Python数据分析_第07课：绘图与可视化_笔记

小灰灰 2021-11-02 11:24 399阅读 0赞

### 文章目录 ###

*  matplotlib创建图表
 *  更多简单的图形
 *  figure与subplot
 *  matplotlib基本设置
 *   *  颜色、标记和线型
     *  设置标题、轴标签、刻度以及刻度标签
     *  添加图例
 *  注释以及在subplot上绘图
 *  画图形
 *  图表的保存
 *  pandas中的绘图函数
 *   *  线图
     *  柱形图
 *  直方图和密度图
 *   *  散点图
 *  Matplotlib作图
 *   *  误差条形图
     *  饼图
     *  等高线图
 *  3D图像
 *   *  3d柱形图
 *  3d直方图

GitHub: [https://github.com/RealEmperor/Python-for-Data-Analysis][https_github.com_RealEmperor_Python-for-Data-Analysis]

# matplotlib创建图表 #

import matplotlib.pyplot as plt
    import numpy as np
    import pandas as pd
    from pandas import Series, DataFrame
    from numpy.random import randn
    from datetime import datetime
    
    np.random.seed(12345)
    plt.rc('figure', figsize=(10, 6))
    
    np.set_printoptions(precision=4)
    
    get_ipython().magic(u'matplotlib inline')
    get_ipython().magic(u'pwd')

plt.plot([1, 2, 3, 2, 3, 2, 2, 1])
    plt.show()

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plt.plot([4, 3, 2, 1], [1, 2, 3, 4])
    plt.show()

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# 更多简单的图形 #

# 更多简单的图形
    x = [1, 2, 3, 4]
    y = [5, 4, 3, 2]
    
    plt.figure()
    
    plt.subplot(2, 3, 1)
    plt.plot(x, y)
    
    plt.subplot(232)
    plt.bar(x, y)
    
    plt.subplot(233)
    plt.barh(x, y)
    
    plt.subplot(234)
    plt.bar(x, y)
    y1 = [7, 8, 5, 3]
    plt.bar(x, y1, bottom=y, color='r')
    
    plt.subplot(235)
    plt.boxplot(x)
    
    plt.subplot(236)
    plt.scatter(x, y)
    
    plt.show()

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# figure与subplot #

# figure对象
    fig = plt.figure()
    
    ax1 = fig.add_subplot(2, 2, 1)
    ax2 = fig.add_subplot(2, 2, 2)
    ax3 = fig.add_subplot(2, 2, 3)
    plt.show()
    
    plt.plot(randn(50).cumsum(), 'k--')
    fig.show()
    
    _ = ax1.hist(randn(100), bins=20, color='k', alpha=0.3)
    ax2.scatter(np.arange(30), np.arange(30) + 3 * randn(30))

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plt.close('all')

fig, axes = plt.subplots(2, 3)

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# 调整subplot周围的间距
    plt.subplots_adjust(left=None, bottom=None, right=None, top=None,
                        wspace=None, hspace=None)
    
    fig, axes = plt.subplots(2, 2, sharex=True, sharey=True)
    for i in range(2):
        for j in range(2):
            axes[i, j].hist(randn(500), bins=50, color='k', alpha=0.5)
    plt.subplots_adjust(wspace=0, hspace=0)

<matplotlib.figure.Figure at 0x29b62776470>

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# matplotlib基本设置 #

## 颜色、标记和线型 ##

plt.figure()
    plt.show()
    
    plt.plot(x, y, linestyle='--', color='g')
    plt.show()
    
    plt.plot(randn(30).cumsum(), 'ko--')
    plt.plot(randn(30).cumsum(), color='k', linestyle='dashed', marker='o')

<matplotlib.figure.Figure at 0x29b62749240>

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[<matplotlib.lines.Line2D at 0x29b61c085f8>]

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plt.close('all')

data = randn(30).cumsum()
    plt.plot(data, 'k--', label='Default')
    plt.plot(data, 'k-', drawstyle='steps-post', label='steps-post')
    plt.legend(loc='best')

<matplotlib.legend.Legend at 0x29b626eb860>

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## 设置标题、轴标签、刻度以及刻度标签 ##

fig = plt.figure()
    ax = fig.add_subplot(1, 1, 1)
    ax.plot(randn(1000).cumsum())
    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')

<matplotlib.text.Text at 0x1f480fc72b0>

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## 添加图例 ##

# 添加图例
    fig = plt.figure()
    ax = fig.add_subplot(1, 1, 1)
    ax.plot(randn(1000).cumsum(), 'k', label='one')
    ax.plot(randn(1000).cumsum(), 'k--', label='two')
    ax.plot(randn(1000).cumsum(), 'k.', label='three')
    
    ax.legend(loc='best')

<matplotlib.legend.Legend at 0x1f481661da0>

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# 注释以及在subplot上绘图 #

fig = plt.figure()
    ax = fig.add_subplot(1, 1, 1)
    
    data = pd.read_csv('data/spx.csv', index_col=0, parse_dates=True)
    spx = data['SPX']
    
    spx.plot(ax=ax, style='k-')
    
    crisis_data = [
        (datetime(2007, 10, 11), 'Peak of bull market'),
        (datetime(2008, 3, 12), 'Bear Stearns Fails'),
        (datetime(2008, 9, 15), 'Lehman Bankruptcy')
    ]
    
    for date, label in crisis_data:
        ax.annotate(label, xy=(date, spx.asof(date) + 50),
                    xytext=(date, spx.asof(date) + 200),
                    arrowprops=dict(facecolor='black'),
                    horizontalalignment='left', verticalalignment='top')
    
    ax.set_xlim(['1/1/2007', '1/1/2011'])
    ax.set_ylim([600, 1800])
    
    ax.set_title('Important dates in 2008-2009 financial crisis')

<matplotlib.text.Text at 0x1f4828fac50>

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# 画图形 #

fig = plt.figure()
    ax = fig.add_subplot(1, 1, 1)
    
    rect = plt.Rectangle((0.2, 0.75), 0.4, 0.15, color='k', alpha=0.3)
    circ = plt.Circle((0.7, 0.2), 0.15, color='b', alpha=0.3)
    pgon = 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(pgon)

<matplotlib.patches.Polygon at 0x1f482943160>

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# 图表的保存 #

# 图表的保存
    fig.savefig('data/figpath.svg')
    
    fig.savefig('data/figpath.png', dpi=400, bbox_inches='tight')

# pandas中的绘图函数 #

## 线图 ##

plt.close('all')
    
    s = Series(np.random.randn(10).cumsum(), index=np.arange(0, 100, 10))
    s.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x1f48170aa90>

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df = DataFrame(np.random.randn(10, 4).cumsum(0),
                   columns=['A', 'B', 'C', 'D'],
                   index=np.arange(0, 100, 10))
    df.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x1f4816a5780>

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## 柱形图 ##

# 柱形图
    fig, axes = plt.subplots(2, 1)
    data = Series(np.random.rand(16), index=list('abcdefghijklmnop'))
    data.plot(kind='bar', ax=axes[0], color='k', alpha=0.7)
    data.plot(kind='barh', ax=axes[1], color='k', alpha=0.7)

<matplotlib.axes._subplots.AxesSubplot at 0x1f482ac03c8>

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df = DataFrame(np.random.rand(6, 4),
                   index=['one', 'two', 'three', 'four', 'five', 'six'],
                   columns=pd.Index(['A', 'B', 'C', 'D'], name='Genus'))
    print(df)
    df.plot(kind='bar')

Genus         A         B         C         D
    one    0.367439  0.498648  0.226575  0.353566
    two    0.650852  0.312933  0.768735  0.781837
    three  0.852409  0.949906  0.107323  0.910725
    four   0.336055  0.826380  0.898101  0.042715
    five   0.195795  0.294501  0.627000  0.086223
    six    0.142945  0.515827  0.689341  0.856626
    
    
    
    
    
    <matplotlib.axes._subplots.AxesSubplot at 0x1f482b20c50>

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df.plot(kind='barh', stacked=True, alpha=0.5)

<matplotlib.axes._subplots.AxesSubplot at 0x1f482d687f0>

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tips = pd.read_csv('data/tips.csv')
    party_counts = pd.crosstab(tips.day, tips['size'])
    print(party_counts)

size  1   2   3   4  5  6
    day                      
    Fri   1  16   1   1  0  0
    Sat   2  53  18  13  1  0
    Sun   0  39  15  18  3  1
    Thur  1  48   4   5  1  3

party_counts = party_counts.loc[:, 2:5]
    
    party_pcts = party_counts.div(party_counts.sum(1).astype(float), axis=0)
    print(party_pcts)

size         2         3         4         5
    day                                         
    Fri   0.888889  0.055556  0.055556  0.000000
    Sat   0.623529  0.211765  0.152941  0.011765
    Sun   0.520000  0.200000  0.240000  0.040000
    Thur  0.827586  0.068966  0.086207  0.017241

party_pcts.plot(kind='bar', stacked=True)

<matplotlib.axes._subplots.AxesSubplot at 0x1f482e2d400>

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# 直方图和密度图 #

plt.figure()
    
    tips['tip_pct'] = tips['tip'] / tips['total_bill']
    tips['tip_pct'].hist(bins=50)

<matplotlib.axes._subplots.AxesSubplot at 0x1f482e75710>

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plt.figure()
    
    tips['tip_pct'].plot(kind='kde')

<matplotlib.axes._subplots.AxesSubplot at 0x1f48304fb38>

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plt.figure()
    
    comp1 = np.random.normal(0, 1, size=200)  # N(0, 1)
    comp2 = np.random.normal(10, 2, size=200)  # N(10, 4)
    values = Series(np.concatenate([comp1, comp2]))
    values.hist(bins=100, alpha=0.3, color='k', normed=True)
    values.plot(kind='kde', style='k--')

<matplotlib.axes._subplots.AxesSubplot at 0x1f48317edd8>

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## 散点图 ##

# 散点图
    macro = pd.read_csv('data/macrodata.csv')
    data = macro[['cpi', 'm1', 'tbilrate', 'unemp']]
    trans_data = np.log(data).diff().dropna()
    trans_data[-5:]

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>cpi</th> 
   <th>m1</th> 
   <th>tbilrate</th> 
   <th>unemp</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>198</th> 
   <td>-0.007904</td> 
   <td>0.045361</td> 
   <td>-0.396881</td> 
   <td>0.105361</td> 
  </tr> 
  <tr> 
   <th>199</th> 
   <td>-0.021979</td> 
   <td>0.066753</td> 
   <td>-2.277267</td> 
   <td>0.139762</td> 
  </tr> 
  <tr> 
   <th>200</th> 
   <td>0.002340</td> 
   <td>0.010286</td> 
   <td>0.606136</td> 
   <td>0.160343</td> 
  </tr> 
  <tr> 
   <th>201</th> 
   <td>0.008419</td> 
   <td>0.037461</td> 
   <td>-0.200671</td> 
   <td>0.127339</td> 
  </tr> 
  <tr> 
   <th>202</th> 
   <td>0.008894</td> 
   <td>0.012202</td> 
   <td>-0.405465</td> 
   <td>0.042560</td> 
  </tr> 
 </tbody> 
</table>

plt.figure()
    
    plt.scatter(trans_data['m1'], trans_data['unemp'])
    plt.title('Changes in log %s vs. log %s' % ('m1', 'unemp'))

<matplotlib.text.Text at 0x1f4848edeb8>

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pd.plotting.scatter_matrix(trans_data, diagonal='kde', color='k', alpha=0.3)
    plt.show()

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# Matplotlib作图 #

## 误差条形图 ##

# 误差条形图
    x = np.arange(0, 10, 1)
    
    y = np.log(x)
    
    xe = 0.1 * np.abs(np.random.randn(len(y)))
    
    plt.bar(x, y, yerr=xe, width=0.4, align='center', ecolor='r', color='cyan',
            label='experiment #1')
    
    plt.xlabel('# measurement')
    plt.ylabel('Measured values')
    plt.title('Measurements')
    plt.legend(loc='upper left')
    
    plt.show()

C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:4: RuntimeWarning: divide by zero encountered in log
      after removing the cwd from sys.path.

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## 饼图 ##

# 饼图
    plt.figure(1, figsize=(8, 8))
    ax = plt.axes([0.1, 0.1, 0.8, 0.8])
    
    labels = 'Spring', 'Summer', 'Autumn', 'Winter'
    values = [15, 16, 16, 28]
    explode = [0.1, 0.1, 0.1, 0.1]
    
    plt.pie(values, explode=explode, labels=labels,
            autopct='%1.1f%%', startangle=67)
    
    plt.title('Rainy days by season')
    
    plt.show()

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## 等高线图 ##

# 等高线图
    import matplotlib as mpl
    
    
    def process_signals(x, y):
        return (1 - (x ** 2 + y ** 2)) * np.exp(-y ** 3 / 3)
    
    
    x = np.arange(-1.5, 1.5, 0.1)
    y = np.arange(-1.5, 1.5, 0.1)
    
    X, Y = np.meshgrid(x, y)
    
    Z = process_signals(X, Y)
    
    N = np.arange(-1, 1.5, 0.3)
    
    CS = plt.contour(Z, N, linewidths=2, cmap=mpl.cm.jet)
    plt.clabel(CS, inline=True, fmt='%1.1f', fontsize=10)
    plt.colorbar(CS)
    
    plt.title('My function: $z=(1-x^2+y^2) e^{-(y^3)/3}$')
    plt.show()

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# 3D图像 #

## 3d柱形图 ##

# 3d柱形图
    from mpl_toolkits.mplot3d import Axes3D
    mpl.rcParams['font.size'] = 10
    
    fig = plt.figure()
    ax = fig.add_subplot(111, projection='3d')
    
    for z in [2011, 2012, 2013, 2014]:
        xs = range(1, 13)
        ys = 1000 * np.random.rand(12)
    
        color = plt.cm.Set2(np.random.choice(range(plt.cm.Set2.N)))
        ax.bar(xs, ys, zs=z, zdir='y', color=color, alpha=0.8)
    
    ax.xaxis.set_major_locator(mpl.ticker.FixedLocator(xs))
    ax.yaxis.set_major_locator(mpl.ticker.FixedLocator(ys))
    
    ax.set_xlabel('Month')
    ax.set_ylabel('Year')
    ax.set_zlabel('Sales Net [usd]')
    
    plt.show()

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# 3d直方图 #

# 3d直方图
    mpl.rcParams['font.size'] = 10
    
    samples = 25
    
    x = np.random.normal(5, 1, samples)
    y = np.random.normal(3, .5, samples)
    
    fig = plt.figure()
    ax = fig.add_subplot(211, projection='3d')
    
    hist, xedges, yedges = np.histogram2d(x, y, bins=10)
    
    elements = (len(xedges) - 1) * (len(yedges) - 1)
    xpos, ypos = np.meshgrid(xedges[:-1] + .25, yedges[:-1] + .25)
    
    xpos = xpos.flatten()
    ypos = ypos.flatten()
    zpos = np.zeros(elements)
    
    dx = .1 * np.ones_like(zpos)
    dy = dx.copy()
    
    dz = hist.flatten()
    
    ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color='b', alpha=0.4)
    ax.set_xlabel('X Axis')
    ax.set_ylabel('Y Axis')
    ax.set_zlabel('Z Axis')
    
    ax2 = fig.add_subplot(212)
    ax2.scatter(x, y)
    ax2.set_xlabel('X Axis')
    ax2.set_ylabel('Y Axis')
    
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjAxODI1OA_size_16_color_FFFFFF_t_70 28]

参考资料：炼数成金Python数据分析课程

[https_github.com_RealEmperor_Python-for-Data-Analysis]: https://github.com/RealEmperor/Python-for-Data-Analysis
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