DatetimeIndex[source]

class pandas.DatetimeIndex [source]

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.

Parameters:

Parameters:	data : array-like (1-dimensional), optional Optional datetime-like data to construct index with copy : bool Make a copy of input ndarray freq : string or pandas offset object, optional One of pandas date offset strings or corresponding objects start : starting value, datetime-like, optional If data is None, start is used as the start point in generating regular timestamp data. periods : int, optional, > 0 Number of periods to generate, if generating index. Takes precedence over end argument end : end time, datetime-like, optional If periods is none, generated index will extend to first conforming time on or just past end argument closed : string or None, default None Make the interval closed with respect to the given frequency to the ?left?, ?right?, or both sides (None) tz : pytz.timezone or dateutil.tz.tzfile ambiguous : ?infer?, bool-ndarray, ?NaT?, default ?raise? ?infer? will attempt to infer fall dst-transition hours based on order bool-ndarray where True signifies a DST time, False signifies a non-DST time (note that this flag is only applicable for ambiguous times) ?NaT? will return NaT where there are ambiguous times ?raise? will raise an AmbiguousTimeError if there are ambiguous times infer_dst : boolean, default False (DEPRECATED) Attempt to infer fall dst-transition hours based on order name : object Name to be stored in the index

data : array-like (1-dimensional), optional

Optional datetime-like data to construct index with

copy : bool

Make a copy of input ndarray

freq : string or pandas offset object, optional

One of pandas date offset strings or corresponding objects

start : starting value, datetime-like, optional

If data is None, start is used as the start point in generating regular timestamp data.

periods : int, optional, > 0

Number of periods to generate, if generating index. Takes precedence over end argument

end : end time, datetime-like, optional

If periods is none, generated index will extend to first conforming time on or just past end argument

closed : string or None, default None

Make the interval closed with respect to the given frequency to the ?left?, ?right?, or both sides (None)

tz : pytz.timezone or dateutil.tz.tzfile

ambiguous : ?infer?, bool-ndarray, ?NaT?, default ?raise?

?infer? will attempt to infer fall dst-transition hours based on order
bool-ndarray where True signifies a DST time, False signifies a non-DST time (note that this flag is only applicable for ambiguous times)
?NaT? will return NaT where there are ambiguous times
?raise? will raise an AmbiguousTimeError if there are ambiguous times

infer_dst : boolean, default False (DEPRECATED)

Attempt to infer fall dst-transition hours based on order

name : object

Name to be stored in the index

Notes

To learn more about the frequency strings, please see this link.

Attributes

`T`	return the transpose, which is by definition self
`asi8`
`asobject`	return object Index which contains boxed values
`base`	return the base object if the memory of the underlying data is
`data`	return the data pointer of the underlying data
`date`	Returns numpy array of python datetime.date objects (namely, the date part of Timestamps without timezone information).
`day`	The days of the datetime
`dayofweek`	The day of the week with Monday=0, Sunday=6
`dayofyear`	The ordinal day of the year
`days_in_month`	The number of days in the month
`daysinmonth`	The number of days in the month
`dtype`
`dtype_str`
`flags`
`freq`	get/set the frequncy of the Index
`freqstr`	Return the frequency object as a string if its set, otherwise None
`has_duplicates`
`hasnans`
`hour`	The hours of the datetime
`inferred_freq`
`inferred_type`
`is_all_dates`
`is_leap_year`	Logical indicating if the date belongs to a leap year
`is_monotonic`	alias for is_monotonic_increasing (deprecated)
`is_monotonic_decreasing`	return if the index is monotonic decreasing (only equal or
`is_monotonic_increasing`	return if the index is monotonic increasing (only equal or
`is_month_end`	Logical indicating if last day of month (defined by frequency)
`is_month_start`	Logical indicating if first day of month (defined by frequency)
`is_normalized`
`is_quarter_end`	Logical indicating if last day of quarter (defined by frequency)
`is_quarter_start`	Logical indicating if first day of quarter (defined by frequency)
`is_unique`
`is_year_end`	Logical indicating if last day of year (defined by frequency)
`is_year_start`	Logical indicating if first day of year (defined by frequency)
`itemsize`	return the size of the dtype of the item of the underlying data
`microsecond`	The microseconds of the datetime
`minute`	The minutes of the datetime
`month`	The month as January=1, December=12
`name`
`names`
`nanosecond`	The nanoseconds of the datetime
`nbytes`	return the number of bytes in the underlying data
`ndim`	return the number of dimensions of the underlying data,
`nlevels`
`offset`
`quarter`	The quarter of the date
`resolution`
`second`	The seconds of the datetime
`shape`	return a tuple of the shape of the underlying data
`size`	return the number of elements in the underlying data
`strides`	return the strides of the underlying data
`time`	Returns numpy array of datetime.time.
`tz`
`tzinfo`	Alias for tz attribute
`values`	return the underlying data as an ndarray
`week`	The week ordinal of the year
`weekday`	The day of the week with Monday=0, Sunday=6
`weekday_name`	The name of day in a week (ex: Friday)
`weekofyear`	The week ordinal of the year
`year`	The year of the datetime

Methods

`all`([other])
`any`([other])
`append`(other)	Append a collection of Index options together
`argmax`([axis])	Returns the indices of the maximum values along an axis.
`argmin`([axis])	Returns the indices of the minimum values along an axis.
`argsort`(\args, \\*kwargs)	Returns the indices that would sort the index and its underlying data.
`asof`(label)	For a sorted index, return the most recent label up to and including the passed label.
`asof_locs`(where, mask)	where : array of timestamps
`astype`(dtype[, copy])	Create an Index with values cast to dtypes.
`ceil`(freq)	ceil the index to the specified freq
`copy`([name, deep, dtype])	Make a copy of this object.
`delete`(loc)	Make a new DatetimeIndex with passed location(s) deleted.
`difference`(other)	Return a new Index with elements from the index that are not in `other`.
`drop`(labels[, errors])	Make new Index with passed list of labels deleted
`drop_duplicates`(\args, \\*kwargs)	Return Index with duplicate values removed
`dropna`([how])	Return Index without NA/NaN values
`duplicated`(\args, \\*kwargs)	Return boolean np.ndarray denoting duplicate values
`equals`(other)	Determines if two Index objects contain the same elements.
`factorize`([sort, na_sentinel])	Encode the object as an enumerated type or categorical variable
`fillna`([value, downcast])	Fill NA/NaN values with the specified value
`floor`(freq)	floor the index to the specified freq
`format`([name, formatter])	Render a string representation of the Index
`get_duplicates`()
`get_indexer`(target[, method, limit, tolerance])	Compute indexer and mask for new index given the current index.
`get_indexer_for`(target, \\kwargs)	guaranteed return of an indexer even when non-unique
`get_indexer_non_unique`(target)	return an indexer suitable for taking from a non unique index
`get_level_values`(level)	Return vector of label values for requested level, equal to the length
`get_loc`(key[, method, tolerance])	Get integer location for requested label
`get_slice_bound`(label, side, kind)	Calculate slice bound that corresponds to given label.
`get_value`(series, key)	Fast lookup of value from 1-dimensional ndarray.
`get_value_maybe_box`(series, key)
`get_values`()	return the underlying data as an ndarray
`groupby`(values)	Group the index labels by a given array of values.
`holds_integer`()
`identical`(other)	Similar to equals, but check that other comparable attributes are
`indexer_at_time`(time[, asof])	Select values at particular time of day (e.g.
`indexer_between_time`(start_time, end_time[, ...])	Select values between particular times of day (e.g., 9:00-9:30AM).
`insert`(loc, item)	Make new Index inserting new item at location
`intersection`(other)	Specialized intersection for DatetimeIndex objects.
`is_`(other)	More flexible, faster check like `is` but that works through views
`is_boolean`()
`is_categorical`()
`is_floating`()
`is_integer`()
`is_lexsorted_for_tuple`(tup)
`is_mixed`()
`is_numeric`()
`is_object`()
`is_type_compatible`(typ)
`isin`(values)	Compute boolean array of whether each index value is found in the
`item`()	return the first element of the underlying data as a python
`join`(other[, how, level, return_indexers])	See Index.join
`map`(f)
`max`([axis])	Return the maximum value of the Index or maximum along an axis.
`memory_usage`([deep])	Memory usage of my values
`min`([axis])	Return the minimum value of the Index or minimum along an axis.
`normalize`()	Return DatetimeIndex with times to midnight.
`nunique`([dropna])	Return number of unique elements in the object.
`order`([return_indexer, ascending])	Return sorted copy of Index
`putmask`(mask, value)	return a new Index of the values set with the mask
`ravel`([order])	return an ndarray of the flattened values of the underlying data
`reindex`(target[, method, level, limit, ...])	Create index with target?s values (move/add/delete values as necessary)
`rename`(name[, inplace])	Set new names on index.
`repeat`(repeats, \args, \\*kwargs)	Analogous to ndarray.repeat
`reshape`(\args, \\*kwargs)	NOT IMPLEMENTED: do not call this method, as reshaping is not supported for Index objects and will raise an error.
`round`(freq, \args, \\*kwargs)	round the index to the specified freq
`searchsorted`(key[, side, sorter])	Find indices where elements should be inserted to maintain order.
`set_names`(names[, level, inplace])	Set new names on index.
`set_value`(arr, key, value)	Fast lookup of value from 1-dimensional ndarray.
`shift`(n[, freq])	Specialized shift which produces a DatetimeIndex
`slice_indexer`([start, end, step, kind])	Return indexer for specified label slice.
`slice_locs`([start, end, step, kind])	Compute slice locations for input labels.
`snap`([freq])	Snap time stamps to nearest occurring frequency
`sort`(\args, \\*kwargs)
`sort_values`([return_indexer, ascending])	Return sorted copy of Index
`sortlevel`([level, ascending, sort_remaining])	For internal compatibility with with the Index API
`str`	alias of `StringMethods`
`strftime`(date_format)	Return an array of formatted strings specified by date_format, which supports the same string format as the python standard library.
`summary`([name])	return a summarized representation
`sym_diff`(\args, \\*kwargs)
`symmetric_difference`(other[, result_name])	Compute the symmetric difference of two Index objects.
`take`(indices[, axis, allow_fill, fill_value])	return a new %(klass)s of the values selected by the indices
`to_datetime`([dayfirst])
`to_julian_date`()	Convert DatetimeIndex to Float64Index of Julian Dates.
`to_native_types`([slicer])	slice and dice then format
`to_period`([freq])	Cast to PeriodIndex at a particular frequency
`to_perioddelta`(freq)	Calcuates TimedeltaIndex of difference between index values and index converted to PeriodIndex at specified freq.
`to_pydatetime`()	Return DatetimeIndex as object ndarray of datetime.datetime objects
`to_series`([keep_tz])	Create a Series with both index and values equal to the index keys
`tolist`()	return a list of the underlying data
`transpose`(\args, \\*kwargs)	return the transpose, which is by definition self
`tz_convert`(tz)	Convert tz-aware DatetimeIndex from one time zone to another (using
`tz_localize`(\args, \\*kwargs)	Localize tz-naive DatetimeIndex to given time zone (using
`union`(other)	Specialized union for DatetimeIndex objects.
`union_many`(others)	A bit of a hack to accelerate unioning a collection of indexes
`unique`()	Return Index of unique values in the object.
`value_counts`([normalize, sort, ascending, ...])	Returns object containing counts of unique values.
`view`([cls])
`where`(cond[, other])	New in version 0.19.0.

Links:

http://pandas.pydata.org/pandas-docs/version/0.19.2/generated/pandas.DatetimeIndex.html

doc_Pandas

2017-01-12 04:48:07

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