groupsummary
Group summary computations
Syntax
Description
Table Data
returns the unique grouping variable combinations and the number of members in
each group for table or timetable G
= groupsummary(T
,groupvars
)T
. Groups are defined by
rows in the variables in groupvars
that have the same unique
combination of values. Each row of the output table corresponds to one group.
For example, G = groupsummary(T,"HealthStatus")
returns a
table with the count of each group in the variable
HealthStatus
.
You can use groupsummary
functionality interactively by
adding the Compute by
Group task to a live script.
For more information, see Group Summary Computation.
applies the groupwise computations specified in G
= groupsummary(T
,groupvars
,method
)method
and
appends the computation results to the output table as additional variables. For
example, G = groupsummary(T,"Location","median")
returns the
median value of every nongrouping variable in T
for each
location, in addition to the number of members in each location group.
specifies additional grouping properties using one or more namevalue arguments
for any of the previous syntaxes. For example, G
= groupsummary(___,Name,Value
)G =
groupsummary(T,"Category1","IncludeMissingGroups",false)
excludes
the group made from missing data of type categorical
indicated by <undefined>
in
Category1
.
Array Data
returns the concatenated results of applying the groupwise computations in
B
= groupsummary(A
,groupvars
,method
)method
to unique groups in vector, matrix, or cell array
A
. Groups are defined by rows in the column vectors in
groupvars
that have the same unique combination of
values. Each row of the output array contains the computation results for one
group.
You can use groupsummary
functionality interactively by
adding the Compute by
Group task to a live script.
specifies additional grouping properties using one or more namevalue arguments
for either of the previous syntaxes for an input array.B
= groupsummary(___,Name,Value
)
Examples
Summary Statistics
Compute summary statistics on table variables.
Create a table T
that contains information about eight individuals.
HealthStatus = categorical(["Poor"; "Good"; "Fair"; "Fair"; "Poor"; "Excellent"; "Good"; "Excellent"]); Age = [38; 43; 38; 40; 49; 51; 52; 35]; Height = [71; 68; 64; 67; 64; 62; 65; 55]; Weight = [176; 153; 131; 133; 119; 120; 140; 129]; T = table(HealthStatus,Age,Height,Weight)
T=8×4 table
HealthStatus Age Height Weight
____________ ___ ______ ______
Poor 38 71 176
Good 43 68 153
Fair 38 64 131
Fair 40 67 133
Poor 49 64 119
Excellent 51 62 120
Good 52 65 140
Excellent 35 55 129
Compute the counts of the health status groups by specifying HealthStatus
as the grouping variable.
G = groupsummary(T,"HealthStatus")
G=4×2 table
HealthStatus GroupCount
____________ __________
Excellent 2
Fair 2
Good 2
Poor 2
Compute the mean age, height, and weight of each health status group.
G = groupsummary(T,"HealthStatus","mean")
G=4×5 table
HealthStatus GroupCount mean_Age mean_Height mean_Weight
____________ __________ ________ ___________ ___________
Excellent 2 43 58.5 124.5
Fair 2 39 65.5 132
Good 2 47.5 66.5 146.5
Poor 2 43.5 67.5 147.5
Still grouping by health status, compute only the median height.
G = groupsummary(T,"HealthStatus","median","Height")
G=4×3 table
HealthStatus GroupCount median_Height
____________ __________ _____________
Excellent 2 58.5
Fair 2 65.5
Good 2 66.5
Poor 2 67.5
Multiple Grouping Variables
Group table data using two grouping variables.
Create a table T
that contains information about eight individuals.
HealthStatus = categorical(["Poor"; "Good"; "Fair"; "Fair"; "Poor"; "Excellent"; "Good"; "Excellent"]); Smoker = logical([1; 0; 0; 1; 1; 0; 0; 1]); Weight = [176; 153; 131; 133; 119; 120; 140; 129]; T = table(HealthStatus,Smoker,Weight)
T=8×3 table
HealthStatus Smoker Weight
____________ ______ ______
Poor true 176
Good false 153
Fair false 131
Fair true 133
Poor true 119
Excellent false 120
Good false 140
Excellent true 129
Compute the mean weight, grouped by health status and smoking status. By default, some combinations of health status and smoking status are not represented in the output because they are empty groups.
G = groupsummary(T,["HealthStatus","Smoker"],"mean","Weight")
G=6×4 table
HealthStatus Smoker GroupCount mean_Weight
____________ ______ __________ ___________
Excellent false 1 120
Excellent true 1 129
Fair false 1 131
Fair true 1 133
Good false 2 146.5
Poor true 2 147.5
Set the value of IncludeEmptyGroups
to true
to see all group combinations, including the empty ones.
G = groupsummary(T,["HealthStatus","Smoker"],"mean","Weight","IncludeEmptyGroups",true)
G=8×4 table
HealthStatus Smoker GroupCount mean_Weight
____________ ______ __________ ___________
Excellent false 1 120
Excellent true 1 129
Fair false 1 131
Fair true 1 133
Good false 2 146.5
Good true 0 NaN
Poor false 0 NaN
Poor true 2 147.5
Specify Group Bins
Group data according to specified bins.
Create a timetable that contains sales information for days within a single month.
TimeStamps = datetime([2017 3 4; 2017 3 2; 2017 3 15; 2017 3 10; ... 2017 3 14; 2017 3 31; 2017 3 25; ... 2017 3 29; 2017 3 21; 2017 3 18]); Profit = [2032 3071 1185 2587 1998 2899 3112 909 2619 3085]'; ItemsSold = [14 13 8 5 10 16 8 6 7 11]'; TT = timetable(TimeStamps,Profit,ItemsSold)
TT=10×2 timetable
TimeStamps Profit ItemsSold
___________ ______ _________
04Mar2017 2032 14
02Mar2017 3071 13
15Mar2017 1185 8
10Mar2017 2587 5
14Mar2017 1998 10
31Mar2017 2899 16
25Mar2017 3112 8
29Mar2017 909 6
21Mar2017 2619 7
18Mar2017 3085 11
Compute the mean and the mode of profit binned by the items sold, binning the groups into intervals of item numbers.
format shorte G = groupsummary(TT,"ItemsSold",[0 4 8 12 16],{"mean","mode"},"Profit")
G=3×4 table
disc_ItemsSold GroupCount mean_Profit mode_Profit
______________ __________ ___________ ___________
[4, 8) 3.0000e+00 2.0383e+03 9.0900e+02
[8, 12) 4.0000e+00 2.3450e+03 1.1850e+03
[12, 16] 3.0000e+00 2.6673e+03 2.0320e+03
Compute the mean profit grouped by day of the week.
G = groupsummary(TT,"TimeStamps","dayname","mean","Profit")
G=5×3 table
dayname_TimeStamps GroupCount mean_Profit
__________________ __________ ___________
Tuesday 2.0000e+00 2.3085e+03
Wednesday 2.0000e+00 1.0470e+03
Thursday 1.0000e+00 3.0710e+03
Friday 2.0000e+00 2.7430e+03
Saturday 3.0000e+00 2.7430e+03
Group Operations with Vector Data
Create a vector of dates and a vector of corresponding profit values.
TimeStamps = datetime([2017 3 4; 2017 3 2; 2017 3 15; 2017 3 10; ... 2017 3 14; 2017 3 31; 2017 3 25; ... 2017 3 29; 2017 3 21; 2017 3 18]); Profit = [2032 3071 1185 2587 1998 2899 3112 909 2619 3085]';
Compute the mean profit for each day of the week. Display the mean, the group names, and the number of members in each group.
format shorte [meanDailyProfit,dayOfWeek,dailyCounts] = groupsummary(Profit,TimeStamps,"dayname","mean")
meanDailyProfit = 5×1
2.3085e+03
1.0470e+03
3.0710e+03
2.7430e+03
2.7430e+03
dayOfWeek = 5x1 categorical
Tuesday
Wednesday
Thursday
Friday
Saturday
dailyCounts = 5×1
2
2
1
2
3
Multiple Grouping Vectors for Vector Input
Compute the mean weights for a set of people grouped by their health status and smoker status.
Store information about the individuals as three vectors of different types.
HealthStatus = categorical(["Poor"; "Good"; "Fair"; "Fair"; "Poor"; "Excellent"; "Good"; "Excellent"]); Smoker = logical([1; 0; 0; 1; 1; 0; 0; 1]); Weight = [176; 153; 131; 133; 119; 120; 140; 129];
Grouping by health status and smoker status, compute the mean weights.
B
contains the mean for each group (NaN
for empty groups). BG
is a cell array containing two vectors that describe the groups as you look at their elements rowwise. For instance, the first row of BG{1}
indicates that the patients in the first group have a health status Excellent
, and the first row of BG{2}
indicates that they are nonsmokers. Finally, BC
contains the number of members in each group for the corresponding groups in BG
.
[B,BG,BC] = groupsummary(Weight,{HealthStatus,Smoker},"mean","IncludeEmptyGroups",true); B
B = 8×1
120.0000
129.0000
131.0000
133.0000
146.5000
NaN
NaN
147.5000
BG{1}
ans = 8x1 categorical
Excellent
Excellent
Fair
Fair
Good
Good
Poor
Poor
BG{2}
ans = 8x1 logical array
0
1
0
1
0
1
0
1
BC
BC = 8×1
1
1
1
1
2
0
0
2
Method Function Handle with Multiple Inputs
Load data containing patient information and create a table describing each patient's location, systolic and diastolic blood pressure, height, and weight.
load patients
Location = categorical(Location);
T = table(Location,Systolic,Diastolic,Height,Weight)
T=100×5 table
Location Systolic Diastolic Height Weight
_________________________ ________ _________ ______ ______
County General Hospital 124 93 71 176
VA Hospital 109 77 69 163
St. Mary's Medical Center 125 83 64 131
VA Hospital 117 75 67 133
County General Hospital 122 80 64 119
St. Mary's Medical Center 121 70 68 142
VA Hospital 130 88 64 142
VA Hospital 115 82 68 180
St. Mary's Medical Center 115 78 68 183
County General Hospital 118 86 66 132
County General Hospital 114 77 68 128
St. Mary's Medical Center 115 68 66 137
VA Hospital 127 74 71 174
VA Hospital 130 95 72 202
St. Mary's Medical Center 114 79 65 129
VA Hospital 130 92 71 181
⋮
Grouping by location, compute the correlation between patient height and weight and the correlation between systolic and diastolic blood pressure. Use the xcov
function as the method to compute the correlation. The first two input arguments to xcov
describe the data to correlate, the third argument describes the lag size, and the fourth argument describes the type of normalization. For each group computation, the x
and y
arguments passed to xcov
are specified pairwise by variable from the two cell elements ["Height","Systolic"]
and ["Weight","Diastolic"]
.
G = groupsummary(T,"Location",@(x,y) xcov(x,y,0,"coeff"),{["Height","Systolic"],["Weight","Diastolic"]})
G=3×4 table
Location GroupCount fun1_Height_Weight fun1_Systolic_Diastolic
_________________________ __________ __________________ _______________________
County General Hospital 39 0.65483 0.44187
St. Mary's Medical Center 24 0.62047 0.44466
VA Hospital 37 0.78438 0.62256
Alternatively, if your data is in vector or matrix form instead of in a table, you can provide the data to correlate as the first input argument of groupsummary
.
[B,BG,BC] = groupsummary({[Height,Systolic],[Weight,Diastolic]},Location,@(x,y) xcov(x,y,0,"coeff"))
B = 3×2
0.6548 0.4419
0.6205 0.4447
0.7844 0.6226
BG = 3x1 categorical
County General Hospital
St. Mary's Medical Center
VA Hospital
BC = 3×1
39
24
37
Input Arguments
T
— Input table
table  timetable
Input table, specified as a table or timetable.
A
— Input array
column vector  matrix  cell array
Input array, specified as a column vector, group of column vectors stored as a matrix, or cell array of column vectors, character row vectors, or matrices.
When you specify a function handle for method
that
takes more than one input argument, the input array A
must be a cell array of column vectors, character row vectors, or matrices.
In each call to the function by group, the input arguments are the
corresponding columns of each element in the cell array. For example:
groupsummary({x1,y1},groupvars,@(x,y) myFun(x,y))
calculatesmyFun(x1,y1)
for each group.groupsummary({[x1 x2],[y1 y2]},groupvars,@(x,y) myFun(x,y))
first calculatesmyFun(x1,y1)
for each group and then calculatesmyFun(x2,y2)
for each group.
groupvars
— Grouping variables or vectors
scalar  vector  matrix  cell array  pattern  function handle  table vartype
subscript
Grouping variables or vectors, specified as one of these options:
For array input data,
groupvars
can be either a column vector with the same number of rows asA
or a group of column vectors arranged in a matrix or cell array.For table or timetable input data,
groupvars
indicates which variables to use to compute groups in the data. You can specify the grouping variables with any of the options in this table.Indexing Scheme Examples Variable names:
A string or character vector
A string array or cell array of character vectors
A
pattern
object
"A"
or'A'
— A variable namedA
["A" "B"]
or{'A','B'}
— Two variables namedA
andB
"Var"+digitsPattern(1)
— Variables named"Var"
followed by a single digit
Variable index:
An index number that refers to the location of a variable in the table
A vector of numbers
A logical vector. Typically, this vector is the same length as the number of variables, but you can omit trailing
0
orfalse
values.
3
— The third variable from the table[2 3]
— The second and third variables from the table[false false true]
— The third variable
Function handle:
A function handle that takes a table variable as input and returns a logical scalar
@isnumeric
— All the variables containing numeric values
Variable type:
A
vartype
subscript that selects variables of a specified type
vartype("numeric")
— All the variables containing numeric values
Example: groupsummary(T,"Var3")
method
— Computation method
"sum"
 "mean"
 "median"
 "all"
 function handle  cell array  ...
Computation method, specified as one of these values:
Method  Description 

"sum"  Sum 
"mean"  Mean 
"median"  Median 
"mode"  Mode 
"var"  Variance 
"std"  Standard deviation 
"min"  Minimum 
"max"  Maximum 
"range"  Maximum minus minimum 
"nummissing"  Number of missing elements 
"numunique"  Number of distinct nonmissing elements 
"nnz"  Number of nonzero and
non 
"all"  All computations previously listed 
You also can specify method
as a function handle that
returns one output per group whose first dimension has length 1. For table
input data, the function operates on each table variable separately.
When the input data is a table T
and you specify a
function handle for method
that takes more than one input
argument, you must specify datavars
. The
datavars
argument must be a cell array whose elements
indicate the table variables to use for each input into the method. In each
call to the function by group, the input arguments are the corresponding
table variables of the cell array elements. For example:
groupsummary(T,groupvars,@(x,y) myFun(x,y),{"x1","y1"})
calculatesmyFun(T.x1,T.y1)
for each group.groupsummary(T,groupvars,@(x,y) myFun(x,y),{["x1" "x2"],["y1" "y2"]})
first calculatesmyFun(T.x1,T.y1)
for each group and then calculatesmyFun(T.x2,T.y2)
for each group.
When the input data is a vector or matrix and you specify a function
handle for method
that takes more than one input
argument, the input data A
must be a cell array of
vectors or matrices. In each call to the function, the input arguments are
the corresponding columns of each element in the cell array. For example:
groupsummary({x1,y1},groupvars,@(x,y) myFun(x,y))
calculatesmyFun(x1,y1)
for each group.groupsummary({[x1 x2],[y1 y2]},groupvars,@(x,y) myFun(x,y))
first calculatesmyFun(x1,y1)
for each group and then calculatesmyFun(x2,y2)
for each group.
To specify multiple computations at a time, list the options in a cell
array, such as {"mean","median"}
or
{myFun1,myFun2}
.
NaN
values in the input data are automatically omitted
when using the method names described here, with the exception of
"nummissing"
. To include NaN
values, use a function handle for the method, such as
@sum
instead of "sum"
.
Data Types: char
 string
 cell
 function_handle
datavars
— Table variables to operate on
scalar  vector  cell array  function handle  pattern  table vartype
subscript
Table variables to operate on, specified as one of the options in this
table. datavars
indicates which variables of the input
table or timetable to apply the methods to. Other variables not specified by
datavars
are not operated on and do not pass through
to the output. When datavars
is not specified,
groupsummary
operates on each nongrouping
variable.
Indexing Scheme  Examples 

Variable names:


Variable index:


Function handle:


Variable type:


When the input data is a table T
and you specify a
function handle for method
that takes more than one input
argument, you must specify datavars
. The
datavars
argument must be a cell array whose elements
are any of the options in the table. The cell array elements indicate the
table variables to use for each input into the method. In each call to the
function by group, the input arguments are the corresponding table variables
of the cell array elements. For example:
groupsummary(T,groupvars,@(x,y) myFun(x,y),{"x1","y1"})
calculatesmyFun(T.x1,T.y1)
for each group.groupsummary(T,groupvars,@(x,y) myFun(x,y),{["x1" "x2"],["y1" "y2"]})
first calculatesmyFun(T.x1,T.y1)
for each group and then calculatesmyFun(T.x2,T.y2)
for each group.
Example: groupsummary(T,groupvars,method,["Var1" "Var2"
"Var4"])
groupbins
— Binning scheme for grouping variables or vectors
"none"
(default)  vector of bin edges  number of bins  length of time (bin width)  name of time unit (bin width)  cell array of binning methods
Binning scheme for grouping variables or vectors, specified as one or more of the following binning methods. Grouping variables or vectors and binning scheme arguments must be the same size, or one of them can be scalar.
"none"
— No binning.Vector of bin edges — The bin edges define the bins. You can specify the edges as numeric values or as
datetime
values fordatetime
grouping variables or vectors.Number of bins — The number determines how many equally spaced bins to create. You can specify the number of bins as a positive integer scalar.
Length of time (bin width) — The length of time determines the width of each bin. You can specify the bin width as a
duration
orcalendarDuration
scalar fordatetime
orduration
grouping variables or vectors.Name of time unit (bin width) — The name of the time unit determines the width of each bin. You can specify the bin width as one of the options in this table for
datetime
orduration
grouping variables or vectors.Value Description Data Type "second"
Each bin is 1 second.
datetime
andduration
"minute"
Each bin is 1 minute.
datetime
andduration
"hour"
Each bin is 1 hour.
datetime
andduration
"day"
Each bin is 1 calendar day. This value accounts for daylight saving time shifts.
datetime
andduration
"week"
Each bin is 1 calendar week. datetime
only"month"
Each bin is 1 calendar month. datetime
only"quarter"
Each bin is 1 calendar quarter. datetime
only"year"
Each bin is 1 calendar year. This value accounts for leap days.
datetime
andduration
"decade"
Each bin is 1 decade (10 calendar years). datetime
only"century"
Each bin is 1 century (100 calendar years). datetime
only"secondofminute"
Bins are seconds from 0 to 59.
datetime
only"minuteofhour"
Bins are minutes from 0 to 59.
datetime
only"hourofday"
Bins are hours from 0 to 23.
datetime
only"dayofweek"
Bins are days from 1 to 7. The first day of the week is Sunday.
datetime
only"dayname"
Bins are full day names, such as "Sunday"
.datetime
only"dayofmonth"
Bins are days from 1 to 31. datetime
only"dayofyear"
Bins are days from 1 to 366. datetime
only"weekofmonth"
Bins are weeks from 1 to 6. datetime
only"weekofyear"
Bins are weeks from 1 to 54. datetime
only"monthname"
Bins are full month names, such as "January"
.datetime
only"monthofyear"
Bins are months from 1 to 12.
datetime
only"quarterofyear"
Bins are quarters from 1 to 4. datetime
only
Example: G = groupsummary(T,"Var1",[Inf 0
Inf])
Example: G = groupsummary(T,["Var1" "Var2"],{"none"
"year"})
NameValue Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Namevalue arguments must appear after other arguments, but the order of the
pairs does not matter.
Example: G =
groupsummary(T,groupvars,groupbins,IncludedEdge="right")
Before R2021a, use commas to separate each name and value, and enclose
Name
in quotes.
Example: G =
groupsummary(T,groupvars,groupbins,"IncludedEdge","right")
IncludedEdge
— Included bin edge for binning scheme
"left"
(default)  "right"
Included bin edge for binning scheme, specified as either
"left"
or "right"
, indicating
which end of the bin interval is inclusive.
You can specify IncludedEdge
only if you also
specify groupbins
, and the value applies to all
binning methods for all grouping variables or vectors.
IncludeMissingGroups
— Option to treat missing values as a group
true
or
1
(default)  false
or 0
Option to treat missing values as a group, specified as a numeric or
logical 1
(true
) or
0
(false
). If
IncludeMissingGroups
is true
,
then groupsummary
treats missing values, such as
NaN
, in a grouping variable or vector as a group.
If a grouping variable or vector has no missing values, or if
IncludeMissingGroups
is false
,
then groupsummary
does not treat missing values as
a group.
IncludeEmptyGroups
— Option to include empty groups in group summary operation
false
or
0
(default)  true
or 1
Option to include empty groups in the group summary operation,
specified as a numeric or logical 0
(false
) or 1
(true
). If IncludeEmptyGroups
is false
, then groupsummary
omits empty groups. If IncludeEmptyGroups
is
true
, then groupsummary
includes empty groups.
An empty group occurs in these cases:
A possible value of a grouping variable or vector is not represented in the input data, such as in a categorical, logical, or binned numeric variable or vector. For example, if no row in the input table has a value of
true
for a logical grouping variable, thentrue
defines an empty group.A unique combination of grouping variables or vectors is not represented in the input data. For example, if there is no row in the input table where the value of grouping variable
A
isA1
and the value of grouping variableB
isB1
, thenA1_B1
defines an empty group.
Output Arguments
G
— Output table
table
Output table for table or timetable input data, returned as a table.
G
contains the computed groups, the number of
elements in each group, and if method
is provided, the
result of the specified computations.
B
— Output array
vector  matrix
Output array for array input data, returned as a vector or matrix.
B
contains the specified computations for each group.
When multiple methods are specified, groupsummary
horizontally concatenates the computations in the order that they were
listed.
BG
— Groups
column vector  cell array of column vectors
Groups for array input data, returned as a column vector or cell array of
column vectors. For a single grouping vector, the output groups are sorted
according to the order returned by the unique
function with the
"sorted"
option.
For more than one input vector, BG
is a cell array
containing column vectors of equal length. Information for each group is
contained in the elements of a row across all vectors in
BG
. Each group maps to the corresponding row of the
output array B
.
BC
— Group counts
column vector
Group counts for array input data, returned as a column vector.
BC
contains the number of elements in each group. The
length of BC
is the same as the length of the group
column vectors returned in BG
.
More About
Group Summary Computation
This table illustrates group summary computations.
Sample Table T  Syntax Example  Resulting Table 


groupsummary(T,"VarA") 

groupsummary(T,"VarA","mean") 
 
groupsummary(T,["VarA" "VarB"],{"none",[Inf 0 Inf]},"min") 
 
groupsummary(T,"VarA",["mean" "median" "mode"],"VarB") 

Tips
When making many calls to
groupsummary
, consider converting grouping variables to typecategorical
orlogical
when possible for improved performance. For example, if you have a string array grouping variable (such asHealthStatus
with elements"Poor"
,"Fair"
,"Good"
, and"Excellent"
), you can convert it to a categorical variable using the commandcategorical(HealthStatus)
.The
groupsummary
function computes onedimensional summary statistics. To compute grouped summaries in two dimensions, consider using thepivot
function.
Alternative Functionality
Live Editor Task
You can use groupsummary
functionality interactively by adding
the Compute by
Group task to a live script.
Extended Capabilities
Tall Arrays
Calculate with arrays that have more rows than fit in memory.
Usage notes and limitations:
If
A
andgroupvars
are both tall matrices, then they must have the same number of rows.If the first input is a tall matrix, then
groupvars
can be a cell array containing tall grouping vectors.The
groupvars
anddatavars
arguments do not support function handles.The
IncludeEmptyGroups
namevalue argument is not supported.The
"median"
,"mode"
, and"numunique"
methods are not supported and are not included when specifying the"all"
method.For tall datetime arrays, the
"std"
method is not supported.If the
method
argument is a function handle, then it must be a valid input forsplitapply
operating on a tall array. If the function handle takes multiple inputs, then the first input togroupsummary
must be a tall table.The order of the groups might be different compared to inmemory
groupsummary
calculations.When grouping by discretized datetime arrays, the categorical group names are different compared to inmemory
groupsummary
calculations.
For more information, see Tall Arrays.
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
Usage notes and limitations:
Sparse inputs are not supported.
Binning scheme is not supported for datetime or duration data.
Input data that is a heterogeneous cell array with variablesized contents is not supported.
Input tables that contain multidimensional arrays are not supported.
Input data that contains cell arrays of character vectors or cell arrays of cell arrays is not supported.
Computation methods must be constant.
Grouping variables must be constant when the first input argument is a table.
Data variables must be constant.
Data variables cannot be nested tables.
Binning scheme specified as character vectors or strings must be constant.
Namevalue arguments must be constant.
Computation methods cannot return sparse, multidimensional, or cell array results.
If the number of group variables can change at runtime, the second output
BG
is a cell array.
ThreadBased Environment
Run code in the background using MATLAB® backgroundPool
or accelerate code with Parallel Computing Toolbox™ ThreadPool
.
This function fully supports threadbased environments. For more information, see Run MATLAB Functions in ThreadBased Environment.
Version History
Introduced in R2018aR2024a: Apply multiple binning methods to grouping variable
Apply multiple binning methods to one grouping variable or vector by specifying a cell array of binning methods.
R2023a: Return the number of unique elements
Compute the number of distinct nonmissing elements in each group of data. Specify
the "numunique"
or "all"
computation
method.
The "all"
computation method now returns the number of unique
values in addition the computation methods in the previous release.
R2022b: Character arrays have no standard missing value
Character arrays have no default definition of a standard missing value.
Therefore, the nummissing
method treats blank character array
elements (' '
) as nonmissing.
R2022a: Code generation support
Generate C or C++ code for the groupsummary
function. For
usage notes and limitations, see C/C++ Code
Generation.
R2022a: Improved performance with small group size
The groupsummary
function shows improved performance,
especially when the data count in each group is small.
For example, this code performs group summary computations on a matrix with 500 groups with a count of 10 each. The code is about 2.70x faster than in the previous release.
function timingGroupsummary data = (1:5000)'; groups = repelem(1:length(data)/10,10)'; p = randperm(length(data)); data = data(p); groups = groups(p); tic for k = 1:300 G = groupsummary(data,groups,"mean"); end toc end
The approximate execution times are:
R2021b: 2.65 s
R2022a: 0.98 s
The code was timed on a Windows^{®} 10, Intel^{®}
Xeon^{®} CPU E51650 v4 @ 3.60 GHz test system by calling the
timingGroupsummary
function.
R2022a: Accept data types with no standard missing value
The "nummissing"
and "nnz"
methods no longer
error for input data types with no default definition of a standard missing
value.
Code that relied on the errors that MATLAB threw for those inputs, such as code
within a try
/catch
block, may no longer catch
those errors.
See Also
Functions
pivot
grouptransform
groupfilter
groupcounts
findgroups
splitapply
discretize
varfun
rowfun
convertvars
vartype
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