问题
This question already has an answer here:
- How to deal with SettingWithCopyWarning in Pandas? 14 answers
Python 3.4 and Pandas 0.15.0
df is a dataframe and col1 is a column. With the code below, I\'m checking for the presence of the value 10 and replacing such values with 1000.
df.col1[df.col1 == 10] = 1000
Here\'s another example. This time, I\'m changing values in col2 based on index.
df.col2[df.index == 151] = 500
Both these produce the warning below:
-c:1: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
Finally,
cols = [\'col1\', \'col2\', \'col3\']
df[cols] = df[cols].applymap(some_function)
This produces a similar warning, with an added suggestion:
Try using .loc[row_indexer,col_indexer] = value instead
I\'m not sure I understand the discussion pointed to in the warnings. What would be a better way to write these three lines of code?
Note that the operations worked.
回答1:
The issue here is that: df.col1[df.col1 == 10] returns a copy.
So I would say:
row_index = df.col1 == 10
# then with the form .loc[row_indexer,col_indexer]
df.loc[row_index, 'col1'] = 100
回答2:
Agreed with Paul about 'loc' usage.
For your applymap case you should be able to do this:
cols = ['col1', 'col2', 'col3']
df.loc[:, cols] = df[cols].applymap(some_function)
来源：https://stackoverflow.com/questions/26724378/pandas-settingwithcopywarning