Numpy函数
- 广播
- 数学函数
- 算术运算
- 加：numpy.add(x1, x2, *args, **kwargs)
- 减：numpy.subtract(x1, x2, *args, **kwargs)
- 乘：numpy.multiply(x1, x2, *args, **kwargs)
- 除：numpy.divide(x1, x2, *args, **kwargs)
- 整除：numpy.floor_divide(x1, x2, *args, **kwargs)
- 幂：numpy.power(x1, x2, *args, **kwargs)
- 开方：numpy.sqrt(x, *args, **kwargs)
- 平方：numpy.square(x, *args, **kwargs)
- 示例
- 三角函数
- 指数、对数函数
- 维度加、累加、累乘
- 临差：numpy.diff(a, n=1, axis=-1, prepend=np._NoValue, append=np._NoValue) — 沿着指定轴计算第N维的离散差值
- 四舍五入
- 其它
- 逻辑函数
数学函数
算术运算
算符为 元素级。也就是说，它们只用于位置相同的元素之间，所得到的运算结果组成一个新的数组。
加：numpy.add(x1, x2, *args, **kwargs)
Add arguments element-wise.
减：numpy.subtract(x1, x2, *args, **kwargs)
Subtract arguments element-wise
乘：numpy.multiply(x1, x2, *args, **kwargs)
Multiply arguments element-wise.
除：numpy.divide(x1, x2, *args, **kwargs)
Returns a true division of the inputs, element-wise.
整除：numpy.floor_divide(x1, x2, *args, **kwargs)
Return the largest integer smaller or equal to the division of the inputs.
幂：numpy.power(x1, x2, *args, **kwargs)
First array elements raised to powers from second array, element-wise.
开方：numpy.sqrt(x, *args, **kwargs)
Return the non-negative square-root of an array, element-wise.
平方：numpy.square(x, *args, **kwargs)
Return the element-wise square of the input.
三角函数 numpy.sin(x, *args, **kwargs)
Trigonometric sine, element-wise.
numpy.cos(x, *args, **kwargs)
Cosine element-wise.
numpy.tan(x, *args, **kwargs)
Compute tangent element-wise.
numpy.arcsin(x, *args, **kwargs)
Inverse sine, element-wise.
numpy.arccos(x, *args, **kwargs)
Trigonometric inverse cosine, element-wise.
numpy.arctan(x, *args, **kwargs)
Trigonometric inverse tangent, element-wise.
指数、对数函数 numpy.exp(x, *args, **kwargs)
Calculate the exponential of all elements in the input array.
numpy.log(x, *args, **kwargs)
Natural logarithm, element-wise.
numpy.exp2(x, *args, **kwargs)
Calculate 2**p for all p in the input array.
numpy.log2(x, *args, **kwargs)
Base-2 logarithm of x.
numpy.log10(x, *args, **kwargs)
Return the base 10 logarithm of the input array, element-wise.
维度加、累加、累乘 维度加：numpy.sum(a[, axis=None, dtype=None, out=None, …])—— 返回给定轴上的数组元素的总和
Sum of array elements over a given axis.
返回给定轴上的数组元素的总和。
通过不同的 axis，numpy 会沿着不同的方向进行操作：如果不设置，那么对所有的元素操作；如果axis=0，则沿着纵轴进行操作；axis=1，则沿着横轴进行操作；axis=i，则 numpy 沿着第i个下标变化的方向进行操作。累加：numpy.cumsum(a, axis=None, dtype=None, out=None) ——返回给定轴上的数组元素的累加和。
Return the cumulative sum of the elements along a given axis.
聚合函数 是指对一组值（比如一个数组）进行操作，返回一个单一值作为结果的函数。因而，求数组所有元素之和的函数就是聚合函数。ndarray类实现了多个这样的函数。维度乘：numpy.prod(a[, axis=None, dtype=None, out=None, …])—— 返回给定轴上数组元素的乘积
Return the product of array elements over a given axis.
参数用法与numpy.sum()相同
示例：
x = np.array([[11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], [26, 27, 28, 29, 30], [31, 32, 33, 34, 35]]) y = np.prod(x) print(y) # 788529152 y = np.prod(x, axis=0) print(y) # [2978976 3877632 4972968 6294624 7875000] y = np.prod(x, axis=1) print(y) # [ 360360 1860480 6375600 17100720 38955840] - 1
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累乘：numpy.cumprod(a, axis=None, dtype=None, out=None) ——返回给定轴上数组元素的累乘
Return the cumulative product of elements along a given axis.临差：numpy.diff(a, n=1, axis=-1, prepend=np._NoValue, append=np._NoValue) — 沿着指定轴计算第N维的离散差值
The first difference is given by out[i] = a[i+1] - a[i] along the given axis, higher differences are calculated by using diff recursively.
Calculate the n-th discrete difference along the given axis
- a：输入矩阵
- n：可选，代表要执行几次差值
- axis：默认是最后一个四舍五入
numpy.around(a, decimals=0, out=None) —— 将数组舍入到给定的小数位数
Evenly round to the given number of decimals
x = np.random.rand(3, 3) * 10 print(x) # [[6.59144457 3.78566113 8.15321227] # [1.68241475 3.78753332 7.68886328] # [2.84255822 9.58106727 7.86678037]] y = np.around(x) print(y) # [[ 7. 4. 8.] # [ 2. 4. 8.] # [ 3. 10. 8.]] y = np.around(x, decimals=2) print(y) # [[6.59 3.79 8.15] # [1.68 3.79 7.69] # [2.84 9.58 7.87]]- 1
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向上\下取整
numpy.ceil(x, *args, **kwargs)——向上取整
Return the ceiling of the input, element-wise.
x = np.random.rand(3, 3) * 10 print(x) # [[0.67847795 1.33073923 4.53920122] # [7.55724676 5.88854047 2.65502046] # [8.67640444 8.80110812 5.97528726]] y = np.ceil(x) print(y) # [[1. 2. 5.] # [8. 6. 3.] # [9. 9. 6.]]- 1
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numpy.floor(x, *args, **kwargs) ——向下取整
Return the floor of the input, element-wise
y = np.floor(x) print(y) # [[0. 1. 4.] # [7. 5. 2.] # [8. 8. 5.]]- 1
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其它
numpy.clip(a, a_min, a_max, out=None, **kwargs) —— 限制值范围
Clip (limit) the values in an array
x = np.array([[11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], [26, 27, 28, 29, 30], [31, 32, 33, 34, 35]]) y = np.clip(x, a_min=20, a_max=30) print(y) # [[20 20 20 20 20] # [20 20 20 20 20] # [21 22 23 24 25] # [26 27 28 29 30] # [30 30 30 30 30]]- 1
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numpy.absolute(x, *args, **kwargs) /numpy.abs(x, *args, **kwargs) —— 绝对值
abs是对absolute的简写形势
x = np.arange(-5, 5) print(x) # [-5 -4 -3 -2 -1 0 1 2 3 4] y = np.abs(x) print(y) # [5 4 3 2 1 0 1 2 3 4] y = np.absolute(x) print(y) # [5 4 3 2 1 0 1 2 3 4]- 1
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numpy.sign(x, *args, **kwargs) ——正负性返回
Returns an element-wise indication of the sign of a number.
x = np.arange(-5, 5) print(x) #[-5 -4 -3 -2 -1 0 1 2 3 4] print(np.sign(x)) #[-1 -1 -1 -1 -1 0 1 1 1 1]- 1
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逻辑函数
真值判断numpy.all（任意真则真）、numpy.any（存在真则真）
- numpy.all(a, axis=None, out=None, keepdims=np._NoValue) Test whether all array elements along a given axis evaluate to True.
- numpy.any(a, axis=None, out=None, keepdims=np._NoValue) Test whether any array element along a given axis evaluates to True.
a = np.array([0, 4, 5]) b = np.copy(a) print(np.all(a == b)) # True print(np.any(a == b)) # True b[0] = 1 print(np.all(a == b)) # False print(np.any(a == b)) # True print(np.all([1.0, np.nan])) # True print(np.any([1.0, np.nan])) # True a = np.eye(3) print(np.all(a, axis=0)) # [False False False] print(np.any(a, axis=0)) # [ True True True]- 1
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逻辑运算
与、或、非、异或
numpy.logical_and(x1, x2, *args, **kwargs)
Compute the truth value of x1 AND x2 element-wise.
numpy.logical_or(x1, x2, *args, **kwargs)
Compute the truth value of x1 OR x2 element-wise.
numpy.logical_not(x, *args, **kwargs)
Compute the truth value of NOT x element-wise.
numpy.logical_xor(x1, x2, *args, **kwargs)
Compute the truth value of x1 XOR x2, element-wise.
print(np.logical_not(3)) # False print(np.logical_not([True, False, 0, 1])) # [False True True False] x = np.arange(5) print(np.logical_not(x < 3)) # [False False False True True] 【例】计算x1 AND x2元素的真值。 print(np.logical_and(True, False)) # False print(np.logical_and([True, False], [True, False])) # [ True False] print(np.logical_and(x > 1, x < 4)) # [False False True True False] 【例】逐元素计算x1 OR x2的真值。 print(np.logical_or(True, False)) # True print(np.logical_or([True, False], [False, False])) # [ True False] print(np.logical_or(x < 1, x > 3)) # [ True False False False True] 【例】计算x1 XOR x2的真值，按元素计算。 print(np.logical_xor(True, False)) # True print(np.logical_xor([True, True, False, False], [True, False, True, False])) # [False True True False] print(np.logical_xor(x < 1, x > 3)) # [ True False False False True] print(np.logical_xor(0, np.eye(2))) # [[ True False] # [False True]]- 1
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比较（大于、小于、等于、不大于、不小于）
numpy.greater(x1, x2, *args, **kwargs)
Return the truth value of (x1 > x2) element-wise.
numpy.greater_equal(x1, x2, *args, **kwargs)
Return the truth value of (x1 >= x2) element-wise.
numpy.equal(x1, x2, *args, **kwargs)
Return (x1 == x2) element-wise.
numpy.not_equal(x1, x2, *args, **kwargs)
Return (x1 != x2) element-wise.
numpy.less(x1, x2, *args, **kwargs)
Return the truth value of (x1 < x2) element-wise.
numpy.less_equal(x1, x2, *args, **kwargs)
Return the truth value of (x1 =< x2) element-wise.
x = np.array([1, 2, 3, 4, 5, 6, 7, 8]) y = x > 2 print(y) print(np.greater(x, 2)) # [False False True True True True True True] y = x >= 2 print(y) print(np.greater_equal(x, 2)) # [False True True True True True True True] y = x == 2 print(y) print(np.equal(x, 2)) # [False True False False False False False False] y = x != 2 print(y) print(np.not_equal(x, 2)) # [ True False True True True True True True] y = x < 2 print(y) print(np.less(x, 2)) # [ True False False False False False False False] y = x <= 2 print(y) print(np.less_equal(x, 2)) # [ True True False False False False False False]- 1
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比较时可以存在广播
x = np.array([[11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], [26, 27, 28, 29, 30], [31, 32, 33, 34, 35]]) np.random.seed(20200611) y = np.random.randint(10, 50, 5) print(y) # [32 37 30 24 10] z = x > y print(z) print(np.greater(x, y)) # [[False False False False True] # [False False False False True] # [False False False False True] # [False False False True True] # [False False True True True]] z = x >= y print(z) print(np.greater_equal(x, y)) # [[False False False False True] # [False False False False True] # [False False False True True] # [False False False True True] # [False False True True True]] z = x == y print(z) print(np.equal(x, y)) # [[False False False False False] # [False False False False False] # [False False False True False] # [False False False False False] # [False False False False False]] z = x != y print(z) print(np.not_equal(x, y)) # [[ True True True True True] # [ True True True True True] # [ True True True False True] # [ True True True True True] # [ True True True True True]] z = x < y print(z) print(np.less(x, y)) # [[ True True True True False] # [ True True True True False] # [ True True True False False] # [ True True True False False] # [ True True False False False]] z = x <= y print(z) print(np.less_equal(x, y)) # [[ True True True True False] # [ True True True True False] # [ True True True True False] # [ True True True False False] # [ True True False False False]]- 1
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