lookup

问题 1) A new variable should be created for each unique observation listed in variable sku , which contains repeated values. 2) These newly created variables should be assigned the value of own product's price at the store/week level, as long as observations' sku value is in the same subcategory ( subc ) as the variable itself. For example, in eta2,3, observations in line 3, 4, and 5 have the same value because they all belong to the same subcategory as sku #3. [ eta2,3 indicates sku 3, subc 2.]

问题 I have a csv which contains "KKR" map to "MBI" data. I want to perform a lookup from a user given data to extract the longest matched phrase from KKR (ignore small phrase if it contains words of long phrase) #os.chdir("kkr_lookup") data = pd.read_csv("KKR_MBI_MAP.csv") dfData = pd.DataFrame(data) dfVerbatim = pd.DataFrame() dataVerbatim = {'verbatim': ['She experienced skin allergy and hair loss after using it for 2-3 weeks']} dfVerbatim = pd.DataFrame(dataVerbatim, columns = ['verbatim'])

问题 I have an application, that shows list of items bound to a typed model. In the list every item has an ID from a lookup table. How to show the value of the lookup data for each item instead of IDs? The best way has to link a html dropdownlist to each ID, from where I would be able to choose the appropriate value. Thanks in advance Gabrile 回答1: Well the first step is to make sure you get all of the values that will be in the DropDownList. You should be able to easily fetch this from your

问题 I have a data.table ( dt_1 ) that contains measured values for 2 columns ( Observed_A and Observed_B ). I need to use the values from those columns and lookup the nearest values to both columns in a second lookup styled data.table ( dt_2 ) referencing columns Modeled_A and Modeled_B , respectively. The ultimate goal is to then, using the rows that have the nearest matches in dt_2 , select the 3 additional modeled column values in dt_2 ( Variable_1 , Variable_2 and Variable_3 ) and add them

问题 I'm trying to make a tuple list store some information in a particular way. Such as scotland belongs to uk , england belongs to uk , etc. Then take two strings as arguments ( String -> String -> Bool ) to make something like: Main> owns "china" "beijing" True Main> owns "uk" "beijing" False Here's my code: lst = [("uk","scotland"),("uk","england"),("uk","wales"),("china","beijing"),("china","hongkong"),("china","shanghai")] owns :: String -> String -> Bool owns a b = [n|(a,b) <- lst, (n == a)

问题 I have a pandas dataframe constructed from a csv file. I ask the user for two values, separated by a space. I then attempt to do a pandas.loc[,] to return that cell value. How do I match their input to my row columns and return just the raw value of that cell? Impossible in Python? My program works well so far in determining which csv table to look up and how to assign the input to variables. It's the actual passing to the chart lookup that doesn't work. Here is some sample code. import

问题 Here is some code, guys. db.collection('bugs').aggregate([{ $match: finder }, { $sort: { name: 1 } }, { $limit: startrecord + settings.pagination_limit }, { $skip: startrecord }, { $lookup: { from: 'users', localField: 'user', foreignField: '_id', as: 'user' } }], { collation: collation }, function(err, docs) { It works perfectly, it's a plain lookup. However I only need a few fields from the collection "users", and the $lookup returns everything. Is there a way to apply a projection to the

问题 I'm working on data from a population of people with allergies. Each person has a unique ExceptionID, and each allergen has a unique AllergenID (451 in total). I have a data table with 2 columns (ExceptionID and AllergenID), where each person's allergies are listed row by row. This means that the ExceptionID column has repeated values for people with multiple allergies, and the AllergenID column has repeated values for the different people who have that allergy. I am trying to count how many

问题 I'm working on data from a population of people with allergies. Each person has a unique ExceptionID, and each allergen has a unique AllergenID (451 in total). I have a data table with 2 columns (ExceptionID and AllergenID), where each person's allergies are listed row by row. This means that the ExceptionID column has repeated values for people with multiple allergies, and the AllergenID column has repeated values for the different people who have that allergy. I am trying to count how many

问题 I have a code that uses a dataframe to look up a value (P) given it's column label (X): df_1 = pd.DataFrame({'X': [1,2,3,1,1,2,1,3,2,1]}) df_2 = pd.DataFrame({ 1 : [1,2,3,4,1,2,3,4,1,2], 2 : [4,1,2,3,4,1,2,1,2,3], 3 : [2,3,4,1,2,3,4,1,2,5]}) df_1['P'] = df_2 .lookup(df_1.index, df_1['X']) When I give it a label in df_1 but don't include that label in df_2, like this: df_1 = pd.DataFrame({'X': [7,2,3,1,1,2,1,3,2,1]}) I get: KeyError: 'One or more column labels was not found' How can I skip