mean

Pandas DataFrame: mean of column B values within column A windows

由 孤者浪人 提交于 2020-07-07 06:45:11
问题 If I have a pandas DataFrame in Python such as follows: import numpy as np import pandas as pd a = np.random.uniform(0,10,20) b = np.random.uniform(0,1,20) data = np.vstack([a,b]).T df = pd.DataFrame(data) df.columns = ['A','B'] df.sort_values(by=['A']) A B 5 0.057519 0.465408 14 1.610972 0.398077 3 1.725556 0.397708 17 1.734124 0.600723 11 1.944105 0.694152 19 3.265799 0.878538 13 3.352460 0.770505 10 3.865299 0.064723 16 4.137863 0.659662 12 5.597172 0.122269 7 5.990105 0.667533 6 6.410582

Counting average on list<T> field

由 丶灬走出姿态 提交于 2020-06-27 06:51:30
问题 I have list of A, and I want to count average on it's field a. What's the best way to do it? class A { int a; int b; } void f() { var L = new List<A>(); for (int i=0; i<3; i++) { L.Add(new A(){a = i}); } } 回答1: Enumerable.Average has an overload that takes a Func<T, int> as an argument. using System.Linq; list.Average(item => item.a); 回答2: You could try this one: var average = ListOfA.Select(x=>x.a).Average(); where ListOfA is a List of objects of type A . 回答3: You can use Enumerable.Average

Add GroupBy mean result as a new column in pandas

由 点点圈 提交于 2020-06-23 08:25:27
问题 I have a dataframe that gives upper and lower values of each indicator as follows df = pd.DataFrame( {'indicator': ['indicator 1', 'indicator 1', 'indicator 2', 'indicator 2'], 'year':[2014,2014,2015,2015], 'value type': ['upper', 'lower', 'upper', 'lower'], 'value':[12.3, 10.2, 15.4, 13.2] }, index=[1,2,3,4]) I want to remove the upper and lower values and replace that with the mean of two values. How can I do that? 回答1: You could groupby and transform by mean . df['value'] = df.groupby(

Getting error suddenly in Angular Error: NodeInjector: NOT_FOUND [ControlContainer]

由 淺唱寂寞╮ 提交于 2020-06-12 04:29:42
问题 core.js:5873 ERROR Error: NodeInjector: NOT_FOUND [ControlContainer]. Sometime when i restart the project it runs perfectly. there are only changes in app.component.html : <div class="container"> <div class="row"> <div class="col-md-4"> <form action=""> <div class="form-group"> <label for="">Username</label> <input type="text" name="username" class="form-control" /> </div> <div class="form-group"> <label>Password</label> <input type="password" class="form-control"> </div> <div class="form

Finding mean and standard deviation across image channels PyTorch

由 对着背影说爱祢 提交于 2020-05-27 04:41:06
问题 Say I have a batch of images in the form of tensors with dimensions (B x C x W x H) where B is the batch size, C is the number of channels in the image, and W and H are the width and height of the image respectively. I'm looking to use the transforms.Normalize() function to normalize my images with respect to the mean and standard deviation of the dataset across the C image channels , meaning that I want a resulting tensor in the form 1 x C. Is there a straightforward way to do this? I tried

Calculate mean and other excluding certain values [duplicate]

由 China☆狼群 提交于 2020-05-09 07:48:27
问题 This question already has an answer here : Calculate mean, median by excluding any given number (1 answer) Closed 2 years ago . I have a date frame ("daten"), in which most columns are of numeric value. They typically range from 0 to 5. However, they can also take on the value 99. I want to calculate the mean of the columns, excluding only the values 99. For example: > mean(c(0, 1, 2, 3, 4, 5, 99)) [1] 16.28571 is not what I need, instead I want it to be calculated as if the vector was > mean

Mean over multiple axis in NumPy

由 戏子无情 提交于 2020-04-13 05:40:59
问题 I Want to write the code below as Pythonic way, applying mean over two axis. What the best way to do this? import numpy as np m = np.random.rand(30, 10, 10) m_mean = np.zeros((30, 1)) for j in range(30): m_mean[j, 0] = m[j, :, :].mean() 回答1: If you have a sufficiently recent NumPy, you can do m_mean = m.mean(axis=(1, 2)) I believe this was introduced in 1.7, though I'm not sure. The documentation was only updated to reflect this in 1.10, but it worked earlier than that. If your NumPy is too

压力测试相关之ab命令

由 拜拜、爱过 提交于 2020-04-08 04:03:34
1. 短时压力测试工具 ab 命令（apache的工具） 关键指标： Requests per second: 98.52 [#/sec] (mean) ###平均每秒的请求数 Time per request: 30449.217 [ms] (mean) ###平均每个请求消耗的时间 Time per request: 10.150 [ms] (mean, across all concurrent requests) ###上面的请求除以并发数 参数含义 -n在测试会话中所执行的请求个数。默认时，仅执行一个请求。 -c一次产生的请求个数。默认是一次一个。 -t测试所进行的最大秒数。其内部隐含值是-n 50000，它可以使对服务器的测试限制在一个固定的总时间以内 -p包含了需要POST的数据的文件。 ab -n 100 -c 100 -p '/root/data.txt' -T 'application/x-www-form-urlencoded' 'http://192.168.1.17:8080/obu-interface/servlet/ObuXmlServlet' 来源： https://www.cnblogs.com/oftenlin/p/4921136.html

task5 模型融合 打卡

由 不想你离开。 提交于 2020-04-05 17:13:25
5.1 模型融合目标 对于多种调参完成的模型进行模型融合。 完成对于多种模型的融合，提交融合结果并打卡。 5.2 内容介绍 模型融合是比赛后期一个重要的环节，大体来说有如下的类型方式。 简单加权融合: 回归（分类概率）：算术平均融合（Arithmetic mean），几何平均融合（Geometric mean）； 分类：投票（Voting) 综合：排序融合(Rank averaging)，log融合 stacking/blending: 构建多层模型，并利用预测结果再拟合预测。 boosting/bagging（在xgboost，Adaboost,GBDT中已经用到）: 多树的提升方法 5.3 Stacking相关理论介绍 什么是 stacking 简单来说 stacking 就是当用初始训练数据学习出若干个基学习器后，将这几个学习器的预测结果作为新的训练集，来学习一个新的学习器。 将个体学习器结合在一起的时候使用的方法叫做结合策略。对于分类问题，我们可以使用投票法来选择输出最多的类。对于回归问题，我们可以将分类器输出的结果求平均值。 上面说的投票法和平均法都是很有效的结合策略，还有一种结合策略是使用另外一个机器学习算法来将个体机器学习器的结果结合在一起，这个方法就是Stacking。 在stacking方法中，我们把个体学习器叫做初级学习器

The mean shift clustering algorithm

由 最后都变了- 提交于 2020-03-20 03:33:20
The mean shift clustering algorithm MEAN SHIFT CLUSTERING Mean shift clustering is a general non-parametric cluster finding procedure — introduced by Fukunaga and Hostetler [ 1 ], and popular within the computer vision field. Nicely, and in contrast to the more-well-known K-means clustering algorithm, the output of mean shift does not depend on any explicit assumptions on the shape of the point distribution, the number of clusters, or any form of random initialization. We describe the mean shift algorithm in some detail in the technical background section at the end of this post. However, its
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