Deleting multiple columns based on column names in Pandas

Question

I have some data and when I import it I get the following unneeded columns I m looking for an easy way to delete all of these      Unnamed  24    Unnamed  25    Unnamed  26    Unnamed  27       Unnamed  28    Unnamed  29    Unnamed  30    Unnamed  31       Unnamed  32    Unnamed  33    Unnamed  34    Unnamed  35       Unnamed  36    Unnamed  37    Unnamed  38    Unnamed  39       Unnamed  40    Unnamed  41    Unnamed  42    Unnamed  43       Unnamed  44    Unnamed  45    Unnamed  46    Unnamed  47       Unnamed  48    Unnamed  49    Unnamed  50    Unnamed  51       Unnamed  52    Unnamed  53    Unnamed  54    Unnamed  55       Unnamed  56    Unnamed  57    Unnamed  58    Unnamed  59       Unnamed  60    They are indexed by 0-indexing so I tried something like       df drop df columns  22  23  24  25       26  27  28  29  30  31  32  55    axis 1  inplace True    But this isn t very efficient  I tried writing some for loops but this struck me as bad Pandas behaviour  Hence i ask the question here   I ve seen some examples which are similar  Drop multiple columns pandas  but this doesn t answer my question

User · Answer

df   df  col for col in df columns if not   Unnamed  in col

User · Answer

This is probably a good way to do what you want  It will delete all columns that contain  Unnamed  in their header   for col in df columns      if  Unnamed  in col          del df col

User · Answer

The below worked for me   for col in df      if  Unnamed  in col           del df col          print col         try              df drop col  axis 1  inplace True          except Exception              pass

User · Answer

The by far the simplest approach is   yourdf drop   columnheading1    columnheading2    axis 1  inplace True

User · Answer

You can do this in one line and one go   df drop  col for col in df columns if  Unnamed  in col   axis 1  inplace True    This involves less moving around copying of the object than the solutions above

User · Answer

Not sure if this solution has been mentioned anywhere yet but one way to do is is pandas Index difference    gt  gt  gt  df   pd DataFrame columns   A   B   C   D     gt  gt  gt  df Empty DataFrame Columns   A  B  C  D  Index      gt  gt  gt  to remove     A   C    gt  gt  gt  df   df df columns difference to remove    gt  gt  gt  df Empty DataFrame Columns   B  D  Index

User · Answer

You can just pass the column names as a list with specifying the axis as 0 or 1    axis 1  Along the Rows    axis 0  Along the Columns    By default axis 0  data drop   Colname1   Colname2   Colname3   Colname4   axis 1

User · Answer

My personal favorite  and easier than the answers I have seen here  for multiple columns   df drop df columns 22 56   axis 1  inplace True

User · Answer

I don t know what you mean by inefficient but if you mean in terms of typing it could be easier to just select the cols of interest and assign back to the df  df   df cols of interest   Where cols of interest is a list of the columns you care about  Or you can slice the columns and pass this to drop  df drop df ix    Unnamed  24   Unnamed  60   head 0  columns  axis 1   The call to head just selects 0 rows as we re only interested in the column names rather than data update Another method  It would be simpler to use the boolean mask from str contains and invert it to mask the columns  In  2   df   pd DataFrame columns   a   Unnamed  1    Unnamed  1   foo    df  Out 2   Empty DataFrame Columns   a  Unnamed  1  Unnamed  1  foo  Index      In  4    df columns str contains  Unnamed     Out 4   array   True  False  False   True   dtype bool   In  5   df df columns  df columns str contains  Unnamed       Out 5   Empty DataFrame Columns   a  foo  Index

User · Answer

Simple and Easy  Remove all columns after the 22th    df drop columns df columns 22      love it

[python] Deleting multiple columns based on column names in Pandas

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