Pandas Arrays
For most data types, pandas uses NumPy arrays as the concrete objects contained with a Index
, Series
, or DataFrame
.
For some data types, pandas extends NumPy’s type system.
Pandas and third-party libraries can extend NumPy’s type system (see Extension Types). The top-level array()
method can be used to create a new array, which may be stored in a Series
, Index
, or as a column in a DataFrame
.
array (data[, dtype, copy]) |
Create an array. |
Datetime Data
NumPy cannot natively represent timezone-aware datetimes. Pandas supports this with the arrays.DatetimeArray
extension array, which can hold timezone-naive or timezone-aware values.
Timestamp
, a subclass of datetime.datetime
, is pandas’ scalar type for timezone-naive or timezone-aware datetime data.
Timestamp |
Pandas replacement for datetime.datetime |
Properties
Methods
Timestamp.astimezone |
Convert tz-aware Timestamp to another time zone. |
Timestamp.ceil |
return a new Timestamp ceiled to this resolution |
Timestamp.combine (date, time) |
date, time -> datetime with same date and time fields |
Timestamp.ctime |
Return ctime() style string. |
Timestamp.date |
Return date object with same year, month and day. |
Timestamp.day_name |
Return the day name of the Timestamp with specified locale. |
Timestamp.dst |
Return self.tzinfo.dst(self). |
Timestamp.floor |
return a new Timestamp floored to this resolution |
Timestamp.freq |
|
Timestamp.freqstr |
|
Timestamp.fromordinal (ordinal[, freq, tz]) |
passed an ordinal, translate and convert to a ts note: by definition there cannot be any tz info on the ordinal itself |
Timestamp.fromtimestamp (ts) |
timestamp[, tz] -> tz’s local time from POSIX timestamp. |
Timestamp.isocalendar |
Return a 3-tuple containing ISO year, week number, and weekday. |
Timestamp.isoformat |
|
Timestamp.isoweekday |
Return the day of the week represented by the date. |
Timestamp.month_name |
Return the month name of the Timestamp with specified locale. |
Timestamp.normalize |
Normalize Timestamp to midnight, preserving tz information. |
Timestamp.now ([tz]) |
Returns new Timestamp object representing current time local to tz. |
Timestamp.replace |
implements datetime.replace, handles nanoseconds |
Timestamp.round |
Round the Timestamp to the specified resolution |
Timestamp.strftime |
format -> strftime() style string. |
Timestamp.strptime |
string, format -> new datetime parsed from a string (like time.strptime()). |
Timestamp.time |
Return time object with same time but with tzinfo=None. |
Timestamp.timestamp |
Return POSIX timestamp as float. |
Timestamp.timetuple |
Return time tuple, compatible with time.localtime(). |
Timestamp.timetz |
Return time object with same time and tzinfo. |
Timestamp.to_datetime64 |
Returns a numpy.datetime64 object with ‘ns’ precision |
Timestamp.to_julian_date |
Convert TimeStamp to a Julian Date. |
Timestamp.to_period |
Return an period of which this timestamp is an observation. |
Timestamp.to_pydatetime |
Convert a Timestamp object to a native Python datetime object. |
Timestamp.today (cls[, tz]) |
Return the current time in the local timezone. |
Timestamp.toordinal |
Return proleptic Gregorian ordinal. |
Timestamp.tz_convert |
Convert tz-aware Timestamp to another time zone. |
Timestamp.tz_localize |
Convert naive Timestamp to local time zone, or remove timezone from tz-aware Timestamp. |
Timestamp.tzname |
Return self.tzinfo.tzname(self). |
Timestamp.utcfromtimestamp (ts) |
Construct a naive UTC datetime from a POSIX timestamp. |
Timestamp.utcnow () |
Return a new Timestamp representing UTC day and time. |
Timestamp.utcoffset |
Return self.tzinfo.utcoffset(self). |
Timestamp.utctimetuple |
Return UTC time tuple, compatible with time.localtime(). |
Timestamp.weekday |
Return the day of the week represented by the date. |
A collection of timestamps may be stored in a arrays.DatetimeArray
. For timezone-aware data, the .dtype
of a DatetimeArray
is a DatetimeTZDtype
. For timezone-naive data, np.dtype("datetime64[ns]")
is used.
If the data are tz-aware, then every value in the array must have the same timezone.
arrays.DatetimeArray (values[, dtype, freq, copy]) |
Pandas ExtensionArray for tz-naive or tz-aware datetime data. |
DatetimeTZDtype ([unit, tz]) |
A np.dtype duck-typed class, suitable for holding a custom datetime with tz dtype. |
Timedelta Data
NumPy can natively represent timedeltas. Pandas provides Timedelta
for symmetry with Timestamp
.
Timedelta |
Represents a duration, the difference between two dates or times. |
Properties
Methods
A collection of timedeltas may be stored in a TimedeltaArray
.
Timespan Data
Pandas represents spans of times as Period
objects.
Period
Period |
Represents a period of time |
Properties
Methods
Period.asfreq |
Convert Period to desired frequency, either at the start or end of the interval |
Period.now |
|
Period.strftime |
Returns the string representation of the Period , depending on the selected fmt . |
Period.to_timestamp |
Return the Timestamp representation of the Period at the target frequency at the specified end (how) of the Period |
A collection of timedeltas may be stored in a arrays.PeriodArray
. Every period in a PeriodArray
must have the same freq
.
arrays.DatetimeArray (values[, dtype, freq, copy]) |
Pandas ExtensionArray for tz-naive or tz-aware datetime data. |
PeriodDtype |
A Period duck-typed class, suitable for holding a period with freq dtype. |
Interval Data
Arbitrary intervals can be represented as Interval
objects.
Interval |
Immutable object implementing an Interval, a bounded slice-like interval. |
Properties
A collection of intervals may be stored in an arrays.IntervalArray
.
Nullable Integer
numpy.ndarray
cannot natively represent integer-data with missing values. Pandas provides this through arrays.IntegerArray
.
Categorical Data
Pandas defines a custom data type for representing data that can take only a limited, fixed set of values. The dtype of a Categorical
can be described by a pandas.api.types.CategoricalDtype
.
CategoricalDtype ([categories, ordered]) |
Type for categorical data with the categories and orderedness |
Categorical data can be stored in a pandas.Categorical
Categorical (values[, categories, ordered, …]) |
Represents a categorical variable in classic R / S-plus fashion |
The alternative Categorical.from_codes()
constructor can be used when you have the categories and integer codes already:
The dtype information is available on the Categorical
np.asarray(categorical)
works by implementing the array interface. Be aware, that this converts the Categorical back to a NumPy array, so categories and order information is not preserved!
A Categorical
can be stored in a Series
or DataFrame
. To create a Series of dtype category
, use cat = s.astype(dtype)
or Series(..., dtype=dtype)
where dtype
is either
- the string
'category'
- an instance of
CategoricalDtype
.
If the Series is of dtype CategoricalDtype
, Series.cat
can be used to change the categorical data. See Categorical Accessor for more.
Sparse Data
Data where a single value is repeated many times (e.g. 0
or NaN
) may be stored efficiently as a SparseArray
.
The Series.sparse
accessor may be used to access sparse-specific attributes and methods if the Series
contains sparse values. See Sparse Accessor for more.