创建数组
>>> np.ones([3,4]) # 值为浮点1的矩阵 array([[ 1., 1., 1., 1.], [ 1., 1., 1., 1.], [ 1., 1., 1., 1.]]) >>> np.zeros([3,4]) # 值为浮点0的矩阵 array([[ 0., 0., 0., 0.], [ 0., 0., 0., 0.], [ 0., 0., 0., 0.]]) >>> np.array([[1,2,3],[4,5,6]]) # 深拷贝 array([[1, 2, 3], [4, 5, 6]]) >>> np.asarray([[1,2,3],[4,5,6]]) # 浅拷贝 array([[1, 2, 3], [4, 5, 6]])
数组属性
>>> d=np.random.rand(2,3,4) >>> d array([[[ 0.75432633, 0.57838693, 0.9298954 , 0.01667251], [ 0.81329288, 0.18277114, 0.40584013, 0.30055277], [ 0.12805651, 0.38665696, 0.29655644, 0.61897223]], [[ 0.1072343 , 0.34588952, 0.96080471, 0.56854714], [ 0.76898631, 0.4881976 , 0.85973732, 0.1127037 ], [ 0.27826845, 0.81381869, 0.03623546, 0.11600406]]]) >>> d.size # 元素个数 24 >>> d.shape # 数组形状 (2, 3, 4) >>> d.dtype # 元素类型 dtype('float64') >>> d.ndim # 数组维度 3
>>> d.itemsize # 元素字节长度
8
随机数（均匀分布）
>>> np.random.uniform(0,100) # 创建指定范围的随机浮点数 79.95384787852716 >>> np.random.randint(0,100) # 创建指定范围的随机整数 13 >>> np.random.rand(3,3) # 创建指定[0, 1]之间的浮点数矩阵 array([[ 0.3672736 , 0.64473369, 0.51615523], [ 0.12016337, 0.80318054, 0.02896355], [ 0.22252509, 0.41416445, 0.9141706 ]])
正态分布
np.random.normal(1, 0.5, (3,4)) # 指定均值、标准差、维度 array([[ 0.86131114, 0.97396862, 1.41560527, 0.9125575 ], [ 0.50615441, 1.30600456, 0.28319673, 0.73952316], [ 1.683847 , 1.88634808, 0.81664031, 1.04438156]])
slice（切片）
>>> a array([[ 0.91108116, 0.38937371, 0.44120738, 0.9711543 , 0.50400801, 0.8159346 ], [ 0.23592619, 0.9446476 , 0.83056827, 0.80585383, 0.98337604, 0.34744683]]) >>> a[:,3:-1] array([[ 0.9711543 , 0.50400801], [ 0.80585383, 0.98337604]])
reshape (reshape前后元素个数要一致)
>>> a=np.random.rand(2,6) >>> b=a.reshape(3,4) >>> a array([[ 0.91108116, 0.38937371, 0.44120738, 0.9711543 , 0.50400801, 0.8159346 ], [ 0.23592619, 0.9446476 , 0.83056827, 0.80585383, 0.98337604, 0.34744683]]) >>> b array([[ 0.91108116, 0.38937371, 0.44120738, 0.9711543 ], [ 0.50400801, 0.8159346 , 0.23592619, 0.9446476 ], [ 0.83056827, 0.80585383, 0.98337604, 0.34744683]])
条件计算
>>> a array([[80, 88], [82, 81], [84, 75], [86, 83], [75, 81]]) >>> a>80 array([[False, True], [ True, True], [ True, False], [ True, True], [False, True]], dtype=bool) >>> np.where(a<=80, 0, 1) # <=80的元素替换为0，否则替换为1 array([[0, 1], [1, 1], [1, 0], [1, 1], [0, 1]])
最大（小）值
>>> a array([[80, 88], [82, 81], [84, 75], [86, 83], [75, 81]]) >>> np.amax(a, axis=0) # 按列求最大值 array([86, 88]) >>> np.amax(a, axis=1) # 按行求最大值 array([88, 82, 84, 86, 81]) >>> np.amin(a, axis=0) # 按列求最小值 array([75, 75]) >>> np.amin(a, axis=1) # 按行求最小值 array([80, 81, 75, 83, 75]) >>> np.mean(a, axis=1) # 平均值 array([ 84. , 81.5, 79.5, 84.5, 78. ]) >>> np.mean(a, axis=0) array([ 81.4, 81.6]) >>> np.std(a, axis=0) # 标准差 array([ 3.77359245, 4.1761226 ]) >>> np.std(a, axis=1) array([ 4. , 0.5, 4.5, 1.5, 3. ])
数组与数值的运算
a array([[84, 92], [86, 85], [88, 79], [90, 87], [79, 85]]) >>> a[:,0]+=4 >>> a array([[88, 92], [90, 85], [92, 79], [94, 87], [83, 85]]) >>> a[2:4,1] -= 10 >>> a array([[88, 92], [90, 85], [92, 69], [94, 77], [83, 85]])
数组拼接
>>> v1 [[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]] >>> v2 [[12, 13, 14, 15, 16, 17], [18, 19, 20, 21, 22, 23]] >>> np.vstack((v1, v2)) # 垂直拼接 array([[ 0, 1, 2, 3, 4, 5], [ 6, 7, 8, 9, 10, 11], [12, 13, 14, 15, 16, 17], [18, 19, 20, 21, 22, 23]]) >>> np.hstack((v1, v2)) # 水平拼接 array([[ 0, 1, 2, 3, 4, 5, 12, 13, 14, 15, 16, 17], [ 6, 7, 8, 9, 10, 11, 18, 19, 20, 21, 22, 23]]) >>>
矩阵乘法
>>> a = np.array([[80, 88], [82, 81], [84, 75], [86, 83], [75, 81]]) >>> b = np.array([[0.4], [0.6]]) >>> np.dot(a, b) array([[ 84.8], [ 81.4], [ 78.6], [ 84.2], [ 78.6]])
参考文档：
https://www.jianshu.com/p/a260a8c43e44
https://docs.scipy.org/doc/numpy/user/quickstart.html