chained-assignment

This question already has an answer here: How to deal with SettingWithCopyWarning in Pandas? 13 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:/

When selecting a sub dataframe from a parent dataframe, I noticed that some programmers make a copy of the data frame using the .copy() method. Why are they making a copy of the data frame? What will happen if I don't make a copy? This expands on Paul's answer. In Pandas, indexing a DataFrame returns a reference to the initial DataFrame. Thus, changing the subset will change the initial DataFrame. Thus, you'd want to use the copy if you want to make sure the initial DataFrame shouldn't change. Consider the following code: df = DataFrame({'x': [1,2]}) df_sub = df[0:1] df_sub.x = -1 print(df)

I'm confused about the rules Pandas uses when deciding that a selection from a dataframe is a copy of the original dataframe, or a view on the original. If I have, for example, df = pd.DataFrame(np.random.randn(8,8), columns=list('ABCDEFGH'), index=range(1,9)) I understand that a query returns a copy so that something like foo = df.query('2 < index <= 5') foo.loc[:,'E'] = 40 will have no effect on the original dataframe, df . I also understand that scalar or named slices return a view, so that assignments to these, such as df.iloc[3] = 70 or df.ix[1,'B':'E'] = 222 will change df . But I'm lost