filtering

问题 I can see that the Android Bacon Library features two algorithms for measuring distance: the running average filter and the ARMA filter. How are these related to the library's implementation (apart from using the filter's formula)? Where can I find some background information about these that explains the theory behind it? Are there any known alternative algorithms that can be studied and tried out for measuring the distance? 回答1: There are two basic steps to giving a distance estimate on BLE

问题 I have a database as the following: And I would like to obtain a pandas dataframe filtered for the 2 rows per date, based on the top ones that have the highest population. The output should look like this: I know that pandas offers a formula called nlargest: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.nlargest.html but I don't think it is usable for this use case. Is there any workaround? Thanks so much in advance! 回答1: I have mimicked your dataframe as below

问题 I need to wite a code to perform a 3D convolution in python using numpy, with 3x3 kernels. I've done it right for 2D arrays like B&W images but when i try to extend it to 3D arrays like RGB is a mess. I need help to improve my method. Here is the 2D code: def convolucion_3x3(arreglo, kernel): (dim_x, dim_y) = arreglo.shape (ker_x, ker_y) = kernel.shape matriz_convolucionada = np.zeros((dim_x, dim_y)) for i in range(dim_x): for j in range(dim_y): resultado = 0 for x in range(-1, 2): try: if i

问题 I would like to filter journalists on the basis of the categories they write about (e.g. Sports, Music). When the user selects multiple categories, e.g. sports and music, it should show only the journalist which are writing about Sports AND Music. Currently, I have only managed to filter on journalists who are writing about Sports AND/OR Music. The categories selected by the user are represented in an array: And the combination journalists and category combination are defined in a linking

问题 The following code gives me Value Error: major_males=[] for row in recent_grads: if recent_grads['Men']>recent_grads['Women']: major_males.append(recent_grads['Major']) display(major_males) ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all() 回答1: If recent_grads is a dataframe, then this is how your for loop would look like major_males=[] for i, row in recent_grads.iterrows(): if row['Men']>row['Women']: major_males.append(row['Major'])

问题 Closed. This question does not meet Stack Overflow guidelines. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 1 year ago . Improve this question I want to use anisotropic diffusion on 2d images. I'd like to use python but don't mind using matlab or c. Are their any libraries I could use as a first step? I did a google search on the subject and found Panda3D and OpenGl. Basically I want to give a set of

问题 Closed. This question does not meet Stack Overflow guidelines. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 1 year ago . Improve this question I want to use anisotropic diffusion on 2d images. I'd like to use python but don't mind using matlab or c. Are their any libraries I could use as a first step? I did a google search on the subject and found Panda3D and OpenGl. Basically I want to give a set of

问题 I have a data.table that I am wanting to aggregate library(data.table) dt1 <- data.table(year=c("2001","2001","2001","2002","2002","2002","2002"), group=c("a","a","b","a","a","b","b"), amt=c(20,40,20,35,30,28,19)) I am wanting to sum the amt by year and group and then filter where the summed amt for any given group is greater than 100. I've got the data.table sum nailed. dt1[, sum(amt),by=list(year,group)] year group V1 1: 2001 a 60 2: 2001 b 20 3: 2002 a 65 4: 2002 b 47 I am having trouble

问题 On a pandas dataframe, I know I can groupby on one or more columns and then filter values that occur more/less than a given number. But I want to do this on every column on the dataframe. I want to remove values that are too infrequent (let's say that occur less than 5% of times) or too frequent. As an example, consider a dataframe with following columns: city of origin, city of destination, distance, type of transport (air/car/foot), time of day, price-interval . import pandas as pd import

问题 On a pandas dataframe, I know I can groupby on one or more columns and then filter values that occur more/less than a given number. But I want to do this on every column on the dataframe. I want to remove values that are too infrequent (let's say that occur less than 5% of times) or too frequent. As an example, consider a dataframe with following columns: city of origin, city of destination, distance, type of transport (air/car/foot), time of day, price-interval . import pandas as pd import