Is .ix() always better than .loc() and .iloc() since it is faster and supports integer and label access?

by

Please refer to the doc Different Choices for Indexing, it states clearly when and why you should use .loc, .iloc over .ix, it’s about explicit use case:

.ix supports mixed integer and label based access. It is primarily
label based, but will fall back to integer positional access unless
the corresponding axis is of integer type. .ix is the most general and
will support any of the inputs in .loc and .iloc. .ix also supports
floating point label schemes. .ix is exceptionally useful when dealing
with mixed positional and label based hierachical indexes.

However, when an axis is integer based, ONLY label based access and
not positional access is supported. Thus, in such cases, it’s usually
better to be explicit and use .iloc or .loc.

Update 22 Mar 2017

Thanks to comment from @Alexander, Pandas is going to deprecate ix in 0.20, details in here.

One of the strong reason behind is because mixing indexes — positional and label (effectively using ix) has been a significant source of problems for users.

It is expected to migrate to use iloc and loc instead, here is a link on how to convert code.

More Related Contents:

  1. Why does the following code cant get value of n_fold = 1?
  2. Converting a Pandas GroupBy output from Series to DataFrame
  3. Pandas long to wide reshape, by two variables
  4. How to avoid Python/Pandas creating an index in a saved csv?
  5. Convert floats to ints in Pandas?
  6. datetime dtypes in pandas read_csv
  7. Calculate average of every x rows in a table and create new table
  8. get previous row’s value and calculate new column pandas python
  9. How to move pandas data from index to column after multiple groupby
  10. sorting by a custom list in pandas
  11. Get last “column” after .str.split() operation on column in pandas DataFrame
  12. How do you update the levels of a pandas MultiIndex after slicing its DataFrame?
  13. Prevent pandas read_csv treating first row as header of column names
  14. How to specify date format when using pandas.to_csv?
  15. Merging dataframes based on date range
  16. Compute *rolling* maximum drawdown of pandas Series
  17. Normalizing pandas DataFrame rows by their sums
  18. How to merge multiple dataframes
  19. How to avoid pandas creating an index in a saved csv
  20. How can I get a value from a cell of a dataframe?
  21. Extract int from string in Pandas
  22. How to conditionally update DataFrame column in Pandas
  23. Returning multiple values from pandas apply on a DataFrame
  24. Create trading holiday calendar with Pandas
  25. Iterating pandas dataframe, checking values and creating some of them
  26. Python: How to drop a row whose particular column is empty/NaN?
  27. How to use ast.literal_eval in a pandas dataframe and handle exceptions
  28. Why is NaN considered as a float?
  29. Python Pandas : pandas.to_datetime() is switching day & month when day is less than 13
  30. Wide to long data transform in pandas

Leave a Comment