Conditional Replace Pandas
.ix indexer works okay for pandas version prior to 0.20.0, but since pandas 0.20.0, the .ix indexer is deprecated, so you should avoid using it. Instead, you can use .loc or iloc indexers. You can solve this problem by:
mask = df.my_channel > 20000column_name = 'my_channel'df.loc[mask, column_name] = 0Or, in one line,
df.loc[df.my_channel > 20000, 'my_channel'] = 0mask helps you to select the rows in which df.my_channel > 20000 is True, while df.loc[mask, column_name] = 0 sets the value 0 to the selected rows where maskholds in the column which name is column_name.
Update:In this case, you should use loc because if you use iloc, you will get a NotImplementedError telling you that iLocation based boolean indexing on an integer type is not available.
Try
df.loc[df.my_channel > 20000, 'my_channel'] = 0Note: Since v0.20.0, ix has been deprecated in favour of loc / iloc.