SettingWithCopyWarning using pandas apply

Trying to figure out why the below function is returning the dreaded SettingWithCopyWarning... Here is my function that intends to modify the dataframe df by reference.

def remove_outliers_by_group(df, cols):
    """
    Removes outliers based on median and median deviation computed using cols
    :param df: The dataframe reference
    :param cols: The columns to compute the median and median dev of
    :return:
    """
    flattened = df[cols].as_matrix().reshape(-1, )
    median = np.nanmedian(flattened)
    median_dev = np.nanmedian(np.abs(flattened) - median)
    for col in cols:
        df[col] = df[col].apply(lambda x: np.nan if get_absolute_median_z_score(x, median, median_dev) >= 2 else x)

And the offending line is df[col] = df[col].apply(lambda x: np.nan if get_absolute_median_z_score(x, median, median_dev) >= 2 else x) as per this error:

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/indexing.html#indexing-view-versus-copy df[col] = df[col].apply(lambda x: np.nan if get_absolute_median_z_score(x, median, median_dev) >= 2 else x)

What I don't understand is that I see this pattern all over the place, using something like df['a'] = df['a'].apply(lambda x: ...), so I can't imagine all of them are doing it wrong.

Am I doing it wrong? What is the best way to do this? I want to modify the original dataframe.

Thanks for your help.