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pandas.IntervalIndex

class pandas.IntervalIndex[source]

Immutable Index implementing an ordered, sliceable set. IntervalIndex represents an Index of intervals that are all closed on the same side.

New in version 0.20.0.

Attributes

T return the transpose, which is by definition self
asi8
base return the base object if the memory of the underlying data is
closed
closed_left
closed_right
data return the data pointer of the underlying data
dtype
dtype_str
empty
flags
has_duplicates
hasnans
inferred_type
is_all_dates
is_monotonic
is_monotonic_decreasing
is_monotonic_increasing
is_non_overlapping_monotonic
is_unique
itemsize return the size of the dtype of the item of the underlying data
left
mid
name
names
nbytes return the number of bytes in the underlying data
ndim return the number of dimensions of the underlying data,
nlevels
open_left
open_right
right
shape return a tuple of the shape of the underlying data
size return the number of elements in the underlying data
strides return the strides of the underlying data
values

Methods

all([other])
any([other])
append(other) Append a collection of Index options together
argmax([axis]) return a ndarray of the maximum argument indexer
argmin([axis]) return a ndarray of the minimum argument indexer
argsort(*args, **kwargs)
asof(label) For a sorted index, return the most recent label up to and including the passed label.
asof_locs(where, mask) where : array of timestamps
astype(dtype[, copy]) Create an Index with values cast to dtypes.
contains(key) return a boolean if this key is IN the index
copy([deep, name]) Make a copy of this object.
delete(loc)
difference(other)
drop(labels[, errors]) Make new Index with passed list of labels deleted
drop_duplicates([keep]) Return Index with duplicate values removed
dropna([how]) Return Index without NA/NaN values
duplicated([keep]) Return boolean np.ndarray denoting duplicate values
equals(other)
factorize([sort, na_sentinel]) Encode the object as an enumerated type or categorical variable
fillna([value, downcast]) Fill NA/NaN values with the specified value
format([name, formatter]) Render a string representation of the Index
from_arrays(left, right[, closed, name, copy]) Construct an IntervalIndex from a a left and right array
from_breaks(breaks[, closed, name, copy]) Construct an IntervalIndex from an array of splits
from_intervals(data[, name, copy]) Construct an IntervalIndex from a 1d array of Interval objects
from_tuples(data[, closed, name, copy]) Construct an IntervalIndex from a list/array of tuples
get_duplicates()
get_indexer(target[, method, limit, tolerance]) Compute indexer and mask for new index given the current index.
get_indexer_for(target, **kwargs) guaranteed return of an indexer even when non-unique
get_indexer_non_unique(target) Compute indexer and mask for new index given the current index.
get_level_values(level) Return an Index of values for requested level, equal to the length
get_loc(key[, method])
get_slice_bound(label, side, kind) Calculate slice bound that corresponds to given label.
get_value(series, key)
get_values() return the underlying data as an ndarray
groupby(values) Group the index labels by a given array of values.
holds_integer()
identical(other) Similar to equals, but check that other comparable attributes are
insert(loc, item)
intersection(other)
is_(other) More flexible, faster check like is but that works through views
is_boolean()
is_categorical()
is_floating()
is_integer()
is_interval()
is_lexsorted_for_tuple(tup)
is_mixed()
is_numeric()
is_object()
is_type_compatible(kind)
isin(values[, level]) Compute boolean array of whether each index value is found in the passed set of values.
isnull() Detect missing values
item() return the first element of the underlying data as a python
join(other[, how, level, return_indexers, sort]) this is an internal non-public method
map(mapper) Apply mapper function to an index.
max() The maximum value of the object
memory_usage([deep]) Memory usage of my values
min() The minimum value of the object
notnull() Reverse of isnull
nunique([dropna]) Return number of unique elements in the object.
putmask(mask, value) return a new Index of the values set with the mask
ravel([order]) return an ndarray of the flattened values of the underlying data
reindex(target[, method, level, limit, ...]) Create index with target’s values (move/add/delete values as necessary)
rename(name[, inplace]) Set new names on index.
repeat(repeats, *args, **kwargs) Repeat elements of an Index.
reshape(*args, **kwargs) NOT IMPLEMENTED: do not call this method, as reshaping is not supported for Index objects and will raise an error.
searchsorted(value[, side, sorter]) Find indices where elements should be inserted to maintain order.
set_names(names[, level, inplace]) Set new names on index.
set_value(arr, key, value) Fast lookup of value from 1-dimensional ndarray.
shift([periods, freq]) Shift Index containing datetime objects by input number of periods and
slice_indexer([start, end, step, kind]) For an ordered Index, compute the slice indexer for input labels and
slice_locs([start, end, step, kind]) Compute slice locations for input labels.
sort(*args, **kwargs)
sort_values([return_indexer, ascending]) Return sorted copy of Index
sortlevel([level, ascending, sort_remaining]) For internal compatibility with with the Index API
str alias of StringMethods
summary([name])
sym_diff(*args, **kwargs)
symmetric_difference(other[, result_name]) Compute the symmetric difference of two Index objects.
symmetric_differnce(other)
take(indices[, axis, allow_fill, fill_value]) return a new IntervalIndex of the values selected by the indices
to_datetime([dayfirst]) DEPRECATED: use pandas.to_datetime() instead.
to_native_types([slicer]) Format specified values of self and return them.
to_series(**kwargs) Create a Series with both index and values equal to the index keys
to_tuples()
tolist() return a list of the Index values
transpose(*args, **kwargs) return the transpose, which is by definition self
union(other)
unique() Return unique values in the object.
value_counts([normalize, sort, ascending, ...]) Returns object containing counts of unique values.
view([cls])
where(cond[, other])

New in version 0.19.0.

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