pandas.Index

class pandas.Index(data=None, dtype=None, copy=False, name=None, tupleize_cols=True, **kwargs)

Immutable sequence used for indexing and alignment. The basic object storing axis labels for all pandas objects.

Parameters

dataarray-like (1-dimensional)

dtypeNumPy dtype (default: object)

If dtype is None, we find the dtype that best fits the data. If an actual dtype is provided, we coerce to that dtype if it’s safe. Otherwise, an error will be raised.

copybool

Make a copy of input ndarray.

nameobject

Name to be stored in the index.

tupleize_colsbool (default: True)

When True, attempt to create a MultiIndex if possible.

See also

RangeIndex

Index implementing a monotonic integer range.

CategoricalIndex

Index of Categorical s.

MultiIndex

A multi-level, or hierarchical Index.

IntervalIndex

An Index of Interval s.

DatetimeIndex

Index of datetime64 data.

TimedeltaIndex

Index of timedelta64 data.

PeriodIndex

Index of Period data.

Int64Index

A special case of Index with purely integer labels.

UInt64Index

A special case of Index with purely unsigned integer labels.

Float64Index

A special case of Index with purely float labels.

Notes

An Index instance can only contain hashable objects

Examples

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

>>> pd.Index(list('abc'))
Index(['a', 'b', 'c'], dtype='object')

Attributes

T	Return the transpose, which is by definition self.
array	The ExtensionArray of the data backing this Series or Index.
asi8	Integer representation of the values.
dtype	Return the dtype object of the underlying data.
has_duplicates	Check if the Index has duplicate values.
hasnans	Return if I have any nans; enables various perf speedups.
inferred_type	Return a string of the type inferred from the values.
is_all_dates	Whether or not the index values only consist of dates.
is_monotonic	Alias for is_monotonic_increasing.
is_monotonic_decreasing	Return if the index is monotonic decreasing (only equal or decreasing) values.
is_monotonic_increasing	Return if the index is monotonic increasing (only equal or increasing) values.
is_unique	Return if the index has unique values.
name	Return Index or MultiIndex name.
nbytes	Return the number of bytes in the underlying data.
ndim	Number of dimensions of the underlying data, by definition 1.
nlevels	Number of levels.
shape	Return a tuple of the shape of the underlying data.
size	Return the number of elements in the underlying data.
values	Return an array representing the data in the Index.

empty
names

Methods

all(*args, **kwargs)	Return whether all elements are Truthy.
any(*args, **kwargs)	Return whether any element is Truthy.
append(other)	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.
argsort(*args, **kwargs)	Return the integer indices that would sort the index.
asof(label)	Return the label from the index, or, if not present, the previous one.
asof_locs(where, mask)	Return the locations (indices) of labels in the index.
astype(dtype[, copy])	Create an Index with values cast to dtypes.
copy([name, deep, dtype, names])	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 of index not in other.
drop(labels[, errors])	Make new Index with passed list of labels deleted.
drop_duplicates([keep])	Return Index with duplicate values removed.
droplevel([level])	Return index with requested level(s) removed.
dropna([how])	Return Index without NA/NaN values.
duplicated([keep])	Indicate duplicate index values.
equals(other)	Determine if two Index object are equal.
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, na_rep])	Render a string representation of the Index.
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.
get_loc(key[, method, tolerance])	Get integer location, slice or boolean mask for requested label.
get_slice_bound(label, side[, kind])	Calculate slice bound that corresponds to given label.
get_value(series, key)	Fast lookup of value from 1-dimensional ndarray.
groupby(values)	Group the index labels by a given array of values.
holds_integer()	Whether the type is an integer type.
identical(other)	Similar to equals, but checks that object attributes and types are also equal.
insert(loc, item)	Make new Index inserting new item at location.
intersection(other[, sort])	Form the intersection of two Index objects.
is_(other)	More flexible, faster check like is but that works through views.
is_boolean()	Check if the Index only consists of booleans.
is_categorical()	Check if the Index holds categorical data.
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_mixed()	Check if the Index holds data with mixed data types.
is_numeric()	Check if the Index only consists of numeric data.
is_object()	Check if the Index is of the object dtype.
is_type_compatible(kind)	Whether the index type is compatible with the provided type.
isin(values[, level])	Return a boolean array where the index values are in values.
isna()	Detect missing values.
isnull()	Detect missing values.
item()	Return the first element of the underlying data as a Python scalar.
join(other[, how, level, return_indexers, sort])	Compute join_index and indexers to conform data structures to the new index.
map(mapper[, na_action])	Map values using input correspondence (a dict, Series, or function).
max([axis, skipna])	Return the maximum value of the Index.
memory_usage([deep])	Memory usage of the values.
min([axis, skipna])	Return the minimum value of the Index.
notna()	Detect existing (non-missing) values.
notnull()	Detect existing (non-missing) values.
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.
rename(name[, inplace])	Alter Index or MultiIndex name.
repeat(repeats[, axis])	Repeat elements of a Index.
searchsorted(value[, side, sorter])	Find indices where elements should be inserted to maintain order.
set_names(names[, level, inplace])	Set Index or MultiIndex name.
set_value(arr, key, value)	(DEPRECATED) Fast lookup of value from 1-dimensional ndarray.
shift([periods, freq])	Shift index by desired number of time frequency increments.
slice_indexer([start, end, step, kind])	Compute the slice indexer for input labels and step.
slice_locs([start, end, step, kind])	Compute slice locations for input labels.
sort(*args, **kwargs)	Use sort_values instead.
sort_values([return_indexer, ascending, …])	Return a sorted copy of the index.
sortlevel([level, ascending, sort_remaining])	For internal compatibility with the Index API.
str	alias of pandas.core.strings.accessor.StringMethods
symmetric_difference(other[, result_name, sort])	Compute the symmetric difference of two Index objects.
take(indices[, axis, allow_fill, fill_value])	Return a new Index of the values selected by the indices.
to_flat_index()	Identity method.
to_frame([index, name])	Create a DataFrame with a column containing the Index.
to_list()	Return a list of the values.
to_native_types([slicer])	(DEPRECATED) Format specified values of self and return them.
to_numpy([dtype, copy, na_value])	A NumPy ndarray representing the values in this Series or 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.
transpose(*args, **kwargs)	Return the transpose, which is by definition self.
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.
where(cond[, other])	Replace values where the condition is False.

view

Index objects pandas.Index.T