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

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])
'''

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
'''

s.plot()  #用pandas中的Seris绘制折线图
plt.show()

#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")

[‘A’, ‘B’, ‘C’, ‘D’]

df = pd.DataFrame(arr,columns=list("ABCD"),index=np.arange(0,100,10))
df

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	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

df.plot()
plt.show()

二、绘制柱状图

# 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()

三、共用坐标轴绘制两种不同类型的图

'''
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()

四、pandas导入excel数据并绘制频率分布直方图

# pandas加载excel数据
df = pd.read_excel("pandas-matplotlib.xlsx","Sheet1")
df

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	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

# 取出数据中的Age这一列
df["Age"]




#结果![这里写图片描述](https://img-blog.csdn.net/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()

#分位值
df.describe()

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	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”])
“`