In Step 1, I find what type of data exists in Database.
In step 2, I retrieve all data from Database and try to store into arrays of varying sizes
1. Accessing data from MongoDB
mong <- mongo(collection = "mycollection", db = "dbname", url = "mongodb://localhost")
agg_df <- mong$aggregate('[{ "$group" :
{ "_id" : "$tagname",
"number_records" : { "$sum" : 1}
}
}]')
print(agg_df)
OUTPUT:
_id number_records
1 raees 100
2 DearZindagi 100
3 FAN 100
4 DDD 21
NOTE: Step 1 output indicates that there are 4 types of categories with records of 100,100,100,21 each.
2. From STEP 1, I need to create 4 arrays consisting of 1 column and varying nos. of rows(100,100,100,21) and give names to those array as Raees,DearZindagi,FAN,DDD
Dataset <- mong$find('{}','{"text":1}')
Dataset$text <- sapply(Dataset$text,function(row) iconv(row, "latin1", "ASCII", sub=""))
typeof(Dataset$text)
> [1] "character"
3. The arrays and their sizes(in rows) to be created is dependent on output of Step 1. There would never be a case where the output of step 1 would be more than 15 rows.
How should i do this?.
The split function would splits the Dataset into arrays, how shall i rename these arrays:
rows <- nrow(agg_df)
for (i in 1:rows){
array<- split(Dataset$text, rep(1:rows, c(agg_df[i,2])))
}
Related
I am trying to count rows in a table based on multiple criteria in different columns of that table. The criteria are not directly in the formula though; they are arrays which I would like to refer to (and not list them in the formula directly).
Range table example:
Group Status
a 1
b 4
b 6
c 4
a 6
d 5
d 4
a 2
b 2
d 3
b 2
c 1
c 2
c 1
a 4
b 3
Criteria/arrays example:
group
a
b
status
1
2
3
I am able to do this if i only have one array search through a range (1 column) in that table:
{=SUM(COUNTIFS(data[Group],group[group]))}
Returns "9" as expected (=sum of rows in the group column of the data table which match any values in group[group])
But if I add a second different range and a different array I get an incorrect result:
{=SUM(COUNTIFS(data[Group],group[group], data[Status],status[status]))}
Returns "3" but should return "5" (=sum of rows which have 1, 2 or 3 in the status column and a or b in the group column)
I searched and googled for various ideas related to using sumproduct or defining arrays within the formula instead of classifying the whole formula as an array but I was not able to get expected results via those means.
Thank you for your help.
Because your group and status criteria are a different number of values (2 values for group, but 3 values for status), I'm not sure you can do this in a single formula. Best way I know of to do this would be to use a helper column (which can be hidden if preferred).
Put this array formula in a helper column and copy down the length of your data (array formulas must be confirmed with Ctrl+Shift+Enter):
=AND(OR(data[#Group]=group[group]),OR(data[#Status]=status[status]))
And then get the count with: =COUNTIF(helpercolumn,TRUE)
You could use a slightly different approach, using Power Query / Power Pivot.
Name your tables Data, Group and Status, then create the following query, named Filtered Data:
let
tbData = Excel.CurrentWorkbook(){[Name="Data"]}[Content],
tbGroup = Excel.CurrentWorkbook(){[Name="Group"]}[Content],
tbStatus = Excel.CurrentWorkbook(){[Name="Status"]}[Content],
#"Merged Group" = Table.NestedJoin(tbData,{"Group"},tbGroup,{"Group"},"tbGroup",JoinKind.Inner),
#"Merged Status" = Table.NestedJoin(#"Merged Group",{"Status"},tbStatus,{"Status"},"Merged Status",JoinKind.Inner),
#"Removed Columns" = Table.RemoveColumns(#"Merged Status",{"tbGroup", "Merged Status"}),
#"Changed Type" = Table.TransformColumnTypes(#"Removed Columns",{{"Status", type number}})
in
#"Changed Type"
Load To as connection only, and tick Load to Data Model
Now create a DAX measure:
Status Sum:=SUM ( 'Filtered Data'[Status] )
You can then use the following formula on your worksheet, to get the Sum of Status values, for rows matching the criteria specified in the Group and Status tables:
=CUBEVALUE("ThisWorkbookDataModel","[Measures].[Status Sum]")
Simply refresh the data connection to update the value.
Need to further prep my data set in order to apply apriori algorithm
There are only two columns:
First column as the transaction_id.
Second column is item_name and is formatted as c("" "a" "b" "c"...)
I run:
rules <- apriori(nz.mb, parameter = list(supp = 0.001, conf = 0.8))
I get an error:
Error in asMethod(object) :
column(s) 2 not logical or a factor. Discretize the columns first.
So I run:
nz.mb$item_name <- discretize(nz.mb$item_name)
I get another error:
Error in min(x, na.rm = TRUE) : invalid 'type' (list) of argument
What is my next step with item_name so that's it's formatted correctly for apriori?
Most Apriori implementation support Dataset like this:
a b c d
1 1 1 0 means a,b,c are there
1 0 0 1 means a,d are there
Either use this form or go to documentation and say the supported data for
I want to be able to create n-dimensional dataframes. I've heard of a method for 3D dataframes using panels in pandas but, if possible, I would like to extend the dimensions past 3 dims by combining different datasets into a super dataframe
I tried this but I cannot figure out how to use these methods with my test dataset ->
Constructing 3D Pandas DataFrame
Also, this did not help for my case -> Pandas Dataframe or Panel to 3d numpy array
I made a random test dataset with arbitrary axis data trying to mimic a real situation; there are 3 axis (i.e. patients, years, and samples). I tried adding a bunch of dataframes to a list and then making a dataframe with that but it didn't work :( I even tried a panel as in the 2nd link above but I couldn't get it to work either.
Does anybody know how to create a N-dimensional pandas dataframe w/ labels?
The first way I tried:
#Reproducibility
np.random.seed(1618033)
#Set 3 axis labels/dims
axis_1 = np.arange(2000,2010) #Years
axis_2 = np.arange(0,20) #Samples
axis_3 = np.array(["patient_%d" % i for i in range(0,3)]) #Patients
#Create random 3D array to simulate data from dims above
A_3D = np.random.random((years.size, samples.size, len(patients))) #(10, 20, 3)
#Create empty list to store 2D dataframes (axis_2=rows, axis_3=columns) along axis_1
list_of_dataframes=[]
#Iterate through all of the year indices
for i in range(axis_1.size):
#Create dataframe of (samples, patients)
DF_slice = pd.DataFrame(A_3D[i,:,:],index=axis_2,columns=axis_3)
list_of_dataframes.append(DF_slice)
# print(DF_slice) #preview of the 2D dataframes "slice" of the 3D array
# patient_0 patient_1 patient_2
# 0 0.727753 0.154701 0.205916
# 1 0.796355 0.597207 0.897153
# 2 0.603955 0.469707 0.580368
# 3 0.365432 0.852758 0.293725
# 4 0.906906 0.355509 0.994513
# 5 0.576911 0.336848 0.265967
# ...
# 19 0.583495 0.400417 0.020099
# DF_3D = pd.DataFrame(list_of_dataframes,index=axis_2, columns=axis_1)
# Error
# Shape of passed values is (1, 10), indices imply (10, 20)
2nd way I tried:
DF = pd.DataFrame(axis_3,columns=axis_2)
#Error:
#Shape of passed values is (1, 3), indices imply (20, 3)
# p={}
# for i in axis_1:
# p[i]=DF
# panel= pd.Panel(p)
I could do something like this I guess, but I really like pandas and would rather use one of their methods if one exists:
#Set data for query
query_year = 2007
query_sample = 15
query_patient = "patient_1"
#Index based on query
A_3D[
(axis_1 == query_year).argmax(),
(axis_2 == query_sample).argmax(),
(axis_3 == query_patient).argmax()
]
#0.1231212416981845
It would be awesome to access the data in this way:
DF_3D[query_year][query_sample][query_patient]
#Where DF_3D[query_year] would give a list of 2D arrays (row=sample, col=patient)
# DF_3D[query_year][query_sample] would give a 1D vector/list of patient data for a particular year, of a particular sample.
# and DF_3D[query_year][query_sample][query_patient] would be a particular sample of a particular patient of a particular year
Rather than using an n-dimensional Panel, you are probably better off using a two dimensional representation of data, but using MultiIndexes for the index, column or both.
For example:
np.random.seed(1618033)
#Set 3 axis labels/dims
years = np.arange(2000,2010) #Years
samples = np.arange(0,20) #Samples
patients = np.array(["patient_%d" % i for i in range(0,3)]) #Patients
#Create random 3D array to simulate data from dims above
A_3D = np.random.random((years.size, samples.size, len(patients))) #(10, 20, 3)
# Create the MultiIndex from years, samples and patients.
midx = pd.MultiIndex.from_product([years, samples, patients])
# Create sample data for each patient, and add the MultiIndex.
patient_data = pd.DataFrame(np.random.randn(len(midx), 3), index = midx)
>>> patient_data.head()
0 1 2
2000 0 patient_0 -0.128005 0.371413 -0.078591
patient_1 -0.378728 -2.003226 -0.024424
patient_2 1.339083 0.408708 1.724094
1 patient_0 -0.997879 -0.251789 -0.976275
patient_1 0.131380 -0.901092 1.456144
Once you have data in this form, it is relatively easy to juggle it around. For example:
>>> patient_data.unstack(level=0).head() # Years.
0 ... 2
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 ... 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
0 patient_0 -0.128005 0.051558 1.251120 0.666061 -1.048103 0.259231 1.535370 0.156281 -0.609149 0.360219 ... -0.078591 -2.305314 -2.253770 0.865997 0.458720 1.479144 -0.214834 -0.791904 0.800452 0.235016
patient_1 -0.378728 -0.117470 -0.306892 0.810256 2.702960 -0.748132 -1.449984 -0.195038 1.151445 0.301487 ... -0.024424 0.114843 0.143700 1.732072 0.602326 1.465946 -1.215020 0.648420 0.844932 -1.261558
patient_2 1.339083 -0.915771 0.246077 0.820608 -0.935617 -0.449514 -1.105256 -0.051772 -0.671971 0.213349 ... 1.724094 0.835418 0.000819 1.149556 -0.318513 -0.450519 -0.694412 -1.535343 1.035295 0.627757
1 patient_0 -0.997879 -0.242597 1.028464 2.093807 1.380361 0.691210 -2.420800 1.593001 0.925579 0.540447 ... -0.976275 1.928454 -0.626332 -0.049824 -0.912860 0.225834 0.277991 0.326982 -0.520260 0.788685
patient_1 0.131380 0.398155 -1.671873 -1.329554 -0.298208 -0.525148 0.897745 -0.125233 -0.450068 -0.688240 ... 1.456144 -0.503815 -1.329334 0.475751 -0.201466 0.604806 -0.640869 -1.381123 0.524899 0.041983
In order to select the data, please refere to the docs for MultiIndexing.
You should consider using xarray instead. From their documentation:
Panel, pandas’ data structure for 3D arrays, was always a second class data structure compared to the Series and DataFrame. To allow pandas developers to focus more on its core functionality built around the DataFrame, pandas removed Panel in favor of directing users who use multi-dimensional arrays to xarray.
An alternative approach (to Alexander) that is derived from the structure of the input data is:
np.random.seed(1618033)
#Set 3 axis labels/dims
years = np.arange(2000,2010) #Years
samples = np.arange(0,20) #Samples
patients = np.array(["patient_%d" % i for i in range(0,3)]) #Patients
#Create random 3D array to simulate data from dims above
A_3D = np.random.random((years.size, samples.size, len(patients))) #(10, 20, 3)
# Reshape data to 2 dimensions
maj_dim = 1
for dim in A_3D.shape[:-1]:
maj_dim = maj_dim*dim
new_dims = (maj_dim, A_3D.shape[-1])
A_3D = A_3D.reshape(new_dims)
# Create the MultiIndex from years, samples and patients.
midx = pd.MultiIndex.from_product([years, samples])
# Note that Cartesian product order is the same as the
# C-order used by default in ``reshape``.
# Create sample data for each patient, and add the MultiIndex.
patient_data = pd.DataFrame(data = A_3D,
index = midx,
columns = patients)
>>>> patient_data.head()
patient_0 patient_1 patient_2
2000 0 0.727753 0.154701 0.205916
1 0.796355 0.597207 0.897153
2 0.603955 0.469707 0.580368
3 0.365432 0.852758 0.293725
4 0.906906 0.355509 0.994513
I have a 4016 x 4 cell, called 'totalSalesCell'. The first two columns contain text the remaining two are numeric.
1st field CompanyName
2nd field UniqueID
3rd field NumberItems
4th field TotalValue
In my code I have a loop which goes over the last month in weekly steps - i.e. 4 loops.
At each loop my code returns a cell of the same structure as totalSalesCell, called weeklySalesCell which generally contains a different number of rows to totalSalesCell.
There are two things I need to do. First if weeklySalesCell contains a company that is not in totalSalesCell it needs to be added to totalSalesCell, which I believe the code below will do for me.
co_list = unique([totalSalesCell(:, 1); weeklySalesCell (:, 1)]);
index = ismember(co_list, totalSalesCell(:, 1));
new_co = co_list(index==0, :);
totalSalesCell = [totalSalesCell; new_co];
The second thing I need to do and am unsure of the best way of going about it is to then add the weeklySalesCell numeric fields to the totalSalesCell. As mentioned the cells will 90% of the time have different row numbers so cannot apply a simple addition. Below is an example of what I wish to achieve.
totalSalesCell weeklySalesCell Result
co_id sales_value co_id sales_value co_id sales_value
23DFG 5 DGH84 3 23DFG 5
DGH84 6 ABC33 1 DGH84 9
12345 7 PLM78 4 ABC33 1
PLM78 4 12345 3 12345 10
KLH11 11 PLM78 8
KLH11 11
I believe the following codes must take care of both of your tasks -
[x1,x2] = ismember(totalSalesCell(:,1),weeklySalesCell(:,1))
corr_c2 = nonzeros(x1.*x2)
newval = cell2mat(totalSalesCell(x1,2)) + cell2mat(weeklySalesCell(corr_c2,2))
totalSalesCell(x1,2) = num2cell(newval)
excl_c2 = ~ismember(weeklySalesCell(:,1),totalSalesCell(:,1))
out = vertcat(totalSalesCell,weeklySalesCell(excl_c2,:)) %// desired output
Output -
out =
'23DFG' [ 5]
'DGH8444' [ 9]
'12345' [10]
'PLM78' [ 8]
'KLH11' [11]
'ABC33' [ 1]
I have a struct mpc with the following structure:
num type col3 col4 ...
mpc.bus = 1 2 ... ...
2 2 ... ...
3 1 ... ...
4 3 ... ...
5 1 ... ...
10 2 ... ...
99 1 ... ...
to from col3 col4 ...
mpc.branch = 1 2 ... ...
1 3 ... ...
2 4 ... ...
10 5 ... ...
10 99 ... ...
What I need to do is:
1: Re-order the rows of mpc.bus, such that all rows of type 1 are first, followed by 2 and at last, 3. There is only one element of type 3, and no other types (4 / 5 etc.).
2: Make the numbering (column 1 of mpc.bus, consecutive, starting at 1.
3: Change the numbers in the to-from columns of mpc.branch, to correspond to the new numbering in mpc.bus.
4: After running simulations, reverse the steps above to turn up with the same order and numbering as above.
It is easy to update mpc.bus using find.
type_1 = find(mpc.bus(:,2) == 1);
type_2 = find(mpc.bus(:,2) == 2);
type_3 = find(mpc.bus(:,2) == 3);
mpc.bus(:,:) = mpc.bus([type1; type2; type3],:);
mpc.bus(:,1) = 1:nb % Where nb is the number of rows of mpc.bus
The numbers in the to/from columns in mpc.branch corresponds to the numbers in column 1 in mpc.bus.
It's OK to update the numbers on the to, from columns of mpc.branch as well.
However, I'm not able to find a non-messy way of retracing my steps. Can I update the numbering using some simple commands?
For the record: I have deliberately not included my code for re-numbering mpc.branch, since I'm sure someone has a smarter, simpler solution (that will make it easier to redo when the simulations are finished).
Edit: It might be easier to create normal arrays (to avoid woriking with structs):
bus = mpc.bus;
branch = mpc.branch;
Edit #2: The order of things:
Re-order and re-number.
Columns (3:end) of bus and branch are changed. (Not part of this question)
Restore original order and indices.
Thanks!
I'm proposing this solution. It generates a n x 2 matrix, where n corresponds to the number of rows in mpc.bus and a temporary copy of mpc.branch:
function [mpc_1, mpc_2, mpc_3] = minimal_example
mpc.bus = [ 1 2;...
2 2;...
3 1;...
4 3;...
5 1;...
10 2;...
99 1];
mpc.branch = [ 1 2;...
1 3;...
2 4;...
10 5;...
10 99];
mpc.bus = sortrows(mpc.bus,2);
mpc_1 = mpc;
mpc_tmp = mpc.branch;
for I=1:size(mpc.bus,1)
PAIRS(I,1) = I;
PAIRS(I,2) = mpc.bus(I,1);
mpc.branch(mpc_tmp(:,1:2)==mpc.bus(I,1)) = I;
mpc.bus(I,1) = I;
end
mpc_2 = mpc;
% (a) the following mpc_tmp is only needed if you want to truly reverse the operation
mpc_tmp = mpc.branch;
%
% do some stuff
%
for I=1:size(mpc.bus,1)
% (b) you can decide not to use the following line, then comment the line below (a)
mpc.branch(mpc_tmp(:,1:2)==mpc.bus(I,1)) = PAIRS(I,2);
mpc.bus(I,1) = PAIRS(I,2);
end
% uncomment the following line, if you commented (a) and (b) above:
% mpc.branch = mpc_tmp;
mpc.bus = sortrows(mpc.bus,1);
mpc_3 = mpc;
The minimal example above can be executed as is. The three outputs (mpc_1, mpc_2 & mpc_3) are just in place to demonstrate the workings of the code but are otherwise not necessary.
1.) mpc.bus is ordered using sortrows, simplifying the approach and not using find three times. It targets the second column of mpc.bus and sorts the remaining matrix accordingly.
2.) The original contents of mpc.branch are stored.
3.) A loop is used to replace the entries in the first column of mpc.bus with ascending numbers while at the same time replacing them correspondingly in mpc.branch. Here, the reference to mpc_tmp is necessary so ensure a correct replacement of the elements.
4.) Afterwards, mpc.branch can be reverted analogously to (3.) - here, one might argue, that if the original mpc.branch was stored earlier on, one could just copy the matrix. Also, the original values of mpc.bus are re-assigned.
5.) Now, sortrows is applied to mpc.bus again, this time with the first column as reference to restore the original format.