pandas.CategoricalIndex

class pandas.CategoricalIndex

Immutable Index implementing an ordered, sliceable set. CategoricalIndex represents a sparsely populated Index with an underlying Categorical.

Parameters:

data : array-like or Categorical, (1-dimensional)

categories : optional, array-like

categories for the CategoricalIndex

ordered : boolean,

designating if the categories are ordered

copy : bool

Make a copy of input ndarray

name : object

Name to be stored in the index

Attributes

T	return the transpose, which is by definition self
base	return the base object if the memory of the underlying data is shared
categories
codes
data	return the data pointer of the underlying data
flags
has_duplicates
inferred_type
is_monotonic	alias for is_monotonic_increasing (deprecated)
is_monotonic_decreasing	return if the index is monotonic decreasing (only equal or
is_monotonic_increasing	return if the index is monotonic increasing (only equal or
itemsize	return the size of the dtype of the item of the underlying data
names
nbytes	return the number of bytes in the underlying data
ndim	return the number of dimensions of the underlying data, by definition 1
nlevels
ordered
shape	return a tuple of the shape of the underlying data
size	return the number of elements in the underlying data
strides	return the strides of the underlying data
values	return the underlying data, which is a Categorical

asi8
dtype
is_all_dates
is_unique
name

Methods

add_categories(*args, **kwargs)	Add new categories.
all([other])
any([other])
append(other)	Append a collection of CategoricalIndex options together
argmax([axis])	return a ndarray of the maximum argument indexer
argmin([axis])	return a ndarray of the minimum argument indexer
argsort(*args, **kwargs)
as_ordered(*args, **kwargs)	Sets the Categorical to be ordered
as_unordered(*args, **kwargs)	Sets the Categorical to be unordered
asof(label)	For a sorted index, return the most recent label up to and including the passed label.
asof_locs(where, mask)	where : array of timestamps
astype(dtype)
copy([names, name, dtype, deep])	Make a copy of this object.
delete(loc)	Make new Index with passed location(-s) deleted
diff(*args, **kwargs)
difference(other)	Compute sorted set difference of two Index objects
drop(labels[, errors])	Make new Index with passed list of labels deleted
drop_duplicates([take_last])	Return Index with duplicate values removed
duplicated([take_last])	Return boolean np.array denoting duplicate values
equals(other)	Determines if two CategorialIndex objects contain the same elements.
factorize([sort, na_sentinel])	Encode the object as an enumerated type or categorical variable
format([name, formatter])	Render a string representation of the Index
get_duplicates()
get_indexer(target[, method, limit])	Compute indexer and mask for new index given the current index.
get_indexer_for(target, **kwargs)	guaranteed return of an indexer even when non-unique
get_indexer_non_unique(target)	this is the same for a CategoricalIndex for get_indexer; the API returns the missing values as well
get_level_values(level)	Return vector of label values for requested level, equal to the length
get_loc(key[, method])	Get integer location for requested label
get_slice_bound(label, side, kind)	Calculate slice bound that corresponds to given label.
get_value(series, key)	Fast lookup of value from 1-dimensional ndarray.
get_values()	return the underlying data as an ndarray
groupby(to_groupby)	Group the index labels by a given array of values.
hasnans()	return if I have any nans; enables various perf speedups
holds_integer()
identical(other)	Similar to equals, but check that other comparable attributes are
insert(loc, item)	Make new Index inserting new item at location.
intersection(other)	Form the intersection of two Index objects.
is_(other)	More flexible, faster check like is but that works through views
is_boolean()
is_categorical()
is_floating()
is_integer()
is_lexsorted_for_tuple(tup)
is_mixed()
is_numeric()
is_object()
is_type_compatible(kind)
isin(values[, level])	Compute boolean array of whether each index value is found in the passed set of values.
item()	return the first element of the underlying data as a python scalar
join(other[, how, level, return_indexers])	this is an internal non-public method
map(mapper)
max(*args, **kwargs)	The maximum value of the object.
min(*args, **kwargs)	The minimum value of the object.
nunique([dropna])	Return number of unique elements in the object.
order([return_indexer, ascending])	Return sorted copy of Index
putmask(mask, value)	return a new Index of the values set with the mask
ravel([order])	return an ndarray of the flattened values of the underlying data
reindex(target[, method, level, limit])	Create index with target’s values (move/add/delete values as necessary)
remove_categories(*args, **kwargs)	Removes the specified categories.
remove_unused_categories(*args, **kwargs)	Removes categories which are not used.
rename(name[, inplace])	Set new names on index.
rename_categories(*args, **kwargs)	Renames categories.
reorder_categories(*args, **kwargs)	Reorders categories as specified in new_categories.
repeat(n)	return a new Index of the values repeated n times
searchsorted(key[, side])	np.ndarray searchsorted compat
set_categories(*args, **kwargs)	Sets the categories to the specified new_categories.
set_names(names[, level, inplace])	Set new names on index.
set_value(arr, key, value)	Fast lookup of value from 1-dimensional ndarray.
shift([periods, freq])	Shift Index containing datetime objects by input number of periods and
slice_indexer([start, end, step, kind])	For an ordered Index, compute the slice indexer for input labels and
slice_locs([start, end, step, kind])	Compute slice locations for input labels.
sort(*args, **kwargs)
str	alias of StringMethods
summary([name])
sym_diff(other[, result_name])	Compute the sorted symmetric difference of two Index objects.
take(indexer[, axis])	return a new CategoricalIndex of the values selected by the indexer
to_datetime([dayfirst])	For an Index containing strings or datetime.datetime objects, attempt
to_native_types([slicer])	slice and dice then format
to_series(**kwargs)	Create a Series with both index and values equal to the index keys
tolist()	return a list of the Index values
transpose()	return the transpose, which is by definition self
union(other)	Form the union of two Index objects and sorts if possible
unique()	Return array of unique values in the object.
value_counts([normalize, sort, ascending, ...])	Returns object containing counts of unique values.
view([cls])