【Python数据分析与可视化】Numpy统计分析-实训

iris_data_arr=np.array(iris_list,dtype=datatype)
print(iris_data_arr.shape)
print(iris_data_arr[:10])

(150,)
[('5.1', '3.5', '1.4', '0.2', 'setosa')
 ('4.9', '3', '1.4', '0.2', 'setosa')
 ('4.7', '3.2', '1.3', '0.2', 'setosa')
 ('4.6', '3.1', '1.5', '0.2', 'setosa')
 ('5', '3.6', '1.4', '0.2', 'setosa')
 ('5.4', '3.9', '1.7', '0.4', 'setosa')
 ('4.6', '3.4', '1.4', '0.3', 'setosa')
 ('5', '3.4', '1.5', '0.2', 'setosa')
 ('4.4', '2.9', '1.4', '0.2', 'setosa')
 ('4.9', '3.1', '1.5', '0.1', 'setosa')]

SepaLong=iris_data_arr["sepaL"].astype(float)
print(SepaLong[:10])

PetalLong=iris_data_arr["petalL"].astype(float)
print(PetalLong[:10])

[5.1 4.9 4.7 4.6 5.  5.4 4.6 5.  4.4 4.9]
[1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5]

#排序
SepaLong.sort()
print(SepaLong[:10])
PetalLong.sort()
print(PetalLong[:10])

[4.3 4.4 4.4 4.4 4.5 4.6 4.6 4.6 4.6 4.7]
[1.  1.1 1.2 1.2 1.3 1.3 1.3 1.3 1.3 1.3]

#去重，并求出和
unique_arr = np.unique(SepaLong)
print(unique_arr)
print(unique_arr.sum())

[4.3 4.4 4.5 4.6 4.7 4.8 4.9 5.  5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 6.
 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 7.  7.1 7.2 7.3 7.4 7.6 7.7 7.9]
210.39999999999998

unique_arr = np.unique(PetalLong)
print(unique_arr)
print(unique_arr.sum())

[1.  1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.9 3.  3.3 3.5 3.6 3.7 3.8 3.9 4.  4.1
 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5.  5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9
 6.  6.1 6.3 6.4 6.6 6.7 6.9]
181.5

#累积和
print(np.cumsum(SepaLong))
#均值
print(np.mean(SepaLong))
#标准差
print(np.std(SepaLong))
#方差
print(np.var(SepaLong))
#最小值
print(np.min(SepaLong))
#最大值
print(np.max(SepaLong))

[  4.3   8.7  13.1  17.5  22.   26.6  31.2  35.8  40.4  45.1  49.8  54.6
  59.4  64.2  69.   73.8  78.7  83.6  88.5  93.4  98.3 103.2 108.2 113.2
 118.2 123.2 128.2 133.2 138.2 143.2 148.2 153.2 158.3 163.4 168.5 173.6
 178.7 183.8 188.9 194.  199.1 204.3 209.5 214.7 219.9 225.2 230.6 236.
 241.4 246.8 252.2 257.6 263.1 268.6 274.1 279.6 285.1 290.6 296.1 301.7
 307.3 312.9 318.5 324.1 329.7 335.4 341.1 346.8 352.5 358.2 363.9 369.6
 375.3 381.1 386.9 392.7 398.5 404.3 410.1 415.9 421.8 427.7 433.6 439.6
 445.6 451.6 457.6 463.6 469.6 475.7 481.8 487.9 494.  500.1 506.2 512.4
 518.6 524.8 531.  537.3 543.6 549.9 556.2 562.5 568.8 575.1 581.4 587.7
 594.1 600.5 606.9 613.3 619.7 626.1 632.5 639.  645.5 652.  658.5 665.
 671.6 678.2 684.9 691.6 698.3 705.  711.7 718.4 725.1 731.8 738.6 745.4
 752.2 759.1 766.  772.9 779.8 786.8 793.9 801.1 808.3 815.5 822.8 830.2
 837.8 845.5 853.2 860.9 868.6 876.5]
5.843333333333334
0.8253012917851409
0.6811222222222223
4.3
7.9

#累积和
print(np.cumsum(PetalLong))
#均值
print(np.mean(PetalLong))
#标准差
print(np.std(PetalLong))
#方差
print(np.var(PetalLong))
#最小值
print(np.min(PetalLong))
#最大值
print(np.max(PetalLong))

[  1.    2.1   3.3   4.5   5.8   7.1   8.4   9.7  11.   12.3  13.6  15.
  16.4  17.8  19.2  20.6  22.   23.4  24.8  26.2  27.6  29.   30.4  31.8
  33.3  34.8  36.3  37.8  39.3  40.8  42.3  43.8  45.3  46.8  48.3  49.8
  51.3  52.9  54.5  56.1  57.7  59.3  60.9  62.5  64.2  65.9  67.6  69.3
  71.2  73.1  76.1  79.4  82.7  86.2  89.7  93.3  97.  100.8 104.7 108.6
 112.5 116.5 120.5 124.5 128.5 132.5 136.6 140.7 144.8 149.  153.2 157.4
 161.6 165.9 170.2 174.6 179.  183.4 187.8 192.3 196.8 201.3 205.8 210.3
 214.8 219.3 223.8 228.4 233.  237.6 242.3 247.  251.7 256.4 261.1 265.9
 270.7 275.5 280.3 285.2 290.1 295.  299.9 304.8 309.8 314.8 319.8 324.8
 329.9 335.  340.1 345.2 350.3 355.4 360.5 365.6 370.8 376.  381.3 386.6
 392.  397.4 402.9 408.4 413.9 419.5 425.1 430.7 436.3 441.9 447.5 453.2
 458.9 464.6 470.4 476.2 482.  487.9 493.8 499.8 505.8 511.9 518.  524.1
 530.4 536.8 543.4 550.1 556.8 563.7]
3.7579999999999996
1.759404065775303
3.0955026666666665
1.0
6.9