Table Of Contents

Search

Enter search terms or a module, class or function name.

pandas.Index

class pandas.Index[source]

Immutable ndarray implementing an ordered, sliceable set. The basic object storing axis labels for all pandas objects

Parameters:
data : array-like (1-dimensional)

dtype : NumPy 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.

copy : bool

Make a copy of input ndarray

name : object

Name to be stored in the index

tupleize_cols : bool (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, TimedeltaIndex, PeriodIndex, Int64Index, UInt64Index, Float64Index

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
base return the base object if the memory of the underlying data is shared
data return the data pointer of the underlying data
dtype return the dtype object of the underlying data
dtype_str return the dtype str of the underlying data
flags
hasnans return if I have any nans; enables various perf speedups
inferred_type return a string of the type inferred from the values
is_monotonic alias for is_monotonic_increasing (deprecated)
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
itemsize return the size of the dtype of the item of the underlying data
nbytes return the number of bytes in the underlying data
ndim return the number of dimensions of the underlying data, by definition 1
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 return the underlying data as an ndarray
asi8  
empty  
has_duplicates  
is_all_dates  
name  
names  
nlevels  

Methods

all(*args, **kwargs) Return whether all elements are True.
any(*args, **kwargs) Return whether any element is True.
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) 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) where : array of timestamps mask : array of booleans where data is not NA
astype(dtype[, copy]) Create an Index with values cast to dtypes.
contains(key) return a boolean if this key is IN the index
copy([name, deep, dtype]) Make a copy of this object.
delete(loc) Make new Index with passed location(-s) deleted
difference(other) Return a new Index with elements from the index that are 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) Determines if two Index objects contain the same elements.
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
get_duplicates() (DEPRECATED) Extract duplicated index elements.
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 This dispatches to get_indexer or get_indexer_nonunique as appropriate
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.
get_values() Return Index data as an numpy.ndarray.
groupby(values) Group the index labels by a given array of values.
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_(other) More flexible, faster check like is but that works through views
is_categorical() Check if the Index holds categorical data.
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]) this is an internal non-public method
map(mapper[, na_action]) Map values using input correspondence (a dict, Series, or function).
max() Return the maximum value of the Index.
memory_usage([deep]) Memory usage of the values
min() 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 (move/add/delete values as necessary)
rename(name[, inplace]) Set new names on index.
repeat(repeats, *args, **kwargs) Repeat elements of an Index.
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 by desired number of time frequency increments.
slice_indexer([start, end, step, kind]) For an ordered or unique index, compute the slice indexer for input labels and step.
slice_locs([start, end, step, kind]) Compute slice locations for input labels.
sort_values([return_indexer, ascending]) Return a sorted copy of the index.
sortlevel([level, ascending, sort_remaining]) For internal compatibility with with the Index API
str alias of pandas.core.strings.StringMethods
summary([name]) (DEPRECATED) Return a summarized representation ..
symmetric_difference(other[, result_name]) 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_frame([index]) Create a DataFrame with a column containing the Index.
to_native_types([slicer]) Format specified values of self and return them.
to_series([index, 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(*args, **kwargs) return the transpose, which is by definition self
union(other) Form the union of two Index objects and sorts if possible.
unique([level]) Return unique values in the index.
value_counts([normalize, sort, ascending, …]) Return a Series containing counts of unique values.
where(cond[, other])
holds_integer  
is_boolean  
is_floating  
is_integer  
is_interval  
is_lexsorted_for_tuple  
is_mixed  
is_numeric  
is_object  
is_type_compatible  
sort  
view  
Scroll To Top