pyplot as plt # This program measures the performance of the NumPy sort function # and plots time vs array size. integers = [] def dosort(): integers.sort() def measure(): timer = timeit.Timer('dosort()', 'from __main__ import dosort') return timer.timeit(10 ** 2) powersOf2 ...
ndarray.size 数组中的元素数量 ndarray.itemsize 一个数组元素的长度(字节) ndarray.dtype 数组元素的类型 代码语言:javascript 代码运行次数:0 运行 AI代码解释 import numpy as np score_data = np.array([[80, 89, 86, 67, 79], [78, 97, 89, 67, 81], [90, 94, 78, 67, 74], [91, 91,...
array.size: 返回一个表示数组元素总数的整数。 array.reshape(): 返回一个新数组,该数组具有不同的形状但相同的数据。 2.数组运算 NumPy提供了大量的函数来进行数组运算,包括数学运算、统计运算、线性代数运算等。数据计算的基本函数如下,详细介绍见后。 2.1 数学运算 # 加法 arr_add = np.array([1, 2, 3...
ndarray.reshape(shape[, order]) Returns an array containing the same data with a new shape. ndarray.T 数组的转置 将数组的行、列进行互换 stock_change.shape (8, 10) stock_change.T.shape (10, 8) ndarray.resize(new_shape[, refcheck]) Change shape and size of array in-place. 类型修改 ...
numpy.array 的基本属性如下表所示 x.ndim 1 X.ndim 2 x.shape (10,) X.shape (3, 5) x.size 10 X.size 15 x.dtype dtype('int32') X.dtype dtype('int32') 3.2 数组索引与切片 x array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) ...
array = np.array([[1,2,3],[2,3,4]]) #构建一个二维矩阵 print(array) ##基本属性 print('number of dim',array.ndim) #维度 print('shape:',array.shape) #形状,几行几列 print('size:',array.size) #元素个数 1. 2. 3. 4. ...
c= np.array([[[1,2,3], [43,5,6]], [[12,23,45], [1,2,3]]]) 分别打印形状 a.shape (2, 3) b.shape (4,) c.shape (2, 2, 3) 三维,简单理解,两行三列两层 打印维数 a.ndim2b.ndim1c.ndim3 打印元素数量 a.size6b.size ...
array([[1, 2, 3], [5, 7, 9]]) >>> np.cumsum(a,axis=1) # sum over columns for each of the 2 rows array([[ 1, 3, 6], [ 4, 9, 15]]) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. numpy.sum() ...
NumPy: N-dimensional array - An ndarray is a (usually fixed-size) multidimensional container of items of the same type and size. The number of dimensions and items in an array is defined by its shape, which is a tuple of N positive integers that specify
from sklearn import datasets%matplotlib inlineimport matplotlib.pyplot as plt## Boston House Prices datasetboston = datasets.load_boston()x = boston.datay = boston.targetboston.feature_namesarray(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD','TAX', 'PTRATIO'...