教你如何用python绘图

拼搏现实的明天。 2022-04-04 04:27 608阅读 0赞

各位工程师累了吗? 推荐一篇可以让你技术能力达到出神入化的网站"持久男"  
1.二维绘图  
a. 一维数据集  
用 Numpy ndarray 作为数据传入 ply

import numpy as np
    import matplotlib as mpl
    import matplotlib.pyplot as plt
    
    np.random.seed(1000)
    y = np.random.standard_normal(10)
    print "y = %s"% y
    x = range(len(y))
    print "x=%s"% x
    plt.plot(y)
    plt.show()

**![加粗样式][20181214200025409.png]**

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5MzYzMDIy_size_16_color_FFFFFF_t_70]

2.操纵坐标轴和增加网格及标签的函数

import numpy as np
    import matplotlib as mpl
    import matplotlib.pyplot as plt
    
    np.random.seed(1000)
    y = np.random.standard_normal(10)
    plt.plot(y.cumsum())
    plt.grid(True) ##增加格点
    plt.axis('tight') # 坐标轴适应数据量 axis 设置坐标轴
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5MzYzMDIy_size_16_color_FFFFFF_t_70 1]

3.plt.xlim 和 plt.ylim 设置每个坐标轴的最小值和最大值

#!/etc/bin/python
    #coding=utf-8
    import numpy as np
    import matplotlib as mpl
    import matplotlib.pyplot as plt
    
    np.random.seed(1000)
    y = np.random.standard_normal(20)
    plt.plot(y.cumsum())
    plt.grid(True) ##增加格点
    plt.xlim(-1,20)
    plt.ylim(np.min(y.cumsum())- 1, np.max(y.cumsum()) + 1)
    
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5MzYzMDIy_size_16_color_FFFFFF_t_70 2]  
4. 添加标题和标签 plt.title, plt.xlabe, plt.ylabel 离散点, 线

#!/etc/bin/python
    #coding=utf-8
    import numpy as np
    import matplotlib as mpl
    import matplotlib.pyplot as plt
    
    np.random.seed(1000)
    y = np.random.standard_normal(20)
    
    plt.figure(figsize=(7,4)) #画布大小
    plt.plot(y.cumsum(),'b',lw = 1.5) # 蓝色的线
    plt.plot(y.cumsum(),'ro') #离散的点
    plt.grid(True)
    plt.axis('tight')
    plt.xlabel('index')
    plt.ylabel('value')
    plt.title('A simple Plot')
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5MzYzMDIy_size_16_color_FFFFFF_t_70 3]  
b. 二维数据集

np.random.seed(2000)
    y = np.random.standard_normal((10, 2)).cumsum(axis=0)   #10行2列   在这个数组上调用cumsum 计算赝本数据在0轴(即第一维)上的总和
    print y

![在这里插入图片描述][20181214200842659.png]  
1.两个数据集绘图

#!/etc/bin/python
    #coding=utf-8
    import numpy as np
    import matplotlib as mpl
    import matplotlib.pyplot as plt
    
    np.random.seed(2000)
    y = np.random.standard_normal((10, 2))
    plt.figure(figsize=(7,5))
    plt.plot(y, lw = 1.5)
    plt.plot(y, 'ro')
    plt.grid(True)
    plt.axis('tight')
    plt.xlabel('index')
    plt.ylabel('value')
    plt.title('A simple plot')
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5MzYzMDIy_size_16_color_FFFFFF_t_70 4]  
2.添加图例 plt.legend(loc = 0)

#!/etc/bin/python
    #coding=utf-8
    import numpy as np
    import matplotlib as mpl
    import matplotlib.pyplot as plt
    
    np.random.seed(2000)
    y = np.random.standard_normal((10, 2))
    plt.figure(figsize=(7,5))
    plt.plot(y[:,0], lw = 1.5,label = '1st')
    plt.plot(y[:,1], lw = 1.5, label = '2st')
    plt.plot(y, 'ro')
    plt.grid(True)
    plt.legend(loc = 0) #图例位置自动
    plt.axis('tight')
    plt.xlabel('index')
    plt.ylabel('value')
    plt.title('A simple plot')
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5MzYzMDIy_size_16_color_FFFFFF_t_70 5]  
3.使用2个 Y轴(左右)fig, ax1 = plt.subplots() ax2 = ax1.twinx()

#!/etc/bin/python
    #coding=utf-8
    import numpy as np
    import matplotlib as mpl
    import matplotlib.pyplot as plt
    
    np.random.seed(2000)
    y = np.random.standard_normal((10, 2))
    
    fig, ax1 = plt.subplots() # 关键代码1 plt first data set using first (left) axis
    
    plt.plot(y[:,0], lw = 1.5,label = '1st')
    
    plt.plot(y[:,0], 'ro')
    plt.grid(True)
    plt.legend(loc = 0) #图例位置自动
    plt.axis('tight')
    plt.xlabel('index')
    plt.ylabel('value')
    plt.title('A simple plot')
    
    ax2 = ax1.twinx()  #关键代码2  plt second data set using second(right) axis
    plt.plot(y[:,1],'g', lw = 1.5, label = '2nd')
    plt.plot(y[:,1], 'ro')
    plt.legend(loc = 0)
    plt.ylabel('value 2nd')
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5MzYzMDIy_size_16_color_FFFFFF_t_70 6]  
4.使用两个子图(上下,左右)plt.subplot(211)

通过使用 plt.subplots 函数,可以直接访问底层绘图对象,例如可以用它生成和第一个子图共享 x 轴的第二个子图.
    
    #!/etc/bin/python
    #coding=utf-8
    import numpy as np
    import matplotlib as mpl
    import matplotlib.pyplot as plt
    
    np.random.seed(2000)
    y = np.random.standard_normal((10, 2))
    
    plt.figure(figsize=(7,5))
    plt.subplot(211)  #两行一列,第一个图
    plt.plot(y[:,0], lw = 1.5,label = '1st')
    plt.plot(y[:,0], 'ro')
    plt.grid(True)
    plt.legend(loc = 0) #图例位置自动
    plt.axis('tight')
    plt.ylabel('value')
    plt.title('A simple plot')
    
    
    plt.subplot(212) #两行一列.第二个图
    plt.plot(y[:,1],'g', lw = 1.5, label = '2nd')
    plt.plot(y[:,1], 'ro')
    plt.grid(True)
    plt.legend(loc = 0)
    plt.xlabel('index')
    plt.ylabel('value 2nd')
    plt.axis('tight')
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5MzYzMDIy_size_16_color_FFFFFF_t_70 7]  
5.左右子图

有时候,选择两个不同的图标类型来可视化数据可能是必要的或者是理想的.利用子图方法:
    
    #!/etc/bin/python
    #coding=utf-8
    import numpy as np
    import matplotlib as mpl
    import matplotlib.pyplot as plt
    
    np.random.seed(2000)
    y = np.random.standard_normal((10, 2))
    
    plt.figure(figsize=(10,5))
    plt.subplot(121)  #两行一列,第一个图
    plt.plot(y[:,0], lw = 1.5,label = '1st')
    plt.plot(y[:,0], 'ro')
    plt.grid(True)
    plt.legend(loc = 0) #图例位置自动
    plt.axis('tight')
    plt.xlabel('index')
    plt.ylabel('value')
    plt.title('1st Data Set')
    
    plt.subplot(122)
    plt.bar(np.arange(len(y)), y[:,1],width=0.5, color='g',label = '2nc')
    plt.grid(True)
    plt.legend(loc=0)
    plt.axis('tight')
    plt.xlabel('index')
    plt.title('2nd Data Set')
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5MzYzMDIy_size_16_color_FFFFFF_t_70 8]  
c.其他绘图样式,散点图,直方图等

1.散点图 scatter
    #!/etc/bin/python
    #coding=utf-8
    import numpy as np
    import matplotlib as mpl
    import matplotlib.pyplot as plt
    
    np.random.seed(2000)
    y = np.random.standard_normal((1000, 2))
    plt.figure(figsize=(7,5))
    plt.scatter(y[:,0],y[:,1],marker='o')
    plt.grid(True)
    plt.xlabel('1st')
    plt.ylabel('2nd')
    plt.title('Scatter Plot')
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5MzYzMDIy_size_16_color_FFFFFF_t_70 9]  
2.直方图 plt.hist

#!/etc/bin/python
    #coding=utf-8
    import numpy as np
    import matplotlib as mpl
    import matplotlib.pyplot as plt
    
    np.random.seed(2000)
    y = np.random.standard_normal((1000, 2))
    plt.figure(figsize=(7,5))
    plt.hist(y,label=['1st','2nd'],bins=25)
    plt.grid(True)
    plt.xlabel('value')
    plt.ylabel('frequency')
    plt.title('Histogram')
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5MzYzMDIy_size_16_color_FFFFFF_t_70 10]  
3.直方图 同一个图中堆叠

#!/etc/bin/python
    #coding=utf-8
    import numpy as np
    import matplotlib as mpl
    import matplotlib.pyplot as plt
    
    np.random.seed(2000)
    y = np.random.standard_normal((1000, 2))
    plt.figure(figsize=(7,5))
    plt.hist(y,label=['1st','2nd'],color=['b','g'],stacked=True,bins=20)
    plt.grid(True)
    plt.xlabel('value')
    plt.ylabel('frequency')
    plt.title('Histogram')
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5MzYzMDIy_size_16_color_FFFFFF_t_70 11]  
4.箱型图 boxplot

#!/etc/bin/python
    #coding=utf-8
    import numpy as np
    import matplotlib as mpl
    import matplotlib.pyplot as plt
    
    np.random.seed(2000)
    y = np.random.standard_normal((1000, 2))
    fig, ax = plt.subplots(figsize=(7,4))
    plt.boxplot(y)
    
    plt.grid(True)
    plt.setp(ax,xticklabels=['1st' , '2nd'])
    plt.xlabel('value')
    plt.ylabel('frequency')
    plt.title('Histogram')
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5MzYzMDIy_size_16_color_FFFFFF_t_70 12]  
5.绘制函数

from matplotlib.patches import Polygon
    import numpy as np
    import matplotlib.pyplot as plt

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5MzYzMDIy_size_16_color_FFFFFF_t_70 13]  
\#1. 定义积分函数

def func(x):
        return 0.5 * np.exp(x)+1
    
    #2.定义积分区间
    
    a,b = 0.5, 1.5
    x = np.linspace(0, 2 )
    y = func(x)
    #3.绘制函数图形
    fig, ax = plt.subplots(figsize=(7,5))
    plt.plot(x,y, 'b',linewidth=2)
    plt.ylim(ymin=0)
    #4.核心， 我们使用Polygon函数生成阴影部分，表示积分面积：
    Ix = np.linspace(a,b)
    Iy = func(Ix)
    verts = [(a,0)] + list(zip(Ix, Iy))+[(b,0)]
    poly = Polygon(verts,facecolor='0.7',edgecolor = '0.5')
    ax.add_patch(poly)
    #5.用plt.text和plt.figtext在图表上添加数学公式和一些坐标轴标签。
    plt.text(0.5 *(a+b),1,r"$\int_a^b f(x)\mathrm{d}x$", horizontalalignment ='center',fontsize=20)
    plt.figtext(0.9, 0.075,'$x$')
    plt.figtext(0.075, 0.9, '$f(x)$')
    #6. 分别设置x,y刻度标签的位置。
    ax.set_xticks((a,b))
    ax.set_xticklabels(('$a$','$b$'))
    ax.set_yticks([func(a),func(b)])
    ax.set_yticklabels(('$f(a)$','$f(b)$'))
    plt.grid(True)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5MzYzMDIy_size_16_color_FFFFFF_t_70 14]  
2.金融学图表 matplotlib.finance

1.烛柱图 candlestick
    #!/etc/bin/python
    #coding=utf-8
    import matplotlib.pyplot as plt
    import matplotlib.finance as mpf
    start = (2014, 5,1)
    end = (2014, 7,1)
    quotes = mpf.quotes_historical_yahoo('^GDAXI',start,end)
    # print quotes[:2]
    
    fig, ax = plt.subplots(figsize=(8,5))
    fig.subplots_adjust(bottom = 0.2)
    mpf.candlestick(ax, quotes, width=0.6, colorup='b',colordown='r')
    plt.grid(True)
    ax.xaxis_date() #x轴上的日期
    ax.autoscale_view()
    plt.setp(plt.gca().get_xticklabels(),rotation=30) #日期倾斜
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5MzYzMDIy_size_16_color_FFFFFF_t_70 15]  
2. plot\_day\_summary  
该函数提供了一个相当类似的图标类型,使用方法和 candlestick 函数相同,使用类似的参数. 这里开盘价和收盘价不是由彩色矩形表示,而是由两条短水平线表示.

#!/etc/bin/python
    #coding=utf-8
    import matplotlib.pyplot as plt
    import matplotlib.finance as mpf
    start = (2014, 5,1)
    end = (2014, 7,1)
    quotes = mpf.quotes_historical_yahoo('^GDAXI',start,end)
    # print quotes[:2]
    
    fig, ax = plt.subplots(figsize=(8,5))
    fig.subplots_adjust(bottom = 0.2)
    mpf.plot_day_summary(ax, quotes,  colorup='b',colordown='r')
    plt.grid(True)
    ax.xaxis_date() #x轴上的日期
    ax.autoscale_view()
    plt.setp(plt.gca().get_xticklabels(),rotation=30) #日期倾斜
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5MzYzMDIy_size_16_color_FFFFFF_t_70 16]  
3.股价数据和成交量

#!/etc/bin/python
    #coding=utf-8
    import matplotlib.pyplot as plt
    import numpy as np
    import matplotlib.finance as mpf
    start = (2014, 5,1)
    end = (2014, 7,1)
    quotes = mpf.quotes_historical_yahoo('^GDAXI',start,end)
    # print quotes[:2]
    
    quotes = np.array(quotes)
    fig, (ax1, ax2) = plt.subplots(2, sharex=True, figsize=(8,6))
    mpf.candlestick(ax1, quotes, width=0.6,colorup='b',colordown='r')
    ax1.set_title('Yahoo Inc.')
    ax1.set_ylabel('index level')
    ax1.grid(True)
    ax1.xaxis_date()
    plt.bar(quotes[:,0] - 0.25, quotes[:, 5], width=0.5)
    
    ax2.set_ylabel('volume')
    ax2.grid(True)
    ax2.autoscale_view()
    plt.setp(plt.gca().get_xticklabels(),rotation=30)
    plt.show()
    
    
    3.3D 绘图
    #!/etc/bin/python
    #coding=utf-8
    import numpy as np
    import matplotlib.pyplot as plt
    
    stike = np.linspace(50, 150, 24)
    ttm = np.linspace(0.5, 2.5, 24)
    stike, ttm = np.meshgrid(stike, ttm)
    print  stike[:2]
    
    iv = (stike - 100) ** 2 / (100 * stike) /ttm
    from mpl_toolkits.mplot3d import Axes3D
    fig = plt.figure(figsize=(9,6))
    ax = fig.gca(projection='3d')
    surf = ax.plot_surface(stike, ttm, iv, rstride=2, cstride=2, cmap=plt.cm.coolwarm, linewidth=0.5, antialiased=True)
    ax.set_xlabel('strike')
    ax.set_ylabel('time-to-maturity')
    ax.set_zlabel('implied volatility')
    
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5MzYzMDIy_size_16_color_FFFFFF_t_70 17]  
岁月里，寒暑交替。人世间，北来南往。铭心的，云烟的。都付往事，不念，不问。  
char:92  
char:59  
char:16  
char955  
**小白欢迎来骚扰**

[20181214200025409.png]: /images/20220404/1cde3190aaf44245a81ae8af246d8ea8.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5MzYzMDIy_size_16_color_FFFFFF_t_70]: /images/20220404/9a45a899139044ff929043deb10127da.png
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[20181214200842659.png]: /images/20220404/5fea56ca68044c15bd468bf63d6d2469.png
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