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import data into matlab from database table -凯发k8网页登录

import data into matlab from database table

syntax

data = sqlread(conn,tablename)
data = sqlread(conn,tablename,opts)
data = sqlread(___,name,value)
[data,metadata] = sqlread(___)

description

example

data = sqlread(conn,tablename) returns a table by importing data into matlab® from a database table. executing this function is the equivalent of writing a select * from tablename sql statement in ansi sql.

example

data = sqlread(conn,tablename,opts) customizes options for importing data from a database table using the object.

example

data = sqlread(___,name,value) specifies additional options using one or more name-value arguments with any of the previous input argument combinations. for example, specify catalog = "cat" to import data from a database table stored in the "cat" catalog.

example

[data,metadata] = sqlread(___) also returns the metadata table, which contains metadata information about the imported data.

examples

use an odbc connection to import product data from a database table into matlab® using a microsoft® sql server® database. then, perform a simple data analysis.

create an odbc database connection to a microsoft sql server database with windows® authentication. specify a blank username and password. the database contains the table producttable.

datasource = 'ms sql server auth';
conn = database(datasource,'','');

check the database connection. if the message property is empty, then the connection is successful.

conn.message

ans =
     []

import data from the database table producttable. the sqlread function returns a matlab® table that contains the product data.

tablename = 'producttable';
data = sqlread(conn,tablename);

display the first five products.

head(data,5)

    productnumber    stocknumber    suppliernumber    unitcost    productdescription
    _____________    ___________    ______________    ________    __________________
          9          1.2597e 05          1003            13       {'victorian doll'}
          8          2.1257e 05          1001             5       {'train set'     }
          7          3.8912e 05          1007            16       {'engine kit'    }
          2          4.0031e 05          1002             9       {'painting set'  }
          4          4.0034e 05          1008            21       {'space cruiser' }

now, import the data using a row filter. the filter condition is that unitcost must be less than 15.

rf = rowfilter("unitcost");
rf = rf.unitcost < 15;
data = sqlread(conn,tablename,"rowfilter",rf);

again, display the first five products.

head(data,5)

    productnumber    stocknumber    suppliernumber    unitcost    productdescription 
    _____________    ___________    ______________    ________    ___________________
          9          1.2597e 05          1003            13       {'victorian doll' }
          8          2.1257e 05          1001             5       {'train set'      }
          2          4.0031e 05          1002             9       {'painting set'   }
          1          4.0034e 05          1001            14       {'building blocks'}
          5          4.0046e 05          1005             3       {'tin soldier'    }

close the database connection.

close(conn)

customize import options when importing data from a database table. control the import options by creating an sqlimportoptions object. then, customize import options for different database columns. import data using the sqlread function.

this example uses the patients.xls file, which contains the columns gender, location, selfassessedhealthstatus, and smoker. the example also uses a microsoft® sql server® version 11.00.2100 database and the microsoft sql server driver 11.00.5058.

create a database connection to a microsoft sql server database with windows® authentication. specify a blank username and password.

datasource = 'ms sql server auth';
conn = database(datasource,'','');

load patient information into the matlab® workspace.

patients = readtable('patients.xls');

create the patients database table using the patient information.

tablename = 'patients';
sqlwrite(conn,tablename,patients)

create an sqlimportoptions object using the patients database table and the databaseimportoptions function.

opts = databaseimportoptions(conn,tablename)
opts = 
  sqlimportoptions with properties:
           excludeduplicates: false
          variablenamingrule: 'modify'
               variablenames: {'lastname', 'gender', 'age' ... and 7 more}
               variabletypes: {'char', 'char', 'double' ... and 7 more}
       selectedvariablenames: {'lastname', 'gender', 'age' ... and 7 more}
                  fillvalues: {'', '',  nan  ... and 7 more }
                   rowfilter:  
             variableoptions: show all 10 variableoptions

display the current import options for the variables selected in the selectedvariablenames property of the sqlimportoptions object.

vars = opts.selectedvariablenames;
varopts = getoptions(opts,vars)
varopts = 
    1x10 sqlvariableimportoptions array with properties:
   variable options:
                      (1) |      (2) |      (3) |        (4) |      (5) |      (6) |      (7) |        (8) |         (9) |                       (10)
         name: 'lastname' | 'gender' |    'age' | 'location' | 'height' | 'weight' | 'smoker' | 'systolic' | 'diastolic' | 'selfassessedhealthstatus'
         type:     'char' |   'char' | 'double' |     'char' | 'double' | 'double' | 'double' |   'double' |    'double' |                     'char'
  missingrule:     'fill' |   'fill' |   'fill' |     'fill' |   'fill' |   'fill' |   'fill' |     'fill' |      'fill' |                     'fill'
    fillvalue:         '' |       '' |      nan |         '' |      nan |      nan |      nan |        nan |         nan |                         ''
	to access sub-properties of each variable, use getoptions

change the data types for the gender, location, selfassessedhealthstatus, and smoker variables using the setoptions function. because the gender, location, and selfassessedhealthstatus variables indicate a finite set of repeating values, change their data type to categorical. because the smoker variable stores the values 0 and 1, change its data type to logical. then, display the updated import options.

opts = setoptions(opts,{'gender','location','selfassessedhealthstatus'}, ...
    'type','categorical');
opts = setoptions(opts,'smoker','type','logical');
varopts = getoptions(opts,{'gender','location','smoker', ...
    'selfassessedhealthstatus'})
varopts = 
    1x4 sqlvariableimportoptions array with properties:
   variable options:
                         (1) |           (2) |       (3) |                        (4)
         name:      'gender' |    'location' |  'smoker' | 'selfassessedhealthstatus'
         type: 'categorical' | 'categorical' | 'logical' |              'categorical'
  missingrule:        'fill' |        'fill' |    'fill' |                     'fill'
    fillvalue:    |    |         0 |                
	to access sub-properties of each variable, use getoptions

import the patients database table using the sqlread function, and display the last eight rows of the table.

data = sqlread(conn,tablename,opts);
tail(data)
      lastname       gender    age            location             height    weight    smoker    systolic    diastolic    selfassessedhealthstatus
    _____________    ______    ___    _________________________    ______    ______    ______    ________    _________    ________________________
    {'foster'   }    female    30     st. mary's medical center      70       124      false       130          91               fair             
    {'gonzales' }    male      48     county general hospital        71       174      false       123          79               good             
    {'bryant'   }    female    48     county general hospital        66       134      false       129          73               excellent        
    {'alexander'}    male      25     county general hospital        69       171      true        128          99               good             
    {'russell'  }    male      44     va hospital                    69       188      true        124          92               good             
    {'griffin'  }    male      49     county general hospital        70       186      false       119          74               fair             
    {'diaz'     }    male      45     county general hospital        68       172      true        136          93               good             
    {'hayes'    }    male      48     county general hospital        66       177      false       114          86               fair

display a summary of the imported data. the sqlread function applies the import options to the variables in the imported data.

summary(data)
variables:
    lastname: 100×1 cell array of character vectors
    gender: 100×1 categorical
        values:
            female       53   
            male         47   
    age: 100×1 double
        values:
            min          25   
            median       39   
            max          50   
    location: 100×1 categorical
        values:
            county general hospital         39   
            st. mary s medical center       24   
            va hospital                     37   
    height: 100×1 double
        values:
            min          60   
            median       67   
            max          72   
    weight: 100×1 double
        values:
            min          111  
            median     142.5  
            max          202  
    smoker: 100×1 logical
        values:
            true        34   
            false       66   
    systolic: 100×1 double
        values:
            min         109   
            median      122   
            max         138   
    diastolic: 100×1 double
        values:
            min           68  
            median      81.5  
            max           99  
    selfassessedhealthstatus: 100×1 categorical
        values:
            excellent       34   
            fair            15   
            good            40   
            poor            11

now set the filter condition to import only data for patients older than 40 years and not taller than 68 inches.

opts.rowfilter = opts.rowfilter.age > 40 & opts.rowfilter.height <= 68
opts = 
  sqlimportoptions with properties:
           excludeduplicates: false
          variablenamingrule: 'modify'
               variablenames: {'lastname', 'gender', 'age' ... and 7 more}
               variabletypes: {'char', 'categorical', 'double' ... and 7 more}
       selectedvariablenames: {'lastname', 'gender', 'age' ... and 7 more}
                  fillvalues: {'',  ,  nan  ... and 7 more }
                   rowfilter: age > 40 & height <= 68 
             variableoptions: show all 10 variableoptions

again, import the patients database table using the sqlread function, and display a summary of the imported data.

data = sqlread(conn,tablename,opts);
summary(data)
variables:
    lastname: 24×1 cell array of character vectors
    gender: 24×1 categorical
        values:
            female       17   
            male          7   
    age: 24×1 double
        values:
            min           41  
            median      45.5  
            max           50  
    location: 24×1 categorical
        values:
            county general hospital         13   
            st. mary s medical center        5   
            va hospital                      6   
    height: 24×1 double
        values:
            min          62   
            median       66   
            max          68   
    weight: 24×1 double
        values:
            min         119   
            median      137   
            max         194   
    smoker: 24×1 logical
        values:
            true         8   
            false       16   
    systolic: 24×1 double
        values:
            min          114  
            median     121.5  
            max          138  
    diastolic: 24×1 double
        values:
            min           68  
            median      81.5  
            max           96  
    selfassessedhealthstatus: 24×1 categorical
        values:
            excellent        7   
            fair             3   
            good            10   
            poor             4

delete the patients database table using the execute function.

sqlquery = ['drop table ' tablename];
execute(conn,sqlquery)

close the database connection.

close(conn)

use an odbc connection to import product data from a database table into matlab® using a microsoft® sql server® database. specify the schema where the database table is stored. then, sort and filter the rows in the imported data and perform a simple data analysis.

create an odbc database connection to a microsoft sql server database with windows® authentication. specify a blank user name and password. the database contains the table producttable.

datasource = 'ms sql server auth';
conn = database(datasource,'','');

check the database connection. if the message property is empty, then the connection is successful.

conn.message

ans =
     []

import data from the table producttable. specify the database schema dbo. the data table contains the product data.

tablename = 'producttable';
data = sqlread(conn,tablename,'schema','dbo');

display the first few products.

data(1:3,:)

ans =
  3×5 table
    productnumber    stocknumber    suppliernumber    unitcost    productdescription
    _____________    ___________    ______________    ________    __________________
          9          1.2597e 05          1003            13        'victorian doll' 
          8          2.1257e 05          1001             5        'train set'      
          7          3.8912e 05          1007            16        'engine kit'

display the first few product descriptions.

data.productdescription(1:3)

ans =
  3×1 cell array
    {'victorian doll'}
    {'train set'     }
    {'engine kit'    }

sort the rows in data by the product description column in alphabetical order.

column = 'productdescription';
data = sortrows(data,column);

display the first few product descriptions after sorting.

data.productdescription(1:3)

ans =
  3×1 cell array
    {'building blocks'}
    {'convertible'    }
    {'engine kit'     }

close the database connection.

close(conn)

use an odbc connection to import product data from a database table into matlab® using a microsoft® sql server® database. specify the maximum number of rows to import from the database table.

create an odbc database connection to a microsoft sql server database with windows® authentication. specify a blank user name and password. the database contains the table producttable.

datasource = 'ms sql server auth';
conn = database(datasource,'','');

check the database connection. if the message property is empty, then the connection is successful.

conn.message

ans =
     []

import data from the table producttable. import only three rows of data from the database table. the data table contains the product data.

tablename = 'producttable';
data = sqlread(conn,tablename,'maxrows',3)

data =
  3×5 table
    productnumber    stocknumber    suppliernumber    unitcost    productdescription
    _____________    ___________    ______________    ________    __________________
          9          1.2597e 05          1003            13        'victorian doll' 
          8          2.1257e 05          1001             5        'train set'      
          7          3.8912e 05          1007            16        'engine kit'

close the database connection.

close(conn)

import product data from a microsoft® sql server® database table into matlab® by using an odbc connection. the table contains a variable name with a non-ascii character. when importing data, preserve the names of all the variables.

create an odbc database connection to an sql server database with windows® authentication. specify a blank user name and password. the database contains the table producttable.

datasource = "mssqlserverauth";
conn = database(datasource,"","");

check the database connection. if the message property is empty, then the connection is successful.

conn.message
ans =
     []

add a column to the database table producttable. the column name contains a non-ascii character.

sqlquery = "alter table producttable add tamaño varchar(30)"; 
execute(conn,sqlquery)

import data from the database table producttable. the sqlread function returns a matlab table that contains the product data. display the first three rows of the data in the table.

tablename = "producttable";
data = sqlread(conn,tablename);
head(data,3)
ans=3×6 table
    productnumber    stocknumber    suppliernumber    unitcost    productdescription      tama_o  
    _____________    ___________    ______________    ________    __________________    __________
          9          1.2597e 05          1003            13       {'victorian doll'}    {0×0 char}
          8          2.1257e 05          1001             5       {'train set'     }    {0×0 char}
          7          3.8912e 05          1007            16       {'engine kit'    }    {0×0 char}

the sqlread function converts the name of the new variable into ascii characters.

preserve the name of the variable that contains the non-ascii character by specifying the variablenamingrule name-value pair argument. import the data again.

data = sqlread(conn,tablename, ...
    'variablenamingrule',"preserve");
head(data,3)
ans=3×6 table
    productnumber    stocknumber    suppliernumber    unitcost    productdescription      tamaño  
    _____________    ___________    ______________    ________    __________________    __________
          9          1.2597e 05          1003            13       {'victorian doll'}    {0×0 char}
          8          2.1257e 05          1001             5       {'train set'     }    {0×0 char}
          7          3.8912e 05          1007            16       {'engine kit'    }    {0×0 char}

the sqlread function preserves the non-ascii character in the variable name.

close the database connection.

close(conn)

retrieve metadata information when importing data from a database table. import data using the sqlread function and explore the metadata information by using dot notation.

this example uses the outages.csv file, which contains outage data. also, the example uses a microsoft® sql server® version 11.00.2100 database and the microsoft sql server driver 11.00.5058.

create a database connection to a microsoft sql server database with windows® authentication. specify a blank user name and password.

datasource = "ms sql server auth";
conn = database(datasource,"","");

load outage information into the matlab® workspace.

outages = readtable("outages.csv");

create the outages database table using the outage information.

tablename = "outages";
sqlwrite(conn,tablename,outages)

import the data into the matlab workspace and return metadata information about the imported data. 

[data,metadata] = sqlread(conn,tablename);

view the names of the variables in the imported data.

metadata.properties.rownames
ans = 6×1 cell array
    {'region'         }
    {'outagetime'     }
    {'loss'           }
    {'customers'      }
    {'restorationtime'}
    {'cause'          }

view the data type of each variable in the imported data.

metadata.variabletype
ans = 6×1 cell array
    {'char'  }
    {'char'  }
    {'double'}
    {'double'}
    {'char'  }
    {'char'  }

view the missing data value for each variable in the imported data.

metadata.fillvalue
ans = 6×1 cell array
    {0×0 char}
    {0×0 char}
    {[   nan]}
    {[   nan]}
    {0×0 char}
    {0×0 char}

view the indices of the missing data for each variable in the imported data.

metadata.missingrows
ans = 6×1 cell array
    {  0×1 double}
    {  0×1 double}
    {604×1 double}
    {328×1 double}
    { 29×1 double}
    {  0×1 double}

display the first eight rows of the imported data that contain missing restoration time. data contains restoration time in the fifth variable. use the numeric indices to find the rows with missing data.

index = metadata.missingrows{5,1};
nullrestoration = data(index,:);
head(nullrestoration)
ans=8×6 table
      region              outagetime             loss     customers     restorationtime          cause       
    ___________    _________________________    ______    __________    _______________    __________________
    'southeast'    '2003-01-23 00:49:00.000'    530.14    2.1204e 05          ''           'winter storm'    
    'northeast'    '2004-09-18 05:54:00.000'         0             0          ''           'equipment fault' 
    'midwest'      '2002-04-20 16:46:00.000'     23141           nan          ''           'unknown'         
    'northeast'    '2004-09-16 19:42:00.000'      4718           nan          ''           'unknown'         
    'southeast'    '2005-09-14 15:45:00.000'    1839.2    3.4144e 05          ''           'severe storm'    
    'southeast'    '2004-08-17 17:34:00.000'     624.1    1.7879e 05          ''           'severe storm'    
    'southeast'    '2006-01-28 23:13:00.000'    498.78           nan          ''           'energy emergency'
    'west'         '2003-06-20 18:22:00.000'         0             0          ''           'energy emergency'

delete the outages database table using the execute function.

sqlstr = "drop table ";
sqlquery = strcat(sqlstr,tablename);
execute(conn,sqlquery)

close the database connection.

close(conn)

input arguments

database connection, specified as an odbc object or jdbc object created using the function.

database table name, specified as a string scalar or character vector denoting the name of a table in the database.

example: "employees"

data types: string | char

database import options, specified as an object.

name-value arguments

specify optional pairs of arguments as name1=value1,...,namen=valuen, where name is the argument name and value is the corresponding value. name-value arguments must appear after other arguments, but the order of the pairs does not matter.

before r2021a, use commas to separate each name and value, and enclose name in quotes.

example: data = sqlread(conn,'inventorytable','catalog','toy_store','schema','dbo','maxrows',5) imports five rows of data from the database table inventorytable stored in the toy_store catalog and the dbo schema.

database catalog name, specified as a string scalar or character vector. a catalog serves as the container for the schemas in a database and contains related metadata information. a database can have multiple catalogs.

example: catalog = "toy_store"

data types: string | char

database schema name, specified as a string scalar or character vector. a schema defines the database tables, views, relationships among tables, and other elements. a database catalog can have numerous schemas.

example: schema = "dbo"

data types: string | char

maximum number of rows to return, specified as the comma-separated pair consisting of 'maxrows' and a positive numeric scalar. by default, the sqlread function returns all rows from the executed sql query. use this name-value pair argument to limit the number of rows imported into matlab.

example: 'maxrows',10

data types: double

variable naming rule, specified as the comma-separated pair consisting of 'variablenamingrule' and one of these values:

  • "modify" — remove non-ascii characters from variable names when the sqlread function imports data.

  • "preserve" — preserve most variable names when the sqlread function imports data. for details, see the limitations section.

example: 'variablenamingrule',"modify"

data types: string

row filter condition, specified as a matlab.io.rowfilter object.

example: rf = rowfilter("productnumber"); rf = rf.productnumber <= 5; sqlread(conn,tablename,"rowfilter",rf)

output arguments

imported data, returned as a table. the rows of the table correspond to the rows in the database table tablename. the variables in the table correspond to each column in the database table. for columns that have numeric data types in the database table, the variable data types in data are double by default. for columns that have text, date, time, or timestamp data types in the database table, the variable data types are cell arrays of character vectors by default.

if the database table contains no data to import, then data is an empty table.

metadata information, returned as a table with these variables.

variable namevariable descriptionvariable data type

variabletype

data type of each variable in the imported data

cell array of character vectors

fillvalue

value of missing data for each variable in the imported data

cell array of missing data values

missingrows

indices for each occurrence of missing data in each variable of the imported data

cell array of numeric indices

by default, the sqlread function imports text data as a character vector and numeric data as a double. fillvalue is an empty character array (for text data) or nan (for numeric data) by default. to change the missing data value to another value, use the object.

the rownames property of the metadata table contains the names of the variables in the imported data.

limitations

  • the sqlread function returns an error when you use the variablenamingrule name-value argument with the object opts.

  • when the variablenamingrule name-value pair argument is set to the value "modify":

    • the variable names properties, rownames, and variablenames are reserved identifiers for the table data type.

    • the length of each variable name must be less than the number returned by .

  • the sqlread function returns an error if you specify the rowfilter name-value argument with the sqlimportoptions object opts. it is ambiguous which of the rowfilter object to use in this case, especially if the filter conditions are different.

version history

introduced in r2018a

see also

functions

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