move column in pandas dataframe

Question

I have the following dataframe      a  b   x  y 0  1  2   3 -1 1  2  4   6 -2 2  3  6   9 -3 3  4  8  12 -4   How can I move columns b and x such that they are the last 2 columns in the dataframe  I would like to specify b and x by name  but not the other columns

User · Answer

similar to Roberto Williams Battista s answer above  but hopefully a bit more robust   df insert len df columns -1   b   df pop  b    df insert len df columns -1   x   df pop  x

User · Answer

cols   list df columns values   Make a list of all of the columns in the df cols pop cols index  b     Remove b from list cols pop cols index  x     Remove x from list df   df cols   b   x     Create new dataframe with columns in the order you want

User · Answer

An alternative  more generic method    from pandas import DataFrame   def move columns df  DataFrame  cols to move  list  new index  int  - gt  DataFrame              This method re-arranges the columns in a dataframe to place the desired columns at the desired index      ex Usage  df   move columns df    Rev    2          param df       param cols to move  The names of the columns to move  They must be a list      param new index  The 0-based location to place the columns       return  Return a dataframe with the columns re-arranged             other    c for c in df if c not in cols to move      start   other 0 new index      end   other new index       return df start   cols to move   end

User · Answer

I use Pok  mon database as an example  the columns for my data base are   Name         Type 1    Type 2    Total    HP    Attack    Defense    Sp  Atk    Sp  Def    Speed    Generation    Legendary    Here is the code   import pandas as pd     df   pd read html  https   gist github com armgilles 194bcff35001e7eb53a2a8b441e8b2c6   0       cols   df columns to list       cos end    quot Name quot    quot Total quot    quot HP quot    quot Defense quot        for i  j in enumerate cos end  start  len cols -len cos end             cols insert i  cols pop cols index j            print cols               df   df reindex columns cols       print df

User · Answer

You can rearrange columns directly by specifying their order   df   df   a    y    b    x      In the case of larger dataframes where the column titles are dynamic  you can use a list comprehension to select every column not in your target set and then append the target set to the end    gt  gt  gt  df  c for c in df if c not in   b    x                b    x       a  y  b   x 0  1 -1  2   3 1  2 -2  4   6 2  3 -3  6   9 3  4 -4  8  12   To make it more bullet proof  you can ensure that your target columns are indeed in the dataframe   cols at end     b    x   df   df  c for c in df if c not in cols at end              c for c in cols at end if c in df

User · Answer

This function will reorder your columns without losing data  Any omitted columns remain in the center of the data set   def reorder columns columns  first cols     last cols     drop cols          columns   list set columns  - set first cols       columns   list set columns  - set drop cols       columns   list set columns  - set last cols       new order   first cols   columns   last cols     return new order   Example usage   my list     first    second    third    fourth    fifth    sixth   reorder columns my list  first cols   fourth    third    last cols   second    drop cols   fifth       Output    fourth    third    first    sixth    second     To assign to your dataframe  use   my list   df columns tolist   reordered cols   reorder columns my list  first cols   fourth    third    last cols   second    drop cols   fifth    df   df reordered cols

User · Answer

You can use to way below  It s very simple  but similar to the good answer given by Charlie Haley   df1   df pop  b     remove column b and store it in df1 df2   df pop  x     remove column x and store it in df2 df  b   df1   add b series as a  new  column  df  x   df2   add b series as a  new  column    Now you have your dataframe with the columns  b  and  x  in the end  You can see this video from OSPY   https   youtu be RlbO27N3Xg4

User · Answer

Simple solution  old cols   df columns values  new cols    a    y    b    x   df   df reindex columns new cols

User · Answer

You can use pd Index difference with np hstack  then reindex or use label-based indexing  In general  it s a good idea to avoid list comprehensions or other explicit loops with NumPy   Pandas objects   cols to move     b    x   new cols   np hstack  df columns difference cols to move   cols to move      OPTION 1  reindex df   df reindex columns new cols     OPTION 2  direct label-based indexing df   df new cols     OPTION 3  loc label-based indexing df   df loc    new cols   print df        a  y  b   x   0  1 -1  2   3   1  2 -2  4   6   2  3 -3  6   9   3  4 -4  8  12

User · Answer

You can also do this as a one-liner   df drop columns   b    x    assign b df  b    x df  x

[python] move column in pandas dataframe

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