Index objects

Index

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

Index([data, dtype, copy, name, tupleize_cols])

Immutable sequence used for indexing and alignment.

Properties

Index.values	Return an array representing the data in the Index.
Index.is_monotonic_increasing	Return a boolean if the values are equal or increasing.
Index.is_monotonic_decreasing	Return a boolean if the values are equal or decreasing.
Index.is_unique	Return if the index has unique values.
Index.has_duplicates	Check if the Index has duplicate values.
Index.hasnans	Return True if there are any NaNs.
Index.dtype	Return the dtype object of the underlying data.
Index.inferred_type	Return a string of the type inferred from the values.
Index.shape	Return a tuple of the shape of the underlying data.
Index.name	Return Index or MultiIndex name.
Index.names	Get names on index.
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	Indicator whether Index is empty.
Index.T	Return the transpose, which is by definition self.
Index.memory_usage([deep])	Memory usage of the values.
Index.array	The ExtensionArray of the data backing this Series or Index.

Modifying and computations

Index.all(*args, **kwargs)	Return whether all elements are Truthy.
Index.any(*args, **kwargs)	Return whether any element is Truthy.
Index.argmin([axis, skipna])	Return int position of the smallest value in the Series.
Index.argmax([axis, skipna])	Return int position of the largest value in the Series.
Index.copy([name, deep])	Make a copy of this object.
Index.delete(loc)	Make new Index with passed location(-s) deleted.
Index.drop(labels[, errors])	Make new Index with passed list of labels deleted.
Index.drop_duplicates(*[, keep])	Return Index with duplicate values removed.
Index.duplicated([keep])	Indicate duplicate index values.
Index.equals(other)	Determine if two Index object are equal.
Index.factorize([sort, use_na_sentinel])	Encode the object as an enumerated type or categorical variable.
Index.identical(other)	Similar to equals, but checks that object attributes and types are also equal.
Index.insert(loc, item)	Make new Index inserting new item at location.
Index.is_(other)	More flexible, faster check like is but that works through views.
Index.min([axis, skipna])	Return the minimum value of the Index.
Index.max([axis, skipna])	Return the maximum value of the Index.
Index.reindex(target[, method, level, ...])	Create index with target's values.
Index.rename(name, *[, inplace])	Alter Index or MultiIndex name.
Index.repeat(repeats[, axis])	Repeat elements of a Index.
Index.where(cond[, other])	Replace values where the condition is False.
Index.take(indices[, axis, allow_fill, ...])	Return a new Index of the values selected by the indices.
Index.putmask(mask, value)	Return a new Index of the values set with the mask.
Index.unique([level])	Return unique values in the index.
Index.nunique([dropna])	Return number of unique elements in the object.
Index.value_counts([normalize, sort, ...])	Return a Series containing counts of unique values.

Compatibility with MultiIndex

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

Missing values

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

Conversion

Index.astype(dtype[, copy])	Create an Index with values cast to dtypes.
Index.item()	Return the first element of the underlying data as a Python scalar.
Index.map(mapper[, na_action])	Map values using an input mapping or function.
Index.ravel([order])	Return a view on self.
Index.to_list()	Return a list of the values.
Index.to_series([index, name])	Create a Series with both index and values equal to the index keys.
Index.to_frame([index, name])	Create a DataFrame with a column containing the Index.
Index.to_numpy([dtype, copy, na_value])	A NumPy ndarray representing the values in this Series or Index.
Index.view([cls])	Return a view of the Index with the specified dtype or a new Index instance.

Sorting

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

Time-specific operations

Index.shift([periods, freq])

Shift index by desired number of time frequency increments.

Combining / joining / set operations

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

Selecting

Index.asof(label)	Return the label from the index, or, if not present, the previous one.
Index.asof_locs(where, mask)	Return the locations (indices) of labels in the index.
Index.get_indexer(target[, method, limit, ...])	Compute indexer and mask for new index given the current index.
Index.get_indexer_for(target)	Guaranteed return of an indexer even when non-unique.
Index.get_indexer_non_unique(target)	Compute indexer and mask for new index given the current index.
Index.get_level_values(level)	Return an Index of values for requested level.
Index.get_loc(key)	Get integer location, slice or boolean mask for requested label.
Index.get_slice_bound(label, side)	Calculate slice bound that corresponds to given label.
Index.isin(values[, level])	Return a boolean array where the index values are in values.
Index.slice_indexer([start, end, step])	Compute the slice indexer for input labels and step.
Index.slice_locs([start, end, step])	Compute slice locations for input labels.

Numeric Index

RangeIndex([start, stop, step, dtype, copy, ...])

Immutable Index implementing a monotonic integer range.

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 pandas.RangeIndex from a range object.

CategoricalIndex

CategoricalIndex([data, categories, ...])

Index based on an underlying Categorical.

Categorical components

CategoricalIndex.codes	The category codes of this categorical index.
CategoricalIndex.categories	The categories of this categorical.
CategoricalIndex.ordered	Whether the categories have an ordered relationship.
CategoricalIndex.rename_categories(...)	Rename categories.
CategoricalIndex.reorder_categories(...[, ...])	Reorder categories as specified in new_categories.
CategoricalIndex.add_categories(new_categories)	Add new categories.
CategoricalIndex.remove_categories(removals)	Remove the specified categories.
CategoricalIndex.remove_unused_categories()	Remove categories which are not used.
CategoricalIndex.set_categories(new_categories)	Set the categories to the specified new categories.
CategoricalIndex.as_ordered()	Set the Categorical to be ordered.
CategoricalIndex.as_unordered()	Set the Categorical to be unordered.

Modifying and computations

CategoricalIndex.map(mapper[, na_action])	Map values using input an input mapping or function.
CategoricalIndex.equals(other)	Determine if two CategoricalIndex objects contain the same elements.

IntervalIndex

IntervalIndex(data[, closed, dtype, copy, ...])

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 left bounds of the intervals in the IntervalIndex.
IntervalIndex.right	Return right bounds of the intervals in the IntervalIndex.
IntervalIndex.mid	Return the midpoint of each interval in the IntervalIndex as an Index.
IntervalIndex.closed	String describing the inclusive side the intervals.
IntervalIndex.length	Calculate the length of each interval in the IntervalIndex.
IntervalIndex.values	Return an array representing the data in the Index.
IntervalIndex.is_empty	Indicates if an interval is empty, meaning it contains no points.
IntervalIndex.is_non_overlapping_monotonic	Return a boolean whether the IntervalArray/IntervalIndex is non-overlapping and monotonic.
IntervalIndex.is_overlapping	Return True if the IntervalIndex has overlapping intervals, else False.
IntervalIndex.get_loc(key)	Get integer location, slice or boolean mask for requested label.
IntervalIndex.get_indexer(target[, method, ...])	Compute indexer and mask for new index given the current index.
IntervalIndex.set_closed(closed)	Return an identical IntervalArray closed on the specified side.
IntervalIndex.contains(other)	Check elementwise if the Intervals contain the value.
IntervalIndex.overlaps(other)	Check elementwise if an Interval overlaps the values in the IntervalArray.
IntervalIndex.to_tuples([na_tuple])	Return an ndarray (if self is IntervalArray) or Index (if self is IntervalIndex) of tuples of the form (left, right).

MultiIndex

MultiIndex([levels, codes, sortorder, ...])

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

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	Levels of the MultiIndex.
MultiIndex.codes	Codes of the MultiIndex.
MultiIndex.nlevels	Integer number of levels in this MultiIndex.
MultiIndex.levshape	A tuple representing the length of each level in the MultiIndex.
MultiIndex.dtypes	Return the dtypes as a Series for the underlying MultiIndex.

MultiIndex components

MultiIndex.set_levels(levels, *[, level, ...])	Set new levels on MultiIndex.
MultiIndex.set_codes(codes, *[, level, ...])	Set new codes on MultiIndex.
MultiIndex.to_flat_index()	Convert a MultiIndex to an Index of Tuples containing the level values.
MultiIndex.to_frame([index, name, ...])	Create a DataFrame with the levels of the MultiIndex as columns.
MultiIndex.sortlevel([level, ascending, ...])	Sort MultiIndex at the requested level.
MultiIndex.droplevel([level])	Return index with requested level(s) removed.
MultiIndex.swaplevel([i, j])	Swap level i with level j.
MultiIndex.reorder_levels(order)	Rearrange levels using input order.
MultiIndex.remove_unused_levels()	Create new MultiIndex from current that removes unused levels.
MultiIndex.drop(codes[, level, errors])	Make a new pandas.MultiIndex with the passed list of codes deleted.
MultiIndex.copy([names, deep, name])	Make a copy of this object.
MultiIndex.append(other)	Append a collection of Index options together.
MultiIndex.truncate([before, after])	Slice index between two labels / tuples, return new MultiIndex.

MultiIndex selecting

MultiIndex.get_loc(key)	Get location for a label or a tuple of labels.
MultiIndex.get_locs(seq)	Get location for a sequence of labels.
MultiIndex.get_loc_level(key[, level, ...])	Get location and sliced index for requested label(s)/level(s).
MultiIndex.get_indexer(target[, method, ...])	Compute indexer and mask for new index given the current index.
MultiIndex.get_level_values(level)	Return vector of label values for requested level.

IndexSlice

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

DatetimeIndex

DatetimeIndex([data, freq, tz, ambiguous, ...])

Immutable ndarray-like of datetime64 data.

Time/date components

DatetimeIndex.year	The year of the datetime.
DatetimeIndex.month	The month as January=1, December=12.
DatetimeIndex.day	The day 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.
DatetimeIndex.time	Returns numpy array of datetime.time objects.
DatetimeIndex.timetz	Returns numpy array of datetime.time objects with timezones.
DatetimeIndex.dayofyear	The ordinal day of the year.
DatetimeIndex.day_of_year	The ordinal day of the year.
DatetimeIndex.dayofweek	The day of the week with Monday=0, Sunday=6.
DatetimeIndex.day_of_week	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	Return the timezone.
DatetimeIndex.freq	Return the frequency object if it is set, otherwise None.
DatetimeIndex.freqstr	Return the frequency object as a string if it's 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	Tries to return a string representing a frequency generated by infer_freq.

Selecting

DatetimeIndex.indexer_at_time(time[, asof])	Return index locations of values at particular time of day.
DatetimeIndex.indexer_between_time(...[, ...])	Return index locations of values between particular times of day.

Time-specific operations

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

Conversion

DatetimeIndex.as_unit(unit[, round_ok])	Convert to a dtype with the given unit resolution.
DatetimeIndex.to_period([freq])	Cast to PeriodArray/PeriodIndex at a particular frequency.
DatetimeIndex.to_pydatetime()	Return an ndarray of datetime.datetime objects.
DatetimeIndex.to_series([index, name])	Create a Series with both index and values equal to the index keys.
DatetimeIndex.to_frame([index, name])	Create a DataFrame with a column containing the Index.

Methods

DatetimeIndex.mean(*[, skipna, axis])	Return the mean value of the Array.
DatetimeIndex.std([axis, dtype, out, ddof, ...])	Return sample standard deviation over requested axis.

TimedeltaIndex

TimedeltaIndex([data, freq, dtype, copy, name])

Immutable Index of timedelta64 data.

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 individual resolution components of the Timedeltas.
TimedeltaIndex.inferred_freq	Tries to return a string representing a frequency generated by infer_freq.

Conversion

TimedeltaIndex.as_unit(unit)	Convert to a dtype with the given unit resolution.
TimedeltaIndex.to_pytimedelta()	Return an ndarray of datetime.timedelta objects.
TimedeltaIndex.to_series([index, name])	Create a Series with both index and values equal to the index keys.
TimedeltaIndex.round(freq[, ambiguous, ...])	Perform round operation on the data to the specified freq.
TimedeltaIndex.floor(freq[, ambiguous, ...])	Perform floor operation on the data to the specified freq.
TimedeltaIndex.ceil(freq[, ambiguous, ...])	Perform ceil operation on the data to the specified freq.
TimedeltaIndex.to_frame([index, name])	Create a DataFrame with a column containing the Index.

Methods

TimedeltaIndex.mean(*[, skipna, axis])

Return the mean value of the Array.

PeriodIndex

PeriodIndex([data, freq, dtype, copy, name])

Immutable ndarray holding ordinal values indicating regular periods in time.

Properties

PeriodIndex.day	The days of the period.
PeriodIndex.dayofweek	The day of the week with Monday=0, Sunday=6.
PeriodIndex.day_of_week	The day of the week with Monday=0, Sunday=6.
PeriodIndex.dayofyear	The ordinal day of the year.
PeriodIndex.day_of_year	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	Get the Timestamp for the end of the period.
PeriodIndex.freq	Return the frequency object if it is set, otherwise None.
PeriodIndex.freqstr	Return the frequency object as a string if it's 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	Fiscal year the Period lies in according to its starting-quarter.
PeriodIndex.second	The second of the period.
PeriodIndex.start_time	Get the Timestamp for the start of the period.
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([freq, how])	Convert the PeriodArray to the specified frequency freq.
PeriodIndex.strftime(date_format)	Convert to Index using specified date_format.
PeriodIndex.to_timestamp([freq, how])	Cast to DatetimeArray/Index.
PeriodIndex.from_fields(*[, year, quarter, ...])	Construct a PeriodIndex from fields (year, month, day, etc.).
PeriodIndex.from_ordinals(ordinals, *, freq)	Construct a PeriodIndex from ordinals.