hipdf.CategoricalIndex

hipdf.CategoricalIndex#

68 min read time

Applies to Linux

class hipdf.CategoricalIndex(data, *args, **kwargs)#

Bases: Index

A categorical of orderable values that represent the indices of another Column

Parameters#

dataarray-like (1-dimensional): The values of the categorical. If categories are given, values not in categories will be replaced with None/NaN.
categorieslist-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.
nameobject, optional: Name to be stored in the index.

Attributes#

codes categories

Methods#

equals

Returns#

CategoricalIndex

Examples#

>>> import cudf
>>> import pandas as pd
>>> cudf.CategoricalIndex(
... data=[1, 2, 3, 4], categories=[1, 2], ordered=False, name="a")
CategoricalIndex([1, 2, <NA>, <NA>], categories=[1, 2], ordered=False, dtype='category', name='a')

>>> cudf.CategoricalIndex(
... data=[1, 2, 3, 4], dtype=pd.CategoricalDtype([1, 2, 3]), name="a")
CategoricalIndex([1, 2, 3, <NA>], categories=[1, 2, 3], ordered=False, dtype='category', name='a')

__init__(data=None, categories=None, ordered=None, dtype=None, copy=False, name=None)#

Methods

`__init__`([data, categories, ordered, dtype, ...])
`add_categories`(new_categories)	Add new categories.
`all`([axis, skipna])	Return whether all elements are True in DataFrame.
`any`()	Return whether any elements is True in DataFrame.
`append`(other)	Append a collection of Index objects together.
`argsort`([axis, kind, order, ascending, ...])	Return the integer indices that would sort the index.
`as_ordered`()	Set the Categorical to be ordered.
`as_unordered`()	Set the Categorical to be unordered.
`astype`(dtype[, copy])	Create an Index with values cast to dtypes.
`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.
`get_indexer`(target[, method, limit, tolerance])	Compute indexer and mask for new index given the current index.
`get_level_values`(level)	Return an Index of values for requested level.
`get_loc`(key)	Get integer location, slice or boolean mask for requested label.
`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.
`isin`(values[, level])	Return a boolean array where the index values are in values.
`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.
`remove_categories`(removals)	Remove the specified categories.
`remove_unused_categories`()	Remove categories which are not used.
`rename`(name[, inplace])	Alter Index name.
`rename_categories`(new_categories)	Rename categories.
`reorder_categories`(new_categories[, ordered])	Reorder categories as specified in new_categories.
`repeat`(repeats[, axis])	Repeat elements of a Index.
`searchsorted`(values[, side, sorter, ...])	Find indices where elements should be inserted to maintain order
`serialize`()	Generate an equivalent serializable representation of an object.
`set_categories`(new_categories[, ordered, rename])	Set the categories to the specified new_categories.
`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 PyArrow Array.
`to_cupy`([dtype, copy, na_value])	Convert the Frame to a CuPy array.
`to_dlpack`()	Converts a cuDF object into a DLPack tensor.
`to_flat_index`()	Identity method.
`to_frame`([index, name])	Create a DataFrame with a column containing this Index
`to_list`()
`to_numpy`([dtype, copy, na_value])	Convert the Frame to a NumPy array.
`to_pandas`(*[, nullable, arrow_type])	Convert to a Pandas Index.
`to_series`([index, name])	Create a Series with both index and values equal to the index keys.
`tolist`()
`transpose`()	Return the transpose, which is by definition self.
`union`(other[, sort])	Form the union of two Index objects.
`unique`([level])	Return unique values in the index.
`where`(cond[, other, inplace])	Replace values where the condition is False.

Attributes

`T`	Return the transpose, which is by definition self.
`categories`	The categories of this categorical.
`codes`	The category codes of this categorical.
`dtype`	dtype of the underlying values in Index.
`empty`
`has_duplicates`
`hasnans`	Return True if there are any NaNs or nulls.
`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 boolean if values in the object are unique.
`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.
`ordered`
`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.

__init__(data=None, categories=None, ordered=None, dtype=None, copy=False, name=None)#

property ordered: bool#

property codes: Index#: The category codes of this categorical.

property categories: Index#: The categories of this categorical.

add_categories(new_categories) → Self#

Add new categories.

new_categories will be included at the last/highest place in the categories and will be unused directly after this call.

as_ordered() → Self#: Set the Categorical to be ordered.

as_unordered() → Self#: Set the Categorical to be unordered.

remove_categories(removals) → Self#

Remove the specified categories.

removals must be included in the old categories.

Parameters#

removalscategory or list of categories: The categories which should be removed.

remove_unused_categories() → Self#

Remove categories which are not used.

This method is currently not supported.

rename_categories(new_categories) → Self#

Rename categories.

This method is currently not supported.

reorder_categories(new_categories, ordered=None) → Self#

Reorder categories as specified in new_categories.

new_categories need to include all old categories and no new category items.

Parameters#

new_categoriesIndex-like: The categories in new order.
orderedbool, optional: Whether or not the categorical is treated as a ordered categorical. If not given, do not change the ordered information.

set_categories(new_categories, ordered=None, rename: bool = False) → Self#

Set the categories to the specified new_categories.

Parameters#

new_categorieslist-like: The categories in new order.
orderedbool, default None: Whether or not the categorical is treated as a ordered categorical. If not given, do not change the ordered information.
renamebool, default False: Whether or not the new_categories should be considered as a rename of the old categories or as reordered categories.

property T#: Return the transpose, which is by definition self.

__getitem__(index)#

all(axis=0, skipna=True, **kwargs)#

Return whether all elements are True in DataFrame.

Parameters#

axis{0 or ‘index’, 1 or ‘columns’, None}, default 0

Indicate which axis or axes should be reduced. For Series this parameter is unused and defaults to 0.

0 or ‘index’reduce the index, return a Series
whose index is the original column labels.
1 or ‘columns’reduce the columns, return a Series
whose index is the original index.
None : reduce all axes, return a scalar.

skipna: bool, default True

Exclude NA/null values. If the entire row/column is NA and skipna is True, then the result will be True, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero.

Returns#

Series

Notes#

Parameters currently not supported are bool_only.

Examples#

>>> import cudf
>>> df = cudf.DataFrame({'a': [3, 2, 3, 4], 'b': [7, 0, 10, 10]})
>>> df.all()
a     True
b    False
dtype: bool

any() → bool#

Return whether any elements is True in DataFrame.

Parameters#

axis{0 or ‘index’, 1 or ‘columns’, None}, default 0

Indicate which axis or axes should be reduced. For Series this parameter is unused and defaults to 0.

0 or ‘index’reduce the index, return a Series
whose index is the original column labels.
1 or ‘columns’reduce the columns, return a Series
whose index is the original index.
None : reduce all axes, return a scalar.

skipna: bool, default True

Exclude NA/null values. If the entire row/column is NA and skipna is True, then the result will be False, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero.

Returns#

Series

Notes#

Parameters currently not supported are bool_only.

Examples#

>>> import cudf
>>> df = cudf.DataFrame({'a': [3, 2, 3, 4], 'b': [7, 0, 10, 10]})
>>> df.any()
a    True
b    True
dtype: bool

append(other)#

Append a collection of Index objects together.

Parameters#

other : Index or list/tuple of indices

Returns#

appended : Index

Examples#

>>> import cudf
>>> idx = cudf.Index([1, 2, 10, 100])
>>> idx
Index([1, 2, 10, 100], dtype='int64')
>>> other = cudf.Index([200, 400, 50])
>>> other
Index([200, 400, 50], dtype='int64')
>>> idx.append(other)
Index([1, 2, 10, 100, 200, 400, 50], dtype='int64')

append accepts list of Index objects

>>> idx.append([other, other])
Index([1, 2, 10, 100, 200, 400, 50, 200, 400, 50], dtype='int64')

argsort(axis=0, kind='quicksort', order=None, ascending=True, na_position='last') → ndarray#

Return the integer indices that would sort the index.

Parameters#

axis{0 or “index”}: Has no effect but is accepted for compatibility with numpy.
kind{‘mergesort’, ‘quicksort’, ‘heapsort’, ‘stable’}, default ‘quicksort’: Choice of sorting algorithm. See numpy.sort() for more information. ‘mergesort’ and ‘stable’ are the only stable algorithms. Only quicksort is supported in cuDF.
orderNone: Has no effect but is accepted for compatibility with numpy.
ascendingbool or list of bool, default True: If True, sort values in ascending order, otherwise descending.
na_position{‘first’ or ‘last’}, default ‘last’: Argument ‘first’ puts NaNs at the beginning, ‘last’ puts NaNs at the end.

Returns#

cupy.ndarray: The indices sorted based on input.

astype(dtype, copy: bool = True) → Index#

Create an Index with values cast to dtypes.

The class of a new Index is determined by dtype. When conversion is impossible, a ValueError exception is raised.

Parameters#

dtypenumpy.dtype: Use a numpy.dtype to cast entire Index object to.
copybool, default False: By default, astype always returns a newly allocated object. If copy is set to False and internal requirements on dtype are satisfied, the original data is used to create a new Index or the original Index is returned.

Returns#

Index: Index with values cast to specified dtype.

Examples#

>>> import cudf
>>> index = cudf.Index([1, 2, 3])
>>> index
Index([1, 2, 3], dtype='int64')
>>> index.astype('float64')
Index([1.0, 2.0, 3.0], dtype='float64')

copy(name: Hashable = None, deep: bool = False) → Self#

Make a copy of this object.

Parameters#

nameobject, default None: Name of index, use original name when None
deepbool, default True: Make a deep copy of the data. With deep=False the original data is used

Returns#

New index instance.

difference(other, sort=None)#

Return a new Index with elements from the index that are not in other.

This is the set difference of two Index objects.

Parameters#

other : Index or array-like sort : False or None, default None

Whether to sort the resulting index. By default, the values are attempted to be sorted, but any TypeError from incomparable elements is caught by cudf.

None : Attempt to sort the result, but catch any TypeErrors from comparing incomparable elements.

False : Do not sort the result.

True : Sort the result (which may raise TypeError).

Returns#

difference : Index

Examples#

>>> import cudf
>>> idx1 = cudf.Index([2, 1, 3, 4])
>>> idx1
Index([2, 1, 3, 4], dtype='int64')
>>> idx2 = cudf.Index([3, 4, 5, 6])
>>> idx2
Index([3, 4, 5, 6], dtype='int64')
>>> idx1.difference(idx2)
Index([1, 2], dtype='int64')
>>> idx1.difference(idx2, sort=False)
Index([2, 1], dtype='int64')

drop_duplicates(keep='first', nulls_are_equal=True)#

Drop duplicate rows in index.

keep{“first”, “last”, False}, default “first”

‘first’ : Drop duplicates except for the first occurrence.
‘last’ : Drop duplicates except for the last occurrence.
False : Drop all duplicates.

nulls_are_equal: bool, default True

Null elements are considered equal to other null elements.

dropna(how='any')#

Drop null rows from Index.

how{“any”, “all”}, default “any”: Specifies how to decide whether to drop a row. “any” (default) drops rows containing at least one null value. “all” drops only rows containing all null values.

property dtype#: dtype of the underlying values in Index.

duplicated(keep='first') → cupy.ndarray#

Indicate duplicate index values.

Duplicated values are indicated as True values in the resulting array. Either all duplicates, all except the first, or all except the last occurrence of duplicates can be indicated.

Parameters#

keep{‘first’, ‘last’, False}, default ‘first’

The value or values in a set of duplicates to mark as missing.

'first' : Mark duplicates as True except for the first occurrence.
'last' : Mark duplicates as True except for the last occurrence.
False : Mark all duplicates as True.

Returns#

cupy.ndarray[bool]

Examples#

By default, for each set of duplicated values, the first occurrence is set to False and all others to True:

>>> import cudf
>>> idx = cudf.Index(['lama', 'cow', 'lama', 'beetle', 'lama'])
>>> idx.duplicated()
array([False, False,  True, False,  True])

which is equivalent to

>>> idx.duplicated(keep='first')
array([False, False,  True, False,  True])

By using ‘last’, the last occurrence of each set of duplicated values is set to False and all others to True:

>>> idx.duplicated(keep='last')
array([ True, False,  True, False, False])

By setting keep to False, all duplicates are True:

>>> idx.duplicated(keep=False)
array([ True, False,  True, False,  True])

property empty#

equals(other) → bool#

Test whether two objects contain the same elements.

This function allows two objects to be compared against each other to see if they have the same shape and elements. NaNs in the same location are considered equal. The column headers do not need to have the same type.

Parameters#

otherIndex, Series, DataFrame: The other object to be compared with.

Returns#

bool: True if all elements are the same in both objects, False otherwise.

Examples#

>>> import cudf

Comparing Series with equals:

>>> s = cudf.Series([1, 2, 3])
>>> other = cudf.Series([1, 2, 3])
>>> s.equals(other)
True
>>> different = cudf.Series([1.5, 2, 3])
>>> s.equals(different)
False

Comparing DataFrames with equals:

>>> df = cudf.DataFrame({1: [10], 2: [20]})
>>> df
    1   2
0  10  20
>>> exactly_equal = cudf.DataFrame({1: [10], 2: [20]})
>>> exactly_equal
    1   2
0  10  20
>>> df.equals(exactly_equal)
True

For two DataFrames to compare equal, the types of column values must be equal, but the types of column labels need not:

>>> different_column_type = cudf.DataFrame({1.0: [10], 2.0: [20]})
>>> different_column_type
   1.0  2.0
0   10   20
>>> df.equals(different_column_type)
True

factorize(sort: bool = False, use_na_sentinel: bool = True) → tuple[cupy.ndarray, cudf.Index]#

Encode the input values as integer labels.

Parameters#

sortbool, default True: Sort uniques and shuffle codes to maintain the relationship.
use_na_sentinelbool, default True: If True, the sentinel -1 will be used for NA values. If False, NA values will be encoded as non-negative integers and will not drop the NA from the uniques of the values.

Returns#

(labels, cats)(cupy.ndarray, cupy.ndarray or Index)

labels contains the encoded values
cats contains the categories in order that the N-th item corresponds to the (N-1) code.

Examples#

>>> import cudf
>>> s = cudf.Series(['a', 'a', 'c'])
>>> codes, uniques = s.factorize()
>>> codes
array([0, 0, 1], dtype=int8)
>>> uniques
Index(['a', 'c'], dtype='object')

fillna(value: None | ScalarLike | cudf.Series = None, method: Literal['ffill', 'bfill', 'pad', 'backfill', None] = None, axis=None, inplace: bool = False, limit=None) → Self | None#

Fill null values with value or specified method.

Parameters#

valuescalar, Series-like or dict: Value to use to fill nulls. If Series-like, null values are filled with values in corresponding indices. A dict can be used to provide different values to fill nulls in different columns. Cannot be used with method.
method{‘ffill’, ‘bfill’}, default None: Method to use for filling null values in the dataframe or series. ffill propagates the last non-null values forward to the next non-null value. bfill propagates backward with the next non-null value. Cannot be used with value.

Deprecated since version 24.04: method is deprecated.

Returns#

resultDataFrame, Series, or Index: Copy with nulls filled.

Examples#

>>> import cudf
>>> df = cudf.DataFrame({'a': [1, 2, None], 'b': [3, None, 5]})
>>> df
      a     b
0     1     3
1     2  <NA>
2  <NA>     5
>>> df.fillna(4)
   a  b
0  1  3
1  2  4
2  4  5
>>> df.fillna({'a': 3, 'b': 4})
   a  b
0  1  3
1  2  4
2  3  5

fillna on a Series object:

>>> ser = cudf.Series(['a', 'b', None, 'c'])
>>> ser
0       a
1       b
2    <NA>
3       c
dtype: object
>>> ser.fillna('z')
0    a
1    b
2    z
3    c
dtype: object

fillna can also supports inplace operation:

>>> ser.fillna('z', inplace=True)
>>> ser
0    a
1    b
2    z
3    c
dtype: object
>>> df.fillna({'a': 3, 'b': 4}, inplace=True)
>>> df
   a  b
0  1  3
1  2  4
2  3  5

fillna specified with fill method

>>> ser = cudf.Series([1, None, None, 2, 3, None, None])
>>> ser.fillna(method='ffill')
  1
  1
  1
  2
  3
  3
  3
dtype: int64
>>> ser.fillna(method='bfill')
     1
     2
     2
     2
     3
  <NA>
  <NA>
dtype: int64

find_label_range(loc: slice) → slice#

Translate a label-based slice to an index-based slice

Parameters#

loc: slice to search for.

Notes#

As with all label-based searches, the slice is right-closed.

Returns#

New slice translated into integer indices of the index (right-open).

classmethod from_arrow(obj) → Index | MultiIndex#

Create from PyArrow Array/ChunkedArray.

Parameters#

arrayPyArrow Array/ChunkedArray: PyArrow Object which has to be converted.

Raises#

TypeError for invalid input type.

Returns#

SingleColumnFrame

Examples#

>>> import cudf
>>> import pyarrow as pa
>>> cudf.Index.from_arrow(pa.array(["a", "b", None]))
Index(['a', 'b', <NA>], dtype='object')

classmethod from_pandas(index: ~pandas.core.indexes.base.Index, nan_as_null=<no_default>)#

Convert from a Pandas Index.

Parameters#

indexPandas Index object: A Pandas Index object which has to be converted to cuDF Index.
nan_as_nullbool, Default None: If None/True, converts np.nan values to null values. If False, leaves np.nan values as is.

Raises#

TypeError for invalid input type.

Examples#

>>> import cudf
>>> import pandas as pd
>>> import numpy as np
>>> data = [10, 20, 30, np.nan]
>>> pdi = pd.Index(data)
>>> cudf.Index.from_pandas(pdi)
Index([10.0, 20.0, 30.0, <NA>], dtype='float64')
>>> cudf.Index.from_pandas(pdi, nan_as_null=False)
Index([10.0, 20.0, 30.0, nan], dtype='float64')

get_indexer(target, method=None, limit=None, tolerance=None)#

Compute indexer and mask for new index given the current index.

The indexer should be then used as an input to ndarray.take to align the current data to the new index.

Parameters#

target : Index method : {None, ‘pad’/’fill’, ‘backfill’/’bfill’, ‘nearest’}, optional

default: exact matches only.

pad / ffill: find the PREVIOUS index value if no exact match.

backfill / bfill: use NEXT index value if no exact match.

nearest: use the NEAREST index value if no exact match. Tied distances are broken by preferring the larger index value.

toleranceint or float, optional: Maximum distance from index value for inexact matches. The value of the index at the matching location must satisfy the equation abs(index[loc] - target) <= tolerance.

Returns#

cupy.ndarray: Integers from 0 to n - 1 indicating that the index at these positions matches the corresponding target values. Missing values in the target are marked by -1.

Examples#

>>> import cudf
>>> index = cudf.Index(['c', 'a', 'b'])
>>> index
Index(['c', 'a', 'b'], dtype='object')
>>> index.get_indexer(['a', 'b', 'x'])
array([ 1,  2, -1], dtype=int32)

get_level_values(level)#

Return an Index of values for requested level.

This is primarily useful to get an individual level of values from a MultiIndex, but is provided on Index as well for compatibility.

Parameters#

levelint or str: It is either the integer position or the name of the level.

Returns#

Index: Calling object, as there is only one level in the Index.

Notes#

For Index, level should be 0, since there are no multiple levels.

Examples#

>>> import cudf
>>> idx = cudf.Index(["a", "b", "c"])
>>> idx.get_level_values(0)
Index(['a', 'b', 'c'], dtype='object')

get_loc(key) → int | slice | ndarray#

Get integer location, slice or boolean mask for requested label.

Parameters#

key : label

Returns#

int or slice or boolean mask

If result is unique, return integer index
If index is monotonic, loc is returned as a slice object
Otherwise, a boolean mask is returned

Examples#

>>> import cudf
>>> unique_index = cudf.Index(list('abc'))
>>> unique_index.get_loc('b')
1
>>> monotonic_index = cudf.Index(list('abbc'))
>>> monotonic_index.get_loc('b')
slice(1, 3, None)
>>> non_monotonic_index = cudf.Index(list('abcb'))
>>> non_monotonic_index.get_loc('b')
array([False,  True, False,  True])
>>> numeric_unique_index = cudf.Index([1, 2, 3])
>>> numeric_unique_index.get_loc(3)
2

MultiIndex

>>> multi_index = cudf.MultiIndex.from_tuples([('a', 'd'), ('b', 'e'), ('b', 'f')])
>>> multi_index
MultiIndex([('a', 'd'),
            ('b', 'e'),
            ('b', 'f')],
        )
>>> multi_index.get_loc('b')
slice(1, 3, None)
>>> multi_index.get_loc(('b', 'e'))
1

get_slice_bound(label, side: Literal['left', 'right']) → int#

Calculate slice bound that corresponds to given label. Returns leftmost (one-past-the-rightmost if side=='right') position of given label.

Parameters#

label : object side : {‘left’, ‘right’}

Returns#

int: Index of label.

property has_duplicates#

property hasnans: bool#

Return True if there are any NaNs or nulls.

Returns#

outbool: If Series has at least one NaN or null value, return True, if not return False.

Examples#

>>> import cudf
>>> import numpy as np
>>> index = cudf.Index([1, 2, np.nan, 3, 4], nan_as_null=False)
>>> index
Index([1.0, 2.0, nan, 3.0, 4.0], dtype='float64')
>>> index.hasnans
True

hasnans returns True for the presence of any NA values:

>>> index = cudf.Index([1, 2, None, 3, 4])
>>> index
Index([1, 2, <NA>, 3, 4], dtype='int64')
>>> index.hasnans
True

intersection(other, sort=False)#

Form the intersection of two Index objects.

This returns a new Index with elements common to the index and other.

Parameters#

other : Index or array-like sort : False or None, default False

Whether to sort the resulting index.

False : do not sort the result.

None : sort the result, except when self and other are equal or when the values cannot be compared.

True : Sort the result (which may raise TypeError).

Returns#

intersection : Index

Examples#

>>> import cudf
>>> import pandas as pd
>>> idx1 = cudf.Index([1, 2, 3, 4])
>>> idx2 = cudf.Index([3, 4, 5, 6])
>>> idx1.intersection(idx2)
Index([3, 4], dtype='int64')

MultiIndex case

>>> idx1 = cudf.MultiIndex.from_pandas(
...    pd.MultiIndex.from_arrays(
...         [[1, 1, 3, 4], ["Red", "Blue", "Red", "Blue"]]
...    )
... )
>>> idx2 = cudf.MultiIndex.from_pandas(
...    pd.MultiIndex.from_arrays(
...         [[1, 1, 2, 2], ["Red", "Blue", "Red", "Blue"]]
...    )
... )
>>> idx1
MultiIndex([(1,  'Red'),
            (1, 'Blue'),
            (3,  'Red'),
            (4, 'Blue')],
        )
>>> idx2
MultiIndex([(1,  'Red'),
            (1, 'Blue'),
            (2,  'Red'),
            (2, 'Blue')],
        )
>>> idx1.intersection(idx2)
MultiIndex([(1,  'Red'),
            (1, 'Blue')],
        )
>>> idx1.intersection(idx2, sort=False)
MultiIndex([(1,  'Red'),
            (1, 'Blue')],
        )

is_boolean()#

Check if the Index only consists of booleans.

Deprecated since version 23.04: Use cudf.api.types.is_bool_dtype instead.

Returns#

bool: Whether or not the Index only consists of booleans.

Examples#

>>> import cudf
>>> idx = cudf.Index([True, False, True])
>>> idx.is_boolean()
True
>>> idx = cudf.Index(["True", "False", "True"])
>>> idx.is_boolean()
False
>>> idx = cudf.Index([1, 2, 3])
>>> idx.is_boolean()
False

is_categorical()#

Check if the Index holds categorical data.

Deprecated since version 23.04: Use cudf.api.types.is_categorical_dtype instead.

Returns#

bool: True if the Index is categorical.

Examples#

>>> import cudf
>>> idx = cudf.Index(["Watermelon", "Orange", "Apple",
...                 "Watermelon"]).astype("category")
>>> idx.is_categorical()
True
>>> idx = cudf.Index([1, 3, 5, 7])
>>> idx.is_categorical()
False
>>> s = cudf.Series(["Peter", "Victor", "Elisabeth", "Mar"])
>>> s
0        Peter
1       Victor
2    Elisabeth
3          Mar
dtype: object
>>> s.index.is_categorical()
False

is_floating()#

Check if the Index is a floating type.

The Index may consist of only floats, NaNs, or a mix of floats, integers, or NaNs.

Deprecated since version 23.04: Use cudf.api.types.is_float_dtype instead.

Returns#

bool: Whether or not the Index only consists of only consists of floats, NaNs, or a mix of floats, integers, or NaNs.

Examples#

>>> import cudf
>>> idx = cudf.Index([1.0, 2.0, 3.0, 4.0])
>>> idx.is_floating()
True
>>> idx = cudf.Index([1.0, 2.0, np.nan, 4.0])
>>> idx.is_floating()
True
>>> idx = cudf.Index([1, 2, 3, 4, np.nan], nan_as_null=False)
>>> idx.is_floating()
True
>>> idx = cudf.Index([1, 2, 3, 4])
>>> idx.is_floating()
False

is_integer()#

Check if the Index only consists of integers.

Deprecated since version 23.04: Use cudf.api.types.is_integer_dtype instead.

Returns#

bool: Whether or not the Index only consists of integers.

Examples#

>>> import cudf
>>> idx = cudf.Index([1, 2, 3, 4])
>>> idx.is_integer()
True
>>> idx = cudf.Index([1.0, 2.0, 3.0, 4.0])
>>> idx.is_integer()
False
>>> idx = cudf.Index(["Apple", "Mango", "Watermelon"])
>>> idx.is_integer()
False

is_interval()#

Check if the Index holds Interval objects.

Deprecated since version 23.04: Use cudf.api.types.is_interval_dtype instead.

Returns#

bool: Whether or not the Index holds Interval objects.

Examples#

>>> import cudf
>>> import pandas as pd
>>> idx = cudf.from_pandas(
...     pd.Index([pd.Interval(left=0, right=5),
...               pd.Interval(left=5, right=10)])
... )
>>> idx.is_interval()
True
>>> idx = cudf.Index([1, 3, 5, 7])
>>> idx.is_interval()
False

property is_monotonic_decreasing: bool#: Return boolean if values in the object are monotonically decreasing.

Returns#

bool

property is_monotonic_increasing: bool#: Return boolean if values in the object are monotonically increasing.

Returns#

bool

is_numeric()#

Check if the Index only consists of numeric data.

Deprecated since version 23.04: Use cudf.api.types.is_any_real_numeric_dtype instead.

Returns#

bool: Whether or not the Index only consists of numeric data.

Examples#

>>> import cudf
>>> idx = cudf.Index([1.0, 2.0, 3.0, 4.0])
>>> idx.is_numeric()
True
>>> idx = cudf.Index([1, 2, 3, 4.0])
>>> idx.is_numeric()
True
>>> idx = cudf.Index([1, 2, 3, 4])
>>> idx.is_numeric()
True
>>> idx = cudf.Index([1, 2, 3, 4.0, np.nan])
>>> idx.is_numeric()
True
>>> idx = cudf.Index(["Apple", "cold"])
>>> idx.is_numeric()
False

is_object()#

Check if the Index is of the object dtype.

Deprecated since version 23.04: Use cudf.api.types.is_object_dtype instead.

Returns#

bool: Whether or not the Index is of the object dtype.

Examples#

>>> import cudf
>>> idx = cudf.Index(["Apple", "Mango", "Watermelon"])
>>> idx.is_object()
True
>>> idx = cudf.Index(["Watermelon", "Orange", "Apple",
...                 "Watermelon"]).astype("category")
>>> idx.is_object()
False
>>> idx = cudf.Index([1.0, 2.0, 3.0, 4.0])
>>> idx.is_object()
False

property is_unique: bool#: Return boolean if values in the object are unique.

Returns#

bool

isin(values, level=None) → ndarray#

Return a boolean array where the index values are in values.

Compute boolean array of whether each index value is found in the passed set of values. The length of the returned boolean array matches the length of the index.

Parameters#

valuesset, list-like, Index: Sought values.
levelstr or int, optional: Name or position of the index level to use (if the index is a MultiIndex).

Returns#

is_containedcupy array: CuPy array of boolean values.

Examples#

>>> idx = cudf.Index([1,2,3])
>>> idx
Index([1, 2, 3], dtype='int64')

Check whether each index value in a list of values.

>>> idx.isin([1, 4])
array([ True, False, False])

isna() → ndarray#

Identify missing values.

Return a boolean same-sized object indicating if the values are <NA>. <NA> values gets mapped to True values. Everything else gets mapped to False values. <NA> values include:

Values where null mask is set.
NaN in float dtype.
NaT in datetime64 and timedelta64 types.

Characters such as empty strings '' or inf in case of float are not considered <NA> values.

Returns#

DataFrame/Series/Index: Mask of bool values for each element in the object that indicates whether an element is an NA value.

Examples#

Show which entries in a DataFrame are NA.

>>> import cudf
>>> import numpy as np
>>> import pandas as pd
>>> df = cudf.DataFrame({'age': [5, 6, np.nan],
...                    'born': [pd.NaT, pd.Timestamp('1939-05-27'),
...                             pd.Timestamp('1940-04-25')],
...                    'name': ['Alfred', 'Batman', ''],
...                    'toy': [None, 'Batmobile', 'Joker']})
>>> df
    age                        born    name        toy
0     5                        <NA>  Alfred       <NA>
1     6  1939-05-27 00:00:00.000000  Batman  Batmobile
2  <NA>  1940-04-25 00:00:00.000000              Joker
>>> df.isna()
     age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False

Show which entries in a Series are NA.

>>> ser = cudf.Series([5, 6, np.nan, np.inf, -np.inf])
>>> ser
0     5.0
1     6.0
2    <NA>
3     Inf
4    -Inf
dtype: float64
>>> ser.isna()
0    False
1    False
2     True
3    False
4    False
dtype: bool

Show which entries in an Index are NA.

>>> idx = cudf.Index([1, 2, None, np.nan, 0.32, np.inf])
>>> idx
Index([1.0, 2.0, <NA>, <NA>, 0.32, Inf], dtype='float64')
>>> idx.isna()
array([False, False,  True,  True, False, False])

isnull() → ndarray#

Identify missing values.

Return a boolean same-sized object indicating if the values are <NA>. <NA> values gets mapped to True values. Everything else gets mapped to False values. <NA> values include:

Values where null mask is set.
NaN in float dtype.
NaT in datetime64 and timedelta64 types.

Characters such as empty strings '' or inf in case of float are not considered <NA> values.

Returns#

DataFrame/Series/Index: Mask of bool values for each element in the object that indicates whether an element is an NA value.

Examples#

Show which entries in a DataFrame are NA.

>>> import cudf
>>> import numpy as np
>>> import pandas as pd
>>> df = cudf.DataFrame({'age': [5, 6, np.nan],
...                    'born': [pd.NaT, pd.Timestamp('1939-05-27'),
...                             pd.Timestamp('1940-04-25')],
...                    'name': ['Alfred', 'Batman', ''],
...                    'toy': [None, 'Batmobile', 'Joker']})
>>> df
    age                        born    name        toy
0     5                        <NA>  Alfred       <NA>
1     6  1939-05-27 00:00:00.000000  Batman  Batmobile
2  <NA>  1940-04-25 00:00:00.000000              Joker
>>> df.isna()
     age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False

Show which entries in a Series are NA.

>>> ser = cudf.Series([5, 6, np.nan, np.inf, -np.inf])
>>> ser
0     5.0
1     6.0
2    <NA>
3     Inf
4    -Inf
dtype: float64
>>> ser.isna()
0    False
1    False
2     True
3    False
4    False
dtype: bool

Show which entries in an Index are NA.

>>> idx = cudf.Index([1, 2, None, np.nan, 0.32, np.inf])
>>> idx
Index([1.0, 2.0, <NA>, <NA>, 0.32, Inf], dtype='float64')
>>> idx.isna()
array([False, False,  True,  True, False, False])

join(other, how='left', level=None, return_indexers=False, sort=False)#

Compute join_index and indexers to conform data structures to the new index.

Parameters#

other : Index. how : {‘left’, ‘right’, ‘inner’, ‘outer’} return_indexers : bool, default False sort : bool, default False

Sort the join keys lexicographically in the result Index. If False, the order of the join keys depends on the join type (how keyword).

Returns: index

Examples#

>>> import cudf
>>> lhs = cudf.DataFrame({
...     "a": [2, 3, 1],
...     "b": [3, 4, 2],
... }).set_index(['a', 'b']).index
>>> lhs
MultiIndex([(2, 3),
            (3, 4),
            (1, 2)],
           names=['a', 'b'])
>>> rhs = cudf.DataFrame({"a": [1, 4, 3]}).set_index('a').index
>>> rhs
Index([1, 4, 3], dtype='int64', name='a')
>>> lhs.join(rhs, how='inner')
MultiIndex([(3, 4),
            (1, 2)],
           names=['a', 'b'])

max(axis=0, skipna=True, numeric_only=False, **kwargs)#

Return the maximum of the values in the DataFrame.

Parameters#

axis: {index (0), columns(1)}: Axis for the function to be applied on.
skipna: bool, default True: Exclude NA/null values when computing the result.
numeric_only: bool, default False: If True, includes only float, int, boolean columns. If False, will raise error in-case there are non-numeric columns.

Returns#

Series

Examples#

>>> import cudf
>>> df = cudf.DataFrame({'a': [1, 2, 3, 4], 'b': [7, 8, 9, 10]})
>>> df.max()
a     4
b    10
dtype: int64

memory_usage(deep: bool = False) → int#

Return the memory usage of an object.

Parameters#

deepbool: The deep parameter is ignored and is only included for pandas compatibility.

Returns#

The total bytes used.

min(axis=0, skipna=True, numeric_only=False, **kwargs)#

Return the minimum of the values in the DataFrame.

Parameters#

axis: {index (0), columns(1)}: Axis for the function to be applied on.
skipna: bool, default True: Exclude NA/null values when computing the result.
numeric_only: bool, default False: If True, includes only float, int, boolean columns. If False, will raise error in-case there are non-numeric columns.

Returns#

Series

Examples#

>>> import cudf
>>> df = cudf.DataFrame({'a': [1, 2, 3, 4], 'b': [7, 8, 9, 10]})
>>> min_series = df.min()
>>> min_series
a    1
b    7
dtype: int64
>>> min_series.min()
1

property name#: Get the name of this object.

property names#: Returns a FrozenList containing the name of the Index.

property ndim: int#: Number of dimensions of the underlying data, by definition 1.

property nlevels: int#: Number of levels.

notna() → ndarray#

Identify non-missing values.

Return a boolean same-sized object indicating if the values are not <NA>. Non-missing values get mapped to True. <NA> values get mapped to False values. <NA> values include:

Values where null mask is set.
NaN in float dtype.
NaT in datetime64 and timedelta64 types.

Characters such as empty strings '' or inf in case of float are not considered <NA> values.

Returns#

DataFrame/Series/Index: Mask of bool values for each element in the object that indicates whether an element is not an NA value.

Examples#

Show which entries in a DataFrame are NA.

>>> import cudf
>>> import numpy as np
>>> import pandas as pd
>>> df = cudf.DataFrame({'age': [5, 6, np.nan],
...                    'born': [pd.NaT, pd.Timestamp('1939-05-27'),
...                             pd.Timestamp('1940-04-25')],
...                    'name': ['Alfred', 'Batman', ''],
...                    'toy': [None, 'Batmobile', 'Joker']})
>>> df
    age                        born    name        toy
0     5                        <NA>  Alfred       <NA>
1     6  1939-05-27 00:00:00.000000  Batman  Batmobile
2  <NA>  1940-04-25 00:00:00.000000              Joker
>>> df.notna()
     age   born  name    toy
0   True  False  True  False
1   True   True  True   True
2  False   True  True   True

Show which entries in a Series are NA.

>>> ser = cudf.Series([5, 6, np.nan, np.inf, -np.inf])
>>> ser
0     5.0
1     6.0
2    <NA>
3     Inf
4    -Inf
dtype: float64
>>> ser.notna()
0     True
1     True
2    False
3     True
4     True
dtype: bool

Show which entries in an Index are NA.

>>> idx = cudf.Index([1, 2, None, np.nan, 0.32, np.inf])
>>> idx
Index([1.0, 2.0, <NA>, <NA>, 0.32, Inf], dtype='float64')
>>> idx.notna()
array([ True,  True, False, False,  True,  True])

notnull() → ndarray#

Identify non-missing values.

Return a boolean same-sized object indicating if the values are not <NA>. Non-missing values get mapped to True. <NA> values get mapped to False values. <NA> values include:

Values where null mask is set.
NaN in float dtype.
NaT in datetime64 and timedelta64 types.

Characters such as empty strings '' or inf in case of float are not considered <NA> values.

Returns#

DataFrame/Series/Index: Mask of bool values for each element in the object that indicates whether an element is not an NA value.

Examples#

Show which entries in a DataFrame are NA.

>>> import cudf
>>> import numpy as np
>>> import pandas as pd
>>> df = cudf.DataFrame({'age': [5, 6, np.nan],
...                    'born': [pd.NaT, pd.Timestamp('1939-05-27'),
...                             pd.Timestamp('1940-04-25')],
...                    'name': ['Alfred', 'Batman', ''],
...                    'toy': [None, 'Batmobile', 'Joker']})
>>> df
    age                        born    name        toy
0     5                        <NA>  Alfred       <NA>
1     6  1939-05-27 00:00:00.000000  Batman  Batmobile
2  <NA>  1940-04-25 00:00:00.000000              Joker
>>> df.notna()
     age   born  name    toy
0   True  False  True  False
1   True   True  True   True
2  False   True  True   True

Show which entries in a Series are NA.

>>> ser = cudf.Series([5, 6, np.nan, np.inf, -np.inf])
>>> ser
0     5.0
1     6.0
2    <NA>
3     Inf
4    -Inf
dtype: float64
>>> ser.notna()
0     True
1     True
2    False
3     True
4     True
dtype: bool

Show which entries in an Index are NA.

>>> idx = cudf.Index([1, 2, None, np.nan, 0.32, np.inf])
>>> idx
Index([1.0, 2.0, <NA>, <NA>, 0.32, Inf], dtype='float64')
>>> idx.notna()
array([ True,  True, False, False,  True,  True])

nunique(dropna: bool = True) → int#

Return count of unique values for the column.

Parameters#

dropnabool, default True: Don’t include NaN in the counts.

Returns#

int: Number of unique values in the column.

rename(name, inplace=False)#

Alter Index name.

Defaults to returning new index.

Parameters#

namelabel: Name(s) to set.

Returns#

Index

Examples#

>>> import cudf
>>> index = cudf.Index([1, 2, 3], name='one')
>>> index
Index([1, 2, 3], dtype='int64', name='one')
>>> index.name
'one'
>>> renamed_index = index.rename('two')
>>> renamed_index
Index([1, 2, 3], dtype='int64', name='two')
>>> renamed_index.name
'two'

repeat(repeats, axis=None) → Self#

Repeat elements of a Index.

Returns a new Index where each element of the current Index is repeated consecutively a given number of times.

Parameters#

repeatsint, or array of ints: The number of repetitions for each element. This should be a non-negative integer. Repeating 0 times will return an empty object.

Returns#

Index: A newly created object of same type as caller with repeated elements.

Examples#

>>> index = cudf.Index([10, 22, 33, 55])
>>> index
Index([10, 22, 33, 55], dtype='int64')
>>> index.repeat(5)
Index([10, 10, 10, 10, 10, 22, 22, 22, 22, 22, 33,
            33, 33, 33, 33, 55, 55, 55, 55, 55],
        dtype='int64')

searchsorted(values, side: Literal['left', 'right'] = 'left', sorter=None, ascending: bool = True, na_position: Literal['first', 'last'] = 'last') → ScalarLike | cupy.ndarray#

Find indices where elements should be inserted to maintain order

Parameters#

valueFrame (Shape must be consistent with self): Values to be hypothetically inserted into Self
sidestr {‘left’, ‘right’} optional, default ‘left’: If ‘left’, the index of the first suitable location found is given If ‘right’, return the last such index
sorter1-D array-like, optional: Optional array of integer indices that sort self into ascending order. They are typically the result of np.argsort. Currently not supported.
ascendingbool optional, default True: Sorted Frame is in ascending order (otherwise descending)
na_positionstr {‘last’, ‘first’} optional, default ‘last’: Position of null values in sorted order

Returns#

1-D cupy array of insertion points

Examples#

>>> s = cudf.Series([1, 2, 3])
>>> s.searchsorted(4)
3
>>> s.searchsorted([0, 4])
array([0, 3], dtype=int32)
>>> s.searchsorted([1, 3], side='left')
array([0, 2], dtype=int32)
>>> s.searchsorted([1, 3], side='right')
array([1, 3], dtype=int32)

If the values are not monotonically sorted, wrong locations may be returned:

>>> s = cudf.Series([2, 1, 3])
>>> s.searchsorted(1)
0   # wrong result, correct would be 1

>>> df = cudf.DataFrame({'a': [1, 3, 5, 7], 'b': [10, 12, 14, 16]})
>>> df
   a   b
0  1  10
1  3  12
2  5  14
3  7  16
>>> values_df = cudf.DataFrame({'a': [0, 2, 5, 6],
... 'b': [10, 11, 13, 15]})
>>> values_df
   a   b
0  0  10
1  2  17
2  5  13
3  6  15
>>> df.searchsorted(values_df, ascending=False)
array([4, 4, 4, 0], dtype=int32)

set_names(names, level=None, inplace=False)#

Set Index or MultiIndex name. Able to set new names partially and by level.

Parameters#

nameslabel or list of label: Name(s) to set.
levelint, label or list of int or label, optional: If the index is a MultiIndex, level(s) to set (None for all levels). Otherwise level must be None.
inplacebool, default False: Modifies the object directly, instead of creating a new Index or MultiIndex.

Returns#

Index: The same type as the caller or None if inplace is True.

Examples#

>>> import cudf
>>> idx = cudf.Index([1, 2, 3, 4])
>>> idx
Index([1, 2, 3, 4], dtype='int64')
>>> idx.set_names('quarter')
Index([1, 2, 3, 4], dtype='int64', name='quarter')
>>> idx = cudf.MultiIndex.from_product([['python', 'cobra'],
... [2018, 2019]])
>>> idx
MultiIndex([('python', 2018),
            ('python', 2019),
            ( 'cobra', 2018),
            ( 'cobra', 2019)],
           )
>>> idx.names
FrozenList([None, None])
>>> idx.set_names(['kind', 'year'], inplace=True)
>>> idx.names
FrozenList(['kind', 'year'])
>>> idx.set_names('species', level=0, inplace=True)
>>> idx.names
FrozenList(['species', 'year'])

property shape: tuple[int]#: Get a tuple representing the dimensionality of the Index.

shift(periods=1, freq=None)#: Not yet implemented

property size: int#

Return the number of elements in the underlying data.

Returns#

size : Size of the DataFrame / Index / Series / MultiIndex

Examples#

Size of an empty dataframe is 0.

>>> import cudf
>>> df = cudf.DataFrame()
>>> df
Empty DataFrame
Columns: []
Index: []
>>> df.size
0
>>> df = cudf.DataFrame(index=[1, 2, 3])
>>> df
Empty DataFrame
Columns: []
Index: [1, 2, 3]
>>> df.size
0

DataFrame with values

>>> df = cudf.DataFrame({'a': [10, 11, 12],
...         'b': ['hello', 'rapids', 'ai']})
>>> df
    a       b
0  10   hello
1  11  rapids
2  12      ai
>>> df.size
6
>>> df.index
RangeIndex(start=0, stop=3)
>>> df.index.size
3

Size of an Index

>>> index = cudf.Index([])
>>> index
Index([], dtype='float64')
>>> index.size
0
>>> index = cudf.Index([1, 2, 3, 10])
>>> index
Index([1, 2, 3, 10], dtype='int64')
>>> index.size
4

Size of a MultiIndex

>>> midx = cudf.MultiIndex(
...                 levels=[["a", "b", "c", None], ["1", None, "5"]],
...                 codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
...                 names=["x", "y"],
...             )
>>> midx
MultiIndex([( 'a',  '1'),
            ( 'a',  '5'),
            ( 'b', <NA>),
            ( 'c', <NA>),
            (<NA>,  '1')],
           names=['x', 'y'])
>>> midx.size
5

sort_values(return_indexer=False, ascending=True, na_position='last', key=None) → Self | tuple[Self, cupy.ndarray]#

Return a sorted copy of the index, and optionally return the indices that sorted the index itself.

Parameters#

return_indexerbool, default False: Should the indices that would sort the index be returned.
ascendingbool, default True: Should the index values be sorted in an ascending order.
na_position{‘first’ or ‘last’}, default ‘last’: Argument ‘first’ puts NaNs at the beginning, ‘last’ puts NaNs at the end.
keyNone, optional: This parameter is NON-FUNCTIONAL.

Returns#

sorted_indexIndex: Sorted copy of the index.
indexercupy.ndarray, optional: The indices that the index itself was sorted by.

Examples#

>>> import cudf
>>> idx = cudf.Index([10, 100, 1, 1000])
>>> idx
Index([10, 100, 1, 1000], dtype='int64')

Sort values in ascending order (default behavior).

>>> idx.sort_values()
Index([1, 10, 100, 1000], dtype='int64')

Sort values in descending order, and also get the indices idx was sorted by.

>>> idx.sort_values(ascending=False, return_indexer=True)
(Index([1000, 100, 10, 1], dtype='int64'), array([3, 1, 0, 2],
                                                    dtype=int32))

Sorting values in a MultiIndex:

>>> midx = cudf.MultiIndex(
...      levels=[[1, 3, 4, -10], [1, 11, 5]],
...      codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
...      names=["x", "y"],
... )
>>> midx
MultiIndex([(  1,  1),
            (  1,  5),
            (  3, 11),
            (  4, 11),
            (-10,  1)],
           names=['x', 'y'])
>>> midx.sort_values()
MultiIndex([(-10,  1),
            (  1,  1),
            (  1,  5),
            (  3, 11),
            (  4, 11)],
           names=['x', 'y'])
>>> midx.sort_values(ascending=False)
MultiIndex([(  4, 11),
            (  3, 11),
            (  1,  5),
            (  1,  1),
            (-10,  1)],
           names=['x', 'y'])

property str#

Vectorized string functions for Series and Index.

This mimics pandas df.str interface. nulls stay null unless handled otherwise by a particular method. Patterned after Python’s string methods, with some inspiration from R’s stringr package.

take(indices, axis=0, allow_fill=True, fill_value=None)#

Return a new index containing the rows specified by indices

Parameters#

indicesarray-like: Array of ints indicating which positions to take.
axisint: The axis over which to select values, always 0.

allow_fill : Unsupported fill_value : Unsupported

Returns#

outIndex: New object with desired subset of rows.

Examples#

>>> idx = cudf.Index(['a', 'b', 'c', 'd', 'e'])
>>> idx.take([2, 0, 4, 3])
Index(['c', 'a', 'e', 'd'], dtype='object')

to_arrow() → pa.Array#

Convert to a PyArrow Array.

Returns#

PyArrow Array

Examples#

>>> import cudf
>>> sr = cudf.Series(["a", "b", None])
>>> sr.to_arrow()
<pyarrow.lib.StringArray object at 0x7f796b0e7600>
[
  "a",
  "b",
  null
]
>>> ind = cudf.Index(["a", "b", None])
>>> ind.to_arrow()
<pyarrow.lib.StringArray object at 0x7f796b0e7750>
[
  "a",
  "b",
  null
]

to_cupy(dtype: Dtype | None = None, copy: bool = False, na_value=None) → cupy.ndarray#

Convert the Frame to a CuPy array.

Parameters#

dtypestr or numpy.dtype, optional: The dtype to pass to numpy.asarray().
copybool, default False: Whether to ensure that the returned value is not a view on another array. Note that copy=False does not ensure that to_cupy() is no-copy. Rather, copy=True ensure that a copy is made, even if not strictly necessary.
na_valueAny, default None: The value to use for missing values. The default value depends on dtype and the dtypes of the DataFrame columns.

Returns#

cupy.ndarray

to_dlpack()#

Converts a cuDF object into a DLPack tensor.

DLPack is an open-source memory tensor structure: dmlc/dlpack.

This function takes a cuDF object and converts it to a PyCapsule object which contains a pointer to a DLPack tensor. This function deep copies the data into the DLPack tensor from the cuDF object.

Parameters#

cudf_obj : DataFrame, Series, Index, or Column

Returns#

pycapsule_objPyCapsule: Output DLPack tensor pointer which is encapsulated in a PyCapsule object.

to_flat_index() → Self#

Identity method.

This is implemented for compatibility with subclass implementations when chaining.

Returns#

pd.Index: Caller.

Parameters#

indexboolean, default True: Set the index of the returned DataFrame as the original Index
nameobject, defaults to index.name: The passed name should substitute for the index name (if it has one).

Returns#

DataFrame: DataFrame containing the original Index data.

Examples#

>>> import cudf
>>> idx = cudf.Index(['Ant', 'Bear', 'Cow'], name='animal')
>>> idx.to_frame()
       animal
animal
Ant       Ant
Bear     Bear
Cow       Cow

By default, the original Index is reused. To enforce a new Index:

>>> idx.to_frame(index=False)
    animal
0   Ant
1  Bear
2   Cow

To override the name of the resulting column, specify name:

>>> idx.to_frame(index=False, name='zoo')
    zoo
0   Ant
1  Bear
2   Cow

to_list()#

to_numpy(dtype: Dtype | None = None, copy: bool = True, na_value=None) → numpy.ndarray#

Convert the Frame to a NumPy array.

Parameters#

dtypestr or numpy.dtype, optional: The dtype to pass to numpy.asarray().
copybool, default True: Whether to ensure that the returned value is not a view on another array. This parameter must be True since cuDF must copy device memory to host to provide a numpy array.
na_valueAny, default None: The value to use for missing values. The default value depends on dtype and the dtypes of the DataFrame columns.

Returns#

numpy.ndarray

to_pandas(*, nullable: bool = False, arrow_type: bool = False) → Index#

Convert to a Pandas Index.

Parameters#

nullablebool, Default False: If nullable is True, the resulting index will have a corresponding nullable Pandas dtype. If there is no corresponding nullable Pandas dtype present, the resulting dtype will be a regular pandas dtype. If nullable is False, the resulting index will either convert null values to np.nan or None depending on the dtype.
arrow_typebool, Default False: Return the Index with a pandas.ArrowDtype

Notes#

nullable and arrow_type cannot both be set to True

Examples#

>>> import cudf
>>> idx = cudf.Index([-3, 10, 15, 20])
>>> idx
Index([-3, 10, 15, 20], dtype='int64')
>>> idx.to_pandas()
Index([-3, 10, 15, 20], dtype='int64')
>>> type(idx.to_pandas())
<class 'pandas.core.indexes.base.Index'>
>>> type(idx)
<class 'cudf.core.index.Index'>
>>> idx.to_pandas(arrow_type=True)
Index([-3, 10, 15, 20], dtype='int64[pyarrow]')

to_series(index=None, name=None)#

Create a Series with both index and values equal to the index keys. Useful with map for returning an indexer based on an index.

Parameters#

indexIndex, optional: Index of resulting Series. If None, defaults to original index.
namestr, optional: Name of resulting Series. If None, defaults to name of original index.

Returns#

Series: The dtype will be based on the type of the Index values.

tolist()#

transpose()#: Return the transpose, which is by definition self.

union(other, sort=None)#

Form the union of two Index objects.

Parameters#

other : Index or array-like sort : bool or None, default None

Whether to sort the resulting Index.

None : Sort the result, except when

self and other are equal.

self or other has length 0.

False : do not sort the result.

True : Sort the result (which may raise TypeError).

Returns#

union : Index

Examples#

Union of an Index >>> import cudf >>> import pandas as pd >>> idx1 = cudf.Index([1, 2, 3, 4]) >>> idx2 = cudf.Index([3, 4, 5, 6]) >>> idx1.union(idx2) Index([1, 2, 3, 4, 5, 6], dtype=’int64’)

MultiIndex case

>>> idx1 = cudf.MultiIndex.from_pandas(
...    pd.MultiIndex.from_arrays(
...         [[1, 1, 2, 2], ["Red", "Blue", "Red", "Blue"]]
...    )
... )
>>> idx1
MultiIndex([(1,  'Red'),
            (1, 'Blue'),
            (2,  'Red'),
            (2, 'Blue')],
           )
>>> idx2 = cudf.MultiIndex.from_pandas(
...    pd.MultiIndex.from_arrays(
...         [[3, 3, 2, 2], ["Red", "Green", "Red", "Green"]]
...    )
... )
>>> idx2
MultiIndex([(3,   'Red'),
            (3, 'Green'),
            (2,   'Red'),
            (2, 'Green')],
           )
>>> idx1.union(idx2)
MultiIndex([(1,  'Blue'),
            (1,   'Red'),
            (2,  'Blue'),
            (2, 'Green'),
            (2,   'Red'),
            (3, 'Green'),
            (3,   'Red')],
           )
>>> idx1.union(idx2, sort=False)
MultiIndex([(1,   'Red'),
            (1,  'Blue'),
            (2,   'Red'),
            (2,  'Blue'),
            (3,   'Red'),
            (3, 'Green'),
            (2, 'Green')],
           )

unique(level: int | None = None) → Self#: Return unique values in the index.

Returns#

Index without duplicates

property values: ndarray#

Return a CuPy representation of the DataFrame.

Only the values in the DataFrame will be returned, the axes labels will be removed.

Returns#

cupy.ndarray: The values of the DataFrame.

property values_host: numpy.ndarray#

Return a NumPy representation of the data.

Only the values in the DataFrame will be returned, the axes labels will be removed.

Returns#

numpy.ndarray: A host representation of the underlying data.

where(cond, other=None, inplace=False) → Index#

Replace values where the condition is False.

Parameters#

condbool Series/DataFrame, array-like

Where cond is True, keep the original value. Where False, replace with corresponding value from other. Callables are not supported.

other: scalar, list of scalars, Series/DataFrame

Entries where cond is False are replaced with corresponding value from other. Callables are not supported. Default is None.

DataFrame expects only Scalar or array like with scalars or dataframe with same dimension as self.

Series expects only scalar or series like with same length

inplacebool, default False

Whether to perform the operation in place on the data.

Returns#

Same type as caller

Examples#

>>> import cudf
>>> df = cudf.DataFrame({"A":[1, 4, 5], "B":[3, 5, 8]})
>>> df.where(df % 2 == 0, [-1, -1])
   A  B
0 -1 -1
1  4 -1
2 -1  8

>>> ser = cudf.Series([4, 3, 2, 1, 0])
>>> ser.where(ser > 2, 10)
0     4
1     3
2    10
3    10
4    10
dtype: int64
>>> ser.where(ser > 2)
0       4
1       3
2    <NA>
3    <NA>
4    <NA>
dtype: int64

hipdf.CategoricalIndex

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