Copy all values in a column to a new column in a pandas dataframe

Question

This is a very basic question  I just can not seem to find an answer   I have a dataframe like this  called df     A     B     C  a 1   b 1   c 1  a 2   b 2   c 2  a 3   b 3   c 3   Then I extract all the rows from df  where column  B  has a value of  b 2    I assign these results to df 2     df 2   df df  B       b 2     df 2 becomes     A     B     C  a 2   b 2   c 2   Then  I copy all the values in column  B  to a new column named  D   Causing df 2 to become      A     B     C     D  a 2   b 2   c 2   b 2   When I preform an assignment like this   df 2  D     df 2  B     I get the following warning      A value is trying to be set on a copy of a slice from a DataFrame  Try   using  loc row indexer col indexer    value instead      See the the caveats in the documentation    http   pandas pydata org pandas-docs stable indexing html indexing-view-versus-copy     I have also tried using  loc when creating df 2 like this   df 2   df loc df  B       b 2     However  I still get the warning    Any help is greatly appreciated

User · Answer

Here is your dataframe  import pandas as pd df   pd DataFrame        A     a 1    a 2    a 3         B     b 1    b 2    b 3         C     c 1    c 2    c 3      Your answer is in the paragraph  quot Setting with enlargement quot  in the section on  quot Indexing and selecting data quot  in the documentation on Pandas  It says   A DataFrame can be enlarged on either axis via  loc   So what you need to do is simply one of these two  df loc     D     df loc     B   df loc     D     df  B

User · Answer

The problem is in the line before the one that throws the warning  When you create df 2 that s where you re creating a copy of a slice of a dataframe  Instead  when you create df 2  use  copy   and you won t get that warning later on   df 2   df df  B       b 2   copy

User · Answer

Following up on these solutions  here is some helpful code illustrating        Copying columns in pandas without slice warning   import numpy as np df   pd DataFrame np random randn 10  3   columns list  ABC         copies column B into new column D df loc    D     df  B   print df      creates new column  E  with values -99      But copy command replaces those where  B  gt 0 while others become NaN  not copied  df  E     -99 print df df  E     df df  B   gt 0   B   copy   print df      creates new column  F  with values -99      Copy command only overwrites values which meet criteria  B  gt 0 df  F   -99 df loc df  B   gt 0  F     df df  B   gt 0   B   copy   print df

User · Answer

I think the correct access method is using the index   df 2 loc    D     df 2  B

User · Answer

You can simply assign the B to the new column   Like -  df  D     df  B       Example Demo -  In  1   import pandas as pd  In  2   df   pd DataFrame    a 1   b 1   c 1     a 2   b 2   c 2     a 3   b 3   c 3    columns   A   B   C     In  3   df Out 3        A    B    C 0  a 1  b 1  c 1 1  a 2  b 2  c 2 2  a 3  b 3  c 3  In  4   df  D     df  B                      lt ---What you want   In  5   df Out 5        A    B    C    D 0  a 1  b 1  c 1  b 1 1  a 2  b 2  c 2  b 2 2  a 3  b 3  c 3  b 3  In  6   df loc 0  D      d 1   In  7   df Out 7        A    B    C    D 0  a 1  b 1  c 1  d 1 1  a 2  b 2  c 2  b 2 2  a 3  b 3  c 3  b 3

User · Answer

How about    df  D     df  B   values

[python] Copy all values in a column to a new column in a pandas dataframe

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