numpy.ptp(a, axis=None, out=None, keepdims=np._NoValue)Range of values (maximum - minimum) along an axis. The name of the function comes from the acronym for ‘peak to peak’. 【例】计算极差 import numpy as np np.random.seed(20201124) x = np.random.randint(0, 20, size=[4,5]...
numpy.ptp(a, axis=None, out=None, keepdims=np._NoValue) Range of values (maximum - minimum) along an axis. The name of the function comes from the acronym for ‘peak to peak’. 【例】 import numpy as np np.random.seed(20200623) x = np.random.randint(0, 20, size=[4, 5]) pr...
numpy.ptp(a, axis=None, out=None, keepdims=np._NoValue) Range of values (maximum - minimum) along an axis. The name of the function comes from the acronym for ‘peak to peak’.【例】计算极差 import numpy as npnp.random.seed(20200623)x = np.random.randint(0, 20, size=[4, 5])...
# Generate an array of 5 values from 0 to 10 (inclusive) arr = np.linspace(0, 10, 5) # Print the array print(arr) [ 0. 2.5 5. 7.5 10. ] numpy.range:用间隔的值创建数组。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 # Generate an array from 0 to 10 (exclusive) with ...
shape[1] # Fancy indexing # Set values on diagonal to 0 # x 0-xmax # y 0-ymax lena[range(xmax), range(ymax)] = 0 # Set values on other diagonal to 0 # x xmax-0 # y 0-ymax lena[range(xmax-1,-1,-1), range(ymax)] = 0 # Plot Lena with diagonal lines set to 0 ...
5]) values_to_insert = [0, 3, 5] insert_positions = np.searchsorted(sorted_array, values_to...
importnumpyasnp# 定义可能的值和它们的概率values=np.array([0.1,0.5,1.0,2.0,5.0])probabilities=np.array([0.3,0.2,0.2,0.2,0.1])# 进行加权随机选择choices=np.random.choice(values,size=10,p=probabilities)print("Weighted random choices from numpyarray.com:",choices) ...
在这里使用np.range()创建一个10(包括)和20(不包括)之间的整数数组x。然后使用np.array()创建第二个包含任意整数的数组y。 一旦我们有两个相同长度的数组,就可以以两个数组为参数调用np.corrcoef()。 >>>r = np.corrcoef(x, y) >>>r array([[1....
NumPy的masked arrays允许我们在数组中标记某些值为无效或缺失。当拼接包含masked values的数组时,mask也会被正确处理。让我们看一个例子: importnumpyasnpimportnumpy.maasma# 创建两个masked arraysarr1=ma.array([1,2,3],mask=[0,0,1])arr2=ma.array([4,5,6],mask=[1,0,0])# 垂直拼接这些masked ...
fig = ff.create_distplot([msft['Daily Pct. Change'].values], ['MSFT Daily Returns'], show_hist=False) plot(fig) 下图显示了前面代码的输出: https://gitcode.net/apachecn/apachecn-ds-zh/-/raw/master/docs/master-num-comp-numpy/img//d49a8f10-1559-47d2-860b-83faf46b9716.png ...