grouping

问题 I am trying to produce a bar graph that has multiple hierarchical / groupings of factors using d3.js . An example from excel of what I am attempting to create, subgrouped by Variety and Irrigation treatment: I have searched for quite some time and have yet to see an example of something like the example graph above. The closet I found is this (as well as this, this, and this). but they all lack the one more level of grouping that I need. Is there a way to create multiple groupings in d3.js ,

问题 On Hackage I see that groupBy's implementation is this: groupBy :: (a -> a -> Bool) -> [a] -> [[a]] groupBy _ [] = [] groupBy eq (x:xs) = (x:ys) : groupBy eq zs where (ys,zs) = span (eq x) xs which means that the preticate eq holds between any two elements of each group . Examples: > difference_eq_1 = ((==1).) . flip (-) > first_isnt_newline = ((/= '\n').) . const > > Data.List.groupBy difference_eq_1 ([1..10] ++ [11,13..21]) [[1,2],[3,4],[5,6],[7,8],[9,10],[11],[13],[15],[17],[19],[21]] > >

问题 Here is my JavaScript object. I want to have a method to dynamically generate an object grouped by one or more attributes. The parameter attrs is an array, which contains some attributes for grouping. var input= [ {fistname:'Joe', age:'10', sex:'boy', class:'3'}, {fistname:'Tom', age:'11', sex:'boy', class:'3'}, {fistname:'Amily', age:'10', sex:'girl', class:'3'}, {fistname:'Bob', age:'11', sex:'boy',class:'4'}, {fistname:'Susan', age:'12', sex:'girl', class:'4'} ] var attrs = ['age', 'class'

问题 I am grouping and partioning a stream as follows: // Partioning Map<Boolean, List<Person>> partitioned = persons.stream(). collect(Collectors.partitioningBy(p -> p.getAge() > 20)); // Grouping Map<String, List<Person>> grouped = persons.stream() .collect(Collectors.groupingBy(p -> p.getCity())); Is there a way i can combine both of these? I tried combining both with using groupingBy inside partioningBy, but did not get the things right. Any suggestion? The expected result is the partition the

问题 Here's my input file : 1.37987 1.21448 0.624999 1.28966 1.77084 1.088 1.41667 I would like to create bins of a size of my choice to get histogram-like output, e.g. something like this for 0.1 bins, starting from 0 : 0 0.1 0 ... 0.5 0.6 0 0.6 0.7 1 ... 1.0 1.1 1 1.1 1.2 0 1.2 1.3 2 1.3 1.4 1 ... My file is too big for R, so I'm looking for an awk solution (also open to anything else that I can understand, as I'm still a Linux beginner). This was sort of already answered in this post : awk

问题 I'm trying to calculate some summary information to help me check for outliers in different groups in a dataset. I can get the sort of output I want using dplyr::group_by() and dplyr::summarise() - a dataframe with summary information for each group for a given variable. Something like this: Sepal.Length_outlier_check <- iris %>% dplyr::group_by(Species) %>% dplyr::summarise(min = min(Sepal.Length, na.rm = TRUE), max = max(Sepal.Length, na.rm = TRUE), median = median(Sepal.Length, na.rm =

问题 I have a large dataframe (from 500k to 1M rows) which contains for example these 3 numeric columns: ID, A, B I want to filter the results in order to obtain a table like the one in the image below, where, for each unique value of column id, i have the maximum and minimum value of A and B. How can i do? EDIT: i have updated the image below in order to be more clear: when i get the max or min from a column i need to get also the data associated to it of the others columns 回答1: Sample data (note

问题 I have this code: from itertools import groupby from itertools import combinations teams = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] combo = list(combinations(teams, 2)) The output is a list of 45 tuples. [(1, 2), (1, 3), (1, 4), (1, 5), (1, 6), (1, 7), (1, 8), (1, 9), (1, 10), (2, 3), (2, 4), (2, 5), (2, 6), (2, 7), (2, 8), (2, 9), (2, 10), (3, 4), (3, 5), (3, 6), (3, 7), (3, 8), (3, 9), (3, 10), (4, 5), (4, 6), (4, 7), (4, 8), (4, 9), (4, 10), (5, 6), (5, 7), (5, 8), (5, 9), (5, 10), (6, 7), (6, 8),

问题 Say I have this dataframe d = { 'Path' : ['abc', 'abc', 'ghi','ghi', 'jkl','jkl'], 'Detail' : ['foo', 'bar', 'bar','foo','foo','foo'], 'Program': ['prog1','prog1','prog1','prog2','prog3','prog3'], 'Value' : [30, 20, 10, 40, 40, 50], 'Field' : [50, 70, 10, 20, 30, 30] } df = DataFrame(d) df.set_index(['Path', 'Detail'], inplace=True) df Field Program Value Path Detail abc foo 50 prog1 30 bar 70 prog1 20 ghi bar 10 prog1 10 foo 20 prog2 40 jkl foo 30 prog3 40 foo 30 prog3 50 I can aggregate it

问题 Say I have this dataframe d = { 'Path' : ['abc', 'abc', 'ghi','ghi', 'jkl','jkl'], 'Detail' : ['foo', 'bar', 'bar','foo','foo','foo'], 'Program': ['prog1','prog1','prog1','prog2','prog3','prog3'], 'Value' : [30, 20, 10, 40, 40, 50], 'Field' : [50, 70, 10, 20, 30, 30] } df = DataFrame(d) df.set_index(['Path', 'Detail'], inplace=True) df Field Program Value Path Detail abc foo 50 prog1 30 bar 70 prog1 20 ghi bar 10 prog1 10 foo 20 prog2 40 jkl foo 30 prog3 40 foo 30 prog3 50 I can aggregate it