Python之matplotlib庫學習：實現數據可視化

1. 安裝和文檔

pip install matplotlib

官方文檔

為了方便顯示圖像，還使用了ipython qtconsole方便顯示。具體怎么弄網上搜一下就很多教程了。

pyplot模塊是提供操作matplotlib庫的經典Python接口。

# 導入pyplot
import matplotlib.pyplot as plt

2. 初探pyplot

plot()的參數表

matplotlib.pyplot.plot(*args, **kwargs)

The following format string characters are accepted to control the line style or marker:

character	description
'-'	solid line style
'--'	dashed line style
'-.'	dash-dot line style
':'	dotted line style
'.'	point marker
','	pixel marker
'o'	circle marker
'v'	triangle_down marker
'^'	triangle_up marker
'<'	triangle_left marker
'>'	triangle_right marker
'1'	tri_down marker
'2'	tri_up marker
'3'	tri_left marker
'4'	tri_right marker
's'	square marker
'p'	pentagon marker
'*'	star marker
'h'	hexagon1 marker
'H'	hexagon2 marker
'+'	plus marker
'x'	x marker
'D'	diamond marker
'd'	thin_diamond marker
'	'
'_'	hline marker

The following color abbreviations are supported:

character	color
‘b’	blue
‘g’	green
‘r’	red
‘c’	cyan
‘m’	magenta
‘y’	yellow
‘k’	black
‘w’	white

演示

plt.axis([0,5,0,20]) # [xmin,xmax,ymin,ymax]對應軸的范圍
plt.title('My first plot') # 圖名
plt.plot([1,2,3,4], [1,4,9,16], 'ro') # 圖上的點,最后一個參數為顯示的模式

Out[5]: [<matplotlib.lines.Line2D at 0x24e0a5bc2b0>]

plt.show() # 展示圖片



In [11]: t = np.arange(0, 2.5, 0.1)
    ...: y1 = list(map(math.sin, math.pi*t))
    ...: y2 = list(map(math.sin, math.pi*t + math.pi/2))
    ...: y3 = list(map(math.sin, math.pi*t - math.pi/2))
    ...: 

In [12]: plt.plot(t, y1, 'b*', t, y2, 'g^', t, y3, 'ys')
Out[12]: 
[<matplotlib.lines.Line2D at 0x24e0a8469b0>,
 <matplotlib.lines.Line2D at 0x24e0a58de48>,
 <matplotlib.lines.Line2D at 0x24e0a846b38>]

In [13]: plt.show()



In [14]: plt.plot(t,y1,'b--',t,y2,'g',t,y3,'r-.')
Out[14]: 
[<matplotlib.lines.Line2D at 0x24e0a8b49e8>,
 <matplotlib.lines.Line2D at 0x24e0a8b4c88>,
 <matplotlib.lines.Line2D at 0x24e0a8b95c0>]

In [15]: plt.show()



3. 處理多個Figure和Axes對象

subplot(nrows, ncols, plot_number)

In [19]: plt.subplot(2,1,1)
    ...: plt.plot(t,y1,'b-.')
    ...: plt.subplot(2,1,2)
    ...: plt.plot(t,y2,'r--')
    ...: 
Out[19]: [<matplotlib.lines.Line2D at 0x24e0b8f47b8>]

In [20]: plt.show()



In [30]: plt.subplot(2,1,1)
    ...: plt.plot(t,y1,'b-.')
    ...: plt.subplot(2,1,2)
    ...: plt.plot(t,y2,'r--')
    ...: 
Out[30]: [<matplotlib.lines.Line2D at 0x24e0bb9fc88>]

In [31]: plt.show()



4. 添加更多元素

4.1. 添加文本

添加標題：title()
添加軸標簽：xlabel()和ylabel()
在圖形的坐標添加文本：text(x, y, string, fontdict=None, **kwargs)，分別表示坐標和字符串以及一些關鍵字參數。
還可以添加LaTeX表達式，繪制邊框等。

In [40]: plt.axis([0,5,0,20])
    ...: plt.title('_Kazusa', fontsize=20, fontname='Times New Roman')
    ...: plt.xlabel('x_axis', color='orange')
    ...: plt.ylabel('y_axis', color='gray')
    ...: plt.text(1, 1.5, 'A')
    ...: plt.text(2, 4.5, 'B')
    ...: plt.text(3, 9.5, 'C')
    ...: plt.text(4, 16.5, 'D')
    ...: plt.text(0.5, 15, r'$y=x^2$', fontsize=20, bbox={'facecolor':'yellow','alpha':0.2})
    ...: plt.plot([1,2,3,4],[1,4,9,16],'ro')
    ...: 
Out[40]: [<matplotlib.lines.Line2D at 0x24e0bf03240>]

In [41]: plt.show()



4.2. 其他元素

網格：grid(True) 表示顯示網格
圖例：matplotlib.pyplot.legend(*args, **kwargs)

---

圖例的位置參數：
loc : int or string or pair of floats, default: ‘upper right’
The location of the legend. Possible codes are:

Location String	Location Code
‘best’	0
‘upper right’	1
‘upper left’	2
‘lower left’	3
‘lower right’	4
‘right’	5
‘center left’	6
‘center right’	7
‘lower center’	8
‘upper center’	9
‘center’	10

Alternatively can be a 2-tuple giving x, y of the lower-left corner of the legend in axes coordinates (in which case bbox_to_anchor will be ignored).

---

In [44]: plt.axis([0,5,0,20])
    ...: plt.title('_Kazusa', fontsize=20, fontname='Times New Roman')
    ...: plt.xlabel('x_axis', color='orange')
    ...: plt.ylabel('y_axis', color='gray')
    ...: plt.text(1, 1.5, 'A')
    ...: plt.text(2, 4.5, 'B')
    ...: plt.text(3, 9.5, 'C')
    ...: plt.text(4, 16.5, 'D')
    ...: plt.text(0.5, 10, r'$y=x^2$', fontsize=20, bbox={'facecolor':'yellow','alpha':0.2})
    ...: plt.plot([1,2,3,4],[1,4,9,16],'ro')
    ...: plt.grid(True)
    ...: plt.plot([1,2,3,4],[0.6,2.3,14.5,15.8], 'g^')
    ...: plt.plot([1,2,3,4],[0.2,9.7,11.6,13.9],'b*')
    ...: plt.legend(['First series', 'Sencond series', 'Third series'], loc=0)
    ...: 
Out[44]: <matplotlib.legend.Legend at 0x24e0c02a630>

In [45]: plt.show()



5. 保存

%save mycode 44
# %save魔術命令后，跟着文件名和代碼對於的命令提示符號碼。例如上面In [44]: plt.axis([0,5,0,20])號碼就是44,運行之后會在工作目錄生成'mycode.py'文件,還可以是'數字-數字'，即保存從一個行數到另一個行數的代碼
ipython qtconsole -m mycode.py
#可以打開之前的代碼
%load mycode.py
#可以加載所有代碼
%run mycode.py
#可以運行代碼

#保存圖片的話，可以在生成圖標的一系列命令之后加上savefig()函數把圖標保存為PNG格式。
#在保存圖像命令之前不要使用plt.show()否則會得到空白圖像
In [30]: plt.subplot(2,1,1)
    ...: plt.plot(t,y1,'b-.')
    ...: plt.subplot(2,1,2)
    ...: plt.plot(t,y2,'r--')
    ...: plt.savefig('pic.png')

6. 線形圖

替換坐標軸的刻度的標簽：xticks()和yticks()。傳入的參數第一個是存儲刻度的位置的列表，第二個是存儲刻度的標簽的列表。要正確顯示標簽，要使用含有LaTeX表達式的字符串。

顯示笛卡爾坐標軸：首先用gca()函數獲取Axes對象。通過這個對象，指定每條邊的位置：上下左右，可選擇組成圖形邊框的每條邊。使用set_color()函數，設置顏色為'none'（這里我不刪除而是換了個顏色方便確認位置），刪除根坐標軸不符合的邊（右上）。用set_positon()函數移動根x軸和y軸相符的邊框，使其穿過原點(0,0)。

annotate()函數可以添加注釋。第一個參數為含有LaTeX表達式、要在圖形中顯示的字符串；隨后是關鍵字參數。文本注釋跟它所解釋的數據點之間的距離用xytext關鍵字參數指定，用曲線箭頭將其表示出來。箭頭的屬性由arrowprops關鍵字參數指定。

In [57]: x = np.arange(-2*np.pi, 2*np.pi, 0.01)
    ...: y1 = np.sin(3*x)/x
    ...: y2 = np.sin(2*x)/x
    ...: y3 = np.sin(x)/x
    ...: plt.plot(x,y1,color='y')
    ...: plt.plot(x,y2,color='r')
    ...: plt.plot(x,y3,color='k')
    ...: plt.xticks([-2*np.pi, -np.pi, 0, np.pi, 2 * np.pi], [r'$-2\pi$', r'$-\pi$', r'$0$', r'$+\pi$', r'$+2\pi$'])
    ...: plt.yticks([-1, 0, +1, +2, +3], [r'$-1$', r'$0$', r'$+1$', r'$+2$', r'$+3$'])
    ...: # 設置坐標軸標簽
    ...: plt.annotate(r'$\lim_{x\to 0}\frac{\sin(x)}{x}=1$', xy=[0,1], xycoords='data', xytext=[30,30], fontsize=18, textcoords='offset points', arrowprops=dict(arrowstyle="->",connectionstyle="arc3,rad=.2")) # 設置注釋
    ...: 
    ...: ax = plt.gca()
    ...: ax.spines['right'].set_color('r')
    ...: ax.spines['top'].set_color('b')
    ...: ax.xaxis.set_ticks_position('bottom')
    ...: ax.spines['bottom'].set_position(('data',0))
    ...: ax.yaxis.set_ticks_position('left')
    ...: ax.spines['left'].set_position(('data',0))
    ...: ax.spines['bottom'].set_color('g')
    ...: ax.spines['left'].set_color('k') # 設置笛卡爾坐標軸
    ...: 

In [58]: plt.show()



為pandas數據結構繪制線形圖##

把DataFrame作為參數傳入plot()函數，就可以得到多序列線形圖。

In [60]: data = {'series1':[1,3,4,3,5], 'series2':[2,4,5,2,4], 'series3':[5,4,4,1,5]}
    ...: df = pd.DataFrame(data)
    ...: x = np.arange(5)
    ...: plt.axis([0,6,0,6])
    ...: plt.plot(x,df)
    ...: plt.legend(data, loc=0)
    ...: 
Out[60]: <matplotlib.legend.Legend at 0x24e0dbda668>

In [61]: plt.show()



7. 直方圖

hist()可以用於繪制直方圖。

In [4]: array = np.random.randint(0, 50, 50)

In [5]: array
Out[5]: 
array([43,  1, 16,  9, 22, 15,  1, 12,  9, 25, 37, 47, 31, 15, 25, 43,  3,
       28, 21, 23, 11, 10, 14, 27, 10, 19, 40, 44, 26, 49, 13, 35, 19, 48,
        7, 21, 37, 47,  2,  4, 15, 20, 47, 11,  2, 49, 31, 31,  1, 46])
        
In [8]: n, bins, patches = plt.hist(array, bins=10) # hist()第二個參數便是划分的面元個數,n表示每個面元有幾個數字,bins表示面元的范圍,patches表示每個長條

In [10]: n
Out[10]: array([ 7.,  5.,  8.,  4.,  4.,  5.,  3.,  3.,  4.,  7.])

In [11]: bins
Out[11]: 
array([  1. ,   5.8,  10.6,  15.4,  20.2,  25. ,  29.8,  34.6,  39.4,
        44.2,  49. ])

In [13]: for patch in patches:
    ...:     print(patch)
    ...:     
Rectangle(1,0;4.8x7)
Rectangle(5.8,0;4.8x5)
Rectangle(10.6,0;4.8x8)
Rectangle(15.4,0;4.8x4)
Rectangle(20.2,0;4.8x4)
Rectangle(25,0;4.8x5)
Rectangle(29.8,0;4.8x3)
Rectangle(34.6,0;4.8x3)
Rectangle(39.4,0;4.8x4)
Rectangle(44.2,0;4.8x7)

In [9]: plt.show()



8. 條狀圖

bar()函數可以生成垂直條狀圖
barh()函數可以生成水平條狀圖，設置的時候坐標軸參數和垂直條狀圖相反，其他都是一樣的。

8.1 普通條狀圖

In [24]: index = np.arange(5)
    ...: values = [5,7,3,4,6]
    ...: plt.bar(index, values)
    ...: plt.title('_Kazusa')
    ...: plt.xticks(index,['A','B','C','D','E'])
    ...: plt.legend('permutation', loc = 0)
    ...: 
Out[24]: <matplotlib.legend.Legend at 0x22abd7a7a20>

In [25]: plt.show()



8.2 多序列條狀圖

In [34]: index = np.arange(5)
    ...: val1 = [5,7,3,4,6]
    ...: val2 = [6,6,4,5,7]
    ...: val3 = [5,6,5,4,6]
    ...: gap = 0.31 # 把每個類別占的空間(總空間為1)分成多個部分,例如這里有三個,因為要增加一點空間區分相鄰的類別,因此取0.31
    ...: plt.axis([0,5,0,8])
    ...: plt.title('_Kazusa')
    ...: plt.bar(index+0.2, val1, gap, color='b')
    ...: plt.bar(index+gap+0.2, val2, gap, color='y')
    ...: plt.bar(index+2*gap+0.2, val3, gap, color='k')
    ...: plt.xticks(index+gap+0.2, ['A','B','C','D','E']) # index+gap即標簽偏移gap的距離
    ...: 
Out[34]: 
([<matplotlib.axis.XTick at 0x22abd72a6d8>,
  <matplotlib.axis.XTick at 0x22abd93fba8>,
  <matplotlib.axis.XTick at 0x22abd9f95f8>,
  <matplotlib.axis.XTick at 0x22abda5e400>,
  <matplotlib.axis.XTick at 0x22abda5edd8>],
 <a list of 5 Text xticklabel objects>)

In [35]: plt.show()



8.3 對序列堆積條狀圖

有時候需要表示總和是由幾個條狀圖相加得到，因此堆積圖表示比較合適。
使用bar()的時候添加參數bottom= 來確定下面是什么。

In [41]: arr1 = np.array([3,4,5,3])
    ...: arr2 = np.array([1,2,2,5])
    ...: arr3 = np.array([2,3,3,4])
    ...: index = np.arange(4)
    ...: plt.axis([0,4,0,15])
    ...: plt.title('_Kazusa')
    ...: plt.bar(index + 0.5, arr1, color='r')
    ...: plt.bar(index + 0.5, arr2, color='b', bottom=arr1)
    ...: plt.bar(index + 0.5, arr3, color='y', bottom=(arr1+arr2))
    ...: plt.xticks(index + 0.5, ['A','B','C','D','E'])
    ...: 
Out[41]: 
([<matplotlib.axis.XTick at 0x22abd953a90>,
  <matplotlib.axis.XTick at 0x22abda95550>,
  <matplotlib.axis.XTick at 0x22abdb99320>,
  <matplotlib.axis.XTick at 0x22abdbff630>],
 <a list of 4 Text xticklabel objects>)

In [42]: plt.show()



8.4 為DataFrame繪制條狀圖

In [47]: data = {'arr1':[1,3,4,3,5], 'arr2':[2,4,5,2,4], 'arr3':[1,3,5,1,2]}
    ...: df = pd.DataFrame(data)
    ...: df.plot(kind='bar') # 若要堆積圖,變成 df.plot(kind='bar', stacked=True)
    ...: 
Out[47]: <matplotlib.axes._subplots.AxesSubplot at 0x22abdcb3cf8>

In [48]: plt.show()



9. 餅圖

pie()函數可以制作餅圖

In [51]: labels = ['A','B','C','D'] # 標簽
    ...: values = [10,30,45,15] # 數值
    ...: colors = ['yellow', 'green', 'red', 'orange'] # 顏色
    ...: explode = [0.3, 0, 0, 0] # 突出顯示,數值0~1
    ...: plt.title('_Kazusa') 
    ...: plt.pie(values, labels=labels, colors=colors, explode=explode, startangle=90, shadow=True, autopct='%1.1f%%') # shadow表示陰影, autopct表示顯示百分比, startangle表示餅旋轉的角度
    ...: plt.axis('equal')
    ...: 
Out[51]: 
(-1.1007797083302826,
 1.1163737124158366,
 -1.1306395753855039,
 1.4003625034945653)

In [52]: plt.show()



10. 等值線圖

由一圈圈封閉的曲線組成的等值線圖表示三維結構的表面，其中封閉的曲線表示的是一個個處於同一層級或z值相同的數據點。
contour()函數可以生成等值線圖。

In [57]: dx = 0.01; dy = 0.01
    ...: x = np.arange(-2.0, 2.0, dx)
    ...: y = np.arange(-2.0, 2.0, dy)
    ...: X, Y = np.meshgrid(x, y) # 這里meshigrid（x，y）的作用是產生一個以向量x為行，向量y為列的矩陣，而x是從-2開始到2，每間隔dx記下一個數據，並把這些數據集成矩陣X；同理y則是從-2到2，每間隔dy記下一個數據，並集成矩陣Y
    ...: def f(x,y): # 生成z的函數
    ...:     return (1 - y**5 + x**5) * np.exp(-x**2 - y**2)
    ...: C = plt.contour(X, Y, f(X,Y), 8, colors='black') # 8表示分層的密集程度，越小越稀疏，colors表示最開始是什么顏色
    ...: plt.contourf(X, Y, f(X,Y), 8) # 使用顏色，8的意義同上
    ...: plt.clabel(C, inline=1, contsize=10) # 生成等值線的標簽
    ...: plt.colorbar() # 側邊的顏色說明
    ...: 
Out[57]: <matplotlib.colorbar.Colorbar at 0x22abf289470>

In [58]: plt.show()



11. 3D圖

3D圖的Axes對象要替換為Axes3D對象。

from mpl_toolkits.mplot3d import Axes3D

11.1. 3D曲面

plot_surface(X, Y, Z, *args, **kwargs)可以顯示面

Argument	Description
X, Y, Z	Data values as 2D arrays
rstride	Array row stride (step size)
cstride	Array column stride (step size)
rcount	Use at most this many rows, defaults to 50
ccount	Use at most this many columns, defaults to 50
color	Color of the surface patches
cmap	A colormap for the surface patches.
facecolors	Face colors for the individual patches
norm	An instance of Normalize to map values to colors
vmin	Minimum value to map
vmax	Maximum value to map
shade	Whether to shade the facecolors

In [9]: fig = plt.figure()
   ...: ax = Axes3D(fig)
   ...: X = np.arange(-2, 2, 0.1)
   ...: Y = np.arange(-2, 2, 0.1)
   ...: X, Y = np.meshgrid(X, Y)
   ...: def f(x,y):
   ...:     return (1 - y ** 5 + x ** 5) * np.exp(-x ** 2 - y ** 2)
   ...: ax.plot_surface(X, Y, f(X,Y), rstride=1, cstride=1) # rstide/cstride表示 行/列數組的跨度
   ...: ax.view_init(elev=30,azim=125) # elev指定從哪個高度查看曲面,azim指定曲面旋轉的角度
   ...: 

In [10]: pt.show()



11.2. 3D散點圖

Axes3D.scatter(xs, ys, zs=0, zdir='z', s=20, c=None, depthshade=True, *args, **kwargs)可以使用散點圖

Argument	Description
xs, ys	Positions of data points.
zs	Either an array of the same length as xs and ys or a single value to place all points in the same plane. Default is 0.
zdir	Which direction to use as z (‘x’, ‘y’ or ‘z’) when plotting a 2D set.
s	Size in points^2. It is a scalar or an array of the same length as x and y.
c	A color. c can be a single color format string, or a sequence of color specifications of length N, or a sequence of N numbers to be mapped to colors using the cmap and norm specified via kwargs (see below).
depthshade	Whether or not to shade the scatter markers to give the appearance of depth. Default is True.

In [16]: x1 = np.random.randint(30, 40, 100)
    ...: y1 = np.random.randint(30, 40, 100)
    ...: z1 = np.random.randint(10, 20, 100)
    ...: x2 = np.random.randint(50, 60, 100)
    ...: y2 = np.random.randint(50, 60, 100)
    ...: z2 = np.random.randint(10, 20, 100)
    ...: x3 = np.random.randint(30, 40, 100)
    ...: y3 = np.random.randint(30, 40, 100)
    ...: z3 = np.random.randint(40, 50, 100)
    ...: fig = plt.figure()
    ...: ax = Axes3D(fig)
    ...: ax.scatter(x1, y1, z1)
    ...: ax.scatter(x2, y2, z2, c = 'r', marker = '^')
    ...: ax.scatter(x3, y3, z3, c = 'y', marker = '*')
    ...: ax.set_xlabel('X axis')
    ...: ax.set_ylabel('Y axis')
    ...: ax.set_zlabel('Z axis')
    ...: 
Out[16]: <matplotlib.text.Text at 0x1b0218fda90>

In [17]: plt.show()



11.3. 3D條狀圖

bar()函數

Axes3D.bar(left, height, zs=0, zdir='z', *args, **kwargs)

Argument	Description
left	The x coordinates of the left sides of the bars.
height	The height of the bars.
zs	Z coordinate of bars, if one value is specified they will all be placed at the same z.
zdir	Which direction to use as z (‘x’, ‘y’ or ‘z’) when plotting a 2D set.

In [22]: x = np.arange(5)
    ...: y1 = np.random.randint(0, 10, 5)
    ...: y2 = y1 + np.random.randint(0, 3, 5)
    ...: y3 = y2 + np.random.randint(0, 3, 5)
    ...: y4 = y3 + np.random.randint(0, 3, 5)
    ...: y5 = y4 + np.random.randint(0, 3, 5)
    ...: clr = ['yellow', 'red', 'blue', 'black', 'green']
    ...: fig = plt.figure()
    ...: ax = Axes3D(fig)
    ...: ax.bar(x, y1, 0, zdir = 'y', color = clr)
    ...: ax.bar(x, y2, 10, zdir = 'y', color = clr)
    ...: ax.bar(x, y3, 20, zdir = 'y', color = clr)
    ...: ax.bar(x, y4, 30, zdir = 'y', color = clr)
    ...: ax.bar(x, y5, 40, zdir = 'y', color = clr)
    ...: ax.set_xlabel('x Axis')
    ...: ax.set_ylabel('y Axis')
    ...: ax.set_zlabel('z Axis')
    ...: ax.view_init(elev=40)
    ...: 

In [23]: plt.show()



12. 多面板圖形

12.1. 在其他子圖中顯示子圖

把主Axes對象（主圖表）跟放置另一個Axes對象實例的框架分開。用figure()函數取到Figure對象，用add_axes()函數在它上面定義兩個Axes對象。

add_axes(*args, **kwargs)
rect = l,b,w,h # (分別是左邊界,下邊界,寬,高)
fig.add_axes(rect)

In [28]: fig = plt.figure()
    ...: ax = fig.add_axes([0.1,0.1,0.8,0.8])
    ...: inner_ax = fig.add_axes([0.6,0.6,0.25,0.25])
    ...: x1 = np.arange(10)
    ...: y1 = np.array([1,2,7,1,5,2,4,2,3,1])
    ...: x2 = np.arange(10)
    ...: y2 = np.array([1,2,3,4,6,4,3,4,5,1])
    ...: ax.plot(x1,y1)
    ...: inner_ax.plot(x2,y2)
    ...: 
Out[28]: [<matplotlib.lines.Line2D at 0x1b022ac94a8>]

In [29]: plt.show()



12.2. 子圖網格

class matplotlib.gridspec.GridSpec(nrows, ncols, left=None, bottom=None, right=None, top=None, wspace=None, hspace=None, width_ratios=None, height_ratios=None)
add_subplot(*args, **kwargs)

In [30]: gs = plt.GridSpec(3,3) # 分成多少塊
    ...: fig = plt.figure(figsize=(8,8)) # 指定圖的尺寸
    ...: x1 = np.array([1,3,2,5])
    ...: y1 = np.array([4,3,7,2])
    ...: x2 = np.arange(5)
    ...: y2 = np.array([3,2,4,6,4])
# 可以看做分作一個3*3的格子,每次給一個表分配格子,[row,col],[0,0]表示左上角
    ...: p1 = fig.add_subplot(gs[1,:2]) 
    ...: p1.plot(x1, y1, 'r')
    ...: p2 = fig.add_subplot(gs[0,:2])
    ...: p2.bar(x2, y2)
    ...: p3 = fig.add_subplot(gs[2,0])
    ...: p3.barh(x2, y2, color='b')
    ...: p4 = fig.add_subplot(gs[:2,2])
    ...: p4.plot(x2, y2, 'k')
    ...: p5 = fig.add_subplot(gs[2,1:])
    ...: p5.plot(x1, y1, 'b^', x2, y2, 'yo')
    ...: 
Out[30]: 
[<matplotlib.lines.Line2D at 0x1b022c8a940>,
 <matplotlib.lines.Line2D at 0x1b022cdc4a8>]
 
 In [32]: plt.show()