cudf.Index

class cudf.Index(data=None, dtype=None, copy=False, name=_NoDefault.no_default, tupleize_cols=True, nan_as_null=True, **kwargs)

The basic object storing row labels for all cuDF objects.

Parameters:

dataarray-like (1-dimensional)/ DataFrame

If it is a DataFrame, it will return a MultiIndex

dtypeNumPy dtype (default: object)

If dtype is None, we find the dtype that best fits the data.

copybool

Make a copy of input data.

nameobject

Name to be stored in the index.

tupleize_colsbool (default: True)

When True, attempt to create a MultiIndex if possible. tupleize_cols == False is not yet supported.

nan_as_nullbool, Default True

If None/True, converts np.nan values to null values. If False, leaves np.nan values as is.

Returns:

Index

cudf Index

Warning

This class should not be subclassed. It is designed as a factory for different subclasses of BaseIndex depending on the provided input. If you absolutely must, and if you’re intimately familiar with the internals of cuDF, subclass BaseIndex instead.

Examples

>>> import cudf
>>> cudf.Index([1, 2, 3], dtype="uint64", name="a")
UInt64Index([1, 2, 3], dtype='uint64', name='a')

Attributes

hasnans	Return True if there are any NaNs or nulls.
is_monotonic	Return boolean if values in the object are monotonic_increasing.
is_unique	Return if the index has unique values.
name	Returns the name of the Index.
names	Returns a tuple 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 data.
str	Not yet implemented.

dtype
empty
has_duplicates
is_monotonic_decreasing
is_monotonic_increasing
size
values

Methods

any()	Return whether any elements is True in Index.
append(other)	Append a collection of Index objects together.
argsort(*args, **kwargs)	Return the integer indices that would sort the index.
astype(dtype[, copy])	Create an Index with values cast to dtypes.
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)	Determine if two Index objects contain the same elements.
fillna(value[, downcast])	Fill null values with the specified value.
find_label_range(loc)	Translate a label-based slice to an index-based slice
from_pandas(index[, nan_as_null])	Convert from a Pandas Index.
get_level_values(level)	Return an Index of values for requested level.
get_slice_bound(label, side[, kind])	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.
isin(values)	Return a boolean array where the index values are in values.
isna()	Detect missing values.
join(other[, how, level, return_indexers, sort])	Compute join_index and indexers to conform data structures to the new index.
max()	The maximum value of the index.
memory_usage([deep])	Return the memory usage of an object.
min()	The minimum value of the index.
notna()	Detect existing (non-missing) values.
rename(name[, inplace])	Alter Index name.
repeat(repeats[, axis])	Repeat elements of a Index.
searchsorted(value[, side, ascending, ...])	Find index 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])	Not yet implemented
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 suitable Arrow object.
to_cupy()	Convert to a cupy array.
to_dlpack()	Converts a cuDF object into a DLPack tensor.
to_frame([index, name])	Create a DataFrame with a column containing this Index
to_numpy()	Convert to a numpy array.
to_pandas(*[, nullable])	Convert to a Pandas Index.
to_series([index, name])	Create a Series with both index and values equal to the index keys.
union(other[, sort])	Form the union of two Index objects.
unique()	Return unique values in the index.
where(cond[, other, inplace])	Replace values where the condition is False.

copy
factorize
from_arrow
get_loc
to_list
tolist