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02_numpy

numpy get started

导入numpy库，并查看numpy版本

import numpy as np
np.__version__
'1.13.0'
import matplotlib.pyplot as plt
 

cat = plt.imread('cat.jpg')
 

print(cat)
 

[[[231 186 131]
  [232 187 132]
  [233 188 133]
  ..., 
  [100  54  54]
  [ 92  48  47]
  [ 85  43  44]]

 [[232 187 132]
  [232 187 132]
  [233 188 133]
  ..., 
  [100  54  54]
  [ 92  48  47]
  [ 84  42  43]]

 [[232 187 132]
  [233 188 133]
  [233 188 133]
  ..., 
  [ 99  53  53]
  [ 91  47  46]
  [ 83  41  42]]

 ..., 
 [[199 119  82]
  [199 119  82]
  [200 120  83]
  ..., 
  [189  99  65]
  [187  97  63]
  [187  97  63]]

 [[199 119  82]
  [199 119  82]
  [199 119  82]
  ..., 
  [188  98  64]
  [186  96  62]
  [188  95  62]]

 [[199 119  82]
  [199 119  82]
  [199 119  82]
  ..., 
  [188  98  64]
  [188  95  62]
  [188  95  62]]]
 

type(cat)
 

numpy.ndarray
 

cat.shape
 

(456, 730, 3)
 

plt.imshow(cat)
plt.show()
 

 
 

#请问电影是什么，nd.array 四维
#(x,456,760,3)
 

一、创建ndarray
 

1. 使用np.array()由python list创建
 

参数为列表：  [1, 4, 2, 5, 3]
 

注意：
 
  
  numpy默认ndarray的所有元素的类型是相同的  
  如果传进来的列表中包含不同的类型，则统一为同一类型，优先级：str>float>int  
  

l = [3,1,4,5,9,6]
n = np.array(l)
 

display(n,l)
 

array([3, 1, 4, 5, 9, 6])
 

[3, 1, 4, 5, 9, 6]
 

display(n.shape,l.shape)
 

--------------------------------------------------------------

AttributeError               Traceback (most recent call last)

<ipython-input-15-5eeacc6c47ae> in <module>()
----> 1 display(n.shape,l.shape)
 

AttributeError: 'list' object has no attribute 'shape'
 

n2 = np.array([[3,4,7,1],[3,0,1,8],[2,4,6,8]])
display(n2.shape)
 

(3, 4)
 

n3 = np.array(['0',9.18,20])
n3
 

array(['0', '9.18', '20'],
      dtype='<U4')
 

n4 = np.array([1,2,3.14])
n4
 

array([ 1.  ,  2.  ,  3.14])
 

2. 使用np的routines函数创建
 

包含以下常见创建方法：
 

1) np.ones(shape, dtype=None, order=‘C‘)
 

n = np.ones((4,5))
n
 

array([[1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1.]])
 

n2 = np.ones((4,5,6), dtype=int)
n2
 

array([[[1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 1]],

       [[1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 1]],

       [[1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 1]],

       [[1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 1]]])
 

2) np.zeros(shape, dtype=float, order=‘C‘)
 

n3 = np.zeros((4,5))
n3
 

array([[0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0.]])
 

3) np.full(shape, fill_value, dtype=None, order=‘C‘)
 

n = np.full((4,5), dtype=int, fill_value=8)
n
 

array([[8, 8, 8, 8, 8],
       [8, 8, 8, 8, 8],
       [8, 8, 8, 8, 8],
       [8, 8, 8, 8, 8]])
 

4) np.eye(N, M=None, k=0, dtype=float)  对角线为1其他的位置为0
 

n = np.eye(4,5)
n
# 满秩矩阵


# x + y = 10
# x - y = 5
# 1  1 
# 1  -1

# 第二行减去第一行
# 1   1
# 0   -2

# 1/2乘于第二行
# 1   1
# 0   -1

# 第二行加上第一行
# 1   0
# 0   -1

# 第二行乘与-1
# 1   0
# 0   1

# x + y 
# 2x + 2Y 
# 无解
# 1    1
# 2    2
 

array([[1., 0., 0., 0., 0.],
       [0., 1., 0., 0., 0.],
       [0., 0., 1., 0., 0.],
       [0., 0., 0., 1., 0.]])
 

5) np.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None)
 

n = np.linspace(0, 100, num=50, dtype=int,retstep=True, endpoint=False)
n
 

(array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32,
        34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66,
        68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98]), 2.0)
 

n = np.linspace(0, 150, num=50, dtype=np.int8)
n
# line 
# 2^(n-1) -1
# lin = linear algebra
 

array([   0,    3,    6,    9,   12,   15,   18,   21,   24,   27,   30,
         33,   36,   39,   42,   45,   48,   52,   55,   58,   61,   64,
         67,   70,   73,   76,   79,   82,   85,   88,   91,   94,   97,
        101,  104,  107,  110,  113,  116,  119,  122,  125, -128, -125,
       -122, -119, -116, -113, -110, -106], dtype=int8)
 

6) np.arange([start, ]stop, [step, ]dtype=None)
 

n = np.arange(10)
n
 

array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
 

n = np.arange(1, 11, step=2)
n
 

array([1, 3, 5, 7, 9])
 

7) np.random.randint(low, high=None, size=None, dtype=‘l‘)
 

n = np.random.randint(10)
n
 

8
 

n = np.random.randint(0, 255, size=(3,4,5))
n
 

array([[[ 89,  68,  18, 202,  49],
        [118, 159,  48, 190, 227],
        [177, 104, 232, 158,  64],
        [112, 125,   0,   7, 216]],

       [[  2, 180,  33, 152, 244],
        [ 46,  66, 185, 155, 253],
        [180, 135,  80, 135,  86],
        [ 64, 218,  69, 128,  90]],

       [[163,   7,  55,  60,  12],
        [ 15,  14, 181,  87,  62],
        [218,   7, 166, 100, 217],
        [137,   0,  42,  49, 194]]])
 

image = np.random.randint(0,255, size=(456,730,3))
image.shape
 

(456, 730, 3)
 

plt.imshow(image)
plt.show(image)
 

 
 

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-97-a28aaec0347e> in <module>()
      1 plt.imshow(image)
----> 2 plt.show(image)
 

C:\ProgramData\Anaconda3\lib\site-packages\matplotlib\pyplot.py in show(*args, **kw)
    251     """
    252     global _show
--> 253     return _show(*args, **kw)
    254 
    255
 

C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\pylab\backend_inline.py in show(close, block)
     39         # only call close('all') if any to close
     40         # close triggers gc.collect, which can be slow
---> 41         if close and Gcf.get_all_fig_managers():
     42             matplotlib.pyplot.close('all')
     43
 

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
 

 
 

8) np.random.randn(d0, d1, ..., dn)
 

标准正太分布
 

n = np.random.randn(10)
n
 

array([-0.4173303 , -0.41736696, -0.11888109, -0.51925789,  1.24985884,
        1.52967696,  0.05327912,  0.84738899,  1.03118302, -0.64532473])
 

9)np.random.normal(loc=0.0, scale=1.0, size=None)
 

n = np.random.normal(175, scale=5.0, size=50)
n
 

array([177.62703208, 176.50746247, 173.26956915, 162.29355083,
       172.05271936, 177.61948035, 172.52243162, 175.43294252,
       181.14225673, 175.21450574, 179.56055092, 170.883815  ,
       170.91435313, 176.25008762, 176.3347509 , 183.90347049,
       178.91856559, 168.84725605, 176.32881783, 172.77973728,
       173.12257339, 174.75054378, 166.60349541, 171.68263799,
       168.83419713, 174.25085091, 175.66113435, 174.12039025,
       177.22772738, 169.01523024, 175.57587527, 172.89083838,
       179.52153939, 173.70318334, 179.06473552, 176.50099117,
       175.83008746, 174.78059027, 175.58909128, 178.11274357,
       183.45771692, 172.43399789, 179.56800892, 182.14239994,
       176.43701867, 177.37866513, 179.55215095, 174.5389049 ,
       175.48698667, 168.73145269])
 

10) np.random.random(size=None)
 

生成0到1的随机数，左闭右开
 

n = np.random.random(10)
n
 

array([0.22608606, 0.62764532, 0.62219649, 0.05348086, 0.94994404,
       0.29048963, 0.49340728, 0.04651386, 0.59005488, 0.59901244])
 

二、ndarray的属性
 

4个必记参数：  ndim：维度  shape：形状（各维度的长度）  size：总长度
 

dtype：元素类型
 

cat.ndim
 

3
 

cat.shape
 

(456, 730, 3)
 

cat.size
 

998640
 

cat.dtype
 

dtype('uint8')
 

三、ndarray的基本操作
 

1. 索引
 

一维与列表完全一致  多维时同理
 

l = [1,2,3,4,5]
l[2:4]
 

[3, 4]
 

n = np.array(l)
n[2]
 

3
 

# 找一个二维ndarray中的某个数
n2 = np.random.randint(0,255, size=(4,4))
n2
 

array([[  8, 117, 209,  86],
       [156, 192, 117, 180],
       [ 33,  70,  53, 179],
       [ 56, 236,  72,  45]])
 

# 查找53
n2[2][2]
 

53
 

n2[2,2]
 

53
 

n3 = np.random.randint(0,255, size=(4,5,6))
n3
 

array([[[128,  60, 108,  12, 112,  60],
        [234, 111, 237,  54,  22,  95],
        [127, 226,  30, 181,  20,  85],
        [239, 233, 210, 165, 186,  57],
        [ 27,  17,  72, 237, 208, 120]],

       [[199, 169, 190, 153, 181,  75],
        [179, 205, 116,  33, 239, 228],
        [154, 204, 138,   5, 231,  97],
        [ 55, 193, 245, 105,  78, 210],
        [157, 227, 239, 230, 242, 185]],

       [[ 67, 232, 113, 189, 245, 206],
        [220,  56, 241, 141, 146,  59],
        [ 46, 206, 152, 240, 105, 105],
        [176, 252, 185, 212, 180, 127],
        [165, 130, 206,  77,  11,  56]],

       [[194,  82,  72,  80,  94, 237],
        [179, 143, 191,  56,  37, 236],
        [194,  65, 223,  45, 223, 125],
        [ 92, 162,  94,  93,  69,   3],
        [ 39, 179, 213, 180,  23, 141]]])
 

n3[1,2,3]
 

5
 

np.random.seed(100)
 

np.random.seed(100)
np.random.randn(10)
 

array([-1.74976547,  0.3426804 ,  1.1530358 , -0.25243604,  0.98132079,
        0.51421884,  0.22117967, -1.07004333, -0.18949583,  0.25500144])
 

n = np.array([1,2,3,np.nan])
np.sum(n)
np.nansum(n)
 

6.0
 

根据索引修改数据
 

n3[1,2,3] = 8
n3
 

array([[[128,  60, 108,  12, 112,  60],
        [234, 111, 237,  54,  22,  95],
        [127, 226,  30, 181,  20,  85],
        [239, 233, 210, 165, 186,  57],
        [ 27,  17,  72, 237, 208, 120]],

       [[199, 169, 190, 153, 181,  75],
        [179, 205, 116,  33, 239, 228],
        [154, 204, 138,   8, 231,  97],
        [ 55, 193, 245, 105,  78, 210],
        [157, 227, 239, 230, 242, 185]],

       [[ 67, 232, 113, 189, 245, 206],
        [220,  56, 241, 141, 146,  59],
        [ 46, 206, 152, 240, 105, 105],
        [176, 252, 185, 212, 180, 127],
        [165, 130, 206,  77,  11,  56]],

       [[194,  82,  72,  80,  94, 237],
        [179, 143, 191,  56,  37, 236],
        [194,  65, 223,  45, 223, 125],
        [ 92, 162,  94,  93,  69,   3],
        [ 39, 179, 213, 180,  23, 141]]])
 

2. 切片
 

一维与列表完全一致  多维时同理
 

l = [1,2,3,4,5]
l[::-1]
 

[5, 4, 3, 2, 1]
 

l[::-2]
l
 

[1, 2, 3, 4, 5]
 

 将数据反转，例如[1,2,3]---->[3,2,1] 
 

n = np.random.randint(0, 255, size=(4,5))
n
 

array([[211, 112,  94, 165,   6],
       [ 86,  15, 241,  38, 139],
       [185, 247,  99,  91,  31],
       [221,  33,  40, 137, 162]])
 

两个::进行切片
 

n[::-1]
n
 

array([[211, 112,  94, 165,   6],
       [ 86,  15, 241,  38, 139],
       [185, 247,  99,  91,  31],
       [221,  33,  40, 137, 162]])
 

3. 变形
 

使用reshape函数，注意参数是一个tuple！
 

n = np.arange(6)
n
 

array([0, 1, 2, 3, 4, 5])
 

n2 = np.reshape(n,(3,2))
n2
 

array([[0, 1],
       [2, 3],
       [4, 5]])
 

cat.shape
 

(456, 730, 3)
 

n = np.reshape(cat, (8322, 120))
n
 

array([[231, 186, 131, ..., 235, 190, 135],
       [237, 192, 137, ..., 237, 192, 137],
       [237, 192, 137, ..., 239, 192, 138],
       ...,
       [203, 125,  89, ..., 201, 121,  86],
       [200, 120,  85, ..., 197, 117,  82],
       [197, 117,  82, ..., 188,  95,  62]], dtype=uint8)
 

4. 级联
 
  
  np.concatenate()  级联需要注意的点：  
  
  
  级联的参数是列表：一定要加中括号或小括号  
  维度必须相同  
  形状相符  
  【重点】级联的方向默认是shape这个tuple的第一个值所代表的维度方向  
  可通过axis参数改变级联的方向  
  

n1 = np.random.randint(0,255, size=(5,6))
n2 = np.random.randint(0,255, size=(5,6))
display(n1,n2)
 

array([[ 67, 115, 248,  66, 212, 248],
       [ 66, 156, 231, 250,  39, 195],
       [248, 172,  19,  21, 200, 206],
       [139,  25,   3,  18,   3,  49],
       [ 55,  21,  12,   6, 218, 116]])
 

array([[182, 251, 137,  33,  60,   6],
       [169, 117, 245, 218,  96, 168],
       [231,  59, 117, 179,  76,  84],
       [  6,  24,  25,  51, 136,  89],
       [ 67, 156, 135, 101, 147,  90]])
 

np.concatenate((n1,n2),axis=1)
 

array([[ 67, 115, 248,  66, 212, 248, 182, 251, 137,  33,  60,   6],
       [ 66, 156, 231, 250,  39, 195, 169, 117, 245, 218,  96, 168],
       [248, 172,  19,  21, 200, 206, 231,  59, 117, 179,  76,  84],
       [139,  25,   3,  18,   3,  49,   6,  24,  25,  51, 136,  89],
       [ 55,  21,  12,   6, 218, 116,  67, 156, 135, 101, 147,  90]])
 
  
  np.hstack与np.vstack  水平级联与垂直级联,处理自己，进行维度的变更  
  

# hstack h 
new_image = np.hstack((cat, image))
plt.imshow(new_image)
plt.show()
 

 
 

# vstack vertical
new_image = np.vstack((cat, image))
plt.imshow(new_image)
plt.show()
 

 
 

5. 切分
 

与级联类似，三个函数完成切分工作：
 
  
  np.split  
  np.vsplit  
  np.hsplit  
  

n = np.random.randint(0,100,size = (4,6))
n
 

array([[92,  7, 55,  5, 20, 53],
       [42, 61, 91, 64, 95, 18],
       [25, 93, 48, 35, 39, 13],
       [42, 97, 73, 57, 14, 59]])
 

np.vsplit(n,(1,2))
 

[array([[92,  7, 55,  5, 20, 53]]),
 array([[42, 61, 91, 64, 95, 18]]),
 array([[25, 93, 48, 35, 39, 13],
        [42, 97, 73, 57, 14, 59]])]
 

n = np.random.randint(0,100, size=(6,6))
n
 

array([[48, 77, 69, 24, 83, 20],
       [80, 92, 21, 97, 16, 37],
       [52, 99,  2, 33, 28,  3],
       [ 5, 53, 34,  3,  0, 95],
       [27, 73, 95, 85,  8, 48],
       [30, 54, 49, 75, 44, 90]])
 

np.vsplit(n, (2,5))
 

[array([[48, 77, 69, 24, 83, 20],
        [80, 92, 21, 97, 16, 37]]), array([[52, 99,  2, 33, 28,  3],
        [ 5, 53, 34,  3,  0, 95],
        [27, 73, 95, 85,  8, 48]]), array([[30, 54, 49, 75, 44, 90]])]
 

np.split(n, 3, axis=1)
 

[array([[48, 77],
        [80, 92],
        [52, 99],
        [ 5, 53],
        [27, 73],
        [30, 54]]), array([[69, 24],
        [21, 97],
        [ 2, 33],
        [34,  3],
        [95, 85],
        [49, 75]]), array([[83, 20],
        [16, 37],
        [28,  3],
        [ 0, 95],
        [ 8, 48],
        [44, 90]])]
 

np.vsplit(n, 3)
 

[array([[48, 77, 69, 24, 83, 20],
        [80, 92, 21, 97, 16, 37]]), array([[52, 99,  2, 33, 28,  3],
        [ 5, 53, 34,  3,  0, 95]]), array([[27, 73, 95, 85,  8, 48],
        [30, 54, 49, 75, 44, 90]])]
 

np.hsplit(n, 3)
 

[array([[48, 77],
        [80, 92],
        [52, 99],
        [ 5, 53],
        [27, 73],
        [30, 54]]), array([[69, 24],
        [21, 97],
        [ 2, 33],
        [34,  3],
        [95, 85],
        [49, 75]]), array([[83, 20],
        [16, 37],
        [28,  3],
        [ 0, 95],
        [ 8, 48],
        [44, 90]])]
 

np.hsplit(n,(2,4))
 

[array([[33, 46],
        [98, 40],
        [47, 53],
        [34, 91]]), array([[53,  7],
        [12, 55],
        [69, 50],
        [32, 52]]), array([[56, 43],
        [18, 64],
        [69,  7],
        [83, 38]])]
 

cat.shape
 

(456, 730, 3)
 

456
730
 

result = np.split(cat, 2, axis = 0)
 

plt.imshow(result[0])
plt.show()
 

 
 

s_result = np.split(cat,2,axis = 1)
 

len(s_result)
 

2
 

plt.imshow(s_result[0])
plt.show()
 

 
 

6. 副本
 

所有赋值运算不会为ndarray的任何元素创建副本。对赋值后的对象的操作也对原来的对象生效。
 

l = [1,2,3,4]
l2 = l
l2[2] = 5
l
 

[1, 2, 5, 4]
 

n1 = np.arange(10)
n2 = n1
n2[3] = 100
n1
 

array([  0,   1,   2, 100,   4,   5,   6,   7,   8,   9])
 

n3 = n1.copy()
n3[5]  = 200
n1
 

array([  0,   1,   2, 100,   4,   5,   6,   7,   8,   9])
 

可使用copy()函数创建副本
 

四、ndarray的聚合操作
 

1. 求和np.sum
 

n = np.arange(11)
n
 

array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])
 

np.sum(n)
 

55
 

n = np.random.randint(0,100, size=(5,6))
n
 

array([[80, 20, 30, 66, 48, 50],
       [52, 33,  3, 76, 35,  9],
       [70, 99, 69, 50, 44, 31],
       [40, 13, 52, 50, 33, 45],
       [69, 42, 55, 30, 61, 22]])
 

np.sum(n, axis=1)
 

array([294, 208, 363, 233, 279])
 

2. 最大最小值：np.max/ np.min
 

同理
 

n = np.arange(11)
n
 

array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])
 

np.median(n)
 

5.0
 

np.mean(n)
 

5.0
 

n = np.random.randint(0,100,size=10)
n
 

array([42, 64, 40,  7,  0, 79, 32, 95, 95, 59])
 

np.mean(n)
 

51.3
 

np.median(n)
 

50.5
 

np.max(n)
 

10
 

np.min(n)
 

0
 

n = np.random.randint(0,100, size=(5,6))
n
 

array([[82, 44,  0, 33, 72, 99],
       [66, 25, 36, 88, 74, 78],
       [ 3, 53, 33, 76, 96, 69],
       [62, 10, 16, 22, 12, 31],
       [41, 57, 43, 79, 34,  7]])
 

np.max(n, axis=0)
 

array([82, 57, 43, 88, 96, 99])
 

3. 其他聚合操作
 

Function Name   NaN-safe Version    Description
np.sum  np.nansum   Compute sum of elements
np.prod np.nanprod  Compute product of elements
np.mean np.nanmean  Compute mean of elements
np.std  np.nanstd   Compute standard deviation
np.var  np.nanvar   Compute variance
np.min  np.nanmin   Find minimum value
np.max  np.nanmax   Find maximum value
np.argmin   np.nanargmin    Find index of minimum value
np.argmax   np.nanargmax    Find index of maximum value
np.median   np.nanmedian    Compute median of elements
np.percentile   np.nanpercentile    Compute rank-based statistics of elements
np.any  N/A Evaluate whether any elements are true
np.all  N/A Evaluate whether all elements are true
np.power 幂运算
 

np.argmin(n, axis=0)
 

array([2, 3, 0, 3, 3, 4], dtype=int64)
 

cat.shape
 

(456, 730, 3)
 

cat2 = cat.reshape((-1,3))
cat2.shape
 

(332880, 3)
 

n = np.random.randint(0,10, size=(4,5))
n
 

array([[8, 8, 9, 1, 5],
       [7, 9, 9, 5, 9],
       [4, 1, 0, 0, 1],
       [6, 5, 4, 2, 9]])
 

np.reshape(n,(-1,))
 

array([8, 8, 9, 1, 5, 7, 9, 9, 5, 9, 4, 1, 0, 0, 1, 6, 5, 4, 2, 9])
 

cat3 = cat.reshape((456*730,3))
cat3.shape
 

(332880, 3)
 

cat3.max(axis = 0)
 

array([255, 242, 219], dtype=uint8)
 

max_cat = cat.max(axis = (0,1))
 

max_cat
 

array([255, 242, 219], dtype=uint8)
 

max_cat.shape
 

(3,)
 

cat.min()
 

0
 

 

 

np.sum 和 np.nansum 的区别  nan not a number
 

a = np.array([1,2,np.nan])
a
 

array([ 1.,  2., nan])
 

np.nansum(a)
 

3.0
 
 操作文件  

使用pandas打开文件president_heights.csv  获取文件中的数据
 

import pandas as pd
data = pd.read_csv('president_heights.csv')
type(data)
data
 
 
  
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

```
.dataframe tbody tr th {
    vertical-align: top;
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.dataframe thead th {
    text-align: right;
}

```

  
   
    
     
       
     order  
     name  
     height(cm)  
     
    
    
     
     0  
     1  
     George Washington  
     189  
     
     
     1  
     2  
     John Adams  
     170  
     
     
     2  
     3  
     Thomas Jefferson  
     189  
     
     
     3  
     4  
     James Madison  
     163  
     
     
     4  
     5  
     James Monroe  
     183  
     
     
     5  
     6  
     John Quincy Adams  
     171  
     
     
     6  
     7  
     Andrew Jackson  
     185  
     
     
     7  
     8  
     Martin Van Buren  
     168  
     
     
     8  
     9  
     William Henry Harrison  
     173  
     
     
     9  
     10  
     John Tyler  
     183  
     
     
     10  
     11  
     James K. Polk  
     173  
     
     
     11  
     12  
     Zachary Taylor  
     173  
     
     
     12  
     13  
     Millard Fillmore  
     175  
     
     
     13  
     14  
     Franklin Pierce  
     178  
     
     
     14  
     15  
     James Buchanan  
     183  
     
     
     15  
     16  
     Abraham Lincoln  
     193  
     
     
     16  
     17  
     Andrew Johnson  
     178  
     
     
     17  
     18  
     Ulysses S. Grant  
     173  
     
     
     18  
     19  
     Rutherford B. Hayes  
     174  
     
     
     19  
     20  
     James A. Garfield  
     183  
     
     
     20  
     21  
     Chester A. Arthur  
     183  
     
     
     21  
     23  
     Benjamin Harrison  
     168  
     
     
     22  
     25  
     William McKinley  
     170  
     
     
     23  
     26  
     Theodore Roosevelt  
     178  
     
     
     24  
     27  
     William Howard Taft  
     182  
     
     
     25  
     28  
     Woodrow Wilson  
     180  
     
     
     26  
     29  
     Warren G. Harding  
     183  
     
     
     27  
     30  
     Calvin Coolidge  
     178  
     
     
     28  
     31  
     Herbert Hoover  
     182  
     
     
     29  
     32  
     Franklin D. Roosevelt  
     188  
     
     
     30  
     33  
     Harry S. Truman  
     175  
     
     
     31  
     34  
     Dwight D. Eisenhower  
     179  
     
     
     32  
     35  
     John F. Kennedy  
     183  
     
     
     33  
     36  
     Lyndon B. Johnson  
     193  
     
     
     34  
     37  
     Richard Nixon  
     182  
     
     
     35  
     38  
     Gerald Ford  
     183  
     
     
     36  
     39  
     Jimmy Carter  
     177  
     
     
     37  
     40  
     Ronald Reagan  
     185  
     
     
     38  
     41  
     George H. W. Bush  
     188  
     
     
     39  
     42  
     Bill Clinton  
     188  
     
     
     40  
     43  
     George W. Bush  
     182  
     
     
     41  
     44  
     Barack Obama  
     185  
     
    
   
 

heights = data['height(cm)']
heights
type(heights)
 

pandas.core.series.Series
 

np.max(heights)
 

193
 

np.mean(heights)
 

179.73809523809524
 

np.std(heights)
 

6.931843442745893
 

np.min(heights)
 

163
 

五、ndarray的矩阵操作
 

1. 基本矩阵操作
 

1) 算术运算符：
 
  
  加减乘除  
  

n = np.random.randint(0,10, size=(4,5))
n
 

array([[2, 5, 0, 4, 6],
       [0, 0, 7, 5, 0],
       [6, 3, 2, 9, 2],
       [5, 7, 0, 4, 5]])
 

# 加
n + 1
 

array([[ 3,  6,  1,  5,  7],
       [ 1,  1,  8,  6,  1],
       [ 7,  4,  3, 10,  3],
       [ 6,  8,  1,  5,  6]])
 

# 减 
n - 1
 

array([[ 1,  4, -1,  3,  5],
       [-1, -1,  6,  4, -1],
       [ 5,  2,  1,  8,  1],
       [ 4,  6, -1,  3,  4]])
 

# 两个矩阵相加
n2 = np.random.randint(0,10,size=(4,5))
n2
 

array([[2, 4, 2, 5, 9],
       [6, 6, 9, 6, 2],
       [9, 7, 5, 6, 1],
       [4, 6, 7, 2, 9]])
 

n + n2
 

array([[ 4,  9,  2,  9, 15],
       [ 6,  6, 16, 11,  2],
       [15, 10,  7, 15,  3],
       [ 9, 13,  7,  6, 14]])
 

n3 = np.random.randint(0,10,size=(2,5))
n3
 

array([[8, 0, 0, 5, 8],
       [4, 0, 3, 6, 7]])
 

n2 + n3
 

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-97-5f0861827bc6> in <module>()
----> 1 n2 + n3
 

ValueError: operands could not be broadcast together with shapes (4,5) (2,5)
 

2) 矩阵积np.dot()
 

n1 = np.random.randint(0,10,size=(2,3))
n1
 

array([[8, 9, 4],
       [1, 7, 5]])
 

n2 = np.random.randint(0,10, size=(3,4))
n2
 

array([[4, 4, 2, 7],
       [4, 4, 7, 3],
       [1, 3, 2, 7]])
 

np.dot(n1,n2)
 

array([[ 72,  80,  87, 111],
       [ 37,  47,  61,  63]])
 

2. 广播机制
 

【重要】ndarray广播机制的两条规则
 
  
  规则一：为缺失的维度补1  
  规则二：假定缺失元素用已有值填充  
  

例1：  m = np.ones((2, 3))  a = np.arange(3)  求M+a
 

m = np.ones((2,3),dtype=int)
m
 

array([[1, 1, 1],
       [1, 1, 1]])
 

n = np.arange(3)
n
 

array([0, 1, 2])
 

m + n
 

array([[1, 2, 3],
       [1, 2, 3]])
 

例2：  a = np.arange(3).reshape((3, 1))  b = np.arange(3)  求a+b
 

a = np.arange(3).reshape((3,1))
a
 

array([[0],
       [1],
       [2]])
 

b = np.arange(3)
b
 

array([0, 1, 2])
 

a + b
 

array([[0, 1, 2],
       [1, 2, 3],
       [2, 3, 4]])
 

习题  a = np.ones((4, 1))  b = np.arange(4)  求a+b
 

六、ndarray的排序
 

小测验：  使用以上所学numpy的知识，对一个ndarray对象进行选择排序。
 

def Sortn(x):
 

代码越短越好
 

n = [5,2,3,6,9]
 

def bubble(n):
    for i in range(len(n) -1):
        for j in range(i+1, len(n)):
            if n[i] > n[j]:
                n[i], n[j] = n[j], n[i]
 

bubble(n)
n
 

[2, 3, 5, 6, 9]
 

# 选择排序
def select(n):
    for i in range(len(n)):
        # 选出最小值的索引
        index = np.argmin(n[i:]) + i
        # 把最小值和当前值的位置换一下
        n[i], n[index] = n[index], n[i]
 

n = [4, 6,1,0,3]
select(n)
n
 

[0, 1, 3, 4, 6]
 

1. 快速排序
 

np.sort()与ndarray.sort()都可以，但有区别：
 
  
  np.sort()不改变输入  
  ndarray.sort()本地处理，不占用空间，但改变输入  
  

n = np.random.randint(0,10,size=6)
n
 

array([6, 7, 1, 1, 8, 3])
 

np.sort(n)
 

array([1, 1, 3, 6, 7, 8])
 

np.sort(n)
n
 

array([6, 7, 1, 1, 8, 3])
 

n.sort()
n
 

array([1, 1, 3, 6, 7, 8])


      
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