Conditional Replace Pandas

Question

I have a DataFrame  and I want to replace the values in a particular column that exceed a value with zero  I had thought this was a way of achieving this  df df my channel  gt  20000  my channel   0  If I copy the channel into a new data frame it s simple  df2   df my channel   df2 df2  gt  20000    0  This does exactly what I want  but seems not to work with the channel as part of the original DataFrame

User · Answer

np where function works as follows   df  X     np where df  Y   gt  50   yes    no     In your case you would want   import numpy as np df  my channel     np where df my channel  gt  20000  0  df my channel

User · Answer

The reason your original dataframe does not update is because chained indexing may cause you to modify a copy rather than a view of your dataframe  The docs give this advice      When setting values in a pandas object  care must be taken to avoid   what is called chained indexing    You have a few alternatives -  loc   Boolean indexing  loc may be used for setting values and supports Boolean masks   df loc df  my channel    gt  20000   my channel     0   mask   Boolean indexing  You can assign to your series   df  my channel     df  my channel   mask df  my channel    gt  20000  0    Or you can update your series in place   df  my channel   mask df  my channel    gt  20000  0  inplace True    np where   Boolean indexing  You can use NumPy by assigning your original series when your condition is not satisfied  however  the first two solutions are cleaner since they explicitly change only specified values   df  my channel     np where df  my channel    gt  20000  0  df  my channel

User · Answer

Try  df loc df my channel  gt  20000   my channel     0   Note   Since v0 20 0  ix has been deprecated in favour of loc   iloc

User · Answer

Try this   df my channel   df my channel where df my channel  lt   20000  other  0   or  df my channel   df my channel mask df my channel  gt  20000  other  0

User · Answer

I would use lambda function on a Series of a DataFrame like this   f   lambda x  0 if x gt 100 else 1 df  my column     df  my column   map f    I do not assert that this is an efficient way  but it works fine

User · Answer

ix indexer works okay for pandas version prior to 0 20 0  but since pandas 0 20 0  the  ix indexer is deprecated  so you should avoid using it  Instead  you can use  loc or iloc indexers  You can solve this problem by   mask   df my channel  gt  20000 column name    my channel  df loc mask  column name    0   Or  in one line   df loc df my channel  gt  20000   my channel     0   mask helps you to select the rows in which df my channel  gt  20000 is True  while df loc mask  column name    0 sets the value 0 to the selected rows where maskholds in the column which name is column name   Update  In this case  you should use loc because if you use iloc  you will get a NotImplementedError telling you that iLocation based boolean indexing on an integer type is not available

[python] Conditional Replace Pandas

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