How to change the order of DataFrame columns

Question

I have the following DataFrame  df    import numpy as np import pandas as pd  df   pd DataFrame np random rand 10  5     I add more column s  by assignment   df  mean     df mean 1    How can I move the column mean to the front  i e  set it as first column leaving the order of the other columns untouched

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A pretty straightforward solution that worked for me is to use  reindex on df columns  df   df df columns reindex   mean   0  1  2  3  4   0

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I have a very specific use case for re-ordering column names in pandas   Sometimes I am creating a new column in a dataframe that is based on an existing column   By default pandas will insert my new column at the end  but I want the new column to be inserted next to the existing column it s derived from       def rearrange list input list  input item to move  input item insert here               Helper function to re-arrange the order of items in a list      Useful for moving column in pandas dataframe       Inputs          input list - list         input item to move - item in list to move         input item insert here - item in list  insert before       returns          output list               make copy for output  make sure it s a list     output list   list input list         index of item to move     idx move   output list index input item to move         pop off the item to move     itm move   output list pop idx move         index of item to insert here     idx insert   output list index input item insert here         insert item to move into here     output list insert idx insert  itm move       return output list   import pandas as pd    step 1  create sample dataframe df   pd DataFrame        motorcycle     motorcycle1    motorcycle2    motorcycle3         initial odometer    101  500  322        final odometer    201  515  463        other col 1     blah    blah    blah         other col 2     blah    blah    blah      print  Step 1  create sample dataframe   display df  print      step 2  add new column that is difference between final and initial df  change odometer     df  final odometer  -df  initial odometer   print  Step 2  add new column   display df  print      step 3  rearrange columns ls cols   df columns ls cols   rearrange list ls cols   change odometer    final odometer   df df ls cols  print  Step 3  rearrange columns   display df

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In your case   df   df reindex columns   mean  0 1 2 3 4     will do exactly what you want   In my case  general form    df   df reindex columns sorted df columns   df   df reindex columns    opened     list  a for a in df columns if a     opened

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You can use a set which is an unordered collection of unique elements to do keep the  quot order of the other columns untouched quot   other columns   list set df columns  difference   quot mean quot       0  1  2  3  4   Then  you can use a lambda to move a specific column to the front by  In  1   import numpy as np                                                                                 In  2   import pandas as pd                                                                                In  3   df   pd DataFrame np random rand 10  5                                                             In  4   df  quot mean quot     df mean 1                                                                             In  5   move col to front   lambda df  col  df  col  list set df columns  difference  col                  In  6   move col to front df   quot mean quot                                                                       Out 6           mean         0         1         2         3         4 0  0 697253  0 600377  0 464852  0 938360  0 945293  0 537384 1  0 609213  0 703387  0 096176  0 971407  0 955666  0 319429 2  0 561261  0 791842  0 302573  0 662365  0 728368  0 321158 3  0 518720  0 710443  0 504060  0 663423  0 208756  0 506916 4  0 616316  0 665932  0 794385  0 163000  0 664265  0 793995 5  0 519757  0 585462  0 653995  0 338893  0 714782  0 305654 6  0 532584  0 434472  0 283501  0 633156  0 317520  0 994271 7  0 640571  0 732680  0 187151  0 937983  0 921097  0 423945 8  0 562447  0 790987  0 200080  0 317812  0 641340  0 862018 9  0 563092  0 811533  0 662709  0 396048  0 596528  0 348642  In  7   move col to front df  2                                                                           Out 7              2         0         1         3         4      mean 0  0 938360  0 600377  0 464852  0 945293  0 537384  0 697253 1  0 971407  0 703387  0 096176  0 955666  0 319429  0 609213 2  0 662365  0 791842  0 302573  0 728368  0 321158  0 561261 3  0 663423  0 710443  0 504060  0 208756  0 506916  0 518720 4  0 163000  0 665932  0 794385  0 664265  0 793995  0 616316 5  0 338893  0 585462  0 653995  0 714782  0 305654  0 519757 6  0 633156  0 434472  0 283501  0 317520  0 994271  0 532584 7  0 937983  0 732680  0 187151  0 921097  0 423945  0 640571 8  0 317812  0 790987  0 200080  0 641340  0 862018  0 562447 9  0 396048  0 811533  0 662709  0 596528  0 348642  0 563092

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To set an existing column right left of another  based on their names  def df move column df  col to move  col left of destiny  quot  quot   right of col bool True       cols   list df columns values      index max   len cols  - 1      if not right of col bool            set left of a column  quot c quot   is like putting right of column previous to  quot c quot                except if left of 1st column  then recursive call to set rest right to it         aux   cols index col left of destiny          if not aux              for g in  x for x in cols   -1  if x    col to move                   df   df move column                          df                           col to move g                           col left of destiny col to move                                       return df         col left of destiny   cols aux - 1       index old   cols index col to move      index new   0     if len col left of destiny           index new   cols index col left of destiny    1      if index old    index new          return df      if index new  lt  index old          index new   np min  index new  index max           cols                 cols  index new                 cols index old                 cols index new index old                cols index old   1                  else          cols                 cols  index old                cols index old   1   index new                 cols index old                 cols index new                  df   df cols      return df  E g  cols   list  quot ABCD quot   df2   pd DataFrame np arange 4  np newaxis      columns cols  for k in cols      print 30    quot - quot       for g in  x for x in cols if x    k           df new   df move column df2  k  g          print f quot  k  after  g     df new columns values  quot   for k in cols      print 30    quot - quot       for g in  x for x in cols if x    k           df new   df move column df2  k  g  right of col bool False          print f quot  k  before  g     df new columns values  quot    Output

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A simple approach is using set    in particular when you have a long list of columns and do not want to handle them manually  cols   list set df columns tolist    - set   mean     cols insert 0   mean   df   df cols

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Just type the column name you want to change  and set the index for the new location   def change column order df  col name  index       cols   df columns tolist       cols remove col name      cols insert index  col name      return df cols    For your case  this would be like   df   change column order df   mean   0

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Simply do   df   df   mean     df columns  -1  tolist

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You could also do something like this   df   df   mean    0    1    2    3      You can get the list of columns with   cols   list df columns values    The output will produce     0    1    2    3    mean        which is then easy to rearrange manually before dropping it into the first function

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This question has been answered before but reindex axis is deprecated now so I would suggest to use  df   df reindex sorted df columns   axis 1   For those who want to specify the order they want instead of just sorting them  here s the solution spelled out  df   df reindex   the   order   you   want    axis 1   Now  how you want to sort the list of column names is really not a pandas question  that s a Python list manipulation question  There are many ways of doing that  and I think this answer has a very neat way of doing it

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Just flipping helps often   df df columns   -1     Or just shuffle for a look   import random cols   list df columns  random shuffle cols  df cols

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I think this is a slightly neater solution  df insert 0   mean   df pop  quot mean quot     This solution is somewhat similar to  JoeHeffer  s solution but this is one liner  Here we remove the column  quot mean quot  from the dataframe and attach it to index 0 with the same column name

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You need to create a new list of your columns in the desired order  then use df   df cols  to rearrange the columns in this new order   cols     mean       col for col in df if col     mean   df   df cols    You can also use a more general approach   In this example  the last column  indicated by -1  is inserted as the first column   cols    df columns -1      col for col in df if col    df columns -1   df   df cols    You can also use this approach for reordering columns in a desired order if they are present in the DataFrame   inserted cols     a    b    c   cols     col for col in inserted cols if col in df              col for col in df if col not in inserted cols   df   df cols

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I liked Shoresh s answer to use set functionality to remove columns when you don t know the location  however this didn t work for my purpose as I need to keep the original column order  which has arbitrary column labels     I got this to work though by using IndexedSet from the boltons package   I also needed to re-add multiple column labels  so for a more general case I used the following code   from boltons setutils import IndexedSet cols   list IndexedSet df columns tolist    - set   mean    std     cols 0 0     mean    std   df   df cols    Hope this is useful to anyone searching this thread for a general solution

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Suppose you have df with columns A B C  The most simple way is  df   df reindex   B   C   A    axis 1

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DataFrame sort index axis 1  is quite clean Check doc here  And then concat

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I ran into a similar question myself  and just wanted to add what I settled on  I liked the reindex axis   method for changing column order  This worked   df   df reindex axis   mean     list df columns  -1    axis 1    An alternate method based on the comment from  Jorge   df   df reindex columns   mean     list df columns  -1      Although reindex axis seems to be slightly faster in micro benchmarks than reindex  I think I prefer the latter for its directness

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Moving any column to any position    import pandas as pd df   pd DataFrame   A    1 2 3                        B    2 4 8                        C    5 5 5     cols   df columns tolist   column to move    C  new position   1  cols insert new position  cols pop cols index column to move    df   df cols

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Here is a very simple answer to this only one line    You can do that after you added the  n  column into your df as follows   import numpy as np import pandas as pd  df   pd DataFrame np random rand 10  5   df  mean     df mean 1  df            0           1           2           3           4        mean 0   0 929616    0 316376    0 183919    0 204560    0 567725    0 440439 1   0 595545    0 964515    0 653177    0 748907    0 653570    0 723143 2   0 747715    0 961307    0 008388    0 106444    0 298704    0 424512 3   0 656411    0 809813    0 872176    0 964648    0 723685    0 805347 4   0 642475    0 717454    0 467599    0 325585    0 439645    0 518551 5   0 729689    0 994015    0 676874    0 790823    0 170914    0 672463 6   0 026849    0 800370    0 903723    0 024676    0 491747    0 449473 7   0 526255    0 596366    0 051958    0 895090    0 728266    0 559587 8   0 818350    0 500223    0 810189    0 095969    0 218950    0 488736 9   0 258719    0 468106    0 459373    0 709510    0 178053    0 414752       here you can add below line and it should work    Don t forget the two       brackets  around columns names Otherwise  it ll give you an error   df   df list   mean  0  1  2 3 4    df          mean           0           1           2           3           4 0   0 440439    0 929616    0 316376    0 183919    0 204560    0 567725 1   0 723143    0 595545    0 964515    0 653177    0 748907    0 653570 2   0 424512    0 747715    0 961307    0 008388    0 106444    0 298704 3   0 805347    0 656411    0 809813    0 872176    0 964648    0 723685 4   0 518551    0 642475    0 717454    0 467599    0 325585    0 439645 5   0 672463    0 729689    0 994015    0 676874    0 790823    0 170914 6   0 449473    0 026849    0 800370    0 903723    0 024676    0 491747 7   0 559587    0 526255    0 596366    0 051958    0 895090    0 728266 8   0 488736    0 818350    0 500223    0 810189    0 095969    0 218950 9   0 414752    0 258719    0 468106    0 459373    0 709510    0 178053

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You can use reindex which can be used for both axis   df             0         1         2         3         4      mean   0  0 943825  0 202490  0 071908  0 452985  0 678397  0 469921   1  0 745569  0 103029  0 268984  0 663710  0 037813  0 363821   2  0 693016  0 621525  0 031589  0 956703  0 118434  0 484254   3  0 284922  0 527293  0 791596  0 243768  0 629102  0 495336   4  0 354870  0 113014  0 326395  0 656415  0 172445  0 324628   5  0 815584  0 532382  0 195437  0 829670  0 019001  0 478415   6  0 944587  0 068690  0 811771  0 006846  0 698785  0 506136   7  0 595077  0 437571  0 023520  0 772187  0 862554  0 538182   8  0 700771  0 413958  0 097996  0 355228  0 656919  0 444974   9  0 263138  0 906283  0 121386  0 624336  0 859904  0 555009  df reindex   mean    range 5    axis 1            mean         0         1         2         3         4   0  0 469921  0 943825  0 202490  0 071908  0 452985  0 678397   1  0 363821  0 745569  0 103029  0 268984  0 663710  0 037813   2  0 484254  0 693016  0 621525  0 031589  0 956703  0 118434   3  0 495336  0 284922  0 527293  0 791596  0 243768  0 629102   4  0 324628  0 354870  0 113014  0 326395  0 656415  0 172445   5  0 478415  0 815584  0 532382  0 195437  0 829670  0 019001   6  0 506136  0 944587  0 068690  0 811771  0 006846  0 698785   7  0 538182  0 595077  0 437571  0 023520  0 772187  0 862554   8  0 444974  0 700771  0 413958  0 097996  0 355228  0 656919   9  0 555009  0 263138  0 906283  0 121386  0 624336  0 859904

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Most of the answers did not generalize enough and pandas reindex axis method is a little tedious  hence I offer a simple function to move an arbitrary number of columns to any position using a dictionary where key   column name and value   position to move to  If your dataframe is large pass True to  big data  then the function will return the ordered columns list  And you could use this list to slice your data   def order column df  columns  big data   False           Re-Orders dataFrame column s         Parameters           df      -- dataframe        columns -- a dictionary                    key     current column position index or column name                   value   position to move it to          big data -- boolean                    True   returns only the ordered columns as a list                           the user user can then slice the data using this                           ordered column                   False   default - return a copy of the dataframe             ordered col   df columns tolist        for key  value in columns items             ordered col remove key          ordered col insert value  key       if big data           return ordered col      return df ordered col     e g  df   pd DataFrame   chicken wings   np random rand 10  1  flatten     taco   np random rand 10 1  flatten                               coffee   np random rand 10  1  flatten     df  mean     df mean 1   df   order column df    mean   0   coffee  1      gt  gt  gt      col   order column df    mean   0   coffee  1    True   col  gt  gt  gt    mean    coffee    chicken wings    taco      you could grab it by doing this  df   df col

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If your column names are too-long-to-type then you could specify the new order through a list of integers with the positions  Data            0         1         2         3         4      mean 0  0 397312  0 361846  0 719802  0 575223  0 449205  0 500678 1  0 287256  0 522337  0 992154  0 584221  0 042739  0 485741 2  0 884812  0 464172  0 149296  0 167698  0 793634  0 491923 3  0 656891  0 500179  0 046006  0 862769  0 651065  0 543382 4  0 673702  0 223489  0 438760  0 468954  0 308509  0 422683 5  0 764020  0 093050  0 100932  0 572475  0 416471  0 389390 6  0 259181  0 248186  0 626101  0 556980  0 559413  0 449972 7  0 400591  0 075461  0 096072  0 308755  0 157078  0 207592 8  0 639745  0 368987  0 340573  0 997547  0 011892  0 471749 9  0 050582  0 714160  0 168839  0 899230  0 359690  0 438500  Generic example  new order    3 2 1 4 5 0  print df df columns new order                 3         2         1         4      mean         0 0  0 575223  0 719802  0 361846  0 449205  0 500678  0 397312 1  0 584221  0 992154  0 522337  0 042739  0 485741  0 287256 2  0 167698  0 149296  0 464172  0 793634  0 491923  0 884812 3  0 862769  0 046006  0 500179  0 651065  0 543382  0 656891 4  0 468954  0 438760  0 223489  0 308509  0 422683  0 673702 5  0 572475  0 100932  0 093050  0 416471  0 389390  0 764020 6  0 556980  0 626101  0 248186  0 559413  0 449972  0 259181 7  0 308755  0 096072  0 075461  0 157078  0 207592  0 400591 8  0 997547  0 340573  0 368987  0 011892  0 471749  0 639745 9  0 899230  0 168839  0 714160  0 359690  0 438500  0 050582  Although it might seem like I m just explicitly typing the column names in a different order  the fact that there s a column  mean  should make it clear that new order relates to actual positions and not column names  For the specific case of OP s question  new order    -1 0 1 2 3 4  df   df df columns new order   print df          mean         0         1         2         3         4 0  0 500678  0 397312  0 361846  0 719802  0 575223  0 449205 1  0 485741  0 287256  0 522337  0 992154  0 584221  0 042739 2  0 491923  0 884812  0 464172  0 149296  0 167698  0 793634 3  0 543382  0 656891  0 500179  0 046006  0 862769  0 651065 4  0 422683  0 673702  0 223489  0 438760  0 468954  0 308509 5  0 389390  0 764020  0 093050  0 100932  0 572475  0 416471 6  0 449972  0 259181  0 248186  0 626101  0 556980  0 559413 7  0 207592  0 400591  0 075461  0 096072  0 308755  0 157078 8  0 471749  0 639745  0 368987  0 340573  0 997547  0 011892 9  0 438500  0 050582  0 714160  0 168839  0 899230  0 359690  The main problem with this approach is that calling the same code multiple times will create different results each time  so one needs to be careful

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import numpy as np import pandas as pd df   pd DataFrame   column names     x   y   z   mean   for col in column names       df col    np random randint 0 100  size 10000    You can try out the following solutions    Solution 1   df   df    mean       col for col in df columns if col     mean        Solution 2     df   df   mean    x    y    z        Solution 3   col   df pop  mean   df   df insert 0  col name  col      Solution 4   df set index df columns -1   inplace True  df reset index inplace True      Solution 5   cols   list df  cols    cols -1     cols  -1  df   df cols      solution 6   order    1 2 3 0    setting column s order df   df  df columns i  for i in order     Time Comparison   Solution 1      CPU times  user 1 05 ms  sys  35   s  total  1 08 ms Wall time  995   s   Solution 2      CPU times  user 933   s  sys  0 ns  total  933   s   Wall time  800   s   Solution 3      CPU times  user 0 ns  sys  1 35 ms  total  1 35 ms   Wall time  1 08 ms   Solution 4      CPU times  user 1 23 ms  sys  45   s  total  1 27 ms   Wall time  986   s   Solution 5      CPU times  user 1 09 ms  sys  19   s  total  1 11 ms   Wall time  949   s   Solution 6      CPU times  user 955   s  sys  34   s  total  989   s   Wall time  859   s

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Here is a function to do this for any number of columns    def mean first df       ncols   df shape 1           Get the number of columns     index   list range ncols     Create an index to reorder the columns     index insert 0 ncols         This puts the last column at the front     return df assign mean df mean 1   iloc   index     new df with last column  mean  first

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I wanted to bring two columns in front from a dataframe where I do not know exactly the names of all columns  because they are generated from a pivot statement before  So  if you are in the same situation  To bring columns in front that you know the name of and then let them follow by  quot all the other columns quot   I came up with the following general solution  df   df reindex axis   Col1   Col2     list df columns drop   Col1   Col2      axis 1

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You could do the following  borrowing parts from Aman s answer    cols   df columns tolist   cols insert 0  cols pop -1    cols  gt  gt  gt   mean   0L  1L  2L  3L  4L   df   df cols

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How about using T  df   df T reindex   mean   0  1  2  3  4   T

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One easy way would be to reassign the dataframe with a list of the columns  rearranged as needed    This is what you have now    In  6   df Out 6             0         1         2         3         4      mean 0  0 445598  0 173835  0 343415  0 682252  0 582616  0 445543 1  0 881592  0 696942  0 702232  0 696724  0 373551  0 670208 2  0 662527  0 955193  0 131016  0 609548  0 804694  0 632596 3  0 260919  0 783467  0 593433  0 033426  0 512019  0 436653 4  0 131842  0 799367  0 182828  0 683330  0 019485  0 363371 5  0 498784  0 873495  0 383811  0 699289  0 480447  0 587165 6  0 388771  0 395757  0 745237  0 628406  0 784473  0 588529 7  0 147986  0 459451  0 310961  0 706435  0 100914  0 345149 8  0 394947  0 863494  0 585030  0 565944  0 356561  0 553195 9  0 689260  0 865243  0 136481  0 386582  0 730399  0 561593  In  7   cols   df columns tolist    In  8   cols Out 8    0L  1L  2L  3L  4L   mean     Rearrange cols in any way you want  This is how I moved the last element to the first position    In  12   cols   cols -1     cols  -1   In  13   cols Out 13     mean   0L  1L  2L  3L  4L    Then reorder the dataframe like this    In  16   df   df cols        OR    df   df ix    cols   In  17   df Out 17          mean         0         1         2         3         4 0  0 445543  0 445598  0 173835  0 343415  0 682252  0 582616 1  0 670208  0 881592  0 696942  0 702232  0 696724  0 373551 2  0 632596  0 662527  0 955193  0 131016  0 609548  0 804694 3  0 436653  0 260919  0 783467  0 593433  0 033426  0 512019 4  0 363371  0 131842  0 799367  0 182828  0 683330  0 019485 5  0 587165  0 498784  0 873495  0 383811  0 699289  0 480447 6  0 588529  0 388771  0 395757  0 745237  0 628406  0 784473 7  0 345149  0 147986  0 459451  0 310961  0 706435  0 100914 8  0 553195  0 394947  0 863494  0 585030  0 565944  0 356561 9  0 561593  0 689260  0 865243  0 136481  0 386582  0 730399

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This function avoids you having to list out every variable in your dataset just to order a few of them    def order frame var       if type var  is str          var    var   let the command take a string or list     varlist   w for w in frame columns if w not in var      frame   frame var varlist      return frame    It takes two arguments  the first is the dataset  the second are the columns in the data set that you want to bring to the front    So in my case I have a data set called Frame with variables A1  A2  B1  B2  Total and Date  If I want to bring Total to the front then all I have to do is    frame   order frame   Total      If I want to bring Total and Date to the front then I do   frame   order frame   Total   Date      EDIT   Another useful way to use this is  if you have an unfamiliar table and you re looking with variables with a particular term in them  like VAR1  VAR2     you may execute something like    frame   order frame  v for v in frame columns if  VAR  in v

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How about   df insert 0   mean   df mean 1     http   pandas pydata org pandas-docs stable dsintro html column-selection-addition-deletion

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Just assign the column names in the order you want them   In  39   df Out 39              0         1         2         3         4  mean 0  0 172742  0 915661  0 043387  0 712833  0 190717     1 1  0 128186  0 424771  0 590779  0 771080  0 617472     1 2  0 125709  0 085894  0 989798  0 829491  0 155563     1 3  0 742578  0 104061  0 299708  0 616751  0 951802     1 4  0 721118  0 528156  0 421360  0 105886  0 322311     1 5  0 900878  0 082047  0 224656  0 195162  0 736652     1 6  0 897832  0 558108  0 318016  0 586563  0 507564     1 7  0 027178  0 375183  0 930248  0 921786  0 337060     1 8  0 763028  0 182905  0 931756  0 110675  0 423398     1 9  0 848996  0 310562  0 140873  0 304561  0 417808     1  In  40   df   df   mean   4 3 2 1     Now   mean  column comes out in the front   In  41   df Out 41       mean         4         3         2         1 0     1  0 190717  0 712833  0 043387  0 915661 1     1  0 617472  0 771080  0 590779  0 424771 2     1  0 155563  0 829491  0 989798  0 085894 3     1  0 951802  0 616751  0 299708  0 104061 4     1  0 322311  0 105886  0 421360  0 528156 5     1  0 736652  0 195162  0 224656  0 082047 6     1  0 507564  0 586563  0 318016  0 558108 7     1  0 337060  0 921786  0 930248  0 375183 8     1  0 423398  0 110675  0 931756  0 182905 9     1  0 417808  0 304561  0 140873  0 310562

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Hackiest method in the book df insert 0   quot test quot   df  quot mean quot    df   df drop columns   quot mean quot    rename columns   quot test quot    quot mean quot

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I believe  Aman s answer is the best if you know the location of the other column   If you don t know the location of mean  but only have its name  you  cannot resort directly to cols   cols -1     cols  -1   Following is the next-best thing I could come up with   meanDf   pd DataFrame df pop  mean      now df doesn t contain  mean  anymore  Order of join will move it to left or right  meanDf join df    has mean as first column df join meanDf    has mean as last column

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I think this function is more straightforward  You Just need to specify a subset of columns at the start or the end or both   def reorder df columns df  start None  end None                   This function reorder columns of a DataFrame          It takes columns given in the list  start  and move them to the left          Its also takes columns in  end  and move them to the right              if start is None          start          if end is None          end          assert isinstance start  list  and isinstance end  list      cols   list df columns      for c in start          if c not in cols              start remove c      for c in end          if c not in cols or c in start              end remove c      for c in start   end          cols remove c      cols   start   cols   end     return df cols

User · Answer

Here s a way to move one existing column that will modify the existing data frame in place   my column   df pop  column name   df insert 3  my column name  my column

[python] How to change the order of DataFrame columns?

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