aggregate

问题 I have a mixed pd.DataFrame : import pandas as pd import numpy as np df = pd.DataFrame({ 'A' : 1., 'B' : pd.Timestamp('20130102'), 'C' : pd.Timestamp('20180101'), 'D' : np.random.rand(10), 'F' : 'foo' }) df Out[12]: A B C D F 0 1.0 2013-01-02 2018-01-01 0.592533 foo 1 1.0 2013-01-02 2018-01-01 0.819248 foo 2 1.0 2013-01-02 2018-01-01 0.298035 foo 3 1.0 2013-01-02 2018-01-01 0.330128 foo 4 1.0 2013-01-02 2018-01-01 0.371705 foo 5 1.0 2013-01-02 2018-01-01 0.541246 foo 6 1.0 2013-01-02 2018-01

问题 i've been assigned a quite simple project as an exam and i had the idea to develop it using the Domain Driven Design. Many of you might say that the application is so simple that going with repositories and UoW is just a waste of time, and you probably be correct but i think of it as an opportunity to learn something more. The application is a "Flight tickets" system and from the following image you could probably well guess it's functionality. The thing is that i am not sure if i am

问题 I need some help with the aggregate function. Here is my data: (it shows three columns however my understanding is that there is 2 columns, named V1 and V3 respectively. The first column showing numbers starting at 330 are the row.names.) I'd like to do a simple aggregate function like mean on V3 using V1 as the key. I have a feeling my data type is not correct, but I don't know how to convert! > testing V1 V3 330 chr1_10440000_11000000 1.59987556934357 335 chr1_10440000_11000000 89

问题 Please help me to count the number of unique IDs per Date. so, initially, there is this data frame of IDs and dates ID Date 1 2009/11/1 1 2009/11/2 1 2009/11/2 2 2009/11/1 2 2009/11/1 2 2009/11/2 3 2009/11/1 3 2009/11/3 It is possible to rearrange it by date. If we do so then we will see that on the 1st there are 3 unique IDs. On the 2ed 2 unique ID and on the 3rd there is one unique ID. So the final table should look like this: Date uniqueIDs 2009/11/1 3 2009/11/2 2 2009/11/3 1 I know that

问题 Using the R programming language, I am trying to follow this tutorial over here: Count number of observations per day, month and year in R I create data at daily intervals and then took weekly sums of this data. To the "y.week" file, I want to add a "count" column that lists the number of observations in each week. Here is the code below I am using: #load libraries library(xts) library(ggplot2) #create data date_decision_made = seq(as.Date("2014/1/1"), as.Date("2016/1/1"),by="day") date

问题 I am looking at a stackoverflow post over here: R: Count Number of Observations within a group Here, daily data is created and summed/grouped at monthly intervals (as well as weekly intervals): library(xts) library(dplyr) #create data date_decision_made = seq(as.Date("2014/1/1"), as.Date("2016/1/1"),by="day") date_decision_made <- format(as.Date(date_decision_made), "%Y/%m/%d") property_damages_in_dollars <- rnorm(731,100,10) final_data <- data.frame(date_decision_made, property_damages_in

问题 I am looking at a stackoverflow post over here: R: Count Number of Observations within a group Here, daily data is created and summed/grouped at monthly intervals (as well as weekly intervals): library(xts) library(dplyr) #create data date_decision_made = seq(as.Date("2014/1/1"), as.Date("2016/1/1"),by="day") date_decision_made <- format(as.Date(date_decision_made), "%Y/%m/%d") property_damages_in_dollars <- rnorm(731,100,10) final_data <- data.frame(date_decision_made, property_damages_in

问题 I am looking at a stackoverflow post over here: R: Count Number of Observations within a group Here, daily data is created and summed/grouped at monthly intervals (as well as weekly intervals): library(xts) library(dplyr) #create data date_decision_made = seq(as.Date("2014/1/1"), as.Date("2016/1/1"),by="day") date_decision_made <- format(as.Date(date_decision_made), "%Y/%m/%d") property_damages_in_dollars <- rnorm(731,100,10) final_data <- data.frame(date_decision_made, property_damages_in

问题 I have two collections: Users: { _id: ObjectId('5e11d2d8ad9c4b6e05e55b82'), name:"vijay" } Followers : { _id:ObjectId('5ed0c8faac47af698ab9f659'), user_id:ObjectId('5e11d2d8ad9c4b6e05e55b82'), following:[ ObjectId(5ee5ca5fac47af698ab9f666'), ObjectId('5df7c0a66243414ad2663088') ] created_at:"2020-05-29T08:34:02.959+00:00" } I need to list all users who are not in the following array from users table for a particular user, I've come up with the below by writing aggregate function in followers

问题 I have two collections: Users: { _id: ObjectId('5e11d2d8ad9c4b6e05e55b82'), name:"vijay" } Followers : { _id:ObjectId('5ed0c8faac47af698ab9f659'), user_id:ObjectId('5e11d2d8ad9c4b6e05e55b82'), following:[ ObjectId(5ee5ca5fac47af698ab9f666'), ObjectId('5df7c0a66243414ad2663088') ] created_at:"2020-05-29T08:34:02.959+00:00" } I need to list all users who are not in the following array from users table for a particular user, I've come up with the below by writing aggregate function in followers