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 with the length of each level.

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

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.