
-1.46790186, -1.17368821, -0.87947456, -0.58526092, -0.29104727,
0.00316638, 0.29738003, 0.59159368, 0.88580733, 1.18002097,
1.47423462, 1.76844827, 2.06266192, 2.35687557, 2.65108921,
2.94530286]),
<a list of 20 Patch objects>)
 
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In [116]:
d = np.random.randn(100)
plt.boxplot(d)
#1) The red bar is the median of the distribution
#2) The blue box includes 50 percent of the data from the lower quartile to the upper quartile.
# Thus, the box is centered on the median of the data.
Out[116]:
{'boxes': [<matplotlib.lines.Line2D at 0x7cca090>],
'caps': [<matplotlib.lines.Line2D at 0x7c02d70>,
<matplotlib.lines.Line2D at 0x7cc2c90>],
'fliers': [<matplotlib.lines.Line2D at 0x7cca850>,
<matplotlib.lines.Line2D at 0x7ccae10>],
'medians': [<matplotlib.lines.Line2D at 0x7cca470>],
'whiskers': [<matplotlib.lines.Line2D at 0x7c02730>,
<matplotlib.lines.Line2D at 0x7cc24b0>]}
 
aaarticlea/png;base64,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" alt="" />
In [118]:
d = np.random.randn(100, 5) # generating multiple box plots
plt.boxplot(d)
Out[118]:
{'boxes': [<matplotlib.lines.Line2D at 0x7f49d70>,
<matplotlib.lines.Line2D at 0x7ea1c90>,
<matplotlib.lines.Line2D at 0x7eafb90>,
<matplotlib.lines.Line2D at 0x7ebea90>,
<matplotlib.lines.Line2D at 0x7ece990>],
'caps': [<matplotlib.lines.Line2D at 0x7f2b3b0>,
<matplotlib.lines.Line2D at 0x7f49990>,
<matplotlib.lines.Line2D at 0x7ea14d0>,
<matplotlib.lines.Line2D at 0x7ea18b0>,
<matplotlib.lines.Line2D at 0x7eaf3d0>,
<matplotlib.lines.Line2D at 0x7eaf7b0>,
<matplotlib.lines.Line2D at 0x7ebe2d0>,
<matplotlib.lines.Line2D at 0x7ebe6b0>,
<matplotlib.lines.Line2D at 0x7ece1d0>,
<matplotlib.lines.Line2D at 0x7ece5b0>],
'fliers': [<matplotlib.lines.Line2D at 0x7e98550>,
<matplotlib.lines.Line2D at 0x7e98930>,
<matplotlib.lines.Line2D at 0x7ea8470>,
<matplotlib.lines.Line2D at 0x7ea8a10>,
<matplotlib.lines.Line2D at 0x7eb6370>,
<matplotlib.lines.Line2D at 0x7eb6730>,
<matplotlib.lines.Line2D at 0x7ec6270>,
<matplotlib.lines.Line2D at 0x7ec6810>,
<matplotlib.lines.Line2D at 0x8030170>,
<matplotlib.lines.Line2D at 0x8030710>],
'medians': [<matplotlib.lines.Line2D at 0x7e98170>,
<matplotlib.lines.Line2D at 0x7ea8090>,
<matplotlib.lines.Line2D at 0x7eaff70>,
<matplotlib.lines.Line2D at 0x7ebee70>,
<matplotlib.lines.Line2D at 0x7eced70>],
'whiskers': [<matplotlib.lines.Line2D at 0x7f2bb50>,
<matplotlib.lines.Line2D at 0x7f491b0>,
<matplotlib.lines.Line2D at 0x7e98cf0>,
<matplotlib.lines.Line2D at 0x7ea10f0>,
<matplotlib.lines.Line2D at 0x7ea8bf0>,
<matplotlib.lines.Line2D at 0x7ea8fd0>,
<matplotlib.lines.Line2D at 0x7eb6cd0>,
<matplotlib.lines.Line2D at 0x7eb6ed0>,
<matplotlib.lines.Line2D at 0x7ec6bd0>,
<matplotlib.lines.Line2D at 0x7ec6dd0>]}
 
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eOJYoXgI8Cb8kdSCbfAbybrrf7CPCdwO9kjSivf5n9+2/AH9PdVKhMNnHEvUF3QD6UO5AAbgFeO3v+KuAJ4B35wgnjTuoecd8MvHr2/OuAv2Tvsh5ao0eAfwb+C3gOuDdvONncQdfDvEQ31ekicFfWiPJ5I/Bpulw8SdffVVe4a55V8s10+8QluimzZ/KGI0mSJEmSJEmSJEmSJEmSJEmSJOm6/D/LHt7iqzqySAAAAABJRU5ErkJggg==" alt="" />

MatplotLib Part 1

2nd 部分:

包含了怎么调整figure的样式和颜色,例如:makers,line,thicness,line patterns和color map.

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
In [22]:
p =np.random.standard_normal((50,2))
p += np.array((-1,1)) # center the distribution at (-1,1) q =np.random.standard_normal((50,2))
q += np.array((1,1)) #center the distribution at (-1,1) plt.scatter(p[:,0], p[:,1], color ='.25')
plt.scatter(q[:,0], q[:,1], color = '.75')
Out[22]:
<matplotlib.collections.PathCollection at 0x71dab90>
 
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" alt="" />
In [34]:
dd =np.random.standard_normal((50,2))
plt.scatter(dd[:,0], dd[:,1], color ='1.0', edgecolor ='0.0') # edge color controls the color of the edge
Out[34]:
<matplotlib.collections.PathCollection at 0x7336670>
 
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" alt="" />
 

Custom Color for Bar charts,Pie charts and box plots: 
The below bar graph, plots x(1 to 50) (vs) y(50 random integers, within 0-100. But you need different colors for each value. For which we create a list containing four colors(color_set). The list comprehension creates 50 different color values from color_set

In [9]:
vals = np.random.random_integers(99, size =50)
color_set = ['.00', '.25', '.50','.75']
color_lists = [color_set[(len(color_set)* val) // 100] for val in vals]
c = plt.bar(np.arange(50), vals, color = color_lists)
 
aaarticlea/png;base64,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" alt="" />
In [8]:
hi =np.random.random_integers(8, size =10)
color_set =['.00', '.25', '.50', '.75']
plt.pie(hi, colors = color_set)# colors attribute accepts a range of values
plt.show()
#If there are less colors than values, then pyplot.pie() will simply cycle through the color list. In the preceding
#example, we gave a list of four colors to color a pie chart that consisted of eight values. Thus, each color will be used twice
 
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" alt="" />
In [27]:
values = np.random.randn(100)
w = plt.boxplot(values)
for att, lines in w.iteritems():
for l in lines:
l.set_color('k')
 
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Color Maps

 

know more about hsv

In [34]:
# how to color scatter plots
#Colormaps are defined in the matplotib.cm module. This module provides
#functions to create and use colormaps. It also provides an exhaustive choice of predefined color maps.
import matplotlib.cm as cm
N = 256
angle = np.linspace(0, 8 * 2 * np.pi, N)
radius = np.linspace(.5, 1., N)
X = radius * np.cos(angle)
Y = radius * np.sin(angle)
plt.scatter(X,Y, c=angle, cmap = cm.hsv)
Out[34]:
<matplotlib.collections.PathCollection at 0x714d9f0>
 
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*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" alt="" />
In [44]:
#Color in bar graphs
import matplotlib.cm as cm
vals = np.random.random_integers(99, size =50)
cmap = cm.ScalarMappable(col.Normalize(0,99), cm.binary)
plt.bar(np.arange(len(vals)),vals, color =cmap.to_rgba(vals))
Out[44]:
<Container object of 50 artists>
 
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NdXn1BVudimOg/u9eBdfHYuOaikfeD901QYxsme3N0DbAXeAC49OOhsjQf54mWrNqAfyfqXstlZS6+CpwlWnPuusxzsjIXAEuAD4HfJlp3v7Rbv+JcNLKD/4Do5NoF1xN18VnWT/RrFsAcoh/urJhMFO67iZZoINvzAVAEXgBuIZtz8SVgFdHSxDPAl4l+PrI4FxCFO8CvgX8jutfXuOaikQH/OrCAkTdB3cPISZSs2g90x4+7GQm60OWAnURXjXy7bDyL8zGTkSshPg+sIOpgszgX3yRq/G4A7gX+A1hDNudiKnDhQ1+nAV8hOn/X1HOR5TdBPQP8CviU6FzE14nOkB+iCS95mmBLiZYljhKF2VtEl9BmcT5+F3iTaC7eJlp/hmzORblljDSAWZyLG4h+Jo4SXUp8IS+zOBeSJEmSJEmSJEmSJEmSJEmSJEmSWtn/A9G4+wcTMn0TAAAAAElFTkSuQmCC" alt="" />
 

Line Styles

In [4]:
# I am creating 3 levels of gray plots, with different line shades 

def pq(I, mu, sigma):
a = 1. / (sigma * np.sqrt(2. * np.pi))
b = -1. / (2. * sigma ** 2)
return a * np.exp(b * (I - mu) ** 2) I =np.linspace(-6,6, 1024) plt.plot(I, pq(I, 0., 1.), color = 'k', linestyle ='solid')
plt.plot(I, pq(I, 0., .5), color = 'k', linestyle ='dashed')
plt.plot(I, pq(I, 0., .25), color = 'k', linestyle ='dashdot')
Out[4]:
[<matplotlib.lines.Line2D at 0x562ffb0>]
 
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" alt="" />
In [12]:
N = 15
A = np.random.random(N)
B= np.random.random(N)
X = np.arange(N)
plt.bar(X, A, color ='.75')
plt.bar(X, A+B , bottom = A, color ='W', linestyle ='dashed') # plot a bar graph
plt.show()
 
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" alt="" />
In [20]:
def gf(X, mu, sigma):
a = 1. / (sigma * np.sqrt(2. * np.pi))
b = -1. / (2. * sigma ** 2)
return a * np.exp(b * (X - mu) ** 2) X = np.linspace(-6, 6, 1024)
for i in range(64):
samples = np.random.standard_normal(50)
mu,sigma = np.mean(samples), np.std(samples)
plt.plot(X, gf(X, mu, sigma), color = '.75', linewidth = .5) plt.plot(X, gf(X, 0., 1.), color ='.00', linewidth = 3.)
Out[20]:
[<matplotlib.lines.Line2D at 0x59fbab0>]
 
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" alt="" />
 

Fill surfaces with pattern

In [27]:
N = 15
A = np.random.random(N)
B= np.random.random(N)
X = np.arange(N)
plt.bar(X, A, color ='w', hatch ='x')
plt.bar(X, A+B,bottom =A, color ='r', hatch ='/') # some other hatch attributes are :
#/
#\
#|
#-
#+
#x
#o
#O
#.
#*
Out[27]:
<Container object of 15 artists>
 
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" alt="" />
 

Marker styles

In [29]:
cd C:\Users\tk\Desktop\Matplot
 
C:\Users\tk\Desktop\Matplot
 

Come back to this section later

In [14]:
X= np.linspace(-6,6,1024)
Ya =np.sinc(X) Yb = np.sinc(X) +1 plt.plot(X, Ya, marker ='o', color ='.75')
plt.plot(X, Yb, marker ='^', color='.00', markevery= 32)# this one marks every 32 nd element
Out[14]:
[<matplotlib.lines.Line2D at 0x7063150>]
 
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" alt="" />
In [31]:
# Marker Size
A = np.random.standard_normal((50,2))
A += np.array((-1,1)) B = np.random.standard_normal((50,2))
B += np.array((1, 1)) plt.scatter(A[:,0], A[:,1], color ='k', s =25.0)
plt.scatter(B[:,0], B[:,1], color ='g', s = 100.0) # size of the marker is specified using 's' attribute
Out[31]:
<matplotlib.collections.PathCollection at 0x7d015f0>
 
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" alt="" />
 

Own Marker Shapes- come back to this later

In [65]:
# more about markers
X =np.linspace(-6,6, 1024)
Y =np.sinc(X)
plt.plot(X,Y, color ='r', marker ='o', markersize =9, markevery = 30, markerfacecolor='w', linewidth = 3.0, markeredgecolor = 'b')
Out[65]:
[<matplotlib.lines.Line2D at 0x84c9750>]
 
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hYLBdjJdCLpNecq94ijXHnTt2b9kKuekMDzLtjOZWbq8netYpx1+zE98e57jZKpAQJ9CItFBXB9deHHwf3TK7666/DDwNLPV15JSxc6G57REpwIr1SCNdVVoYO8qB78mPGwLx5zrYpJFnvRrhEAr1IekmTq24O9Nu3Q2Oja00R6UUCvUh6SZOr3rUrdO+uy7W1sHevu+0RaUMCvUh6TbnqoTTlqo8b52ybwpLhG+ECCfQi6SVVrrrcP1a4QAK9SHqZmVCQX0t+zvuUr64DoGytYto0b6RWBjH36CXQC4dYsQSCEK7L2r+D2aUjmXf171lw2gVk/+dFFBZ6qCffRIZuhAsk0IvUsG0bPhqZUJEHp4yACSVutyg0GboRLpChG5EaPLbGTVgydCNcIIFepIZkCfR9+xp3vdq9W6dZCmEzCfQiNSRLoD/qKDjxRF1WKnxeqBAWkkAvUkOyBHqQ4RvhOAn0IjWYM1g8cMORiCTzRjhMAr1IfgcOwDff6PJRR0GvXu62JxrJvBEOk0Avkp85WPbvD+08/raWoRvhMI9/IoSIQTKNz0Nwj16GboQDJNCL5GcOlskQ6KVHLxwmgV4kv82bjfIpp7jXjlj16qWvJYC+trB/v7vtESlPAr1IfskW6Nu1k169cJQVgX40sAHYBOSF+P2tQCmwBvgQGGRBnUIYtmwxyskQ6EECvXBUooua+YC5wCigAlgOvAmsN22zFbgE2I/+UngKGJ5gvUJodXWwY4fxOBnG6EECvXBUoj36YcBmYDtQB7wEXNdim4/RQR7gU+DEBOsUwrBjh3EvwRNP1IvTJwPJvBEOSjTQ9wZ2mR7vDvwsnJ8DCxOsUwiDeXx+wAD32hEv6dELByU6dKPi2PZS4C7gwnAbzJgxo7mck5NDTk5OW9sl0kWyXYhtYu7Rm68xCBFFSUkJJSUlcT0nI8E6hwMz0GPvAJOBRmBWi+0GAa8FtttMaEqpeL43hAB+9SsoLNTlhx+GyZPdbU+svv0Wjj1Wlzt0gEOHPHg7LJEMMvSy1xFjeaJDNyuAU4H+QEfgJvTFWLO+6CD/U8IHeSHaJll79MccY6zJU1cnyxULWyUa6OuB8cA7wDpgATrjZkzgH0A+0A34I7AKWJZgnUIYkjG1som5vZs2udcOkfISHbqxkgzdiPg0NkLnzsZdmvbvN4ZDksFdd8Gzz+ryvHkwdqy77RFJyYmhGyHcU1FhBPkePZIryIP06IVjJNCL5JWsqZVNTj3VKG+Wy1fCPhLoRfJK1guxTcyBXnr0wkaJ5tGLFOD3Q3ExVFbqDL+GBsjOhtxcj080NV+ITcYevbnNW7dCfT209+ZHMmnfIwKQQJ/2/H6YNAny8qBvX+PnO3fCxIlQUABZWe61L6Jk79E3pVju3atTLHft8uRaPUn9HhGADN2kveJiyPutCvoAg/5AFxRAfr6xlIznJHuPHpJi+Cap3yMCkECf9irL9tK3X+jMrKwsGPNfDcyb68G0V6WSv0cPwe326AXZyh01kd8jP69n3mOHHW6ViIcE+nQ2eza+TRsibjJwkI/KTzwYgPbsgYMHdblrVz1gnIy83qMvL8e3dnXETQae3Z7Kd1frAXzhSRLo09WTT8Kvf02DP3JPrHx1HdkfvA5z5zrUsBht3GiUv/tdyPDS3L84eLlHX1kJV15Jw/5vI25WvrqO7HVLYdQovYaP8BwJ9Olo8WIYPx6A7F2r2bm9MeRm1dUw//aPGFcxGX7zGygtdbKVkbUM9MnKqz36xka47TbYtSv6e+SOwHuktBRuv10/V3iKBPp0U1kJP/1p89Wz3OP/zqyZiurq4M2qq2HK5AYeJB8fjXDkCNx6q84O8YJUCfTmHn1TiqUXPPMMvP02ALl7H2DW/ftDv0emwIM/36nfIwCvvw5FRQ43ViQTJWzW2KjUD3+olL6UqVTPnkpVVKiqKqXuuUepsjK92dq1So0fr1RVlVJqwwalOnc2nlNY6OohNLvySqNNr7zidmsSc/zxxrFs3ux2a5SqrFTquOOMNk2cGPk9opRS995rbN+pk37fCEcQw31BvDSwGWizN6TkBJGXX4abbjIe/+MfcNVVgD6+efP08WZnw7hxpuXRH31UJ1KDvvC5aZNeW8ZNJ59s3Jlp7Vo46yx325OIkSNhyRJd/vvf4eqr3W3PvffCnDm63K8frFsHnTtHfo8cOQLDhuEv/ZziXvlUnpWD74LzaGjMSLrPTbJ99mNZ1MxL3P5ibFZTo9TYsUrt2BH88x079M+bezHJ5Ntvlerd2+h1jRkT+3Nra5U69VTjuffdZ187Y3HokFIZGbotGRn6cTL7xS+M1/aRR9xtS0WFUkcdZbTn9ddjfmrNx6Vq7DkfJfXnJhk/+8R3pz/Xuf16NSssbP2HblJVpdQ9l5Wp+oKZSq1f72zD4lRTo49l6lSlpt+2RU0d+pYq7PV75e/RV6nq6vh29te/qhoyVWGv36up5y9S0+8/oqZO1fv3++1pf1hr1xqB6KSTHK7cBnPmGMdz113utmXCBKMtQ4fq4b4YFRYqtWNbQ8jfNQ391Ndb1VCLHTyo1IIFqvD7C5PuGJBA3zZTp0b+fdmqI6qo9yylQNVc+SNVOPUrHUynK/eCXwtheybbGtTYG7+Mu2dS822DGnv+Sm/0dF55xQhGo0c7WLFNFi0yjueCC9xrx549wb35f/4zrqdH/dyUKVVUlED77FBbq9TMmc3XJKYOfSvi5l48BmII9LLWTQi+wzVAl7C/Hzi4Awv6nIO/IpNJe35N3t3ZnlsDpLi49dokAH37t6PgqR7k58Ps2bHfprT46XbkvTg44jT4ePaXEHPGzemnO1ChzczHsH69DrMOzgtoHpP+5CC+4W/T4D9Mdv0+cnOuJJ4h6Wh/+4EDYcGChJqasKDx98M1NKwsJXudIvfrGjIBX+dOEZ8/cCAseEmRREPinuP2F6P28stq6vBFETcpK61XRSNeVYW9fu/Z0zyre1ee6q397GdGr/OPf3SoUhs1Nip19NHGMe3d61jVYc/8tjfGfabmqfdICBHPcod8rKr6fE9N/cmmiPsoW3VEFeW8ptT+/Ta2ND7E0KOXPHqzOXPgxz8me/uKyBNE/uRj3OIfUnnTWPr2D/0SZmXBmDE6S8ENsfSu4pmxbvX+ErLBtGxDMufQN8nICO7Vb4i8LIWVwp759cuIe8Gy7Ozw9zivrob583WmjlsinuUuHk7+davpPuyUyMdw+0eMK7lBZ0ol0ZIPEuibzJyp08oITBC5dU34CSIP6sDny4p86zpHg18L0T6c5eXxLQ9j9f7aTKnUmSxldsYZRnn9eseqraxsHfiaxNtZyc2FWbMI/bm58fPmz41boh7rf7ejoSHCMdy2gwfXXKcnh332GYwYAV98YX/DLSCBHuAPf4DJk5sfZl4whILn+5OfrwMYQFkZTJsWPO7umeAXQvZxKvJZSZy9K8/01r74Qt8EHHRO//HHO1CpA8yB3sEevZVnapmZxvWa5s/NqjqmjXifgneHkVX+YWKNTZC+9hbewIFw4ECIY2j67D/Xj6ynHmm+fuJft42i7/+TaXlHmDFDb1NUBIcO2XwgSc6R8ayglMPpSk29c7dOOaSTHh8dOVLnnCs9vl5UpNS0afr/luPtUdMwb9zj2hi9//W39bhjizHWqqq2Zcn4/aGfV1Wl1Ngx9c5l3bz9tjGWfeGFDlXqgNdeM47riiscq9aOcfWgz80Vf1f1tNPHdcklcaVrWqqyUk295P2Im5iPNeJn/8UXVY3vGDV28IeeyEJD0iuDRb0Yc+FVceVFRgx+536qqjr0UGr5cgtaHqeGBqUGD1ZVdFX3DCpRZavrlFIhpq3HqXka/Fr9YV278ogaP6hEVf3xRataHt1jjxkBMTfXuXrttn69cVx9+jhWbeFj9fYmFGzZolT79saxvRU5fdEWDQ1KXXGFpckThbnlnknEwKFAPxrYAGwC8sJsMyfw+1LgnDDb2J6DHrUH/ou6uP84rdYAWdOoxp+/QlXRVb+xBwxw/gr9ggXNH6z6Tl1UUcG3Yc9K4tXc0/npVlXUe5burV12mTXtjsWddxpBY84c5+q125EjwQHxwAFHqvW/utDSM7+Qxo41juucc3TgddLjjysFyk8ny47VSxlGOBDofcBmoD/QAVgNnNFimx8ACwPl84BPwuxLKWXvqY9df5xWp3kbNyt1zDHGm/uWW5w7ZT1yJHi5grw8e+rZtSt4GYKdO+2pp6Vhw4xjW7zYmTqdMnCgcWwff+xMnTfcYPmZXytffKFUZqZxbAsWWLTjGKxerVTHjs11V03Ij7w4W4ymT4++zbRpcbc2JkHDz/mNjgT684G3TY/vC/wzexIwraTFBqBniH01H4hdpz6O/nFeeMF4Y4NSzz5r0Y6jeOopo86uXZX6+mv76ho1yqiroMC+epo0NCjVpYtR57599tfppJtvNo5t/nz769u/v3kmbD3tVNG0ryw782slL884ttNOU6quzuIKQvD7lTrzTKPeIUOUqq2Neu0tFm716EMNP+NAoL8BKDY9/inwPy22+Ttwgenxu8B/hNiX7S/U1Psiv7ksr/Ouu4w3WZcuSn3+uYU7D8HvD1647OGH7a3vueeMur73PXvrUkqpbduM+rKz7a/PaQ8/bBzf+PH212f++519tr11ffON7ng01VdcbG99SunXsKm+zExLl06OOgz84722jNGHqhcHlkCI9Zuk5XzhkM+bMWNGczknJ4fKypw2NSqkujqyl77Ozu03hJzk1JQiOHu2dVUyZw58+KHO+66p0Tfu+PBD6NDBwkpMnngCKip0uWdPmDDBnnqaXHcddOoEhw/rpYLXrw9OE7RaWZlRTuZlicMZNMgor1ljf33m9QjMy1fboVs3+O1v9UQUgAce0DfA6RR5yYE2W7gw+PaXhYWWzrnIzQ29zEl1NUy5fBkFq36Ab9FfYPRoy+oEneq6dWsJzzxTYul+oxlO8NDNZFpfkH0S+InpcdShG6V0ZodlveuGBqVuv93SizExW7lSqQ4djJUfb9poz+Jn1dXBN4uYO9eiHUdhvpFJLGNjiZg506hr3Dh763LDjh3G8XXrZu91naoqpTp0MOrbssW+upocPKjUd75j1PnYY/bUs3dvcD3XXWfLaxk1EaNLF6WWLbO0zum/ap3YgQNDN+2BLeiLsR2JfjF2OFEuxja/gCPLVH3N4cRfmYYGpe6+27gYQ1d1zzVbE74YE4+agtn259xOnhy8dG9trQU7jcHLLxv1nn66vcHplltSa42bFmoONqrCfo+rqUPfUtNHLFFTJx6yLwvt2WeN1/Lcc22oIAzzkszHHWdZRlrzBcopjWr69aXGktw9+yv11VeW1BFKq/H+nRVK9esXPMS4caM1lS1eHHIdLhxKr7wS2IjOvmmaXjom8K/J3MDvS4EhYfajlDLloNNVp+0l8kaorw8K8gqU+vnPVX1dY8IXY+JROLvB3pzbzZuDMgvUX/6SwM7iVFMTfIG0tNS+uk4/3ajnk0/sq8cFjt/wYvRo47V08mYnhw8r1a+fcYZ744aEz3Ajzo+5pcr5m4Vs2BB0dl3T62RVOOXLxJYyf+45pTp2DDkXgGSbMLV2TaMaf+Vm49QncIW+Znl58GzWEC9Uqxmv99Wpwov/asx4Bb3qoQtTVW2/Qn/ttcYxnnee83nKP/mJUf/999tTx8GDRjpnu3b6D55Col7cszILrbIyOGc/XMU2qXnmJUvPcB197WL18cdKZWaqGjITO9b6eqUmTmz+W/nppMYOXxH0PJIt0BcVKVVf16jU735nfBuSqcYO+TjiCxV1xitdXQvySlmf1hn0pXbP18ZpKp2U+vTTNrezzV5/3QgaAwbYM3zz0UdGHWeeaf3+XeZoup45Bff88y3aaewKZzdaeobrpclLQUpKVGHfx2I+1lad1V8dVIXn/l9wZ/XMM1VV6Y6gawMkW6AP8sILSnXuHNO05ccfj/KNftVmVV/r3v2/rHwjRvxS+/4md+5peeiQUscea7wZV6ywvo65c43933qr9fsJuBmRAAAON0lEQVR3maNzPC67zHgtCwst2mnsrA7Mbk5eimbqhMi37Gw61pg6q9dc0zyUbb42QAyB3rurV958M3z6KZXfvTDymu93N7Lqg28jLz86awDznnRvfdSoKz8+0RDzyo8R19R+6ZS41g+3TKdOOtWyiYW3EfL79YqA094dwYwRS5g29C2K1ISUWyHQsZVQ9+2DJUt0OSMDbrzRgp3Gx+p7GzRs3xXx926uIuvr1jXi7wcOhMoNX1F8/zbyfqvCr5V/bSkNr74Bx+ql0X0+nT394IOxtcO7gR7grLPwXXxBxE0Gfq8dGfV1kbdxcV14iLJO9+XLeHD3XfgyQi8p3JKV64dbypyH/fLLur+YIL8fJk2C66+Hh14/ixklOTy0bDTXPzyUiRNbv57JzLFloF99FRoD77WLL4YTTrBgp/Gx9Ett6VKyF71o6ZLcVop6rKvryH7zWSo/2kjffqFvT5iVBWMe7se8P3o7XMcq9KlPDDfqvu2iyDnAbt/CTKkQOber6/XKj00XnidOjGls27OnqbW1Ove7aUjAgrVaPHmRzSYRV0K1Muvmkkucn2vRgmV/19JSpbp3d2d+TIyiHuugElVPOzV9xJKo+wr3uSapx+hjfaEuWa0ev/hVzywZGkmrnNvx9wanfubl6VzqCBlGU38TOSfL1S8185IPv/xlwrvz7EU2mzR3Bkr1m3XtyiNq/GXl1gWqiorgzCUH701rFnV579/F8AW0dm3QpKiq7FPUPXcfcnR+TCwiHuuNX6qqW8YqNWqUmvr9yEkUkd7rpEKgj6Wn41hvyGp1dXrWnjnDaNTG8BlGS9eowtPmefdL7V//MgL9CScknObp2bMXG9XXK1U0eY+aNvxfehno/gOs23lRkfH3GTnSuv22Qasz3FV1wWe406YpVV/fOhNlqlKF4zcp/zGmma/HHqvU8uWWLFZmh1bHGuJLKJGzHFIh0Mf6QsWyjSfV1jbnwUfNMBq8VH1LZ8+epqq6OqV69DA+gO9HvqNPNOnWo29WWxs8Aa6y0pr9XnCBsU8nVseMIigwP1qr6i8aEXSGWzN8pBp7S1XkTJSjj1bqww9daX88on0JJdJZJVUCfSwvVKzbeFJtrVJ3362mDo18952yVUdUUe9ZquqYPuqeW7/25pfaf/+38WEdOzahXaXTGH0r5nX3rbgr086dxv58PluXBWizgweDlr6O2vEZ9rGqX2njTGyHtbWzSioF+nQwfcyeqNtMu+ITpdav9+6X2pIlxvT2C99T0/Mb2zy93e9XauzdR7x59mK3e+4xAnO0U5tYPPqosb/vfz/x/dmlrk6/qdu3j97xsXLhQ49oy+caCfTJJepQRWm9Kip0eHmDONUcqFdjhy6zbHp71Ytv6bsfrTqilPLY2YudTLeDVCNGJL4/8xnC008nvj+7bdmipt8cfTGwVLtG0xYk9YSpNBQ1l/pPPsaN9/afrPgZH3kv/0friR999drd8U7oylqzlNlrRrL46tnk37SR997TS4ub1wBPSRdfbJQ//RRqa9u+r23bYNkyXe7QQU9M8LqTT6ZhwGkRN3FzIlSySfTGI8JCEW9mMEX/PNqsQrdVVhJ5JnNgQlfM90RZsgQfjUyoyIMfD4AfWXfzCE87/ngYMAC2bNE3dvnsM7gg8uTBlvx+PZO6crnCN2IJDf7DZHf8ltxO3ci0qdlWaur4hJogaMuNgoQj3D4D8oSkzR4KsDQl8sABfeEQdP63VdknyeKOO4zhlpkz43pq2LVTtjcmzfWNpE2bdhgydJN8srJ0L2XxYj3MkWxDFZZOb//gA2OHZ58Nxx2XUNuSziWXGOXFi+N6atg1kfpltGkIzQ2ZmcZwX3m5/llZGUyb1vqsV0QmQzce1LRgUTKy9HT7vfeM8siRlrQvqYwaZZSXLtVjMZ07x/TUWNdE8vr7rKnjM2+eXisvO1t3fLw+hOk10qMXloq4gNsUvdpezB/Sd981yukY6Pv0gTPP1OXaWh3sY2T1CpFuMq/UOGGCBPm2kEAvLBV0ur1Gjw2Urapj2tWr4jvd3rEDSkt1uWPH4GGMdDJ6tFF+++2Yn+bYssciKUigF5Zrvs6wsJb8Cxfz3jWzKfzwXLIqymPfyZtvGuWRI+GYY6xvaDIwB/qFC2Ne/jk7G88u3SucJ4Fe2MLngwn3debBE55kQkUePhrhT3+KfQdvvGGUzTc1STcXXwxduujypk2wdm1MT8sdtZVZP1pmzRCaSHoS6IW9/uu/jPIzz8CBA9Gf8803wePR115rfbuSRadOwcf/0ksxPS3z6bkUrBxN/oj3KV+tb8wjGSvpK/QtTdwRSAkVKaWxUV9Q3LhRP37kET0rLJInnjDGFYYN0zND09mbbxpnNSedpCdRZUT46NbUQO/esH8/DbRj3r2bqDz2ZLKz9csqPfnUkqHfCxFjuQR6Yb/iYp2OAzoAbd2qL7CGM2wYLF+uy3PnymBybS307An79+vH//43XHhh+O0ffVTfgxHg1FNhwwZoJyfvqSqWQJ/oX787sAj4HPgXEOqEsA+wBCgHygCPZ+4Ky912mw5UABUV8L//G37bzz4zgnzHjvom8enuqKOCb+I9d274bQ8ehD/8wXj8m99IkBcJB/r70IH+NGBx4HFLdcCvgIHAcGAccEaC9Ypk0qkT/PKXxuP8fB2QQnnkEaN8ww3Qvbu9bUsW5rOaV14Jv/pdYSF89ZUu9+sHd95pf9uE5yU6dLMBGAHsA3oBJcDpUZ7zBvA/6C8GMxm6SWV+P5x2mu7Rg56fP3Nm8DalpTBkiB7XB1i1CgYPdradXjZiBCxdip9Mii//K5XnXYXPp3Pms7Mh94rtZA45Qy+CBvDUU3D33e62WdjOiTH6KqCbaV/fmB6H0h94H927b9mlk0Cf6v78Z7jjDgD8GV0o/tU6Kjv31cGqrpHsfz1P7opcMjkMV16p88aFoaQE/6U/YNLgd8n72wVBSxzs3KGY9bNyCpZeRBb79Rfk8uXQXlY5SXVWBfpF6N56S1OAPxMc2L9Bj9uHcjS6x/87dK++JTV9+vTmBzk5OeTk5MTQPJE0Ghvh8svxv/dx6GC1vZFZP1pGQenVZK39AM6QEb6Wii76K9f/5Uchl4Kurob8Ee8zu+xyfB99AOed50ILhd1KSkooKSlpfvzAAw+AzT36DUAOsBc4Hn3RNdTQTQfgH8BbQGGYfUmPPh3s3k1Rzmtc/+748MHq9h3Mfq2fpAGGMG3SYR56pFPY35evrmPxIyuZ8LwE+XThRNbNm8DtgfLthO6pZwBPA+sIH+RFujjxRCqvuiPyzUkK+jJvnsPtShK+LuGDPMDAwR2oPHmYQ60RySLRQD8TuBydXjky8BjgBOCfgfKFwE+BS4FVgX+jEWnL1+3YiL8feFZG0qys6LSoi5WVKbJ7eGl6jPCCRK/UfAOMCvHzL4CrAuV/I0stCBNZWbHtoq73/1SG3F5PtCIBWDgu6k3QZWXFsCxd71+kDQn0wnESrNpObq8n2sJLg3mSdZNGqqt1sBozRt/tqKxM9+QfekiCVSwaGvTt9SorkcXK0pwsaiY8TYKVEImTQC+EECnOiTx6IYQQHieBXgghUpwEeiGESHES6IUQIsVJoBdCiBQngV4IIVKcBHohhEhxEuiFECLFSaAXQogUJ4FeCCFSnAR6IYRIcRLohRAixUmgF0KIFCeBXgghUpwEeiGESHES6IUQIsVJoBdCiBSXSKDvDiwCPgf+BUS606cPWAX8PYH6hBBCtEEigf4+dKA/DVgceBzOvcA6IG3vFVhSUuJ2E2yTyscGcnzJLtWPLxaJBPprgT8Hyn8G/jPMdicCPwD+hLfuUeuoVH6zpfKxgRxfskv144tFIoG+J7AvUN4XeBzKbGAS0JhAXUIIIdqofZTfLwJ6hfj5lBaPFaGHZa4GvkSPz+fE2zghhBCJS2QoZQM6eO8FjgeWAKe32OZh4DagHugEHAu8CvwsxP42AwMSaI8QQqSjLcApdu38D0BeoHwfMDPK9iOQrBshhEgq3YF3aZ1eeQLwzxDbjwDedKZpQgghhBBCCFfcA6wHyoBZLrfFLr9BZyB1d7shFnsE/bcrBV4DurrbHMuMRl+P2oQxVJkq+qCvrZWjP3MT3G2OLVJ5smYW8Ar6c7cOGO5uc2JzKTrLp0PgcQ8X22KXPsDbwDZSL9BfjpGuO5Po12ySgQ+dJNAf/b5cDZzhZoMs1gsYHCgfDWwktY4P4NfA86TmsPGfgbsC5fYkSefqZWCk242w2V+BQaRmoDe7HviL242wwPnoL+Ym9xF5BniyewO4zO1GWOhE9HXES0m9Hn1XYGusG3tpUbNTgUuAT4AS4FxXW2O964DdwBq3G+KAu4CFbjfCAr2BXabHuwM/S0X9gXOAT11uh5VSebLmScBXwLPASqAY6Bxu42gTpqwWaQJWe6AbepxpKLqHf7JzTbNEpOObDFxh+lkyLgcR7vjux+gxTQGOAC841SgbpcvaTEejx3rvBQ663BarpPpkzfbAEGA8sBwoRJ9t5rvZqFi8hU7BbLIZOM6ltljtLPQyEdsC/+qA7cB3XGyTHe4APkRPjksFwwkeuplM6l2Q7QC8A/zS7YZY7GH02dg2YA9QA/yfqy2yVi/0sTW5CPiHS22JyxjggUD5NGCni22xWyqO0Y9GZ29ku90QC7VHzzrsD3Qk9S7GZqCD32y3G2KzVJ2suRQdKwFmkCSZih2A54C1wGek5ulWk62kXqDfBOxAnyqvAp5wtzmWuRKdjbIZ3aNPJRehx69XY/zdRrvaInuk6mTNs9HDNqmW0iyEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEOH9Pyxi8Uwly3R3AAAAAElFTkSuQmCC" alt="" />
In [20]:
import matplotlib as mpl
mpl.rc('lines', linewidth =3)
mpl.rc('xtick', color ='w') # color of x axis numbers
mpl.rc('ytick', color = 'w') # color of y axis numbers
mpl.rc('axes', facecolor ='g', edgecolor ='y') # color of axes
mpl.rc('figure', facecolor ='.00',edgecolor ='w') # color of figure
mpl.rc('axes', color_cycle = ('y','r')) # color of plots
x = np.linspace(0, 7, 1024)
plt.plot(x, np.sin(x))
plt.plot(x, np.cos(x))
Out[20]:
[<matplotlib.lines.Line2D at 0x7b0fb70>]
 
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" alt="" />

MatplotLib Part2

3rd 部分:

图的注释--包含若干图,控制坐标轴范围,长款比和坐标轴。

Annotation

In [1]:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
In [28]:
X =np.linspace(-6,6, 1024)
Y =np.sinc(X)
plt.title('A simple marker exercise')# a title notation
plt.xlabel('array variables') # adding xlabel
plt.ylabel(' random variables') # adding ylabel
plt.text(-5, 0.4, 'Matplotlib') # -5 is the x value and 0.4 is y value
plt.plot(X,Y, color ='r', marker ='o', markersize =9, markevery = 30, markerfacecolor='w', linewidth = 3.0, markeredgecolor = 'b')
Out[28]:
[<matplotlib.lines.Line2D at 0x84b6430>]
 
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" alt="" />
In [39]:
def pq(I, mu, sigma):
a = 1. / (sigma * np.sqrt(2. * np.pi))
b = -1. / (2. * sigma ** 2)
return a * np.exp(b * (I - mu) ** 2) I =np.linspace(-6,6, 1024) plt.plot(I, pq(I, 0., 1.), color = 'k', linestyle ='solid')
plt.plot(I, pq(I, 0., .5), color = 'k', linestyle ='dashed')
plt.plot(I, pq(I, 0., .25), color = 'k', linestyle ='dashdot') # I have created a dictinary of styles
design = {
'facecolor' : 'y', # color used for the text box
'edgecolor' : 'g',
'boxstyle' : 'round'
}
plt.text(-4, 1.5, 'Matplot Lib', bbox = design)
plt.plot(X, Y, c='k')
plt.show() #This sets the style of the box, which can either be 'round' or 'square'
#'pad': If 'boxstyle' is set to 'square', it defines the amount of padding between the text and the box's sides
 
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" alt="" />
 

Alignment Control

The text is bound by a box. This box is used to relatively align the text to the coordinates passed to pyplot.text(). Using the verticalalignment and horizontalalignment parameters (respective shortcut equivalents are va and ha), we can control how the alignment is done.

The vertical alignment options are as follows:
'center': This is relative to the center of the textbox
'top': This is relative to the upper side of the textbox
'bottom': This is relative to the lower side of the textbox
'baseline': This is relative to the text's baseline

Horizontal alignment options are as follows:

align ='bottom'                               align ='baseline'
------------------------align = center-------------------------------------- 
align= 'top

In [41]:
cd C:\Users\tk\Desktop
 
C:\Users\tk\Desktop
In [44]:
from IPython.display import Image
Image(filename='text alignment.png')
#The horizontal alignment options are as follows:
#'center': This is relative to the center of the textbox
#'left': This is relative to the left side of the textbox
#'right': This is relative to the right-hand side of the textbox
Out[44]:
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" alt="" />
In [76]:
X = np.linspace(-4, 4, 1024)
Y = .25 * (X + 4.) * (X + 1.) * (X - 2.) plt.annotate('Big Data',
ha ='center', va ='bottom',
xytext =(-1.5, 3.0), xy =(0.75, -2.7),
arrowprops ={'facecolor': 'green', 'shrink':0.05, 'edgecolor': 'black'}) #arrow properties
plt.plot(X, Y)
Out[76]:
[<matplotlib.lines.Line2D at 0x9d1def0>]
 
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" alt="" />
In [74]:
#arrow styles are :

from IPython.display import Image
Image(filename='arrows.png')
Out[74]:
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" alt="" />
 

Legend properties: 
'loc': This is the location of the legend. The default value is 'best', which will place it automatically. Other valid values are
'upper left', 'lower left', 'lower right', 'right', 'center left', 'center right', 'lower center', 'upper center', and 'center'.

'shadow': This can be either True or False, and it renders the legend with a shadow effect.

'fancybox': This can be either True or False and renders the legend with a rounded box.

'title': This renders the legend with the title passed as a parameter.

'ncol': This forces the passed value to be the number of columns for the legend

In [101]:
x =np.linspace(0, 6,1024)
y1 =np.sin(x)
y2 =np.cos(x)
plt.xlabel('Sin Wave')
plt.ylabel('Cos Wave')
plt.plot(x, y1, c='b', lw =3.0, label ='Sin(x)') # labels are specified
plt.plot(x, y2, c ='r', lw =3.0, ls ='--', label ='Cos(x)')
plt.legend(loc ='best', shadow = True, fancybox = False, title ='Waves', ncol =1) # displays the labels
plt.grid(True, lw = 2, ls ='--', c='.75') # adds grid lines to the figure
plt.show()
 
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" alt="" />
 

Shapes

In [4]:
#Paths for several kinds of shapes are available in the matplotlib.patches module
import matplotlib.patches as patches dis = patches.Circle((0,0), radius = 1.0, color ='.75' )
plt.gca().add_patch(dis) # used to render the image. dis = patches.Rectangle((2.5, -.5), 2.0, 1.0, color ='.75') #patches.rectangle((x & y coordinates), length, breadth)
plt.gca().add_patch(dis) dis = patches.Ellipse((0, -2.0), 2.0, 1.0, angle =45, color ='.00')
plt.gca().add_patch(dis) dis = patches.FancyBboxPatch((2.5, -2.5), 2.0, 1.0, boxstyle ='roundtooth', color ='g')
plt.gca().add_patch(dis) plt.grid(True)
plt.axis('scaled') # displays the images within the prescribed axis
plt.show() #FancyBox: This is like a rectangle but takes an additional boxstyle parameter
#(either 'larrow', 'rarrow', 'round', 'round4', 'roundtooth', 'sawtooth', or 'square')
 
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mfsZl9fNEwVrYMyc7Oplu3boZL9nqnKAqZmZlRz80VQhiDoWr0rfn9ftavX09dXV0CQ0ot6enpjB49WqayCmEgpqvRt2a1WsnNzTXdcsZasdvt5ObmSpIXwoQMm+gheHF25MiR2O12rUPpkJ5r9DabjZEjR57yxVez10BlfMZm9vFFw9CJHsDpdBom2euR3W5n5MiRpKenax2KECJBDFujb6uhoYENGzbg9XrjGJJ5WSyWljN5SfJCGFc0NXrTJHoAr9dLUVER9fX1BAKBOIVlPoqi4HQ6yc3NJS0tTetwhBAxMPXF2HAcDgejRo2ie/fuupt6qZcavaIodOvWjVGjRsUtyZu9BirjMzazjy8a+sqGcdD8qc6cnBzdJXutKYpCv379GDZsmMyuESKFmKp001ZFRQVbtmwhEAgQ72MbjdVqZejQoXTv3l3rUIQQcZRyNfpwvF4v27dv5/jx4ylZt1cUhYyMDAYPHiz1eCFMKOVq9OE4HA5GjBjB4MGDsVqtmt1AQ4savdVq5ZxzzmHEiBEJTfJmr4HK+IzN7OOLRizr0RtKVlYWmZmZKXF2L2fxQojWTF+6CaeyspJdu3ZRV1dnqoSvKArp6ekMHDiQzMxMrcMRQiSB1Ojb74yKigp27dqF1+s1dMJXFAWHw8GAAQPo3r273N9ViBQiNfp2WCwWunfvzgUXXMDgwYPxeDwJnY6ZiBq9oii43W4GDRrEBRdcQI8ePTRJ8mavgcr4jM3s44tGLDX664EHgcHA+cCGCPtNBZ4FrMCLwB9j6DPuLBYLWVlZZGVlUVNTQ2lpKeXl5QC6PMtv/mN0+umn07dvXzwej8YRCSH0LpbTv8FAAJgP3E34RG8FdhC8ifgBYC1wA7AtzL5JLd20x+fzUV5ezqFDh6iurkZRFE3vU2u1WgkEAng8Hnr16kXPnj2x2VLmOroQoh3RlG5iyRbbo9hnDLAbKA5tLwauJnyi1w2bzUbv3r3p3bs3Pp+PiooKysvLqaioaCmNJDLxK4qCxWJBVVW6detGVlYW3bp1k+QuhDglic4cZwIlrbZLgQsS3Gdc2Wy2ltKOqqrU1dVRXV1NZWUllZWV1NfXY7FYWv4ARPoUbmFhIXl5eS3bFoulpQyjqiqqqpKenk5GRgYZGRl4PB5cLpchlnEoKChg4sSJWoeRMDI+YzP7+KLRUaL/GOgV5vH7gPejOH6najH5+fkt9yvNzMwkLy+v5QfUfEFFD9tut5vt24NvaCZMmEBDQwOffPIJTU1NnH/++TQ2NvLZZ5/h9/sZPXo0qqpy4MABPB4PY8eOxel0smHDBux2O5MmTcLhcLBq1Srq6uoYM2aM5uOTbdmWbf1uFxQUsGDBAoCo7+8cjykaK4hcox9L8ILt1ND2vQTr+uEuyOqmRi+EEEaRzOmVkTpZBwwEcgAH8H1gSZz6FEIIEYVYEv01BOvvY4GlwEehx3uHtgF8wExgGbAVeAudX4hNlOa3XmZk5rGBjM/ozD6+aMRyMfafoa+2yoArW21/xDd/BIQQQiSZnj4rLzV6IYToJFkCQQghhCT6ZDFzndDMYwMZn9GZfXzRkEQvhBAmJzV6IYQwMKnRCyGESJ1bCWqtIM7rbRw9epRt27axbds2iouLqaqqora2lrq6Onr06EF2dja9evVi0KBBjBw5ErvdHre+24r32PRGxmdsZh9fNCTRG0B9fT2ffPIJ7777LuvWrWPPnj00NjaSnp5OU1MTdXV1J7Wx2WykpaWhKAper5fc3FxuuukmbrnlFrp06aLBKIQQIlijFyEVFRXqSy+9pE6ePFlNS0tTu3btqlosFpXgQnGn9OV2u1WXy6Xefvvt6pEjR7QeYsqo89apT375pNrziZ6qZ45HzXg0Q/3Nst+oR+uORtXe5/epCwsXqtlPZ6ueOR6166Nd1ds/uF09XHM4qvb+gF99c/Obas6zOapnjkft8kgX9Sfv/kQtqyqLqn0gEFD/sfUf6tl/Orul/Y/+8SN1//H9Ubd/f8f76qC5g1TPHI/qecSjXv/369U9FXuibv+vXf9Sh88b3tL+6jevVnd+vTOq9qqqqv/Z8x817y95Le2veP0KdWv51qjbr9y3Uh3919Et7acsnKIWHSqKuv2a0jXquJfHqZ5HPKpnjkcd/dfR6oq9K6Ju3x6iWDxSLsbqiNfr5cMPP+Qvf/kLBQUF2Gw2amtr496Pw+EgPT2dV155hWuuuSbuxxffWFe2jitev4J6Xz21Td/8LJ02J1aLlbeue4srz7kyYvuy6jImL5zMgeoD1HhrWh5Ps6ZhVawsnL6Q64ZeF7F9eW05l7x6CXuO7Tmhf4fVgdViZf535nPTiJsitq+or+Dy1y5n+9HtJ/RvV+zYFBvPTH2Gn4/6ecT2VY1VTHtjGoWHCk/o32axYbPaeGTKI9w19q6I7Wu9tUxfPJ3/lv73hPZWixW71c794+/n/vH3R7yFZoOvgev/fj0rilec0F6xKKRZ07hz7J3MmTwnYnuv38uN79zI0l1LqWuqO6n9baNu4+nLn0axhL/c6Qv4uHXJrby95W0afA2orXKy2+7mouyLeOd77+B2uCP+G3REbg6uI+3VCY8dO8YTTzzB3LlzsVgsVFdXJyUml8vFD3/4Q+bPnx/Tuvdmr4Ge6vjWHFjD5IWTT0gwbaXb0pl35Tzy8/JPem7n0Z2Mf2U8FfUV+AK+iO2fvOxJZpw/46Tnio8XM+7lcZTXltMUaIrY/paMW3hh5gsnPXeg6gDjXhlHWXUZXr83bHuX3cWsC2cxe8Lsk5Ll4ZrDXLzgYvYd30ejvzFi+xnnz+DxSx4/qf3RuqNMWjiJXRW7aPA1RGx/S+4tPD/t+ZOSbWVDJZe+dilFq4rwZoeP3213c93Q63jpqpewKtYTnqvx1nDF61ew/uB66n31EdtPGziNN777BnbridfB6pvquXrx1XxR8sUJfyRac9qcDOkxhC9v/RKnzRl2n47IrBudO3bsGPfddx99+vThmWeeoaamJmlJHqCuro5FixZx8803h71Zijh1Nd4apr4+td0kD1Dvq2fG0hlMe2Ma244E1/uraqzigRUPcN788zhSeyRikm9uP+vjWVz66qVsPrwZCJ4FP/zZwwyfN5yDNQcjJvnm9q8UvsLFr1zMhoPBlcbrm+p5/IvHGfLnIZRWlUZM8gB1TXU8+eWTfOulb7G6dDUAjb5G/rTqTwx6fhB7j+2NmOSb27+w9gVG/200n+//HIAmfxPz1s5j4NyB7Di6I2KSb27/atGr5P4llxV7VwDBs+iXNrzEgLkD2HR4U7vx1zbV8vbWtxk2bxjLdi9DVVX8AT+vFb3GgOcGsK5sXcQk39x+6a6lDP7zYN7f8T6qqhJQA7z11VsMnDuQz/d/HjHJQ/Adx/avt/PLD38ZcZ94kDN6DTSfwf/pT38iEAjQ0BD5hZwMLpeLe+65hwceeEDTOMzksc8f4w+f/aHdX/LWrBYrNsXWkpScNme7CaYtxaLgsDpo9AWTaro9Peq+ASxYcNqcLeUFt93d4R+peLaH4Nl5fVP9Kbd3293UNdWhouJxeE4oNUXbvrlPj91DTVPn2rfus7P9O21Ods7cSd+Mvp3qE6R0ozt6S/Ctpaens2rVKkaMGKF1KKaQ+VgmlY2VWochDMKu2Jlx/gyenfpsp9tK6UYnGhsbuemmm1pKNHV1dbpK8gANDQ1873vfIxAIdLqt2dcSOZXxGSrJ79U6gAQzwPiaAk3sq9yXsONLok+w1atXM2jQIN5++21dJvhmaui+tsuXL9c6FCFSkiWBBRZJ9AlSX1/PHXfcwaRJk9i3bx+NjZEvSOlFTU0Njz76aKfbmXnGDZza+IadPiz+gSTKWVoHkGAGGJ/b7mZK/ykJO74k+gRYuXIlAwYM4MUXX6S+PvoLanqwZs0aSktLtQ7D8OZMnoPH4dE6DGEQNsXGrefdmrDjS6KPo5qaGm677TYuv/xyysrKDJfkIbh0Qmdr0lKjP9l3Bn2Hs087G5tigFVGDFDDjonOx+eyu5gzec4pz6OPRiyJ/npgC+AHRrazXzGwCdgIrImhP1375JNPOPvss3nttdcMmeCb1dTUsGzZMq3DMDzFovDRjz6ii0PWFRKRpdvS+c453wn7gbd4iqX6PxgIAPOBu4ENEfbbC4wCKjo4niGnV/p8Pu666y5efvnlsIuLGVG/fv0oLi7WOgxTWFW6ikkLJ7X7oR+Rus7pdg5FtxfFdDaf6OmV24Gd0cYSQz+6dezYMSZNmmSqJA9w5MgRrUMwjbF9xjIpZ1JCZ1QIY/I4PDx1+VMJLdk0S0aNXgX+DawDfpaE/pJi27ZtnHvuuaxdu9ZUSR6Cc+o7M0tIavTte2DCA7jsrvgEkwg6r2HHTKfjy3RmMm3gtKT01dGVoo+BXmEevw94P8o+LgIOAqeHjrcdWBlux/z8fHJycgDIzMwkLy+vZWpb8y+bHrY//PBDrr32Wt3OiY+V0+lkyZIlnH766br49zb69oBuA/D+zxu8mtU81a85+ch2ym5nZmW2LMTWmddTQUEBCxYsAGjJlx2Jx/vJFbRfo29tNlADPBXmOd3X6FVV5ZFHHmHOnDmGvuDaEZfLxY4dO+jTp4/WoZhCeW05/Z7tJ3V6cYJv9fkWX976ZczHSeYSCJE6cQHN0w7cwGXA5jj1mVT19fVce+21PProo6ZO8gCBQACnM/F1w1Tx7vZ3sVqsHe8oUkrhoUIq6juaoxIfsST6a4ASYCywFPgo9Hjv0DYEyz4rgUJgNfABYLjP2JeWljJy5Ej+9a9/JeRGIHrj8/lIT0+Pen+p0UfmD/h56NOHOr0SY1LptIYdNzoe33Orn0tKP7F8muOfoa+2yoDmW+bsAfJi6ENzO3fu5MILL6SyshKfL/K64GYjZ/Txcd8n91HZYKAFzkTS1PvqeeLLJ7hm8DXk9spNaF96mvOluxr9li1bGD9+PMePH0+pG3P07duX/fv3ax2G4f3f1v/jlndv6dS68CL1ZLmz2DJjCz1cPU6pvSxTHIPCwkIuuuiilEvyAEOHDtU6BMM7WneU/HfzJcmLDh2rP8ZPl/w0oX1Iog9j7dq1XHzxxVRWVqZcklcUhVGjRnWqjdToTzZ3zVwCaufX9teEjmvYcaHz8TUFmlj2v2XsPZa4QCXRt7F+/XomT56c1Hu36onb7WbYMAMtsatTz6x6plO3AhSpzR/w88yqZxJ2fEn0rWzatInJkydTU9O5e0WaicViYciQIZ1qI+vRn6yqsSr+gSSKAdZrj4kBxtcUaKKkqiRhx5dEH7Jt2zYuvvhiqqoM9AuaAF6vV87ohTAZSfTA7t27GTduXMoneYDJkyfjcDg61UZq9Cfrn9k//oEkis5r2DEzwPhcdhcX9r0wYcdP+UR/7NgxJk6cmJKza9ryeDz88Ic/1DoMU3hw4oNyhykRNQsWfj7q5wk8vn4kfR69z+dj0qRJrFmzBq/Xm9S+9cjhcHDw4EG6deumdSiG1+Rv4uznzqa0qhSV1D6BEO1z2V3cNfYuHp788Cm1l3n0HbjzzjvZsGGDJPmQvLw8SfJxYrfaWXbjMn0vTyw057A6GNlrJA9NfCih/aRson/llVd45ZVXTLeW/KnyeDzMmHFqtzOTGn14Q04fwqJrF5Fui37dIE0YoIYdEx2P7zTnaSy5YQlWJbGL3qVkol+1ahUzZ86UJN+Kz+fju9/9rtZhmM5Vg64it2di1zERxuRxeHjqsqc4Lf20hPeVcjX6AwcOcO6553Ls2LGE92UkV111Fe+9957WYZjS8v8t59q/X0uNN3U/nyFO1j29OwfvPojdao/pOFKjb6O+vp5LL700ZT/1GonH4+FXv/qV1mGY1sgzRuILpM7KpyI6g3sMjjnJRytlEr2qqtx4440UFxen1HLD0cjJyWHKlCmn3F5q9O3T/bRdHdew40Kn40vm6yJlEv3cuXNZtmyZ6e8O1Vlut5snn3yy+e2fSICC4gLsSnLO3IRxbCrflLTVTfX0252wGv3evXsZPny4XHwNY/jw4WzatEmaDzilAAATy0lEQVQSfYKoqsqQPw9hx9EdWocidMZtd/PolEf51QWxlU2lRk/wF+2GG26gsbFR61B0R87mE2/eunkJXaxKGFdtUy2/X/F79h3fl/C+TJ/o//rXv/LVV1/h9/u1DkV3zjrrLC677LKYjyM1+vBW7lvJrOWz9H/zEZ3WsONGx+Or9dZy2euXJfw1EkuifwLYBhQB7wAZEfabCmwHdgH3xNBfp5WUlHD33XenxA29O0vO5hOr1lvL1YuvljXpRbv8qp/9lfu58193JrSfWH7LLwU+AQLAY6HHftdmHyuwA7gEOACsBW4g+AeirbjW6FVVZdKkSXzxxRcyyyYMqc0n1tzVc7n3k3upbZKTDNExp81J8a+L6enp2em2ia7Rf0wwyQOsBvqE2WcMsBsoBpqAxcDVMfQZtVdffZV169ZJkg9DzuYT7+GVD0uSF1FTVZXnVj+XsOPHq0b/E+DDMI+fCbS+ElUaeiyhDh48yMyZM6VkE4bNZmP06NFxqc03kxr9ycpry+MfSKLouIYdFwYYX6O/ka1fb03Y8W0dPP8x0CvM4/cB74e+vx/wAovC7NepWkx+fj45OTkAZGZmkpeX13Ibt+Zftmi2f/zjH8t8+QjS0tJ4/fXX+fTTT4Ho/j1lu/PbLcml+TZ2si3bHWwf8R+hWXuvr4KCAhYsWADQki87Eut793zgZ8AUoCHM82OBBwlekAW4l2C5549h9o1Ljb6goIBvf/vbcjYfhtvt5qmnnuLnP0/cDQ5EUM8nexrrrF5oymlz8puxv2HOlDmdbpvoGv1UYBbBmnu4JA+wDhgI5AAO4PvAkhj67NCsWbMkyYdhtVo599xzue2227QOJSX8fvzvcdvdWochDOTXY3+dsGPHkujnAh6C5Z2NwLzQ472BpaHvfcBMYBmwFXiL8DNu4qKgoIBt2xJ2eENLS0tj0aJFCbkAKzX6k/3kvJ/gsHbu3ruaMUANOyY6H5/T5uSmETeR5c5KWB8d1ejbMzDC42XAla22Pwp9JZyczYfndruZM2cOZ511Vsc7i7hwO9wsuWEJl712mcylFxFZLVayM7J5duqzCe1HT/PrYqrRS20+PEVRyM3NZd26dSiK6T8IrTt/Xvtnfvvxb/X/6Vihia5pXdl8+2ayM7JP+RgptdaNnM2H53Q6eeuttyTJa2TG6Bn07dpX6zCEDrntbh6e9HBMST5apvjtl9p8eG63m9mzZzNwYKQqW3xIjT4yi8XCHyb9gS6OLvELKN50XsOOmU7HZ7FYuHXkrUnpyxSJXs7mT+Z0Opk0aRKzZs3SOpSUNzFnIk2BJq3DEDozImsELrsrKX0ZPtF//vnncjbfhtVqpW/fvixevDgpyxy0fEjIpGIdn+6XmjD7NXqdji+ZrwvDJ/rnn39ebijShsfj4eOPP8btlnncerDh4AZsSiwT3IQZbf96O03+5LzTM3Sir6urY8mSJfq/J2cSpaen8/7779OvX7+k9Sk1+vbNXjGbGm9NfIJJBJ3WsONGp+Nr9Dfy9y1/T0pfhk70S5YswWaTM6VmLpeLp59+mvHjx2sdighZsmMJRYeLtA5D6FCNt4a7l9/NsfpjCe9LT8XDTs+jnzBhAp999lmCwjEWl8vFDTfcwIsvvqh1KCJk25FtnP+382W5YhGRw+pgTO8xFOQXYFWsp3SMaObRGzbRV1VVcfrpp+P1ehMYkjE4HA7y8vL4/PPPsdvtWocjgCZ/E2c/dzalVaWonVvEVaQYl93FXWPv4uHJD59Se1N/YGr58uWkpaVpHYbmLBYLp512GkuXLtUsyUuN/mSLv1rMsYZjxkjyOq1hx43Ox1fXVMezq56lqrEqYX0YNtEvXryY6upqrcPQXEZGBgUFBfTo0UPrUEQrDxY8qO8LsEJXVFTmr5+fsOMbsnTj8/nIzMxM+Q9Jde3alS+++ILhw4drHYpow/KQnn61hBFMHzydf37/n51uZ9rSzc6dO/X/IZQE83g8rFixQpK8EKJDhkz027ZtS+lFutxuN//+978ZOXKk1qEAUqMPp2ta1/gHkig6r2HHzADjsyv2hC5+Z8hsuXXr1pT9NKzH42H58uVccMEFWoci2nHX2LtIt6VrHYYwCKti5a6xdyXs+Hqqf0Rdo58+fTrvvfdegsPRny5duvCf//yH0aNHax2K6EBFfQXZz2TLHHrRIbtiZ9rAabz7g3dPqb1pa/RfffWV1iEklcViISMjg5UrV0qSN4hu6d1YOH1h0lYnFMZ1WvppvHhVYj/oGEuif4Lg/V+LgHeAjAj7FQObCN5Xdk0M/bUoKSmJx2EMwWq1kpmZyZdffklubq7W4YQlNfrwrh16LTPPn6n/m4QboIYdEx2Pz2V3sfzG5fRwJXZ6dCyJfjkwDMgFdgL3RthPBSYC5wFjYugPCE6tbGpKjbW9nU4n/fv3Z+PGjQwdOlTrcMQpeGTKI2Q4I50DiVSWbktn1oWzyO2V+BO4WBL9x0Ag9P1qoE87+8btWkB9fX1KLGTmdru55JJL2LhxY1JXojwVsh59ZFbFyuwJs/V9Vq/T9drjRsfju+OCO5LST7xq9D8BPozwnAr8G1gH/CzWjhoaGrBaT23xH6NwuVzcfffdLFmyRNaUN4Hpg6fjV/1ahyF0Jq9XHt3SuyWlr44S/cfA5jBf32m1z/2AF1gU4RgXESzbXAH8Eoh5DV2zrj9vsVhwu90sWrSIhx56yDAfCpMaffsUi87nPOi4hh0XOh1fMl8XHdVALu3g+XxgGjClnX0Ohv5/BPgnwTr9yrAHy88nJycHgMzMTPLy8lreNjf/so0fP96UNXqbzUaXLl347LPPGD58eMt4245fto23vbtidzDZ+PimjNCcfGQ7Zbd3HN5B4McBFIvSqddTQUEBCxYsAGjJlx2J5ZRxKvAUMAH4OsI+LsAKVANughdwHwr9v62o59FnZmZSWVnZ2Xh1y+Vyce6557J06VK6d++udTgizqa9MY1/7f6XMVayFEnjcXh489o3+fY5347pOImeRz8X8BAs72wE5oUe7w0sDX3fi+DZeyHBC7YfED7Jd0pWVlash9ANl8vFjTfeyMqVKyXJm9Cq0lWsKF4hSV6cpMZbw2+W/YYGX0PC+4ol0Q8E+hGsv58HzAg9XgZcGfp+D5AX+hoOPBpDfy0mTpxomPp1JE6nkx49evDOO+8wf/58Q98wRGr04ZVVlzHtjWlJ+UWOiU5r2HGj4/GVVpWS/25+wq876vwqUXiXXnopHo9H6zBOWfNt//73v/9x+eWXax2OSICAGmDaG9Oo9so9E0Rk9b563t/5PvPWzut45xjo6bQ46hp9eXk52dnZNDY2Jjik+EpPTycjI4NFixYxadIkrcMRCfTe9ve48Z83ys1HRFQy0jI4ePdB0u2dXwjPtGvdZGVlcfHFF2sdRqekp6eTn5/P7t27JcmngPv/c78keRE1X8DHy4UvJ+z4hkz0APfee68hyjfp6en06dOHxx9/nHnz5pnyA1BSoz/ZliNb4h9Ioui4hh0XBhhfbVMtn+z5JGHHN2yinzhxIv3799ftDUicTicul4tf//rX7Nq1S+4EJYRoVyJnZhmyRt9s+/btjBw5kvr6+gSF1HlOpxNFUfjlL3/JPffcI1MmU1TmY5lUNprnsx4iseyKnRnnz+DZqc92uq1pa/TNBg8ezOzZs3G5tF/zu/kM/le/+hX79+/n8ccflySfwn437nedWovearGSZk3DEvqvs3enUiwKTpuzpX1n18Fv7tMSyhedXYQt1vYQXLI3lvZuuzvm9s08js6XhVu36Wx7q2Ll7m/d3ek+o2XoRA/w29/+lh/84AeaJftoE7yZ69hmHhuc2vhmjplJmjUtqn3TbelMHTCVwl8UEpgdoPJ3lcy6cNYJia89LruLKWdNYe3P1hKYHaD63mruG3cfbrs7qvVU0krSGJ89ni9+8gWB2QHq7qtj9sTZdHF0waZ0vFKs2+5mzJljKMgvIDA7QMP9DcHlmdMyom4/8oyRLL9xOYHZAby/9/LEZU9wmvM0HFZHVO2HZw3n/RveJzA7QNP/a+K5K56jh6tH8GfQQY3eZXcxqPsg3vn+OwQeCOD7fz5euPIFerp74rQ5O+zfZXfR/7T+vHntmwQeCOB/wM9LV73EmV3OjOoPdrotnR+d+yP6ZiTunrGGLt20asgdd9zByy+/nLR7yXbp0oVAIMCMGTOiKtEUFBSYdjlfM48NTn18aw+sZdLCSe3eTjDdls68K+eRn5d/0nO7ju5i3CvjqKivwBfwRWz/5GVPMuP8GSc9V3y8mHEvj6O8tpymQPj1oVx2Fzd3vZkXZr5w0nMHqg4w/pXxHKg+gNfvjdj+txf+lgcmPHDShxgP1xxmwoIJFB8vptEffiq0y+7il+f/kj9e8seT2lfUVzBp4SR2Ht0Z8UNnLruL/Nx85k6be9IftcqGSi597VKKVhXhzQ4fv9vu5rqh1/HSVS9hVU5cFbfGW8O0N6axrmwd9b7w5WGX3cWVA6/kje++gd164oce65vquXrx1XxR8gV1TeHzktPmZEiPIXx565dR/VEJJ5rSjSkSfbMPPviAm2++mbq6uoTMsXe73fh8Pi6++GJuv/12pk2bRlpadGdtIjWtK1vHFa9fQb2v/oSE77Q6sSpW3rruLa4858qI7cuqy5i8cDKlVaUntE+zpmFVrCycvpDrhl4XsX15bTmXvHoJe47tOaG9w+rAarEy/zvzuWnETRHbV9RXcPlrl7Pt620ntLcrdmyKjWenPstto26L2L6qsYorF13JxoMbT2hvs9iwWW08MuWRdm+KXeutZfri6fy39L8ntLdarNitdn5/8e+5b9x9ET8p3+Br4Pq/X8+K4hUntFcsCmnWNO4ceydzJs+J2N7r93LTOzfxwa4PTkjWze1vG3UbT1/+dMR3Tr6Aj58u+Slvb32b+qb6Ey64uu1uxmWP4x/f+wdux6nPxosm0euJGg8VFRXqHXfcobrdbtXtdqsE18M/pS+LxaJ27dpVTUtLUydOnKj+7W9/U48ePRqXOEXqqPPWqU99+ZTa84meqmeOR814NEO9e9nd6tG66F5LPr9PfbXwVTX76WzVM8ejdn20qzrjgxnq4ZrDUbX3B/zq4s2L1Zxnc1TPHI/a5ZEu6q3v3aqWVZVF1T4QCKjvbH1HHfDcgJb2P/rHj9T9x/dH3f6DHR+og58frHrmeFTPIx71e3//nrqnYk/U7ZftXqYOnze8pf30N6erO7/eGVV7VVXVFXtXqHl/yWtpP+2NaerW8q1Rt/983+fq+X89v6X9JQsvUTcd2hR1+zWla9TxL49XPY94VM8cjzr6r6PVgr0FUbdvTyhftUtPfwVCMcdHbW0tr732GgsXLqSwsBC73Y6qqjQ0NODznfw22OVyYbfbqa+vx+Fw0L9/f0aNGsU111zDlClTYr4GYObyhpnHBjI+ozP7+KI5ozftPfncbje/+MUv+MUvfoHP52Pjxo3s2LGDQ4cOsX//fo4cOYLL5cLj8eDxeOjXrx9DhgxhyJAh9OiR2Bv1CiFEMpn2jF4IIVKB6efRCyGE6Jgk+iQx81xzM48NZHxGZ/bxRUMSvRBCmJzU6IUQwsCkRi+EECKmRP8HoIjgjb8/ASIt1DAV2A7sAu6JoT9DM3Od0MxjAxmf0Zl9fNGIJdE/DuQSvPH3u8DsMPtYgecJJvuhwA3AkBj6NKzCwkKtQ0gYM48NZHxGZ/bxRSOWRN/6rsce4Osw+4wBdgPFQBOwGLg6hj4N6/jx41qHkDBmHhvI+IzO7OOLRqyfjJ0D3ATUAWPDPH8mUNJquxS4IMY+hRBCdEJHZ/QfA5vDfH0n9Pz9QDawAHgmTHuZRhNSXFysdQgJY+axgYzP6Mw+vmjEa3plNvAh0PbGqGOBBwnW6AHuBQLAH8McYzdwdpziEUKIVPE/YECiDj6w1fe/Al4Ls48tFEQO4CA4QyclL8YKIYQR/R/BMk4h8A8gK/R4b2Bpq/2uAHYQPGO/N5kBCiGEEEIIIZLoemAL4AdGahxLPJn5A2MvA4cJvrMzo77ACoKvy6+AO7QNJ+6cwGqC78q3Ao9qG05CWIGNwPtaB5IAxcAmguNbo20o0RsMnEPwF8ssid5KsGSVA9gx3zWK8cB5mDfR9yL4gUAIflZkB+b6+QE03zrNBqwCxmkYSyL8BngDWKJ1IAmwF+gWzY56WutmO7BT6yDizOwfGFsJHNM6iAQ6RPCPM0ANsI3gNSgzab7jtYPgiUmFhrHEWx9gGvAi+lrAMZ6iGpeeEr0ZhfvA2JkaxSJik0Pw3ctqjeOIN4XgH7PDBN9Nb9U2nLh6BphFcEq3GanAv4F1wM/a2zHZ94z9mODb4bbuw5w1NPnAmDl4CM4y+zXBM3szCRAsT2UAy4CJQIGG8cTLt4FygvXridqGkjAXAQeB0wnm1u0E32WfJNmJ/tIk96e1A5y4qmdfgmf1wjjsBKcPv05w8T6zqiQ4LXo05kj0FwJXESzdOIGuwKvAzVoGFWcHQ/8/AvyTYKk4bKLXoxXAKK2DiJNU+MBYDua9GGshmBzCLe9hBj2AzND36cBnwBTtwkmYCZivYuACuoS+dwNfAJdpF070riFYz64neBHsI23DiRszf2DsTaAMaCT4s/uxtuHE3TiCpY1CgiWAjXyznIcZnAtsIDi+TQTr2WY0AfPNujmL4M+tkODUX7PlFiGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGECO//Az5O8d+MvyJCAAAAAElFTkSuQmCC" alt="" />
In [22]:
import matplotlib.patches as patches
theta = np.linspace(0, 2 * np.pi, 8) # generates an array
vertical = np.vstack((np.cos(theta), np.sin(theta))).transpose() # vertical stack clubs the two arrays.
#print vertical, print and see how the array looks
plt.gca().add_patch(patches.Polygon(vertical, color ='y'))
plt.axis('scaled')
plt.grid(True)
plt.show() #The matplotlib.patches.Polygon()constructor takes a list of coordinates as the inputs, that is, the vertices of the polygon
 
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" alt="" />
In [34]:
# a polygon can be imbided into a circle
theta = np.linspace(0, 2 * np.pi, 6) # generates an array
vertical = np.vstack((np.cos(theta), np.sin(theta))).transpose() # vertical stack clubs the two arrays.
#print vertical, print and see how the array looks
plt.gca().add_patch(plt.Circle((0,0), radius =1.0, color ='b'))
plt.gca().add_patch(plt.Polygon(vertical, fill =None, lw =4.0, ls ='dashed', edgecolor ='w'))
plt.axis('scaled')
plt.grid(True)
plt.show()
 
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" 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Ticks in Matplotlib

In [54]:
#In matplotlib, ticks are small marks on both the axes of a figure
import matplotlib.ticker as ticker
X = np.linspace(-12, 12, 1024)
Y = .25 * (X + 4.) * (X + 1.) * (X - 2.)
pl =plt.axes() #the object that manages the axes of a figure
pl.xaxis.set_major_locator(ticker.MultipleLocator(5))
pl.xaxis.set_minor_locator(ticker.MultipleLocator(1))
plt.plot(X, Y, c = 'y')
plt.grid(True, which ='major') # which can take three values: minor, major and both
plt.show()
 
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" alt="" />
In [59]:
name_list = ('Omar', 'Serguey', 'Max', 'Zhou', 'Abidin')
value_list = np.random.randint(0, 99, size = len(name_list))
pos_list = np.arange(len(name_list))
ax = plt.axes()
ax.xaxis.set_major_locator(ticker.FixedLocator((pos_list)))
ax.xaxis.set_major_formatter(ticker.FixedFormatter((name_list)))
plt.bar(pos_list, value_list, color = '.75',align = 'center')
plt.show()
 
aaarticlea/png;base64,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" alt="" />

MatplotLib Part3

4th 部分:

包含了一些复杂图形。

Working with figures

In [4]:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
In [5]:
T = np.linspace(-np.pi, np.pi, 1024) #
fig, (ax0, ax1) = plt.subplots(ncols =2)
ax0.plot(np.sin(2 * T), np.cos(0.5 * T), c = 'k')
ax1.plot(np.cos(3 * T), np.sin(T), c = 'k')
plt.show()
 
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" alt="" />
 

Setting aspect ratio

In [7]:
T = np.linspace(0, 2 * np.pi, 1024)
plt.plot(2. * np.cos(T), np.sin(T), c = 'k', lw = 3.)
plt.axes().set_aspect('equal') # remove this line of code and see how the figure looks
plt.show()
 
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z1vrVCCBEXF0dAQAAhISHauhEjRrBq1aoya3nQozvnIaAlpou71YFnyH8h1z1HUN2y5vMmfYsppZg9e3aupN+xY0f27t0rSV8IoYtatWoRHBzMuHHjtHUbN25k5MiRpKSk6BaXpYk/HXgZ2AacAoKASOCFrAngaeA4ph4//wbGWnjMfJRSvPnmm3z00Z9fNPr27cuvv/6Kh4dHWR9OCCGKzcHBgRUrVvDqq69q67Zu3cqIESNITk7WJaYKf+euUoq3336bDz74QFs3fPhwgoKCcHR0LMv4hBCi1JRSzJkzh/fff19b179/fzZv3oyTk1Op91sl79x9//33cyX9YcOGsWbNGkn6QghDsbGx4b333uO9997T1u3cuZNnnnmG9PR068Zi1aMVrsRn/AsXLmTatGna8pAhQ1i3bp101xRCGNq//vUv3n77z0EMJk+ezOLFi7PP3kukSg3StmXLFkaMGEFmZiYATz75JJs2bZKkL4SoEGbNmpXruuSsWbP48MMPS7yfKpP49+/fT9++fbULI126dCE0NBRXV9fyjE8IIcqMUoopU6bw/fffa+u+/vprXnjhhUK2yq9KJP6YmBg6d+7MjRs3AGjWrBlhYWG4u7uXd3xCCFGm0tPTGTlyJFu2bAHA3t6eX375hV69ehV7H5U+8aelpfHEE0+wZ88eAOrUqUNYWBitWpm9EVgIIQwvKSmJnj17cvToUQDq16/PwYMHadKkSbG2r/S9el599VUt6dva2hIUFCRJXwhRoTk7O7Nx40bc3NwAuH37NqNHj+bBgwfldswKk/jXrFnDvHnztOV//etfMvaOEKJSaNy4MevWrcPe3jR82sGDB/nnP/9ZbserEE09169f55FHHtGebxkQEMC6detK1fVJCCGM6tNPP+W1117Tlrdt28bAgQML3aZStvErpfD392fbtm2A6ZMxIiKCWrVqWTs+IYQoV5mZmQwePFjLdw0bNuTkyZM89NBDBW5TKdv4Fy9erFWCjY0Ny5Ytk6QvhKiUbG1tWbZsmdZL8fr167z++utlfhxDn/HfunWLNm3aaE08M2fO5NNPP9UjNiGEsJp169bx9NNPa8u7du3iiSeeMFu20jX1TJo0iaVLlwKmcfVPnDhh0WBGQghRUYwaNYr169cD0Lp1a44fP061atXylatUTT1hYWFa0geYP3++JH0hRJUxf/58atasCcCZM2dYsGBBme3bkIlfKcUbb7yhLY8aNQp/f38dIxJCCOtq0KBBroHc5syZw717ZfMMK0Mm/uDgYH777TfAdAtzzoGMhBCiqpg+fTrNmzcH4P79+3zyySdlsl/DJf7sB6tk++tf/4q3t7eOEQkhhD4cHBxyjdg5f/78MjnrN1zi37VrlzZmhZOTU7nevSaEEEb39NNP065dOwASEhJyjWBQWoZL/Dm7a06ePFmemSuEqNJsbW156623tOV58+ZZ/KxeQ3XnPHHiBI888ghg+mXPnj0rzTxCiCovIyODli1bcuHCBQCWLFnCs88+C1SC7pzfffedNj9ixAhJ+kIIAdjZ2eV6zOxXX31l0f4Mdcbv5ubGrVu3ANi6dat04RRCiCy3b9/Gy8tLG645IiKC9u3bV/wz/uyk37BhwyJHpBNCiKqkfv36jBgxQltes2ZNqfdlqMSfbcKECdjZ2ekdhhBCGMozzzyjza9evZriPqc8L0M19WTPhIWF0b17dz1jEUIIw0lOTqZ+/fokJiYCkKNDTMVt6gFwc3OjW7dueochhBCG4+TkxJNPPqkt79q1q1T7MVzi9/f3x9bWcGEJIYQh9OvXT5uvNIm/V69eeocghBCGlTPx//rrr6Xah+Ha+E+dOkXbtm31jkUIIQxJKYWbmxt37tzJubritvHXrFmT1q1b6x2GEEIYlo2NDR07drRoH4ZK/G3btpX2fSGEKMKjjz5q0faGyrJyti+EEEWztDncUIlfxuYRQoiieXl5WbR9WST+QcBp4BzwRgFl5mW9Hg50KmhH7u7uZRCOEEJUbo0aNbJoe0sTvx0wH1PybweMA/J+BxkMtABaAn8FCnxicL169SwMRwghKr/69etbtL2lib8bEAVcBNKAVcBTecoMB5Zmze8HHgLMntrXrl3bwnCEEKLyc3Jysmh7SxO/J3Alx/LVrHVFlTHbQCUDswkhRNEsTfz2Fh6/uEPD5b25wOx2S5YsISQkBAA/Pz/8/PxKH5kQQlRCoaGhWp4sLUvv3O0OzMHUxg8wG8gEPspR5msgFFMzEJguBPcBbubZlwoJCZFkL4QQRUhKSsLFxSXnKqveuXsI00XbpkB14Blgc54ym4HArPnuQCz5kz4AsbGxFoYjhBCVX1JSkkXbW9rUkw68DGzD1MPnWyASeCHr9YXAVkw9e6KAROC5gnZ27949C8MRQojK7/79+xZtb2niBwjOmnJamGf55eLsKPvRi0IIIQp29epVi7Y31J2758+f1zsEIYQwvEqV+KOiovQOQQghDO/cuXMWbW+oxH/q1KlSPzxYCCGqimPHjlm0vaES/82bN7ly5UrRBYUQogqrVIkfYN++fXqHIIQQhnXt2jXtBNnBwaFU+zBc4v/tt9/0DkEIIQzrl19+0eZ79OhRqn0YLvFv3bpV2vmFEKIAORN/zgevl4ThEv/58+c5e/as3mEIIYThZGRksHXrVm35iSeeKNV+DJf4ATZs2KB3CEIIYTi7d+/WbnT18PCgW7dupdqPIRP/8uXLpblHCCHyWL16tTY/atSoUg9lb6jEnz3a3KlTpzh69KjO0QghhHEkJSWxatUqbXn06NGl3pehEv/IkSO1+UWLFukYiRBCGEtQUJA2grG3tze9evUq9b4MlfinTJmizS9btoy7d+/qGI0QQhiDUoqvvvpKW542bRq2tqVP34ZK/L1796ZTp04AJCcns3Bh3kE+hRCi6gkNDeXQoUOA6aat554rcHT7YjFU4rexseEf//iHtvzvf/+bhIQEHSMSQgj9/fd//7c2/+yzz1K3bl2L9meoxA8wZswYGjduDMDt27eZN2+ezhEJIYR+9uzZoz1j187OjlmzZlm8T8Ml/urVq/POO+9oy5988onFT5sRQoiKSCnF66+/ri1PnDiRZs2aWbxfwyV+MH2VadmyJWB6Du97772nc0RCCGF9QUFBhIWFAaaT4rfffrtM9mvIxG9vb88HH3ygLX/xxReEh4frGJEQQlhXUlISb7zxhrY8Y8YMvL29y2Tfhkz8YLorLXsAoszMTF566SUyMzN1jkoIIazjnXfe4fLlywDUr1+ft956q8z2bdjEb2NjwxdffIG9vel58Hv37mXBggU6RyWEEOXvwIEDfP7559ry//7v/1KrVq0y279Nme3Jcsrc+DxvvvkmH374IQBOTk6Eh4dr7f9CCFHZpKSk4OPjw4kTJwDo378/27dvx8bGfLrOWl+iXG74xJ+amkrXrl21SvD19WX37t3aNwEhhKhMXnnlFebPnw+As7MzJ06cKLQnT2kSv2GberI5ODiwbNkyLdGHhYWV2ZVtIYQwkk2bNmlJH+DTTz8tk+6beRn+jD/bBx98kOvixqZNmxg+fLg14hJCiHJ3/vx5fHx8uHfvHgABAQGsW7euwCaebJWyqSdbZmYmQ4cOJTg4GICHHnqIgwcP0qJFC2vFJ4QQ5SI+Pp4ePXpoTdpeXl6Eh4dTp06dIretlE092WxtbVm+fLk2nENsbCyDBw+WETyFEBVaZmYmEydO1JJ+9erVWb16dbGSfmlVmMQPULduXdauXYujoyMA586dY+TIkaSmpuocmRBClM7bb7/Npk2btOVFixbh6+tbrsesUIkfwMfHhxUrVmjLu3fvZsqUKXJzlxCiwpk7d67WXR1g5syZPPvss+V+3ArTxp/Xxx9/nOt25pdffpl58+YVeSFECCGMYPny5QQGBmrLgwcPZvPmzSV+jm6lvrhrpjDTpk3L9YjG1157jY8++kiSvxDC0LZs2UJAQAAZGRkA9OjRgx07duDs7FzifVWpxA+QkZHBX/7yF4KCgrR177zzjozmKYQwrE2bNjFmzBgePHgAQPv27fn111+pXbt2qfZX5RI/QFpaGqNHj851cWTWrFl88MEHcuYvhDCUNWvWMH78eNLT0wFo3rw5e/bsoUGDBqXep7UTfx0gCGgCXATGALFmyl0E/gAygDSgWwH7K1XiB9OwDk899RTbtm3T1k2bNo0vv/zSogcSCyFEWfnhhx8IDAzUOqK0aNGCXbt2aV3US8va/fhnATuAVsCurGVzFOAHdKLgpG8RBwcHNmzYwNChQ7V1X3/9NRMmTCAtLa08DimEEMWilOKzzz5j4sSJWtJv06YNv/76q8VJv7QsSfzDgaVZ80uBEYWULfc2FycnJ9avX8/48eO1dT/++CNDhgwhNtbcFxEhhChfGRkZzJgxg5kzZ5LdovHII48QGhpKw4YNdYvLkoR8H8i+GmED3MuxnNN5IA5TU89C4JsC9lfqpp6cMjMzefnll3ON3d+2bVt++uknmjdvbvH+hRCiOJKSkhg/fnyu64+PP/44mzZtom7dumV2nNI09RQ1tvEOwMPM+ryPglFZkzmPAzFA/az9nQZ+M1dwzpw52ryfnx9+fn5FhJefra0tX375Je7u7tr+IiMj6datGxs3bqRnz54l3qcQQpTEpUuXGDlyJEeOHNHWjR49mmXLlmkjD5RWaGgooaGhFkZYeqf580OhQdZyUd4FZhbwmiprK1euVA4ODtkfSqpatWrqyy+/VJmZmWV+LCGEUEqpnTt3qrp162p5B1CvvvqqysjIKJfjUfBJd4EsaePfDGTfW/wssNFMGWegRta8CzAQOG7BMUtk3LhxhISE4ObmBpi6fr700ktMnDiRhIQEa4UhhKgClFJ8+umnDBw4UBs8slq1aixYsIBPPvnEUD0MLe3OuRpoTO7unA0xteMPAZoD67PK2wM/AB/m3VGWrA+vsnfx4kVGjhzJ0aNHtXXt2rVj7dq1tG3btlyOKYSoOu7evcuUKVNyted7eHiwdu1aHn/88XI9dpW8gau4kpOTmTFjBt988+e1ZRcXFz7//HOmTp0qN3sJIUolJCSECRMmcP36dW2dr68va9eutUrPnUo9Hr+lnJycWLRoEUuWLMHJyQmAxMRE/vrXvzJixAhu3bqlc4RCiIokLS2Nt956i379+uVK+q+88oru3TWLYqTT3HI9488pIiKCsWPHEhkZqa1zc3Pj22+/zXUTmBBCmBMeHs5zzz2Xq/m4Xr16fP/991bPIXLGX0wdOnTg8OHDTJ8+XVt369Ythg0bRmBgIHfu3NExOiGEUT148IA5c+bQtWvXXEm/X79+hIeHV5gTxyp5xp/T9u3bmTRpEjExMdq6evXqMXfuXMaNGydt/0IIAA4fPszkyZOJiIjQ1jk4OPA///M//OMf/9Ct105pzviNpFz6uBbHnTt31Lhx43L1uwWUv7+/unDhgm5xCSH0d//+ffXSSy8pW1vbXPmhR48e6vTp03qHV6p+/Eaid/2pn376STVq1CjXH9fR0VG9++67KjExUe/whBBWlJmZqZYuXarc3Nxy5QQnJyf1+eefq/T0dL1DVEpJ4i8Tf/zxh3rllVeUjY1Nrj9248aN1Zo1a+SuXyGqgMOHD6tevXrlawUYOHCgOnfunN7h5YIk/rITFhamOnfunO8P37dvX3X48GG9wxNClIPLly+riRMn5nvfe3l5qbVr1xryxA9J/GUrPT1dLVq0SNWrVy/fP8K4ceNUVFSU3iEKIcpAXFycmj17tnJ0dMz1Pre3t1evv/66io+P1zvEAiGJv3zcu3dPTZ8+XdnZ2eX7p3j55ZfVzZs39Q5RCFEKiYmJ6pNPPjF7cjd8+HAVGRmpd4hFQhJ/+Tp16pQaMWJEvn8QFxcX9frrr8sHgBAVRFJSkvrss8/yXbgFVJcuXVRISIjeIRYbkvitY+/evapnz575/mGcnZ3VzJkzVUxMjN4hCiHMSE5OVnPnzlUeHh753r+NGzdWK1asKLfhk8sLkvitJzMzU23ZskVTozB/AAAMvklEQVS1b98+3z+Qo6OjmjFjhrp06ZLeYQohlKkv/ocffmg24Xt5eakFCxaolJQUvcMsFSTxW19GRoZat26devTRR/P9Q9nZ2amxY8eqgwcP6h2mEFXSpUuX1N///nfl6uqa7/3ZsGFDNX/+/Aqb8LMhiV8/mZmZauPGjWa7gAKqd+/eatOmTRXua6QQFdGxY8fUhAkTlL29vdmEP3fuXJWcnKx3mGWCUiR+I43vkPU7VGxKKYKDg/n0008JCQnJ93qrVq146aWXCAwM5KGHHtIhQiEqp9TUVNavX8+XX37J3r17873+8MMP8+qrrzJ+/HiqV6+uQ4TlQx7EYjBHjhzhs88+IygoiPT09FyvOTk5MXbsWF588UW6du0qg8EJUUqXL19m4cKFLF682OxzNfr06cNrr72Gv7+/oR5/WFYk8RvUlStX+OKLL1i4cCF//PFHvtc7d+7MtGnTGDduHK6urjpEKETFkpaWRnBwMN9++y0//fQTmZmZuV63t7dn1KhRzJw5Ex8fH52itA5J/AYXHx/PypUrWbBgAeHh4fled3FxYdSoUQQGBuLn54ednZ0OUQphXBERESxZsoQVK1Zw+/btfK97eXnxwgsvMHXqVDw8PHSI0Pok8VcQSin279/P119/TVBQECkpKfnKeHl5MWHCBAIDA+WB8KJKu337Nj/++CNLlizJ9fCTnAYMGMDf/vY3hg4dir29vZUj1Jck/gro3r17LF26lG+++SbXoyBz6tq1K8888wxPP/00TZs2tW6AQujg7t27bNy4kaCgIH755RcyMjLylfH09CQwMJBJkybRqlUrHaI0Bkn8FZhSiiNHjrBs2TJWrlxZ4OMffXx8ePrppxk9ejTNmjWzcpRClJ/79++zadMmgoKC2LlzZ74OEQCOjo4EBAQwadIk+vXrJ82hSOKvNNLS0vj5559ZtmwZmzdv5sGDB2bLdenShVGjRjF8+HDatWsnPYNEhXP16lW2bNnC5s2b2bVrF2lpaWbLPf744wQGBjJmzBjpBp2HJP5K6P79+2zcuJE1a9awc+fOAt8YzZo1Y+jQoQwdOpQ+ffrg4OBg5UiFKFr2N9vsZF9Qmz1A9+7dGTNmDKNHj8bLy8uKUVYskvgrufv377N582bWrFnD9u3bC/wQcHFxYeDAgQwZMoQBAwbQuHFjK0cqxJ/u3r3Lrl272LFjB8HBwVy7dq3AstnXs0aPHk2TJk2sGGXFJYm/ComNjdXOmrZt20Z8fHyBZVu2bEn//v3p378/ffv2pXbt2laMVFQ1qamp/P777+zYsYPt27dz5MgRCnpvV6tWDT8/P4YPH86wYcMk2ZeCJP4q6sGDB/z222/89NNPbNmyhejo6ALL2tjY0KVLF/r160efPn3w9fWVNlNhkZSUFA4cOMBvv/3G7t272bNnD0lJSQWWr1OnDkOGDGHYsGE8+eST1KxZ04rRVj6S+AVKKc6ePcuWLVvYsWMHu3fvNnufQDYbGxvat29Pz549talRo0ZWjFhUNHFxcfz+++9aoj948GCBHRAA7Ozs6NatGwMGDGDAgAF07969yvW1L0+S+EU+KSkphIWFsWvXLnbu3MnBgwfz3d6eV+PGjfH19cXHxwcfHx86depEjRo1rBSxMJIHDx5w/PhxDhw4oE2RkZEFNt1ka9GihZbo+/btK98qy5EkflGk2NhYfv31V3755Rf27t3L0aNHi/wgsLGxoU2bNvj4+NC1a1e6du1K+/btZVyhSiY1NZXIyEjCw8M5fPgwBw4c4NixY6Smpha5bevWrenVqxe9evWid+/ecqOhFUniFyUWHx/P/v372bNnD3v27GHfvn0kJiYWa1tvb2/at2+fa2rRooV8jTc4pRQxMTFEREQQERFBeHg4ERERnD592uxNU3nZ2dnx6KOPakm+Z8+euLm5WSFyYY4kfmGx9PR0wsPDOXDgAIcOHeLgwYOcPHmyyG8F2RwdHWnbti2tW7emdevWtGrVSpvkIp51paamEh0dzenTpzlz5kyun3FxccXeT/PmzenWrRs+Pj5069aNTp064eLiUo6Ri5KQxC/KRVJSEseOHePgwYMcOnSIw4cPc/bsWbPjpxTGw8ODVq1a0bJlS5o2bUqTJk20n56ennL7fQkppbhz5w4XLlzINV28eJHo6GguXLhQ7A/sbN7e3nTo0IFHH31US/b16tUrp99AlAVrJ/7RwBygDeADHCmg3CDg34AdsBj4qIByFSLxh4aG4ufnp3cYhbJGjCkpKZw+fZqIiAiOHz+uTdevXy/V/uzt7fHy8qJJkyY0btyYBg0amJ30uK6gx988LS2NGzducP36dbPTtWvXuHjxYrGb5fKqUaMG7du315J8hw4daN++vVUu4leE9xBUnDhLk/gtaYw9DgQACwspYwfMB/oD14CDwGbA/DCUFUBF+GewRoyOjo507NiRjh075lp/9+5dTp06xblz5zhz5gxnz57l7NmzREVFFdrlLz09nYsXL3Lx4sVCj+vq6oq7uzt169albt261KlTR/uZPV+7dm1q1KiBq6trrsnZ2blUT2AqTX0qpUhNTSU+Pt7sFBcXx927d7lz5w537tzJN3///v0Sx5mXjY0NjRs3pk2bNrRu3TrXzwYNGug2tlNFeA9BxYmzNCxJ/KeLUaYbEAVczFpeBTxFBU78onB169bVenfklJGRwaVLlzhz5gznz5/n0qVL/Oc//8HV1ZVLly5x8+bNYu0/ISGBhISEQm9SK4yLiwsuLi44ODhQvXp1bapWrVqueVtbW63LYlRUlPYMV6UUSinS09NJTU3NNT148ECbT05OLnBIjbJUo0YNmjVrRrNmzbhx4wbjxo3Tlr29vXF2di73GETFU97dLzyBKzmWrwKPlfMxhQHZ2dnRvHlzmjdvrq1zdnZmzpw5ACQnJ3P58mUuXrzI1atXiYmJ4caNG8TExOSaitO1sDCJiYmlah45f/68RcctKRsbG9zc3PD09KRhw4ZmpyZNmlC7dm3tzH3OnDnMmDHDqnGKiqmo73o7AHPPL3sT2JI1HwLMxHwb/yhMbfzPZy1PwJT4XzFTNgrwLiIeIYQQuUUDLUqyQVFn/ANKHwtgatfPef9/I0xn/eaUKHAhhBD6CQG6FPCaPaZPo6ZAdeAYIA+QFUKICioAU/t9MnADCM5a3xD4T45y/sAZTE05s60ZoBBCCCGEEEInn2Dq0hkOrAdqFVBuEKZuo+eAN6wTWi6jgZNABtC5kHIXgQjgKHCg/MPKp7hx6l2fdTB1GDgLbAcKGrLxIvrUZ3HqZ17W6+FAJyvFlVdRcfoBcZjq7yjwttUi+9N3wE1M9/sUxAh1WVScfuhfl40wNamfBE4A0wsoZ4T6LNQAIPtOmv/NmvKyw9Q81BSohj7XB9oArTBVemEJ9QKmpKaX4sRphPr8GHg9a/4NzP/dQZ/6LE79DAa2Zs0/BuyzVnA5FCdOP0w3SuqpF6bkU1BCNUJdQtFx+qF/XXoA2XdKumJqOrfof7PktzGWjR1A9iAi+wFzT1LOefNXGn/e/GVNpzGdnRaHnuMeFSdOI9TncGBp1vxSYEQhZa1dn8Wpn5zx78f0jcXdSvFlK+7fUe9xuH4DCrv92Ah1CUXHCfrX5Q1MH/AACZhaSxrmKVOi+tQr8ec0mT8/qXIyd/OXp1UiKjkF7AQO8ec9C0ZjhPp0x/S1mqyfBf1j6lGfxakfc2XMnbSUp+LEqYAemL7ybwXaWSe0EjFCXRaH0eqyKaZvKPvzrC9RfZbnnbvFufnrLeABsNJMOWuN2FacOIvyOBAD1M/a32lMZxJlydI49a7Pt/IsKwqOyRr1mVdx6yfv2Z+1RxYszvGOYGoXTsLUq24jpqZAo9G7LovDSHXpCqwFZmA688+r2PVZnom/qJu/JmFql+pXwOslufnLEpbepAamJAVwG9iA6et4WScqa95MZ4nC4ryJ6UPhBtAAuFVAOWvUZ17FqZ+8Zbyy1llTceKMzzEfDHyF6ZrJvfINrUSMUJfFYZS6rAasA1Zg+vDJq0LU5yBMV6gLG+jbSDd/FXaTmjOQPZatC7AXGGiNoMww+s10H/NnL5RZmL+4q1d9Fqd+cl5A644+FySLE6c7f579dePPQRKtrSnFu7irV11ma0rBcRqhLm2AZcDnhZQxUn0W6BxwiT+7SH2Vtd5oN38V5ya15pjefMcwdbUyapygf33WwdR2n7c7p1Hq01z9vJA1ZZuf9Xo4hff0Kk9FxfkSpro7BvyOKRFY24/AdUxNuVcwXcszYl0WFacR6rInps4wx/gzZ/pjzPoUQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIUQe/w9VBDIJWnSvcAAAAABJRU5ErkJggg==" alt="" />
In [12]:
X = np.linspace(-6, 6, 1024)
Y1, Y2 = np.sinc(X), np.cos(X)
plt.figure(figsize=(10.24, 2.56)) #sets size of the figure
plt.plot(X, Y1, c='r', lw = 3.)
plt.plot(X, Y2, c='.75', lw = 3.)
plt.show()
 
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" alt="" />
In [8]:
X = np.linspace(-6, 6, 1024)
plt.ylim(-.5, 1.5)
plt.plot(X, np.sinc(X), c = 'k')
plt.show()
 
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" alt="" />
In [16]:
X = np.linspace(-6, 6, 1024)
Y = np.sinc(X)
X_sub = np.linspace(-3, 3, 1024)#coordinates of subplot
Y_sub = np.sinc(X_sub) # coordinates of sub plot
plt.plot(X, Y, c = 'b')
sub_axes = plt.axes([.6, .6, .25, .25])# coordinates, length and width of the subplot frame
sub_axes.plot(X_detail, Y_detail, c = 'r')
plt.show()
 
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" alt="" />
 

Log Scale

In [20]:
X = np.linspace(1, 10, 1024)
plt.yscale('log') # set y scale as log. we would use plot.xscale()
plt.plot(X, X, c = 'k', lw = 2., label = r'$f(x)=x$')
plt.plot(X, 10 ** X, c = '.75', ls = '--', lw = 2., label = r'$f(x)=e^x$')
plt.plot(X, np.log(X), c = '.75', lw = 2., label = r'$f(x)=\log(x)$')
plt.legend()
plt.show() #The logarithm base is 10 by default, but it can be changed with the optional parameters basex and basey.
 
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" alt="" />
 

Polar Coordinates

In [23]:
T = np.linspace(0 , 2 * np.pi, 1024)
plt.axes(polar = True) # show polar coordinates
plt.plot(T, 1. + .25 * np.sin(16 * T), c= 'k')
plt.show()
 
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" alt="" />
In [25]:
import matplotlib.patches as patches # import patch module from matplotlib
ax = plt.axes(polar = True)
theta = np.linspace(0, 2 * np.pi, 8, endpoint = False)
radius = .25 + .75 * np.random.random(size = len(theta))
points = np.vstack((theta, radius)).transpose()
plt.gca().add_patch(patches.Polygon(points, color = '.75'))
plt.show()
 
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" alt="" />
In [2]:
x = np.linspace(-6,6,1024)
y= np.sin(x)
plt.plot(x,y)
plt.savefig('bigdata.png', c= 'y', transparent = True) #savefig function writes that data to a file
# will create a file named bigdata.png. Its resolution will be 800 x 600 pixels, in 8-bit colors (24-bits per pixel)
 
aaarticlea/png;base64,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" alt="" />
In [3]:
theta =np.linspace(0, 2 *np.pi, 8)
points =np.vstack((np.cos(theta), np.sin(theta))).T
plt.figure(figsize =(6.0, 6.0))
plt.gca().add_patch(plt.Polygon(points, color ='r'))
plt.axis('scaled')
plt.grid(True)
plt.savefig('pl.png', dpi =300) # try 'pl.pdf', pl.svg'
#dpi is dots per inch. 300*8 x 6*300 = 2400 x 1800 pixels
 
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" 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MatplotLib Part4

总结

你学习Python时能犯的最简单的错误之一就是同时去尝试学习过多的库。当你努力一下子学会每样东西时,你会花费很多时间来切换这些不同概念之间,变得沮丧,最后转移到其他事情上。

所以,坚持关注这个过程:

  • 理解Python基础

  • 学习Numpy

  • 学习Pandas

  • 学习Matplolib

下载链接:

你可以从我的github上下载这些文件。这些文件是以.ipynb格式存放。这些文件也包含了我用来说明的一些图片。

    1. Python and Numpy Basics

    2. Pandas Basics

    3. Matplotlib

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