cudf.Index

class cudf.Index(*args, **kwargs)

Immutable sequence used for indexing and alignment.

The basic object storing axis labels for all pandas objects.

Parameters:

dataarray-like (1-dimensional)

dtypestr, numpy.dtype, or ExtensionDtype, optional

Data type for the output Index. If not specified, this will be inferred from data.

copybool, default False

Copy input data.

nameobject

Name to be stored in the index.

tupleize_colsbool (default: True)

When True, attempt to create a MultiIndex if possible. Currently not supported.

Attributes

T	Return the transpose, which is by definition self.
empty	Indicator whether DataFrame or Series is empty.
name	Get the name of this object.
names	Returns a FrozenList containing the name of the Index.
ndim	Number of dimensions of the underlying data, by definition 1.
nlevels	Number of levels.
shape	Get a tuple representing the dimensionality of the Index.
size	Return the number of elements in the underlying data.
str	Vectorized string functions for Series and Index.
values	Return a CuPy representation of the DataFrame.
values_host	Return a NumPy representation of the data.

dtype
has_duplicates
hasnans
inferred_type	Return a string of the type inferred from the values. Examples ——– >>> import cudf >>> idx = cudf.Index([1, 2, 3]) >>> idx Index([1, 2, 3], dtype=’int64’) >>> idx.inferred_type ‘integer’
is_monotonic_decreasing
is_monotonic_increasing
is_unique

Methods

all([axis, skipna])	Return whether all elements are True in DataFrame.
any()	Return whether any elements is True in DataFrame.
argsort([axis, kind, order, ascending, ...])	Return the integer indices that would sort the index.
copy([name, deep])	Make a copy of this object.
deserialize(header, frames)	Generate an object from a serialized representation.
device_deserialize(header, frames)	Perform device-side deserialization tasks.
device_serialize()	Serialize data and metadata associated with device memory.
difference(other[, sort])	Return a new Index with elements from the index that are not in other.
drop_duplicates([keep, nulls_are_equal])	Drop duplicate rows in index.
dropna([how])	Drop null rows from Index.
duplicated([keep])	Indicate duplicate index values.
equals(other)	Test whether two objects contain the same elements.
factorize([sort, use_na_sentinel])	Encode the input values as integer labels.
fillna([value, method, axis, inplace, limit])	Fill null values with value or specified method.
find_label_range(loc)	Translate a label-based slice to an index-based slice
from_arrow(obj)	Create from PyArrow Array/ChunkedArray.
from_pandas(index[, nan_as_null])	Convert from a Pandas Index.
from_pylibcudf(col[, metadata])	Create a Index from a pylibcudf.Column.
get_level_values(level)	Return an Index of values for requested level.
get_slice_bound(label, side)	Calculate slice bound that corresponds to given label.
host_deserialize(header, frames)	Perform device-side deserialization tasks.
host_serialize()	Serialize data and metadata associated with host memory.
intersection(other[, sort])	Form the intersection of two Index objects.
is_boolean()	Check if the Index only consists of booleans.
is_categorical()	Check if the Index holds categorical data.
is_floating()	Check if the Index is a floating type.
is_integer()	Check if the Index only consists of integers.
is_interval()	Check if the Index holds Interval objects.
is_numeric()	Check if the Index only consists of numeric data.
is_object()	Check if the Index is of the object dtype.
isna()	Identify missing values.
isnull()	Identify missing values.
join(other[, how, level, return_indexers, sort])	Compute join_index and indexers to conform data structures to the new index.
max([axis, skipna, numeric_only])	Return the maximum of the values in the DataFrame.
memory_usage([deep])	Return the memory usage of an object.
min([axis, skipna, numeric_only])	Return the minimum of the values in the DataFrame.
notna()	Identify non-missing values.
notnull()	Identify non-missing values.
nunique([dropna])	Return count of unique values for the column.
rename(name[, inplace])	Alter Index name.
searchsorted(values[, side, sorter, ...])	Find indices where elements should be inserted to maintain order
serialize()	Generate an equivalent serializable representation of an object.
set_names(names[, level, inplace])	Set Index or MultiIndex name.
shift([periods, freq])	Shift index by desired number of time frequency increments.
sort_values([return_indexer, ascending, ...])	Return a sorted copy of the index, and optionally return the indices that sorted the index itself.
take(indices[, axis, allow_fill, fill_value])	Return a new index containing the rows specified by indices
to_arrow()	Convert to a PyArrow Array.
to_cupy([dtype, copy, na_value])	Convert the SingleColumnFrame (e.g., Series) to a CuPy array.
to_dlpack()	Converts a cuDF object to a DLPack tensor.
to_frame([index, name])	Create a DataFrame with a column containing this Index
to_list()	Conversion to host memory lists is currently unsupported
to_numpy([dtype, copy, na_value])	Convert the Frame to a NumPy array.
to_pylibcudf([copy])	Convert this Index to a pylibcudf.Column.
to_series([index, name])	Create a Series with both index and values equal to the index keys.
tolist()	Conversion to host memory lists is currently unsupported
transpose()	Return the transpose, which is by definition self.
union(other[, sort])	Form the union of two Index objects.
where(cond[, other, inplace])	Replace values where the condition is False.

append
astype
get_indexer
get_loc
isin
nans_to_nulls
repeat
to_pandas
unique