Line Plots and curve(plt.annotate)

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本文详细介绍了使用Python的Matplotlib库进行数据可视化的方法，包括基本的线条绘制、自定义颜色和样式、调整线宽和标记，以及如何在子图上进行注释和绘制形状。通过实例展示了如何从文件读取数据并进行绘图，涵盖了Series和DataFrame的绘图方法，以及如何创建和调整子图。

In [1]: import matplotlib.pyplot as plt

In [2]: X = range(100)

list(X)[:5]

Out[2]: [0, 1, 2, 3, 4]

In [3]: Y=[value **2 for value in X]

Y[:5]

Out[3]: [0, 1, 4, 9, 16]

In [15]: plt.plot(X,Y)

Out[15]: # if not plt.show()

[<matplotlib.lines.Line2D at 0x5219b00>]

In [13]: plt.plot(X,Y)

plt.show()

Out[13]:

In [29]: import numpy as np

import matplotlib.pyplot as plt

In [30]: XList = np.linspace(-3,2,200)

YList = XList**2 - 2*XList +1 #ploting Y= X^2 - 2X -1

In [31]: plt.plot(XList, YList)

plt.show()

In [49]: import matplotlib.pyplot as plt

In [54]: XList, YList=[], []

for line in open('my_data.txt', 'r'):

values =[float(v) for v in line.split()]

XList.append(values[0]) #x-axis

YList.append(values[1]) #y-axis

In [55]: plt.plot(XList, YList)

plt.show()

In [56]: import matplotlib.pyplot as plt

In [57]: with open('my_data.txt', 'r') as f:

X,Y= zip(*[

[ float(s) for s in line.split() ] for line in f

])#2D-list, each element is a list in this 2D-list

In [58]: plt.plot(X,Y)

plt.show()

In [59]: import matplotlib.pyplot as plt

import numpy as np

In [60]: data = np.loadtxt('my_data.txt') #return 2D array or called full-blown matrices

In [61]: plt.plot(data[:,0], data[:,1])

#0-->columnIndex-->X- axis, 1-->columnIndex --> Y-axis

plt.show()

##################################

'my_data.txt'

0 0 6
1 1 5
2 4 4
4 16 3
5 25 2
6 36 1

##################################

In [62]: import numpy as np

import matplotlib.pyplot as plt

In [63]: data = np.loadtxt('my_data.txt')

data.T

Out[63]: array([[ 0., 1., 2., 4., 5., 6.],

[ 0., 1., 4., 16., 25., 36.],

[ 6., 5., 4., 3., 2., 1.]])

In [67]: for columnList in data.T[1:]: #excluding data.T[0]

plt.plot(data[:,0], columnList) # the len(columnList) == the number of curves

plt.show()

In [68]: data

Out[68]: array([[ 0., 0., 6.], [ 1., 1., 5.], [ 2., 4., 4.], [ 4., 16., 3.], [ 5., 25., 2.], [ 6., 36., 1.]])

#######################################################################

cp2_customizing the Color and Styles

1 Alias Colors

b Blue
g Green
r Red
c Cyan m Magenta
y Yellow
k Black
w White

1.0.1 Gray-level strings: matplotlib will interpret a string representation of a floating point- value as a shade of gray, such as 0.75 for a medium light gray.

In [1]: import numpy as np

import matplotlib.pyplot as plt

In [2]: #Gaussian Distribution

#Probability density function

def pdf(X, mu, sigma):

a = 1./(sigma*np.sqrt(2.*np.pi))

b =-1./(2* sigma**2)

return a * np.exp(b* (X-mu)**2 ) #f(x)

In [3]: X = np.linspace(-6,6,1000)

for i in range(5):

#generates five sets of 50 samples from a normal distribution

samples = np.random.standard_normal(50)

mu, sigma = np.mean(samples), np.std(samples)

#For each of the 5 sets, we plot the estimated probability density in light gray

plt.plot(X, pdf(X, mu, sigma), color='0.75')

plt.plot(X, pdf(X,0., 1.), color='k')# standard normal distribution(mean=0,stdev=1)

plt.show()

In [6]: samples=np.random.standard_normal(50)

samples.shape

Out[6]: (50L,)

In [7]: mu, sigma = np.mean(samples), np.std(samples)

mu,sigma

Out[7]: (-0.20688694794049467, 0.8455170647061793)

Controlling a line pattern and thickness

# In[20]:

import numpy as np
import matplotlib.pyplot as plt

# ### Normal Distribution~(mean,Variance)=(0,1)
# #### Gaussians Probability Density Functions~(mean,Variance)=(u,sigma**2 )

# In[21]:

#Gaussians PDF
def pdf(X, mu, sigma):
a=1./( sigma * np.sqrt(2. * np.pi))
b=-1./( 2. * sigma **2)
return a*np.exp(b*(X-mu)**2)

# In[22]:

X = np.linspace(-6,6,1024)

# In[23]:

plt.plot(X, pdf(X, 0., 1.), color='k', linestyle='solid')
plt.plot(X, pdf(X, 0., .5), color='k', linestyle='dashed')
plt.plot(X, pdf(X, 0., .25), color='k', linestyle='dashdot')
plt.show()

The line width

# In[62]:
import numpy as np
import matplotlib.pyplot as plt

# In[65]:
def pdf(X, mu, sigma):
a = 1./(sigma*np.sqrt(2. * np.pi))
b= -1./(2.* sigma**2)
return a*np.exp(b* (X-mu)**2)

# In[69]:
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, pdf(X, mu, sigma), color='.75', linewidth=0.5)

plt.plot(X, pdf(X,0,1), color='k', linewidth=3.) #u =0, std.dev=1 ~ standard normal distribution
plt.show()

markevery=32 will plot every 32th marker starting from the first data point

# In[104]:
import numpy as np
import matplotlib.pyplot as plt

# In[100]:
X = np.linspace(-6,6,1024)
Y1 = np.sinc(X) #The sinc function is sin(pi x)/(pi x).
Y2= np.sinc(X)+1
Y2

# In[103]:
plt.plot(X,Y1, marker='o', color='.75')
#markevery=32 will plot every 32th marker starting from the first data point
plt.plot(X,Y2, marker='o', color='k', markevery=32)

plt.show()

# In[117]:
import numpy as np
import matplotlib.pyplot as plt

# In[118]:
X = np.linspace(-6,6,1024)
Y = np.sinc(X)

# In[120]:
plt.plot(X,Y,
linewidth=3.,
color='k',
markersize=9,
markeredgewidth=3.5,
markerfacecolor='.75',
markeredgecolor='k',
marker='o',
markevery=32)

plt.show()

# In[121]:
import numpy as np
import matplotlib as mpl
from matplotlib import pyplot as plt

# In[173]:
mpl.rc('lines', linewidth=5)
mpl.rc('axes', facecolor='y', edgecolor='w')
mpl.rc('xtick', color='m')
mpl.rc('ytick', color='m')
mpl.rc('text', color='m')
mpl.rc('figure', facecolor='r', edgecolor='y')
#mpl.rc('axes', color_cycle = ('w', '.5', '.75'))

# In[172]:
X = np.linspace(0,7,1024)

# In[170]:
plt.plot(X, np.sin(X))
plt.plot(X, np.cos(X))

plt.show()

If a matplotlibrc file is found in your current directory (that is, the directory from where you launched your script from), it will override matplotlib's default settings. You can also save your matplotlibrc file in a specific location to make your own default settings. In the interactive Python shell, run the following command: import matplotlib mpl.get_configdir() This command will display the location where you can place your matplotlibrc file so that those settings will be your own default settings.

Line Plots¶

The Series object’s index is passed to matplotlib for plotting on the x-axis, though you can disable this by passing use_index=False.

The x-axis ticks and limits can be adjusted with the xticks and xlim options, and y-axis respectively with yticks and ylim.

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

#Y-axis #X-axis

s = pd.Series( np.random.randn(10).cumsum(), index=np.arange(0,100,10) )

s.plot()

plt.show()

DataFrame’s plot method plots each of its columns as a different line on the same subplot, creating a legend automatically; The plot attribute contains a “family” of methods for different plot types. For example, df.plot() is equivalent to df.plot.line().

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

df = pd.DataFrame(np.random.randn(10,4).cumsum(0),

columns=['A','B','C','D'],

index = np.arange(0,100,10))

df.plot()

plt.show()

Figures and Subplots

Plots in matplotlib reside within a Figure object.

import matplotlib.pyplot as plt

fig = plt.figure()

ax = fig.add_subplot(1,1,1)

#pass no label or label='_nolegend_'

ax.plot(randn(1000).cumsum(), color='k', label='one')

ax.plot(randn(1000).cumsum(), color='k', linestyle='--', label='two')

ax.plot(randn(1000).cumsum(), color='k', linestyle='dotted',label='three')

ticks = ax.set_xticks([0,250,500,750,1000])

labels = ax.set_xticklabels(['one','two', 'three', 'four', 'five'], rotation=30, fontsize='small')

ax.set_title('My first matplotlib plot')

ax.set_xlabel('Stages')

//////////////////////////////////////// OR

props = {

'title': 'My first matplotlib plot',

'xlabel': 'Stages'

}

ax.set(**props)

////////////////////////////////////////

ax.legend(loc='best')

plt.show()

matplotlib draws on the last figure and subplot used (creating one if necessary), thus hiding the figure and subplot creation.

plt.plot(np.random.randn(50).cumsum(), color='black', ls='--')

matplotlib includes a convenience method, plt.subplots, that creates a new figure and returns a NumPy array containing the created subplot objects, the axes array can be easily indexed like a two-dimensional array; for example, axes[0, 1].

Adjusting the spacing around subplots

plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=None)

# 1

fig, axes = plt.subplots(2,2, sharex=True, sharey= True)

for i in range(2):

for j in range(2):

axes[i,j].hist(np.random.randn(500), bins =5, color='k', alpha=0.5) #hist: frequency

plt.subplots_adjust(wspace=0.05, hspace=0.05)

# 2

from numpy.random import randn

arr=randn(30)

arrCumSum=arr.cumsum()

plt.plot(arrCumSum, color='k', linestyle='dashed', drawstyle='steps-post', label='steps-post', marker='o')

plt.legend(loc='best') #label='steps-post'

plt.show()

Annotations and Drawing on a Subplot

spx.csv file

......

import numpy as np

import pandas as pd

from datetime import datetime

#index_col : int or sequence or False, default None

data = pd.read_csv('../examples/spx.csv',parse_dates=True, index_col=0)

spx = data['SPX'] #'SPX' column # spx: pandas.core.series.Series

crisis_data=[

(datetime(2007, 10, 11), 'Peak of bull market'), #tuple

(datetime(2008, 3, 12), 'Bear Stearns Fails'),

(datetime(2008, 9, 15), 'Lehman Bankruptcy')

]

# // matplotlib Configuration

plt.rc('figure', figsize=(10,10))

font_options={

'family': 'monospace',

'weight': 'bold',

'size': 16

}

plt.rc('font', **font_options)

fig = plt.figure()

ax = fig.add_subplot(1,1,1)

# spx: date: X-axis # SPX: y-axis

spx.plot(ax=ax, color='green', linestyle='-')

for date, label in crisis_data:

ax.annotate( label,

ha='left',

va='top',

xytext=(date, spx.asof(date) + 225), #The xytext parameter specifies the text position

xy=(date, spx.asof(date) + 75), #The xy parameter specifies the arrow's destination

arrowprops=dict(facecolor='blue', headwidth=10, headlength=4, width=2 ),

#arrowprops={'facecolor':'blue', 'headwidth':10, 'headlength':4, 'width':2} #OR

)

#Zoom in on 2007-2010

ax.set_xlim(['1/1/2007', '1/1/2011'])

ax.set_ylim([600,1800])

ax.set_title('Important dates in the 2008-2009 financial crisis')

plt.show()

Adding arrows

The aspect of the arrow is controlled by a dictionary passed to the arrowprops parameter: 'arrowstyle': The parameters ''<-'', ''<'', ''-'', ''wedge'',''simple'', and ''fancy'' control the style of the arrow 'facecolor': This is the color used for the arrow. It will be used to set the background and the edge color 'edgecolor': This is the color used for the edges of the arrow's shape 'alpha': This is used to set the transparency level so that the arrow blends with the background

The shrink parameter controls the gap between the arrow's endpoints and the arrow itself.

import matplotlib.pyplot as plt

fig = plt.figure()

ax = fig.add_subplot(1,1,1) # return first plot’s reference

#Left-Bottom corner position

#Width, Height

rect = plt.Rectangle( (0.2, 0.75), 0.4, 0.15, color='k', alpha=0.3 )

#center point position

circ = plt.Circle( (0.7, 0.2), radius=0.15, color='b', alpha=0.3 )

polygon = plt.Polygon( [ [0.15, 0.15],

[0.35, 0.4],

[0.2,0.6]

color='g',

alpha=0.5

)

ax.add_patch(rect)

ax.add_patch(circ)

ax.add_patch(polygon)

plt.show() # plt.savefig('figpath.png',dpi=400, bbox_inches='tight')

#dpi, which controls the dots-per-inch resolution

#bbox_inches, which can trim the whitespace around the actual figure.

from io import BytesIO

buffer = BytesIO()

plt.savefig(buffer)

plot_data = buffer.getvalue()

Adding shapes

Internally, a shape is described as a path called patch in matplotlib API. Paths for several kinds of shapes are available in the matplotlib.patches module. As is the case for lines, creating a path won't be enough to render it; you will have to signal that you want to render it. This is done by pyplot.gca().add_patch().

A lot of path constructors are available. Let's review those used in the example:

Circle: This takes the coordinates of its center and the radius as the parameters

Rectangle: This takes the coordinates of its lower-left corner and its size as the parameters

Ellipse: This takes the coordinates of its center and the half-length of its two axes as the parameters

FancyBox: This is like a rectangle but takes an additional boxstyle parameter (either 'larrow', 'rarrow', 'round', 'round4', 'roundtooth', 'sawtooth', or 'square')

import matplotlib.patches as patches

import matplotlib.pyplot as plt

#Circle #center point position

shape = patches.Circle((0,0), radius=1., color='.75')

plt.gca().add_patch(shape)

#Rectangle #Left-Bottom corner position

shape = patches.Rectangle((2.5,-.5), 2., 1., color='.75')

plt.gca().add_patch(shape) #Length, Width

#Ellipse #center position

shape = patches.Ellipse((0,-2.), 2., 1., angle=45., color='.75')

plt.gca().add_patch(shape) #2a：long-axis=2*2, 2b: short-axis=1*2

#Fancy box #inner-rectangle Left-Bottom