IntervalIndex

class IntervalIndex

Immutable index backed by interval data.

Example

import pandasCore as pd

# Create IntervalIndex
idx = pd.IntervalIndex([1, 2, 3], name='my_index')
print(len(idx))  # 3

Attributes

Attribute	Description	Example
closed	String describing the inclusive side(s) of the intervals
dtype	Return the dtype object of the underlying data
empty	Indicator whether IntervalIndex is empty
has_duplicates	Return if the index has duplicate values
is_empty	Indicates if an interval is empty (left == right and not both-closed)
is_monotonic_decreasing	Return if the index is monotonic decreasing
is_monotonic_increasing	Return if the index is monotonic increasing
is_non_overlapping_monotonic	Return True if intervals are non-overlapping and monotonically sorted
is_overlapping	Return True if any intervals overlap
is_unique	Return if the index has unique values
left	Return the left bounds of the intervals
length	Return the length of each interval
mid	Return the midpoint of each interval
name	Return the name of the IntervalIndex
ndim	Number of dimensions of the underlying data
nlevels	Number of levels in this index
right	Return the right bounds of the intervals
shape	Return a tuple of the shape of the underlying data
size	Return the number of elements in the IntervalIndex
str	Vectorized string functions for Index.	View

Construction

Method	Description	Example
__init__() (*args, **kwargs) | () | (data: collections.abc.Sequence[tuple[typing.SupportsFloat, typing.SupportsFloat]], closed: object = None, dtype: object = None, copy: bool = False, name: object = None, verify_integrity: bool = True)	Create an empty IntervalIndex	View

Indexing / Selection

Method	Description	Example
__getitem__() (arg0: typing.SupportsInt)
take() (indices: collections.abc.Sequence[typing.SupportsInt], axis: typing.SupportsInt = 0, allow_fill: bool = True, fill_value: object = None, **kwargs)	Return new IntervalIndex with elements at specified positions
where() (cond: object, other: object = None)	Replace values where the condition is False.

Data Manipulation

Method	Description	Example
drop() (labels: object, errors: str = 'raise')	Make new Index with passed list of labels deleted
droplevel() (level: typing.SupportsInt = 0)	Return index with requested level(s) removed.
insert() (loc: typing.SupportsInt, item: object)	Make new IntervalIndex inserting new item at location
reindex() (target: pandasCore.IntervalIndex, method: object = None, level: object = None, limit: object = None, tolerance: object = None)	Conform Index to new index with optional filling logic
rename() (name: object, *, inplace: bool = False)	Alter IntervalIndex name.
set_names() (names: object, *, level: object = None, inplace: bool = False)	Set IntervalIndex name

Missing Data

Method	Description	Example
dropna() (how: str = 'any')	Return IntervalIndex without NA values
fillna() (value: object = None, downcast: object = None)	Fill NA/NaN values with the specified value
isna() ()	Detect missing values
isnull() ()	Detect missing values (alias for isna)
notna() ()	Detect existing (non-missing) values
notnull() ()	Detect existing (non-missing) values (alias for notna)

Statistics

Method	Description	Example
max() (axis: object = None, skipna: bool = True, *args, **kwargs)	Return the maximum interval (by left bound)
min() (axis: object = None, skipna: bool = True, *args, **kwargs)	Return the minimum interval (by left bound)
nunique() (dropna: bool = True)	Return number of unique elements in the index
value_counts() (normalize: bool = False, sort: bool = True, ascending: bool = False, bins: object = None, dropna: bool = True)	Return a dict containing counts of unique values

Aggregation

Method	Description	Example
groupby() (values: collections.abc.Sequence[typing.SupportsInt])	Group the index labels by a given array of values
map() (mapper: object, na_action: object = None)	Map values using an input mapping or function

Comparison

Method	Description	Example
__eq__() (self, value, /)	Return self==value.
__ge__() (self, value, /)	Return self>=value.
__gt__() (self, value, /)	Return self>value.
__le__() (self, value, /)	Return self<=value.
__lt__() (self, value, /)	Return self<value.
__ne__() (self, value, /)	Return self!=value.
equals() (other: pandasCore.IntervalIndex)	Determine if two IntervalIndex objects are equal

Sorting

Method	Description	Example
argsort() (*args, **kwargs)	Return the indices that would sort the index
searchsorted() (value: object, side: str = 'left', sorter: object = None)	Find indices where elements should be inserted to maintain order
sort_values() (*, return_indexer: bool = False, ascending: bool = True, na_position: str = 'last', key: object = None)	Return a sorted copy of the index

Reshaping

Method	Description	Example
to_frame() (index: bool = True, name: object = None)	Create a DataFrame with a column containing the IntervalIndex values
transpose() ()	Return the transpose (self for 1D index)

Combining

Method	Description	Example
append() (other: pandasCore.IntervalIndex)	Append another IntervalIndex to this one
join() (other: pandasCore.IntervalIndex, *, how: str = 'left', level: object = None, return_indexers: bool = False, sort: bool = False)	Join this index with another

Time Series

Method	Description	Example
asof() (label: object)	Return the label from the index, or, if not present, the previous one.
diff() (periods: typing.SupportsInt = 1)	Compute first-order differences
shift() (periods: typing.SupportsInt = 1, freq: object = None)	Shift index by desired number of periods

I/O

Method	Description	Example
to_flat_index() ()	Return a flattened index
to_list() ()	Return a list of the intervals as tuples
to_numpy() (dtype: object = None, copy: bool = False, na_value: object = None, **kwargs)	A NumPy ndarray representing the values in this Series or Index
to_series() (index: object = None, name: object = None)	Create a Series with the IntervalIndex values
to_tuples() ()	Return a list of tuples of the form (left, right)
tolist() ()	Return a list of the intervals as tuples

Conversion

Method	Description	Example
astype() (dtype: str, copy: bool = True)	Cast to a specified dtype.
copy() (name: object = None, deep: bool = False)	Make a copy of this IntervalIndex.
infer_objects() (copy: bool = True)	Infer objects type and return a converted version.
view() (cls: object = None)	Return a view of the index.

Iteration

Method	Description	Example
__contains__() (key: tuple[typing.SupportsFloat, typing.SupportsFloat])
__iter__() ()
__len__() ()

Set Operations

Method	Description	Example
difference() (other: pandasCore.IntervalIndex, sort: object = None)	Form the set difference of two IntervalIndex objects
drop_duplicates() (*, keep: str = 'first')	Return IntervalIndex with duplicate values removed
duplicated() (keep: str = 'first')	Indicate duplicate index values
intersection() (other: pandasCore.IntervalIndex, sort: bool = False)	Form the intersection of two IntervalIndex objects
isin() (values: collections.abc.Sequence[tuple[typing.SupportsFloat, typing.SupportsFloat]], level: object = None)	Check if values are in the IntervalIndex
symmetric_difference() (other: pandasCore.IntervalIndex, result_name: object = None, sort: object = None)	Form the symmetric difference of two IntervalIndex objects
union() (other: pandasCore.IntervalIndex, sort: object = None)	Form the union of two IntervalIndex objects
unique() (level: object = None)	Return unique values in the index

String Methods

Method	Description	Example
contains() (other: typing.SupportsFloat)	Check elementwise if the Intervals contain the value.

Datetime Methods

Method	Description	Example
round() (decimals: typing.SupportsInt = 0)	Round interval bounds to specified number of decimals

Other Methods

Method	Description	Example
__hash__() (self, /)	Return hash(self).
__repr__() ()
__str__() ()
all() (*args, **kwargs)	Return whether all elements are truthy
any() (*args, **kwargs)	Return whether any element is truthy
argmax() (axis: object = None, skipna: bool = True, *args, **kwargs)	Return the index of the maximum value
argmin() (axis: object = None, skipna: bool = True, *args, **kwargs)	Return the index of the minimum value
asof_locs() (where: object, mask: object = None)	Return the locations (indices) of labels in the index.
delete() (loc: typing.SupportsInt)	Make new IntervalIndex with element at position deleted
factorize() (sort: bool = False, use_na_sentinel: bool = True)	Encode the object as an enumerated type or categorical variable
format() (name: bool = False, formatter: object = None, na_rep: str = 'NaN')	Format the IntervalIndex as list of strings
get_indexer() (target: pandasCore.IntervalIndex, method: object = None, limit: object = None, tolerance: object = None)	Compute indexer for new index given the current index
get_indexer_for() (target: pandasCore.IntervalIndex)	Guaranteed return of an indexer even when non-unique
get_indexer_non_unique() (target: pandasCore.IntervalIndex)	Compute indexer and mask for new index given the current index.
get_level_values() (level: object)	Return an IntervalIndex of values for requested level
get_loc() (key: tuple[typing.SupportsFloat, typing.SupportsFloat])	Get integer location for requested label
get_slice_bound() (label: object, side: str)	Calculate slice bound that corresponds to given label.
holds_integer() ()	Check if the index holds integers (always False for float64 Interva…
identical() (other: pandasCore.IntervalIndex)	Determine if two IntervalIndex objects are identical
item() ()	Return the first element as a scalar (only for single-element index)
memory_usage() (deep: bool = False)	Return memory usage of the index in bytes
overlaps() (left: typing.SupportsFloat, right: typing.SupportsFloat)	Check elementwise if an interval overlaps the values.
putmask() (mask: object, value: object)	Return a new IntervalIndex with values put where mask is True.
ravel() (order: str = 'C')	Return a flattened view of the index.
repeat() (repeats: typing.SupportsInt, axis: object = None)	Repeat elements of the index
set_closed() (closed: str)	Return an IntervalIndex with the specified closed side.
slice_indexer() (start: object = None, end: object = None, step: object = None)	Compute slice indexer for input labels.
slice_locs() (start: object = None, end: object = None, step: object = None)	Compute slice locations for input labels
sort() (ascending: bool = True)	Return a sorted copy of the index.
sortlevel() (level: object = None, ascending: bool = True, sort_remaining: object = None, na_position: str = 'first')	Sort index by level

Code Examples

The following examples are extracted from the test suite.

str (test_interval_multi_range_str.py:79)

def test_intervalindex_str_call_with_data():
    """Test IntervalIndex.str(data) call."""
    print("Testing IntervalIndex.str(data) call...")

    # Create an IntervalIndex
    ii = pandasCore.IntervalIndex.from_breaks([0, 1, 2])

    # Call IntervalIndex.str with the index as argument
    try:
        str_accessor = pandasCore.IntervalIndex.str(ii)
        print(f"  IntervalIndex.str(ii) returned: {type(str_accessor)}")
        print("  PASSED: IntervalIndex.str(data) call works")
    except Exception as e:
        # Expected to raise error since IntervalIndex doesn't contain strings
        print(f"  Error (expected for non-string data): {e}")
        print("  PASSED: Appropriate error raised for non-string Index")

# =============================================================================
# MultiIndex.str tests
# =============================================================================

__init__ (test_intervalindex.py:87)

    ctx.assert_equal((0.0, 1.0), ii[0], "from_tuples first interval")
    ctx.assert_equal((2.0, 3.0), ii[1], "from_tuples second interval")
    ctx.assert_equal((4.0, 5.0), ii[2], "from_tuples third interval")


def test_constructor():
    """Test IntervalIndex constructor with list of tuples"""
    f_print_header("Test: constructor")

    data = [(0.0, 1.0), (1.0, 2.0), (2.0, 3.0)]
    ii = pandasCore.IntervalIndex(data)

    ctx.assert_equal(3, ii.size, "constructor size")
    ctx.assert_equal("right", ii.closed, "constructor closed")

    # With closed parameter
    ii_both = pandasCore.IntervalIndex(data, closed="both")
    ctx.assert_equal("both", ii_both.closed, "constructor closed=both")


def test_properties():