import matplotlib.pylab as plt

# 读取图片二进制数据
img_arr = plt.imread(‘img_dir‘)

# 将图片展示
plt.imshow(img_arr) 

# 将图片所有数据减少
plt.imshow(img_arr - 100)

# 操作该numpy数据，该操作会同步到图片中

np.linspace(0,100,num=20)

out: array([  0.        ,   5.26315789,  10.52631579,  15.78947368,
        21.05263158,  26.31578947,  31.57894737,  36.84210526,
        42.10526316,  47.36842105,  52.63157895,  57.89473684,
        63.15789474,  68.42105263,  73.68421053,  78.94736842,
        84.21052632,  89.47368421,  94.73684211, 100.        ])

np.arange(0,100,2)

out: 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])

#固定随机性
#随机因子：系统的时间
np.random.seed(100)
arr = np.random.randint(0,100,size=(4,5))

np.random.random(size=(4,5))


out: array([[0.56229626, 0.00581719, 0.30742321, 0.95018431, 0.12665424],
       [0.07898787, 0.31135313, 0.63238359, 0.69935892, 0.64196495],
       [0.92002378, 0.29887635, 0.56874553, 0.17862432, 0.5325737 ],
       [0.64669147, 0.14206538, 0.58138896, 0.47918994, 0.38641911]])

arr.shape
arr.size
img_arr.size
arr.dtype

print(arr)

out: array([[ 8, 24, 67, 87, 79],
       [48, 10, 94, 52, 98],
       [53, 66, 98, 14, 34],
       [24, 15, 60, 58, 16]])
print(arr[1])

out:　array([48, 10, 94, 52, 98])

#获取二维数组前两行
arr[0:2]

#获取二维数组前两列
arr[:,0:2]  #arr[hang,lie]

#获取二维数组前两行和前两列数据
arr[0:2,0:2]

#将数组的行倒序
arr[::-1]

#列倒序
arr[:,::-1]

#全部倒序
arr[::-1,::-1]

import numpy as np

arr_1 = np.random.randint(0, 100, size=(20,))

arr_1.reshape((2, 10))
# 把其中一维的数据设为-1，会自动计算
arr_1.reshape((5, -1))

arr_1 = arr.reshape(20,)

# 按照行来进行级联
np.concatenate((arr,arr,arr),axis=1)

# 按照列来进行级联
np.concatenate((arr,arr,arr),axis=0)

arr.sum(axis=1)

arr.max()

arr.min()

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.sort(arr,axis=0)