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spark-instrumented-optimizer/python/pyspark/pandas/datetimes.py
Luka Sturtewagen fd8081cd27 [SPARK-34983][PYTHON] Renaming the package alias from pp to ps 
### What changes were proposed in this pull request?

This PR proposes to fix:

```python
import pyspark.pandas as pp
```

to

```python
import pyspark.pandas as ps
```

### Why are the changes needed?

`pp` might sound offensive in some contexts.

### Does this PR introduce _any_ user-facing change?

The change is in master only. We'll use `ps` as the short name instead of `pp`.

### How was this patch tested?

The CI in this PR will test it out.

Closes #32108 from LSturtew/renaming_pyspark.pandas.

Authored-by: Luka Sturtewagen <luka.sturtewagen@linkit.nl>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2021-04-12 11:18:08 +09:00

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#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""
Date/Time related functions on Koalas Series
"""
from typing import TYPE_CHECKING
import numpy as np # noqa: F401 (SPARK-34943)
import pandas as pd # noqa: F401
import pyspark.sql.functions as F
from pyspark.sql.types import DateType, TimestampType, LongType
if TYPE_CHECKING:
import pyspark.pandas as ps # noqa: F401 (SPARK-34943)
class DatetimeMethods(object):
"""Date/Time methods for Koalas Series"""
def __init__(self, series: "ps.Series"):
if not isinstance(series.spark.data_type, (DateType, TimestampType)):
raise ValueError(
"Cannot call DatetimeMethods on type {}".format(series.spark.data_type)
)
self._data = series
# Properties
@property
def date(self) -> "ps.Series":
"""
Returns a Series of python datetime.date objects (namely, the date
part of Timestamps without timezone information).
"""
# TODO: Hit a weird exception
# syntax error in attribute name: `to_date(`start_date`)` with alias
return self._data.spark.transform(F.to_date)
@property
def time(self) -> "ps.Series":
raise NotImplementedError()
@property
def timetz(self) -> "ps.Series":
raise NotImplementedError()
@property
def year(self) -> "ps.Series":
"""
The year of the datetime.
"""
return self._data.spark.transform(lambda c: F.year(c).cast(LongType()))
@property
def month(self) -> "ps.Series":
"""
The month of the timestamp as January = 1 December = 12.
"""
return self._data.spark.transform(lambda c: F.month(c).cast(LongType()))
@property
def day(self) -> "ps.Series":
"""
The days of the datetime.
"""
return self._data.spark.transform(lambda c: F.dayofmonth(c).cast(LongType()))
@property
def hour(self) -> "ps.Series":
"""
The hours of the datetime.
"""
return self._data.spark.transform(lambda c: F.hour(c).cast(LongType()))
@property
def minute(self) -> "ps.Series":
"""
The minutes of the datetime.
"""
return self._data.spark.transform(lambda c: F.minute(c).cast(LongType()))
@property
def second(self) -> "ps.Series":
"""
The seconds of the datetime.
"""
return self._data.spark.transform(lambda c: F.second(c).cast(LongType()))
@property
def microsecond(self) -> "ps.Series":
"""
The microseconds of the datetime.
"""
def pandas_microsecond(s) -> "ps.Series[np.int64]":
return s.dt.microsecond
return self._data.koalas.transform_batch(pandas_microsecond)
@property
def nanosecond(self) -> "ps.Series":
raise NotImplementedError()
@property
def week(self) -> "ps.Series":
"""
The week ordinal of the year.
"""
return self._data.spark.transform(lambda c: F.weekofyear(c).cast(LongType()))
@property
def weekofyear(self) -> "ps.Series":
return self.week
weekofyear.__doc__ = week.__doc__
@property
def dayofweek(self) -> "ps.Series":
"""
The day of the week with Monday=0, Sunday=6.
Return the day of the week. It is assumed the week starts on
Monday, which is denoted by 0 and ends on Sunday which is denoted
by 6. This method is available on both Series with datetime
values (using the `dt` accessor).
Returns
-------
Series
Containing integers indicating the day number.
See Also
--------
Series.dt.dayofweek : Alias.
Series.dt.weekday : Alias.
Series.dt.day_name : Returns the name of the day of the week.
Examples
--------
>>> s = ps.from_pandas(pd.date_range('2016-12-31', '2017-01-08', freq='D').to_series())
>>> s.dt.dayofweek
2016-12-31 5
2017-01-01 6
2017-01-02 0
2017-01-03 1
2017-01-04 2
2017-01-05 3
2017-01-06 4
2017-01-07 5
2017-01-08 6
dtype: int64
"""
def pandas_dayofweek(s) -> "ps.Series[np.int64]":
return s.dt.dayofweek
return self._data.koalas.transform_batch(pandas_dayofweek)
@property
def weekday(self) -> "ps.Series":
return self.dayofweek
weekday.__doc__ = dayofweek.__doc__
@property
def dayofyear(self) -> "ps.Series":
"""
The ordinal day of the year.
"""
def pandas_dayofyear(s) -> "ps.Series[np.int64]":
return s.dt.dayofyear
return self._data.koalas.transform_batch(pandas_dayofyear)
@property
def quarter(self) -> "ps.Series":
"""
The quarter of the date.
"""
def pandas_quarter(s) -> "ps.Series[np.int64]":
return s.dt.quarter
return self._data.koalas.transform_batch(pandas_quarter)
@property
def is_month_start(self) -> "ps.Series":
"""
Indicates whether the date is the first day of the month.
Returns
-------
Series
For Series, returns a Series with boolean values.
See Also
--------
is_month_end : Return a boolean indicating whether the date
is the last day of the month.
Examples
--------
This method is available on Series with datetime values under
the ``.dt`` accessor.
>>> s = ps.Series(pd.date_range("2018-02-27", periods=3))
>>> s
0 2018-02-27
1 2018-02-28
2 2018-03-01
dtype: datetime64[ns]
>>> s.dt.is_month_start
0 False
1 False
2 True
dtype: bool
"""
def pandas_is_month_start(s) -> "ps.Series[bool]":
return s.dt.is_month_start
return self._data.koalas.transform_batch(pandas_is_month_start)
@property
def is_month_end(self) -> "ps.Series":
"""
Indicates whether the date is the last day of the month.
Returns
-------
Series
For Series, returns a Series with boolean values.
See Also
--------
is_month_start : Return a boolean indicating whether the date
is the first day of the month.
Examples
--------
This method is available on Series with datetime values under
the ``.dt`` accessor.
>>> s = ps.Series(pd.date_range("2018-02-27", periods=3))
>>> s
0 2018-02-27
1 2018-02-28
2 2018-03-01
dtype: datetime64[ns]
>>> s.dt.is_month_end
0 False
1 True
2 False
dtype: bool
"""
def pandas_is_month_end(s) -> "ps.Series[bool]":
return s.dt.is_month_end
return self._data.koalas.transform_batch(pandas_is_month_end)
@property
def is_quarter_start(self) -> "ps.Series":
"""
Indicator for whether the date is the first day of a quarter.
Returns
-------
is_quarter_start : Series
The same type as the original data with boolean values. Series will
have the same name and index.
See Also
--------
quarter : Return the quarter of the date.
is_quarter_end : Similar property for indicating the quarter start.
Examples
--------
This method is available on Series with datetime values under
the ``.dt`` accessor.
>>> df = ps.DataFrame({'dates': pd.date_range("2017-03-30",
... periods=4)})
>>> df
dates
0 2017-03-30
1 2017-03-31
2 2017-04-01
3 2017-04-02
>>> df.dates.dt.quarter
0 1
1 1
2 2
3 2
Name: dates, dtype: int64
>>> df.dates.dt.is_quarter_start
0 False
1 False
2 True
3 False
Name: dates, dtype: bool
"""
def pandas_is_quarter_start(s) -> "ps.Series[bool]":
return s.dt.is_quarter_start
return self._data.koalas.transform_batch(pandas_is_quarter_start)
@property
def is_quarter_end(self) -> "ps.Series":
"""
Indicator for whether the date is the last day of a quarter.
Returns
-------
is_quarter_end : Series
The same type as the original data with boolean values. Series will
have the same name and index.
See Also
--------
quarter : Return the quarter of the date.
is_quarter_start : Similar property indicating the quarter start.
Examples
--------
This method is available on Series with datetime values under
the ``.dt`` accessor.
>>> df = ps.DataFrame({'dates': pd.date_range("2017-03-30",
... periods=4)})
>>> df
dates
0 2017-03-30
1 2017-03-31
2 2017-04-01
3 2017-04-02
>>> df.dates.dt.quarter
0 1
1 1
2 2
3 2
Name: dates, dtype: int64
>>> df.dates.dt.is_quarter_start
0 False
1 False
2 True
3 False
Name: dates, dtype: bool
"""
def pandas_is_quarter_end(s) -> "ps.Series[bool]":
return s.dt.is_quarter_end
return self._data.koalas.transform_batch(pandas_is_quarter_end)
@property
def is_year_start(self) -> "ps.Series":
"""
Indicate whether the date is the first day of a year.
Returns
-------
Series
The same type as the original data with boolean values. Series will
have the same name and index.
See Also
--------
is_year_end : Similar property indicating the last day of the year.
Examples
--------
This method is available on Series with datetime values under
the ``.dt`` accessor.
>>> dates = ps.Series(pd.date_range("2017-12-30", periods=3))
>>> dates
0 2017-12-30
1 2017-12-31
2 2018-01-01
dtype: datetime64[ns]
>>> dates.dt.is_year_start
0 False
1 False
2 True
dtype: bool
"""
def pandas_is_year_start(s) -> "ps.Series[bool]":
return s.dt.is_year_start
return self._data.koalas.transform_batch(pandas_is_year_start)
@property
def is_year_end(self) -> "ps.Series":
"""
Indicate whether the date is the last day of the year.
Returns
-------
Series
The same type as the original data with boolean values. Series will
have the same name and index.
See Also
--------
is_year_start : Similar property indicating the start of the year.
Examples
--------
This method is available on Series with datetime values under
the ``.dt`` accessor.
>>> dates = ps.Series(pd.date_range("2017-12-30", periods=3))
>>> dates
0 2017-12-30
1 2017-12-31
2 2018-01-01
dtype: datetime64[ns]
>>> dates.dt.is_year_end
0 False
1 True
2 False
dtype: bool
"""
def pandas_is_year_end(s) -> "ps.Series[bool]":
return s.dt.is_year_end
return self._data.koalas.transform_batch(pandas_is_year_end)
@property
def is_leap_year(self) -> "ps.Series":
"""
Boolean indicator if the date belongs to a leap year.
A leap year is a year, which has 366 days (instead of 365) including
29th of February as an intercalary day.
Leap years are years which are multiples of four with the exception
of years divisible by 100 but not by 400.
Returns
-------
Series
Booleans indicating if dates belong to a leap year.
Examples
--------
This method is available on Series with datetime values under
the ``.dt`` accessor.
>>> dates_series = ps.Series(pd.date_range("2012-01-01", "2015-01-01", freq="Y"))
>>> dates_series
0 2012-12-31
1 2013-12-31
2 2014-12-31
dtype: datetime64[ns]
>>> dates_series.dt.is_leap_year
0 True
1 False
2 False
dtype: bool
"""
def pandas_is_leap_year(s) -> "ps.Series[bool]":
return s.dt.is_leap_year
return self._data.koalas.transform_batch(pandas_is_leap_year)
@property
def daysinmonth(self) -> "ps.Series":
"""
The number of days in the month.
"""
def pandas_daysinmonth(s) -> "ps.Series[np.int64]":
return s.dt.daysinmonth
return self._data.koalas.transform_batch(pandas_daysinmonth)
@property
def days_in_month(self) -> "ps.Series":
return self.daysinmonth
days_in_month.__doc__ = daysinmonth.__doc__
# Methods
def tz_localize(self, tz) -> "ps.Series":
"""
Localize tz-naive Datetime column to tz-aware Datetime column.
"""
# Neither tz-naive or tz-aware datetime exists in Spark
raise NotImplementedError()
def tz_convert(self, tz) -> "ps.Series":
"""
Convert tz-aware Datetime column from one time zone to another.
"""
# tz-aware datetime doesn't exist in Spark
raise NotImplementedError()
def normalize(self) -> "ps.Series":
"""
Convert times to midnight.
The time component of the date-time is converted to midnight i.e.
00:00:00. This is useful in cases, when the time does not matter.
Length is unaltered. The timezones are unaffected.
This method is available on Series with datetime values under
the ``.dt`` accessor, and directly on Datetime Array.
Returns
-------
Series
The same type as the original data. Series will have the same
name and index.
See Also
--------
floor : Floor the series to the specified freq.
ceil : Ceil the series to the specified freq.
round : Round the series to the specified freq.
Examples
--------
>>> series = ps.Series(pd.Series(pd.date_range('2012-1-1 12:45:31', periods=3, freq='M')))
>>> series.dt.normalize()
0 2012-01-31
1 2012-02-29
2 2012-03-31
dtype: datetime64[ns]
"""
def pandas_normalize(s) -> "ps.Series[np.datetime64]":
return s.dt.normalize()
return self._data.koalas.transform_batch(pandas_normalize)
def strftime(self, date_format) -> "ps.Series":
"""
Convert to a string Series using specified date_format.
Return an series of formatted strings specified by date_format, which
supports the same string format as the python standard library. Details
of the string format can be found in python string format
doc.
Parameters
----------
date_format : str
Date format string (e.g. "%%Y-%%m-%%d").
Returns
-------
Series
Series of formatted strings.
See Also
--------
to_datetime : Convert the given argument to datetime.
normalize : Return series with times to midnight.
round : Round the series to the specified freq.
floor : Floor the series to the specified freq.
Examples
--------
>>> series = ps.Series(pd.date_range(pd.Timestamp("2018-03-10 09:00"),
... periods=3, freq='s'))
>>> series
0 2018-03-10 09:00:00
1 2018-03-10 09:00:01
2 2018-03-10 09:00:02
dtype: datetime64[ns]
>>> series.dt.strftime('%B %d, %Y, %r')
0 March 10, 2018, 09:00:00 AM
1 March 10, 2018, 09:00:01 AM
2 March 10, 2018, 09:00:02 AM
dtype: object
"""
def pandas_strftime(s) -> "ps.Series[str]":
return s.dt.strftime(date_format)
return self._data.koalas.transform_batch(pandas_strftime)
def round(self, freq, *args, **kwargs) -> "ps.Series":
"""
Perform round operation on the data to the specified freq.
Parameters
----------
freq : str or Offset
The frequency level to round the index to. Must be a fixed
frequency like 'S' (second) not 'ME' (month end).
nonexistent : 'shift_forward', 'shift_backward, 'NaT', timedelta, default 'raise'
A nonexistent time does not exist in a particular timezone
where clocks moved forward due to DST.
- 'shift_forward' will shift the nonexistent time forward to the
closest existing time
- 'shift_backward' will shift the nonexistent time backward to the
closest existing time
- 'NaT' will return NaT where there are nonexistent times
- timedelta objects will shift nonexistent times by the timedelta
- 'raise' will raise an NonExistentTimeError if there are
nonexistent times
.. note:: this option only works with pandas 0.24.0+
Returns
-------
Series
a Series with the same index for a Series.
Raises
------
ValueError if the `freq` cannot be converted.
Examples
--------
>>> series = ps.Series(pd.date_range('1/1/2018 11:59:00', periods=3, freq='min'))
>>> series
0 2018-01-01 11:59:00
1 2018-01-01 12:00:00
2 2018-01-01 12:01:00
dtype: datetime64[ns]
>>> series.dt.round("H")
0 2018-01-01 12:00:00
1 2018-01-01 12:00:00
2 2018-01-01 12:00:00
dtype: datetime64[ns]
"""
def pandas_round(s) -> "ps.Series[np.datetime64]":
return s.dt.round(freq, *args, **kwargs)
return self._data.koalas.transform_batch(pandas_round)
def floor(self, freq, *args, **kwargs) -> "ps.Series":
"""
Perform floor operation on the data to the specified freq.
Parameters
----------
freq : str or Offset
The frequency level to floor the index to. Must be a fixed
frequency like 'S' (second) not 'ME' (month end).
nonexistent : 'shift_forward', 'shift_backward, 'NaT', timedelta, default 'raise'
A nonexistent time does not exist in a particular timezone
where clocks moved forward due to DST.
- 'shift_forward' will shift the nonexistent time forward to the
closest existing time
- 'shift_backward' will shift the nonexistent time backward to the
closest existing time
- 'NaT' will return NaT where there are nonexistent times
- timedelta objects will shift nonexistent times by the timedelta
- 'raise' will raise an NonExistentTimeError if there are
nonexistent times
.. note:: this option only works with pandas 0.24.0+
Returns
-------
Series
a Series with the same index for a Series.
Raises
------
ValueError if the `freq` cannot be converted.
Examples
--------
>>> series = ps.Series(pd.date_range('1/1/2018 11:59:00', periods=3, freq='min'))
>>> series
0 2018-01-01 11:59:00
1 2018-01-01 12:00:00
2 2018-01-01 12:01:00
dtype: datetime64[ns]
>>> series.dt.floor("H")
0 2018-01-01 11:00:00
1 2018-01-01 12:00:00
2 2018-01-01 12:00:00
dtype: datetime64[ns]
"""
def pandas_floor(s) -> "ps.Series[np.datetime64]":
return s.dt.floor(freq, *args, **kwargs)
return self._data.koalas.transform_batch(pandas_floor)
def ceil(self, freq, *args, **kwargs) -> "ps.Series":
"""
Perform ceil operation on the data to the specified freq.
Parameters
----------
freq : str or Offset
The frequency level to round the index to. Must be a fixed
frequency like 'S' (second) not 'ME' (month end).
nonexistent : 'shift_forward', 'shift_backward, 'NaT', timedelta, default 'raise'
A nonexistent time does not exist in a particular timezone
where clocks moved forward due to DST.
- 'shift_forward' will shift the nonexistent time forward to the
closest existing time
- 'shift_backward' will shift the nonexistent time backward to the
closest existing time
- 'NaT' will return NaT where there are nonexistent times
- timedelta objects will shift nonexistent times by the timedelta
- 'raise' will raise an NonExistentTimeError if there are
nonexistent times
.. note:: this option only works with pandas 0.24.0+
Returns
-------
Series
a Series with the same index for a Series.
Raises
------
ValueError if the `freq` cannot be converted.
Examples
--------
>>> series = ps.Series(pd.date_range('1/1/2018 11:59:00', periods=3, freq='min'))
>>> series
0 2018-01-01 11:59:00
1 2018-01-01 12:00:00
2 2018-01-01 12:01:00
dtype: datetime64[ns]
>>> series.dt.ceil("H")
0 2018-01-01 12:00:00
1 2018-01-01 12:00:00
2 2018-01-01 13:00:00
dtype: datetime64[ns]
"""
def pandas_ceil(s) -> "ps.Series[np.datetime64]":
return s.dt.ceil(freq, *args, **kwargs)
return self._data.koalas.transform_batch(pandas_ceil)
def month_name(self, locale=None) -> "ps.Series":
"""
Return the month names of the series with specified locale.
Parameters
----------
locale : str, optional
Locale determining the language in which to return the month name.
Default is English locale.
Returns
-------
Series
Series of month names.
Examples
--------
>>> series = ps.Series(pd.date_range(start='2018-01', freq='M', periods=3))
>>> series
0 2018-01-31
1 2018-02-28
2 2018-03-31
dtype: datetime64[ns]
>>> series.dt.month_name()
0 January
1 February
2 March
dtype: object
"""
def pandas_month_name(s) -> "ps.Series[str]":
return s.dt.month_name(locale=locale)
return self._data.koalas.transform_batch(pandas_month_name)
def day_name(self, locale=None) -> "ps.Series":
"""
Return the day names of the series with specified locale.
Parameters
----------
locale : str, optional
Locale determining the language in which to return the day name.
Default is English locale.
Returns
-------
Series
Series of day names.
Examples
--------
>>> series = ps.Series(pd.date_range(start='2018-01-01', freq='D', periods=3))
>>> series
0 2018-01-01
1 2018-01-02
2 2018-01-03
dtype: datetime64[ns]
>>> series.dt.day_name()
0 Monday
1 Tuesday
2 Wednesday
dtype: object
"""
def pandas_day_name(s) -> "ps.Series[str]":
return s.dt.day_name(locale=locale)
return self._data.koalas.transform_batch(pandas_day_name)
def _test():
import os
import doctest
import sys
from pyspark.sql import SparkSession
import pyspark.pandas.datetimes
os.chdir(os.environ["SPARK_HOME"])
globs = pyspark.pandas.datetimes.__dict__.copy()
globs["ps"] = pyspark.pandas
spark = (
SparkSession.builder.master("local[4]")
.appName("pyspark.pandas.datetimes tests")
.getOrCreate()
)
(failure_count, test_count) = doctest.testmod(
pyspark.pandas.datetimes,
globs=globs,
optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE,
)
spark.stop()
if failure_count:
sys.exit(-1)
if __name__ == "__main__":
_test()
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