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

Do you take falling damage if falling from 20 feet or less while grappled by someone affected by the Cat's Grace option of the Enhance Ability spell?

99 coins into the sacks

Should one save up to purchase a house/condo or maximize their 401k first?

Names of the Six Tastes

Company stopped paying my salary. What are my options?

Is this strange Morse signal type common?

How is it believable that Euron could so easily pull off this ambush?

Exactly which act of bravery are Luke and Han awarded a medal for?

What's an appropriate age to involve kids in life changing decisions?

How do integrated charger ICs dissipate differences in VCC and the battery voltage?

Light Switch Neutrals: Bundle all together?

mini sub panel?

Is your maximum jump distance halved by grappling?

Illegal assignment from Id to List

What are my options legally if NYC company is not paying salary?

Why are thrust reversers not used down to taxi speeds?

What's the difference between "ricochet" and "bounce"?

How can it be that ssh somename works, while nslookup somename does not?

Align a table column at a specific symbol

Cyclic queue using an array in C#

Are wands in any sort of book going to be too much like Harry Potter?

What happens when the drag force exceeds the weight of an object falling into earth?

How can I test a shell script in a "safe environment" to avoid harm to my computer?

Is it a good idea to copy a trader when investing?



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






.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty height:90px;width:728px;box-sizing:border-box;








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















    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

          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






























            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






            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%2f55309626%2fhow-do-i-drop-all-columns-that-include-id-python%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









            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

















                    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%2f55309626%2fhow-do-i-drop-all-columns-that-include-id-python%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







                    Popular posts from this blog

                    Kamusi Yaliyomo Aina za kamusi | Muundo wa kamusi | Faida za kamusi | Dhima ya picha katika kamusi | Marejeo | Tazama pia | Viungo vya nje | UrambazajiKuhusu kamusiGo-SwahiliWiki-KamusiKamusi ya Kiswahili na Kiingerezakuihariri na kuongeza habari

                    Swift 4 - func physicsWorld not invoked on collision? The Next CEO of Stack OverflowHow to call Objective-C code from Swift#ifdef replacement in the Swift language@selector() in Swift?#pragma mark in Swift?Swift for loop: for index, element in array?dispatch_after - GCD in Swift?Swift Beta performance: sorting arraysSplit a String into an array in Swift?The use of Swift 3 @objc inference in Swift 4 mode is deprecated?How to optimize UITableViewCell, because my UITableView lags

                    Access current req object everywhere in Node.js ExpressWhy are global variables considered bad practice? (node.js)Using req & res across functionsHow do I get the path to the current script with Node.js?What is Node.js' Connect, Express and “middleware”?Node.js w/ express error handling in callbackHow to access the GET parameters after “?” in Express?Modify Node.js req object parametersAccess “app” variable inside of ExpressJS/ConnectJS middleware?Node.js Express app - request objectAngular Http Module considered middleware?Session variables in ExpressJSAdd properties to the req object in expressjs with Typescript