pyspark.pandas.CategoricalIndex

class pyspark.pandas.CategoricalIndex

Index based on an underlying Categorical.

CategoricalIndex can only take on a limited, and usually fixed, number of possible values (categories). Also, it might have an order, but numerical operations (additions, divisions, …) are not possible.

Parameters

dataarray-like (1-dimensional)

The values of the categorical. If categories are given, values not in categories will be replaced with NaN.

categoriesindex-like, optional

The categories for the categorical. Items need to be unique. If the categories are not given here (and also not in dtype), they will be inferred from the data.

orderedbool, optional

Whether or not this categorical is treated as an ordered categorical. If not given here or in dtype, the resulting categorical will be unordered.

dtypeCategoricalDtype or “category”, optional

If CategoricalDtype, cannot be used together with categories or ordered.

copybool, default False

Make a copy of input ndarray.

nameobject, optional

Name to be stored in the index.

See also

Index

The base pandas-on-Spark Index type.

Examples

>>> ps.CategoricalIndex(["a", "b", "c", "a", "b", "c"])  
CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
                 categories=['a', 'b', 'c'], ordered=False, dtype='category')

CategoricalIndex can also be instantiated from a Categorical:

>>> c = pd.Categorical(["a", "b", "c", "a", "b", "c"])
>>> ps.CategoricalIndex(c)  
CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
                 categories=['a', 'b', 'c'], ordered=False, dtype='category')

Ordered CategoricalIndex can have a min and max value.

>>> ci = ps.CategoricalIndex(
...     ["a", "b", "c", "a", "b", "c"], ordered=True, categories=["c", "b", "a"]
... )
>>> ci  
CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
                 categories=['c', 'b', 'a'], ordered=True, dtype='category')

From a Series:

>>> s = ps.Series(["a", "b", "c", "a", "b", "c"], index=[10, 20, 30, 40, 50, 60])
>>> ps.CategoricalIndex(s)  
CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
                 categories=['a', 'b', 'c'], ordered=False, dtype='category')

From an Index:

>>> idx = ps.Index(["a", "b", "c", "a", "b", "c"])
>>> ps.CategoricalIndex(idx)  
CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
                 categories=['a', 'b', 'c'], ordered=False, dtype='category')

Methods

add_categories(new_categories[, inplace])	Add new categories.
all([axis])	Return whether all elements are True.
any([axis])	Return whether any element is True.
append(other)	Append a collection of Index options together.
argmax()	Return a maximum argument indexer.
argmin()	Return a minimum argument indexer.
as_ordered([inplace])	Set the Categorical to be ordered.
as_unordered([inplace])	Set the Categorical to be unordered.
asof(label)	Return the label from the index, or, if not present, the previous one.
astype(dtype)	Cast a pandas-on-Spark object to a specified dtype dtype.
copy([name, deep])	Make a copy of this object.
delete(loc)	Make new Index with passed location(-s) deleted.
difference(other[, sort])	Return a new Index with elements from the index that are not in other.
drop(labels)	Make new Index with passed list of labels deleted.
drop_duplicates()	Return Index with duplicate values removed.
droplevel(level)	Return index with requested level(s) removed.
dropna()	Return Index or MultiIndex without NA/NaN values
equals(other)	Determine if two Index objects contain the same elements.
factorize([sort, na_sentinel])	Encode the object as an enumerated type or categorical variable.
fillna(value)	Fill NA/NaN values with the specified value.
get_level_values(level)	Return Index if a valid level is given.
holds_integer()	Whether the type is an integer type.
identical(other)	Similar to equals, but check that other comparable attributes are also equal.
insert(loc, item)	Make new Index inserting new item at location.
intersection(other)	Form the intersection of two Index objects.
is_boolean()	Return if the current index type is a boolean type.
is_categorical()	Return if the current index type is a categorical type.
is_floating()	Return if the current index type is a floating type.
is_integer()	Return if the current index type is a integer type.
is_interval()	Return if the current index type is an interval type.
is_numeric()	Return if the current index type is a numeric type.
is_object()	Return if the current index type is a object type.
is_type_compatible(kind)	Whether the index type is compatible with the provided type.
isin(values)	Check whether values are contained in Series or Index.
isna()	Detect existing (non-missing) values.
isnull()	Detect existing (non-missing) values.
item()	Return the first element of the underlying data as a python scalar.
map(mapper)	Map values using input correspondence (a dict, Series, or function).
max()	Return the maximum value of the Index.
min()	Return the minimum value of the Index.
notna()	Detect existing (non-missing) values.
notnull()	Detect existing (non-missing) values.
nunique([dropna, approx, rsd])	Return number of unique elements in the object.
remove_categories(removals[, inplace])	Remove the specified categories.
remove_unused_categories([inplace])	Remove categories which are not used.
rename(name[, inplace])	Alter Index or MultiIndex name.
rename_categories(new_categories[, inplace])	Rename categories.
reorder_categories(new_categories[, …])	Reorder categories as specified in new_categories.
repeat(repeats)	Repeat elements of a Index/MultiIndex.
set_categories(new_categories[, ordered, …])	Set the categories to the specified new_categories.
set_names(names[, level, inplace])	Set Index or MultiIndex name.
shift([periods, fill_value])	Shift Series/Index by desired number of periods.
sort(*args, **kwargs)	Use sort_values instead.
sort_values([ascending])	Return a sorted copy of the index.
symmetric_difference(other[, result_name, sort])	Compute the symmetric difference of two Index objects.
take(indices)	Return the elements in the given positional indices along an axis.
to_frame([index, name])	Create a DataFrame with a column containing the Index.
to_list()	Return a list of the values.
to_numpy([dtype, copy])	A NumPy ndarray representing the values in this Index or MultiIndex.
to_pandas()	Return a pandas Index.
to_series([name])	Create a Series with both index and values equal to the index keys useful with map for returning an indexer based on an index.
tolist()	Return a list of the values.
transpose()	Return the transpose, For index, It will be index itself.
union(other[, sort])	Form the union of two Index objects.
unique([level])	Return unique values in the index.
value_counts([normalize, sort, ascending, …])	Return a Series containing counts of unique values.
view()	this is defined as a copy with the same identity

Attributes

T	Return the transpose, For index, It will be index itself.
asi8	Integer representation of the values.
categories	The categories of this categorical.
codes	The category codes of this categorical.
dtype	Return the dtype object of the underlying data.
empty	Returns true if the current object is empty.
has_duplicates	If index has duplicates, return True, otherwise False.
hasnans	Return True if it has any missing values.
inferred_type	Return a string of the type inferred from the values.
is_all_dates	Return if all data types of the index are datetime.
is_monotonic	Return boolean if values in the object are monotonically increasing.
is_monotonic_decreasing	Return boolean if values in the object are monotonically decreasing.
is_monotonic_increasing	Return boolean if values in the object are monotonically increasing.
is_unique	Return if the index has unique values.
name	Return name of the Index.
names	Return names of the Index.
ndim	Return an int representing the number of array dimensions.
nlevels	Number of levels in Index & MultiIndex.
ordered	Whether the categories have an ordered relationship.
shape	Return a tuple of the shape of the underlying data.
size	Return an int representing the number of elements in this object.
values	Return an array representing the data in the Index.

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