modin.pandas.Index¶

Bases: object

Methods

`all`(args, *kwargs)	Return whether all elements are Truthy.
`any`(args, *kwargs)	Return whether any element is Truthy.
`append`()	Append a collection of Index options together.
`argmax`([axis, skipna])	Return int position of the largest value in the Series.
`argmin`([axis, skipna])	Return int position of the smallest value in the Series.
`astype`(dtype[, copy])	Create an Index with values cast to dtypes.
`copy`([name, deep])	Make a copy of this object.
`delete`()	Make new Index with passed location(-s) deleted.
`difference`(other[, sort])	Return a new Index with elements of index not in other.
`drop`(labels[, errors])	Make new Index with the passed list of labels deleted.
`drop_duplicates`()	Return Index with duplicate values removed.
`dropna`()	Return Index without NA/NaN values.
`duplicated`([keep])	Indicate duplicate index values.
`equals`(other)	Determine if two Index objects are equal.
`fillna`()	Fill NA/NaN values with the specified value.
`get_indexer_for`(target)	Guaranteed return of an indexer even when non-unique.
`get_level_values`(level)	Return an Index of values for requested level.
`hasnans`()	Return True if there are any NaNs.
`identical`(other)	Similar to equals, but checks that object attributes and types are also equal.
`insert`()	Make new Index inserting new item at location.
`intersection`(other[, sort])	Form the intersection of two Index objects.
`is_boolean`()	Check if the Index only consists of booleans.
`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`()	Return a boolean array where the index values are in values.
`isna`()	Detect missing values.
`item`()	Return the first element of the underlying data as a Python scalar.
`join`()	Compute join_index and indexers to conform data structures to the new index.
`max`([axis, skipna])	Return the maximum value of the Index.
`min`([axis, skipna])	Return the minimum value of the Index.
`notna`()	Detect existing (non-missing) values.
`nunique`([dropna])	Return number of unique elements in the object.
`reindex`(target[, method, level, limit, ...])	Create index with target's values.
`rename`(name[, inplace])	Alter Index or MultiIndex name.
`set_names`(names[, level, inplace])	Set Index name.
`slice_indexer`([start, end, step])	Compute the slice indexer for input labels and step.
`sort_values`([return_indexer, ascending, ...])	Return a sorted copy of the index.
`to_frame`([index, name])	Create a `DataFrame` with a column containing the Index.
`to_list`()	Return a list of the values.
`to_pandas`(*[, statement_params])	Convert Snowpark pandas Index to pandas Index.
`to_series`([index, name])	Create a Series with both index and values equal to the index keys.
`tolist`()	Return a list of the values.
`union`(other[, sort])	Form the union of two Index objects.
`unique`([level])	Return unique values in the index.
`value_counts`([normalize, sort, ascending, ...])	Return a Series containing counts of unique values.

Attributes

`T`	Return the transpose, which is by definition self.
`array`	return the array of values
`dtype`	Get the dtype object of the underlying data.
`empty`	Whether the index is empty.
`has_duplicates`	Check if the Index has duplicate values.
`is_monotonic_decreasing`	Return a boolean if the values are equal or decreasing.
`is_monotonic_increasing`	Return a boolean if the values are equal or increasing.
`is_unique`	Return if the index has unique values.
`name`	Get the index name.
`names`	Get names of index
`ndim`	Number of dimensions of the underlying data, by definition 1.
`nlevels`	Number of levels.
`shape`	Get a tuple of the shape of the underlying data.
`size`	Get the number of elements in the underlying data.
`str`	Vectorized string functions for Series and Index.
`values`	Return an array representing the data in the Index.