Pandas: Split a Dataframe into separate Dataframes based on certain Column's string values












2















Haven't found any answers that I could apply to my problem so here it goes:



I have an initial dataframe of images that I would like to split into two, based on the description of that image, which is a string in the "Description" column.



My problem issue is that not all descriptions are equally written. Here's an example of what I mean:



enter image description here



Some images are accelerated and others aren't. That's the criteria I want to use to split the dataset.



However even accelerated and non-accelerated image descriptions vary among them.



My strategy would be to rename every string that has "ACC" in it - this would cover all accelerated images - to "ACCELERATED IMAGE".



Then I could do:



df_Accl = df[df.Description == "ACCELERATED IMAGE"]

df_NonAccl = df[df.Description != "ACCELERATED IMAGE"]


How can I achieve this? This was just a strategy that I came up with, if there's any other more efficient way of doing this feel free to speak it.






python string pandas dataframe







share|improve this question


















  • 1





    Please try to avoid images and put some data that can be easily loaded next time..

    – Franco Piccolo
    Nov 18 '18 at 17:58











  • Related: Splitting a dataframe based on condition

    – jpp
    Nov 18 '18 at 18:07
















2















Haven't found any answers that I could apply to my problem so here it goes:



I have an initial dataframe of images that I would like to split into two, based on the description of that image, which is a string in the "Description" column.



My problem issue is that not all descriptions are equally written. Here's an example of what I mean:



enter image description here



Some images are accelerated and others aren't. That's the criteria I want to use to split the dataset.



However even accelerated and non-accelerated image descriptions vary among them.



My strategy would be to rename every string that has "ACC" in it - this would cover all accelerated images - to "ACCELERATED IMAGE".



Then I could do:



df_Accl = df[df.Description == "ACCELERATED IMAGE"]

df_NonAccl = df[df.Description != "ACCELERATED IMAGE"]


How can I achieve this? This was just a strategy that I came up with, if there's any other more efficient way of doing this feel free to speak it.






python string pandas dataframe







share|improve this question


















  • 1





    Please try to avoid images and put some data that can be easily loaded next time..

    – Franco Piccolo
    Nov 18 '18 at 17:58











  • Related: Splitting a dataframe based on condition

    – jpp
    Nov 18 '18 at 18:07














2












2








2








Haven't found any answers that I could apply to my problem so here it goes:



I have an initial dataframe of images that I would like to split into two, based on the description of that image, which is a string in the "Description" column.



My problem issue is that not all descriptions are equally written. Here's an example of what I mean:



enter image description here



Some images are accelerated and others aren't. That's the criteria I want to use to split the dataset.



However even accelerated and non-accelerated image descriptions vary among them.



My strategy would be to rename every string that has "ACC" in it - this would cover all accelerated images - to "ACCELERATED IMAGE".



Then I could do:



df_Accl = df[df.Description == "ACCELERATED IMAGE"]

df_NonAccl = df[df.Description != "ACCELERATED IMAGE"]


How can I achieve this? This was just a strategy that I came up with, if there's any other more efficient way of doing this feel free to speak it.






python string pandas dataframe







share|improve this question














Haven't found any answers that I could apply to my problem so here it goes:



I have an initial dataframe of images that I would like to split into two, based on the description of that image, which is a string in the "Description" column.



My problem issue is that not all descriptions are equally written. Here's an example of what I mean:



enter image description here



Some images are accelerated and others aren't. That's the criteria I want to use to split the dataset.



However even accelerated and non-accelerated image descriptions vary among them.



My strategy would be to rename every string that has "ACC" in it - this would cover all accelerated images - to "ACCELERATED IMAGE".



Then I could do:



df_Accl = df[df.Description == "ACCELERATED IMAGE"]

df_NonAccl = df[df.Description != "ACCELERATED IMAGE"]


How can I achieve this? This was just a strategy that I came up with, if there's any other more efficient way of doing this feel free to speak it.






python string pandas dataframe




python string pandas dataframe






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked Nov 18 '18 at 17:49









J. DevezJ. Devez

599




599








  • 1





    Please try to avoid images and put some data that can be easily loaded next time..

    – Franco Piccolo
    Nov 18 '18 at 17:58











  • Related: Splitting a dataframe based on condition

    – jpp
    Nov 18 '18 at 18:07














  • 1





    Please try to avoid images and put some data that can be easily loaded next time..

    – Franco Piccolo
    Nov 18 '18 at 17:58











  • Related: Splitting a dataframe based on condition

    – jpp
    Nov 18 '18 at 18:07








1




1





Please try to avoid images and put some data that can be easily loaded next time..

– Franco Piccolo
Nov 18 '18 at 17:58





Please try to avoid images and put some data that can be easily loaded next time..

– Franco Piccolo
Nov 18 '18 at 17:58













Related: Splitting a dataframe based on condition

– jpp
Nov 18 '18 at 18:07





Related: Splitting a dataframe based on condition

– jpp
Nov 18 '18 at 18:07












2 Answers
2






active

oldest

votes


















2














You can use str.contains for boolean mask - then filter by boolean indexing.



For invert mask is use ~, filter rows not contains ACC:



mask = df.Description.str.contains("ACC")

df_Accl = df[mask]

df_NonAccl = df[~mask]





share|improve this answer
























  • @KhalilAlHooti - thank you.

    – jezrael
    Nov 18 '18 at 18:00






  • 1





    This works (plus it's much more efficient than the strategy I was thinking)!

    – J. Devez
    Nov 18 '18 at 18:21



















0














You can use contains to find the rows that contain the substring ACC:



df['Description'].str.contains('ACC')





share|improve this answer























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    2 Answers
    2






    active

    oldest

    votes








    2 Answers
    2






    active

    oldest

    votes









    active

    oldest

    votes






    active

    oldest

    votes









    2














    You can use str.contains for boolean mask - then filter by boolean indexing.



    For invert mask is use ~, filter rows not contains ACC:



    mask = df.Description.str.contains("ACC")
    
df_Accl = df[mask]
    
df_NonAccl = df[~mask]





    share|improve this answer
























    • @KhalilAlHooti - thank you.

      – jezrael
      Nov 18 '18 at 18:00






    • 1





      This works (plus it's much more efficient than the strategy I was thinking)!

      – J. Devez
      Nov 18 '18 at 18:21
















    2














    You can use str.contains for boolean mask - then filter by boolean indexing.



    For invert mask is use ~, filter rows not contains ACC:



    mask = df.Description.str.contains("ACC")
    
df_Accl = df[mask]
    
df_NonAccl = df[~mask]





    share|improve this answer
























    • @KhalilAlHooti - thank you.

      – jezrael
      Nov 18 '18 at 18:00






    • 1





      This works (plus it's much more efficient than the strategy I was thinking)!

      – J. Devez
      Nov 18 '18 at 18:21














    2












    2








    2







    You can use str.contains for boolean mask - then filter by boolean indexing.



    For invert mask is use ~, filter rows not contains ACC:



    mask = df.Description.str.contains("ACC")
    
df_Accl = df[mask]
    
df_NonAccl = df[~mask]





    share|improve this answer













    You can use str.contains for boolean mask - then filter by boolean indexing.



    For invert mask is use ~, filter rows not contains ACC:



    mask = df.Description.str.contains("ACC")
    
df_Accl = df[mask]
    
df_NonAccl = df[~mask]






    share|improve this answer












    share|improve this answer



    share|improve this answer










    answered Nov 18 '18 at 17:56









    jezraeljezrael

    333k24276352




    333k24276352













    • @KhalilAlHooti - thank you.

      – jezrael
      Nov 18 '18 at 18:00






    • 1





      This works (plus it's much more efficient than the strategy I was thinking)!

      – J. Devez
      Nov 18 '18 at 18:21



















    • @KhalilAlHooti - thank you.

      – jezrael
      Nov 18 '18 at 18:00






    • 1





      This works (plus it's much more efficient than the strategy I was thinking)!

      – J. Devez
      Nov 18 '18 at 18:21

















    @KhalilAlHooti - thank you.

    – jezrael
    Nov 18 '18 at 18:00





    @KhalilAlHooti - thank you.

    – jezrael
    Nov 18 '18 at 18:00




    1




    1





    This works (plus it's much more efficient than the strategy I was thinking)!

    – J. Devez
    Nov 18 '18 at 18:21





    This works (plus it's much more efficient than the strategy I was thinking)!

    – J. Devez
    Nov 18 '18 at 18:21













    0














    You can use contains to find the rows that contain the substring ACC:



    df['Description'].str.contains('ACC')





    share|improve this answer




























      0














      You can use contains to find the rows that contain the substring ACC:



      df['Description'].str.contains('ACC')





      share|improve this answer


























        0












        0








        0







        You can use contains to find the rows that contain the substring ACC:



        df['Description'].str.contains('ACC')





        share|improve this answer













        You can use contains to find the rows that contain the substring ACC:



        df['Description'].str.contains('ACC')






        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered Nov 18 '18 at 17:56









        Franco PiccoloFranco Piccolo

        1,576712




        1,576712






























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