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..\pandas\reference\api\pandas.DataFrame.set_axis.html
|
pandas.DataFrame.set_axis
|
DataFrame.set_axis(labels, *, axis=0, copy=None)[source]# Assign desired index to given axis. Indexes for column or row labels can be changed by assigning a list-like or Index.
|
Parameters: labelslist-like, IndexThe values for the new index. axis{0 or ‘index’, 1 or ‘columns’}, default 0The axis to update. The value 0 identifies the rows. For Series this parameter is unused and defaults to 0. copybool, default TrueWhether to make a copy of the underlying data. Note The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas. You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True Returns: DataFrameAn object of type DataFrame.
|
['>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})', ">>> df.set_axis(['a', 'b', 'c'], axis='index')\n A B\na 1 4\nb 2 5\nc 3 6", ">>> df.set_axis(['I', 'II'], axis='columns')\n I II\n0 1 4\n1 2 5\n2 3 6"]
|
pandas.DataFrame.set_axis
DataFrame.set_axis(labels, *, axis=0, copy=None)[source]# Assign desired index to given axis. Indexes for column or row labels can be changed by assigning a list-like or Index.
|
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pandas.tseries.offsets.BYearBegin.n
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BYearBegin.n#
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] |
102 |
..\pandas\reference\api\pandas.core.groupby.SeriesGroupBy.rolling.html
|
pandas.core.groupby.SeriesGroupBy.rolling
|
SeriesGroupBy.rolling(*args, **kwargs)[source]# Return a rolling grouper, providing rolling functionality per group.
|
Parameters: windowint, timedelta, str, offset, or BaseIndexer subclassSize of the moving window. If an integer, the fixed number of observations used for each window. If a timedelta, str, or offset, the time period of each window. Each window will be a variable sized based on the observations included in the time-period. This is only valid for datetimelike indexes. To learn more about the offsets & frequency strings, please see this link. If a BaseIndexer subclass, the window boundaries based on the defined get_window_bounds method. Additional rolling keyword arguments, namely min_periods, center, closed and step will be passed to get_window_bounds. min_periodsint, default NoneMinimum number of observations in window required to have a value; otherwise, result is np.nan. For a window that is specified by an offset, min_periods will default to 1. For a window that is specified by an integer, min_periods will default to the size of the window. centerbool, default FalseIf False, set the window labels as the right edge of the window index. If True, set the window labels as the center of the window index. win_typestr, default NoneIf None, all points are evenly weighted. If a string, it must be a valid scipy.signal window function. Certain Scipy window types require additional parameters to be passed in the aggregation function. The additional parameters must match the keywords specified in the Scipy window type method signature. onstr, optionalFor a DataFrame, a column label or Index level on which to calculate the rolling window, rather than the DataFrame’s index. Provided integer column is ignored and excluded from result since an integer index is not used to calculate the rolling window. axisint or str, default 0If 0 or 'index', roll across the rows. If 1 or 'columns', roll across the columns. For Series this parameter is unused and defaults to 0. closedstr, default NoneIf 'right', the first point in the window is excluded from calculations. If 'left', the last point in the window is excluded from calculations. If 'both', no points in the window are excluded from calculations. If 'neither', the first and last points in the window are excluded from calculations. Default None ('right'). methodstr {‘single’, ‘table’}, default ‘single’Execute the rolling operation per single column or row ('single') or over the entire object ('table'). This argument is only implemented when specifying engine='numba' in the method call. Returns: pandas.api.typing.RollingGroupbyReturn a new grouper with our rolling appended.
|
[">>> df = pd.DataFrame({'A': [1, 1, 2, 2],\n... 'B': [1, 2, 3, 4],\n... 'C': [0.362, 0.227, 1.267, -0.562]})\n>>> df\n A B C\n0 1 1 0.362\n1 1 2 0.227\n2 2 3 1.267\n3 2 4 -0.562", ">>> df.groupby('A').rolling(2).sum()\n B C\nA\n1 0 NaN NaN\n 1 3.0 0.589\n2 2 NaN NaN\n 3 7.0 0.705", ">>> df.groupby('A').rolling(2, min_periods=1).sum()\n B C\nA\n1 0 1.0 0.362\n 1 3.0 0.589\n2 2 3.0 1.267\n 3 7.0 0.705", ">>> df.groupby('A').rolling(2, on='B').sum()\n B C\nA\n1 0 1 NaN\n 1 2 0.589\n2 2 3 NaN\n 3 4 0.705"]
|
pandas.core.groupby.SeriesGroupBy.rolling
SeriesGroupBy.rolling(*args, **kwargs)[source]# Return a rolling grouper, providing rolling functionality per group.
|
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103 |
..\pandas\reference\api\pandas.DataFrame.set_flags.html
|
pandas.DataFrame.set_flags
|
DataFrame.set_flags(*, copy=False, allows_duplicate_labels=None)[source]# Return a new object with updated flags. Notes This method returns a new object that’s a view on the same data as the input. Mutating the input or the output values will be reflected in the other. This method is intended to be used in method chains. “Flags” differ from “metadata”. Flags reflect properties of the pandas object (the Series or DataFrame). Metadata refer to properties of the dataset, and should be stored in DataFrame.attrs.
|
Parameters: copybool, default FalseSpecify if a copy of the object should be made. Note The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas. You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True allows_duplicate_labelsbool, optionalWhether the returned object allows duplicate labels. Returns: Series or DataFrameThe same type as the caller.
|
['>>> df = pd.DataFrame({"A": [1, 2]})\n>>> df.flags.allows_duplicate_labels\nTrue\n>>> df2 = df.set_flags(allows_duplicate_labels=False)\n>>> df2.flags.allows_duplicate_labels\nFalse']
|
pandas.DataFrame.set_flags
DataFrame.set_flags(*, copy=False, allows_duplicate_labels=None)[source]# Return a new object with updated flags. Notes This method returns a new object that’s a view on the same data as the input. Mutating the input or the output values will be reflected in the other. This method is intended to be used in method chains. “Flags” differ from “metadata”. Flags reflect properties of the pandas object (the Series or DataFrame). Metadata refer to properties of the dataset, and should be stored in DataFrame.attrs.
|
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] |
104 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray.nbytes.html
|
pandas.api.extensions.ExtensionArray.nbytes
|
property ExtensionArray.nbytes[source]# The number of bytes needed to store this object in memory.
|
No parameters found
|
['>>> pd.array([1, 2, 3]).nbytes\n27']
|
pandas.api.extensions.ExtensionArray.nbytes
property ExtensionArray.nbytes[source]# The number of bytes needed to store this object in memory.
|
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] |
105 |
..\pandas\reference\api\pandas.Series.describe.html
|
pandas.Series.describe
|
Series.describe(percentiles=None, include=None, exclude=None)[source]# Generate descriptive statistics. Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values. Analyzes both numeric and object series, as well as DataFrame column sets of mixed data types. The output will vary depending on what is provided. Refer to the notes below for more detail. Notes For numeric data, the result’s index will include count, mean, std, min, max as well as lower, 50 and upper percentiles. By default the lower percentile is 25 and the upper percentile is 75. The 50 percentile is the same as the median. For object data (e.g. strings or timestamps), the result’s index will include count, unique, top, and freq. The top is the most common value. The freq is the most common value’s frequency. Timestamps also include the first and last items. If multiple object values have the highest count, then the count and top results will be arbitrarily chosen from among those with the highest count. For mixed data types provided via a DataFrame, the default is to return only an analysis of numeric columns. If the dataframe consists only of object and categorical data without any numeric columns, the default is to return an analysis of both the object and categorical columns. If include='all' is provided as an option, the result will include a union of attributes of each type. The include and exclude parameters can be used to limit which columns in a DataFrame are analyzed for the output. The parameters are ignored when analyzing a Series.
|
Parameters: percentileslist-like of numbers, optionalThe percentiles to include in the output. All should fall between 0 and 1. The default is [.25, .5, .75], which returns the 25th, 50th, and 75th percentiles. include‘all’, list-like of dtypes or None (default), optionalA white list of data types to include in the result. Ignored for Series. Here are the options: ‘all’ : All columns of the input will be included in the output. A list-like of dtypes : Limits the results to the provided data types. To limit the result to numeric types submit numpy.number. To limit it instead to object columns submit the numpy.object data type. Strings can also be used in the style of select_dtypes (e.g. df.describe(include=['O'])). To select pandas categorical columns, use 'category' None (default) : The result will include all numeric columns. excludelist-like of dtypes or None (default), optional,A black list of data types to omit from the result. Ignored for Series. Here are the options: A list-like of dtypes : Excludes the provided data types from the result. To exclude numeric types submit numpy.number. To exclude object columns submit the data type numpy.object. Strings can also be used in the style of select_dtypes (e.g. df.describe(exclude=['O'])). To exclude pandas categorical columns, use 'category' None (default) : The result will exclude nothing. Returns: Series or DataFrameSummary statistics of the Series or Dataframe provided.
|
['>>> s = pd.Series([1, 2, 3])\n>>> s.describe()\ncount 3.0\nmean 2.0\nstd 1.0\nmin 1.0\n25% 1.5\n50% 2.0\n75% 2.5\nmax 3.0\ndtype: float64', ">>> s = pd.Series(['a', 'a', 'b', 'c'])\n>>> s.describe()\ncount 4\nunique 3\ntop a\nfreq 2\ndtype: object", '>>> s = pd.Series([\n... np.datetime64("2000-01-01"),\n... np.datetime64("2010-01-01"),\n... np.datetime64("2010-01-01")\n... ])\n>>> s.describe()\ncount 3\nmean 2006-09-01 08:00:00\nmin 2000-01-01 00:00:00\n25% 2004-12-31 12:00:00\n50% 2010-01-01 00:00:00\n75% 2010-01-01 00:00:00\nmax 2010-01-01 00:00:00\ndtype: object', ">>> df = pd.DataFrame({'categorical': pd.Categorical(['d', 'e', 'f']),\n... 'numeric': [1, 2, 3],\n... 'object': ['a', 'b', 'c']\n... })\n>>> df.describe()\n numeric\ncount 3.0\nmean 2.0\nstd 1.0\nmin 1.0\n25% 1.5\n50% 2.0\n75% 2.5\nmax 3.0", ">>> df.describe(include='all') \n categorical numeric object\ncount 3 3.0 3\nunique 3 NaN 3\ntop f NaN a\nfreq 1 NaN 1\nmean NaN 2.0 NaN\nstd NaN 1.0 NaN\nmin NaN 1.0 NaN\n25% NaN 1.5 NaN\n50% NaN 2.0 NaN\n75% NaN 2.5 NaN\nmax NaN 3.0 NaN", '>>> df.numeric.describe()\ncount 3.0\nmean 2.0\nstd 1.0\nmin 1.0\n25% 1.5\n50% 2.0\n75% 2.5\nmax 3.0\nName: numeric, dtype: float64', '>>> df.describe(include=[np.number])\n numeric\ncount 3.0\nmean 2.0\nstd 1.0\nmin 1.0\n25% 1.5\n50% 2.0\n75% 2.5\nmax 3.0', '>>> df.describe(include=[object]) \n object\ncount 3\nunique 3\ntop a\nfreq 1', ">>> df.describe(include=['category'])\n categorical\ncount 3\nunique 3\ntop d\nfreq 1", '>>> df.describe(exclude=[np.number]) \n categorical object\ncount 3 3\nunique 3 3\ntop f a\nfreq 1 1', '>>> df.describe(exclude=[object]) \n categorical numeric\ncount 3 3.0\nunique 3 NaN\ntop f NaN\nfreq 1 NaN\nmean NaN 2.0\nstd NaN 1.0\nmin NaN 1.0\n25% NaN 1.5\n50% NaN 2.0\n75% NaN 2.5\nmax NaN 3.0']
|
pandas.Series.describe
Series.describe(percentiles=None, include=None, exclude=None)[source]# Generate descriptive statistics. Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values. Analyzes both numeric and object series, as well as DataFrame column sets of mixed data types. The output will vary depending on what is provided. Refer to the notes below for more detail. Notes For numeric data, the result’s index will include count, mean, std, min, max as well as lower, 50 and upper percentiles. By default the lower percentile is 25 and the upper percentile is 75. The 50 percentile is the same as the median. For object data (e.g. strings or timestamps), the result’s index will include count, unique, top, and freq. The top is the most common value. The freq is the most common value’s frequency. Timestamps also include the first and last items. If multiple object values have the highest count, then the count and top results will be arbitrarily chosen from among those with the highest count. For mixed data types provided via a DataFrame, the default is to return only an analysis of numeric columns. If the dataframe consists only of object and categorical data without any numeric columns, the default is to return an analysis of both the object and categorical columns. If include='all' is provided as an option, the result will include a union of attributes of each type. The include and exclude parameters can be used to limit which columns in a DataFrame are analyzed for the output. The parameters are ignored when analyzing a Series.
|
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] |
106 |
..\pandas\reference\api\pandas.Series.to_string.html
|
pandas.Series.to_string
|
Series.to_string(buf=None, na_rep='NaN', float_format=None, header=True, index=True, length=False, dtype=False, name=False, max_rows=None, min_rows=None)[source]# Render a string representation of the Series.
|
Parameters: bufStringIO-like, optionalBuffer to write to. na_repstr, optionalString representation of NaN to use, default ‘NaN’. float_formatone-parameter function, optionalFormatter function to apply to columns’ elements if they are floats, default None. headerbool, default TrueAdd the Series header (index name). indexbool, optionalAdd index (row) labels, default True. lengthbool, default FalseAdd the Series length. dtypebool, default FalseAdd the Series dtype. namebool, default FalseAdd the Series name if not None. max_rowsint, optionalMaximum number of rows to show before truncating. If None, show all. min_rowsint, optionalThe number of rows to display in a truncated repr (when number of rows is above max_rows). Returns: str or NoneString representation of Series if buf=None, otherwise None.
|
[">>> ser = pd.Series([1, 2, 3]).to_string()\n>>> ser\n'0 1\\n1 2\\n2 3'"]
|
pandas.Series.to_string
Series.to_string(buf=None, na_rep='NaN', float_format=None, header=True, index=True, length=False, dtype=False, name=False, max_rows=None, min_rows=None)[source]# Render a string representation of the Series.
|
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] |
107 |
..\pandas\reference\api\pandas.Int8Dtype.html
|
pandas.Int8Dtype
|
class pandas.Int8Dtype[source]# An ExtensionDtype for int8 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.Int8Dtype
class pandas.Int8Dtype[source]# An ExtensionDtype for int8 integer data. Uses pandas.NA as its missing value, rather than numpy.nan. Attributes None Methods None
|
[
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] |
108 |
..\pandas\reference\api\pandas.tseries.offsets.Milli.is_year_start.html
|
pandas.tseries.offsets.Milli.is_year_start
|
Milli.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.Milli.is_year_start
Milli.is_year_start(ts)# Return boolean whether a timestamp occurs on the year start.
|
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109 |
..\pandas\reference\api\pandas.tseries.offsets.BYearBegin.name.html
|
pandas.tseries.offsets.BYearBegin.name
|
BYearBegin.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.BYearBegin.name
BYearBegin.name# Return a string representing the base frequency.
|
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] |
110 |
..\pandas\reference\api\pandas.core.groupby.SeriesGroupBy.sample.html
|
pandas.core.groupby.SeriesGroupBy.sample
|
SeriesGroupBy.sample(n=None, frac=None, replace=False, weights=None, random_state=None)[source]# Return a random sample of items from each group. You can use random_state for reproducibility.
|
Parameters: nint, optionalNumber of items to return for each group. Cannot be used with frac and must be no larger than the smallest group unless replace is True. Default is one if frac is None. fracfloat, optionalFraction of items to return. Cannot be used with n. replacebool, default FalseAllow or disallow sampling of the same row more than once. weightslist-like, optionalDefault None results in equal probability weighting. If passed a list-like then values must have the same length as the underlying DataFrame or Series object and will be used as sampling probabilities after normalization within each group. Values must be non-negative with at least one positive element within each group. random_stateint, array-like, BitGenerator, np.random.RandomState, np.random.Generator, optionalIf int, array-like, or BitGenerator, seed for random number generator. If np.random.RandomState or np.random.Generator, use as given. Changed in version 1.4.0: np.random.Generator objects now accepted Returns: Series or DataFrameA new object of same type as caller containing items randomly sampled within each group from the caller object.
|
['>>> df = pd.DataFrame(\n... {"a": ["red"] * 2 + ["blue"] * 2 + ["black"] * 2, "b": range(6)}\n... )\n>>> df\n a b\n0 red 0\n1 red 1\n2 blue 2\n3 blue 3\n4 black 4\n5 black 5', '>>> df.groupby("a").sample(n=1, random_state=1)\n a b\n4 black 4\n2 blue 2\n1 red 1', '>>> df.groupby("a")["b"].sample(frac=0.5, random_state=2)\n5 5\n2 2\n0 0\nName: b, dtype: int64', '>>> df.groupby("a").sample(\n... n=1,\n... weights=[1, 1, 1, 0, 0, 1],\n... random_state=1,\n... )\n a b\n5 black 5\n2 blue 2\n0 red 0']
|
pandas.core.groupby.SeriesGroupBy.sample
SeriesGroupBy.sample(n=None, frac=None, replace=False, weights=None, random_state=None)[source]# Return a random sample of items from each group. You can use random_state for reproducibility.
|
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] |
111 |
..\pandas\reference\api\pandas.DataFrame.set_index.html
|
pandas.DataFrame.set_index
|
DataFrame.set_index(keys, *, drop=True, append=False, inplace=False, verify_integrity=False)[source]# Set the DataFrame index using existing columns. Set the DataFrame index (row labels) using one or more existing columns or arrays (of the correct length). The index can replace the existing index or expand on it.
|
Parameters: keyslabel or array-like or list of labels/arraysThis parameter can be either a single column key, a single array of the same length as the calling DataFrame, or a list containing an arbitrary combination of column keys and arrays. Here, “array” encompasses Series, Index, np.ndarray, and instances of Iterator. dropbool, default TrueDelete columns to be used as the new index. appendbool, default FalseWhether to append columns to existing index. inplacebool, default FalseWhether to modify the DataFrame rather than creating a new one. verify_integritybool, default FalseCheck the new index for duplicates. Otherwise defer the check until necessary. Setting to False will improve the performance of this method. Returns: DataFrame or NoneChanged row labels or None if inplace=True.
|
[">>> df = pd.DataFrame({'month': [1, 4, 7, 10],\n... 'year': [2012, 2014, 2013, 2014],\n... 'sale': [55, 40, 84, 31]})\n>>> df\n month year sale\n0 1 2012 55\n1 4 2014 40\n2 7 2013 84\n3 10 2014 31", ">>> df.set_index('month')\n year sale\nmonth\n1 2012 55\n4 2014 40\n7 2013 84\n10 2014 31", ">>> df.set_index(['year', 'month'])\n sale\nyear month\n2012 1 55\n2014 4 40\n2013 7 84\n2014 10 31", ">>> df.set_index([pd.Index([1, 2, 3, 4]), 'year'])\n month sale\n year\n1 2012 1 55\n2 2014 4 40\n3 2013 7 84\n4 2014 10 31", '>>> s = pd.Series([1, 2, 3, 4])\n>>> df.set_index([s, s**2])\n month year sale\n1 1 1 2012 55\n2 4 4 2014 40\n3 9 7 2013 84\n4 16 10 2014 31']
|
pandas.DataFrame.set_index
DataFrame.set_index(keys, *, drop=True, append=False, inplace=False, verify_integrity=False)[source]# Set the DataFrame index using existing columns. Set the DataFrame index (row labels) using one or more existing columns or arrays (of the correct length). The index can replace the existing index or expand on it.
|
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] |
112 |
..\pandas\reference\api\pandas.Interval.closed.html
|
pandas.Interval.closed
|
Interval.closed# String describing the inclusive side the intervals. Either left, right, both or neither.
|
No parameters found
|
[">>> interval = pd.Interval(left=1, right=2, closed='left')\n>>> interval\nInterval(1, 2, closed='left')\n>>> interval.closed\n'left'"]
|
pandas.Interval.closed
Interval.closed# String describing the inclusive side the intervals. Either left, right, both or neither.
|
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] |
113 |
..\pandas\reference\api\pandas.Series.to_timestamp.html
|
pandas.Series.to_timestamp
|
Series.to_timestamp(freq=None, how='start', copy=None)[source]# Cast to DatetimeIndex of Timestamps, at beginning of period.
|
Parameters: freqstr, default frequency of PeriodIndexDesired frequency. how{‘s’, ‘e’, ‘start’, ‘end’}Convention for converting period to timestamp; start of period vs. end. copybool, default TrueWhether or not to return a copy. Note The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas. You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True Returns: Series with DatetimeIndex
|
[">>> idx = pd.PeriodIndex(['2023', '2024', '2025'], freq='Y')\n>>> s1 = pd.Series([1, 2, 3], index=idx)\n>>> s1\n2023 1\n2024 2\n2025 3\nFreq: Y-DEC, dtype: int64", '>>> s1 = s1.to_timestamp()\n>>> s1\n2023-01-01 1\n2024-01-01 2\n2025-01-01 3\nFreq: YS-JAN, dtype: int64', ">>> s2 = pd.Series([1, 2, 3], index=idx)\n>>> s2 = s2.to_timestamp(freq='M')\n>>> s2\n2023-01-31 1\n2024-01-31 2\n2025-01-31 3\nFreq: YE-JAN, dtype: int64"]
|
pandas.Series.to_timestamp
Series.to_timestamp(freq=None, how='start', copy=None)[source]# Cast to DatetimeIndex of Timestamps, at beginning of period.
|
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] |
114 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray.ndim.html
|
pandas.api.extensions.ExtensionArray.ndim
|
property ExtensionArray.ndim[source]# Extension Arrays are only allowed to be 1-dimensional.
|
No parameters found
|
['>>> arr = pd.array([1, 2, 3])\n>>> arr.ndim\n1']
|
pandas.api.extensions.ExtensionArray.ndim
property ExtensionArray.ndim[source]# Extension Arrays are only allowed to be 1-dimensional.
|
[
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] |
115 |
..\pandas\reference\api\pandas.Series.diff.html
|
pandas.Series.diff
|
Series.diff(periods=1)[source]# First discrete difference of element. Calculates the difference of a Series element compared with another element in the Series (default is element in previous row). Notes For boolean dtypes, this uses operator.xor() rather than operator.sub(). The result is calculated according to current dtype in Series, however dtype of the result is always float64.
|
Parameters: periodsint, default 1Periods to shift for calculating difference, accepts negative values. Returns: SeriesFirst differences of the Series.
|
['>>> s = pd.Series([1, 1, 2, 3, 5, 8])\n>>> s.diff()\n0 NaN\n1 0.0\n2 1.0\n3 1.0\n4 2.0\n5 3.0\ndtype: float64', '>>> s.diff(periods=3)\n0 NaN\n1 NaN\n2 NaN\n3 2.0\n4 4.0\n5 6.0\ndtype: float64', '>>> s.diff(periods=-1)\n0 0.0\n1 -1.0\n2 -1.0\n3 -2.0\n4 -3.0\n5 NaN\ndtype: float64', '>>> s = pd.Series([1, 0], dtype=np.uint8)\n>>> s.diff()\n0 NaN\n1 255.0\ndtype: float64']
|
pandas.Series.diff
Series.diff(periods=1)[source]# First discrete difference of element. Calculates the difference of a Series element compared with another element in the Series (default is element in previous row). Notes For boolean dtypes, this uses operator.xor() rather than operator.sub(). The result is calculated according to current dtype in Series, however dtype of the result is always float64.
|
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..\pandas\reference\api\pandas.tseries.offsets.BYearBegin.nanos.html
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pandas.tseries.offsets.BYearBegin.nanos
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BYearBegin.nanos#
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No parameters found
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[]
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pandas.tseries.offsets.BYearBegin.nanos
BYearBegin.nanos#
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] |
117 |
..\pandas\reference\api\pandas.tseries.offsets.Milli.kwds.html
|
pandas.tseries.offsets.Milli.kwds
|
Milli.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.Milli.kwds
Milli.kwds# Return a dict of extra parameters for the offset.
|
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] |
118 |
..\pandas\reference\api\pandas.core.groupby.SeriesGroupBy.sem.html
|
pandas.core.groupby.SeriesGroupBy.sem
|
SeriesGroupBy.sem(ddof=1, numeric_only=False)[source]# Compute standard error of the mean of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex.
|
Parameters: ddofint, default 1Degrees of freedom. numeric_onlybool, default FalseInclude only float, int or boolean data. Added in version 1.5.0. Changed in version 2.0.0: numeric_only now defaults to False. Returns: Series or DataFrameStandard error of the mean of values within each group.
|
[">>> lst = ['a', 'a', 'b', 'b']\n>>> ser = pd.Series([5, 10, 8, 14], index=lst)\n>>> ser\na 5\na 10\nb 8\nb 14\ndtype: int64\n>>> ser.groupby(level=0).sem()\na 2.5\nb 3.0\ndtype: float64", '>>> data = [[1, 12, 11], [1, 15, 2], [2, 5, 8], [2, 6, 12]]\n>>> df = pd.DataFrame(data, columns=["a", "b", "c"],\n... index=["tuna", "salmon", "catfish", "goldfish"])\n>>> df\n a b c\n tuna 1 12 11\n salmon 1 15 2\n catfish 2 5 8\ngoldfish 2 6 12\n>>> df.groupby("a").sem()\n b c\na\n1 1.5 4.5\n2 0.5 2.0', ">>> ser = pd.Series([1, 3, 2, 4, 3, 8],\n... index=pd.DatetimeIndex(['2023-01-01',\n... '2023-01-10',\n... '2023-01-15',\n... '2023-02-01',\n... '2023-02-10',\n... '2023-02-15']))\n>>> ser.resample('MS').sem()\n2023-01-01 0.577350\n2023-02-01 1.527525\nFreq: MS, dtype: float64"]
|
pandas.core.groupby.SeriesGroupBy.sem
SeriesGroupBy.sem(ddof=1, numeric_only=False)[source]# Compute standard error of the mean of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex.
|
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] |
119 |
..\pandas\reference\api\pandas.DataFrame.shape.html
|
pandas.DataFrame.shape
|
property DataFrame.shape[source]# Return a tuple representing the dimensionality of the DataFrame.
|
No parameters found
|
[">>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})\n>>> df.shape\n(2, 2)", ">>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4],\n... 'col3': [5, 6]})\n>>> df.shape\n(2, 3)"]
|
pandas.DataFrame.shape
property DataFrame.shape[source]# Return a tuple representing the dimensionality of the DataFrame.
|
[
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] |
120 |
..\pandas\reference\api\pandas.Interval.closed_left.html
|
pandas.Interval.closed_left
|
Interval.closed_left# Check if the interval is closed on the left side. For the meaning of closed and open see Interval.
|
Returns: boolTrue if the Interval is closed on the left-side.
|
[">>> iv = pd.Interval(0, 5, closed='left')\n>>> iv.closed_left\nTrue", ">>> iv = pd.Interval(0, 5, closed='right')\n>>> iv.closed_left\nFalse"]
|
pandas.Interval.closed_left
Interval.closed_left# Check if the interval is closed on the left side. For the meaning of closed and open see Interval.
|
[
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] |
121 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray.ravel.html
|
pandas.api.extensions.ExtensionArray.ravel
|
ExtensionArray.ravel(order='C')[source]# Return a flattened view on this array. Notes Because ExtensionArrays are 1D-only, this is a no-op. The “order” argument is ignored, is for compatibility with NumPy.
|
Parameters: order{None, ‘C’, ‘F’, ‘A’, ‘K’}, default ‘C’ Returns: ExtensionArray
|
['>>> pd.array([1, 2, 3]).ravel()\n<IntegerArray>\n[1, 2, 3]\nLength: 3, dtype: Int64']
|
pandas.api.extensions.ExtensionArray.ravel
ExtensionArray.ravel(order='C')[source]# Return a flattened view on this array. Notes Because ExtensionArrays are 1D-only, this is a no-op. The “order” argument is ignored, is for compatibility with NumPy.
|
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122 |
..\pandas\reference\api\pandas.Series.div.html
|
pandas.Series.div
|
Series.div(other, level=None, fill_value=None, axis=0)[source]# Return Floating division of series and other, element-wise (binary operator truediv). Equivalent to series / other, but with support to substitute a fill_value for missing data in either one of the inputs.
|
Parameters: otherSeries or scalar value levelint or nameBroadcast across a level, matching Index values on the passed MultiIndex level. fill_valueNone or float value, default None (NaN)Fill existing missing (NaN) values, and any new element needed for successful Series alignment, with this value before computation. If data in both corresponding Series locations is missing the result of filling (at that location) will be missing. axis{0 or ‘index’}Unused. Parameter needed for compatibility with DataFrame. Returns: SeriesThe result of the operation.
|
[">>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])\n>>> a\na 1.0\nb 1.0\nc 1.0\nd NaN\ndtype: float64\n>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])\n>>> b\na 1.0\nb NaN\nd 1.0\ne NaN\ndtype: float64\n>>> a.divide(b, fill_value=0)\na 1.0\nb inf\nc inf\nd 0.0\ne NaN\ndtype: float64"]
|
pandas.Series.div
Series.div(other, level=None, fill_value=None, axis=0)[source]# Return Floating division of series and other, element-wise (binary operator truediv). Equivalent to series / other, but with support to substitute a fill_value for missing data in either one of the inputs.
|
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] |
123 |
..\pandas\reference\api\pandas.core.groupby.SeriesGroupBy.shift.html
|
pandas.core.groupby.SeriesGroupBy.shift
|
SeriesGroupBy.shift(periods=1, freq=None, axis=<no_default>, fill_value=<no_default>, suffix=None)[source]# Shift each group by periods observations. If freq is passed, the index will be increased using the periods and the freq.
|
Parameters: periodsint | Sequence[int], default 1Number of periods to shift. If a list of values, shift each group by each period. freqstr, optionalFrequency string. axisaxis to shift, default 0Shift direction. Deprecated since version 2.1.0: For axis=1, operate on the underlying object instead. Otherwise the axis keyword is not necessary. fill_valueoptionalThe scalar value to use for newly introduced missing values. Changed in version 2.1.0: Will raise a ValueError if freq is provided too. suffixstr, optionalA string to add to each shifted column if there are multiple periods. Ignored otherwise. Returns: Series or DataFrameObject shifted within each group.
|
[">>> lst = ['a', 'a', 'b', 'b']\n>>> ser = pd.Series([1, 2, 3, 4], index=lst)\n>>> ser\na 1\na 2\nb 3\nb 4\ndtype: int64\n>>> ser.groupby(level=0).shift(1)\na NaN\na 1.0\nb NaN\nb 3.0\ndtype: float64", '>>> data = [[1, 2, 3], [1, 5, 6], [2, 5, 8], [2, 6, 9]]\n>>> df = pd.DataFrame(data, columns=["a", "b", "c"],\n... index=["tuna", "salmon", "catfish", "goldfish"])\n>>> df\n a b c\n tuna 1 2 3\n salmon 1 5 6\n catfish 2 5 8\ngoldfish 2 6 9\n>>> df.groupby("a").shift(1)\n b c\n tuna NaN NaN\n salmon 2.0 3.0\n catfish NaN NaN\ngoldfish 5.0 8.0']
|
pandas.core.groupby.SeriesGroupBy.shift
SeriesGroupBy.shift(periods=1, freq=None, axis=<no_default>, fill_value=<no_default>, suffix=None)[source]# Shift each group by periods observations. If freq is passed, the index will be increased using the periods and the freq.
|
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] |
124 |
..\pandas\reference\api\pandas.Series.to_xarray.html
|
pandas.Series.to_xarray
|
Series.to_xarray()[source]# Return an xarray object from the pandas object. Notes See the xarray docs
|
Returns: xarray.DataArray or xarray.DatasetData in the pandas structure converted to Dataset if the object is a DataFrame, or a DataArray if the object is a Series.
|
[">>> df = pd.DataFrame([('falcon', 'bird', 389.0, 2),\n... ('parrot', 'bird', 24.0, 2),\n... ('lion', 'mammal', 80.5, 4),\n... ('monkey', 'mammal', np.nan, 4)],\n... columns=['name', 'class', 'max_speed',\n... 'num_legs'])\n>>> df\n name class max_speed num_legs\n0 falcon bird 389.0 2\n1 parrot bird 24.0 2\n2 lion mammal 80.5 4\n3 monkey mammal NaN 4", ">>> df.to_xarray() \n<xarray.Dataset>\nDimensions: (index: 4)\nCoordinates:\n * index (index) int64 32B 0 1 2 3\nData variables:\n name (index) object 32B 'falcon' 'parrot' 'lion' 'monkey'\n class (index) object 32B 'bird' 'bird' 'mammal' 'mammal'\n max_speed (index) float64 32B 389.0 24.0 80.5 nan\n num_legs (index) int64 32B 2 2 4 4", ">>> df['max_speed'].to_xarray() \n<xarray.DataArray 'max_speed' (index: 4)>\narray([389. , 24. , 80.5, nan])\nCoordinates:\n * index (index) int64 0 1 2 3", ">>> dates = pd.to_datetime(['2018-01-01', '2018-01-01',\n... '2018-01-02', '2018-01-02'])\n>>> df_multiindex = pd.DataFrame({'date': dates,\n... 'animal': ['falcon', 'parrot',\n... 'falcon', 'parrot'],\n... 'speed': [350, 18, 361, 15]})\n>>> df_multiindex = df_multiindex.set_index(['date', 'animal'])", '>>> df_multiindex\n speed\ndate animal\n2018-01-01 falcon 350\n parrot 18\n2018-01-02 falcon 361\n parrot 15', ">>> df_multiindex.to_xarray() \n<xarray.Dataset>\nDimensions: (date: 2, animal: 2)\nCoordinates:\n * date (date) datetime64[ns] 2018-01-01 2018-01-02\n * animal (animal) object 'falcon' 'parrot'\nData variables:\n speed (date, animal) int64 350 18 361 15"]
|
pandas.Series.to_xarray
Series.to_xarray()[source]# Return an xarray object from the pandas object. Notes See the xarray docs
|
[
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..\pandas\reference\api\pandas.tseries.offsets.Milli.n.html
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pandas.tseries.offsets.Milli.n
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Milli.n#
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No parameters found
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[]
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pandas.tseries.offsets.Milli.n
Milli.n#
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126 |
..\pandas\reference\api\pandas.DataFrame.shift.html
|
pandas.DataFrame.shift
|
DataFrame.shift(periods=1, freq=None, axis=0, fill_value=<no_default>, suffix=None)[source]# Shift index by desired number of periods with an optional time freq. When freq is not passed, shift the index without realigning the data. If freq is passed (in this case, the index must be date or datetime, or it will raise a NotImplementedError), the index will be increased using the periods and the freq. freq can be inferred when specified as “infer” as long as either freq or inferred_freq attribute is set in the index.
|
Parameters: periodsint or SequenceNumber of periods to shift. Can be positive or negative. If an iterable of ints, the data will be shifted once by each int. This is equivalent to shifting by one value at a time and concatenating all resulting frames. The resulting columns will have the shift suffixed to their column names. For multiple periods, axis must not be 1. freqDateOffset, tseries.offsets, timedelta, or str, optionalOffset to use from the tseries module or time rule (e.g. ‘EOM’). If freq is specified then the index values are shifted but the data is not realigned. That is, use freq if you would like to extend the index when shifting and preserve the original data. If freq is specified as “infer” then it will be inferred from the freq or inferred_freq attributes of the index. If neither of those attributes exist, a ValueError is thrown. axis{0 or ‘index’, 1 or ‘columns’, None}, default NoneShift direction. For Series this parameter is unused and defaults to 0. fill_valueobject, optionalThe scalar value to use for newly introduced missing values. the default depends on the dtype of self. For numeric data, np.nan is used. For datetime, timedelta, or period data, etc. NaT is used. For extension dtypes, self.dtype.na_value is used. suffixstr, optionalIf str and periods is an iterable, this is added after the column name and before the shift value for each shifted column name. Returns: DataFrameCopy of input object, shifted.
|
['>>> df = pd.DataFrame({"Col1": [10, 20, 15, 30, 45],\n... "Col2": [13, 23, 18, 33, 48],\n... "Col3": [17, 27, 22, 37, 52]},\n... index=pd.date_range("2020-01-01", "2020-01-05"))\n>>> df\n Col1 Col2 Col3\n2020-01-01 10 13 17\n2020-01-02 20 23 27\n2020-01-03 15 18 22\n2020-01-04 30 33 37\n2020-01-05 45 48 52', '>>> df.shift(periods=3)\n Col1 Col2 Col3\n2020-01-01 NaN NaN NaN\n2020-01-02 NaN NaN NaN\n2020-01-03 NaN NaN NaN\n2020-01-04 10.0 13.0 17.0\n2020-01-05 20.0 23.0 27.0', '>>> df.shift(periods=1, axis="columns")\n Col1 Col2 Col3\n2020-01-01 NaN 10 13\n2020-01-02 NaN 20 23\n2020-01-03 NaN 15 18\n2020-01-04 NaN 30 33\n2020-01-05 NaN 45 48', '>>> df.shift(periods=3, fill_value=0)\n Col1 Col2 Col3\n2020-01-01 0 0 0\n2020-01-02 0 0 0\n2020-01-03 0 0 0\n2020-01-04 10 13 17\n2020-01-05 20 23 27', '>>> df.shift(periods=3, freq="D")\n Col1 Col2 Col3\n2020-01-04 10 13 17\n2020-01-05 20 23 27\n2020-01-06 15 18 22\n2020-01-07 30 33 37\n2020-01-08 45 48 52', '>>> df.shift(periods=3, freq="infer")\n Col1 Col2 Col3\n2020-01-04 10 13 17\n2020-01-05 20 23 27\n2020-01-06 15 18 22\n2020-01-07 30 33 37\n2020-01-08 45 48 52', ">>> df['Col1'].shift(periods=[0, 1, 2])\n Col1_0 Col1_1 Col1_2\n2020-01-01 10 NaN NaN\n2020-01-02 20 10.0 NaN\n2020-01-03 15 20.0 10.0\n2020-01-04 30 15.0 20.0\n2020-01-05 45 30.0 15.0"]
|
pandas.DataFrame.shift
DataFrame.shift(periods=1, freq=None, axis=0, fill_value=<no_default>, suffix=None)[source]# Shift index by desired number of periods with an optional time freq. When freq is not passed, shift the index without realigning the data. If freq is passed (in this case, the index must be date or datetime, or it will raise a NotImplementedError), the index will be increased using the periods and the freq. freq can be inferred when specified as “infer” as long as either freq or inferred_freq attribute is set in the index.
|
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127 |
..\pandas\reference\api\pandas.tseries.offsets.BYearBegin.normalize.html
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pandas.tseries.offsets.BYearBegin.normalize
|
BYearBegin.normalize#
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No parameters found
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[]
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pandas.tseries.offsets.BYearBegin.normalize
BYearBegin.normalize#
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] |
128 |
..\pandas\reference\api\pandas.Interval.closed_right.html
|
pandas.Interval.closed_right
|
Interval.closed_right# Check if the interval is closed on the right side. For the meaning of closed and open see Interval.
|
Returns: boolTrue if the Interval is closed on the left-side.
|
[">>> iv = pd.Interval(0, 5, closed='both')\n>>> iv.closed_right\nTrue", ">>> iv = pd.Interval(0, 5, closed='left')\n>>> iv.closed_right\nFalse"]
|
pandas.Interval.closed_right
Interval.closed_right# Check if the interval is closed on the right side. For the meaning of closed and open see Interval.
|
[
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] |
129 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray.repeat.html
|
pandas.api.extensions.ExtensionArray.repeat
|
ExtensionArray.repeat(repeats, axis=None)[source]# Repeat elements of a ExtensionArray. Returns a new ExtensionArray where each element of the current ExtensionArray is repeated consecutively a given number of times.
|
Parameters: repeatsint or array of intsThe number of repetitions for each element. This should be a non-negative integer. Repeating 0 times will return an empty ExtensionArray. axisNoneMust be None. Has no effect but is accepted for compatibility with numpy. Returns: ExtensionArrayNewly created ExtensionArray with repeated elements.
|
[">>> cat = pd.Categorical(['a', 'b', 'c'])\n>>> cat\n['a', 'b', 'c']\nCategories (3, object): ['a', 'b', 'c']\n>>> cat.repeat(2)\n['a', 'a', 'b', 'b', 'c', 'c']\nCategories (3, object): ['a', 'b', 'c']\n>>> cat.repeat([1, 2, 3])\n['a', 'b', 'b', 'c', 'c', 'c']\nCategories (3, object): ['a', 'b', 'c']"]
|
pandas.api.extensions.ExtensionArray.repeat
ExtensionArray.repeat(repeats, axis=None)[source]# Repeat elements of a ExtensionArray. Returns a new ExtensionArray where each element of the current ExtensionArray is repeated consecutively a given number of times.
|
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130 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray.searchsorted.html
|
pandas.api.extensions.ExtensionArray.searchsorted
|
ExtensionArray.searchsorted(value, side='left', sorter=None)[source]# Find indices where elements should be inserted to maintain order. Find the indices into a sorted array self (a) such that, if the corresponding elements in value were inserted before the indices, the order of self would be preserved. Assuming that self is sorted: side returned index i satisfies left self[i-1] < value <= self[i] right self[i-1] <= value < self[i]
|
Parameters: valuearray-like, list or scalarValue(s) to insert into self. side{‘left’, ‘right’}, optionalIf ‘left’, the index of the first suitable location found is given. If ‘right’, return the last such index. If there is no suitable index, return either 0 or N (where N is the length of self). sorter1-D array-like, optionalOptional array of integer indices that sort array a into ascending order. They are typically the result of argsort. Returns: array of ints or intIf value is array-like, array of insertion points. If value is scalar, a single integer.
|
['>>> arr = pd.array([1, 2, 3, 5])\n>>> arr.searchsorted([4])\narray([3])']
|
pandas.api.extensions.ExtensionArray.searchsorted
ExtensionArray.searchsorted(value, side='left', sorter=None)[source]# Find indices where elements should be inserted to maintain order. Find the indices into a sorted array self (a) such that, if the corresponding elements in value were inserted before the indices, the order of self would be preserved. Assuming that self is sorted: side returned index i satisfies left self[i-1] < value <= self[i] right self[i-1] <= value < self[i]
|
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131 |
..\pandas\reference\api\pandas.Interval.html
|
pandas.Interval
|
class pandas.Interval# Immutable object implementing an Interval, a bounded slice-like interval. Notes The parameters left and right must be from the same type, you must be able to compare them and they must satisfy left <= right. A closed interval (in mathematics denoted by square brackets) contains its endpoints, i.e. the closed interval [0, 5] is characterized by the conditions 0 <= x <= 5. This is what closed='both' stands for. An open interval (in mathematics denoted by parentheses) does not contain its endpoints, i.e. the open interval (0, 5) is characterized by the conditions 0 < x < 5. This is what closed='neither' stands for. Intervals can also be half-open or half-closed, i.e. [0, 5) is described by 0 <= x < 5 (closed='left') and (0, 5] is described by 0 < x <= 5 (closed='right'). Examples It is possible to build Intervals of different types, like numeric ones: You can check if an element belongs to it, or if it contains another interval: You can test the bounds (closed='right', so 0 < x <= 5): Calculate its length You can operate with + and * over an Interval and the operation is applied to each of its bounds, so the result depends on the type of the bound elements To create a time interval you can use Timestamps as the bounds Attributes closed String describing the inclusive side the intervals. closed_left Check if the interval is closed on the left side. closed_right Check if the interval is closed on the right side. is_empty Indicates if an interval is empty, meaning it contains no points. left Left bound for the interval. length Return the length of the Interval. mid Return the midpoint of the Interval. open_left Check if the interval is open on the left side. open_right Check if the interval is open on the right side. right Right bound for the interval.
|
Parameters: leftorderable scalarLeft bound for the interval. rightorderable scalarRight bound for the interval. closed{‘right’, ‘left’, ‘both’, ‘neither’}, default ‘right’Whether the interval is closed on the left-side, right-side, both or neither. See the Notes for more detailed explanation.
|
[">>> iv = pd.Interval(left=0, right=5)\n>>> iv\nInterval(0, 5, closed='right')", ">>> 2.5 in iv\nTrue\n>>> pd.Interval(left=2, right=5, closed='both') in iv\nTrue", '>>> 0 in iv\nFalse\n>>> 5 in iv\nTrue\n>>> 0.0001 in iv\nTrue', '>>> iv.length\n5', ">>> shifted_iv = iv + 3\n>>> shifted_iv\nInterval(3, 8, closed='right')\n>>> extended_iv = iv * 10.0\n>>> extended_iv\nInterval(0.0, 50.0, closed='right')", ">>> year_2017 = pd.Interval(pd.Timestamp('2017-01-01 00:00:00'),\n... pd.Timestamp('2018-01-01 00:00:00'),\n... closed='left')\n>>> pd.Timestamp('2017-01-01 00:00') in year_2017\nTrue\n>>> year_2017.length\nTimedelta('365 days 00:00:00')"]
|
pandas.Interval
class pandas.Interval# Immutable object implementing an Interval, a bounded slice-like interval. Notes The parameters left and right must be from the same type, you must be able to compare them and they must satisfy left <= right. A closed interval (in mathematics denoted by square brackets) contains its endpoints, i.e. the closed interval [0, 5] is characterized by the conditions 0 <= x <= 5. This is what closed='both' stands for. An open interval (in mathematics denoted by parentheses) does not contain its endpoints, i.e. the open interval (0, 5) is characterized by the conditions 0 < x < 5. This is what closed='neither' stands for. Intervals can also be half-open or half-closed, i.e. [0, 5) is described by 0 <= x < 5 (closed='left') and (0, 5] is described by 0 < x <= 5 (closed='right'). Examples It is possible to build Intervals of different types, like numeric ones: You can check if an element belongs to it, or if it contains another interval: You can test the bounds (closed='right', so 0 < x <= 5): Calculate its length You can operate with + and * over an Interval and the operation is applied to each of its bounds, so the result depends on the type of the bound elements To create a time interval you can use Timestamps as the bounds Attributes closed String describing the inclusive side the intervals. closed_left Check if the interval is closed on the left side. closed_right Check if the interval is closed on the right side. is_empty Indicates if an interval is empty, meaning it contains no points. left Left bound for the interval. length Return the length of the Interval. mid Return the midpoint of the Interval. open_left Check if the interval is open on the left side. open_right Check if the interval is open on the right side. right Right bound for the interval.
|
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] |
132 |
..\pandas\reference\api\pandas.DataFrame.size.html
|
pandas.DataFrame.size
|
property DataFrame.size[source]# Return an int representing the number of elements in this object. Return the number of rows if Series. Otherwise return the number of rows times number of columns if DataFrame.
|
No parameters found
|
[">>> s = pd.Series({'a': 1, 'b': 2, 'c': 3})\n>>> s.size\n3", ">>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})\n>>> df.size\n4"]
|
pandas.DataFrame.size
property DataFrame.size[source]# Return an int representing the number of elements in this object. Return the number of rows if Series. Otherwise return the number of rows times number of columns if DataFrame.
|
[
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] |
133 |
..\pandas\reference\api\pandas.core.groupby.SeriesGroupBy.size.html
|
pandas.core.groupby.SeriesGroupBy.size
|
SeriesGroupBy.size()[source]# Compute group sizes.
|
Returns: DataFrame or SeriesNumber of rows in each group as a Series if as_index is True or a DataFrame if as_index is False.
|
[">>> lst = ['a', 'a', 'b']\n>>> ser = pd.Series([1, 2, 3], index=lst)\n>>> ser\na 1\na 2\nb 3\ndtype: int64\n>>> ser.groupby(level=0).size()\na 2\nb 1\ndtype: int64", '>>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]]\n>>> df = pd.DataFrame(data, columns=["a", "b", "c"],\n... index=["owl", "toucan", "eagle"])\n>>> df\n a b c\nowl 1 2 3\ntoucan 1 5 6\neagle 7 8 9\n>>> df.groupby("a").size()\na\n1 2\n7 1\ndtype: int64', ">>> ser = pd.Series([1, 2, 3], index=pd.DatetimeIndex(\n... ['2023-01-01', '2023-01-15', '2023-02-01']))\n>>> ser\n2023-01-01 1\n2023-01-15 2\n2023-02-01 3\ndtype: int64\n>>> ser.resample('MS').size()\n2023-01-01 2\n2023-02-01 1\nFreq: MS, dtype: int64"]
|
pandas.core.groupby.SeriesGroupBy.size
SeriesGroupBy.size()[source]# Compute group sizes.
|
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] |
134 |
..\pandas\reference\api\pandas.Series.transform.html
|
pandas.Series.transform
|
Series.transform(func, axis=0, *args, **kwargs)[source]# Call func on self producing a Series with the same axis shape as self. Notes Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See Mutating with User Defined Function (UDF) methods for more details.
|
Parameters: funcfunction, str, list-like or dict-likeFunction to use for transforming the data. If a function, must either work when passed a Series or when passed to Series.apply. If func is both list-like and dict-like, dict-like behavior takes precedence. Accepted combinations are: function string function name list-like of functions and/or function names, e.g. [np.exp, 'sqrt'] dict-like of axis labels -> functions, function names or list-like of such. axis{0 or ‘index’}Unused. Parameter needed for compatibility with DataFrame. *argsPositional arguments to pass to func. **kwargsKeyword arguments to pass to func. Returns: SeriesA Series that must have the same length as self. Raises: ValueErrorIf the returned Series has a different length than self.
|
[">>> df = pd.DataFrame({'A': range(3), 'B': range(1, 4)})\n>>> df\n A B\n0 0 1\n1 1 2\n2 2 3\n>>> df.transform(lambda x: x + 1)\n A B\n0 1 2\n1 2 3\n2 3 4", '>>> s = pd.Series(range(3))\n>>> s\n0 0\n1 1\n2 2\ndtype: int64\n>>> s.transform([np.sqrt, np.exp])\n sqrt exp\n0 0.000000 1.000000\n1 1.000000 2.718282\n2 1.414214 7.389056', '>>> df = pd.DataFrame({\n... "Date": [\n... "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05",\n... "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05"],\n... "Data": [5, 8, 6, 1, 50, 100, 60, 120],\n... })\n>>> df\n Date Data\n0 2015-05-08 5\n1 2015-05-07 8\n2 2015-05-06 6\n3 2015-05-05 1\n4 2015-05-08 50\n5 2015-05-07 100\n6 2015-05-06 60\n7 2015-05-05 120\n>>> df.groupby(\'Date\')[\'Data\'].transform(\'sum\')\n0 55\n1 108\n2 66\n3 121\n4 55\n5 108\n6 66\n7 121\nName: Data, dtype: int64', '>>> df = pd.DataFrame({\n... "c": [1, 1, 1, 2, 2, 2, 2],\n... "type": ["m", "n", "o", "m", "m", "n", "n"]\n... })\n>>> df\n c type\n0 1 m\n1 1 n\n2 1 o\n3 2 m\n4 2 m\n5 2 n\n6 2 n\n>>> df[\'size\'] = df.groupby(\'c\')[\'type\'].transform(len)\n>>> df\n c type size\n0 1 m 3\n1 1 n 3\n2 1 o 3\n3 2 m 4\n4 2 m 4\n5 2 n 4\n6 2 n 4']
|
pandas.Series.transform
Series.transform(func, axis=0, *args, **kwargs)[source]# Call func on self producing a Series with the same axis shape as self. Notes Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See Mutating with User Defined Function (UDF) methods for more details.
|
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] |
135 |
..\pandas\reference\api\pandas.tseries.offsets.Milli.name.html
|
pandas.tseries.offsets.Milli.name
|
Milli.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.Milli.name
Milli.name# Return a string representing the base frequency.
|
[
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] |
136 |
..\pandas\reference\api\pandas.Series.dot.html
|
pandas.Series.dot
|
Series.dot(other)[source]# Compute the dot product between the Series and the columns of other. This method computes the dot product between the Series and another one, or the Series and each columns of a DataFrame, or the Series and each columns of an array. It can also be called using self @ other. Notes The Series and other has to share the same index if other is a Series or a DataFrame.
|
Parameters: otherSeries, DataFrame or array-likeThe other object to compute the dot product with its columns. Returns: scalar, Series or numpy.ndarrayReturn the dot product of the Series and other if other is a Series, the Series of the dot product of Series and each rows of other if other is a DataFrame or a numpy.ndarray between the Series and each columns of the numpy array.
|
['>>> s = pd.Series([0, 1, 2, 3])\n>>> other = pd.Series([-1, 2, -3, 4])\n>>> s.dot(other)\n8\n>>> s @ other\n8\n>>> df = pd.DataFrame([[0, 1], [-2, 3], [4, -5], [6, 7]])\n>>> s.dot(df)\n0 24\n1 14\ndtype: int64\n>>> arr = np.array([[0, 1], [-2, 3], [4, -5], [6, 7]])\n>>> s.dot(arr)\narray([24, 14])']
|
pandas.Series.dot
Series.dot(other)[source]# Compute the dot product between the Series and the columns of other. This method computes the dot product between the Series and another one, or the Series and each columns of a DataFrame, or the Series and each columns of an array. It can also be called using self @ other. Notes The Series and other has to share the same index if other is a Series or a DataFrame.
|
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137 |
..\pandas\reference\api\pandas.tseries.offsets.BYearBegin.rule_code.html
|
pandas.tseries.offsets.BYearBegin.rule_code
|
BYearBegin.rule_code#
|
No parameters found
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[]
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pandas.tseries.offsets.BYearBegin.rule_code
BYearBegin.rule_code#
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] |
138 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray.shape.html
|
pandas.api.extensions.ExtensionArray.shape
|
property ExtensionArray.shape[source]# Return a tuple of the array dimensions.
|
No parameters found
|
['>>> arr = pd.array([1, 2, 3])\n>>> arr.shape\n(3,)']
|
pandas.api.extensions.ExtensionArray.shape
property ExtensionArray.shape[source]# Return a tuple of the array dimensions.
|
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] |
139 |
..\pandas\reference\api\pandas.Interval.is_empty.html
|
pandas.Interval.is_empty
|
Interval.is_empty# Indicates if an interval is empty, meaning it contains no points.
|
Returns: bool or ndarrayA boolean indicating if a scalar Interval is empty, or a boolean ndarray positionally indicating if an Interval in an IntervalArray or IntervalIndex is empty.
|
[">>> pd.Interval(0, 1, closed='right').is_empty\nFalse", ">>> pd.Interval(0, 0, closed='right').is_empty\nTrue\n>>> pd.Interval(0, 0, closed='left').is_empty\nTrue\n>>> pd.Interval(0, 0, closed='neither').is_empty\nTrue", ">>> pd.Interval(0, 0, closed='both').is_empty\nFalse", ">>> ivs = [pd.Interval(0, 0, closed='neither'),\n... pd.Interval(1, 2, closed='neither')]\n>>> pd.arrays.IntervalArray(ivs).is_empty\narray([ True, False])", ">>> ivs = [pd.Interval(0, 0, closed='neither'), np.nan]\n>>> pd.IntervalIndex(ivs).is_empty\narray([ True, False])"]
|
pandas.Interval.is_empty
Interval.is_empty# Indicates if an interval is empty, meaning it contains no points.
|
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] |
140 |
..\pandas\reference\api\pandas.tseries.offsets.BYearEnd.copy.html
|
pandas.tseries.offsets.BYearEnd.copy
|
BYearEnd.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.BYearEnd.copy
BYearEnd.copy()# Return a copy of the frequency.
|
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] |
141 |
..\pandas\reference\api\pandas.core.groupby.SeriesGroupBy.skew.html
|
pandas.core.groupby.SeriesGroupBy.skew
|
SeriesGroupBy.skew(axis=<no_default>, skipna=True, numeric_only=False, **kwargs)[source]# Return unbiased skew within groups. Normalized by N-1.
|
Parameters: axis{0 or ‘index’, 1 or ‘columns’, None}, default 0Axis for the function to be applied on. This parameter is only for compatibility with DataFrame and is unused. Deprecated since version 2.1.0: For axis=1, operate on the underlying object instead. Otherwise the axis keyword is not necessary. skipnabool, default TrueExclude NA/null values when computing the result. numeric_onlybool, default FalseInclude only float, int, boolean columns. Not implemented for Series. **kwargsAdditional keyword arguments to be passed to the function. Returns: Series
|
['>>> ser = pd.Series([390., 350., 357., np.nan, 22., 20., 30.],\n... index=[\'Falcon\', \'Falcon\', \'Falcon\', \'Falcon\',\n... \'Parrot\', \'Parrot\', \'Parrot\'],\n... name="Max Speed")\n>>> ser\nFalcon 390.0\nFalcon 350.0\nFalcon 357.0\nFalcon NaN\nParrot 22.0\nParrot 20.0\nParrot 30.0\nName: Max Speed, dtype: float64\n>>> ser.groupby(level=0).skew()\nFalcon 1.525174\nParrot 1.457863\nName: Max Speed, dtype: float64\n>>> ser.groupby(level=0).skew(skipna=False)\nFalcon NaN\nParrot 1.457863\nName: Max Speed, dtype: float64']
|
pandas.core.groupby.SeriesGroupBy.skew
SeriesGroupBy.skew(axis=<no_default>, skipna=True, numeric_only=False, **kwargs)[source]# Return unbiased skew within groups. Normalized by N-1.
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142 |
..\pandas\reference\api\pandas.Series.truediv.html
|
pandas.Series.truediv
|
Series.truediv(other, level=None, fill_value=None, axis=0)[source]# Return Floating division of series and other, element-wise (binary operator truediv). Equivalent to series / other, but with support to substitute a fill_value for missing data in either one of the inputs.
|
Parameters: otherSeries or scalar value levelint or nameBroadcast across a level, matching Index values on the passed MultiIndex level. fill_valueNone or float value, default None (NaN)Fill existing missing (NaN) values, and any new element needed for successful Series alignment, with this value before computation. If data in both corresponding Series locations is missing the result of filling (at that location) will be missing. axis{0 or ‘index’}Unused. Parameter needed for compatibility with DataFrame. Returns: SeriesThe result of the operation.
|
[">>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])\n>>> a\na 1.0\nb 1.0\nc 1.0\nd NaN\ndtype: float64\n>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])\n>>> b\na 1.0\nb NaN\nd 1.0\ne NaN\ndtype: float64\n>>> a.divide(b, fill_value=0)\na 1.0\nb inf\nc inf\nd 0.0\ne NaN\ndtype: float64"]
|
pandas.Series.truediv
Series.truediv(other, level=None, fill_value=None, axis=0)[source]# Return Floating division of series and other, element-wise (binary operator truediv). Equivalent to series / other, but with support to substitute a fill_value for missing data in either one of the inputs.
|
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] |
143 |
..\pandas\reference\api\pandas.DataFrame.skew.html
|
pandas.DataFrame.skew
|
DataFrame.skew(axis=0, skipna=True, numeric_only=False, **kwargs)[source]# Return unbiased skew over requested axis. Normalized by N-1.
|
Parameters: axis{index (0), columns (1)}Axis for the function to be applied on. For Series this parameter is unused and defaults to 0. For DataFrames, specifying axis=None will apply the aggregation across both axes. Added in version 2.0.0. skipnabool, default TrueExclude NA/null values when computing the result. numeric_onlybool, default FalseInclude only float, int, boolean columns. Not implemented for Series. **kwargsAdditional keyword arguments to be passed to the function. Returns: Series or scalar
|
['>>> s = pd.Series([1, 2, 3])\n>>> s.skew()\n0.0', ">>> df = pd.DataFrame({'a': [1, 2, 3], 'b': [2, 3, 4], 'c': [1, 3, 5]},\n... index=['tiger', 'zebra', 'cow'])\n>>> df\n a b c\ntiger 1 2 1\nzebra 2 3 3\ncow 3 4 5\n>>> df.skew()\na 0.0\nb 0.0\nc 0.0\ndtype: float64", '>>> df.skew(axis=1)\ntiger 1.732051\nzebra -1.732051\ncow 0.000000\ndtype: float64', ">>> df = pd.DataFrame({'a': [1, 2, 3], 'b': ['T', 'Z', 'X']},\n... index=['tiger', 'zebra', 'cow'])\n>>> df.skew(numeric_only=True)\na 0.0\ndtype: float64"]
|
pandas.DataFrame.skew
DataFrame.skew(axis=0, skipna=True, numeric_only=False, **kwargs)[source]# Return unbiased skew over requested axis. Normalized by N-1.
|
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] |
144 |
..\pandas\reference\api\pandas.tseries.offsets.Milli.nanos.html
|
pandas.tseries.offsets.Milli.nanos
|
Milli.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.Milli.nanos
Milli.nanos# Return an integer of the total number of nanoseconds.
|
[
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] |
145 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray.shift.html
|
pandas.api.extensions.ExtensionArray.shift
|
ExtensionArray.shift(periods=1, fill_value=None)[source]# Shift values by desired number. Newly introduced missing values are filled with self.dtype.na_value. Notes If self is empty or periods is 0, a copy of self is returned. If periods > len(self), then an array of size len(self) is returned, with all values filled with self.dtype.na_value. For 2-dimensional ExtensionArrays, we are always shifting along axis=0.
|
Parameters: periodsint, default 1The number of periods to shift. Negative values are allowed for shifting backwards. fill_valueobject, optionalThe scalar value to use for newly introduced missing values. The default is self.dtype.na_value. Returns: ExtensionArrayShifted.
|
['>>> arr = pd.array([1, 2, 3])\n>>> arr.shift(2)\n<IntegerArray>\n[<NA>, <NA>, 1]\nLength: 3, dtype: Int64']
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pandas.api.extensions.ExtensionArray.shift
ExtensionArray.shift(periods=1, fill_value=None)[source]# Shift values by desired number. Newly introduced missing values are filled with self.dtype.na_value. Notes If self is empty or periods is 0, a copy of self is returned. If periods > len(self), then an array of size len(self) is returned, with all values filled with self.dtype.na_value. For 2-dimensional ExtensionArrays, we are always shifting along axis=0.
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] |
146 |
..\pandas\reference\api\pandas.Series.drop.html
|
pandas.Series.drop
|
Series.drop(labels=None, *, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise')[source]# Return Series with specified index labels removed. Remove elements of a Series based on specifying the index labels. When using a multi-index, labels on different levels can be removed by specifying the level.
|
Parameters: labelssingle label or list-likeIndex labels to drop. axis{0 or ‘index’}Unused. Parameter needed for compatibility with DataFrame. indexsingle label or list-likeRedundant for application on Series, but ‘index’ can be used instead of ‘labels’. columnssingle label or list-likeNo change is made to the Series; use ‘index’ or ‘labels’ instead. levelint or level name, optionalFor MultiIndex, level for which the labels will be removed. inplacebool, default FalseIf True, do operation inplace and return None. errors{‘ignore’, ‘raise’}, default ‘raise’If ‘ignore’, suppress error and only existing labels are dropped. Returns: Series or NoneSeries with specified index labels removed or None if inplace=True. Raises: KeyErrorIf none of the labels are found in the index.
|
[">>> s = pd.Series(data=np.arange(3), index=['A', 'B', 'C'])\n>>> s\nA 0\nB 1\nC 2\ndtype: int64", ">>> s.drop(labels=['B', 'C'])\nA 0\ndtype: int64", ">>> midx = pd.MultiIndex(levels=[['llama', 'cow', 'falcon'],\n... ['speed', 'weight', 'length']],\n... codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],\n... [0, 1, 2, 0, 1, 2, 0, 1, 2]])\n>>> s = pd.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3],\n... index=midx)\n>>> s\nllama speed 45.0\n weight 200.0\n length 1.2\ncow speed 30.0\n weight 250.0\n length 1.5\nfalcon speed 320.0\n weight 1.0\n length 0.3\ndtype: float64", ">>> s.drop(labels='weight', level=1)\nllama speed 45.0\n length 1.2\ncow speed 30.0\n length 1.5\nfalcon speed 320.0\n length 0.3\ndtype: float64"]
|
pandas.Series.drop
Series.drop(labels=None, *, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise')[source]# Return Series with specified index labels removed. Remove elements of a Series based on specifying the index labels. When using a multi-index, labels on different levels can be removed by specifying the level.
|
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] |
147 |
..\pandas\reference\api\pandas.Interval.left.html
|
pandas.Interval.left
|
Interval.left# Left bound for the interval.
|
No parameters found
|
[">>> interval = pd.Interval(left=1, right=2, closed='left')\n>>> interval\nInterval(1, 2, closed='left')\n>>> interval.left\n1"]
|
pandas.Interval.left
Interval.left# Left bound for the interval.
|
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148 |
..\pandas\reference\api\pandas.tseries.offsets.BYearEnd.freqstr.html
|
pandas.tseries.offsets.BYearEnd.freqstr
|
BYearEnd.freqstr# Return a string representing the frequency.
|
No parameters found
|
[">>> pd.DateOffset(5).freqstr\n'<5 * DateOffsets>'", ">>> pd.offsets.BusinessHour(2).freqstr\n'2bh'", ">>> pd.offsets.Nano().freqstr\n'ns'", ">>> pd.offsets.Nano(-3).freqstr\n'-3ns'"]
|
pandas.tseries.offsets.BYearEnd.freqstr
BYearEnd.freqstr# Return a string representing the frequency.
|
[
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] |
149 |
..\pandas\reference\api\pandas.Series.truncate.html
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pandas.Series.truncate
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Series.truncate(before=None, after=None, axis=None, copy=None)[source]# Truncate a Series or DataFrame before and after some index value. This is a useful shorthand for boolean indexing based on index values above or below certain thresholds. Notes If the index being truncated contains only datetime values, before and after may be specified as strings instead of Timestamps.
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Parameters: beforedate, str, intTruncate all rows before this index value. afterdate, str, intTruncate all rows after this index value. axis{0 or ‘index’, 1 or ‘columns’}, optionalAxis to truncate. Truncates the index (rows) by default. For Series this parameter is unused and defaults to 0. copybool, default is True,Return a copy of the truncated section. Note The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas. You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True Returns: type of callerThe truncated Series or DataFrame.
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[">>> df = pd.DataFrame({'A': ['a', 'b', 'c', 'd', 'e'],\n... 'B': ['f', 'g', 'h', 'i', 'j'],\n... 'C': ['k', 'l', 'm', 'n', 'o']},\n... index=[1, 2, 3, 4, 5])\n>>> df\n A B C\n1 a f k\n2 b g l\n3 c h m\n4 d i n\n5 e j o", '>>> df.truncate(before=2, after=4)\n A B C\n2 b g l\n3 c h m\n4 d i n', '>>> df.truncate(before="A", after="B", axis="columns")\n A B\n1 a f\n2 b g\n3 c h\n4 d i\n5 e j', ">>> df['A'].truncate(before=2, after=4)\n2 b\n3 c\n4 d\nName: A, dtype: object", ">>> dates = pd.date_range('2016-01-01', '2016-02-01', freq='s')\n>>> df = pd.DataFrame(index=dates, data={'A': 1})\n>>> df.tail()\n A\n2016-01-31 23:59:56 1\n2016-01-31 23:59:57 1\n2016-01-31 23:59:58 1\n2016-01-31 23:59:59 1\n2016-02-01 00:00:00 1", ">>> df.truncate(before=pd.Timestamp('2016-01-05'),\n... after=pd.Timestamp('2016-01-10')).tail()\n A\n2016-01-09 23:59:56 1\n2016-01-09 23:59:57 1\n2016-01-09 23:59:58 1\n2016-01-09 23:59:59 1\n2016-01-10 00:00:00 1", ">>> df.truncate('2016-01-05', '2016-01-10').tail()\n A\n2016-01-09 23:59:56 1\n2016-01-09 23:59:57 1\n2016-01-09 23:59:58 1\n2016-01-09 23:59:59 1\n2016-01-10 00:00:00 1", ">>> df.loc['2016-01-05':'2016-01-10', :].tail()\n A\n2016-01-10 23:59:55 1\n2016-01-10 23:59:56 1\n2016-01-10 23:59:57 1\n2016-01-10 23:59:58 1\n2016-01-10 23:59:59 1"]
|
pandas.Series.truncate
Series.truncate(before=None, after=None, axis=None, copy=None)[source]# Truncate a Series or DataFrame before and after some index value. This is a useful shorthand for boolean indexing based on index values above or below certain thresholds. Notes If the index being truncated contains only datetime values, before and after may be specified as strings instead of Timestamps.
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150 |
..\pandas\reference\api\pandas.core.groupby.SeriesGroupBy.std.html
|
pandas.core.groupby.SeriesGroupBy.std
|
SeriesGroupBy.std(ddof=1, engine=None, engine_kwargs=None, numeric_only=False)[source]# Compute standard deviation of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex.
|
Parameters: ddofint, default 1Degrees of freedom. enginestr, default None 'cython' : Runs the operation through C-extensions from cython. 'numba' : Runs the operation through JIT compiled code from numba. None : Defaults to 'cython' or globally setting compute.use_numba Added in version 1.4.0. engine_kwargsdict, default None For 'cython' engine, there are no accepted engine_kwargs For 'numba' engine, the engine can accept nopython, nogil and parallel dictionary keys. The values must either be True or False. The default engine_kwargs for the 'numba' engine is {{'nopython': True, 'nogil': False, 'parallel': False}} Added in version 1.4.0. numeric_onlybool, default FalseInclude only float, int or boolean data. Added in version 1.5.0. Changed in version 2.0.0: numeric_only now defaults to False. Returns: Series or DataFrameStandard deviation of values within each group.
|
[">>> lst = ['a', 'a', 'a', 'b', 'b', 'b']\n>>> ser = pd.Series([7, 2, 8, 4, 3, 3], index=lst)\n>>> ser\na 7\na 2\na 8\nb 4\nb 3\nb 3\ndtype: int64\n>>> ser.groupby(level=0).std()\na 3.21455\nb 0.57735\ndtype: float64", ">>> data = {'a': [1, 3, 5, 7, 7, 8, 3], 'b': [1, 4, 8, 4, 4, 2, 1]}\n>>> df = pd.DataFrame(data, index=['dog', 'dog', 'dog',\n... 'mouse', 'mouse', 'mouse', 'mouse'])\n>>> df\n a b\n dog 1 1\n dog 3 4\n dog 5 8\nmouse 7 4\nmouse 7 4\nmouse 8 2\nmouse 3 1\n>>> df.groupby(level=0).std()\n a b\ndog 2.000000 3.511885\nmouse 2.217356 1.500000"]
|
pandas.core.groupby.SeriesGroupBy.std
SeriesGroupBy.std(ddof=1, engine=None, engine_kwargs=None, numeric_only=False)[source]# Compute standard deviation of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex.
|
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] |
151 |
..\pandas\reference\api\pandas.Series.droplevel.html
|
pandas.Series.droplevel
|
Series.droplevel(level, axis=0)[source]# Return Series/DataFrame with requested index / column level(s) removed.
|
Parameters: levelint, str, or list-likeIf a string is given, must be the name of a level If list-like, elements must be names or positional indexes of levels. axis{0 or ‘index’, 1 or ‘columns’}, default 0Axis along which the level(s) is removed: 0 or ‘index’: remove level(s) in column. 1 or ‘columns’: remove level(s) in row. For Series this parameter is unused and defaults to 0. Returns: Series/DataFrameSeries/DataFrame with requested index / column level(s) removed.
|
[">>> df = pd.DataFrame([\n... [1, 2, 3, 4],\n... [5, 6, 7, 8],\n... [9, 10, 11, 12]\n... ]).set_index([0, 1]).rename_axis(['a', 'b'])", ">>> df.columns = pd.MultiIndex.from_tuples([\n... ('c', 'e'), ('d', 'f')\n... ], names=['level_1', 'level_2'])", '>>> df\nlevel_1 c d\nlevel_2 e f\na b\n1 2 3 4\n5 6 7 8\n9 10 11 12', ">>> df.droplevel('a')\nlevel_1 c d\nlevel_2 e f\nb\n2 3 4\n6 7 8\n10 11 12", ">>> df.droplevel('level_2', axis=1)\nlevel_1 c d\na b\n1 2 3 4\n5 6 7 8\n9 10 11 12"]
|
pandas.Series.droplevel
Series.droplevel(level, axis=0)[source]# Return Series/DataFrame with requested index / column level(s) removed.
|
[
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] |
152 |
..\pandas\reference\api\pandas.Interval.length.html
|
pandas.Interval.length
|
Interval.length# Return the length of the Interval.
|
No parameters found
|
[">>> interval = pd.Interval(left=1, right=2, closed='left')\n>>> interval\nInterval(1, 2, closed='left')\n>>> interval.length\n1"]
|
pandas.Interval.length
Interval.length# Return the length of the Interval.
|
[
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] |
153 |
..\pandas\reference\api\pandas.DataFrame.sort_index.html
|
pandas.DataFrame.sort_index
|
DataFrame.sort_index(*, axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True, ignore_index=False, key=None)[source]# Sort object by labels (along an axis). Returns a new DataFrame sorted by label if inplace argument is False, otherwise updates the original DataFrame and returns None.
|
Parameters: axis{0 or ‘index’, 1 or ‘columns’}, default 0The axis along which to sort. The value 0 identifies the rows, and 1 identifies the columns. levelint or level name or list of ints or list of level namesIf not None, sort on values in specified index level(s). ascendingbool or list-like of bools, default TrueSort ascending vs. descending. When the index is a MultiIndex the sort direction can be controlled for each level individually. inplacebool, default FalseWhether to modify the DataFrame rather than creating a new one. kind{‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, default ‘quicksort’Choice of sorting algorithm. See also numpy.sort() for more information. mergesort and stable are the only stable algorithms. For DataFrames, this option is only applied when sorting on a single column or label. na_position{‘first’, ‘last’}, default ‘last’Puts NaNs at the beginning if first; last puts NaNs at the end. Not implemented for MultiIndex. sort_remainingbool, default TrueIf True and sorting by level and index is multilevel, sort by other levels too (in order) after sorting by specified level. ignore_indexbool, default FalseIf True, the resulting axis will be labeled 0, 1, …, n - 1. keycallable, optionalIf not None, apply the key function to the index values before sorting. This is similar to the key argument in the builtin sorted() function, with the notable difference that this key function should be vectorized. It should expect an Index and return an Index of the same shape. For MultiIndex inputs, the key is applied per level. Returns: DataFrame or NoneThe original DataFrame sorted by the labels or None if inplace=True.
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[">>> df = pd.DataFrame([1, 2, 3, 4, 5], index=[100, 29, 234, 1, 150],\n... columns=['A'])\n>>> df.sort_index()\n A\n1 4\n29 2\n100 1\n150 5\n234 3", '>>> df.sort_index(ascending=False)\n A\n234 3\n150 5\n100 1\n29 2\n1 4', '>>> df = pd.DataFrame({"a": [1, 2, 3, 4]}, index=[\'A\', \'b\', \'C\', \'d\'])\n>>> df.sort_index(key=lambda x: x.str.lower())\n a\nA 1\nb 2\nC 3\nd 4']
|
pandas.DataFrame.sort_index
DataFrame.sort_index(*, axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True, ignore_index=False, key=None)[source]# Sort object by labels (along an axis). Returns a new DataFrame sorted by label if inplace argument is False, otherwise updates the original DataFrame and returns None.
|
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154 |
..\pandas\reference\api\pandas.tseries.offsets.Milli.normalize.html
|
pandas.tseries.offsets.Milli.normalize
|
Milli.normalize#
|
No parameters found
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[]
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pandas.tseries.offsets.Milli.normalize
Milli.normalize#
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155 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray.take.html
|
pandas.api.extensions.ExtensionArray.take
|
ExtensionArray.take(indices, *, allow_fill=False, fill_value=None)[source]# Take elements from an array. Notes ExtensionArray.take is called by Series.__getitem__, .loc, iloc, when indices is a sequence of values. Additionally, it’s called by Series.reindex(), or any other method that causes realignment, with a fill_value.
|
Parameters: indicessequence of int or one-dimensional np.ndarray of intIndices to be taken. allow_fillbool, default FalseHow to handle negative values in indices. False: negative values in indices indicate positional indices from the right (the default). This is similar to numpy.take(). True: negative values in indices indicate missing values. These values are set to fill_value. Any other other negative values raise a ValueError. fill_valueany, optionalFill value to use for NA-indices when allow_fill is True. This may be None, in which case the default NA value for the type, self.dtype.na_value, is used. For many ExtensionArrays, there will be two representations of fill_value: a user-facing “boxed” scalar, and a low-level physical NA value. fill_value should be the user-facing version, and the implementation should handle translating that to the physical version for processing the take if necessary. Returns: ExtensionArray Raises: IndexErrorWhen the indices are out of bounds for the array. ValueErrorWhen indices contains negative values other than -1 and allow_fill is True.
|
[]
|
pandas.api.extensions.ExtensionArray.take
ExtensionArray.take(indices, *, allow_fill=False, fill_value=None)[source]# Take elements from an array. Notes ExtensionArray.take is called by Series.__getitem__, .loc, iloc, when indices is a sequence of values. Additionally, it’s called by Series.reindex(), or any other method that causes realignment, with a fill_value.
|
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156 |
..\pandas\reference\api\pandas.tseries.offsets.BYearEnd.html
|
pandas.tseries.offsets.BYearEnd
|
class pandas.tseries.offsets.BYearEnd# DateOffset increments between the last business day of the year. Examples Attributes base Returns a copy of the calling offset object with n=1 and all other attributes equal. freqstr Return a string representing the frequency. kwds Return a dict of extra parameters for the offset. month n name Return a string representing the base frequency. nanos normalize rule_code
|
Parameters: nint, default 1The number of years represented. normalizebool, default FalseNormalize start/end dates to midnight before generating date range. monthint, default 12A specific integer for the month of the year.
|
[">>> from pandas.tseries.offsets import BYearEnd\n>>> ts = pd.Timestamp('2020-05-24 05:01:15')\n>>> ts - BYearEnd()\nTimestamp('2019-12-31 05:01:15')\n>>> ts + BYearEnd()\nTimestamp('2020-12-31 05:01:15')\n>>> ts + BYearEnd(3)\nTimestamp('2022-12-30 05:01:15')\n>>> ts + BYearEnd(-3)\nTimestamp('2017-12-29 05:01:15')\n>>> ts + BYearEnd(month=11)\nTimestamp('2020-11-30 05:01:15')"]
|
pandas.tseries.offsets.BYearEnd
class pandas.tseries.offsets.BYearEnd# DateOffset increments between the last business day of the year. Examples Attributes base Returns a copy of the calling offset object with n=1 and all other attributes equal. freqstr Return a string representing the frequency. kwds Return a dict of extra parameters for the offset. month n name Return a string representing the base frequency. nanos normalize rule_code
|
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] |
157 |
..\pandas\reference\api\pandas.Series.tz_convert.html
|
pandas.Series.tz_convert
|
Series.tz_convert(tz, axis=0, level=None, copy=None)[source]# Convert tz-aware axis to target time zone.
|
Parameters: tzstr or tzinfo object or NoneTarget time zone. Passing None will convert to UTC and remove the timezone information. axis{0 or ‘index’, 1 or ‘columns’}, default 0The axis to convert levelint, str, default NoneIf axis is a MultiIndex, convert a specific level. Otherwise must be None. copybool, default TrueAlso make a copy of the underlying data. Note The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas. You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True Returns: Series/DataFrameObject with time zone converted axis. Raises: TypeErrorIf the axis is tz-naive.
|
[">>> s = pd.Series(\n... [1],\n... index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']),\n... )\n>>> s.tz_convert('Asia/Shanghai')\n2018-09-15 07:30:00+08:00 1\ndtype: int64", ">>> s = pd.Series([1],\n... index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']))\n>>> s.tz_convert(None)\n2018-09-14 23:30:00 1\ndtype: int64"]
|
pandas.Series.tz_convert
Series.tz_convert(tz, axis=0, level=None, copy=None)[source]# Convert tz-aware axis to target time zone.
|
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158 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray.tolist.html
|
pandas.api.extensions.ExtensionArray.tolist
|
ExtensionArray.tolist()[source]# Return a list of the values. These are each a scalar type, which is a Python scalar (for str, int, float) or a pandas scalar (for Timestamp/Timedelta/Interval/Period)
|
Returns: list
|
['>>> arr = pd.array([1, 2, 3])\n>>> arr.tolist()\n[1, 2, 3]']
|
pandas.api.extensions.ExtensionArray.tolist
ExtensionArray.tolist()[source]# Return a list of the values. These are each a scalar type, which is a Python scalar (for str, int, float) or a pandas scalar (for Timestamp/Timedelta/Interval/Period)
|
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159 |
..\pandas\reference\api\pandas.Interval.mid.html
|
pandas.Interval.mid
|
Interval.mid# Return the midpoint of the Interval.
|
No parameters found
|
['>>> iv = pd.Interval(0, 5)\n>>> iv.mid\n2.5']
|
pandas.Interval.mid
Interval.mid# Return the midpoint of the Interval.
|
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160 |
..\pandas\reference\api\pandas.core.groupby.SeriesGroupBy.sum.html
|
pandas.core.groupby.SeriesGroupBy.sum
|
SeriesGroupBy.sum(numeric_only=False, min_count=0, engine=None, engine_kwargs=None)[source]# Compute sum of group values.
|
Parameters: numeric_onlybool, default FalseInclude only float, int, boolean columns. Changed in version 2.0.0: numeric_only no longer accepts None. min_countint, default 0The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. enginestr, default None None 'cython' : Runs rolling apply through C-extensions from cython. 'numba'Runs rolling apply through JIT compiled code from numba.Only available when raw is set to True. None : Defaults to 'cython' or globally setting compute.use_numba engine_kwargsdict, default None None For 'cython' engine, there are no accepted engine_kwargs For 'numba' engine, the engine can accept nopython, nogiland parallel dictionary keys. The values must either be True or False. The default engine_kwargs for the 'numba' engine is {'nopython': True, 'nogil': False, 'parallel': False} and will be applied to both the func and the apply groupby aggregation. Returns: Series or DataFrameComputed sum of values within each group.
|
[">>> lst = ['a', 'a', 'b', 'b']\n>>> ser = pd.Series([1, 2, 3, 4], index=lst)\n>>> ser\na 1\na 2\nb 3\nb 4\ndtype: int64\n>>> ser.groupby(level=0).sum()\na 3\nb 7\ndtype: int64", '>>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]]\n>>> df = pd.DataFrame(data, columns=["a", "b", "c"],\n... index=["tiger", "leopard", "cheetah", "lion"])\n>>> df\n a b c\n tiger 1 8 2\nleopard 1 2 5\ncheetah 2 5 8\n lion 2 6 9\n>>> df.groupby("a").sum()\n b c\na\n1 10 7\n2 11 17']
|
pandas.core.groupby.SeriesGroupBy.sum
SeriesGroupBy.sum(numeric_only=False, min_count=0, engine=None, engine_kwargs=None)[source]# Compute sum of group values.
|
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] |
161 |
..\pandas\reference\api\pandas.DataFrame.sort_values.html
|
pandas.DataFrame.sort_values
|
DataFrame.sort_values(by, *, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last', ignore_index=False, key=None)[source]# Sort by the values along either axis.
|
Parameters: bystr or list of strName or list of names to sort by. if axis is 0 or ‘index’ then by may contain index levels and/or column labels. if axis is 1 or ‘columns’ then by may contain column levels and/or index labels. axis“{0 or ‘index’, 1 or ‘columns’}”, default 0Axis to be sorted. ascendingbool or list of bool, default TrueSort ascending vs. descending. Specify list for multiple sort orders. If this is a list of bools, must match the length of the by. inplacebool, default FalseIf True, perform operation in-place. kind{‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, default ‘quicksort’Choice of sorting algorithm. See also numpy.sort() for more information. mergesort and stable are the only stable algorithms. For DataFrames, this option is only applied when sorting on a single column or label. na_position{‘first’, ‘last’}, default ‘last’Puts NaNs at the beginning if first; last puts NaNs at the end. ignore_indexbool, default FalseIf True, the resulting axis will be labeled 0, 1, …, n - 1. keycallable, optionalApply the key function to the values before sorting. This is similar to the key argument in the builtin sorted() function, with the notable difference that this key function should be vectorized. It should expect a Series and return a Series with the same shape as the input. It will be applied to each column in by independently. Returns: DataFrame or NoneDataFrame with sorted values or None if inplace=True.
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[">>> df = pd.DataFrame({\n... 'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],\n... 'col2': [2, 1, 9, 8, 7, 4],\n... 'col3': [0, 1, 9, 4, 2, 3],\n... 'col4': ['a', 'B', 'c', 'D', 'e', 'F']\n... })\n>>> df\n col1 col2 col3 col4\n0 A 2 0 a\n1 A 1 1 B\n2 B 9 9 c\n3 NaN 8 4 D\n4 D 7 2 e\n5 C 4 3 F", ">>> df.sort_values(by=['col1'])\n col1 col2 col3 col4\n0 A 2 0 a\n1 A 1 1 B\n2 B 9 9 c\n5 C 4 3 F\n4 D 7 2 e\n3 NaN 8 4 D", ">>> df.sort_values(by=['col1', 'col2'])\n col1 col2 col3 col4\n1 A 1 1 B\n0 A 2 0 a\n2 B 9 9 c\n5 C 4 3 F\n4 D 7 2 e\n3 NaN 8 4 D", ">>> df.sort_values(by='col1', ascending=False)\n col1 col2 col3 col4\n4 D 7 2 e\n5 C 4 3 F\n2 B 9 9 c\n0 A 2 0 a\n1 A 1 1 B\n3 NaN 8 4 D", ">>> df.sort_values(by='col1', ascending=False, na_position='first')\n col1 col2 col3 col4\n3 NaN 8 4 D\n4 D 7 2 e\n5 C 4 3 F\n2 B 9 9 c\n0 A 2 0 a\n1 A 1 1 B", ">>> df.sort_values(by='col4', key=lambda col: col.str.lower())\n col1 col2 col3 col4\n0 A 2 0 a\n1 A 1 1 B\n2 B 9 9 c\n3 NaN 8 4 D\n4 D 7 2 e\n5 C 4 3 F", '>>> df = pd.DataFrame({\n... "time": [\'0hr\', \'128hr\', \'72hr\', \'48hr\', \'96hr\'],\n... "value": [10, 20, 30, 40, 50]\n... })\n>>> df\n time value\n0 0hr 10\n1 128hr 20\n2 72hr 30\n3 48hr 40\n4 96hr 50\n>>> from natsort import index_natsorted\n>>> df.sort_values(\n... by="time",\n... key=lambda x: np.argsort(index_natsorted(df["time"]))\n... )\n time value\n0 0hr 10\n3 48hr 40\n2 72hr 30\n4 96hr 50\n1 128hr 20']
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pandas.DataFrame.sort_values
DataFrame.sort_values(by, *, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last', ignore_index=False, key=None)[source]# Sort by the values along either axis.
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162 |
..\pandas\reference\api\pandas.Series.dropna.html
|
pandas.Series.dropna
|
Series.dropna(*, axis=0, inplace=False, how=None, ignore_index=False)[source]# Return a new Series with missing values removed. See the User Guide for more on which values are considered missing, and how to work with missing data.
|
Parameters: axis{0 or ‘index’}Unused. Parameter needed for compatibility with DataFrame. inplacebool, default FalseIf True, do operation inplace and return None. howstr, optionalNot in use. Kept for compatibility. ignore_indexbool, default FalseIf True, the resulting axis will be labeled 0, 1, …, n - 1. Added in version 2.0.0. Returns: Series or NoneSeries with NA entries dropped from it or None if inplace=True.
|
['>>> ser = pd.Series([1., 2., np.nan])\n>>> ser\n0 1.0\n1 2.0\n2 NaN\ndtype: float64', '>>> ser.dropna()\n0 1.0\n1 2.0\ndtype: float64', ">>> ser = pd.Series([np.nan, 2, pd.NaT, '', None, 'I stay'])\n>>> ser\n0 NaN\n1 2\n2 NaT\n3\n4 None\n5 I stay\ndtype: object\n>>> ser.dropna()\n1 2\n3\n5 I stay\ndtype: object"]
|
pandas.Series.dropna
Series.dropna(*, axis=0, inplace=False, how=None, ignore_index=False)[source]# Return a new Series with missing values removed. See the User Guide for more on which values are considered missing, and how to work with missing data.
|
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163 |
..\pandas\reference\api\pandas.tseries.offsets.Milli.rule_code.html
|
pandas.tseries.offsets.Milli.rule_code
|
Milli.rule_code#
|
No parameters found
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[]
|
pandas.tseries.offsets.Milli.rule_code
Milli.rule_code#
|
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164 |
..\pandas\reference\api\pandas.tseries.offsets.BYearEnd.is_anchored.html
|
pandas.tseries.offsets.BYearEnd.is_anchored
|
BYearEnd.is_anchored()# Return boolean whether the frequency is a unit frequency (n=1). Deprecated since version 2.2.0: is_anchored is deprecated and will be removed in a future version. Use obj.n == 1 instead.
|
No parameters found
|
['>>> pd.DateOffset().is_anchored()\nTrue\n>>> pd.DateOffset(2).is_anchored()\nFalse']
|
pandas.tseries.offsets.BYearEnd.is_anchored
BYearEnd.is_anchored()# Return boolean whether the frequency is a unit frequency (n=1). Deprecated since version 2.2.0: is_anchored is deprecated and will be removed in a future version. Use obj.n == 1 instead.
|
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..\pandas\reference\api\pandas.Series.tz_localize.html
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pandas.Series.tz_localize
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Series.tz_localize(tz, axis=0, level=None, copy=None, ambiguous='raise', nonexistent='raise')[source]# Localize tz-naive index of a Series or DataFrame to target time zone. This operation localizes the Index. To localize the values in a timezone-naive Series, use Series.dt.tz_localize().
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Parameters: tzstr or tzinfo or NoneTime zone to localize. Passing None will remove the time zone information and preserve local time. axis{0 or ‘index’, 1 or ‘columns’}, default 0The axis to localize levelint, str, default NoneIf axis ia a MultiIndex, localize a specific level. Otherwise must be None. copybool, default TrueAlso make a copy of the underlying data. Note The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas. You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True ambiguous‘infer’, bool-ndarray, ‘NaT’, default ‘raise’When clocks moved backward due to DST, ambiguous times may arise. For example in Central European Time (UTC+01), when going from 03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at 00:30:00 UTC and at 01:30:00 UTC. In such a situation, the ambiguous parameter dictates how ambiguous times should be handled. ‘infer’ will attempt to infer fall dst-transition hours based on order bool-ndarray where True signifies a DST time, False designates 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. nonexistentstr, default ‘raise’A nonexistent time does not exist in a particular timezone where clocks moved forward due to DST. Valid values are: ‘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. Returns: Series/DataFrameSame type as the input. Raises: TypeErrorIf the TimeSeries is tz-aware and tz is not None.
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[">>> s = pd.Series(\n... [1],\n... index=pd.DatetimeIndex(['2018-09-15 01:30:00']),\n... )\n>>> s.tz_localize('CET')\n2018-09-15 01:30:00+02:00 1\ndtype: int64", ">>> s = pd.Series([1],\n... index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']))\n>>> s.tz_localize(None)\n2018-09-15 01:30:00 1\ndtype: int64", ">>> s = pd.Series(range(7),\n... index=pd.DatetimeIndex(['2018-10-28 01:30:00',\n... '2018-10-28 02:00:00',\n... '2018-10-28 02:30:00',\n... '2018-10-28 02:00:00',\n... '2018-10-28 02:30:00',\n... '2018-10-28 03:00:00',\n... '2018-10-28 03:30:00']))\n>>> s.tz_localize('CET', ambiguous='infer')\n2018-10-28 01:30:00+02:00 0\n2018-10-28 02:00:00+02:00 1\n2018-10-28 02:30:00+02:00 2\n2018-10-28 02:00:00+01:00 3\n2018-10-28 02:30:00+01:00 4\n2018-10-28 03:00:00+01:00 5\n2018-10-28 03:30:00+01:00 6\ndtype: int64", ">>> s = pd.Series(range(3),\n... index=pd.DatetimeIndex(['2018-10-28 01:20:00',\n... '2018-10-28 02:36:00',\n... '2018-10-28 03:46:00']))\n>>> s.tz_localize('CET', ambiguous=np.array([True, True, False]))\n2018-10-28 01:20:00+02:00 0\n2018-10-28 02:36:00+02:00 1\n2018-10-28 03:46:00+01:00 2\ndtype: int64", ">>> s = pd.Series(range(2),\n... index=pd.DatetimeIndex(['2015-03-29 02:30:00',\n... '2015-03-29 03:30:00']))\n>>> s.tz_localize('Europe/Warsaw', nonexistent='shift_forward')\n2015-03-29 03:00:00+02:00 0\n2015-03-29 03:30:00+02:00 1\ndtype: int64\n>>> s.tz_localize('Europe/Warsaw', nonexistent='shift_backward')\n2015-03-29 01:59:59.999999999+01:00 0\n2015-03-29 03:30:00+02:00 1\ndtype: int64\n>>> s.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1h'))\n2015-03-29 03:30:00+02:00 0\n2015-03-29 03:30:00+02:00 1\ndtype: int64"]
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pandas.Series.tz_localize
Series.tz_localize(tz, axis=0, level=None, copy=None, ambiguous='raise', nonexistent='raise')[source]# Localize tz-naive index of a Series or DataFrame to target time zone. This operation localizes the Index. To localize the values in a timezone-naive Series, use Series.dt.tz_localize().
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] |
166 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray.unique.html
|
pandas.api.extensions.ExtensionArray.unique
|
ExtensionArray.unique()[source]# Compute the ExtensionArray of unique values.
|
Returns: pandas.api.extensions.ExtensionArray
|
['>>> arr = pd.array([1, 2, 3, 1, 2, 3])\n>>> arr.unique()\n<IntegerArray>\n[1, 2, 3]\nLength: 3, dtype: Int64']
|
pandas.api.extensions.ExtensionArray.unique
ExtensionArray.unique()[source]# Compute the ExtensionArray of unique values.
|
[
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] |
167 |
..\pandas\reference\api\pandas.DataFrame.sparse.density.html
|
pandas.DataFrame.sparse.density
|
DataFrame.sparse.density[source]# Ratio of non-sparse points to total (dense) data points.
|
No parameters found
|
['>>> df = pd.DataFrame({"A": pd.arrays.SparseArray([0, 1, 0, 1])})\n>>> df.sparse.density\n0.5']
|
pandas.DataFrame.sparse.density
DataFrame.sparse.density[source]# Ratio of non-sparse points to total (dense) data points.
|
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] |
168 |
..\pandas\reference\api\pandas.Interval.open_left.html
|
pandas.Interval.open_left
|
Interval.open_left# Check if the interval is open on the left side. For the meaning of closed and open see Interval.
|
Returns: boolTrue if the Interval is not closed on the left-side.
|
[">>> iv = pd.Interval(0, 5, closed='neither')\n>>> iv.open_left\nTrue", ">>> iv = pd.Interval(0, 5, closed='both')\n>>> iv.open_left\nFalse"]
|
pandas.Interval.open_left
Interval.open_left# Check if the interval is open on the left side. For the meaning of closed and open see Interval.
|
[
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pandas.tseries.offsets.Minute.copy
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Minute.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.Minute.copy
Minute.copy()# Return a copy of the frequency.
|
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] |
170 |
..\pandas\reference\api\pandas.core.groupby.SeriesGroupBy.tail.html
|
pandas.core.groupby.SeriesGroupBy.tail
|
SeriesGroupBy.tail(n=5)[source]# Return last n rows of each group. Similar to .apply(lambda x: x.tail(n)), but it returns a subset of rows from the original DataFrame with original index and order preserved (as_index flag is ignored).
|
Parameters: nintIf positive: number of entries to include from end of each group. If negative: number of entries to exclude from start of each group. Returns: Series or DataFrameSubset of original Series or DataFrame as determined by n.
|
[">>> df = pd.DataFrame([['a', 1], ['a', 2], ['b', 1], ['b', 2]],\n... columns=['A', 'B'])\n>>> df.groupby('A').tail(1)\n A B\n1 a 2\n3 b 2\n>>> df.groupby('A').tail(-1)\n A B\n1 a 2\n3 b 2"]
|
pandas.core.groupby.SeriesGroupBy.tail
SeriesGroupBy.tail(n=5)[source]# Return last n rows of each group. Similar to .apply(lambda x: x.tail(n)), but it returns a subset of rows from the original DataFrame with original index and order preserved (as_index flag is ignored).
|
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] |
171 |
..\pandas\reference\api\pandas.Series.drop_duplicates.html
|
pandas.Series.drop_duplicates
|
Series.drop_duplicates(*, keep='first', inplace=False, ignore_index=False)[source]# Return Series with duplicate values removed.
|
Parameters: keep{‘first’, ‘last’, False}, default ‘first’Method to handle dropping duplicates: ‘first’ : Drop duplicates except for the first occurrence. ‘last’ : Drop duplicates except for the last occurrence. False : Drop all duplicates. inplacebool, default FalseIf True, performs operation inplace and returns None. ignore_indexbool, default FalseIf True, the resulting axis will be labeled 0, 1, …, n - 1. Added in version 2.0.0. Returns: Series or NoneSeries with duplicates dropped or None if inplace=True.
|
[">>> s = pd.Series(['llama', 'cow', 'llama', 'beetle', 'llama', 'hippo'],\n... name='animal')\n>>> s\n0 llama\n1 cow\n2 llama\n3 beetle\n4 llama\n5 hippo\nName: animal, dtype: object", '>>> s.drop_duplicates()\n0 llama\n1 cow\n3 beetle\n5 hippo\nName: animal, dtype: object', ">>> s.drop_duplicates(keep='last')\n1 cow\n3 beetle\n4 llama\n5 hippo\nName: animal, dtype: object", '>>> s.drop_duplicates(keep=False)\n1 cow\n3 beetle\n5 hippo\nName: animal, dtype: object']
|
pandas.Series.drop_duplicates
Series.drop_duplicates(*, keep='first', inplace=False, ignore_index=False)[source]# Return Series with duplicate values removed.
|
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] |
172 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray.view.html
|
pandas.api.extensions.ExtensionArray.view
|
ExtensionArray.view(dtype=None)[source]# Return a view on the array.
|
Parameters: dtypestr, np.dtype, or ExtensionDtype, optionalDefault None. Returns: ExtensionArray or np.ndarrayA view on the ExtensionArray’s data.
|
['>>> arr = pd.array([1, 2, 3])\n>>> arr2 = arr.view()\n>>> arr[0] = 2\n>>> arr2\n<IntegerArray>\n[2, 2, 3]\nLength: 3, dtype: Int64']
|
pandas.api.extensions.ExtensionArray.view
ExtensionArray.view(dtype=None)[source]# Return a view on the array.
|
[
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] |
173 |
..\pandas\reference\api\pandas.Series.unique.html
|
pandas.Series.unique
|
Series.unique()[source]# Return unique values of Series object. Uniques are returned in order of appearance. Hash table-based unique, therefore does NOT sort. Notes Returns the unique values as a NumPy array. In case of an extension-array backed Series, a new ExtensionArray of that type with just the unique values is returned. This includes Categorical Period Datetime with Timezone Datetime without Timezone Timedelta Interval Sparse IntegerNA See Examples section.
|
Returns: ndarray or ExtensionArrayThe unique values returned as a NumPy array. See Notes.
|
[">>> pd.Series([2, 1, 3, 3], name='A').unique()\narray([2, 1, 3])", ">>> pd.Series([pd.Timestamp('2016-01-01') for _ in range(3)]).unique()\n<DatetimeArray>\n['2016-01-01 00:00:00']\nLength: 1, dtype: datetime64[ns]", ">>> pd.Series([pd.Timestamp('2016-01-01', tz='US/Eastern')\n... for _ in range(3)]).unique()\n<DatetimeArray>\n['2016-01-01 00:00:00-05:00']\nLength: 1, dtype: datetime64[ns, US/Eastern]", ">>> pd.Series(pd.Categorical(list('baabc'))).unique()\n['b', 'a', 'c']\nCategories (3, object): ['a', 'b', 'c']\n>>> pd.Series(pd.Categorical(list('baabc'), categories=list('abc'),\n... ordered=True)).unique()\n['b', 'a', 'c']\nCategories (3, object): ['a' < 'b' < 'c']"]
|
pandas.Series.unique
Series.unique()[source]# Return unique values of Series object. Uniques are returned in order of appearance. Hash table-based unique, therefore does NOT sort. Notes Returns the unique values as a NumPy array. In case of an extension-array backed Series, a new ExtensionArray of that type with just the unique values is returned. This includes Categorical Period Datetime with Timezone Datetime without Timezone Timedelta Interval Sparse IntegerNA See Examples section.
|
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] |
174 |
..\pandas\reference\api\pandas.tseries.offsets.BYearEnd.is_month_end.html
|
pandas.tseries.offsets.BYearEnd.is_month_end
|
BYearEnd.is_month_end(ts)# Return boolean whether a timestamp occurs on the month end.
|
No parameters found
|
['>>> ts = pd.Timestamp(2022, 1, 1)\n>>> freq = pd.offsets.Hour(5)\n>>> freq.is_month_end(ts)\nFalse']
|
pandas.tseries.offsets.BYearEnd.is_month_end
BYearEnd.is_month_end(ts)# Return boolean whether a timestamp occurs on the month end.
|
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] |
175 |
..\pandas\reference\api\pandas.DataFrame.sparse.from_spmatrix.html
|
pandas.DataFrame.sparse.from_spmatrix
|
classmethod DataFrame.sparse.from_spmatrix(data, index=None, columns=None)[source]# Create a new DataFrame from a scipy sparse matrix.
|
Parameters: datascipy.sparse.spmatrixMust be convertible to csc format. index, columnsIndex, optionalRow and column labels to use for the resulting DataFrame. Defaults to a RangeIndex. Returns: DataFrameEach column of the DataFrame is stored as a arrays.SparseArray.
|
['>>> import scipy.sparse\n>>> mat = scipy.sparse.eye(3, dtype=float)\n>>> pd.DataFrame.sparse.from_spmatrix(mat)\n 0 1 2\n0 1.0 0 0\n1 0 1.0 0\n2 0 0 1.0']
|
pandas.DataFrame.sparse.from_spmatrix
classmethod DataFrame.sparse.from_spmatrix(data, index=None, columns=None)[source]# Create a new DataFrame from a scipy sparse matrix.
|
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] |
176 |
..\pandas\reference\api\pandas.Interval.open_right.html
|
pandas.Interval.open_right
|
Interval.open_right# Check if the interval is open on the right side. For the meaning of closed and open see Interval.
|
Returns: boolTrue if the Interval is not closed on the left-side.
|
[">>> iv = pd.Interval(0, 5, closed='left')\n>>> iv.open_right\nTrue", '>>> iv = pd.Interval(0, 5)\n>>> iv.open_right\nFalse']
|
pandas.Interval.open_right
Interval.open_right# Check if the interval is open on the right side. For the meaning of closed and open see Interval.
|
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] |
177 |
..\pandas\reference\api\pandas.Series.dt.as_unit.html
|
pandas.Series.dt.as_unit
|
Series.dt.as_unit(*args, **kwargs)[source]#
|
No parameters found
|
[]
|
pandas.Series.dt.as_unit
Series.dt.as_unit(*args, **kwargs)[source]#
|
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] |
178 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray._accumulate.html
|
pandas.api.extensions.ExtensionArray._accumulate
|
ExtensionArray._accumulate(name, *, skipna=True, **kwargs)[source]# Return an ExtensionArray performing an accumulation operation. The underlying data type might change.
|
Parameters: namestrName of the function, supported values are: - cummin - cummax - cumsum - cumprod skipnabool, default TrueIf True, skip NA values. **kwargsAdditional keyword arguments passed to the accumulation function. Currently, there is no supported kwarg. Returns: array Raises: NotImplementedErrorsubclass does not define accumulations
|
[">>> arr = pd.array([1, 2, 3])\n>>> arr._accumulate(name='cumsum')\n<IntegerArray>\n[1, 3, 6]\nLength: 3, dtype: Int64"]
|
pandas.api.extensions.ExtensionArray._accumulate
ExtensionArray._accumulate(name, *, skipna=True, **kwargs)[source]# Return an ExtensionArray performing an accumulation operation. The underlying data type might change.
|
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] |
179 |
..\pandas\reference\api\pandas.Series.unstack.html
|
pandas.Series.unstack
|
Series.unstack(level=-1, fill_value=None, sort=True)[source]# Unstack, also known as pivot, Series with MultiIndex to produce DataFrame. Notes Reference the user guide for more examples.
|
Parameters: levelint, str, or list of these, default last levelLevel(s) to unstack, can pass level name. fill_valuescalar value, default NoneValue to use when replacing NaN values. sortbool, default TrueSort the level(s) in the resulting MultiIndex columns. Returns: DataFrameUnstacked Series.
|
[">>> s = pd.Series([1, 2, 3, 4],\n... index=pd.MultiIndex.from_product([['one', 'two'],\n... ['a', 'b']]))\n>>> s\none a 1\n b 2\ntwo a 3\n b 4\ndtype: int64", '>>> s.unstack(level=-1)\n a b\none 1 2\ntwo 3 4', '>>> s.unstack(level=0)\n one two\na 1 3\nb 2 4']
|
pandas.Series.unstack
Series.unstack(level=-1, fill_value=None, sort=True)[source]# Unstack, also known as pivot, Series with MultiIndex to produce DataFrame. Notes Reference the user guide for more examples.
|
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] |
180 |
..\pandas\reference\api\pandas.DataFrame.sparse.html
|
pandas.DataFrame.sparse
|
DataFrame.sparse()[source]# DataFrame accessor for sparse data.
|
No parameters found
|
['>>> df = pd.DataFrame({"a": [1, 2, 0, 0],\n... "b": [3, 0, 0, 4]}, dtype="Sparse[int]")\n>>> df.sparse.density\n0.5']
|
pandas.DataFrame.sparse
DataFrame.sparse()[source]# DataFrame accessor for sparse data.
|
[
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181 |
..\pandas\reference\api\pandas.core.groupby.SeriesGroupBy.take.html
|
pandas.core.groupby.SeriesGroupBy.take
|
SeriesGroupBy.take(indices, axis=<no_default>, **kwargs)[source]# Return the elements in the given positional indices in each group. This means that we are not indexing according to actual values in the index attribute of the object. We are indexing according to the actual position of the element in the object. If a requested index does not exist for some group, this method will raise. To get similar behavior that ignores indices that don’t exist, see SeriesGroupBy.nth().
|
Parameters: indicesarray-likeAn array of ints indicating which positions to take in each group. axis{0 or ‘index’, 1 or ‘columns’, None}, default 0The axis on which to select elements. 0 means that we are selecting rows, 1 means that we are selecting columns. For SeriesGroupBy this parameter is unused and defaults to 0. Deprecated since version 2.1.0: For axis=1, operate on the underlying object instead. Otherwise the axis keyword is not necessary. **kwargsFor compatibility with numpy.take(). Has no effect on the output. Returns: SeriesA Series containing the elements taken from each group.
|
['>>> df = pd.DataFrame([(\'falcon\', \'bird\', 389.0),\n... (\'parrot\', \'bird\', 24.0),\n... (\'lion\', \'mammal\', 80.5),\n... (\'monkey\', \'mammal\', np.nan),\n... (\'rabbit\', \'mammal\', 15.0)],\n... columns=[\'name\', \'class\', \'max_speed\'],\n... index=[4, 3, 2, 1, 0])\n>>> df\n name class max_speed\n4 falcon bird 389.0\n3 parrot bird 24.0\n2 lion mammal 80.5\n1 monkey mammal NaN\n0 rabbit mammal 15.0\n>>> gb = df["name"].groupby([1, 1, 2, 2, 2])', '>>> gb.take([0, 1])\n1 4 falcon\n 3 parrot\n2 2 lion\n 1 monkey\nName: name, dtype: object', '>>> gb.take([-1, -2])\n1 3 parrot\n 4 falcon\n2 0 rabbit\n 1 monkey\nName: name, dtype: object']
|
pandas.core.groupby.SeriesGroupBy.take
SeriesGroupBy.take(indices, axis=<no_default>, **kwargs)[source]# Return the elements in the given positional indices in each group. This means that we are not indexing according to actual values in the index attribute of the object. We are indexing according to the actual position of the element in the object. If a requested index does not exist for some group, this method will raise. To get similar behavior that ignores indices that don’t exist, see SeriesGroupBy.nth().
|
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] |
182 |
..\pandas\reference\api\pandas.Interval.overlaps.html
|
pandas.Interval.overlaps
|
Interval.overlaps(other)# Check whether two Interval objects overlap. Two intervals overlap if they share a common point, including closed endpoints. Intervals that only have an open endpoint in common do not overlap.
|
Parameters: otherIntervalInterval to check against for an overlap. Returns: boolTrue if the two intervals overlap.
|
['>>> i1 = pd.Interval(0, 2)\n>>> i2 = pd.Interval(1, 3)\n>>> i1.overlaps(i2)\nTrue\n>>> i3 = pd.Interval(4, 5)\n>>> i1.overlaps(i3)\nFalse', ">>> i4 = pd.Interval(0, 1, closed='both')\n>>> i5 = pd.Interval(1, 2, closed='both')\n>>> i4.overlaps(i5)\nTrue", ">>> i6 = pd.Interval(1, 2, closed='neither')\n>>> i4.overlaps(i6)\nFalse"]
|
pandas.Interval.overlaps
Interval.overlaps(other)# Check whether two Interval objects overlap. Two intervals overlap if they share a common point, including closed endpoints. Intervals that only have an open endpoint in common do not overlap.
|
[
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183 |
..\pandas\reference\api\pandas.tseries.offsets.Minute.delta.html
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pandas.tseries.offsets.Minute.delta
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Minute.delta#
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No parameters found
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[]
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pandas.tseries.offsets.Minute.delta
Minute.delta#
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184 |
..\pandas\reference\api\pandas.tseries.offsets.BYearEnd.is_month_start.html
|
pandas.tseries.offsets.BYearEnd.is_month_start
|
BYearEnd.is_month_start(ts)# Return boolean whether a timestamp occurs on the month start.
|
No parameters found
|
['>>> ts = pd.Timestamp(2022, 1, 1)\n>>> freq = pd.offsets.Hour(5)\n>>> freq.is_month_start(ts)\nTrue']
|
pandas.tseries.offsets.BYearEnd.is_month_start
BYearEnd.is_month_start(ts)# Return boolean whether a timestamp occurs on the month start.
|
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] |
185 |
..\pandas\reference\api\pandas.Series.dt.ceil.html
|
pandas.Series.dt.ceil
|
Series.dt.ceil(*args, **kwargs)[source]# Perform ceil operation on the data to the specified freq. Notes If the timestamps have a timezone, ceiling will take place relative to the local (“wall”) time and re-localized to the same timezone. When ceiling near daylight savings time, use nonexistent and ambiguous to control the re-localization behavior.
|
Parameters: freqstr or OffsetThe frequency level to ceil the index to. Must be a fixed frequency like ‘S’ (second) not ‘ME’ (month end). See frequency aliases for a list of possible freq values. ambiguous‘infer’, bool-ndarray, ‘NaT’, default ‘raise’Only relevant for DatetimeIndex: ‘infer’ will attempt to infer fall dst-transition hours based on order bool-ndarray where True signifies a DST time, False designates 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. 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. Returns: DatetimeIndex, TimedeltaIndex, or SeriesIndex of the same type for a DatetimeIndex or TimedeltaIndex, or a Series with the same index for a Series. Raises: ValueError if the freq cannot be converted.
|
[">>> rng = pd.date_range('1/1/2018 11:59:00', periods=3, freq='min')\n>>> rng\nDatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00',\n '2018-01-01 12:01:00'],\n dtype='datetime64[ns]', freq='min')\n>>> rng.ceil('h')\nDatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00',\n '2018-01-01 13:00:00'],\n dtype='datetime64[ns]', freq=None)", '>>> pd.Series(rng).dt.ceil("h")\n0 2018-01-01 12:00:00\n1 2018-01-01 12:00:00\n2 2018-01-01 13:00:00\ndtype: datetime64[ns]', '>>> rng_tz = pd.DatetimeIndex(["2021-10-31 01:30:00"], tz="Europe/Amsterdam")', '>>> rng_tz.ceil("h", ambiguous=False)\nDatetimeIndex([\'2021-10-31 02:00:00+01:00\'],\n dtype=\'datetime64[ns, Europe/Amsterdam]\', freq=None)', '>>> rng_tz.ceil("h", ambiguous=True)\nDatetimeIndex([\'2021-10-31 02:00:00+02:00\'],\n dtype=\'datetime64[ns, Europe/Amsterdam]\', freq=None)']
|
pandas.Series.dt.ceil
Series.dt.ceil(*args, **kwargs)[source]# Perform ceil operation on the data to the specified freq. Notes If the timestamps have a timezone, ceiling will take place relative to the local (“wall”) time and re-localized to the same timezone. When ceiling near daylight savings time, use nonexistent and ambiguous to control the re-localization behavior.
|
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] |
186 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray._concat_same_type.html
|
pandas.api.extensions.ExtensionArray._concat_same_type
|
classmethod ExtensionArray._concat_same_type(to_concat)[source]# Concatenate multiple array of this dtype.
|
Parameters: to_concatsequence of this type Returns: ExtensionArray
|
['>>> arr1 = pd.array([1, 2, 3])\n>>> arr2 = pd.array([4, 5, 6])\n>>> pd.arrays.IntegerArray._concat_same_type([arr1, arr2])\n<IntegerArray>\n[1, 2, 3, 4, 5, 6]\nLength: 6, dtype: Int64']
|
pandas.api.extensions.ExtensionArray._concat_same_type
classmethod ExtensionArray._concat_same_type(to_concat)[source]# Concatenate multiple array of this dtype.
|
[
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] |
187 |
..\pandas\reference\api\pandas.Series.update.html
|
pandas.Series.update
|
Series.update(other)[source]# Modify Series in place using values from passed Series. Uses non-NA values from passed Series to make updates. Aligns on index.
|
Parameters: otherSeries, or object coercible into Series
|
['>>> s = pd.Series([1, 2, 3])\n>>> s.update(pd.Series([4, 5, 6]))\n>>> s\n0 4\n1 5\n2 6\ndtype: int64', ">>> s = pd.Series(['a', 'b', 'c'])\n>>> s.update(pd.Series(['d', 'e'], index=[0, 2]))\n>>> s\n0 d\n1 b\n2 e\ndtype: object", '>>> s = pd.Series([1, 2, 3])\n>>> s.update(pd.Series([4, 5, 6, 7, 8]))\n>>> s\n0 4\n1 5\n2 6\ndtype: int64', '>>> s = pd.Series([1, 2, 3])\n>>> s.update(pd.Series([4, np.nan, 6]))\n>>> s\n0 4\n1 2\n2 6\ndtype: int64', '>>> s = pd.Series([1, 2, 3])\n>>> s.update([4, np.nan, 6])\n>>> s\n0 4\n1 2\n2 6\ndtype: int64', '>>> s = pd.Series([1, 2, 3])\n>>> s.update({1: 9})\n>>> s\n0 1\n1 9\n2 3\ndtype: int64']
|
pandas.Series.update
Series.update(other)[source]# Modify Series in place using values from passed Series. Uses non-NA values from passed Series to make updates. Aligns on index.
|
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188 |
..\pandas\reference\api\pandas.core.groupby.SeriesGroupBy.transform.html
|
pandas.core.groupby.SeriesGroupBy.transform
|
SeriesGroupBy.transform(func, *args, engine=None, engine_kwargs=None, **kwargs)[source]# Call function producing a same-indexed Series on each group. Returns a Series having the same indexes as the original object filled with the transformed values. Notes Each group is endowed the attribute ‘name’ in case you need to know which group you are working on. The current implementation imposes three requirements on f: f must return a value that either has the same shape as the input subframe or can be broadcast to the shape of the input subframe. For example, if f returns a scalar it will be broadcast to have the same shape as the input subframe. if this is a DataFrame, f must support application column-by-column in the subframe. If f also supports application to the entire subframe, then a fast path is used starting from the second chunk. f must not mutate groups. Mutation is not supported and may produce unexpected results. See Mutating with User Defined Function (UDF) methods for more details. When using engine='numba', there will be no “fall back” behavior internally. The group data and group index will be passed as numpy arrays to the JITed user defined function, and no alternative execution attempts will be tried. Changed in version 1.3.0: The resulting dtype will reflect the return value of the passed func, see the examples below. Changed in version 2.0.0: When using .transform on a grouped DataFrame and the transformation function returns a DataFrame, pandas now aligns the result’s index with the input’s index. You can call .to_numpy() on the result of the transformation function to avoid alignment.
|
Parameters: ffunction, strFunction to apply to each group. See the Notes section below for requirements. Accepted inputs are: String Python function Numba JIT function with engine='numba' specified. Only passing a single function is supported with this engine. If the 'numba' engine is chosen, the function must be a user defined function with values and index as the first and second arguments respectively in the function signature. Each group’s index will be passed to the user defined function and optionally available for use. If a string is chosen, then it needs to be the name of the groupby method you want to use. *argsPositional arguments to pass to func. enginestr, default None 'cython' : Runs the function through C-extensions from cython. 'numba' : Runs the function through JIT compiled code from numba. None : Defaults to 'cython' or the global setting compute.use_numba engine_kwargsdict, default None For 'cython' engine, there are no accepted engine_kwargs For 'numba' engine, the engine can accept nopython, nogil and parallel dictionary keys. The values must either be True or False. The default engine_kwargs for the 'numba' engine is {'nopython': True, 'nogil': False, 'parallel': False} and will be applied to the function **kwargsKeyword arguments to be passed into func. Returns: Series
|
['>>> ser = pd.Series([390.0, 350.0, 30.0, 20.0],\n... index=["Falcon", "Falcon", "Parrot", "Parrot"],\n... name="Max Speed")\n>>> grouped = ser.groupby([1, 1, 2, 2])\n>>> grouped.transform(lambda x: (x - x.mean()) / x.std())\n Falcon 0.707107\n Falcon -0.707107\n Parrot 0.707107\n Parrot -0.707107\n Name: Max Speed, dtype: float64', '>>> grouped.transform(lambda x: x.max() - x.min())\nFalcon 40.0\nFalcon 40.0\nParrot 10.0\nParrot 10.0\nName: Max Speed, dtype: float64', '>>> grouped.transform("mean")\nFalcon 370.0\nFalcon 370.0\nParrot 25.0\nParrot 25.0\nName: Max Speed, dtype: float64', '>>> grouped.transform(lambda x: x.astype(int).max())\nFalcon 390\nFalcon 390\nParrot 30\nParrot 30\nName: Max Speed, dtype: int64']
|
pandas.core.groupby.SeriesGroupBy.transform
SeriesGroupBy.transform(func, *args, engine=None, engine_kwargs=None, **kwargs)[source]# Call function producing a same-indexed Series on each group. Returns a Series having the same indexes as the original object filled with the transformed values. Notes Each group is endowed the attribute ‘name’ in case you need to know which group you are working on. The current implementation imposes three requirements on f: f must return a value that either has the same shape as the input subframe or can be broadcast to the shape of the input subframe. For example, if f returns a scalar it will be broadcast to have the same shape as the input subframe. if this is a DataFrame, f must support application column-by-column in the subframe. If f also supports application to the entire subframe, then a fast path is used starting from the second chunk. f must not mutate groups. Mutation is not supported and may produce unexpected results. See Mutating with User Defined Function (UDF) methods for more details. When using engine='numba', there will be no “fall back” behavior internally. The group data and group index will be passed as numpy arrays to the JITed user defined function, and no alternative execution attempts will be tried. Changed in version 1.3.0: The resulting dtype will reflect the return value of the passed func, see the examples below. Changed in version 2.0.0: When using .transform on a grouped DataFrame and the transformation function returns a DataFrame, pandas now aligns the result’s index with the input’s index. You can call .to_numpy() on the result of the transformation function to avoid alignment.
|
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] |
189 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray._explode.html
|
pandas.api.extensions.ExtensionArray._explode
|
ExtensionArray._explode()[source]# Transform each element of list-like to a row. For arrays that do not contain list-like elements the default implementation of this method just returns a copy and an array of ones (unchanged index).
|
Returns: ExtensionArrayArray with the exploded values. np.ndarray[uint64]The original lengths of each list-like for determining the resulting index.
|
['>>> import pyarrow as pa\n>>> a = pd.array([[1, 2, 3], [4], [5, 6]],\n... dtype=pd.ArrowDtype(pa.list_(pa.int64())))\n>>> a._explode()\n(<ArrowExtensionArray>\n[1, 2, 3, 4, 5, 6]\nLength: 6, dtype: int64[pyarrow], array([3, 1, 2], dtype=int32))']
|
pandas.api.extensions.ExtensionArray._explode
ExtensionArray._explode()[source]# Transform each element of list-like to a row. For arrays that do not contain list-like elements the default implementation of this method just returns a copy and an array of ones (unchanged index).
|
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] |
190 |
..\pandas\reference\api\pandas.Interval.right.html
|
pandas.Interval.right
|
Interval.right# Right bound for the interval.
|
No parameters found
|
[">>> interval = pd.Interval(left=1, right=2, closed='left')\n>>> interval\nInterval(1, 2, closed='left')\n>>> interval.right\n2"]
|
pandas.Interval.right
Interval.right# Right bound for the interval.
|
[
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] |
191 |
..\pandas\reference\api\pandas.tseries.offsets.BYearEnd.is_on_offset.html
|
pandas.tseries.offsets.BYearEnd.is_on_offset
|
BYearEnd.is_on_offset(dt)# Return boolean whether a timestamp intersects with this frequency.
|
Parameters: dtdatetime.datetimeTimestamp to check intersections with frequency.
|
['>>> ts = pd.Timestamp(2022, 1, 1)\n>>> freq = pd.offsets.Day(1)\n>>> freq.is_on_offset(ts)\nTrue', ">>> ts = pd.Timestamp(2022, 8, 6)\n>>> ts.day_name()\n'Saturday'\n>>> freq = pd.offsets.BusinessDay(1)\n>>> freq.is_on_offset(ts)\nFalse"]
|
pandas.tseries.offsets.BYearEnd.is_on_offset
BYearEnd.is_on_offset(dt)# Return boolean whether a timestamp intersects with this frequency.
|
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192 |
..\pandas\reference\api\pandas.DataFrame.sparse.to_coo.html
|
pandas.DataFrame.sparse.to_coo
|
DataFrame.sparse.to_coo()[source]# Return the contents of the frame as a sparse SciPy COO matrix. Notes The dtype will be the lowest-common-denominator type (implicit upcasting); that is to say if the dtypes (even of numeric types) are mixed, the one that accommodates all will be chosen. e.g. If the dtypes are float16 and float32, dtype will be upcast to float32. By numpy.find_common_type convention, mixing int64 and and uint64 will result in a float64 dtype.
|
Returns: scipy.sparse.spmatrixIf the caller is heterogeneous and contains booleans or objects, the result will be of dtype=object. See Notes.
|
['>>> df = pd.DataFrame({"A": pd.arrays.SparseArray([0, 1, 0, 1])})\n>>> df.sparse.to_coo()\n<COOrdinate sparse matrix of dtype \'int64\'\n with 2 stored elements and shape (4, 1)>']
|
pandas.DataFrame.sparse.to_coo
DataFrame.sparse.to_coo()[source]# Return the contents of the frame as a sparse SciPy COO matrix. Notes The dtype will be the lowest-common-denominator type (implicit upcasting); that is to say if the dtypes (even of numeric types) are mixed, the one that accommodates all will be chosen. e.g. If the dtypes are float16 and float32, dtype will be upcast to float32. By numpy.find_common_type convention, mixing int64 and and uint64 will result in a float64 dtype.
|
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] |
193 |
..\pandas\reference\api\pandas.Series.dt.components.html
|
pandas.Series.dt.components
|
Series.dt.components[source]# Return a Dataframe of the components of the Timedeltas.
|
Returns: DataFrame
|
[">>> s = pd.Series(pd.to_timedelta(np.arange(5), unit='s'))\n>>> s\n0 0 days 00:00:00\n1 0 days 00:00:01\n2 0 days 00:00:02\n3 0 days 00:00:03\n4 0 days 00:00:04\ndtype: timedelta64[ns]\n>>> s.dt.components\n days hours minutes seconds milliseconds microseconds nanoseconds\n0 0 0 0 0 0 0 0\n1 0 0 0 1 0 0 0\n2 0 0 0 2 0 0 0\n3 0 0 0 3 0 0 0\n4 0 0 0 4 0 0 0"]
|
pandas.Series.dt.components
Series.dt.components[source]# Return a Dataframe of the components of the Timedeltas.
|
[
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194 |
..\pandas\reference\api\pandas.tseries.offsets.Minute.freqstr.html
|
pandas.tseries.offsets.Minute.freqstr
|
Minute.freqstr# Return a string representing the frequency.
|
No parameters found
|
[">>> pd.DateOffset(5).freqstr\n'<5 * DateOffsets>'", ">>> pd.offsets.BusinessHour(2).freqstr\n'2bh'", ">>> pd.offsets.Nano().freqstr\n'ns'", ">>> pd.offsets.Nano(-3).freqstr\n'-3ns'"]
|
pandas.tseries.offsets.Minute.freqstr
Minute.freqstr# Return a string representing the frequency.
|
[
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] |
195 |
..\pandas\reference\api\pandas.Series.values.html
|
pandas.Series.values
|
property Series.values[source]# Return Series as ndarray or ndarray-like depending on the dtype. Warning We recommend using Series.array or Series.to_numpy(), depending on whether you need a reference to the underlying data or a NumPy array.
|
Returns: numpy.ndarray or ndarray-like
|
['>>> pd.Series([1, 2, 3]).values\narray([1, 2, 3])', ">>> pd.Series(list('aabc')).values\narray(['a', 'a', 'b', 'c'], dtype=object)", ">>> pd.Series(list('aabc')).astype('category').values\n['a', 'a', 'b', 'c']\nCategories (3, object): ['a', 'b', 'c']", ">>> pd.Series(pd.date_range('20130101', periods=3,\n... tz='US/Eastern')).values\narray(['2013-01-01T05:00:00.000000000',\n '2013-01-02T05:00:00.000000000',\n '2013-01-03T05:00:00.000000000'], dtype='datetime64[ns]')"]
|
pandas.Series.values
property Series.values[source]# Return Series as ndarray or ndarray-like depending on the dtype. Warning We recommend using Series.array or Series.to_numpy(), depending on whether you need a reference to the underlying data or a NumPy array.
|
[
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196 |
..\pandas\reference\api\pandas.core.groupby.SeriesGroupBy.unique.html
|
pandas.core.groupby.SeriesGroupBy.unique
|
SeriesGroupBy.unique()[source]# Return unique values for each group. It returns unique values for each of the grouped values. Returned in order of appearance. Hash table-based unique, therefore does NOT sort.
|
Returns: SeriesUnique values for each of the grouped values.
|
[">>> df = pd.DataFrame([('Chihuahua', 'dog', 6.1),\n... ('Beagle', 'dog', 15.2),\n... ('Chihuahua', 'dog', 6.9),\n... ('Persian', 'cat', 9.2),\n... ('Chihuahua', 'dog', 7),\n... ('Persian', 'cat', 8.8)],\n... columns=['breed', 'animal', 'height_in'])\n>>> df\n breed animal height_in\n0 Chihuahua dog 6.1\n1 Beagle dog 15.2\n2 Chihuahua dog 6.9\n3 Persian cat 9.2\n4 Chihuahua dog 7.0\n5 Persian cat 8.8\n>>> ser = df.groupby('animal')['breed'].unique()\n>>> ser\nanimal\ncat [Persian]\ndog [Chihuahua, Beagle]\nName: breed, dtype: object"]
|
pandas.core.groupby.SeriesGroupBy.unique
SeriesGroupBy.unique()[source]# Return unique values for each group. It returns unique values for each of the grouped values. Returned in order of appearance. Hash table-based unique, therefore does NOT sort.
|
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197 |
..\pandas\reference\api\pandas.Series.dt.date.html
|
pandas.Series.dt.date
|
Series.dt.date[source]# Returns numpy array of python datetime.date objects. Namely, the date part of Timestamps without time and timezone information.
|
No parameters found
|
['>>> s = pd.Series(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"])\n>>> s = pd.to_datetime(s)\n>>> s\n0 2020-01-01 10:00:00+00:00\n1 2020-02-01 11:00:00+00:00\ndtype: datetime64[ns, UTC]\n>>> s.dt.date\n0 2020-01-01\n1 2020-02-01\ndtype: object', '>>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00",\n... "2/1/2020 11:00:00+00:00"])\n>>> idx.date\narray([datetime.date(2020, 1, 1), datetime.date(2020, 2, 1)], dtype=object)']
|
pandas.Series.dt.date
Series.dt.date[source]# Returns numpy array of python datetime.date objects. Namely, the date part of Timestamps without time and timezone information.
|
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] |
198 |
..\pandas\reference\api\pandas.DataFrame.sparse.to_dense.html
|
pandas.DataFrame.sparse.to_dense
|
DataFrame.sparse.to_dense()[source]# Convert a DataFrame with sparse values to dense.
|
Returns: DataFrameA DataFrame with the same values stored as dense arrays.
|
['>>> df = pd.DataFrame({"A": pd.arrays.SparseArray([0, 1, 0])})\n>>> df.sparse.to_dense()\n A\n0 0\n1 1\n2 0']
|
pandas.DataFrame.sparse.to_dense
DataFrame.sparse.to_dense()[source]# Convert a DataFrame with sparse values to dense.
|
[
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] |
199 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray._formatter.html
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pandas.api.extensions.ExtensionArray._formatter
|
ExtensionArray._formatter(boxed=False)[source]# Formatting function for scalar values. This is used in the default ‘__repr__’. The returned formatting function receives instances of your scalar type.
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Parameters: boxedbool, default FalseAn indicated for whether or not your array is being printed within a Series, DataFrame, or Index (True), or just by itself (False). This may be useful if you want scalar values to appear differently within a Series versus on its own (e.g. quoted or not). Returns: Callable[[Any], str]A callable that gets instances of the scalar type and returns a string. By default, repr() is used when boxed=False and str() is used when boxed=True.
|
[">>> class MyExtensionArray(pd.arrays.NumpyExtensionArray):\n... def _formatter(self, boxed=False):\n... return lambda x: '*' + str(x) + '*' if boxed else repr(x) + '*'\n>>> MyExtensionArray(np.array([1, 2, 3, 4]))\n<MyExtensionArray>\n[1*, 2*, 3*, 4*]\nLength: 4, dtype: int64"]
|
pandas.api.extensions.ExtensionArray._formatter
ExtensionArray._formatter(boxed=False)[source]# Formatting function for scalar values. This is used in the default ‘__repr__’. The returned formatting function receives instances of your scalar type.
|
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