map list to bin numbers












1












$begingroup$


Does WL have the equivalent of Matlab's discretize or NumPy's digitize? I.e., a function that takes a length-N list and a list of bin edges and returns a length-N list of bin numbers, mapping each list item to its bin number?






list-manipulation data







share|improve this question











$endgroup$












  • $begingroup$
    HistogramList seems similar. This could also be done efficiently with GroupBy and some easy little Compile-d selection determiner. Or maybe hit it first with Sort then write something that only checks the next bin up. Again, can be easily Compile-d.
    $endgroup$
    – b3m2a1
    6 hours ago












  • $begingroup$
    I need it to work like a map (in terms of the order of the items in the resulting list). Of course it is possible to write something ...
    $endgroup$
    – Alan
    4 hours ago












  • $begingroup$
    Related: 140577
    $endgroup$
    – Carl Woll
    1 hour ago












  • $begingroup$
    Did you try BinCounts? I guess it is what you need.
    $endgroup$
    – Rom38
    18 mins ago
















1












$begingroup$


Does WL have the equivalent of Matlab's discretize or NumPy's digitize? I.e., a function that takes a length-N list and a list of bin edges and returns a length-N list of bin numbers, mapping each list item to its bin number?






list-manipulation data







share|improve this question











$endgroup$












  • $begingroup$
    HistogramList seems similar. This could also be done efficiently with GroupBy and some easy little Compile-d selection determiner. Or maybe hit it first with Sort then write something that only checks the next bin up. Again, can be easily Compile-d.
    $endgroup$
    – b3m2a1
    6 hours ago












  • $begingroup$
    I need it to work like a map (in terms of the order of the items in the resulting list). Of course it is possible to write something ...
    $endgroup$
    – Alan
    4 hours ago












  • $begingroup$
    Related: 140577
    $endgroup$
    – Carl Woll
    1 hour ago












  • $begingroup$
    Did you try BinCounts? I guess it is what you need.
    $endgroup$
    – Rom38
    18 mins ago














1












1








1





$begingroup$


Does WL have the equivalent of Matlab's discretize or NumPy's digitize? I.e., a function that takes a length-N list and a list of bin edges and returns a length-N list of bin numbers, mapping each list item to its bin number?






list-manipulation data







share|improve this question











$endgroup$




Does WL have the equivalent of Matlab's discretize or NumPy's digitize? I.e., a function that takes a length-N list and a list of bin edges and returns a length-N list of bin numbers, mapping each list item to its bin number?






list-manipulation data




list-manipulation data






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited 1 hour ago









Carl Woll

73k396189




73k396189










asked 6 hours ago









AlanAlan

6,6331125




6,6331125












  • $begingroup$
    HistogramList seems similar. This could also be done efficiently with GroupBy and some easy little Compile-d selection determiner. Or maybe hit it first with Sort then write something that only checks the next bin up. Again, can be easily Compile-d.
    $endgroup$
    – b3m2a1
    6 hours ago












  • $begingroup$
    I need it to work like a map (in terms of the order of the items in the resulting list). Of course it is possible to write something ...
    $endgroup$
    – Alan
    4 hours ago












  • $begingroup$
    Related: 140577
    $endgroup$
    – Carl Woll
    1 hour ago












  • $begingroup$
    Did you try BinCounts? I guess it is what you need.
    $endgroup$
    – Rom38
    18 mins ago


















  • $begingroup$
    HistogramList seems similar. This could also be done efficiently with GroupBy and some easy little Compile-d selection determiner. Or maybe hit it first with Sort then write something that only checks the next bin up. Again, can be easily Compile-d.
    $endgroup$
    – b3m2a1
    6 hours ago












  • $begingroup$
    I need it to work like a map (in terms of the order of the items in the resulting list). Of course it is possible to write something ...
    $endgroup$
    – Alan
    4 hours ago












  • $begingroup$
    Related: 140577
    $endgroup$
    – Carl Woll
    1 hour ago












  • $begingroup$
    Did you try BinCounts? I guess it is what you need.
    $endgroup$
    – Rom38
    18 mins ago
















$begingroup$
HistogramList seems similar. This could also be done efficiently with GroupBy and some easy little Compile-d selection determiner. Or maybe hit it first with Sort then write something that only checks the next bin up. Again, can be easily Compile-d.
$endgroup$
– b3m2a1
6 hours ago






$begingroup$
HistogramList seems similar. This could also be done efficiently with GroupBy and some easy little Compile-d selection determiner. Or maybe hit it first with Sort then write something that only checks the next bin up. Again, can be easily Compile-d.
$endgroup$
– b3m2a1
6 hours ago














$begingroup$
I need it to work like a map (in terms of the order of the items in the resulting list). Of course it is possible to write something ...
$endgroup$
– Alan
4 hours ago






$begingroup$
I need it to work like a map (in terms of the order of the items in the resulting list). Of course it is possible to write something ...
$endgroup$
– Alan
4 hours ago














$begingroup$
Related: 140577
$endgroup$
– Carl Woll
1 hour ago






$begingroup$
Related: 140577
$endgroup$
– Carl Woll
1 hour ago














$begingroup$
Did you try BinCounts? I guess it is what you need.
$endgroup$
– Rom38
18 mins ago




$begingroup$
Did you try BinCounts? I guess it is what you need.
$endgroup$
– Rom38
18 mins ago










2 Answers
2






active

oldest

votes


















4












$begingroup$

This is a very quick-n-dirty, but may serve as a simple example.



This creates a piecewise function following the first definition in Matlab's discretize documentation, then applies that to the data.



disc[data_, edges_] := Module[{e = Partition[edges, 2, 1], p, l},

   l = Length@e;

   Table[Piecewise[

                   Append[Table[{i, e[[i, 1]] <= x < e[[i, 2]]}, {i, l - 1}]

                          , {l,e[[l, 1]] <= x <= e[[l, 2]]}]

                   , "NaN"]

          , {x, data}]];


From the first example in the above referenced documentation:



data={1, 1, 2, 3, 6, 5, 8, 10, 4, 4};

edges={2, 4, 6, 8, 10};



disc[data,edges]



{NaN,NaN,1,1,3,2,4,4,2,2}




I'm sure there are more efficient/elegant solutions, and will revisit as time permits.






share|improve this answer









$endgroup$





















    0












    $begingroup$

    Here's a version based on Nearest:



    digitize[edges_] := DigitizeFunction[edges, Nearest[edges -> "Index"]]
    
digitize[data_, edges_] := digitize[edges][data]
    

    
DigitizeFunction[edges_, nf_NearestFunction][data_] := With[{init = nf[data][[All, 1]]},
    
    init + UnitStep[data - edges[[init]]] - 1
    
]


    For example:



    SeedRandom[1]
    
data = RandomReal[10, 10]
    
digitize[data, {2, 4, 5, 7, 8}]



    {8.17389, 1.1142, 7.89526, 1.87803, 2.41361, 0.657388, 5.42247, 2.31155, 3.96006, 7.00474}



    {5, 0, 4, 0, 1, 0, 3, 1, 1, 4}




    Note that I broke up the definition of digitize into two pieces, so that if you do this for multiple data sets with the same edges list, you only need to compute the nearest function once.






    share|improve this answer











    $endgroup$














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      2 Answers
      2






      active

      oldest

      votes








      2 Answers
      2






      active

      oldest

      votes









      active

      oldest

      votes






      active

      oldest

      votes









      4












      $begingroup$

      This is a very quick-n-dirty, but may serve as a simple example.



      This creates a piecewise function following the first definition in Matlab's discretize documentation, then applies that to the data.



      disc[data_, edges_] := Module[{e = Partition[edges, 2, 1], p, l},
      
   l = Length@e;
      
   Table[Piecewise[
      
                   Append[Table[{i, e[[i, 1]] <= x < e[[i, 2]]}, {i, l - 1}]
      
                          , {l,e[[l, 1]] <= x <= e[[l, 2]]}]
      
                   , "NaN"]
      
          , {x, data}]];


      From the first example in the above referenced documentation:



      data={1, 1, 2, 3, 6, 5, 8, 10, 4, 4};
      
edges={2, 4, 6, 8, 10};
      

      
disc[data,edges]



      {NaN,NaN,1,1,3,2,4,4,2,2}




      I'm sure there are more efficient/elegant solutions, and will revisit as time permits.






      share|improve this answer









      $endgroup$


















        4












        $begingroup$

        This is a very quick-n-dirty, but may serve as a simple example.



        This creates a piecewise function following the first definition in Matlab's discretize documentation, then applies that to the data.



        disc[data_, edges_] := Module[{e = Partition[edges, 2, 1], p, l},
        
   l = Length@e;
        
   Table[Piecewise[
        
                   Append[Table[{i, e[[i, 1]] <= x < e[[i, 2]]}, {i, l - 1}]
        
                          , {l,e[[l, 1]] <= x <= e[[l, 2]]}]
        
                   , "NaN"]
        
          , {x, data}]];


        From the first example in the above referenced documentation:



        data={1, 1, 2, 3, 6, 5, 8, 10, 4, 4};
        
edges={2, 4, 6, 8, 10};
        

        
disc[data,edges]



        {NaN,NaN,1,1,3,2,4,4,2,2}




        I'm sure there are more efficient/elegant solutions, and will revisit as time permits.






        share|improve this answer









        $endgroup$
















          4












          4








          4





          $begingroup$

          This is a very quick-n-dirty, but may serve as a simple example.



          This creates a piecewise function following the first definition in Matlab's discretize documentation, then applies that to the data.



          disc[data_, edges_] := Module[{e = Partition[edges, 2, 1], p, l},
          
   l = Length@e;
          
   Table[Piecewise[
          
                   Append[Table[{i, e[[i, 1]] <= x < e[[i, 2]]}, {i, l - 1}]
          
                          , {l,e[[l, 1]] <= x <= e[[l, 2]]}]
          
                   , "NaN"]
          
          , {x, data}]];


          From the first example in the above referenced documentation:



          data={1, 1, 2, 3, 6, 5, 8, 10, 4, 4};
          
edges={2, 4, 6, 8, 10};
          

          
disc[data,edges]



          {NaN,NaN,1,1,3,2,4,4,2,2}




          I'm sure there are more efficient/elegant solutions, and will revisit as time permits.






          share|improve this answer









          $endgroup$



          This is a very quick-n-dirty, but may serve as a simple example.



          This creates a piecewise function following the first definition in Matlab's discretize documentation, then applies that to the data.



          disc[data_, edges_] := Module[{e = Partition[edges, 2, 1], p, l},
          
   l = Length@e;
          
   Table[Piecewise[
          
                   Append[Table[{i, e[[i, 1]] <= x < e[[i, 2]]}, {i, l - 1}]
          
                          , {l,e[[l, 1]] <= x <= e[[l, 2]]}]
          
                   , "NaN"]
          
          , {x, data}]];


          From the first example in the above referenced documentation:



          data={1, 1, 2, 3, 6, 5, 8, 10, 4, 4};
          
edges={2, 4, 6, 8, 10};
          

          
disc[data,edges]



          {NaN,NaN,1,1,3,2,4,4,2,2}




          I'm sure there are more efficient/elegant solutions, and will revisit as time permits.







          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered 4 hours ago









          ciaociao

          17.4k138109




          17.4k138109























              0












              $begingroup$

              Here's a version based on Nearest:



              digitize[edges_] := DigitizeFunction[edges, Nearest[edges -> "Index"]]
              
digitize[data_, edges_] := digitize[edges][data]
              

              
DigitizeFunction[edges_, nf_NearestFunction][data_] := With[{init = nf[data][[All, 1]]},
              
    init + UnitStep[data - edges[[init]]] - 1
              
]


              For example:



              SeedRandom[1]
              
data = RandomReal[10, 10]
              
digitize[data, {2, 4, 5, 7, 8}]



              {8.17389, 1.1142, 7.89526, 1.87803, 2.41361, 0.657388, 5.42247, 2.31155, 3.96006, 7.00474}



              {5, 0, 4, 0, 1, 0, 3, 1, 1, 4}




              Note that I broke up the definition of digitize into two pieces, so that if you do this for multiple data sets with the same edges list, you only need to compute the nearest function once.






              share|improve this answer











              $endgroup$


















                0












                $begingroup$

                Here's a version based on Nearest:



                digitize[edges_] := DigitizeFunction[edges, Nearest[edges -> "Index"]]
                
digitize[data_, edges_] := digitize[edges][data]
                

                
DigitizeFunction[edges_, nf_NearestFunction][data_] := With[{init = nf[data][[All, 1]]},
                
    init + UnitStep[data - edges[[init]]] - 1
                
]


                For example:



                SeedRandom[1]
                
data = RandomReal[10, 10]
                
digitize[data, {2, 4, 5, 7, 8}]



                {8.17389, 1.1142, 7.89526, 1.87803, 2.41361, 0.657388, 5.42247, 2.31155, 3.96006, 7.00474}



                {5, 0, 4, 0, 1, 0, 3, 1, 1, 4}




                Note that I broke up the definition of digitize into two pieces, so that if you do this for multiple data sets with the same edges list, you only need to compute the nearest function once.






                share|improve this answer











                $endgroup$
















                  0












                  0








                  0





                  $begingroup$

                  Here's a version based on Nearest:



                  digitize[edges_] := DigitizeFunction[edges, Nearest[edges -> "Index"]]
                  
digitize[data_, edges_] := digitize[edges][data]
                  

                  
DigitizeFunction[edges_, nf_NearestFunction][data_] := With[{init = nf[data][[All, 1]]},
                  
    init + UnitStep[data - edges[[init]]] - 1
                  
]


                  For example:



                  SeedRandom[1]
                  
data = RandomReal[10, 10]
                  
digitize[data, {2, 4, 5, 7, 8}]



                  {8.17389, 1.1142, 7.89526, 1.87803, 2.41361, 0.657388, 5.42247, 2.31155, 3.96006, 7.00474}



                  {5, 0, 4, 0, 1, 0, 3, 1, 1, 4}




                  Note that I broke up the definition of digitize into two pieces, so that if you do this for multiple data sets with the same edges list, you only need to compute the nearest function once.






                  share|improve this answer











                  $endgroup$



                  Here's a version based on Nearest:



                  digitize[edges_] := DigitizeFunction[edges, Nearest[edges -> "Index"]]
                  
digitize[data_, edges_] := digitize[edges][data]
                  

                  
DigitizeFunction[edges_, nf_NearestFunction][data_] := With[{init = nf[data][[All, 1]]},
                  
    init + UnitStep[data - edges[[init]]] - 1
                  
]


                  For example:



                  SeedRandom[1]
                  
data = RandomReal[10, 10]
                  
digitize[data, {2, 4, 5, 7, 8}]



                  {8.17389, 1.1142, 7.89526, 1.87803, 2.41361, 0.657388, 5.42247, 2.31155, 3.96006, 7.00474}



                  {5, 0, 4, 0, 1, 0, 3, 1, 1, 4}




                  Note that I broke up the definition of digitize into two pieces, so that if you do this for multiple data sets with the same edges list, you only need to compute the nearest function once.







                  share|improve this answer














                  share|improve this answer



                  share|improve this answer








                  edited 1 hour ago

























                  answered 1 hour ago









                  Carl WollCarl Woll

                  73k396189




                  73k396189






























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