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..\pandas\reference\api\pandas.tseries.offsets.YearEnd.is_quarter_end.html
|
pandas.tseries.offsets.YearEnd.is_quarter_end
|
YearEnd.is_quarter_end(ts)# Return boolean whether a timestamp occurs on the quarter end.
|
No parameters found
|
['>>> ts = pd.Timestamp(2022, 1, 1)\n>>> freq = pd.offsets.Hour(5)\n>>> freq.is_quarter_end(ts)\nFalse']
|
pandas.tseries.offsets.YearEnd.is_quarter_end
YearEnd.is_quarter_end(ts)# Return boolean whether a timestamp occurs on the quarter end.
|
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] |
2,101 |
..\pandas\reference\api\pandas.tseries.offsets.Micro.is_quarter_end.html
|
pandas.tseries.offsets.Micro.is_quarter_end
|
Micro.is_quarter_end(ts)# Return boolean whether a timestamp occurs on the quarter end.
|
No parameters found
|
['>>> ts = pd.Timestamp(2022, 1, 1)\n>>> freq = pd.offsets.Hour(5)\n>>> freq.is_quarter_end(ts)\nFalse']
|
pandas.tseries.offsets.Micro.is_quarter_end
Micro.is_quarter_end(ts)# Return boolean whether a timestamp occurs on the quarter end.
|
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] |
2,102 |
..\pandas\reference\api\pandas.tseries.offsets.YearEnd.is_quarter_start.html
|
pandas.tseries.offsets.YearEnd.is_quarter_start
|
YearEnd.is_quarter_start(ts)# Return boolean whether a timestamp occurs on the quarter start.
|
No parameters found
|
['>>> ts = pd.Timestamp(2022, 1, 1)\n>>> freq = pd.offsets.Hour(5)\n>>> freq.is_quarter_start(ts)\nTrue']
|
pandas.tseries.offsets.YearEnd.is_quarter_start
YearEnd.is_quarter_start(ts)# Return boolean whether a timestamp occurs on the quarter start.
|
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] |
2,103 |
..\pandas\reference\api\pandas.tseries.offsets.Micro.is_quarter_start.html
|
pandas.tseries.offsets.Micro.is_quarter_start
|
Micro.is_quarter_start(ts)# Return boolean whether a timestamp occurs on the quarter start.
|
No parameters found
|
['>>> ts = pd.Timestamp(2022, 1, 1)\n>>> freq = pd.offsets.Hour(5)\n>>> freq.is_quarter_start(ts)\nTrue']
|
pandas.tseries.offsets.Micro.is_quarter_start
Micro.is_quarter_start(ts)# Return boolean whether a timestamp occurs on the quarter start.
|
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] |
2,104 |
..\pandas\reference\api\pandas.tseries.offsets.YearEnd.is_year_end.html
|
pandas.tseries.offsets.YearEnd.is_year_end
|
YearEnd.is_year_end(ts)# Return boolean whether a timestamp occurs on the year end.
|
No parameters found
|
['>>> ts = pd.Timestamp(2022, 1, 1)\n>>> freq = pd.offsets.Hour(5)\n>>> freq.is_year_end(ts)\nFalse']
|
pandas.tseries.offsets.YearEnd.is_year_end
YearEnd.is_year_end(ts)# Return boolean whether a timestamp occurs on the year end.
|
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] |
2,105 |
..\pandas\reference\api\pandas.tseries.offsets.YearEnd.is_year_start.html
|
pandas.tseries.offsets.YearEnd.is_year_start
|
YearEnd.is_year_start(ts)# Return boolean whether a timestamp occurs on the year start.
|
No parameters found
|
['>>> ts = pd.Timestamp(2022, 1, 1)\n>>> freq = pd.offsets.Hour(5)\n>>> freq.is_year_start(ts)\nTrue']
|
pandas.tseries.offsets.YearEnd.is_year_start
YearEnd.is_year_start(ts)# Return boolean whether a timestamp occurs on the year start.
|
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2,106 |
..\pandas\reference\api\pandas.tseries.offsets.Micro.is_year_end.html
|
pandas.tseries.offsets.Micro.is_year_end
|
Micro.is_year_end(ts)# Return boolean whether a timestamp occurs on the year end.
|
No parameters found
|
['>>> ts = pd.Timestamp(2022, 1, 1)\n>>> freq = pd.offsets.Hour(5)\n>>> freq.is_year_end(ts)\nFalse']
|
pandas.tseries.offsets.Micro.is_year_end
Micro.is_year_end(ts)# Return boolean whether a timestamp occurs on the year end.
|
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2,107 |
..\pandas\reference\api\pandas.tseries.offsets.YearEnd.kwds.html
|
pandas.tseries.offsets.YearEnd.kwds
|
YearEnd.kwds# Return a dict of extra parameters for the offset.
|
No parameters found
|
['>>> pd.DateOffset(5).kwds\n{}', ">>> pd.offsets.FY5253Quarter().kwds\n{'weekday': 0,\n 'startingMonth': 1,\n 'qtr_with_extra_week': 1,\n 'variation': 'nearest'}"]
|
pandas.tseries.offsets.YearEnd.kwds
YearEnd.kwds# Return a dict of extra parameters for the offset.
|
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] |
2,108 |
..\pandas\reference\api\pandas.tseries.offsets.Micro.is_year_start.html
|
pandas.tseries.offsets.Micro.is_year_start
|
Micro.is_year_start(ts)# Return boolean whether a timestamp occurs on the year start.
|
No parameters found
|
['>>> ts = pd.Timestamp(2022, 1, 1)\n>>> freq = pd.offsets.Hour(5)\n>>> freq.is_year_start(ts)\nTrue']
|
pandas.tseries.offsets.Micro.is_year_start
Micro.is_year_start(ts)# Return boolean whether a timestamp occurs on the year start.
|
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..\pandas\reference\api\pandas.tseries.offsets.YearEnd.month.html
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pandas.tseries.offsets.YearEnd.month
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YearEnd.month#
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[]
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pandas.tseries.offsets.YearEnd.month
YearEnd.month#
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] |
2,110 |
..\pandas\reference\api\pandas.tseries.offsets.Micro.kwds.html
|
pandas.tseries.offsets.Micro.kwds
|
Micro.kwds# Return a dict of extra parameters for the offset.
|
No parameters found
|
['>>> pd.DateOffset(5).kwds\n{}', ">>> pd.offsets.FY5253Quarter().kwds\n{'weekday': 0,\n 'startingMonth': 1,\n 'qtr_with_extra_week': 1,\n 'variation': 'nearest'}"]
|
pandas.tseries.offsets.Micro.kwds
Micro.kwds# Return a dict of extra parameters for the offset.
|
[
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..\pandas\reference\api\pandas.tseries.offsets.YearEnd.n.html
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pandas.tseries.offsets.YearEnd.n
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YearEnd.n#
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No parameters found
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[]
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pandas.tseries.offsets.YearEnd.n
YearEnd.n#
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..\pandas\reference\api\pandas.tseries.offsets.Micro.n.html
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pandas.tseries.offsets.Micro.n
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Micro.n#
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[]
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pandas.tseries.offsets.Micro.n
Micro.n#
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2,113 |
..\pandas\reference\api\pandas.tseries.offsets.YearEnd.name.html
|
pandas.tseries.offsets.YearEnd.name
|
YearEnd.name# Return a string representing the base frequency.
|
No parameters found
|
[">>> pd.offsets.Hour().name\n'h'", ">>> pd.offsets.Hour(5).name\n'h'"]
|
pandas.tseries.offsets.YearEnd.name
YearEnd.name# Return a string representing the base frequency.
|
[
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] |
2,114 |
..\pandas\reference\api\pandas.tseries.offsets.Micro.name.html
|
pandas.tseries.offsets.Micro.name
|
Micro.name# Return a string representing the base frequency.
|
No parameters found
|
[">>> pd.offsets.Hour().name\n'h'", ">>> pd.offsets.Hour(5).name\n'h'"]
|
pandas.tseries.offsets.Micro.name
Micro.name# Return a string representing the base frequency.
|
[
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] |
2,115 |
..\pandas\reference\api\pandas.tseries.offsets.Micro.nanos.html
|
pandas.tseries.offsets.Micro.nanos
|
Micro.nanos# Return an integer of the total number of nanoseconds.
|
Raises: ValueErrorIf the frequency is non-fixed.
|
['>>> pd.offsets.Hour(5).nanos\n18000000000000']
|
pandas.tseries.offsets.Micro.nanos
Micro.nanos# Return an integer of the total number of nanoseconds.
|
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..\pandas\reference\api\pandas.tseries.offsets.YearEnd.nanos.html
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pandas.tseries.offsets.YearEnd.nanos
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YearEnd.nanos#
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No parameters found
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[]
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pandas.tseries.offsets.YearEnd.nanos
YearEnd.nanos#
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2,117 |
..\pandas\reference\api\pandas.tseries.offsets.Micro.normalize.html
|
pandas.tseries.offsets.Micro.normalize
|
Micro.normalize#
|
No parameters found
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[]
|
pandas.tseries.offsets.Micro.normalize
Micro.normalize#
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2,118 |
..\pandas\reference\api\pandas.tseries.offsets.YearEnd.normalize.html
|
pandas.tseries.offsets.YearEnd.normalize
|
YearEnd.normalize#
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No parameters found
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[]
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pandas.tseries.offsets.YearEnd.normalize
YearEnd.normalize#
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2,119 |
..\pandas\reference\api\pandas.tseries.offsets.YearEnd.rule_code.html
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pandas.tseries.offsets.YearEnd.rule_code
|
YearEnd.rule_code#
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No parameters found
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[]
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pandas.tseries.offsets.YearEnd.rule_code
YearEnd.rule_code#
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2,120 |
..\pandas\reference\api\pandas.tseries.offsets.Micro.rule_code.html
|
pandas.tseries.offsets.Micro.rule_code
|
Micro.rule_code#
|
No parameters found
|
[]
|
pandas.tseries.offsets.Micro.rule_code
Micro.rule_code#
|
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] |
2,121 |
..\pandas\reference\api\pandas.UInt16Dtype.html
|
pandas.UInt16Dtype
|
class pandas.UInt16Dtype[source]# An ExtensionDtype for uint16 integer data. Uses pandas.NA as its missing value, rather than numpy.nan. Attributes None Methods None
|
No parameters found
|
['>>> ser = pd.Series([2, pd.NA], dtype=pd.Int8Dtype())\n>>> ser.dtype\nInt8Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.Int16Dtype())\n>>> ser.dtype\nInt16Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.Int32Dtype())\n>>> ser.dtype\nInt32Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.Int64Dtype())\n>>> ser.dtype\nInt64Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt8Dtype())\n>>> ser.dtype\nUInt8Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt16Dtype())\n>>> ser.dtype\nUInt16Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt32Dtype())\n>>> ser.dtype\nUInt32Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt64Dtype())\n>>> ser.dtype\nUInt64Dtype()']
|
pandas.UInt16Dtype
class pandas.UInt16Dtype[source]# An ExtensionDtype for uint16 integer data. Uses pandas.NA as its missing value, rather than numpy.nan. Attributes None Methods None
|
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] |
2,122 |
..\pandas\reference\api\pandas.tseries.offsets.Milli.copy.html
|
pandas.tseries.offsets.Milli.copy
|
Milli.copy()# Return a copy of the frequency.
|
No parameters found
|
['>>> freq = pd.DateOffset(1)\n>>> freq_copy = freq.copy()\n>>> freq is freq_copy\nFalse']
|
pandas.tseries.offsets.Milli.copy
Milli.copy()# Return a copy of the frequency.
|
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] |
2,123 |
..\pandas\reference\api\pandas.UInt32Dtype.html
|
pandas.UInt32Dtype
|
class pandas.UInt32Dtype[source]# An ExtensionDtype for uint32 integer data. Uses pandas.NA as its missing value, rather than numpy.nan. Attributes None Methods None
|
No parameters found
|
['>>> ser = pd.Series([2, pd.NA], dtype=pd.Int8Dtype())\n>>> ser.dtype\nInt8Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.Int16Dtype())\n>>> ser.dtype\nInt16Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.Int32Dtype())\n>>> ser.dtype\nInt32Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.Int64Dtype())\n>>> ser.dtype\nInt64Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt8Dtype())\n>>> ser.dtype\nUInt8Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt16Dtype())\n>>> ser.dtype\nUInt16Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt32Dtype())\n>>> ser.dtype\nUInt32Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt64Dtype())\n>>> ser.dtype\nUInt64Dtype()']
|
pandas.UInt32Dtype
class pandas.UInt32Dtype[source]# An ExtensionDtype for uint32 integer data. Uses pandas.NA as its missing value, rather than numpy.nan. Attributes None Methods None
|
[
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] |
2,124 |
..\pandas\reference\api\pandas.UInt64Dtype.html
|
pandas.UInt64Dtype
|
class pandas.UInt64Dtype[source]# An ExtensionDtype for uint64 integer data. Uses pandas.NA as its missing value, rather than numpy.nan. Attributes None Methods None
|
No parameters found
|
['>>> ser = pd.Series([2, pd.NA], dtype=pd.Int8Dtype())\n>>> ser.dtype\nInt8Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.Int16Dtype())\n>>> ser.dtype\nInt16Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.Int32Dtype())\n>>> ser.dtype\nInt32Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.Int64Dtype())\n>>> ser.dtype\nInt64Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt8Dtype())\n>>> ser.dtype\nUInt8Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt16Dtype())\n>>> ser.dtype\nUInt16Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt32Dtype())\n>>> ser.dtype\nUInt32Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt64Dtype())\n>>> ser.dtype\nUInt64Dtype()']
|
pandas.UInt64Dtype
class pandas.UInt64Dtype[source]# An ExtensionDtype for uint64 integer data. Uses pandas.NA as its missing value, rather than numpy.nan. Attributes None Methods None
|
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] |
2,125 |
..\pandas\reference\api\pandas.UInt8Dtype.html
|
pandas.UInt8Dtype
|
class pandas.UInt8Dtype[source]# An ExtensionDtype for uint8 integer data. Uses pandas.NA as its missing value, rather than numpy.nan. Attributes None Methods None
|
No parameters found
|
['>>> ser = pd.Series([2, pd.NA], dtype=pd.Int8Dtype())\n>>> ser.dtype\nInt8Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.Int16Dtype())\n>>> ser.dtype\nInt16Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.Int32Dtype())\n>>> ser.dtype\nInt32Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.Int64Dtype())\n>>> ser.dtype\nInt64Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt8Dtype())\n>>> ser.dtype\nUInt8Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt16Dtype())\n>>> ser.dtype\nUInt16Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt32Dtype())\n>>> ser.dtype\nUInt32Dtype()', '>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt64Dtype())\n>>> ser.dtype\nUInt64Dtype()']
|
pandas.UInt8Dtype
class pandas.UInt8Dtype[source]# An ExtensionDtype for uint8 integer data. Uses pandas.NA as its missing value, rather than numpy.nan. Attributes None Methods None
|
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] |
2,126 |
..\pandas\reference\api\pandas.unique.html
|
pandas.unique
|
pandas.unique(values)[source]# Return unique values based on a hash table. Uniques are returned in order of appearance. This does NOT sort. Significantly faster than numpy.unique for long enough sequences. Includes NA values.
|
Parameters: values1d array-like Returns: numpy.ndarray or ExtensionArrayThe return can be: Index : when the input is an Index Categorical : when the input is a Categorical dtype ndarray : when the input is a Series/ndarray Return numpy.ndarray or ExtensionArray.
|
['>>> pd.unique(pd.Series([2, 1, 3, 3]))\narray([2, 1, 3])', '>>> pd.unique(pd.Series([2] + [1] * 5))\narray([2, 1])', '>>> pd.unique(pd.Series([pd.Timestamp("20160101"), pd.Timestamp("20160101")]))\narray([\'2016-01-01T00:00:00.000000000\'], dtype=\'datetime64[ns]\')', '>>> pd.unique(\n... pd.Series(\n... [\n... pd.Timestamp("20160101", tz="US/Eastern"),\n... pd.Timestamp("20160101", tz="US/Eastern"),\n... ]\n... )\n... )\n<DatetimeArray>\n[\'2016-01-01 00:00:00-05:00\']\nLength: 1, dtype: datetime64[ns, US/Eastern]', '>>> pd.unique(\n... pd.Index(\n... [\n... pd.Timestamp("20160101", tz="US/Eastern"),\n... pd.Timestamp("20160101", tz="US/Eastern"),\n... ]\n... )\n... )\nDatetimeIndex([\'2016-01-01 00:00:00-05:00\'],\n dtype=\'datetime64[ns, US/Eastern]\',\n freq=None)', '>>> pd.unique(np.array(list("baabc"), dtype="O"))\narray([\'b\', \'a\', \'c\'], dtype=object)', '>>> pd.unique(pd.Series(pd.Categorical(list("baabc"))))\n[\'b\', \'a\', \'c\']\nCategories (3, object): [\'a\', \'b\', \'c\']', '>>> pd.unique(pd.Series(pd.Categorical(list("baabc"), categories=list("abc"))))\n[\'b\', \'a\', \'c\']\nCategories (3, object): [\'a\', \'b\', \'c\']', '>>> pd.unique(\n... pd.Series(\n... pd.Categorical(list("baabc"), categories=list("abc"), ordered=True)\n... )\n... )\n[\'b\', \'a\', \'c\']\nCategories (3, object): [\'a\' < \'b\' < \'c\']', '>>> pd.unique(pd.Series([("a", "b"), ("b", "a"), ("a", "c"), ("b", "a")]).values)\narray([(\'a\', \'b\'), (\'b\', \'a\'), (\'a\', \'c\')], dtype=object)']
|
pandas.unique
pandas.unique(values)[source]# Return unique values based on a hash table. Uniques are returned in order of appearance. This does NOT sort. Significantly faster than numpy.unique for long enough sequences. Includes NA values.
|
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] |
2,127 |
..\pandas\reference\api\pandas.util.hash_array.html
|
pandas.util.hash_array
|
pandas.util.hash_array(vals, encoding='utf8', hash_key='0123456789123456', categorize=True)[source]# Given a 1d array, return an array of deterministic integers.
|
Parameters: valsndarray or ExtensionArray encodingstr, default ‘utf8’Encoding for data & key when strings. hash_keystr, default _default_hash_keyHash_key for string key to encode. categorizebool, default TrueWhether to first categorize object arrays before hashing. This is more efficient when the array contains duplicate values. Returns: ndarray[np.uint64, ndim=1]Hashed values, same length as the vals.
|
['>>> pd.util.hash_array(np.array([1, 2, 3]))\narray([ 6238072747940578789, 15839785061582574730, 2185194620014831856],\n dtype=uint64)']
|
pandas.util.hash_array
pandas.util.hash_array(vals, encoding='utf8', hash_key='0123456789123456', categorize=True)[source]# Given a 1d array, return an array of deterministic integers.
|
[
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0.20212434232234955,
0.3340880274772644,
0.2800041437149048,
0.0751248449087143,
0.20909348130226135,
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0.21805915236473083,
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] |
2,128 |
..\pandas\reference\api\pandas.util.hash_pandas_object.html
|
pandas.util.hash_pandas_object
|
pandas.util.hash_pandas_object(obj, index=True, encoding='utf8', hash_key='0123456789123456', categorize=True)[source]# Return a data hash of the Index/Series/DataFrame.
|
Parameters: objIndex, Series, or DataFrame indexbool, default TrueInclude the index in the hash (if Series/DataFrame). encodingstr, default ‘utf8’Encoding for data & key when strings. hash_keystr, default _default_hash_keyHash_key for string key to encode. categorizebool, default TrueWhether to first categorize object arrays before hashing. This is more efficient when the array contains duplicate values. Returns: Series of uint64, same length as the object
|
['>>> pd.util.hash_pandas_object(pd.Series([1, 2, 3]))\n0 14639053686158035780\n1 3869563279212530728\n2 393322362522515241\ndtype: uint64']
|
pandas.util.hash_pandas_object
pandas.util.hash_pandas_object(obj, index=True, encoding='utf8', hash_key='0123456789123456', categorize=True)[source]# Return a data hash of the Index/Series/DataFrame.
|
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] |
2,129 |
..\pandas\reference\api\pandas.wide_to_long.html
|
pandas.wide_to_long
|
pandas.wide_to_long(df, stubnames, i, j, sep='', suffix='\\d+')[source]# Unpivot a DataFrame from wide to long format. Less flexible but more user-friendly than melt. With stubnames [‘A’, ‘B’], this function expects to find one or more group of columns with format A-suffix1, A-suffix2,…, B-suffix1, B-suffix2,… You specify what you want to call this suffix in the resulting long format with j (for example j=’year’) Each row of these wide variables are assumed to be uniquely identified by i (can be a single column name or a list of column names) All remaining variables in the data frame are left intact. Notes All extra variables are left untouched. This simply uses pandas.melt under the hood, but is hard-coded to “do the right thing” in a typical case.
|
Parameters: dfDataFrameThe wide-format DataFrame. stubnamesstr or list-likeThe stub name(s). The wide format variables are assumed to start with the stub names. istr or list-likeColumn(s) to use as id variable(s). jstrThe name of the sub-observation variable. What you wish to name your suffix in the long format. sepstr, default “”A character indicating the separation of the variable names in the wide format, to be stripped from the names in the long format. For example, if your column names are A-suffix1, A-suffix2, you can strip the hyphen by specifying sep=’-’. suffixstr, default ‘\d+’A regular expression capturing the wanted suffixes. ‘\d+’ captures numeric suffixes. Suffixes with no numbers could be specified with the negated character class ‘\D+’. You can also further disambiguate suffixes, for example, if your wide variables are of the form A-one, B-two,.., and you have an unrelated column A-rating, you can ignore the last one by specifying suffix=’(!?one|two)’. When all suffixes are numeric, they are cast to int64/float64. Returns: DataFrameA DataFrame that contains each stub name as a variable, with new index (i, j).
|
['>>> np.random.seed(123)\n>>> df = pd.DataFrame({"A1970" : {0 : "a", 1 : "b", 2 : "c"},\n... "A1980" : {0 : "d", 1 : "e", 2 : "f"},\n... "B1970" : {0 : 2.5, 1 : 1.2, 2 : .7},\n... "B1980" : {0 : 3.2, 1 : 1.3, 2 : .1},\n... "X" : dict(zip(range(3), np.random.randn(3)))\n... })\n>>> df["id"] = df.index\n>>> df\n A1970 A1980 B1970 B1980 X id\n0 a d 2.5 3.2 -1.085631 0\n1 b e 1.2 1.3 0.997345 1\n2 c f 0.7 0.1 0.282978 2\n>>> pd.wide_to_long(df, ["A", "B"], i="id", j="year")\n... \n X A B\nid year\n0 1970 -1.085631 a 2.5\n1 1970 0.997345 b 1.2\n2 1970 0.282978 c 0.7\n0 1980 -1.085631 d 3.2\n1 1980 0.997345 e 1.3\n2 1980 0.282978 f 0.1', ">>> df = pd.DataFrame({\n... 'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3],\n... 'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3],\n... 'ht1': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],\n... 'ht2': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9]\n... })\n>>> df\n famid birth ht1 ht2\n0 1 1 2.8 3.4\n1 1 2 2.9 3.8\n2 1 3 2.2 2.9\n3 2 1 2.0 3.2\n4 2 2 1.8 2.8\n5 2 3 1.9 2.4\n6 3 1 2.2 3.3\n7 3 2 2.3 3.4\n8 3 3 2.1 2.9\n>>> l = pd.wide_to_long(df, stubnames='ht', i=['famid', 'birth'], j='age')\n>>> l\n... \n ht\nfamid birth age\n1 1 1 2.8\n 2 3.4\n 2 1 2.9\n 2 3.8\n 3 1 2.2\n 2 2.9\n2 1 1 2.0\n 2 3.2\n 2 1 1.8\n 2 2.8\n 3 1 1.9\n 2 2.4\n3 1 1 2.2\n 2 3.3\n 2 1 2.3\n 2 3.4\n 3 1 2.1\n 2 2.9", ">>> w = l.unstack()\n>>> w.columns = w.columns.map('{0[0]}{0[1]}'.format)\n>>> w.reset_index()\n famid birth ht1 ht2\n0 1 1 2.8 3.4\n1 1 2 2.9 3.8\n2 1 3 2.2 2.9\n3 2 1 2.0 3.2\n4 2 2 1.8 2.8\n5 2 3 1.9 2.4\n6 3 1 2.2 3.3\n7 3 2 2.3 3.4\n8 3 3 2.1 2.9", ">>> np.random.seed(0)\n>>> df = pd.DataFrame({'A(weekly)-2010': np.random.rand(3),\n... 'A(weekly)-2011': np.random.rand(3),\n... 'B(weekly)-2010': np.random.rand(3),\n... 'B(weekly)-2011': np.random.rand(3),\n... 'X' : np.random.randint(3, size=3)})\n>>> df['id'] = df.index\n>>> df \n A(weekly)-2010 A(weekly)-2011 B(weekly)-2010 B(weekly)-2011 X id\n0 0.548814 0.544883 0.437587 0.383442 0 0\n1 0.715189 0.423655 0.891773 0.791725 1 1\n2 0.602763 0.645894 0.963663 0.528895 1 2", ">>> pd.wide_to_long(df, ['A(weekly)', 'B(weekly)'], i='id',\n... j='year', sep='-')\n... \n X A(weekly) B(weekly)\nid year\n0 2010 0 0.548814 0.437587\n1 2010 1 0.715189 0.891773\n2 2010 1 0.602763 0.963663\n0 2011 0 0.544883 0.383442\n1 2011 1 0.423655 0.791725\n2 2011 1 0.645894 0.528895", ">>> stubnames = sorted(\n... set([match[0] for match in df.columns.str.findall(\n... r'[A-B]\\(.*\\)').values if match != []])\n... )\n>>> list(stubnames)\n['A(weekly)', 'B(weekly)']", ">>> df = pd.DataFrame({\n... 'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3],\n... 'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3],\n... 'ht_one': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],\n... 'ht_two': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9]\n... })\n>>> df\n famid birth ht_one ht_two\n0 1 1 2.8 3.4\n1 1 2 2.9 3.8\n2 1 3 2.2 2.9\n3 2 1 2.0 3.2\n4 2 2 1.8 2.8\n5 2 3 1.9 2.4\n6 3 1 2.2 3.3\n7 3 2 2.3 3.4\n8 3 3 2.1 2.9", ">>> l = pd.wide_to_long(df, stubnames='ht', i=['famid', 'birth'], j='age',\n... sep='_', suffix=r'\\w+')\n>>> l\n... \n ht\nfamid birth age\n1 1 one 2.8\n two 3.4\n 2 one 2.9\n two 3.8\n 3 one 2.2\n two 2.9\n2 1 one 2.0\n two 3.2\n 2 one 1.8\n two 2.8\n 3 one 1.9\n two 2.4\n3 1 one 2.2\n two 3.3\n 2 one 2.3\n two 3.4\n 3 one 2.1\n two 2.9"]
|
pandas.wide_to_long
pandas.wide_to_long(df, stubnames, i, j, sep='', suffix='\\d+')[source]# Unpivot a DataFrame from wide to long format. Less flexible but more user-friendly than melt. With stubnames [‘A’, ‘B’], this function expects to find one or more group of columns with format A-suffix1, A-suffix2,…, B-suffix1, B-suffix2,… You specify what you want to call this suffix in the resulting long format with j (for example j=’year’) Each row of these wide variables are assumed to be uniquely identified by i (can be a single column name or a list of column names) All remaining variables in the data frame are left intact. Notes All extra variables are left untouched. This simply uses pandas.melt under the hood, but is hard-coded to “do the right thing” in a typical case.
|
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