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#
Return an array representing the data in the Index.
Return a boolean if the values are equal or increasing.
Return a boolean if the values are equal or decreasing.
Return if the index has unique values.
Check if the Index has duplicate values.
Return True if there are any NaNs.
Return the dtype object of the underlying data.
Return a string of the type inferred from the values.
Return a tuple of the shape of the underlying data.
Return Index or MultiIndex name.
Return the number of bytes in the underlying data.
Number of dimensions of the underlying data, by definition 1.
Return the number of elements in the underlying data.
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 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.
(DEPRECATED) Check if the Index only consists of booleans.
(DEPRECATED) Check if the Index holds categorical data.
(DEPRECATED) Check if the Index is a floating type.
(DEPRECATED) Check if the Index only consists of integers.
(DEPRECATED) Check if the Index holds Interval objects.
(DEPRECATED) Check if the Index only consists of numeric data.
(DEPRECATED) 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.
Detect missing values.
Detect existing (non-missing) values.
Conversion#
Index.astype(dtype[, copy])
Create an Index with values cast to dtypes.
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.
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.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)
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.
The value of the start parameter (0 if this was not supplied).
The value of the stop parameter.
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#
The category codes of this categorical index.
The categories of this categorical.
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[, 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.
String describing the inclusive side the intervals.
Return an array representing the data in the Index.
Indicates if an interval is empty, meaning it contains no points.
IntervalIndex.is_non_overlapping_monotonic
Return a boolean whether the IntervalArray is non-overlapping and monotonic.
Return True if the IntervalIndex has overlapping intervals, else False.
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 identical IntervalArray 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 (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#
Names of levels in MultiIndex.
Levels of the MultiIndex.
Integer number of levels in this MultiIndex.
A tuple with the length of each level.
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.
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.
Create an object to more easily perform multi-index slicing.
DatetimeIndex#
DatetimeIndex([data, freq, tz, normalize, ...])
Immutable ndarray-like of datetime64 data.
Time/date components#
The year of the datetime.
The month as January=1, December=12.
The day of the datetime.
The hours of the datetime.
The minutes of the datetime.
The seconds of the datetime.
The microseconds of the datetime.
The nanoseconds of the datetime.
Returns numpy array of python datetime.date objects.
Returns numpy array of datetime.time objects.
Returns numpy array of datetime.time objects with timezones.
The ordinal day of the year.
The ordinal day of the year.
The day of the week with Monday=0, Sunday=6.
The day of the week with Monday=0, Sunday=6.
The day of the week with Monday=0, Sunday=6.
The quarter of the date.
Return the timezone.
Return the frequency object as a string if it's set, otherwise None.
Indicates whether the date is the first day of the month.
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.
Indicator for whether the date is the last day of a quarter.
Indicate whether the date is the first day of a year.
Indicate whether the date is the last day of the year.
Boolean indicator if the date belongs to a leap year.
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(*args, **kwargs)
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.
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 with specified locale.
DatetimeIndex.day_name(*args, **kwargs)
Return the day names with specified locale.
Conversion#
DatetimeIndex.as_unit(*args, **kwargs)
Convert to a dtype with the given unit resolution.
DatetimeIndex.to_period(*args, **kwargs)
Cast to PeriodArray/PeriodIndex at a particular frequency.
DatetimeIndex.to_pydatetime(*args, **kwargs)
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(*args, **kwargs)
Return sample standard deviation over requested axis.
TimedeltaIndex#
TimedeltaIndex([data, unit, freq, closed, ...])
Immutable Index of timedelta64 data.
Components#
Number of days for each element.
Number of seconds (>= 0 and less than 1 day) for each element.
Number of microseconds (>= 0 and less than 1 second) for each element.
Number of nanoseconds (>= 0 and less than 1 microsecond) for each element.
Return a DataFrame of the individual resolution components of the Timedeltas.
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(*args, **kwargs)
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(*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(*[, skipna, axis])
Return the mean value of the Array.
PeriodIndex#
PeriodIndex([data, ordinal, freq, dtype, ...])
Immutable ndarray holding ordinal values indicating regular periods in time.
Properties#
The days of the period.
The day of the week with Monday=0, Sunday=6.
The day of the week with Monday=0, Sunday=6.
The ordinal day of the year.
The ordinal day of the year.
The number of days in the month.
The number of days in the month.
Get the Timestamp for the end of the period.
Return the frequency object as a string if it's set, otherwise None.
The hour of the period.
Logical indicating if the date belongs to a leap year.
The minute of the period.
The month as January=1, December=12.
The quarter of the date.
The second of the period.
Get the Timestamp for the start of the period.
The week ordinal of the year.
The day of the week with Monday=0, Sunday=6.
The week ordinal of the year.
The year of the period.
Methods#
PeriodIndex.asfreq([freq, how])
Convert the PeriodArray to the specified frequency freq.
PeriodIndex.strftime(*args, **kwargs)
Convert to Index using specified date_format.
PeriodIndex.to_timestamp([freq, how])
Cast to DatetimeArray/Index.
PeriodIndex.from_fields(*[, year, quarter, ...])
PeriodIndex.from_ordinals(ordinals, *, freq)