Matplotlib-利用pandas进行数据分析并作可视化展示一、简单的绘图展示二、绘制柱状图三、共用坐标轴绘制两种不同类型的图四、pandas导入excel数据并绘制频率分布直方图五、绘制箱线图

• 一、简单的绘图展示

• 二、绘制柱状图

• 三、共用坐标轴绘制两种不同类型的图

• 四、pandas导入excel数据并绘制频率分布直方图

• 五、绘制箱线图

• • *

一、简单的绘图展示

    randomList = np.random.randn(10).cumsum()
    randomList
    
    #结果
    '''
        array([ 0.43692622, -0.17404988,  0.8479853 ,  1.39711286,  1.67546532,
                4.37286221,  4.22259538,  4.40355887,  4.38907365,  4.45077964])
    '''
[/code]
​
```code
    s = pd.Series(randomList,
                  index=np.arange(0,100,10))# index 指定序列中每个元素的索引
    s
    
    
    
    
    #结果
    '''
        0     0.436926
        10   -0.174050
        20    0.847985
        30    1.397113
        40    1.675465
        50    4.372862
        60    4.222595
        70    4.403559
        80    4.389074
        90    4.450780
        dtype: float64
    '''
[/code]
​
```code
    s.plot()  #用pandas中的Seris绘制折线图
    plt.show()
[/code]
​
![这里写图片描述](https://img-
blog.youkuaiyun.com/20180731212158931?watermark/2/text/aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzI3MTcxMzQ3/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70)
​
```code
    #利用pandas的DataFrame绘图
    arr = np.random.randn(10,4)  #产生一组10行4列的随机数组
    arr
    
    
    
    #结果
    
        array([[ 0.01616026,  0.57473119,  0.65414164,  0.93159686],
               [-0.03817341, -0.64962119,  0.27062599,  1.87690331],
               [ 1.15445861,  0.26759284,  0.69272073, -1.03753846],
               [ 0.11747495,  0.07197997,  0.15004073, -1.42265905],
               [-1.03527018,  0.38356526, -0.60570823,  0.45902491],
               [ 1.00210782, -1.18924028, -1.15890713,  0.7904771 ],
               [-0.70293899,  1.34306577,  0.63224563,  1.36712281],
               [-0.61717437,  0.31562477, -0.16665483,  0.08683415],
               [-0.9461549 , -0.11139913, -0.54149887, -1.12147449],
               [-0.15181162,  0.6141104 , -0.11115217,  0.43228114]])
    

    list("ABCD")
[/code]
​
[‘A’, ‘B’, ‘C’, ‘D’]
​
```code
    df = pd.DataFrame(arr,columns=list("ABCD"),index=np.arange(0,100,10))
    df
[/code]
​
.dataframe thead tr:only-child th { text-align: right; } .dataframe thead th {
text-align: left; } .dataframe tbody tr th { vertical-align: top; }  |  A  |
B  |  C  |  D  
---|---|---|---|---  
0  |  0.016160  |  0.574731  |  0.654142  |  0.931597  
10  |  -0.038173  |  -0.649621  |  0.270626  |  1.876903  
20  |  1.154459  |  0.267593  |  0.692721  |  -1.037538  
30  |  0.117475  |  0.071980  |  0.150041  |  -1.422659  
40  |  -1.035270  |  0.383565  |  -0.605708  |  0.459025  
50  |  1.002108  |  -1.189240  |  -1.158907  |  0.790477  
60  |  -0.702939  |  1.343066  |  0.632246  |  1.367123  
70  |  -0.617174  |  0.315625  |  -0.166655  |  0.086834  
80  |  -0.946155  |  -0.111399  |  -0.541499  |  -1.121474  
90  |  -0.151812  |  0.614110  |  -0.111152  |  0.432281
​
```code
    df.plot()
    plt.show()
[/code]  
  
![这里写图片描述](https://img-
blog.youkuaiyun.com/20180731212434555?watermark/2/text/aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzI3MTcxMzQ3/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70)
​
#  二、绘制柱状图
​
```code
    # Series 绘制柱状图
    np.random.randn(16)
    
    
    
    
    #结果
        array([ 1.65970298, -2.34573948,  0.04198811,  1.24727844,  0.08232593,
                0.94127546,  0.24426673,  0.05756959, -2.0821717 ,  0.08035341,
               -1.25196654,  0.08303011,  1.44323599,  0.32131152, -1.07353378,
                1.10811569])

    list('abcdefghijklmnop')
    
    data = pd.Series(np.random.randn(16),index=list("abcdefghijklmnop"))
    data
    
    
    
        a   -2.156393
        b    1.420026
        c    0.209807
        d    0.777654
        e    0.652906
        f   -1.704662
        g   -0.478381
        h   -0.234059
        i   -1.888555
        j    0.127597
        k   -0.211189
        l    0.960216
        m    0.491695
        n   -0.166496
        o    0.494728
        p    1.112572
        dtype: float64

    #创建一个带有两行一列绘图区域的画板
    fig,axes = plt.subplots(2,1)
    #Series在画板上绘制第一个柱状图
    data.plot(kind="bar",ax=axes[0],color="k",alpha=0.7)
    #Series在画板上绘制第二个柱状图
    data.plot(kind="barh",ax=axes[1],color='r',alpha=0.7)
    plt.show()
[/code]
​
![这里写图片描述](https://img-
blog.youkuaiyun.com/20180731215708487?watermark/2/text/aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzI3MTcxMzQ3/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70)
​
#  三、共用坐标轴绘制两种不同类型的图
​
```code
    '''
    pyplot.subplots(nrows,ncols,sharex,sharey)方法使用
    
    nrows 创建几行绘图区域
    ncols 创建几列绘图区域
    sharex 是否共用x轴
    sharey 是否共用y轴
    
    
    '''
    
    x = np.linspace(0,2*pi,400)
    y = np.sin(x**2)
    
    #创建一个有一行两列绘图对象的画板赋值给fig,两个绘图区域分别赋值给ax1,ax2
    fig,(ax1,ax2) = plt.subplots(1,2,sharey=True) 
    
    #在第一个绘图区域上绘制折线图
    ax1.plot(x,y)
    ax1.set_title("共用 Y 轴")
    #在第二个绘图区域上绘制散点图
    ax2.scatter(x,y)
    plt.show()
[/code]
​
![这里写图片描述](https://img-
blog.youkuaiyun.com/20180731215836146?watermark/2/text/aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzI3MTcxMzQ3/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70)
​
#  四、pandas导入excel数据并绘制频率分布直方图
​
```code
    # pandas加载excel数据
    df = pd.read_excel("pandas-matplotlib.xlsx","Sheet1")
    df
[/code]
​
.dataframe thead tr:only-child th { text-align: right; } .dataframe thead th {
text-align: left; } .dataframe tbody tr th { vertical-align: top; }  |  EMPID
|  Gender  |  Age  |  Sales  |  BMI  |  Income  
---|---|---|---|---|---|---  
0  |  E001  |  M  |  34  |  123  |  Normal  |  350  
1  |  E002  |  F  |  40  |  114  |  Overweight  |  450  
2  |  E003  |  F  |  37  |  135  |  Obesity  |  169  
3  |  E004  |  M  |  30  |  139  |  Underweight  |  189  
4  |  E005  |  F  |  44  |  117  |  Underweight  |  183  
5  |  E006  |  M  |  36  |  121  |  Normal  |  80  
6  |  E007  |  M  |  32  |  133  |  Obesity  |  166  
7  |  E008  |  F  |  26  |  140  |  Normal  |  120  
8  |  E009  |  M  |  32  |  133  |  Normal  |  75  
9  |  E010  |  M  |  36  |  133  |  Underweight  |  40
​
```code
    # 取出数据中的Age这一列
    df["Age"]
    
    
    
    
    #结果![这里写图片描述](https://img-blog.youkuaiyun.com/20180731220500412?watermark/2/text/aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzI3MTcxMzQ3/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70)
        0    34
        1    40
        2    37
        3    30
        4    44
        5    36
        6    32
        7    26
        8    32
        9    36
        Name: Age, dtype: int64

    #创建画板
    fig = plt.figure()
    #在画板上添加一个绘图区域,并返回选中的绘图区域
    ax = fig.add_subplot(111)
    #在ax中绘制频率分布直方图
    ax.hist(df["Age"],bins=7)
    plt.show()
[/code]  
  
![这里写图片描述](https://img-
blog.youkuaiyun.com/20180731220511286?watermark/2/text/aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzI3MTcxMzQ3/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70)
​
```code
    #分位值
    df.describe()
[/code]
​
.dataframe thead tr:only-child th { text-align: right; } .dataframe thead th {
text-align: left; } .dataframe tbody tr th { vertical-align: top; }  |  Age  |
Sales  |  Income  
---|---|---|---  
count  |  10.000000  |  10.000000  |  10.000000  
mean  |  34.700000  |  128.800000  |  182.200000  
std  |  5.121849  |  9.271222  |  127.533699  
min  |  26.000000  |  114.000000  |  40.000000  
25%  |  32.000000  |  121.500000  |  90.000000  
50%  |  35.000000  |  133.000000  |  167.500000  
75%  |  36.750000  |  134.500000  |  187.500000  
max  |  44.000000  |  140.000000  |  450.000000  
  
#  五、绘制箱线图
​
“`python  
fig = plt.figure()  
ax = fig.add_subplot(111)
​
ax.boxplot(df[“Age”])  
![这里写图片描述](https://img-
blog.youkuaiyun.com/20180731221126769?watermark/2/text/aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzI3MTcxMzQ3/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70)
“`
​
​
![在这里插入图片描述](https://img-blog.csdnimg.cn/20210608151750993.gif)