Subset pandas DataFrame based on two columns ignoring what order the match happens in











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I have two Pandas DataFrames and I want to subset df_all based on the values within to_keep. Unfortunately this isn't straight forward pd.merge() or df.join() because I have multiple columns that I want to match on, and I don't care what order the match happens.




  • I don't care if df_all['source'] matches in either to_keep['from'] OR 'to_keep['to']

  • And then df_all['target'] matches in either to_keep['from'] OR to_keep['to'].


What I have below currently works, but it seems like a lot of work and hopefully this operation could be optimized.





import pandas as pd
import numpy as np

# create sample dataframe
df_all = pd.DataFrame({'from': ['a', 'a', 'b', 'a', 'b', 'c', 'd', 'd', 'd'],
'to': ['b', 'b', 'a', 'c', 'c', 'd', 'c', 'f', 'e'],
'time': np.random.randint(50, size=9),
'category': np.random.randn(9)
})

# create a key based on from & to
df_all['key'] = df_all['from'] + '-' + df_all['to']

df_all

category from time to key
0 0.374312 a 38 b a-b
1 -0.425700 a 0 b a-b
2 0.928008 b 34 a b-a
3 -0.160849 a 44 c a-c
4 0.462712 b 4 c b-c
5 -0.223074 c 33 d c-d
6 -0.778988 d 47 c d-c
7 -1.392306 d 0 f d-f
8 0.910363 d 34 e d-e

# create another sample datframe
to_keep = pd.DataFrame({'source': ['a', 'a', 'b'],
'target': ['b', 'c', 'c']
})

to_keep

source target
0 a b
1 a c
2 b c

# create a copy of to_keep
to_keep_flipped = to_keep.copy()

# flip source and target column names
to_keep_flipped.rename(columns={'source': 'target', 'target': 'source'}, inplace=True)

# extend to_keep with flipped version
to_keep_all = pd.concat([to_keep, to_keep_flipped], ignore_index=True)

to_keep_all

source target
0 a b
1 a c
2 b c
3 b a
4 c a
5 c b

# create a key based on source & target
keys = to_keep_all['source'] + '-' + to_keep_all['target']

keys

0 a-b
1 a-c
2 b-c
3 b-a
4 c-a
5 c-b
dtype: object

df_all[df_all['key'].isin(keys)]

category from time to key
0 0.374312 a 38 b a-b
1 -0.425700 a 0 b a-b
2 0.928008 b 34 a b-a
3 -0.160849 a 44 c a-c
4 0.462712 b 4 c b-c









share|improve this question




























    up vote
    2
    down vote

    favorite












    I have two Pandas DataFrames and I want to subset df_all based on the values within to_keep. Unfortunately this isn't straight forward pd.merge() or df.join() because I have multiple columns that I want to match on, and I don't care what order the match happens.




    • I don't care if df_all['source'] matches in either to_keep['from'] OR 'to_keep['to']

    • And then df_all['target'] matches in either to_keep['from'] OR to_keep['to'].


    What I have below currently works, but it seems like a lot of work and hopefully this operation could be optimized.





    import pandas as pd
    import numpy as np

    # create sample dataframe
    df_all = pd.DataFrame({'from': ['a', 'a', 'b', 'a', 'b', 'c', 'd', 'd', 'd'],
    'to': ['b', 'b', 'a', 'c', 'c', 'd', 'c', 'f', 'e'],
    'time': np.random.randint(50, size=9),
    'category': np.random.randn(9)
    })

    # create a key based on from & to
    df_all['key'] = df_all['from'] + '-' + df_all['to']

    df_all

    category from time to key
    0 0.374312 a 38 b a-b
    1 -0.425700 a 0 b a-b
    2 0.928008 b 34 a b-a
    3 -0.160849 a 44 c a-c
    4 0.462712 b 4 c b-c
    5 -0.223074 c 33 d c-d
    6 -0.778988 d 47 c d-c
    7 -1.392306 d 0 f d-f
    8 0.910363 d 34 e d-e

    # create another sample datframe
    to_keep = pd.DataFrame({'source': ['a', 'a', 'b'],
    'target': ['b', 'c', 'c']
    })

    to_keep

    source target
    0 a b
    1 a c
    2 b c

    # create a copy of to_keep
    to_keep_flipped = to_keep.copy()

    # flip source and target column names
    to_keep_flipped.rename(columns={'source': 'target', 'target': 'source'}, inplace=True)

    # extend to_keep with flipped version
    to_keep_all = pd.concat([to_keep, to_keep_flipped], ignore_index=True)

    to_keep_all

    source target
    0 a b
    1 a c
    2 b c
    3 b a
    4 c a
    5 c b

    # create a key based on source & target
    keys = to_keep_all['source'] + '-' + to_keep_all['target']

    keys

    0 a-b
    1 a-c
    2 b-c
    3 b-a
    4 c-a
    5 c-b
    dtype: object

    df_all[df_all['key'].isin(keys)]

    category from time to key
    0 0.374312 a 38 b a-b
    1 -0.425700 a 0 b a-b
    2 0.928008 b 34 a b-a
    3 -0.160849 a 44 c a-c
    4 0.462712 b 4 c b-c









    share|improve this question


























      up vote
      2
      down vote

      favorite









      up vote
      2
      down vote

      favorite











      I have two Pandas DataFrames and I want to subset df_all based on the values within to_keep. Unfortunately this isn't straight forward pd.merge() or df.join() because I have multiple columns that I want to match on, and I don't care what order the match happens.




      • I don't care if df_all['source'] matches in either to_keep['from'] OR 'to_keep['to']

      • And then df_all['target'] matches in either to_keep['from'] OR to_keep['to'].


      What I have below currently works, but it seems like a lot of work and hopefully this operation could be optimized.





      import pandas as pd
      import numpy as np

      # create sample dataframe
      df_all = pd.DataFrame({'from': ['a', 'a', 'b', 'a', 'b', 'c', 'd', 'd', 'd'],
      'to': ['b', 'b', 'a', 'c', 'c', 'd', 'c', 'f', 'e'],
      'time': np.random.randint(50, size=9),
      'category': np.random.randn(9)
      })

      # create a key based on from & to
      df_all['key'] = df_all['from'] + '-' + df_all['to']

      df_all

      category from time to key
      0 0.374312 a 38 b a-b
      1 -0.425700 a 0 b a-b
      2 0.928008 b 34 a b-a
      3 -0.160849 a 44 c a-c
      4 0.462712 b 4 c b-c
      5 -0.223074 c 33 d c-d
      6 -0.778988 d 47 c d-c
      7 -1.392306 d 0 f d-f
      8 0.910363 d 34 e d-e

      # create another sample datframe
      to_keep = pd.DataFrame({'source': ['a', 'a', 'b'],
      'target': ['b', 'c', 'c']
      })

      to_keep

      source target
      0 a b
      1 a c
      2 b c

      # create a copy of to_keep
      to_keep_flipped = to_keep.copy()

      # flip source and target column names
      to_keep_flipped.rename(columns={'source': 'target', 'target': 'source'}, inplace=True)

      # extend to_keep with flipped version
      to_keep_all = pd.concat([to_keep, to_keep_flipped], ignore_index=True)

      to_keep_all

      source target
      0 a b
      1 a c
      2 b c
      3 b a
      4 c a
      5 c b

      # create a key based on source & target
      keys = to_keep_all['source'] + '-' + to_keep_all['target']

      keys

      0 a-b
      1 a-c
      2 b-c
      3 b-a
      4 c-a
      5 c-b
      dtype: object

      df_all[df_all['key'].isin(keys)]

      category from time to key
      0 0.374312 a 38 b a-b
      1 -0.425700 a 0 b a-b
      2 0.928008 b 34 a b-a
      3 -0.160849 a 44 c a-c
      4 0.462712 b 4 c b-c









      share|improve this question















      I have two Pandas DataFrames and I want to subset df_all based on the values within to_keep. Unfortunately this isn't straight forward pd.merge() or df.join() because I have multiple columns that I want to match on, and I don't care what order the match happens.




      • I don't care if df_all['source'] matches in either to_keep['from'] OR 'to_keep['to']

      • And then df_all['target'] matches in either to_keep['from'] OR to_keep['to'].


      What I have below currently works, but it seems like a lot of work and hopefully this operation could be optimized.





      import pandas as pd
      import numpy as np

      # create sample dataframe
      df_all = pd.DataFrame({'from': ['a', 'a', 'b', 'a', 'b', 'c', 'd', 'd', 'd'],
      'to': ['b', 'b', 'a', 'c', 'c', 'd', 'c', 'f', 'e'],
      'time': np.random.randint(50, size=9),
      'category': np.random.randn(9)
      })

      # create a key based on from & to
      df_all['key'] = df_all['from'] + '-' + df_all['to']

      df_all

      category from time to key
      0 0.374312 a 38 b a-b
      1 -0.425700 a 0 b a-b
      2 0.928008 b 34 a b-a
      3 -0.160849 a 44 c a-c
      4 0.462712 b 4 c b-c
      5 -0.223074 c 33 d c-d
      6 -0.778988 d 47 c d-c
      7 -1.392306 d 0 f d-f
      8 0.910363 d 34 e d-e

      # create another sample datframe
      to_keep = pd.DataFrame({'source': ['a', 'a', 'b'],
      'target': ['b', 'c', 'c']
      })

      to_keep

      source target
      0 a b
      1 a c
      2 b c

      # create a copy of to_keep
      to_keep_flipped = to_keep.copy()

      # flip source and target column names
      to_keep_flipped.rename(columns={'source': 'target', 'target': 'source'}, inplace=True)

      # extend to_keep with flipped version
      to_keep_all = pd.concat([to_keep, to_keep_flipped], ignore_index=True)

      to_keep_all

      source target
      0 a b
      1 a c
      2 b c
      3 b a
      4 c a
      5 c b

      # create a key based on source & target
      keys = to_keep_all['source'] + '-' + to_keep_all['target']

      keys

      0 a-b
      1 a-c
      2 b-c
      3 b-a
      4 c-a
      5 c-b
      dtype: object

      df_all[df_all['key'].isin(keys)]

      category from time to key
      0 0.374312 a 38 b a-b
      1 -0.425700 a 0 b a-b
      2 0.928008 b 34 a b-a
      3 -0.160849 a 44 c a-c
      4 0.462712 b 4 c b-c






      python pandas






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      edited Jun 16 at 2:42

























      asked Jun 15 at 18:57









      CurtLH

      3051313




      3051313






















          1 Answer
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          up vote
          0
          down vote













          First, some comments on your question-asking: in your initial statement, you talk about matching 'col1' etc., but in your actual code, you have 'from, 'to', 'source', and 'target'. You talk about subsetting, but then you talk about pd.merge(), and those are completely different things. You formatting is poor, resulting your column names not lining up with the actual columns when you show the output in your code. When you generate random sample data, you should set a seed so that people will get the same data, and can check whether their code is doing the same thing as yours. Your test data is poorly chosen. For instance, does row for which 'from' matches one of the to_keep columns have to match the row in which 'to' matches, or can they match different rows? Can they both match the same column (e.g. 'from' and 'to' both match 'target'), or do they have to match different columns. Neither your test cases nor your problem description are clear on those points; one has to go through your code to figure out what you mean.



          Assuming that they have match on the same row, and in different columns, this code should work:



          def check(row):
          forward = (to_keep['source'] == row['from']) & (to_keep['target'] == row['to'])
          reverse = (to_keep['source'] == row['to']) & (to_keep['target'] == row['from'])
          return any(forward) | any(reverse)


          kept_df = df_all.loc[[check(row) for row in df_all.iterrows()]]





          share|improve this answer























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            1 Answer
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            active

            oldest

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            oldest

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            active

            oldest

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            up vote
            0
            down vote













            First, some comments on your question-asking: in your initial statement, you talk about matching 'col1' etc., but in your actual code, you have 'from, 'to', 'source', and 'target'. You talk about subsetting, but then you talk about pd.merge(), and those are completely different things. You formatting is poor, resulting your column names not lining up with the actual columns when you show the output in your code. When you generate random sample data, you should set a seed so that people will get the same data, and can check whether their code is doing the same thing as yours. Your test data is poorly chosen. For instance, does row for which 'from' matches one of the to_keep columns have to match the row in which 'to' matches, or can they match different rows? Can they both match the same column (e.g. 'from' and 'to' both match 'target'), or do they have to match different columns. Neither your test cases nor your problem description are clear on those points; one has to go through your code to figure out what you mean.



            Assuming that they have match on the same row, and in different columns, this code should work:



            def check(row):
            forward = (to_keep['source'] == row['from']) & (to_keep['target'] == row['to'])
            reverse = (to_keep['source'] == row['to']) & (to_keep['target'] == row['from'])
            return any(forward) | any(reverse)


            kept_df = df_all.loc[[check(row) for row in df_all.iterrows()]]





            share|improve this answer



























              up vote
              0
              down vote













              First, some comments on your question-asking: in your initial statement, you talk about matching 'col1' etc., but in your actual code, you have 'from, 'to', 'source', and 'target'. You talk about subsetting, but then you talk about pd.merge(), and those are completely different things. You formatting is poor, resulting your column names not lining up with the actual columns when you show the output in your code. When you generate random sample data, you should set a seed so that people will get the same data, and can check whether their code is doing the same thing as yours. Your test data is poorly chosen. For instance, does row for which 'from' matches one of the to_keep columns have to match the row in which 'to' matches, or can they match different rows? Can they both match the same column (e.g. 'from' and 'to' both match 'target'), or do they have to match different columns. Neither your test cases nor your problem description are clear on those points; one has to go through your code to figure out what you mean.



              Assuming that they have match on the same row, and in different columns, this code should work:



              def check(row):
              forward = (to_keep['source'] == row['from']) & (to_keep['target'] == row['to'])
              reverse = (to_keep['source'] == row['to']) & (to_keep['target'] == row['from'])
              return any(forward) | any(reverse)


              kept_df = df_all.loc[[check(row) for row in df_all.iterrows()]]





              share|improve this answer

























                up vote
                0
                down vote










                up vote
                0
                down vote









                First, some comments on your question-asking: in your initial statement, you talk about matching 'col1' etc., but in your actual code, you have 'from, 'to', 'source', and 'target'. You talk about subsetting, but then you talk about pd.merge(), and those are completely different things. You formatting is poor, resulting your column names not lining up with the actual columns when you show the output in your code. When you generate random sample data, you should set a seed so that people will get the same data, and can check whether their code is doing the same thing as yours. Your test data is poorly chosen. For instance, does row for which 'from' matches one of the to_keep columns have to match the row in which 'to' matches, or can they match different rows? Can they both match the same column (e.g. 'from' and 'to' both match 'target'), or do they have to match different columns. Neither your test cases nor your problem description are clear on those points; one has to go through your code to figure out what you mean.



                Assuming that they have match on the same row, and in different columns, this code should work:



                def check(row):
                forward = (to_keep['source'] == row['from']) & (to_keep['target'] == row['to'])
                reverse = (to_keep['source'] == row['to']) & (to_keep['target'] == row['from'])
                return any(forward) | any(reverse)


                kept_df = df_all.loc[[check(row) for row in df_all.iterrows()]]





                share|improve this answer














                First, some comments on your question-asking: in your initial statement, you talk about matching 'col1' etc., but in your actual code, you have 'from, 'to', 'source', and 'target'. You talk about subsetting, but then you talk about pd.merge(), and those are completely different things. You formatting is poor, resulting your column names not lining up with the actual columns when you show the output in your code. When you generate random sample data, you should set a seed so that people will get the same data, and can check whether their code is doing the same thing as yours. Your test data is poorly chosen. For instance, does row for which 'from' matches one of the to_keep columns have to match the row in which 'to' matches, or can they match different rows? Can they both match the same column (e.g. 'from' and 'to' both match 'target'), or do they have to match different columns. Neither your test cases nor your problem description are clear on those points; one has to go through your code to figure out what you mean.



                Assuming that they have match on the same row, and in different columns, this code should work:



                def check(row):
                forward = (to_keep['source'] == row['from']) & (to_keep['target'] == row['to'])
                reverse = (to_keep['source'] == row['to']) & (to_keep['target'] == row['from'])
                return any(forward) | any(reverse)


                kept_df = df_all.loc[[check(row) for row in df_all.iterrows()]]






                share|improve this answer














                share|improve this answer



                share|improve this answer








                edited Jun 15 at 22:31

























                answered Jun 15 at 21:59









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