QST: Misleading `SettingWithCopyWarning` when assigning on result of DataFrame.dropna?

[astrojuanlu]

• I have searched the [pandas] tag on StackOverflow for similar questions.

• I have asked my usage related question on StackOverflow.

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Under certain cases, doing an assignment on a DataFrame created using .dropna() emits the infamous SettingWithCopyWarning. So I followed what this SO answer suggests, and inspected the ._is_view and ._is_copy attributes:

In [1]: import pandas as pd                                                                                                                                                                                                                   

In [2]: import numpy as np                                                                                                                                                                                                                    

In [3]: df1 = pd.DataFrame([[1.0, np.nan, 2, np.nan], 
   ...:                    [2.0, 3.0, 5, np.nan], 
   ...:                    [np.nan, 4.0, 6, np.nan]], columns=list("ABCD")); df1                                                                                                                                                              
Out[3]: 
     A    B  C   D
0  1.0  NaN  2 NaN
1  2.0  3.0  5 NaN
2  NaN  4.0  6 NaN

In [4]: df2 = df1.dropna(axis="columns", how="all")   

In [5]: df2._is_view, df2._is_copy                                                                                                                                                                                                            
Out[5]: (False, <weakref at 0x7f56e8ecfd60; to 'DataFrame' at 0x7f56e916f9d0>)

In [6]: df2["A"] = df2["A"].fillna(df2["B"])                                                                                                                                                                                                  
<ipython-input-6-9c99ac454f90>:1: SettingWithCopyWarning: 
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 caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df2["A"] = df2["A"].fillna(df2["B"])                                                                                                                                                                                        

However, this _is_copy is set to None if you actually call .copy() (misleading variable name?), which on the other hand removes the warning:

In [17]: df3 = df1.dropna(axis="columns", how="all").copy()                                                                                                                                                                                   

In [18]: df3._is_view, df3._is_copy                                                                                                                                                                                                           
Out[18]: (False, None)

In [19]: df3["A"] = df3["A"].fillna(df3["B"])                                                                                                                                                                                                 

In [20]: 

But, finally, .fillna result does have _is_copy set to None:

In [20]: df1 = pd.DataFrame([[1.0, np.nan], [2.0, 3.0]], columns=["A", "B"]); df1                                                                                                                                                             
Out[20]: 
     A    B
0  1.0  NaN
1  2.0  3.0

In [21]: df2 = df1.dropna(axis="columns", how="all")                                                                                                                                                                                          

In [22]: df2._is_view, df2._is_copy                                                                                                                                                                                                           
Out[22]: (False, None)

In [23]: df2["A"] = df2["A"].fillna(df2["B"])                                                                                                                                                                                                 

In [24]:  

Can anybody explain what's the meaning of the SettingWithCopyWarning in this context and why do I still get despite _is_view being False in all cases?

I am particularly worried about this, in light of what the pandas docs say:

Sometimes a SettingWithCopy warning will arise at times when there’s no obvious chained indexing going on. These are the bugs that SettingWithCopy is designed to catch!

Originally asked at https://stackoverflow.com/q/65926892/554319

[tnwei]

I posted a reply to the original post on stackoverflow. This issue does raise a good point however; let me summarize below so that it is easier for the devs to weigh in.

Based on the definition for dropna, if no rows are to be dropped, the original dataframe will be returned as a copy(). Else, the function will return a .loc of the original dataframe:

...
        if np.all(mask):
            result = self.copy()
        else:
            result = self.loc(axis=axis)[mask]
...

If rows were removed with dropna, any further assignment on the result will become chain assignment and raise SettingWithCopyWarnings. Is this intended behaviour?

[rhshadrach]

I don't believe so - we should not have value-dependent behavior determining whether a copy is returned. I'm uncertain if In this situation we show always make a copy, or only do so if _is_copy is not None.

[jreback]

this should probably copy