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 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	Check if the Index has duplicate values.
Index.hasnans	Return if I have any nans; enables various perf speedups.
Index.dtype	Return the dtype object of the underlying data.
Index.inferred_type	Return a string of the type inferred from the values.
Index.is_all_dates	Whether or not the index values only consist of dates.
Index.shape	Return a tuple of the shape of the underlying data.
Index.name	Return Index or MultiIndex 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.T	Return the transpose, which is by definition self.
Index.memory_usage([deep])	Memory usage of the values.

Modifying and computations

Index.all(*args, **kwargs)	Return whether all elements are True.
Index.any(*args, **kwargs)	Return whether any element is True.
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, dtype, names])	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, 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.is_boolean()	Check if the Index only consists of booleans.
Index.is_categorical()	Check if the Index holds categorical data.
Index.is_floating()	Check if the Index is a floating type.
Index.is_integer()	Check if the Index only consists of integers.
Index.is_interval()	Check if the Index holds Interval objects.
Index.is_mixed()	Check if the Index holds data with mixed data types.
Index.is_numeric()	Check if the Index only consists of numeric data.
Index.is_object()	Check if the Index is of the object dtype.
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, downcast])	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 input correspondence (a dict, Series, or function).
Index.ravel([order])	Return an ndarray of the flattened values of the underlying data.
Index.to_list()	Return a list of the values.
Index.to_native_types([slicer])	Format specified values of self and return them.
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.view([cls])

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, **kwargs)	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[, method, tolerance])	Get integer location, slice or boolean mask for requested label.
Index.get_slice_bound(label, side, kind)	Calculate slice bound that corresponds to given label.
Index.get_value(series, key)	Fast lookup of value from 1-dimensional ndarray.
Index.isin(values[, level])	Return a boolean array where the index values are in values.
Index.slice_indexer([start, end, step, kind])	Compute the slice indexer for input labels and step.
Index.slice_locs([start, end, step, kind])	Compute slice locations for input labels.

Numeric Index

RangeIndex([start, stop, step, dtype, copy, …])	Immutable Index implementing a monotonic integer range.
Int64Index([data, dtype, copy, name])	Immutable ndarray implementing an ordered, sliceable set.
UInt64Index([data, dtype, copy, name])	Immutable ndarray implementing an ordered, sliceable set.
Float64Index([data, dtype, copy, name])	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([data, categories, …])

Index based on an underlying Categorical.

Categorical components

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

Modifying and computations

CategoricalIndex.map(mapper)	Map values using input correspondence (a dict, Series, 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 the left endpoints of each Interval in the IntervalArray as an Index.
IntervalIndex.right	Return the right endpoints of each Interval in the IntervalArray as an Index.
IntervalIndex.mid	Return the midpoint of each Interval in the IntervalArray 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 IntervalArray.
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 IntervalArray 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(key[, method, tolerance])	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(*args, **kwargs)	Return an IntervalArray identical to the current one, but closed on the specified side.
IntervalIndex.contains(*args, **kwargs)	Check elementwise if the Intervals contain the value.
IntervalIndex.overlaps(*args, **kwargs)	Check elementwise if an Interval overlaps the values in the IntervalArray.
IntervalIndex.to_tuples(*args, **kwargs)	Return an ndarray of tuples of the form (left, right).

MultiIndex

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

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(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.is_lexsorted()	Return True if the codes are lexicographically sorted.
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 selecting

MultiIndex.get_loc(key[, method])	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.

DatetimeIndex

DatetimeIndex([data, freq, tz, normalize, …])

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 (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	(DEPRECATED) The week ordinal of the year.
DatetimeIndex.week	(DEPRECATED) 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	Return timezone, if any.
DatetimeIndex.freq	Return the frequency object if it is set, otherwise None.
DatetimeIndex.freqstr	Return the frequency object as a string if its 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 guess, generated by infer_freq.

Selecting

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

Time-specific operations

DatetimeIndex.normalize(*args, **kwargs)	Convert times to midnight.
DatetimeIndex.strftime(*args, **kwargs)	Convert to Index using specified date_format.
DatetimeIndex.snap([freq])	Snap time stamps to nearest occurring frequency.
DatetimeIndex.tz_convert(*args, **kwargs)	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(*args, **kwargs)	Perform round operation on the data to the specified freq.
DatetimeIndex.floor(*args, **kwargs)	Perform floor operation on the data to the specified freq.
DatetimeIndex.ceil(*args, **kwargs)	Perform ceil operation on the data to the specified freq.
DatetimeIndex.month_name(*args, **kwargs)	Return the month names of the DateTimeIndex with specified locale.
DatetimeIndex.day_name(*args, **kwargs)	Return the day names of the DateTimeIndex with specified locale.

Conversion

DatetimeIndex.to_period(*args, **kwargs)	Cast to PeriodArray/Index at a particular frequency.
DatetimeIndex.to_perioddelta(*args, **kwargs)	Calculate TimedeltaArray of difference between index values and index converted to PeriodArray at specified freq.
DatetimeIndex.to_pydatetime(*args, **kwargs)	Return Datetime Array/Index as object ndarray of datetime.datetime objects.
DatetimeIndex.to_series([keep_tz, 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.
DatetimeIndex.to_frame([index, name])	Create a DataFrame with a column containing the Index.

Methods

DatetimeIndex.mean(*args, **kwargs)

Return the mean value of the Array.

TimedeltaIndex

TimedeltaIndex([data, unit, freq, closed, …])

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	Tries to return a string representing a frequency guess, generated by infer_freq.

Conversion

TimedeltaIndex.to_pytimedelta(*args, **kwargs)	Return Timedelta Array/Index as object 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(*args, **kwargs)	Perform round operation on the data to the specified freq.
TimedeltaIndex.floor(*args, **kwargs)	Perform floor operation on the data to the specified freq.
TimedeltaIndex.ceil(*args, **kwargs)	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(*args, **kwargs)

Return the mean value of the Array.

PeriodIndex

PeriodIndex([data, ordinal, freq, tz, …])

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.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 its 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([freq, how])	Convert the Period Array/Index to the specified frequency freq.
PeriodIndex.strftime(*args, **kwargs)	Convert to Index using specified date_format.
PeriodIndex.to_timestamp(*args, **kwargs)	Cast to DatetimeArray/Index.

Panel pandas.Index