pyspark.pandas.Int64Index

class pyspark.pandas.Int64Index

Immutable sequence used for indexing and alignment. The basic object storing axis labels for all pandas objects. Int64Index is a special case of Index with purely integer labels.

Parameters

dataarray-like (1-dimensional)

dtypeNumPy dtype (default: int64)

copybool

Make a copy of input ndarray.

nameobject

Name to be stored in the index.

See also

Index

The base pandas-on-Spark Index type.

Float64Index

A special case of Index with purely float labels.

Notes

An Index instance can only contain hashable objects.

Examples

>>> ps.Int64Index([1, 2, 3])
Int64Index([1, 2, 3], dtype='int64')

From a Series:

>>> s = ps.Series([1, 2, 3], index=[10, 20, 30])
>>> ps.Int64Index(s)
Int64Index([1, 2, 3], dtype='int64')

From an Index:

>>> idx = ps.Index([1, 2, 3])
>>> ps.Int64Index(idx)
Int64Index([1, 2, 3], dtype='int64')

Methods

all([axis, skipna])	Return whether all elements are True.
any([axis])	Return whether any element is True.
append(other)	Append a collection of Index options together.
argmax()	Return a maximum argument indexer.
argmin()	Return a minimum argument indexer.
asof(label)	Return the label from the index, or, if not present, the previous one.
astype(dtype)	Cast a pandas-on-Spark object to a specified dtype dtype.
copy([name, deep])	Make a copy of this object.
delete(loc)	Make new Index with passed location(-s) deleted.
difference(other[, sort])	Return a new Index with elements from the index that are not in other.
drop(labels)	Make new Index with passed list of labels deleted.
drop_duplicates()	Return Index with duplicate values removed.
droplevel(level)	Return index with requested level(s) removed.
dropna()	Return Index or MultiIndex without NA/NaN values
equals(other)	Determine if two Index objects contain the same elements.
factorize([sort, na_sentinel])	Encode the object as an enumerated type or categorical variable.
fillna(value)	Fill NA/NaN values with the specified value.
get_level_values(level)	Return Index if a valid level is given.
holds_integer()	Whether the type is an integer type.
identical(other)	Similar to equals, but check that other comparable attributes are also equal.
insert(loc, item)	Make new Index inserting new item at location.
intersection(other)	Form the intersection of two Index objects.
is_boolean()	Return if the current index type is a boolean type.
is_categorical()	Return if the current index type is a categorical type.
is_floating()	Return if the current index type is a floating type.
is_integer()	Return if the current index type is a integer type.
is_interval()	Return if the current index type is an interval type.
is_numeric()	Return if the current index type is a numeric type.
is_object()	Return if the current index type is a object type.
is_type_compatible(kind)	Whether the index type is compatible with the provided type.
isin(values)	Check whether values are contained in Series or Index.
isna()	Detect existing (non-missing) values.
isnull()	Detect existing (non-missing) values.
item()	Return the first element of the underlying data as a python scalar.
map(mapper[, na_action])	Map values using input correspondence (a dict, Series, or function).
max()	Return the maximum value of the Index.
min()	Return the minimum value of the Index.
notna()	Detect existing (non-missing) values.
notnull()	Detect existing (non-missing) values.
nunique([dropna, approx, rsd])	Return number of unique elements in the object.
rename(name[, inplace])	Alter Index or MultiIndex name.
repeat(repeats)	Repeat elements of a Index/MultiIndex.
set_names(names[, level, inplace])	Set Index or MultiIndex name.
shift([periods, fill_value])	Shift Series/Index by desired number of periods.
sort(*args, **kwargs)	Use sort_values instead.
sort_values([ascending])	Return a sorted copy of the index.
symmetric_difference(other[, result_name, sort])	Compute the symmetric difference of two Index objects.
take(indices)	Return the elements in the given positional indices along an axis.
to_frame([index, name])	Create a DataFrame with a column containing the Index.
to_list()	Return a list of the values.
to_numpy([dtype, copy])	A NumPy ndarray representing the values in this Index or MultiIndex.
to_pandas()	Return a pandas Index.
to_series([name])	Create a Series with both index and values equal to the index keys useful with map for returning an indexer based on an index.
tolist()	Return a list of the values.
transpose()	Return the transpose, For index, It will be index itself.
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.
view()	this is defined as a copy with the same identity

Attributes

T	Return the transpose, For index, It will be index itself.
asi8	Integer representation of the values.
dtype	Return the dtype object of the underlying data.
empty	Returns true if the current object is empty.
has_duplicates	If index has duplicates, return True, otherwise False.
hasnans	Return True if it has any missing values.
inferred_type	Return a string of the type inferred from the values.
is_all_dates	Return if all data types of the index are datetime.
is_monotonic	Return boolean if values in the object are monotonically increasing.
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 if the index has unique values.
name	Return name of the Index.
names	Return names of the Index.
ndim	Return an int representing the number of array dimensions.
nlevels	Number of levels in Index & MultiIndex.
shape	Return a tuple of the shape of the underlying data.
size	Return an int representing the number of elements in this object.
values	Return an array representing the data in the Index.

pyspark.pandas.Index.isin pyspark.pandas.Float64Index