Conditional replacement in pandas for each row

Multi tool use
Multi tool use












1















I am probably doing something very simple, but I cant figure out the trick there.



I have a dataframe, and I want to replace the values in a particular column that exceed a value from zero with some random value. I had thought this was a way of achieving this:



self.dfile['foo'] = np.where(self.dfile['foo'] >= 0, random.uniform(4, 9), self.dfile['foo'])


It seems to be giving the same random value across all values that exceed 0. How do I get different values?










share|improve this question





























    1















    I am probably doing something very simple, but I cant figure out the trick there.



    I have a dataframe, and I want to replace the values in a particular column that exceed a value from zero with some random value. I had thought this was a way of achieving this:



    self.dfile['foo'] = np.where(self.dfile['foo'] >= 0, random.uniform(4, 9), self.dfile['foo'])


    It seems to be giving the same random value across all values that exceed 0. How do I get different values?










    share|improve this question



























      1












      1








      1








      I am probably doing something very simple, but I cant figure out the trick there.



      I have a dataframe, and I want to replace the values in a particular column that exceed a value from zero with some random value. I had thought this was a way of achieving this:



      self.dfile['foo'] = np.where(self.dfile['foo'] >= 0, random.uniform(4, 9), self.dfile['foo'])


      It seems to be giving the same random value across all values that exceed 0. How do I get different values?










      share|improve this question
















      I am probably doing something very simple, but I cant figure out the trick there.



      I have a dataframe, and I want to replace the values in a particular column that exceed a value from zero with some random value. I had thought this was a way of achieving this:



      self.dfile['foo'] = np.where(self.dfile['foo'] >= 0, random.uniform(4, 9), self.dfile['foo'])


      It seems to be giving the same random value across all values that exceed 0. How do I get different values?







      python pandas numpy series






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Jan 3 at 9:21









      jpp

      102k2165116




      102k2165116










      asked Jan 3 at 9:15









      tandemtandem

      3511323




      3511323
























          2 Answers
          2






          active

          oldest

          votes


















          2














          Use numpy.random.uniform with specifying length by length of DataFrame:



          v = np.random.uniform(4, 9, size=len(self.dfile))
          self.dfile['foo'] = np.where(self.dfile['foo'] >= 0, v,self.dfile['foo'])


          Sample:



          np.random.seed(123)

          dfile = pd.DataFrame({
          'foo':[0,5.1,1,0,20.4,10.7],
          })

          v = np.random.uniform(4, 9, size=len(dfile))

          dfile['foo1'] = np.where(dfile['foo'] >= 0, v, dfile['foo'])
          dfile['foo2'] = np.where(dfile['foo'].between(0, 10), v ,dfile['foo'])
          print (dfile)

          foo foo1 foo2
          0 0.0 7.482346 7.482346
          1 5.1 5.430697 5.430697
          2 1.0 5.134257 5.134257
          3 0.0 6.756574 6.756574
          4 20.4 7.597345 20.400000
          5 10.7 6.115532 10.700000





          share|improve this answer


























          • Thanks. Would this also function? self.dfile['foo'] = np.where(self.dfile['foo'] >= 0 and self.dfile['foo'] <= 10, v, self.dfile['foo'])

            – tandem
            Jan 3 at 9:25








          • 2





            @tandem - You are close, need & for bitwise AND and also () like self.dfile['foo'] = np.where((self.dfile['foo'] >= 0) & (self.dfile['foo'] <= 10), v, self.dfile['foo'])

            – jezrael
            Jan 3 at 9:26











          • @tandem - or better self.dfile['foo'] = np.where(self.dfile['foo'].between(0, 10), v,self.dfile['foo'])

            – jezrael
            Jan 3 at 9:27



















          2














          random.uniform(4, 9) returns an integer, which np.where then broadcasts across all rows. Instead, use np.random, which gives an array of specified length:



          self.dfile['foo'] = np.where(self.dfile['foo'] >= 0,
          np.random.uniform(4, 9, len(self.dfile.index)),
          self.dfile['foo'])





          share|improve this answer























            Your Answer






            StackExchange.ifUsing("editor", function () {
            StackExchange.using("externalEditor", function () {
            StackExchange.using("snippets", function () {
            StackExchange.snippets.init();
            });
            });
            }, "code-snippets");

            StackExchange.ready(function() {
            var channelOptions = {
            tags: "".split(" "),
            id: "1"
            };
            initTagRenderer("".split(" "), "".split(" "), channelOptions);

            StackExchange.using("externalEditor", function() {
            // Have to fire editor after snippets, if snippets enabled
            if (StackExchange.settings.snippets.snippetsEnabled) {
            StackExchange.using("snippets", function() {
            createEditor();
            });
            }
            else {
            createEditor();
            }
            });

            function createEditor() {
            StackExchange.prepareEditor({
            heartbeatType: 'answer',
            autoActivateHeartbeat: false,
            convertImagesToLinks: true,
            noModals: true,
            showLowRepImageUploadWarning: true,
            reputationToPostImages: 10,
            bindNavPrevention: true,
            postfix: "",
            imageUploader: {
            brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
            contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
            allowUrls: true
            },
            onDemand: true,
            discardSelector: ".discard-answer"
            ,immediatelyShowMarkdownHelp:true
            });


            }
            });














            draft saved

            draft discarded


















            StackExchange.ready(
            function () {
            StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f54019278%2fconditional-replacement-in-pandas-for-each-row%23new-answer', 'question_page');
            }
            );

            Post as a guest















            Required, but never shown

























            2 Answers
            2






            active

            oldest

            votes








            2 Answers
            2






            active

            oldest

            votes









            active

            oldest

            votes






            active

            oldest

            votes









            2














            Use numpy.random.uniform with specifying length by length of DataFrame:



            v = np.random.uniform(4, 9, size=len(self.dfile))
            self.dfile['foo'] = np.where(self.dfile['foo'] >= 0, v,self.dfile['foo'])


            Sample:



            np.random.seed(123)

            dfile = pd.DataFrame({
            'foo':[0,5.1,1,0,20.4,10.7],
            })

            v = np.random.uniform(4, 9, size=len(dfile))

            dfile['foo1'] = np.where(dfile['foo'] >= 0, v, dfile['foo'])
            dfile['foo2'] = np.where(dfile['foo'].between(0, 10), v ,dfile['foo'])
            print (dfile)

            foo foo1 foo2
            0 0.0 7.482346 7.482346
            1 5.1 5.430697 5.430697
            2 1.0 5.134257 5.134257
            3 0.0 6.756574 6.756574
            4 20.4 7.597345 20.400000
            5 10.7 6.115532 10.700000





            share|improve this answer


























            • Thanks. Would this also function? self.dfile['foo'] = np.where(self.dfile['foo'] >= 0 and self.dfile['foo'] <= 10, v, self.dfile['foo'])

              – tandem
              Jan 3 at 9:25








            • 2





              @tandem - You are close, need & for bitwise AND and also () like self.dfile['foo'] = np.where((self.dfile['foo'] >= 0) & (self.dfile['foo'] <= 10), v, self.dfile['foo'])

              – jezrael
              Jan 3 at 9:26











            • @tandem - or better self.dfile['foo'] = np.where(self.dfile['foo'].between(0, 10), v,self.dfile['foo'])

              – jezrael
              Jan 3 at 9:27
















            2














            Use numpy.random.uniform with specifying length by length of DataFrame:



            v = np.random.uniform(4, 9, size=len(self.dfile))
            self.dfile['foo'] = np.where(self.dfile['foo'] >= 0, v,self.dfile['foo'])


            Sample:



            np.random.seed(123)

            dfile = pd.DataFrame({
            'foo':[0,5.1,1,0,20.4,10.7],
            })

            v = np.random.uniform(4, 9, size=len(dfile))

            dfile['foo1'] = np.where(dfile['foo'] >= 0, v, dfile['foo'])
            dfile['foo2'] = np.where(dfile['foo'].between(0, 10), v ,dfile['foo'])
            print (dfile)

            foo foo1 foo2
            0 0.0 7.482346 7.482346
            1 5.1 5.430697 5.430697
            2 1.0 5.134257 5.134257
            3 0.0 6.756574 6.756574
            4 20.4 7.597345 20.400000
            5 10.7 6.115532 10.700000





            share|improve this answer


























            • Thanks. Would this also function? self.dfile['foo'] = np.where(self.dfile['foo'] >= 0 and self.dfile['foo'] <= 10, v, self.dfile['foo'])

              – tandem
              Jan 3 at 9:25








            • 2





              @tandem - You are close, need & for bitwise AND and also () like self.dfile['foo'] = np.where((self.dfile['foo'] >= 0) & (self.dfile['foo'] <= 10), v, self.dfile['foo'])

              – jezrael
              Jan 3 at 9:26











            • @tandem - or better self.dfile['foo'] = np.where(self.dfile['foo'].between(0, 10), v,self.dfile['foo'])

              – jezrael
              Jan 3 at 9:27














            2












            2








            2







            Use numpy.random.uniform with specifying length by length of DataFrame:



            v = np.random.uniform(4, 9, size=len(self.dfile))
            self.dfile['foo'] = np.where(self.dfile['foo'] >= 0, v,self.dfile['foo'])


            Sample:



            np.random.seed(123)

            dfile = pd.DataFrame({
            'foo':[0,5.1,1,0,20.4,10.7],
            })

            v = np.random.uniform(4, 9, size=len(dfile))

            dfile['foo1'] = np.where(dfile['foo'] >= 0, v, dfile['foo'])
            dfile['foo2'] = np.where(dfile['foo'].between(0, 10), v ,dfile['foo'])
            print (dfile)

            foo foo1 foo2
            0 0.0 7.482346 7.482346
            1 5.1 5.430697 5.430697
            2 1.0 5.134257 5.134257
            3 0.0 6.756574 6.756574
            4 20.4 7.597345 20.400000
            5 10.7 6.115532 10.700000





            share|improve this answer















            Use numpy.random.uniform with specifying length by length of DataFrame:



            v = np.random.uniform(4, 9, size=len(self.dfile))
            self.dfile['foo'] = np.where(self.dfile['foo'] >= 0, v,self.dfile['foo'])


            Sample:



            np.random.seed(123)

            dfile = pd.DataFrame({
            'foo':[0,5.1,1,0,20.4,10.7],
            })

            v = np.random.uniform(4, 9, size=len(dfile))

            dfile['foo1'] = np.where(dfile['foo'] >= 0, v, dfile['foo'])
            dfile['foo2'] = np.where(dfile['foo'].between(0, 10), v ,dfile['foo'])
            print (dfile)

            foo foo1 foo2
            0 0.0 7.482346 7.482346
            1 5.1 5.430697 5.430697
            2 1.0 5.134257 5.134257
            3 0.0 6.756574 6.756574
            4 20.4 7.597345 20.400000
            5 10.7 6.115532 10.700000






            share|improve this answer














            share|improve this answer



            share|improve this answer








            edited Jan 3 at 9:31

























            answered Jan 3 at 9:19









            jezraeljezrael

            351k26314389




            351k26314389













            • Thanks. Would this also function? self.dfile['foo'] = np.where(self.dfile['foo'] >= 0 and self.dfile['foo'] <= 10, v, self.dfile['foo'])

              – tandem
              Jan 3 at 9:25








            • 2





              @tandem - You are close, need & for bitwise AND and also () like self.dfile['foo'] = np.where((self.dfile['foo'] >= 0) & (self.dfile['foo'] <= 10), v, self.dfile['foo'])

              – jezrael
              Jan 3 at 9:26











            • @tandem - or better self.dfile['foo'] = np.where(self.dfile['foo'].between(0, 10), v,self.dfile['foo'])

              – jezrael
              Jan 3 at 9:27



















            • Thanks. Would this also function? self.dfile['foo'] = np.where(self.dfile['foo'] >= 0 and self.dfile['foo'] <= 10, v, self.dfile['foo'])

              – tandem
              Jan 3 at 9:25








            • 2





              @tandem - You are close, need & for bitwise AND and also () like self.dfile['foo'] = np.where((self.dfile['foo'] >= 0) & (self.dfile['foo'] <= 10), v, self.dfile['foo'])

              – jezrael
              Jan 3 at 9:26











            • @tandem - or better self.dfile['foo'] = np.where(self.dfile['foo'].between(0, 10), v,self.dfile['foo'])

              – jezrael
              Jan 3 at 9:27

















            Thanks. Would this also function? self.dfile['foo'] = np.where(self.dfile['foo'] >= 0 and self.dfile['foo'] <= 10, v, self.dfile['foo'])

            – tandem
            Jan 3 at 9:25







            Thanks. Would this also function? self.dfile['foo'] = np.where(self.dfile['foo'] >= 0 and self.dfile['foo'] <= 10, v, self.dfile['foo'])

            – tandem
            Jan 3 at 9:25






            2




            2





            @tandem - You are close, need & for bitwise AND and also () like self.dfile['foo'] = np.where((self.dfile['foo'] >= 0) & (self.dfile['foo'] <= 10), v, self.dfile['foo'])

            – jezrael
            Jan 3 at 9:26





            @tandem - You are close, need & for bitwise AND and also () like self.dfile['foo'] = np.where((self.dfile['foo'] >= 0) & (self.dfile['foo'] <= 10), v, self.dfile['foo'])

            – jezrael
            Jan 3 at 9:26













            @tandem - or better self.dfile['foo'] = np.where(self.dfile['foo'].between(0, 10), v,self.dfile['foo'])

            – jezrael
            Jan 3 at 9:27





            @tandem - or better self.dfile['foo'] = np.where(self.dfile['foo'].between(0, 10), v,self.dfile['foo'])

            – jezrael
            Jan 3 at 9:27













            2














            random.uniform(4, 9) returns an integer, which np.where then broadcasts across all rows. Instead, use np.random, which gives an array of specified length:



            self.dfile['foo'] = np.where(self.dfile['foo'] >= 0,
            np.random.uniform(4, 9, len(self.dfile.index)),
            self.dfile['foo'])





            share|improve this answer




























              2














              random.uniform(4, 9) returns an integer, which np.where then broadcasts across all rows. Instead, use np.random, which gives an array of specified length:



              self.dfile['foo'] = np.where(self.dfile['foo'] >= 0,
              np.random.uniform(4, 9, len(self.dfile.index)),
              self.dfile['foo'])





              share|improve this answer


























                2












                2








                2







                random.uniform(4, 9) returns an integer, which np.where then broadcasts across all rows. Instead, use np.random, which gives an array of specified length:



                self.dfile['foo'] = np.where(self.dfile['foo'] >= 0,
                np.random.uniform(4, 9, len(self.dfile.index)),
                self.dfile['foo'])





                share|improve this answer













                random.uniform(4, 9) returns an integer, which np.where then broadcasts across all rows. Instead, use np.random, which gives an array of specified length:



                self.dfile['foo'] = np.where(self.dfile['foo'] >= 0,
                np.random.uniform(4, 9, len(self.dfile.index)),
                self.dfile['foo'])






                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered Jan 3 at 9:19









                jppjpp

                102k2165116




                102k2165116






























                    draft saved

                    draft discarded




















































                    Thanks for contributing an answer to Stack Overflow!


                    • Please be sure to answer the question. Provide details and share your research!

                    But avoid



                    • Asking for help, clarification, or responding to other answers.

                    • Making statements based on opinion; back them up with references or personal experience.


                    To learn more, see our tips on writing great answers.




                    draft saved


                    draft discarded














                    StackExchange.ready(
                    function () {
                    StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f54019278%2fconditional-replacement-in-pandas-for-each-row%23new-answer', 'question_page');
                    }
                    );

                    Post as a guest















                    Required, but never shown





















































                    Required, but never shown














                    Required, but never shown












                    Required, but never shown







                    Required, but never shown

































                    Required, but never shown














                    Required, but never shown












                    Required, but never shown







                    Required, but never shown







                    kOSKg45a1KG z,MmiaqfbQPtD6Q7VBqaaW4FC1AlQEOYPN urTH,dig4WmUurhNTcXhqJKSb k,31xaC5y,HQ7tUUYT4y4IaJ0e
                    3W9eJpAQl4gs,dn x0,uUck,1z6gfO,Z,hv

                    Popular posts from this blog

                    Monofisismo

                    Angular Downloading a file using contenturl with Basic Authentication

                    Olmecas