lookup | 易学教程

MongoDB nested lookup with 3 levels

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问题 I need to retrieve the entire single object hierarchy from the database as a JSON. Actually the proposal about any other solution to achive this result would be highly appriciated. I decided to use MongoDB with its $lookup support. So I have three collections: party { "_id" : "2", "name" : "party2" } { "_id" : "5", "name" : "party5" } { "_id" : "4", "name" : "party4" } { "_id" : "1", "name" : "party1" } { "_id" : "3", "name" : "party3" } address { "_id" : "a3", "street" : "Address3", "party

Lookup Tables Best Practices: DB Tables… or Enumerations

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问题 If we have to store the available positions at a company (i.e. Manager, Team Lead, ... etc). What are the best practices for storing it? I have two opinions with comments... "sure, welcoming yours" Storing it as DB table with columns ID and Name, and deal with it using queries and joins. Storing it as Enum and forget about the DB table. In my opinion, I will choose the first solution if I have changing items. So that I won't hard code these options as Enum. I may choose the Enum solution, if

A dictionary object that uses ranges of values for keys

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I have need of a sort of specialized dictionary. My use case is this: The user wants to specify ranges of values (the range could be a single point as well) and assign a value to a particular range. We then want to perform a lookup using a single value as a key. If this single value occurs within one of the ranges then we will return the value associated to the range. For example: // represents the keyed value struct Interval { public int Min; public int Max; } // some code elsewhere in the program var dictionary = new Dictionary<Interval, double>(); dictionary.Add(new Interval { Min = 0, Max

Replace specific values based on another dataframe

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问题 First, let's start with DataFrame 1 (DF1) : DF1 <- data.frame(c("06/19/2016", "06/20/2016", "06/21/2016", "06/22/2016", "06/23/2016", "06/19/2016", "06/20/2016", "06/21/2016", "06/22/2016", "06/23/2016"), c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2), c(149, 150, 151, 152, 155, 84, 83, 80, 81, 97), c(101, 102, 104, 107, 99, 55, 55, 56, 57, 58), c("MTL", "MTL", "MTL", "MTL", "MTL", "NY", "NY", "NY", "NY", "NY")) colnames(DF1) <- c("date", "id", "sales", "cost", "city") I also have DataFrame 2 (DF2) : DF2 <-

Row data for specific column headers based on row ID

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问题 I have a worksheet with a few hundred rows of employee data. At its most basic level it lists EmployeeID 's, Name and lists the skills that they have. What I have done is on another worksheet (in my Employee Lookup workbook) I have used a combination of the index and match functions to look up an EmployeeID that I enter and return the skill level from the employee skills data sheet. What I want to do is try to simplify how the data is obtained (I have limited vba knowledge but some excel

Change values in multiple columns of a dataframe using a lookup table

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问题 I am trying to change the value of a number of columns at once using a lookup table. They all use the same lookup table. I know how to do this for just one column -- I'd just use a merge , but am having trouble with multiple columns. Below is an example dataframe and an example lookup table. My actual data is much larger (~10K columns with 8 rows). example <- data.frame(a = seq(1,5), b = seq(5,1), c=c(1,4,3,2,5)) lookup <- data.frame(number = seq(1,5), letter = LETTERS[seq(1,5)]) Ideally, I

Interpolating data from a look up table

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问题 read the look up table LUT = np.genfromtxt('test.out', delimiter=',', dtype=float) LUT: 12, 25, 136, 6743 13, 26, 139, 6786 14, 27, 142, 6791 15, 28, 145, 6789 Values to be read from the LUT are as follows: x1, x2, x3 = 12.5, 25.5, 137 Reading the neighboring two values in the LUT for each of the given values (3 columns), I have to linearly interpolate the results (4th column in LUT). The given values (x1, x2, x3) belongs to between 1st and 2nd row of the LUT. Based on this how to read the

Dynamically build call for lookup multiple columns

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How can I dynamically lookup multiple fields and add by reference using character vector variable as argument. In below case I want to lookup two columns and get rid of i. prefix in them. Of course they can override already existing columns with the same name. library(data.table) set.seed(1) ID <- data.table(id = 1:3, meta = rep(1,3), key = "id") JN <- data.table(idd = sample(ID$id, 3, FALSE), value = sample(letters, 3, FALSE), meta = rep(1,3), key = "idd") select <- c("value","meta") # my fields to lookup j.lkp <- call(":=", select, lapply(paste0("i.",select), as.symbol)) j.lkp # `:=`(c(

What is the point of Lookup<TKey, TElement>?

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The MSDN explains Lookup like this: A Lookup<TKey, TElement> resembles a Dictionary<TKey, TValue> . The difference is that a Dictionary<TKey, TValue> maps keys to single values, whereas a Lookup<TKey, TElement> maps keys to collections of values. I don't find that explanation particularly helpful. What is Lookup used for? Jon Skeet It's a cross between an IGrouping and a dictionary. It lets you group items together by a key, but then access them via that key in an efficient manner (rather than just iterating over them all, which is what GroupBy lets you do). For example, you could take a load

Vectorized lookup on a pandas dataframe

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I have two DataFrames . . . df1 is a table I need to pull values from using index, column pairs retrieved from multiple columns in df2. I see there is a function get_value which works perfectly when given an index and column value, but when trying to vectorize this function to create a new column I am failing... df1 = pd.DataFrame(np.arange(20).reshape((4, 5))) df1.columns = list('abcde') df1.index = ['cat', 'dog', 'fish', 'bird'] a b c d e cat 0 1 2 3 4 dog 5 6 7 8 9 fish 10 11 12 13 14 bird 15 16 17 18 19 df1.get_value('bird, 'c') 17 Now what I need to do is to create an entire new column on