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How do i drop all columns that include '_id' - Python


How can I safely create a nested directory in Python?How to get the current time in PythonHow can I make a time delay in Python?How do I list all files of a directory?Find all files in a directory with extension .txt in PythonDrop data frame columns by nameRenaming columns in pandasAdding new column to existing DataFrame in Python pandasSelect rows from a DataFrame based on values in a column in pandasGet list from pandas DataFrame column headers






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I have a dataframe with 247 columns. Many of the column names contain "_id" in the column name. How do I drop all columns that contain "_id"??










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    0















    I have a dataframe with 247 columns. Many of the column names contain "_id" in the column name. How do I drop all columns that contain "_id"??










    share|improve this question
























      0












      0








      0








      I have a dataframe with 247 columns. Many of the column names contain "_id" in the column name. How do I drop all columns that contain "_id"??










      share|improve this question














      I have a dataframe with 247 columns. Many of the column names contain "_id" in the column name. How do I drop all columns that contain "_id"??







      python dataframe data-cleaning






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Mar 23 at 1:04









      GuyGuyGuyGuyGuyGuy

      104




      104






















          2 Answers
          2






          active

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          3














          This is pretty straight forward as well. Select the columns that contain "_id" and then invert it, use .loc to restrict the columns, and you're done.



          df = df.loc[:, ~df.columns.str.contains("_id")]





          share|improve this answer






























            2














            Try this:



            df = df[df.columns.drop(list(df.filter(like='_id')), axis = 1, inplace = True)]


            What this code does is:
            To filter all those columns which will have _id anywhere in its name and then dropping all those columns.



            let me know if you didn't understand or need any help in this regard.






            share|improve this answer























            • let me know if you need any other help in this regard. And if you find the answer useful, please consider accepting the answer by clicking on the tick mark(grey colored) on the left of the answer. And also upvote the answer. Thanks

              – Faizan Khan
              Mar 23 at 1:35











            Your Answer






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            2 Answers
            2






            active

            oldest

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            2 Answers
            2






            active

            oldest

            votes









            active

            oldest

            votes






            active

            oldest

            votes









            3














            This is pretty straight forward as well. Select the columns that contain "_id" and then invert it, use .loc to restrict the columns, and you're done.



            df = df.loc[:, ~df.columns.str.contains("_id")]





            share|improve this answer



























              3














              This is pretty straight forward as well. Select the columns that contain "_id" and then invert it, use .loc to restrict the columns, and you're done.



              df = df.loc[:, ~df.columns.str.contains("_id")]





              share|improve this answer

























                3












                3








                3







                This is pretty straight forward as well. Select the columns that contain "_id" and then invert it, use .loc to restrict the columns, and you're done.



                df = df.loc[:, ~df.columns.str.contains("_id")]





                share|improve this answer













                This is pretty straight forward as well. Select the columns that contain "_id" and then invert it, use .loc to restrict the columns, and you're done.



                df = df.loc[:, ~df.columns.str.contains("_id")]






                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered Mar 23 at 7:11









                run-outrun-out

                1,2571214




                1,2571214























                    2














                    Try this:



                    df = df[df.columns.drop(list(df.filter(like='_id')), axis = 1, inplace = True)]


                    What this code does is:
                    To filter all those columns which will have _id anywhere in its name and then dropping all those columns.



                    let me know if you didn't understand or need any help in this regard.






                    share|improve this answer























                    • let me know if you need any other help in this regard. And if you find the answer useful, please consider accepting the answer by clicking on the tick mark(grey colored) on the left of the answer. And also upvote the answer. Thanks

                      – Faizan Khan
                      Mar 23 at 1:35















                    2














                    Try this:



                    df = df[df.columns.drop(list(df.filter(like='_id')), axis = 1, inplace = True)]


                    What this code does is:
                    To filter all those columns which will have _id anywhere in its name and then dropping all those columns.



                    let me know if you didn't understand or need any help in this regard.






                    share|improve this answer























                    • let me know if you need any other help in this regard. And if you find the answer useful, please consider accepting the answer by clicking on the tick mark(grey colored) on the left of the answer. And also upvote the answer. Thanks

                      – Faizan Khan
                      Mar 23 at 1:35













                    2












                    2








                    2







                    Try this:



                    df = df[df.columns.drop(list(df.filter(like='_id')), axis = 1, inplace = True)]


                    What this code does is:
                    To filter all those columns which will have _id anywhere in its name and then dropping all those columns.



                    let me know if you didn't understand or need any help in this regard.






                    share|improve this answer













                    Try this:



                    df = df[df.columns.drop(list(df.filter(like='_id')), axis = 1, inplace = True)]


                    What this code does is:
                    To filter all those columns which will have _id anywhere in its name and then dropping all those columns.



                    let me know if you didn't understand or need any help in this regard.







                    share|improve this answer












                    share|improve this answer



                    share|improve this answer










                    answered Mar 23 at 1:34









                    Faizan KhanFaizan Khan

                    437312




                    437312












                    • let me know if you need any other help in this regard. And if you find the answer useful, please consider accepting the answer by clicking on the tick mark(grey colored) on the left of the answer. And also upvote the answer. Thanks

                      – Faizan Khan
                      Mar 23 at 1:35

















                    • let me know if you need any other help in this regard. And if you find the answer useful, please consider accepting the answer by clicking on the tick mark(grey colored) on the left of the answer. And also upvote the answer. Thanks

                      – Faizan Khan
                      Mar 23 at 1:35
















                    let me know if you need any other help in this regard. And if you find the answer useful, please consider accepting the answer by clicking on the tick mark(grey colored) on the left of the answer. And also upvote the answer. Thanks

                    – Faizan Khan
                    Mar 23 at 1:35





                    let me know if you need any other help in this regard. And if you find the answer useful, please consider accepting the answer by clicking on the tick mark(grey colored) on the left of the answer. And also upvote the answer. Thanks

                    – Faizan Khan
                    Mar 23 at 1:35

















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