Set value to an entire column of a pandas dataframe

Question

I m trying to set the entire column of a dataframe to a specific value   In   1   df Out  1         issueid   industry 0        001        xxx 1        002        xxx 2        003        xxx 3        004        xxx 4        005        xxx   From what I ve seen  loc is the best practice when replacing values in a dataframe  or isn t it     In   2   df loc    industry      yyy    However  I still received this much talked-about warning message   A value is trying to be set on a copy of a slice from a DataFrame  Try using  loc row index col indexer    value instead   If I do  In   3   df  industry      yyy    I got the same warning message   Any ideas   Working with Python 3 5 2 and pandas 0 18 1

User · Accepted Answer

Python can do unexpected things when new objects are defined from existing ones. You stated in a comment above that your dataframe is defined along the lines of df = df_all.loc[df_all['issueid']==specific_id,:]. In this case, df is really just a stand-in for the rows stored in the df_all object: a new object is NOT created in memory.

To avoid these issues altogether, I often have to remind myself to use the copy module, which explicitly forces objects to be copied in memory so that methods called on the new objects are not applied to the source object. I had the same problem as you, and avoided it using the deepcopy function.

In your case, this should get rid of the warning message:

from copy import deepcopy
df = deepcopy(df_all.loc[df_all['issueid']==specific_id,:])
df['industry'] = 'yyy'

EDIT: Also see David M.'s excellent comment below!

df = df_all.loc[df_all['issueid']==specific_id,:].copy()
df['industry'] = 'yyy'

User · Answer

You can do     df  industry      yyy

User · Answer

Assuming your Data frame is like  Data  you have to consider if your data is a string or an integer  Both are treated differently  So in this case you need be specific about that   import pandas as pd  data      001   xxx      002   xxx      003   xxx      004   xxx      005   xxx     df   pd DataFrame data columns   issueid    industry     print  Old DataFrame   print df   df loc    industry     str  yyy    print  New DataFrame   print df    Now if want to put numbers instead of letters you must create and array  list of ones    1 1 1 1 1  df loc    industry     list of ones print df    Or if you are using Numpy  import numpy as np n   len df  df loc    industry     np ones n  print df

User · Answer

You can use the assign function   df   df assign industry  yyy

User · Answer

df loc    industry      yyy    This does the magic  You are to add   loc  with     for all rows  Hope it helps

User · Answer

Seems to me that   df1   df df  col1    some value  WILL NOT create a new DataFrame  basically  changes in df1 will be reflected in the parent df  This leads to the warning  Whereas   df1   df df  col1    some value  copy   WILL create a new DataFrame  and changes in df1 will not be reflected in df  the copy   method is recommended if you don t want to make changes to your original df

User · Answer

I had a similar issue before even with this approach df loc    industry      yyy   but once I refreshed the notebook  it ran well   You may want to try refreshing the cells after you have df loc    industry      yyy

User · Answer

This provides you with the possibility of adding conditions on the rows and then change all the cells of a specific column corresponding to those rows   df loc  df  issueid       001     industry     str  yyy

[python] Set value to an entire column of a pandas dataframe

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