python dataframe pandas drop column using int

Question

I understand that to drop a column you use df drop  column name   axis 1   Is there a way to drop a column using a numerical index instead of the column name

User · Answer

You can delete column on i index like this   df drop df columns i   axis 1    It could work strange  if you have duplicate names in columns  so to do this you can rename column you want to delete column by new name  Or you can reassign DataFrame like this   df   df iloc     j for j  c in enumerate df columns  if j    i

User · Answer

You can simply supply columns parameter to df drop command so you don t to specify axis in that case  like so columns list    1  2  4    index numbers of columns you want to delete df   df drop columns df columns columns list    For reference see columns parameter here  https   pandas pydata org pandas-docs stable reference api pandas DataFrame drop html highlight drop pandas DataFrame drop

User · Answer

If you have two columns with the same name  One simple way is to manually rename the columns like this -  df columns     column1    column2    column3     Then you can drop via column index as you requested  like this -  df drop df columns 1   axis 1  inplace True    df column 1  will drop index 1   Remember axis 1   columns and axis 0   rows

User · Answer

You need to identify the columns based on their position in dataframe  For example  if you want to drop  del  column number 2 3 and 5  it will be   df drop df columns  2 3 5    axis   1

User · Answer

Since there can be multiple columns with same name   we should first rename the columns  Here is code for the solution   df columns list range 0 len df columns    df drop columns  1 2   drop second and third columns

User · Answer

You can use the following line to drop the first two columns  or any column you don t need    df drop  df columns 0   df columns 1    axis 1    Reference

User · Answer

Drop multiple columns like this   cols    1 2 4 5 12  df drop df columns cols  axis 1 inplace True    inplace True is used to make the changes in the dataframe itself without doing the column dropping on a copy of the data frame  If you need to keep your original intact  use   df after dropping   df drop df columns cols  axis 1

User · Answer

If there are multiple columns with identical names  the solutions given here so far will remove all of the columns   which may not be what one is looking for  This may be the case if one is trying to remove duplicate columns except one instance  The example below clarifies this situation     make a df with duplicate columns  x  df   pd DataFrame   x   range 5     x  range 5    y  range 6  11    columns     x    x    y       df Out 495       x  x   y 0  0  0   6 1  1  1   7 2  2  2   8 3  3  3   9 4  4  4  10    attempting to drop the first column according to the solution offered so far      df drop df columns 0   axis   1      y 0  6 1  7 2  8 3  9 4  10   As you can see  both Xs columns were dropped  Alternative solution   column numbers    x for x in range df shape 1       list of columns  integer indices  column numbers  remove 0   removing column integer index 0 df iloc    column numbers   return all columns except the 0th column     x  y 0  0  6 1  1  7 2  2  8 3  3  9 4  4  10   As you can see  this truly removed only the 0th column  first  x

User · Answer

if you really want to do it with integers  but why    then you could build a dictionary   col dict    x  col for x  col in enumerate df columns     then df   df drop col dict 0   1  will work as desired  edit  you can put it in a function that does that for you  though this way it creates the dictionary every time you call it  def drop col n df  col n to drop       col dict    x  col for x  col in enumerate df columns       return df drop col dict col n to drop   1   df   drop col n df  2

[python] python dataframe pandas drop column using int

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