numpy | 易学教程

Plotting stochastic processes in Python

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问题 Say I have a stochastic process defined between [0... N] , e.g. N=50 . For every location, I have several samples (e.g. m=100 samples) (representing my sampling distribution at each location). One way to look at this is as a numpy 2D array of size (m,N) . How can I plot this intuitively in matplotlib ? One possibility is to plot the process as a 1D plot along with an envelope of varying thickness and shade that captures the density of these distributions, something along the lines of what I

Merging two DataFrames

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问题 I have 2 DataFrames which I would like to merge. I have looked at the documentation and tried to perform the following operation but an getting confused as to how to do it. Like I said I have 2 DataFrames : df1: id name type currency 0 BTA.S Applewood Hard GBp 1 VOD.S Softwood Soft GBp and df2: id BTA.S 301.221525 VOD.S 213.791400 and I would like to return: id name type currency price 0 BTA.S Applewood Hard GBp 301.221525 1 VOD.S Softwood Soft GBp 213.791400 Where the price column from the

Merging two DataFrames

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Merging two DataFrames

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Merging two DataFrames

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Numpy-vectorized function to repeat blocks of consecutive elements

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问题 Numpy has а repeat function, that repeats each element of the array a given (per element) number of times. I want to implement a function that does similar thing but repeats not individual elements, but variably sized blocks of consecutive elements. Essentially I want the following function: import numpy as np def repeat_blocks(a, sizes, repeats): b = [] start = 0 for i, size in enumerate(sizes): end = start + size b.extend([a[start:end]] * repeats[i]) start = end return np.concatenate(b) For

efficient per column matrix indexing in numpy

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问题 I have two matrices of the same size, A, B. I want to use the columns of B to acsses the columns of A, on a per column basis. For example, A = np.array([[1, 4, 7], [2, 5, 8], [3, 6, 9]]) and B = np.array([[0, 0, 2], [1, 2, 1], [2, 1, 0]]) I want something like: A[B] = [[1, 4, 9], [2, 6, 8], [3, 5, 7]] I.e., I've used the j'th column of B as indices to the j'th column of A. Is there any effiecnt way of doing so? Thanks! 回答1: You can use advanced indexing: A[B, np.arange(A.shape[0])] array([[1,

Get indices of matches from one array in another

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问题 Given two np.arrays; a = np.array([1, 6, 5, 3, 8, 345, 34, 6, 2, 867]) b = np.array([867, 8, 34, 75]) I would like to get an np.array with same dimensions as b, where each value is the index where the value in b appears in a, or np.nan if the value in b is not present in a. The result should be; [9, 4, 6, nan] a and b will always have the same number of dimensions, but the sizes of the dimensions may differ. something like; (pseudo code) c = np.where(b in a) but which works for arrays ("in"

机器学习--matplotlib绘制各种图表

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机器学习三剑客：numpy、pandas、matplotlib NumPy系统是Python的一种开源的数值计算扩展。这种工具可用来存储和处理大型矩阵。 pandas 是基于numpy的一种工具，该工具是为了解决数据分析任务而创建的。 Matplotlib 是一个 Python 的 2D绘图库，它以各种硬拷贝格式和跨平台的交互式环境生成出版质量级别的图形。柱状图bar from matplotlib import pyplot as plt import matplotlib # 显示图表，仅限于jupyter使用 % matplotlib inline # 指定默认字体 matplotlib.rcParams[ ' font.sans-serif ' ] = [ ' SimHei ' ] # 第一个参数：索引 # 第二个参数：高度参数必须对应否则报错 plt.bar(range(5),[100,200,300,400,500],color= ' red ' ) plt.xticks(range( 5),[ ' A ' , ' B ' , ' C ' , ' D ' , ' E ' ]) plt.xlabel( ' 姓名 ' ) plt.ylabel( ' 得分 ' ) plt.title( ' 学生得分 ' ) # 或显示图标Plt.show() 饼图pie labels =

opencv 机器视觉学习笔记

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import numpy as np import cv2,os # img = numpy.zeros((3,3),dtype=numpy.uint8) # print(img) # print(img.shape) # img = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR) # print("=============",img) #修改图片格式 # img = cv2.imread("1123.jpg") # cv2.imwrite("1123.png",img) # # grayimg = cv2.imread('1123.png',cv2.IMREAD_GRAYSCALE) # cv2.imwrite("11231.png",grayimg) # # print(grayimg[0,0]) # print(grayimg.item((0,0))) # #grayimg.setitem((0,0),108) # #grayimg[0,0] =128 # # byteimage = bytearray(grayimg) # print(byteimage) #将含有随机字节的bytearray转换为灰度图像和BGR图像 # randombytearray = bytearray(os.urandom(120000)) #