Perform Calculations by Group in Table- MATLAB & Simulink- MathWorks Deutschland (2024)

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You can perform calculations on groups of data within table variables. In such calculations, you split one or more table variables into groups of data, perform a calculation on each group, and combine the results into one or more output variables. MATLAB® provides several functions that split data into groups and combine the results for you. You need only specify which table variables contain data, which variables define groups, and the function to apply to the groups of data.

For example, this diagram shows a simple grouped calculation that splits a numeric table variable into two groups of data, calculates the mean of each group, and then combines the mean values into an output variable.

You can perform grouped calculations on table variables by using any of these functions:

groupsummary, groupcounts, groupfilter, and grouptransform
varfun and rowfun
findgroups and splitapply

In most cases, groupsummary is the recommended function for grouped calculations. It is simple to use and returns a table with labels that describe results. The other listed functions, however, also offer capabilities that can be useful in some situations.

This topic has examples that use each of these functions. It ends with a summary of their behaviors and recommended usages.

Create Table from File

The sample spreadsheet outages.csv contains data values that represent electric utility power outages in the United States. To create a table from the file, use the readtable function. To read text data from the file into table variables that are string arrays, specify the TextType name-value argument as "string".

outages = readtable("outages.csv","TextType","string")

outages=1468×6 table Region OutageTime Loss Customers RestorationTime Cause ___________ ________________ ______ __________ ________________ _________________ "SouthWest" 2002-02-01 12:18 458.98 1.8202e+06 2002-02-07 16:50 "winter storm" "SouthEast" 2003-01-23 00:49 530.14 2.1204e+05 NaT "winter storm" "SouthEast" 2003-02-07 21:15 289.4 1.4294e+05 2003-02-17 08:14 "winter storm" "West" 2004-04-06 05:44 434.81 3.4037e+05 2004-04-06 06:10 "equipment fault" "MidWest" 2002-03-16 06:18 186.44 2.1275e+05 2002-03-18 23:23 "severe storm" "West" 2003-06-18 02:49 0 0 2003-06-18 10:54 "attack" "West" 2004-06-20 14:39 231.29 NaN 2004-06-20 19:16 "equipment fault" "West" 2002-06-06 19:28 311.86 NaN 2002-06-07 00:51 "equipment fault" "NorthEast" 2003-07-16 16:23 239.93 49434 2003-07-17 01:12 "fire" "MidWest" 2004-09-27 11:09 286.72 66104 2004-09-27 16:37 "equipment fault" "SouthEast" 2004-09-05 17:48 73.387 36073 2004-09-05 20:46 "equipment fault" "West" 2004-05-21 21:45 159.99 NaN 2004-05-22 04:23 "equipment fault" "SouthEast" 2002-09-01 18:22 95.917 36759 2002-09-01 19:12 "severe storm" "SouthEast" 2003-09-27 07:32 NaN 3.5517e+05 2003-10-04 07:02 "severe storm" "West" 2003-11-12 06:12 254.09 9.2429e+05 2003-11-17 02:04 "winter storm" "NorthEast" 2004-09-18 05:54 0 0 NaT "equipment fault" ⋮

Create `categorical` Grouping Variables

Table variables can have any data type. But conceptually, you can also think of tables as having two general kinds of variables: data variables and grouping variables.

Data variables enable you to describe individual events or observations. For example, in outages you can think of the OutageTime, Loss, Customers, and RestorationTime variables as data variables.
Grouping variables enable you to group together events or observations that have something in common. For example, in outages you can think of the Region and Cause variables as grouping variables. You can group together and analyze the power outages that occur in the same region or share the same cause.

Often, grouping variables contain a discrete set of fixed values that specify categories. The categories specify groups that data values can belong to. The categorical data type can be a convenient type for working with categories.

To convert Region and Cause to categorical variables, use the convertvars function.

outages = convertvars(outages,["Region","Cause"],"categorical")

outages=1468×6 table Region OutageTime Loss Customers RestorationTime Cause _________ ________________ ______ __________ ________________ _______________ SouthWest 2002-02-01 12:18 458.98 1.8202e+06 2002-02-07 16:50 winter storm SouthEast 2003-01-23 00:49 530.14 2.1204e+05 NaT winter storm SouthEast 2003-02-07 21:15 289.4 1.4294e+05 2003-02-17 08:14 winter storm West 2004-04-06 05:44 434.81 3.4037e+05 2004-04-06 06:10 equipment fault MidWest 2002-03-16 06:18 186.44 2.1275e+05 2002-03-18 23:23 severe storm West 2003-06-18 02:49 0 0 2003-06-18 10:54 attack West 2004-06-20 14:39 231.29 NaN 2004-06-20 19:16 equipment fault West 2002-06-06 19:28 311.86 NaN 2002-06-07 00:51 equipment fault NorthEast 2003-07-16 16:23 239.93 49434 2003-07-17 01:12 fire MidWest 2004-09-27 11:09 286.72 66104 2004-09-27 16:37 equipment fault SouthEast 2004-09-05 17:48 73.387 36073 2004-09-05 20:46 equipment fault West 2004-05-21 21:45 159.99 NaN 2004-05-22 04:23 equipment fault SouthEast 2002-09-01 18:22 95.917 36759 2002-09-01 19:12 severe storm SouthEast 2003-09-27 07:32 NaN 3.5517e+05 2003-10-04 07:02 severe storm West 2003-11-12 06:12 254.09 9.2429e+05 2003-11-17 02:04 winter storm NorthEast 2004-09-18 05:54 0 0 NaT equipment fault ⋮

Calculate Statistics by Group in Table

You can calculate statistics by group in a table using functions such as groupsummary, varfun, and splitapply. These functions enable you to specify groups of data within a table and methods that perform calculations on each group. You can store the results in another table or in output arrays.

For example, determine the mean power loss and customers affected due to the outages in each region in the outages table. The recommended way to perform this calculation is to use the groupsummary function. Specify Region as the grouping variable, mean as the method to apply to each group, and Loss and Customers as the data variables. The output lists the regions (in the Region variable), the number of power outages per region (in the GroupCount variable), and the mean power loss and customers affected in each region (in the mean_Loss and mean_Customers variables, respectively).

meanLossByRegion = groupsummary(outages,"Region","mean",["Loss","Customers"])

meanLossByRegion=5×4 table Region GroupCount mean_Loss mean_Customers _________ __________ _________ ______________ MidWest 142 1137.7 2.4015e+05 NorthEast 557 551.65 1.4917e+05 SouthEast 389 495.35 1.6776e+05 SouthWest 26 493.88 2.6975e+05 West 354 433.37 1.5201e+05

The groupsummary function is recommended for several reasons:

You can specify many common methods (such as max, min, and mean) by name, without using function handles.
You can specify multiple methods in one call.
NaNs, NaTs, and other missing values in the data variables are automatically omitted when calculating results.

The third point explains why the mean_Loss and mean_Customers variables do not have NaNs in the meanLossByRegion output table.

To specify multiple methods in one call to groupsummary, list them in an array. For example, calculate the maximum, mean, and minimum power loss by region.

lossStatsByRegion = groupsummary(outages,"Region",["max","mean","min"],"Loss")

lossStatsByRegion=5×5 table Region GroupCount max_Loss mean_Loss min_Loss _________ __________ ________ _________ ________ MidWest 142 23141 1137.7 0 NorthEast 557 23418 551.65 0 SouthEast 389 8767.3 495.35 0 SouthWest 26 2796 493.88 0 West 354 16659 433.37 0

The minimum loss in every region is zero. To analyze only those outages that resulted in losses greater than zero, exclude the rows in outages where the loss is zero. First create a vector of logical indices whose values are logical 1 (true) for rows where outages.Loss is greater than zero. Then index into outages to return a table that includes only those rows. Again, calculate the maximum, mean, and minimum power loss by region.

nonZeroLossIndices = outages.Loss > 0;nonZeroLossOutages = outages(nonZeroLossIndices,:);nonZeroLossStats = groupsummary(nonZeroLossOutages,"Region",["max","mean","min"],"Loss")

nonZeroLossStats=5×5 table Region GroupCount max_Loss mean_Loss min_Loss _________ __________ ________ _________ ________ MidWest 81 23141 1264.1 8.9214 NorthEast 180 23418 827.47 0.74042 SouthEast 234 8767.3 546.16 2.3096 SouthWest 23 2796 515.35 27.882 West 175 16659 549.76 0.71847

Use Alternative Functions for Grouped Calculations

There are alternative functions that perform grouped calculations in tables. While groupsummary is recommended, the alternative functions are also useful in some situations.

The varfun function performs calculations on variables. It is similar to groupsummary, but varfun can perform both grouped and ungrouped calculations.
The rowfun function performs calculations along rows. You can specify methods that take multiple inputs or that return multiple outputs.
The findgroups and splitapply functions can perform calculations on variables or along rows. You can specify methods that take multiple inputs or that return multiple outputs. The outputs of splitapply are arrays, not tables.

Call `varfun` on Variables

For example, calculate the maximum power loss by region using varfun. The output table has a similar format to the output of groupsummary.

maxLossByVarfun = varfun(@max, ... outages, ... "InputVariables","Loss", ... "GroupingVariables","Region")

maxLossByVarfun=5×3 table Region GroupCount max_Loss _________ __________ ________ MidWest 142 23141 NorthEast 557 23418 SouthEast 389 8767.3 SouthWest 26 2796 West 354 16659

However, there are significant differences when you use varfun:

You must always specify the method by using a function handle.
You can specify only one method.
You can perform grouped or ungrouped calculations.
NaNs, NaTs, and other missing values in the data variables are automatically included when calculating results.

The last point is a significant difference in behavior between groupsummary and varfun. For example, the Loss variable has NaNs. If you use varfun to calculate the mean losses, then by default the results are NaNs, unlike the default groupsummary results.

meanLossByVarfun = varfun(@mean, ... outages, ... "InputVariables","Loss", ... "GroupingVariables","Region")

meanLossByVarfun=5×3 table Region GroupCount mean_Loss _________ __________ _________ MidWest 142 NaN NorthEast 557 NaN SouthEast 389 NaN SouthWest 26 NaN West 354 NaN

To omit missing values when using varfun, wrap the method in an anonymous function so that you can specify the "omitnan" option.

omitnanMean = @(x)(mean(x,"omitnan"));meanLossOmitNaNs = varfun(omitnanMean, ... outages, ... "InputVariables","Loss", ... "GroupingVariables","Region")

meanLossOmitNaNs=5×3 table Region GroupCount Fun_Loss _________ __________ ________ MidWest 142 1137.7 NorthEast 557 551.65 SouthEast 389 495.35 SouthWest 26 493.88 West 354 433.37

Another point refers to a different but related use case, which is to perform ungrouped calculations on table variables. To apply a method to all table variables without grouping, use varfun. For example, calculate the maximum power loss and the maximum number of customers affected in the entire table.

maxValuesInOutages = varfun(@max, ... outages, ... "InputVariables",["Loss","Customers"])

maxValuesInOutages=1×2 table max_Loss max_Customers ________ _____________ 23418 5.9689e+06

Call `rowfun` on Rows

The rowfun function applies a method along the rows of a table. Where varfun applies a method to each specified variable, one by one, rowfun takes all specified table variables as input arguments to the method and applies the method once.

For example, calculate the median loss per customer in each region. To perform this calculation, first specify a function that takes two input arguments (loss and customers), divides the loss by the number of customers, and then returns the median.

medianLossCustFcn = @(loss,customers)(median(loss ./ customers,"omitnan"));

Then call rowfun. You can specify a meaningful output variable name by using the OutputVariablesNames name-value argument.

meanLossPerCustomer = rowfun(medianLossCustFcn, ... outages, ... "InputVariables",["Loss","Customers"], ... "GroupingVariables","Region", ... "OutputVariableNames","MedianLossPerCustomer")

meanLossPerCustomer=5×3 table Region GroupCount MedianLossPerCustomer _________ __________ _____________________ MidWest 142 0.0042139 NorthEast 557 0.0028512 SouthEast 389 0.0032057 SouthWest 26 0.0026353 West 354 0.002527

You can also use rowfun when the method returns multiple outputs. For example, use bounds to calculate the minimum and maximum loss per region in one call to rowfun. The bounds function returns two output arguments.

boundsLossPerRegion = rowfun(@bounds, ... outages, ... "InputVariables","Loss", ... "GroupingVariables","Region", ... "OutputVariableNames",["MinLoss","MaxLoss"])

boundsLossPerRegion=5×4 table Region GroupCount MinLoss MaxLoss _________ __________ _______ _______ MidWest 142 0 23141 NorthEast 557 0 23418 SouthEast 389 0 8767.3 SouthWest 26 0 2796 West 354 0 16659

Call `findgroups` and `splitapply` on Variables or Rows

You can use the findgroups function to define groups and then use splitapply to apply a method to each group. The findgroups function returns a vector of group numbers that identifies which group a row of data is part of. The splitapply function returns a numeric array of the outputs of the method applied to the groups.

For example, calculate the maximum power loss by region using findgroups and splitapply.

G = findgroups(outages.Region)

G = 1468×1 4 3 3 5 1 5 5 5 2 1 ⋮

maxLossArray = splitapply(@max,outages.Loss,G)

maxLossArray = 5×110⁴ × 2.3141 2.3418 0.8767 0.2796 1.6659

Like rowfun, splitapply enables you to specify methods that return multiple outputs. Calculate both minima and maxima by using bounds.

[minLossArray,maxLossArray] = splitapply(@bounds,outages.Loss,G)

minLossArray = 5×1 0 0 0 0 0

maxLossArray = 5×110⁴ × 2.3141 2.3418 0.8767 0.2796 1.6659

You can also specify methods that take multiple inputs. For example, use the medianLossCustFcn function again to calculate the median loss per customer. But this time, return the median loss per customer in each region as an array.

medianLossCustFcn = @(loss,customers)(median(loss ./ customers,"omitnan"));medianLossArray = splitapply(medianLossCustFcn,outages.Loss,outages.Customers,G)

medianLossArray = 5×1 0.0042 0.0029 0.0032 0.0026 0.0025

The numeric outputs of findgroups and splitapply are not annotated like the output of groupsummary. However, returning numeric outputs can have other benefits:

You can use the output of findgroups in multiple calls to splitapply. You might want to use findgroups and splitapply for efficiency when you make many grouped calculations on a large table.
You can create a results table with a different format by building it from the outputs of findgroups and splitapply.
You can call methods that return multiple outputs.
You can append the outputs of splitapply to an existing table.

Append New Calculation to Existing Table

If you already have a table of results, you can append the results of another calculation to that table. For example, calculate the mean duration of power outages in each region in hours. Append the mean durations as a new variable to the lossStatsByRegion table.

First subtract the outage times from the restoration times to return the durations of the power outages. Convert these durations to hours by using the hours function.

D = outages.RestorationTime - outages.OutageTime;H = hours(D)

H = 1468×110⁵ × 0.0015 NaN 0.0023 0.0000 0.0007 0.0001 0.0000 0.0001 0.0001 0.0001 ⋮

Next use mean to calculate the mean durations. The outage durations have some NaN values because the outage and restoration times have some missing values. As before, wrap the method in an anonymous function to specify the "omitnan" option.

omitnanMean = @(x)(mean(x,"omitnan"));

Calculate the mean duration of power outages by region. Append it to lossStatsByRegion as a new table variable.

G = findgroups(outages.Region);lossStatsByRegion.mean_Outage = splitapply(omitnanMean,H,G)

lossStatsByRegion=5×6 table Region GroupCount max_Loss mean_Loss min_Loss mean_Outage _________ __________ ________ _________ ________ ___________ MidWest 142 23141 1137.7 0 819.25 NorthEast 557 23418 551.65 0 581.04 SouthEast 389 8767.3 495.35 0 40.83 SouthWest 26 2796 493.88 0 59.519 West 354 16659 433.37 0 673.45

Specify Groups as Bins

There is another way to specify groups. Instead of specifying categories as unique values in a grouping variable, you can bin values in a variable where values are distributed continuously. Then you can use those bins to specify groups.

For example, bin the power outages by year. To count the number of power outages per year, use the groupcounts function.

outagesByYear = groupcounts(outages,"OutageTime","year")

outagesByYear=13×3 table year_OutageTime GroupCount Percent _______________ __________ _______ 2002 36 2.4523 2003 62 4.2234 2004 79 5.3815 2005 74 5.0409 2006 108 7.3569 2007 91 6.1989 2008 115 7.8338 2009 142 9.673 2010 177 12.057 2011 190 12.943 2012 207 14.101 2013 186 12.67 2014 1 0.06812

Visualize the number of outages per year. The number per year increases over time in this data set.

bar(outagesByYear.year_OutageTime,outagesByYear.GroupCount)

You can use groupsummary with bins as groups. For example, calculate the median values for customers affected and power losses by year.

medianLossesByYear = groupsummary(outages,"OutageTime","year","median",["Customers","Loss"])

medianLossesByYear=13×4 table year_OutageTime GroupCount median_Customers median_Loss _______________ __________ ________________ ___________ 2002 36 1.7101e+05 277.02 2003 62 1.0204e+05 295.6 2004 79 1.0108e+05 252.44 2005 74 91536 265.16 2006 108 86020 210.08 2007 91 1.0529e+05 232.12 2008 115 86356 205.77 2009 142 63119 83.491 2010 177 66212 155.76 2011 190 48200 75.286 2012 207 66994 78.289 2013 186 55669 69.596 2014 1 NaN NaN

Visualize the median number of customers affected by outages per year. Although the number of outages increased over time, the median number of affected customers decreased.

plot(medianLossesByYear,"year_OutageTime","median_Customers")

Return the rows of outages for years with more than 75 outages. To index into outages by those years, use the groupfilter function. To find the bins with more than 75 rows, specify an anonymous function that returns a logical 1 if the number of rows in a bin is greater than 75.

outages75 = groupfilter(outages,"OutageTime","year",@(x) numel(x) > 75)

outages75=1295×7 table Region OutageTime Loss Customers RestorationTime Cause year_OutageTime _________ ________________ ______ __________ ________________ _______________ _______________ West 2004-04-06 05:44 434.81 3.4037e+05 2004-04-06 06:10 equipment fault 2004 West 2004-06-20 14:39 231.29 NaN 2004-06-20 19:16 equipment fault 2004 MidWest 2004-09-27 11:09 286.72 66104 2004-09-27 16:37 equipment fault 2004 SouthEast 2004-09-05 17:48 73.387 36073 2004-09-05 20:46 equipment fault 2004 West 2004-05-21 21:45 159.99 NaN 2004-05-22 04:23 equipment fault 2004 NorthEast 2004-09-18 05:54 0 0 NaT equipment fault 2004 NorthEast 2004-11-13 10:42 NaN 1.4227e+05 2004-11-19 02:31 winter storm 2004 SouthEast 2004-12-06 23:18 NaN 37136 2004-12-14 03:21 winter storm 2004 West 2004-12-21 18:50 112.05 7.985e+05 2004-12-29 03:46 winter storm 2004 NorthEast 2004-12-26 22:18 255.45 1.0444e+05 2004-12-27 14:11 winter storm 2004 SouthWest 2004-06-06 05:27 559.41 2.19e+05 2004-06-06 05:55 equipment fault 2004 MidWest 2004-07-02 09:16 15128 2.0104e+05 2004-07-06 14:11 thunder storm 2004 SouthWest 2004-07-18 14:40 340.35 1.4963e+05 2004-07-26 23:34 severe storm 2004 NorthEast 2004-09-16 19:42 4718 NaN NaT unknown 2004 SouthEast 2004-09-20 12:37 8767.3 2.2249e+06 2004-10-02 06:00 severe storm 2004 MidWest 2004-11-09 18:44 470.83 67587 2004-11-09 21:24 wind 2004 ⋮

Summary of Behavior and Recommendations

Use these tips and recommendations to decide which functions to use to perform group calculations.

Specify groups using either grouping variables or bins created from numeric, datetime, or duration variables.
To perform calculations by group on data in tables or timetables, use the recommended function groupsummary. The related functions groupcounts, groupfilter, and grouptransform also are useful.
Consider using varfun to automatically include missing values (such as NaNs and NaTs) when applying methods to groups of data. Also, varfun can perform both grouped and ungrouped calculations.
Consider using findgroups and splitapply for efficiency when you make many consecutive grouped calculations on a large table.
Consider using findgroups and splitapply to append new arrays to an existing table of results.
To perform calculations using a method that returns multiple outputs, such as bounds, use either rowfun or splitapply.
To perform calculations along rows using a method that requires multiple input arguments, use either rowfun or splitapply.

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Perform Calculations by Group in Table - MATLAB & Simulink - MathWorks Deutschland? ›

You can calculate statistics by group in a table using functions such as groupsummary , varfun , and splitapply . These functions enable you to specify groups of data within a table and methods that perform calculations on each group. You can store the results in another table or in output arrays.

Tell Me More ›

How do you find groups in Matlab table? ›

G = findgroups( T ) returns G , a vector of group numbers created from the variables in table T . The findgroups function treats all the variables in T as grouping variables. [ G , TID ] = findgroups( T ) also returns TID , a table that contains the unique values for each group.

Learn More Now ›

How to divide data into groups in Matlab? ›

To split data into groups and apply a function to the groups, use the findgroups and splitapply functions together. For more information about calculations on groups of data, see Calculations on Groups of Data.

How to add a grouping variable? ›

A grouping variable must have the same number of observations (rows) as the table, dataset array, or numeric array you are grouping. Observations that have the same grouping variable value belong to the same group. For example, the following variables comprise the same groups.

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What is the function of group in Matlab table? ›

You can calculate statistics by group in a table using functions such as groupsummary , varfun , and splitapply . These functions enable you to specify groups of data within a table and methods that perform calculations on each group.

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How do you group data in a table? ›

Select the data (including any summary rows or columns). Go to Data > Outline > Group > Group, and then select Rows or Columns.

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What is group method of data handling MATLAB? ›

Group Method of Data Handling (GMDH)[1] is a family of algorithms for computer-based mathematical modeling and structural identification. Most of GMDH algorithms are characterized by inductive self-organizing procedure used for obtaining multi-parametric model.

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How do you calculate division in MATLAB? ›

Description. X = A / b performs right-matrix division. X = mrdivide( A , b ) is an alternative way to execute X = A/b .

Can you group by multiple variables? ›

One great feature of the group_by function is its ability to group by more than one variable to show what the aggregated data looks like for combinations of the different variables across the response variable.

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How do you factor by grouping two variables? ›

Step 1: Arrange the polynomial such that neighboring terms, in groups of two, have a common factor. Step 2: Define the groups by putting parentheses around every two terms. Step 3: Factor the common factor out of each group. Step 4: Factor the common expression out of the resulting polynomial.

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How to do complex calculations in MATLAB? ›

You can use i to enter complex numbers. You also can use the character j as the imaginary unit. To create a complex number without using i and j , use the complex function. z = a + b i returns a complex numerical constant, z .

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How do you do a cumulative calculation in MATLAB? ›

B = c*msum( A , dim ) returns the cumulative sum of the elements along dimension dim . For example, if A is a matrix, then c*msum(A,2) returns the cumulative sum along the rows of A . B = c*msum(___, direction ) specifies the direction for any of the previous syntaxes.

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Solve an Equation

If eqn is an equation, solve(eqn, x) solves eqn for the symbolic variable x . Use the == operator to specify the familiar quadratic equation and solve it using solve . solx is a symbolic vector containing the two solutions of the quadratic equation.

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How do I find categories in Matlab? ›

To return a list of the categories in A , use the categories function. The order of the categories was determined when you created A . If you do not specify an order when you use categorical , then categorical calls the unique function to determine the order of the categories from the input array.

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How do you access the elements of a table in Matlab? ›

To access the data in a table, you can index into the table by specifying rows and variables, just as you can index into a matrix by specifying rows and columns. Table variables have names, just as the fields of a structure have names. The rows of a table also can have names, but row names are not required.

Perform Calculations by Group in Table - MATLAB & Simulink - MathWorks Deutschland (2024)

Create Table from File

Create `categorical` Grouping Variables

Calculate Statistics by Group in Table

Use Alternative Functions for Grouped Calculations

Call `varfun` on Variables

Call `rowfun` on Rows

Call `findgroups` and `splitapply` on Variables or Rows

Append New Calculation to Existing Table

Specify Groups as Bins

Summary of Behavior and Recommendations

See Also

Related Topics

MATLAB-Befehl

Americas

Europe

Asia Pacific

FAQs

Perform Calculations by Group in Table - MATLAB & Simulink - MathWorks Deutschland? ›

Can you group by multiple variables? ›

Perform Calculations by Group in Table - MATLAB & Simulink - MathWorks Deutschland (2024)

Create Table from File

Create categorical Grouping Variables

Calculate Statistics by Group in Table

Use Alternative Functions for Grouped Calculations

Call varfun on Variables

Call rowfun on Rows

Call findgroups and splitapply on Variables or Rows

Append New Calculation to Existing Table

Specify Groups as Bins

Summary of Behavior and Recommendations

See Also

Related Topics

MATLAB-Befehl

Americas

Europe

Asia Pacific

FAQs

Perform Calculations by Group in Table - MATLAB & Simulink - MathWorks Deutschland? ›

Can you group by multiple variables? ›

Create `categorical` Grouping Variables

Call `varfun` on Variables

Call `rowfun` on Rows

Call `findgroups` and `splitapply` on Variables or Rows