Table Of Contents
- What’s New
- Installation
- Contributing to pandas
- Frequently Asked Questions (FAQ)
- Package overview
- 10 Minutes to pandas
- Tutorials
- Cookbook
- Intro to Data Structures
- Essential Basic Functionality
- Working with Text Data
- Options and Settings
- Indexing and Selecting Data
- MultiIndex / Advanced Indexing
- Computational tools
- Working with missing data
- Group By: split-apply-combine
- Merge, join, and concatenate
- Reshaping and Pivot Tables
- Time Series / Date functionality
- Time Deltas
- Categorical Data
- Visualization
- Style
- IO Tools (Text, CSV, HDF5, ...)
- Remote Data Access
- Enhancing Performance
- Sparse data structures
- Caveats and Gotchas
- rpy2 / R interface
- pandas Ecosystem
- Comparison with R / R libraries
- Comparison with SQL
- Comparison with SAS
- API Reference
- Input/Output
- General functions
- Series
- DataFrame
- Panel
- Panel4D
- Index
- CategoricalIndex
- pandas.CategoricalIndex
- pandas.CategoricalIndex.T
- pandas.CategoricalIndex.asi8
- pandas.CategoricalIndex.base
- pandas.CategoricalIndex.categories
- pandas.CategoricalIndex.codes
- pandas.CategoricalIndex.data
- pandas.CategoricalIndex.dtype
- pandas.CategoricalIndex.dtype_str
- pandas.CategoricalIndex.flags
- pandas.CategoricalIndex.has_duplicates
- pandas.CategoricalIndex.hasnans
- pandas.CategoricalIndex.inferred_type
- pandas.CategoricalIndex.is_all_dates
- pandas.CategoricalIndex.is_monotonic
- pandas.CategoricalIndex.is_monotonic_decreasing
- pandas.CategoricalIndex.is_monotonic_increasing
- pandas.CategoricalIndex.is_unique
- pandas.CategoricalIndex.itemsize
- pandas.CategoricalIndex.name
- pandas.CategoricalIndex.names
- pandas.CategoricalIndex.nbytes
- pandas.CategoricalIndex.ndim
- pandas.CategoricalIndex.nlevels
- pandas.CategoricalIndex.ordered
- pandas.CategoricalIndex.shape
- pandas.CategoricalIndex.size
- pandas.CategoricalIndex.strides
- pandas.CategoricalIndex.values
- pandas.CategoricalIndex.add_categories
- pandas.CategoricalIndex.all
- pandas.CategoricalIndex.any
- pandas.CategoricalIndex.append
- pandas.CategoricalIndex.argmax
- pandas.CategoricalIndex.argmin
- pandas.CategoricalIndex.argsort
- pandas.CategoricalIndex.as_ordered
- pandas.CategoricalIndex.as_unordered
- pandas.CategoricalIndex.asof
- pandas.CategoricalIndex.asof_locs
- pandas.CategoricalIndex.astype
- pandas.CategoricalIndex.copy
- pandas.CategoricalIndex.delete
- pandas.CategoricalIndex.difference
- pandas.CategoricalIndex.drop
- pandas.CategoricalIndex.drop_duplicates
- pandas.CategoricalIndex.dropna
- pandas.CategoricalIndex.duplicated
- pandas.CategoricalIndex.equals
- pandas.CategoricalIndex.factorize
- pandas.CategoricalIndex.fillna
- pandas.CategoricalIndex.format
- pandas.CategoricalIndex.get_duplicates
- pandas.CategoricalIndex.get_indexer
- pandas.CategoricalIndex.get_indexer_for
- pandas.CategoricalIndex.get_indexer_non_unique
- pandas.CategoricalIndex.get_level_values
- pandas.CategoricalIndex.get_loc
- pandas.CategoricalIndex.get_slice_bound
- pandas.CategoricalIndex.get_value
- pandas.CategoricalIndex.get_values
- pandas.CategoricalIndex.groupby
- pandas.CategoricalIndex.holds_integer
- pandas.CategoricalIndex.identical
- pandas.CategoricalIndex.insert
- pandas.CategoricalIndex.intersection
- pandas.CategoricalIndex.is
- pandas.CategoricalIndex.is_boolean
- pandas.CategoricalIndex.is_categorical
- pandas.CategoricalIndex.is_floating
- pandas.CategoricalIndex.is_integer
- pandas.CategoricalIndex.is_lexsorted_for_tuple
- pandas.CategoricalIndex.is_mixed
- pandas.CategoricalIndex.is_numeric
- pandas.CategoricalIndex.is_object
- pandas.CategoricalIndex.is_type_compatible
- pandas.CategoricalIndex.isin
- pandas.CategoricalIndex.item
- pandas.CategoricalIndex.join
- pandas.CategoricalIndex.map
- pandas.CategoricalIndex.max
- pandas.CategoricalIndex.memory_usage
- pandas.CategoricalIndex.min
- pandas.CategoricalIndex.nunique
- pandas.CategoricalIndex.order
- pandas.CategoricalIndex.putmask
- pandas.CategoricalIndex.ravel
- pandas.CategoricalIndex.reindex
- pandas.CategoricalIndex.remove_categories
- pandas.CategoricalIndex.remove_unused_categories
- pandas.CategoricalIndex.rename
- pandas.CategoricalIndex.rename_categories
- pandas.CategoricalIndex.reorder_categories
- pandas.CategoricalIndex.repeat
- pandas.CategoricalIndex.reshape
- pandas.CategoricalIndex.searchsorted
- pandas.CategoricalIndex.set_categories
- pandas.CategoricalIndex.set_names
- pandas.CategoricalIndex.set_value
- pandas.CategoricalIndex.shift
- pandas.CategoricalIndex.slice_indexer
- pandas.CategoricalIndex.slice_locs
- pandas.CategoricalIndex.sort
- pandas.CategoricalIndex.sort_values
- pandas.CategoricalIndex.sortlevel
- pandas.CategoricalIndex.str
- pandas.CategoricalIndex.summary
- pandas.CategoricalIndex.sym_diff
- pandas.CategoricalIndex.symmetric_difference
- pandas.CategoricalIndex.take
- pandas.CategoricalIndex.to_datetime
- pandas.CategoricalIndex.to_native_types
- pandas.CategoricalIndex.to_series
- pandas.CategoricalIndex.tolist
- pandas.CategoricalIndex.transpose
- pandas.CategoricalIndex.union
- pandas.CategoricalIndex.unique
- pandas.CategoricalIndex.value_counts
- pandas.CategoricalIndex.view
- pandas.CategoricalIndex.where
- Categorical Components
- pandas.CategoricalIndex
- MultiIndex
- DatetimeIndex
- TimedeltaIndex
- Window
- GroupBy
- Resampling
- Style
- General utility functions
- Internals
- Release Notes
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pandas.CategoricalIndex.map¶
-
CategoricalIndex.
map
(mapper)[source]¶ Apply mapper function to its categories (not codes).
Parameters: mapper : callable
Function to be applied. When all categories are mapped to different categories, the result will be Categorical which has the same order property as the original. Otherwise, the result will be np.ndarray.
Returns: applied : Categorical or np.ndarray.