average = np.mean(sub_arr) 打印结果:将平均值打印出来。 代码语言:txt 复制 print("指定部分的平均值为:", average) 完整代码示例: 代码语言:txt 复制 import numpy as np arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) start_index = 2 end_index = 6 sub_arr = arr[start_i...
1. Quick Examples of NumPy Average If you are in a hurry, below are some quick examples of how to calculate the average of an array by using the NumPy average() function.# Quick examples of numpy average # Example 1: Get the average of 2-D array arr = np.array([[6, 8, 4],[...
array1= np.array([[1,2,3], [4,5,6]]) # by default all values have the same weight(1)result1 = np.average(array1)# assign variable weightsresult2 = np.average(array1, weights = np.arange(0,6,1).reshape(2,3))# assign 0 weight to first column# to get average of 2nd and ...
...,列,投影等信息,所有的源文件这些信息都是一致的 print ('rows and cols is '),rows,cols filesum = [[0.0]*cols]*rows #栅格值和...,二维数组 average= [[0.0]*cols]*rows# 存放平均值,二维数组 filesum=np.array(filesum)#转换类型为np.array average...filepath = os.path.join(dirpath,...
X = np.array([[1, 2], [3, 4]])poly = PolynomialFeatures(degree=2, include_bias=False)X_poly = poly.fit_transform(X) print(X_poly)# 输出: [[1. 2. 1. 2. 4.]# [3. 4. 9. 12. 16.]] print(poly.g...
指数移动平均线(exponential moving average)是另一种技术指标。指数移动平均线使用的权重是指数衰减的。对历史数据点赋予的权重以指数速度减小,但不会到达0。在计算权重的过程中使用 exp 和 linspace 函数。 1)先了解numpy中的exp 和 linspace 函数 x = np.arange(5)y = np.arange(10)print ("Exp", np.exp...
importnumpyasnpfromnumpy.typingimportArrayLikedefmoving_average(ts:ArrayLike,win:int,padding=True)->np.ndarray:kernel=np.ones(win)/winarr=np.convolve(ts,kernel,'valid')ifpadding:returnnp.insert(arr,0,[np.nan]*(win-1))else:returnarrprint(moving_average(np.arange(5),3))print(moving_average...
输入array。 max_line_width:int, 可选 如果文本长于max_line_width,则插入换行符。 默认为numpy.get_printoptions()['linewidth']。 precision:int或None, 可选 浮点精度。默认为numpy.get_printoptions()['precision']。 suppress_small:bool, 可选 ...
random.randint(10, size=10) print('array: ', Z) # Method 1 moving_average(Z, n=3).round(2) # Method 2: # Thanks AlanLRH! # np.ones(3)/3 gives equal weights. Use np.ones(4)/4 for window size 4. np.convolve(Z, np.ones(3)/3, mode='valid') . #> array: [8 8 3 ...
array(['CRIM','ZN','INDUS','CHAS','NOX','RM','AGE','DIS','RAD','TAX','PTRATIO','B','LSTAT'], dtype='<U7') x.shape (506,13) y.shape (506,)## We will consider "lower status of population" as independent variable for its importancelstat = x[0:,-1] ...