Index objects

Index

Many of these methods or variants thereof are available on the objects that contain an index (Series/DataFrame) and those should most likely be used before calling these methods directly.

cudf.Index([data, dtype, copy, name, ...])

The basic object storing row labels for all cuDF objects.

Properties

Index.has_duplicates
Index.duplicated([keep])	Indicate duplicate index values.
Index.hasnans	Return True if there are any NaNs or nulls.
Index.is_monotonic	Return boolean if values in the object are monotonic_increasing.
Index.is_monotonic_increasing	Return boolean if values in the object are monotonically increasing.
Index.is_monotonic_decreasing	Return boolean if values in the object are monotonically decreasing.
Index.names	Returns a tuple containing the name of the Index.
Index.nlevels	Number of levels.
Index.size
Index.values

Modifying and computations

Index.any()	Return whether any elements is True in Index.
Index.copy([deep])
Index.drop_duplicates([keep, nulls_are_equal])	Drop duplicate rows in index.
Index.is_boolean()	Check if the Index only consists of booleans.
Index.is_categorical()	Check if the Index holds categorical data.
Index.is_floating()	Check if the Index is a floating type.
Index.is_integer()	Check if the Index only consists of integers.
Index.is_interval()	Check if the Index holds Interval objects.
Index.is_numeric()	Check if the Index only consists of numeric data.
Index.is_object()	Check if the Index is of the object dtype.
Index.rename(name[, inplace])	Alter Index name.
Index.repeat(repeats[, axis])	Repeat elements of a Index.
Index.take(indices[, axis, allow_fill, ...])	Return a new index containing the rows specified by indices
Index.unique()	Return unique values in the index.

Compatibility with MultiIndex

Index.set_names(names[, level, inplace])

Set Index or MultiIndex name.

Missing values

Index.fillna(value[, downcast])	Fill null values with the specified value.
Index.dropna([how])	Drop null rows from Index.
Index.isna()	Detect missing values.
Index.notna()	Detect existing (non-missing) values.

Memory usage

Conversion

Index.to_series([index, name])	Create a Series with both index and values equal to the index keys.
Index.to_frame([index, name])	Create a DataFrame with a column containing this Index
Index.to_pandas([nullable])	Convert to a Pandas Index.
Index.to_dlpack()	Converts a cuDF object into a DLPack tensor.
Index.from_pandas(index[, nan_as_null])	Convert from a Pandas Index.
Index.from_arrow(obj)

Sorting

Index.sort_values([return_indexer, ...])

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

Time-specific operations

Combining / joining / set operations

Index.append(other)	Append a collection of Index objects together.
Index.union(other[, sort])	Form the union of two Index objects.
Index.intersection(other[, sort])	Form the intersection of two Index objects.
Index.join(other[, how, level, ...])	Compute join_index and indexers to conform data structures to the new index.
Index.difference(other[, sort])	Return a new Index with elements from the index that are not in other.

Selecting

Index.get_level_values(level)	Return an Index of values for requested level.
Index.get_loc(key[, method, tolerance])
Index.get_slice_bound(label, side[, kind])	Calculate slice bound that corresponds to given label.
Index.isin(values)	Return a boolean array where the index values are in values.

Numeric Index

RangeIndex(start[, stop, step, dtype, copy, ...])	Immutable Index implementing a monotonic integer range.
Int64Index([data, dtype, copy, name])	Immutable, ordered and sliceable sequence of labels.
UInt64Index([data, dtype, copy, name])	Immutable, ordered and sliceable sequence of labels.
Float64Index([data, dtype, copy, name])	Immutable, ordered and sliceable sequence of labels.

CategoricalIndex

CategoricalIndex([data, categories, ...])

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

Categorical components

CategoricalIndex.codes	The category codes of this categorical.
CategoricalIndex.categories	The categories of this categorical.

Modifying and computations

CategoricalIndex.equals(other, **kwargs)

Determine if two Index objects contain the same elements.

IntervalIndex

IntervalIndex(data[, closed, dtype, copy, name])

Immutable index of intervals that are closed on the same side.

IntervalIndex components

IntervalIndex.from_breaks([closed, name, ...])	Construct an IntervalIndex from an array of splits.
IntervalIndex.values	Return a CuPy representation of the DataFrame.
IntervalIndex.get_loc(key[, method, tolerance])	Get integer location, slice or boolean mask for requested label.

MultiIndex

MultiIndex([levels, codes, sortorder, ...])

A multi-level or hierarchical index.

MultiIndex constructors

MultiIndex.from_tuples(tuples[, names])	Convert list of tuples to MultiIndex.
MultiIndex.from_product(arrays[, names])	Make a MultiIndex from the cartesian product of multiple iterables.
MultiIndex.from_frame(df[, names])	Make a MultiIndex from a DataFrame.
MultiIndex.from_arrow(data)	Convert from PyArrow Table to Frame

MultiIndex properties

MultiIndex.names	Returns a tuple containing the name of the Index.
MultiIndex.levels	Returns list of levels in the MultiIndex
MultiIndex.codes	Returns the codes of the underlying MultiIndex.
MultiIndex.nlevels	Integer number of levels in this MultiIndex.

MultiIndex components

MultiIndex.to_frame([index, name])	Create a DataFrame with a column containing this Index
MultiIndex.droplevel([level])	Removes the specified levels from the MultiIndex.

MultiIndex selecting

MultiIndex.get_loc(key[, method, tolerance])	Get location for a label or a tuple of labels.
MultiIndex.get_level_values(level)	Return the values at the requested level

DatetimeIndex

DatetimeIndex([data, freq, tz, normalize, ...])

Immutable , ordered and sliceable sequence of datetime64 data, represented internally as int64.

Time/date components

DatetimeIndex.year	The year of the datetime.
DatetimeIndex.month	The month as January=1, December=12.
DatetimeIndex.day	The day of the datetime.
DatetimeIndex.hour	The hours of the datetime.
DatetimeIndex.minute	The minutes of the datetime.
DatetimeIndex.second	The seconds of the datetime.
DatetimeIndex.microsecond	The microseconds of the datetime.
DatetimeIndex.nanosecond	The nanoseconds of the datetime.
DatetimeIndex.day_of_year	The day of the year, from 1-365 in non-leap years and from 1-366 in leap years.
DatetimeIndex.dayofyear	The day of the year, from 1-365 in non-leap years and from 1-366 in leap years.
DatetimeIndex.dayofweek	The day of the week with Monday=0, Sunday=6.
DatetimeIndex.weekday	The day of the week with Monday=0, Sunday=6.
DatetimeIndex.quarter	Integer indicator for which quarter of the year the date belongs in.
DatetimeIndex.is_leap_year	Boolean indicator if the date belongs to a leap year.
DatetimeIndex.isocalendar()	Returns a DataFrame with the year, week, and day calculated according to the ISO 8601 standard.

Time-specific operations

DatetimeIndex.round(freq)	Perform round operation on the data to the specified freq.
DatetimeIndex.ceil(freq)	Perform ceil operation on the data to the specified freq.
DatetimeIndex.floor(freq)	Perform floor operation on the data to the specified freq.

Conversion

DatetimeIndex.to_series([index, name])	Create a Series with both index and values equal to the index keys.
DatetimeIndex.to_frame([index, name])	Create a DataFrame with a column containing this Index

TimedeltaIndex

TimedeltaIndex([data, unit, freq, closed, ...])

Immutable, ordered and sliceable sequence of timedelta64 data, represented internally as int64.

Components

TimedeltaIndex.days	Number of days for each element.
TimedeltaIndex.seconds	Number of seconds (>= 0 and less than 1 day) for each element.
TimedeltaIndex.microseconds	Number of microseconds (>= 0 and less than 1 second) for each element.
TimedeltaIndex.nanoseconds	Number of nanoseconds (>= 0 and less than 1 microsecond) for each element.
TimedeltaIndex.components	Return a dataframe of the components (days, hours, minutes, seconds, milliseconds, microseconds, nanoseconds) of the Timedeltas.
TimedeltaIndex.inferred_freq	Infers frequency of TimedeltaIndex.

Conversion

TimedeltaIndex.to_series([index, name])	Create a Series with both index and values equal to the index keys.
TimedeltaIndex.to_frame([index, name])	Create a DataFrame with a column containing this Index