Index objects

Index objects

Index

Many of these methods or variants thereof are available on the objectsthat contain an index (Series/DataFrame) and those should most likely beused before calling these methods directly.

Index Immutable ndarray implementing an ordered, sliceable set.

Properties

`Index.values`	Return an array representing the data in the Index.
`Index.is_monotonic`	Alias for is_monotonic_increasing.
`Index.is_monotonic_increasing`	Return if the index is monotonic increasing (only equal or increasing) values.
`Index.is_monotonic_decreasing`	Return if the index is monotonic decreasing (only equal or decreasing) values.
`Index.is_unique`	Return if the index has unique values.
`Index.has_duplicates`
`Index.hasnans`	Return if I have any nans; enables various perf speedups.
`Index.dtype`	Return the dtype object of the underlying data.
`Index.dtype_str`	(DEPRECATED) Return the dtype str of the underlying data.
`Index.inferred_type`	Return a string of the type inferred from the values.
`Index.is_all_dates`
`Index.shape`	Return a tuple of the shape of the underlying data.
`Index.name`
`Index.names`
`Index.nbytes`	Return the number of bytes in the underlying data.
`Index.ndim`	Number of dimensions of the underlying data, by definition 1.
`Index.size`	Return the number of elements in the underlying data.
`Index.empty`
`Index.strides`	(DEPRECATED) Return the strides of the underlying data.
`Index.itemsize`	(DEPRECATED) Return the size of the dtype of the item of the underlying data.
`Index.base`	(DEPRECATED) Return the base object if the memory of the underlying data is shared.
`Index.T`	Return the transpose, which is by
`Index.memory_usage`(self[, deep])	Memory usage of the values

Modifying and computations

`Index.all`(self, args, *kwargs)	Return whether all elements are True.
`Index.any`(self, args, *kwargs)	Return whether any element is True.
`Index.argmin`(self[, axis, skipna])	Return a ndarray of the minimum argument indexer.
`Index.argmax`(self[, axis, skipna])	Return an ndarray of the maximum argument indexer.
`Index.copy`(self[, name, deep, dtype])	Make a copy of this object.
`Index.delete`(self, loc)	Make new Index with passed location(-s) deleted.
`Index.drop`(self, labels[, errors])	Make new Index with passed list of labels deleted.
`Index.drop_duplicates`(self[, keep])	Return Index with duplicate values removed.
`Index.duplicated`(self[, keep])	Indicate duplicate index values.
`Index.equals`(self, other)	Determine if two Index objects contain the same elements.
`Index.factorize`(self[, sort, nasentinel])	Encode the object as an enumerated type or categorical variable.
`Index.identical`(self, other)	Similar to equals, but check that other comparable attributes are also equal.
`Index.insert`(self, loc, item)	Make new Index inserting new item at location.
[`Index.is`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.is_.html#pandas.Index.is_)(self, other)	More flexible, faster check like `is` but that works through views.
`Index.is_boolean`(self)
`Index.is_categorical`(self)	Check if the Index holds categorical data.
`Index.is_floating`(self)
`Index.is_integer`(self)
`Index.is_interval`(self)
`Index.is_mixed`(self)
`Index.is_numeric`(self)
`Index.is_object`(self)
`Index.min`(self[, axis, skipna])	Return the minimum value of the Index.
`Index.max`(self[, axis, skipna])	Return the maximum value of the Index.
`Index.reindex`(self, target[, method, level, …])	Create index with target’s values (move/add/delete values as necessary).
`Index.rename`(self, name[, inplace])	Alter Index or MultiIndex name.
`Index.repeat`(self, repeats[, axis])	Repeat elements of a Index.
`Index.where`(self, cond[, other])	Return an Index of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from other.
`Index.take`(self, indices[, axis, …])	Return a new Index of the values selected by the indices.
`Index.putmask`(self, mask, value)	Return a new Index of the values set with the mask.
`Index.unique`(self[, level])	Return unique values in the index.
`Index.nunique`(self[, dropna])	Return number of unique elements in the object.
`Index.value_counts`(self[, normalize, sort, …])	Return a Series containing counts of unique values.

Compatibility with MultiIndex

`Index.set_names`(self, names[, level, inplace])	Set Index or MultiIndex name.
`Index.is_lexsorted_for_tuple`(self, tup)
`Index.droplevel`(self[, level])	Return index with requested level(s) removed.

Missing values

`Index.fillna`(self[, value, downcast])	Fill NA/NaN values with the specified value
`Index.dropna`(self[, how])	Return Index without NA/NaN values
`Index.isna`(self)	Detect missing values.
`Index.notna`(self)	Detect existing (non-missing) values.

Conversion

`Index.astype`(self, dtype[, copy])	Create an Index with values cast to dtypes.
`Index.item`(self)	Return the first element of the underlying data as a python scalar.
`Index.map`(self, mapper[, naaction])	Map values using input correspondence (a dict, Series, or function).
`Index.ravel`(self[, order])	Return an ndarray of the flattened values of the underlying data.
`Index.to_list`(self)	Return a list of the values.
`Index.to_native_types`(self[, slicer])	Format specified values of _self and return them.
`Index.to_series`(self[, 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.
`Index.to_frame`(self[, index, name])	Create a DataFrame with a column containing the Index.
`Index.view`(self[, cls])

Sorting

`Index.argsort`(self, args, *kwargs)	Return the integer indices that would sort the index.
`Index.searchsorted`(self, value[, side, sorter])	Find indices where elements should be inserted to maintain order.
`Index.sort_values`(self[, return_indexer, …])	Return a sorted copy of the index.

Time-specific operations

Index.shift(self[, periods, freq]) Shift index by desired number of time frequency increments.

Combining / joining / set operations

`Index.append`(self, other)	Append a collection of Index options together.
`Index.join`(self, other[, how, level, …])	Compute joinindex and indexers to conform data structures to the new index.
`Index.intersection`(self, other[, sort])	Form the intersection of two Index objects.
`Index.union`(self, other[, sort])	Form the union of two Index objects.
`Index.difference`(self, other[, sort])	Return a new Index with elements from the index that are not in _other.
`Index.symmetric_difference`(self, other[, …])	Compute the symmetric difference of two Index objects.

Selecting

`Index.asof`(self, label)	Return the label from the index, or, if not present, the previous one.
`Index.asof_locs`(self, where, mask)	Find the locations (indices) of the labels from the index for every entry in the where argument.
`Index.contains`(self, key)	(DEPRECATED) Return a boolean indicating whether the provided key is in the index.
`Index.get_duplicates`(self)	(DEPRECATED) Extract duplicated index elements.
`Index.get_indexer`(self, target[, method, …])	Compute indexer and mask for new index given the current index.
`Index.get_indexer_for`(self, target, **kwargs)	Guaranteed return of an indexer even when non-unique.
`Index.get_indexer_non_unique`(self, target)	Compute indexer and mask for new index given the current index.
`Index.get_level_values`(self, level)	Return an Index of values for requested level.
`Index.get_loc`(self, key[, method, tolerance])	Get integer location, slice or boolean mask for requested label.
`Index.get_slice_bound`(self, label, side, kind)	Calculate slice bound that corresponds to given label.
`Index.get_value`(self, series, key)	Fast lookup of value from 1-dimensional ndarray.
`Index.get_values`(self)	(DEPRECATED) Return Index data as an numpy.ndarray.
`Index.set_value`(self, arr, key, value)	Fast lookup of value from 1-dimensional ndarray.
`Index.isin`(self, values[, level])	Return a boolean array where the index values are in values.
`Index.slice_indexer`(self[, start, end, …])	For an ordered or unique index, compute the slice indexer for input labels and step.
`Index.slice_locs`(self[, start, end, step, kind])	Compute slice locations for input labels.

Numeric Index

`RangeIndex`	Immutable Index implementing a monotonic integer range.
`Int64Index`	Immutable ndarray implementing an ordered, sliceable set.
`UInt64Index`	Immutable ndarray implementing an ordered, sliceable set.
`Float64Index`	Immutable ndarray implementing an ordered, sliceable set.

`RangeIndex.start`	The value of the start parameter (`0` if this was not supplied)
`RangeIndex.stop`	The value of the stop parameter
`RangeIndex.step`	The value of the step parameter (`1` if this was not supplied)
`RangeIndex.from_range`(data[, name, dtype])	Create RangeIndex from a range object.

CategoricalIndex

CategoricalIndex Index based on an underlying Categorical.

Categorical components

`CategoricalIndex.codes`
`CategoricalIndex.categories`
`CategoricalIndex.ordered`
`CategoricalIndex.rename_categories`(self, …)	Rename categories.
`CategoricalIndex.reorder_categories`(self, …)	Reorder categories as specified in new_categories.
`CategoricalIndex.add_categories`(self, …)	Add new categories.
`CategoricalIndex.remove_categories`(self, …)	Remove the specified categories.
`CategoricalIndex.remove_unused_categories`(…)	Remove categories which are not used.
`CategoricalIndex.set_categories`(self, …)	Set the categories to the specified new_categories.
`CategoricalIndex.as_ordered`(self, *args, …)	Set the Categorical to be ordered.
`CategoricalIndex.as_unordered`(self, *args, …)	Set the Categorical to be unordered.

Modifying and computations

`CategoricalIndex.map`(self, mapper)	Map values using input correspondence (a dict, Series, or function).
`CategoricalIndex.equals`(self, other)	Determine if two CategoricalIndex objects contain the same elements.

IntervalIndex

IntervalIndex Immutable index of intervals that are closed on the same side.

IntervalIndex components

`IntervalIndex.from_arrays`(left, right[, …])	Construct from two arrays defining the left and right bounds.
`IntervalIndex.from_tuples`(data[, closed, …])	Construct an IntervalIndex from an array-like of tuples
`IntervalIndex.from_breaks`(breaks[, closed, …])	Construct an IntervalIndex from an array of splits.
`IntervalIndex.left`	Return the left endpoints of each Interval in the IntervalIndex as an Index
`IntervalIndex.right`	Return the right endpoints of each Interval in the IntervalIndex as an Index
`IntervalIndex.mid`	Return the midpoint of each Interval in the IntervalIndex as an Index
`IntervalIndex.closed`	Whether the intervals are closed on the left-side, right-side, both or neither
`IntervalIndex.length`	Return an Index with entries denoting the length of each Interval in the IntervalIndex
`IntervalIndex.values`	Return the IntervalIndex’s data as an IntervalArray.
`IntervalIndex.is_empty`	Indicates if an interval is empty, meaning it contains no points.
`IntervalIndex.is_non_overlapping_monotonic`	Return True if the IntervalIndex is non-overlapping (no Intervals share points) and is either monotonic increasing or monotonic decreasing, else False
`IntervalIndex.is_overlapping`	Return True if the IntervalIndex has overlapping intervals, else False.
`IntervalIndex.get_loc`(self, key, method, …)	Get integer location, slice or boolean mask for requested label.
`IntervalIndex.get_indexer`(self, target, …)	Compute indexer and mask for new index given the current index.
`IntervalIndex.set_closed`(self, closed)	Return an IntervalIndex identical to the current one, but closed on the specified side
`IntervalIndex.contains`(self, other)	Check elementwise if the Intervals contain the value.
`IntervalIndex.overlaps`(self, other)	Check elementwise if an Interval overlaps the values in the IntervalIndex.
`IntervalIndex.to_tuples`(self[, na_tuple])	Return an Index of tuples of the form (left, right)

MultiIndex

MultiIndex A multi-level, or hierarchical, index object for pandas objects.

IndexSlice Create an object to more easily perform multi-index slicing

MultiIndex constructors

`MultiIndex.from_arrays`(arrays[, sortorder, …])	Convert arrays to MultiIndex.
`MultiIndex.from_tuples`(tuples[, sortorder, …])	Convert list of tuples to MultiIndex.
`MultiIndex.from_product`(iterables[, …])	Make a MultiIndex from the cartesian product of multiple iterables.
`MultiIndex.from_frame`(df[, sortorder, names])	Make a MultiIndex from a DataFrame.

MultiIndex properties

`MultiIndex.names`	Names of levels in MultiIndex
`MultiIndex.levels`
`MultiIndex.codes`
`MultiIndex.nlevels`	Integer number of levels in this MultiIndex.
`MultiIndex.levshape`	A tuple with the length of each level.

MultiIndex components

`MultiIndex.set_levels`(self, levels[, level, …])	Set new levels on MultiIndex.
`MultiIndex.set_codes`(self, codes[, level, …])	Set new codes on MultiIndex.
`MultiIndex.to_hierarchical`(self, n_repeat[, …])	(DEPRECATED) Return a MultiIndex reshaped to conform to the shapes given by n_repeat and n_shuffle.
`MultiIndex.to_flat_index`(self)	Convert a MultiIndex to an Index of Tuples containing the level values.
`MultiIndex.to_frame`(self[, index, name])	Create a DataFrame with the levels of the MultiIndex as columns.
`MultiIndex.is_lexsorted`(self)	Return True if the codes are lexicographically sorted
`MultiIndex.sortlevel`(self[, level, …])	Sort MultiIndex at the requested level.
`MultiIndex.droplevel`(self[, level])	Return index with requested level(s) removed.
`MultiIndex.swaplevel`(self[, i, j])	Swap level i with level j.
`MultiIndex.reorder_levels`(self, order)	Rearrange levels using input order.
`MultiIndex.remove_unused_levels`(self)	Create a new MultiIndex from the current that removes unused levels, meaning that they are not expressed in the labels.

MultiIndex selecting

`MultiIndex.get_loc`(self, key[, method])	Get location for a label or a tuple of labels as an integer, slice or boolean mask.
`MultiIndex.get_loc_level`(self, key[, level, …])	Get both the location for the requested label(s) and the resulting sliced index.
`MultiIndex.get_indexer`(self, target[, …])	Compute indexer and mask for new index given the current index.
`MultiIndex.get_level_values`(self, level)	Return vector of label values for requested level, equal to the length of the index.

DatetimeIndex

DatetimeIndex Immutable ndarray of datetime64 data, represented internally as int64, and which can be boxed to Timestamp objects that are subclasses of datetime and carry metadata such as frequency information.

Time/Date components

`DatetimeIndex.year`	The year of the datetime.
`DatetimeIndex.month`	The month as January=1, December=12.
`DatetimeIndex.day`	The days of the datetime.
`DatetimeIndex.hour`	The hours of the datetime.
`DatetimeIndex.minute`	The minutes of the datetime.
`DatetimeIndex.second`	The seconds of the datetime.
`DatetimeIndex.microsecond`	The microseconds of the datetime.
`DatetimeIndex.nanosecond`	The nanoseconds of the datetime.
`DatetimeIndex.date`	Returns numpy array of python datetime.date objects (namely, the date part of Timestamps without timezone information).
`DatetimeIndex.time`	Returns numpy array of datetime.time.
`DatetimeIndex.timetz`	Returns numpy array of datetime.time also containing timezone information.
`DatetimeIndex.dayofyear`	The ordinal day of the year.
`DatetimeIndex.weekofyear`	The week ordinal of the year.
`DatetimeIndex.week`	The week ordinal of the year.
`DatetimeIndex.dayofweek`	The day of the week with Monday=0, Sunday=6.
`DatetimeIndex.weekday`	The day of the week with Monday=0, Sunday=6.
`DatetimeIndex.quarter`	The quarter of the date.
`DatetimeIndex.tz`
`DatetimeIndex.freq`	Return the frequency object if it is set, otherwise None.
`DatetimeIndex.freqstr`	Return the frequency object as a string if it is set, otherwise None.
`DatetimeIndex.is_month_start`	Indicates whether the date is the first day of the month.
`DatetimeIndex.is_month_end`	Indicates whether the date is the last day of the month.
`DatetimeIndex.is_quarter_start`	Indicator for whether the date is the first day of a quarter.
`DatetimeIndex.is_quarter_end`	Indicator for whether the date is the last day of a quarter.
`DatetimeIndex.is_year_start`	Indicate whether the date is the first day of a year.
`DatetimeIndex.is_year_end`	Indicate whether the date is the last day of the year.
`DatetimeIndex.is_leap_year`	Boolean indicator if the date belongs to a leap year.
`DatetimeIndex.inferred_freq`	Tryies to return a string representing a frequency guess, generated by infer_freq.

Selecting

`DatetimeIndex.indexer_at_time`(self, time[, asof])	Return index locations of index values at particular time of day (e.g.
`DatetimeIndex.indexer_between_time`(self, …)	Return index locations of values between particular times of day (e.g., 9:00-9:30AM).

Time-specific operations

`DatetimeIndex.normalize`(self, args, *kwargs)	Convert times to midnight.
`DatetimeIndex.strftime`(self, args, *kwargs)	Convert to Index using specified dateformat.
`DatetimeIndex.snap`(self[, freq])	Snap time stamps to nearest occurring frequency
`DatetimeIndex.tz_convert`(self, *args, …)	Convert tz-aware Datetime Array/Index from one time zone to another.
`DatetimeIndex.tz_localize`(self, *args, …)	Localize tz-naive Datetime Array/Index to tz-aware Datetime Array/Index.
`DatetimeIndex.round`(self, args, *kwargs)	Perform round operation on the data to the specified _freq.
`DatetimeIndex.floor`(self, args, *kwargs)	Perform floor operation on the data to the specified freq.
`DatetimeIndex.ceil`(self, args, *kwargs)	Perform ceil operation on the data to the specified freq.
`DatetimeIndex.month_name`(self, *args, …)	Return the month names of the DateTimeIndex with specified locale.
`DatetimeIndex.day_name`(self, args, *kwargs)	Return the day names of the DateTimeIndex with specified locale.

Conversion

`DatetimeIndex.to_period`(self, args, *kwargs)	Cast to PeriodArray/Index at a particular frequency.
`DatetimeIndex.to_perioddelta`(self, *args, …)	Calculate TimedeltaArray of difference between index values and index converted to PeriodArray at specified freq.
`DatetimeIndex.to_pydatetime`(self, *args, …)	Return Datetime Array/Index as object ndarray of datetime.datetime objects
`DatetimeIndex.to_series`(self[, keep_tz, …])	Create a Series with both index and values equal to the index keys useful with map for returning an indexer based on an index
`DatetimeIndex.to_frame`(self[, index, name])	Create a DataFrame with a column containing the Index.

Methods

DatetimeIndex.mean(self, *args, **kwargs) Return the mean value of the Array.

TimedeltaIndex

TimedeltaIndex Immutable ndarray of timedelta64 data, represented internally as int64, and which can be boxed to timedelta objects

Components

`TimedeltaIndex.days`	Number of days for each element.
`TimedeltaIndex.seconds`	Number of seconds (>= 0 and less than 1 day) for each element.
`TimedeltaIndex.microseconds`	Number of microseconds (>= 0 and less than 1 second) for each element.
`TimedeltaIndex.nanoseconds`	Number of nanoseconds (>= 0 and less than 1 microsecond) for each element.
`TimedeltaIndex.components`	Return a dataframe of the components (days, hours, minutes, seconds, milliseconds, microseconds, nanoseconds) of the Timedeltas.
`TimedeltaIndex.inferred_freq`	Tryies to return a string representing a frequency guess, generated by infer_freq.

Conversion

`TimedeltaIndex.to_pytimedelta`(self, *args, …)	Return Timedelta Array/Index as object ndarray of datetime.timedelta objects.
`TimedeltaIndex.to_series`(self[, 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.
`TimedeltaIndex.round`(self, freq[, …])	Perform round operation on the data to the specified freq.
`TimedeltaIndex.floor`(self, freq[, …])	Perform floor operation on the data to the specified freq.
`TimedeltaIndex.ceil`(self, freq[, ambiguous, …])	Perform ceil operation on the data to the specified freq.
`TimedeltaIndex.to_frame`(self[, index, name])	Create a DataFrame with a column containing the Index.

Methods

TimedeltaIndex.mean(self, *args, **kwargs) Return the mean value of the Array.

PeriodIndex

PeriodIndex Immutable ndarray holding ordinal values indicating regular periods in time such as particular years, quarters, months, etc.

Properties

`PeriodIndex.day`	The days of the period
`PeriodIndex.dayofweek`	The day of the week with Monday=0, Sunday=6
`PeriodIndex.dayofyear`	The ordinal day of the year
`PeriodIndex.days_in_month`	The number of days in the month
`PeriodIndex.daysinmonth`	The number of days in the month
`PeriodIndex.end_time`
`PeriodIndex.freq`	Return the frequency object if it is set, otherwise None.
`PeriodIndex.freqstr`	Return the frequency object as a string if it is set, otherwise None.
`PeriodIndex.hour`	The hour of the period
`PeriodIndex.is_leap_year`	Logical indicating if the date belongs to a leap year
`PeriodIndex.minute`	The minute of the period
`PeriodIndex.month`	The month as January=1, December=12
`PeriodIndex.quarter`	The quarter of the date
`PeriodIndex.qyear`
`PeriodIndex.second`	The second of the period
`PeriodIndex.start_time`
`PeriodIndex.week`	The week ordinal of the year
`PeriodIndex.weekday`	The day of the week with Monday=0, Sunday=6
`PeriodIndex.weekofyear`	The week ordinal of the year
`PeriodIndex.year`	The year of the period

Methods

`PeriodIndex.asfreq`(self, args, *kwargs)	Convert the Period Array/Index to the specified frequency freq.
`PeriodIndex.strftime`(self, args, *kwargs)	Convert to Index using specified date_format.
`PeriodIndex.to_timestamp`(self, *args, …)	Cast to DatetimeArray/Index.