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'

Create it with numpy by reshaping the data.

import pandas as pd
import numpy as np

pd.DataFrame(df.to_numpy().flatten('F'), 
             index=np.tile(df.index, df.shape[1]), 
             columns=['items'])

Output:

            items
Saturday   2540.0
Sunday     1313.0
Monday     1360.0
Tuesday    1089.0
Wednesday  1329.0
Thursday    798.0
Saturday   2441.0
...
Sunday     1798.0
Monday     1752.0
Tuesday    1457.0
Wednesday   874.0
Thursday   1269.0

With large size series with duplicates the set(some_series) execution-time will evolve exponentially with series size.

Better practice would be to set(some_series.unique()).

Creating dataframe:

df
Out[]: 
   x  y
A  1  1
B  2  2
C  3  3

You can use iloc function and pass array to it as follows:

df['y'].iloc[0:3]=[56, 45, 34]

df
Out[]: 
   x   y
A  1  56
B  2  45
C  3  34