27, 64]) >>> # equivalent to a[0:6:2] = 1000; >>> # from start to position 6, exclusive, set every 2nd element to 1000 >>> a[:6:2] = 1000 >>> a array([1000, 1, 1000, 27, 1000, 125, 216, 343, 512,
>>> np.set_printoptions(threshold=sys.maxsize) # sys module should be imported 基本运算 数组上的算术运算符以逐元素方式应用。将创建一个新数组并填充结果。 >>> a = np.array([20, 30, 40, 50]) >>> b = np.arange(4) >>> b array([0, 1, 2, 3]) >>> c = a - b >>> c...
Z = np.arange(9).reshape(3,3) for index, value in np.ndenumerate(Z): print(index, value) for index in np.ndindex(Z.shape): print(index, Z[index]) 56. 构造一个二维高斯矩阵(★★☆) (提示: np.meshgrid, np.exp) X, Y = np.meshgrid(np.linspace(-1, 1, 10), np.linspace(-1...
slope, intercept, r_value, p_value, std_err = stats.linregress(rm, y)print(slope, intercept, r_value, p_value, std_err)print("r-squared:", r_value**2)## 9.102108981180308 -34.670620776438554 0.6953599470715394 2.48722887100781e-74 0.4190265601213402## r-squared: 0.483525455991334 以下代码块绘制...
Index是Pandas中非常重要的数据结构,它是一种类似于元组的不可变序列。Index主要用于对DataFrame和Series中的行和列进行标记和索引,使用索引可以方便地对数据进行筛选、切片等操作。 import pandas as pd import numpy as np s = pd.Series([1, 3, 5, np.nan, 6, 8], index=['a', 'b', 'c', 'd'...
np.set_printoptions(threshold=np.nan)Z = np.zeros((16,16))print(Z) 1. 50.如何找到向量中最接近给定标量的值?(★★☆) Z = np.arange(100)v = np.random.uniform(0,100)index = (np.abs(Z-v)).argmin()print(Z[index]) 1.
def argmax(a, axis=None, out=None, *, keepdims=<no value>): """ Returns the indices of the maximum values along an axis. args: a : array_like Input array. axis : int, optional By default, the index is into the flattened array, otherwise along the specified axis. keepdims : ...
>>> set_printoptions(threshold='nan') 基本运算 数组的算术运算是按元素的。新的数组被创建并且被结果填充。 代码语言:javascript 复制 >>> a = array( [20,30,40,50] ) >>> b = arange( 4 ) >>> b array([0, 1, 2, 3]) >>> c = a-b >>> c array([20, 29, 38, 47]) >>> ...
(...a_2d,axis=0,return_counts=True,return_index=True)>>>print(unique_rows)[[1234][5678][9101112]]>>>print(indices)[012]>>>print(occurrence_count)[211]#改变二维数组形状>>>data.reshape(2,3)array([[1,2,3],[4,5,6]])>>>data.reshape(3,2)array([[1,2],[3,4],[5,6]])>...
(506,)from scipy import statsslope, intercept, r_value, p_value, std_err = stats.linregress(lstat, y)print(slope, intercept, r_value, p_value, std_err)-0.9500493537579909 34.55384087938311 -0.737662726174015 5.081103394387796e-88 0.03873341621263942print("r-squared:", r_value**2)r-squared: ...