mean

问题 I want to find the mean, median and mode value for each year given a specific ZIP code how can I achieve this, I already read the data from CSV file and convert it to json file and define it as DataFrame my data sample is not limited to the following table it's larger 回答1: Use SciPy.mstats: In [2295]: df.DATE = pd.to_datetime(df.DATE).dt.year In [2291]: import scipy.stats.mstats as mstats In [2313]: def mode(x): ...: return mstats.mode(x, axis=None)[0] ...: In [2314]: df.groupby(['DATE',

问题 I have data named cluster_1. It has nominal variable from first column to the third. # select the columns based on the clustering results cluster_1 <- mat[which(groups==1),] m_cluster_1 <- mean(cluster_1[c(-(1:3))]) By the last statement, I can get the mean of all columns'. However, what I want is to attach the mean of each variable(column) to the bottom of the column. How can I make it? Please let me know. 回答1: colMeans() will give you the mean of each column in a data frame or matrix. And

问题 I have a data.frame with many rows and columns and I want to calculate the mean difference of each value to each of the other values within a group. Here an example: ID value 1 4 1 5 1 7 2 8 2 6 2 5 2 6 This is what I want to calculate: ID value value_mean_diff 1 4 (4-5)^2 + (4-7)^2 /groupsize = 3 1 5 (5-4)^2 + (5-7)^2 / 3 1 7 (7-4)^2 + (7-5)^2 / 3 2 8 (8-6)^2 + (8-5)^2 + (8-6)^2 / 4 2 6 (6-8)^2 + (6-5)^2 + (6-6)^2 / 4 2 5 (5-8)^2 + (5-6)^2 + (5-6)^2 / 4 2 6 (6-8)^2 + (6-6)^2 + (6-5)^2 / 4 I

问题 The following DF represents events received from users. Id of the user and the timestamp of the event: id timestamp 0 1 2020-09-01 18:14:35 1 1 2020-09-01 18:14:39 2 1 2020-09-01 18:14:40 3 1 2020-09-01 02:09:22 4 1 2020-09-01 02:09:35 5 1 2020-09-01 02:09:53 6 1 2020-09-01 02:09:57 7 2 2020-09-01 18:14:35 8 2 2020-09-01 18:14:39 9 2 2020-09-01 18:14:40 10 2 2020-09-01 02:09:22 11 2 2020-09-01 02:09:35 12 2 2020-09-01 02:09:53 13 2 2020-09-01 02:09:57 I would like to get the average expanding

问题 I have this i x j x k 3d matrix (it's a movie). Without loops, I'm trying to take the mean of the non-zero positive elements in each ixj array and put these values into a 1x1xk matrix. I've been searching for quite a while now, and although there's plenty of solutions to accomplish this for a 2d matrix, I can't for the life of me find a way to do it for a 3d matrix without using a loop. 回答1: What if you convert each image (frame) into an array: % Remove negative and zero elements A(A<=0) = 0;

问题 I want to calculate the mean of all the values in selected columns in a dataframe. For example, I have a dataframe with columns A, B, C, D and E and I want the mean of all the values in columns A, C and E. import pandas as pd df1 = pd.DataFrame( ( {'A': [1,2,3,4,5], 'B': [10,20,30,40,50], 'C': [11,21,31,41,51], 'D': [12,22,32,42,52], 'E': [13,23,33,43,53]} ) ) print( df1 ) print( "Mean of df1:", df1.mean() ) df2 = pd.concat( [df1['A'], df1['C'], df1['E'] ], ignore_index=True ) print( df2 )

问题 I'm building a web application using the MEAN stack. The site contains authentication (using passport.js) so I would like to secure our connection with SSL/TLS. For our deployment we're using nginx as a reverse proxy to the Node app running on the same AWS EC2 instance. My question is: With my setup, what is the best practice way to setup an https (SSL/TLS) connection? Should I get a certificate and set it up at the nginx layer? Should I do it in my node app directly? Is there some other

问题 I got the following dataset: data <- read.table(text=" wake_time sleep_time 08:38:00 23:05:00 09:30:00 00:50:00 06:45:00 22:15:00 07:27:00 23:34:00 09:00:00 23:00:00 09:05:00 00:10:00 06:40:00 23:28:00 10:00:00 23:30:00 08:10:00 00:10:00 08:07:00 00:38:00", header=T) I used the chron-package to calculate the average wake_time: > mean(times(data$wake_time)) [1] 08:20:12 But when I do the same for the variable sleep_time, this happens: > mean(times(data$sleep_time)) [1] 14:04:00 I guess the

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 3 years ago . Improve this question Example of some entries in the data frame: I need to find the mean of this column in the data frame, but can't find the mean as it says: " argument is not numeric or logical: returning NA" The non-numeric entries are dash signs, I have tried converting them

问题 I have a dataframe like below: df = pd.DataFrame({'price': ['480,000,000','477,000,000', '608,700,000', '580,000,000', '350,000,000'], 'sale_date': ['1396/10/30','1396/10/30', '1396/11/01', '1396/11/03', '1396/11/07']}) df Out[7]: price sale_date 0 480,000,000 1396/10/30 1 477,000,000 1396/10/30 2 608,700,000 1396/11/01 3 580,000,000 1396/11/04 4 350,000,000 1396/11/04 So then i define period datetime and resample them by day df['sale_date']=df['sale_date'].str.replace('/','').astype(int) df[