id int64 0 2.13k | file_path stringlengths 38 93 | function_signature stringlengths 9 64 | body stringlengths 6 16.2k | parameters stringlengths 8 16.8k | examples stringlengths 2 6.12k | content stringlengths 31 16.3k | embeddings listlengths 768 768 |
|---|---|---|---|---|---|---|---|
2,100 | ..\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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0.12231417000... |
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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0.172090... |
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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0.09516459... |
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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0.11857508122... |
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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0.17541487514... |
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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0.05359570309519... |
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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0.20137055218... |
2,109 | ..\pandas\reference\api\pandas.tseries.offsets.YearEnd.month.html | pandas.tseries.offsets.YearEnd.month | YearEnd.month# | No parameters found | [] | pandas.tseries.offsets.YearEnd.month
YearEnd.month# | [
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-0.2453174740076065,
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0.31319504976272583,
0.237261906266212... |
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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0.1369849294... |
2,111 | ..\pandas\reference\api\pandas.tseries.offsets.YearEnd.n.html | pandas.tseries.offsets.YearEnd.n | YearEnd.n# | No parameters found | [] | pandas.tseries.offsets.YearEnd.n
YearEnd.n# | [
-0.20423287153244019,
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0.30036213994026184,
0.22039231657... |
2,112 | ..\pandas\reference\api\pandas.tseries.offsets.Micro.n.html | pandas.tseries.offsets.Micro.n | Micro.n# | No parameters found | [] | pandas.tseries.offsets.Micro.n
Micro.n# | [
-0.23689626157283783,
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0.14327536523342133,
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0.10172326862812042,
0.3829907774... |
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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0.150128617882... |
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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0.337053090333938... |
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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0.38520216941833... |
2,116 | ..\pandas\reference\api\pandas.tseries.offsets.YearEnd.nanos.html | pandas.tseries.offsets.YearEnd.nanos | YearEnd.nanos# | No parameters found | [] | pandas.tseries.offsets.YearEnd.nanos
YearEnd.nanos# | [
-0.11232902854681015,
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0.22618310153484344,
0.329605638... |
2,117 | ..\pandas\reference\api\pandas.tseries.offsets.Micro.normalize.html | pandas.tseries.offsets.Micro.normalize | Micro.normalize# | No parameters found | [] | pandas.tseries.offsets.Micro.normalize
Micro.normalize# | [
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0.15342801809310913,
0.16540925204753876,
0.361971288919448... |
2,118 | ..\pandas\reference\api\pandas.tseries.offsets.YearEnd.normalize.html | pandas.tseries.offsets.YearEnd.normalize | YearEnd.normalize# | No parameters found | [] | pandas.tseries.offsets.YearEnd.normalize
YearEnd.normalize# | [
-0.3795486390590668,
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0.04032624885439873,
0.13737021386623383,
0.34186291694641113,
0.25133806467056274... |
2,119 | ..\pandas\reference\api\pandas.tseries.offsets.YearEnd.rule_code.html | pandas.tseries.offsets.YearEnd.rule_code | YearEnd.rule_code# | No parameters found | [] | pandas.tseries.offsets.YearEnd.rule_code
YearEnd.rule_code# | [
-0.12699052691459656,
-0.30534565448760986,
-0.22695700824260712,
0.03425873443484306,
0.20659908652305603,
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0.366034597158432,
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0.27893680334091187,
0.12624894082546234,
0.12197598069906235,
0.2888693511486... |
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# | [
-0.16399288177490234,
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0.20931975543498993,
0.12365417182445526,
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0.433885931968... |
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>>>... | 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 | [
0.11114408075809479,
-0.35680636763572693,
-0.1306423842906952,
0.17110465466976166,
0.5058011412620544,
0.27801597118377686,
0.3988853991031647,
0.2844623923301697,
0.12488795816898346,
0.2845558822154999,
0.1636364907026291,
-0.07220964133739471,
-0.0146547332406044,
-0.0728624239563942,... |
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. | [
-0.2190794050693512,
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0.19174052774906158,
0.1640525609254837,
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0.24030804634094238,
0.12083294242620468,
0.18281576... |
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>>>... | 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 | [
0.13898353278636932,
-0.3720096945762634,
-0.1283545196056366,
0.16641585528850555,
0.4746212363243103,
0.31992143392562866,
0.3686973750591278,
0.2907209098339081,
0.014791855588555336,
0.2791750133037567,
0.1156415045261383,
-0.0477260947227478,
-0.002908392110839486,
-0.0829220488667488... |
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>>>... | 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 | [
0.21337014436721802,
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0.18312090635299683,
0.4724567234516144,
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0.3360399007797241,
0.35993871092796326,
0.00009693613537820056,
0.23651893436908722,
0.1559544950723648,
-0.058934833854436874,
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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>>>... | 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... ... | 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 duplica... | ['>>> 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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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 ... | ['>>> 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 spec... | 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... | ['>>> 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(r... | 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, ... | [
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