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..\pandas\reference\api\pandas.Series.cat.reorder_categories.html
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pandas.Series.cat.reorder_categories
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Series.cat.reorder_categories(*args, **kwargs)[source]# Reorder categories as specified in new_categories. new_categories need to include all old categories and no new category items.
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Parameters: new_categoriesIndex-likeThe categories in new order. orderedbool, optionalWhether or not the categorical is treated as a ordered categorical. If not given, do not change the ordered information. Returns: CategoricalCategorical with reordered categories. Raises: ValueErrorIf the new categories do not contain all old category items or any new ones
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[">>> ser = pd.Series(['a', 'b', 'c', 'a'], dtype='category')\n>>> ser = ser.cat.reorder_categories(['c', 'b', 'a'], ordered=True)\n>>> ser\n0 a\n1 b\n2 c\n3 a\ndtype: category\nCategories (3, object): ['c' < 'b' < 'a']", ">>> ser.sort_values()\n2 c\n1 b\n0 a\n3 a\ndtype: category\nCategories (3, object): ['c' < 'b' < 'a']", ">>> ci = pd.CategoricalIndex(['a', 'b', 'c', 'a'])\n>>> ci\nCategoricalIndex(['a', 'b', 'c', 'a'], categories=['a', 'b', 'c'],\n ordered=False, dtype='category')\n>>> ci.reorder_categories(['c', 'b', 'a'], ordered=True)\nCategoricalIndex(['a', 'b', 'c', 'a'], categories=['c', 'b', 'a'],\n ordered=True, dtype='category')"]
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pandas.Series.cat.reorder_categories
Series.cat.reorder_categories(*args, **kwargs)[source]# Reorder categories as specified in new_categories. new_categories need to include all old categories and no new category items.
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] |
1 |
..\pandas\reference\api\pandas.Index.union.html
|
pandas.Index.union
|
final Index.union(other, sort=None)[source]# Form the union of two Index objects. If the Index objects are incompatible, both Index objects will be cast to dtype(‘object’) first.
|
Parameters: otherIndex or array-like sortbool or None, default NoneWhether to sort the resulting Index. None : Sort the result, except when self and other are equal. self or other has length 0. Some values in self or other cannot be compared. A RuntimeWarning is issued in this case. False : do not sort the result. True : Sort the result (which may raise TypeError). Returns: Index
|
[">>> idx1 = pd.Index([1, 2, 3, 4])\n>>> idx2 = pd.Index([3, 4, 5, 6])\n>>> idx1.union(idx2)\nIndex([1, 2, 3, 4, 5, 6], dtype='int64')", ">>> idx1 = pd.Index(['a', 'b', 'c', 'd'])\n>>> idx2 = pd.Index([1, 2, 3, 4])\n>>> idx1.union(idx2)\nIndex(['a', 'b', 'c', 'd', 1, 2, 3, 4], dtype='object')", '>>> idx1 = pd.MultiIndex.from_arrays(\n... [[1, 1, 2, 2], ["Red", "Blue", "Red", "Blue"]]\n... )\n>>> idx1\nMultiIndex([(1, \'Red\'),\n (1, \'Blue\'),\n (2, \'Red\'),\n (2, \'Blue\')],\n )\n>>> idx2 = pd.MultiIndex.from_arrays(\n... [[3, 3, 2, 2], ["Red", "Green", "Red", "Green"]]\n... )\n>>> idx2\nMultiIndex([(3, \'Red\'),\n (3, \'Green\'),\n (2, \'Red\'),\n (2, \'Green\')],\n )\n>>> idx1.union(idx2)\nMultiIndex([(1, \'Blue\'),\n (1, \'Red\'),\n (2, \'Blue\'),\n (2, \'Green\'),\n (2, \'Red\'),\n (3, \'Green\'),\n (3, \'Red\')],\n )\n>>> idx1.union(idx2, sort=False)\nMultiIndex([(1, \'Red\'),\n (1, \'Blue\'),\n (2, \'Red\'),\n (2, \'Blue\'),\n (3, \'Red\'),\n (3, \'Green\'),\n (2, \'Green\')],\n )']
|
pandas.Index.union
final Index.union(other, sort=None)[source]# Form the union of two Index objects. If the Index objects are incompatible, both Index objects will be cast to dtype(‘object’) first.
|
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] |
2 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray.argsort.html
|
pandas.api.extensions.ExtensionArray.argsort
|
ExtensionArray.argsort(*, ascending=True, kind='quicksort', na_position='last', **kwargs)[source]# Return the indices that would sort this array.
|
Parameters: ascendingbool, default TrueWhether the indices should result in an ascending or descending sort. kind{‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, optionalSorting algorithm. na_position{‘first’, ‘last’}, default ‘last’If 'first', put NaN values at the beginning. If 'last', put NaN values at the end. *args, **kwargs:Passed through to numpy.argsort(). Returns: np.ndarray[np.intp]Array of indices that sort self. If NaN values are contained, NaN values are placed at the end.
|
['>>> arr = pd.array([3, 1, 2, 5, 4])\n>>> arr.argsort()\narray([1, 2, 0, 4, 3])']
|
pandas.api.extensions.ExtensionArray.argsort
ExtensionArray.argsort(*, ascending=True, kind='quicksort', na_position='last', **kwargs)[source]# Return the indices that would sort this array.
|
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] |
3 |
..\pandas\reference\api\pandas.DataFrame.reset_index.html
|
pandas.DataFrame.reset_index
|
DataFrame.reset_index(level=None, *, drop=False, inplace=False, col_level=0, col_fill='', allow_duplicates=<no_default>, names=None)[source]# Reset the index, or a level of it. Reset the index of the DataFrame, and use the default one instead. If the DataFrame has a MultiIndex, this method can remove one or more levels.
|
Parameters: levelint, str, tuple, or list, default NoneOnly remove the given levels from the index. Removes all levels by default. dropbool, default FalseDo not try to insert index into dataframe columns. This resets the index to the default integer index. inplacebool, default FalseWhether to modify the DataFrame rather than creating a new one. col_levelint or str, default 0If the columns have multiple levels, determines which level the labels are inserted into. By default it is inserted into the first level. col_fillobject, default ‘’If the columns have multiple levels, determines how the other levels are named. If None then the index name is repeated. allow_duplicatesbool, optional, default lib.no_defaultAllow duplicate column labels to be created. Added in version 1.5.0. namesint, str or 1-dimensional list, default NoneUsing the given string, rename the DataFrame column which contains the index data. If the DataFrame has a MultiIndex, this has to be a list or tuple with length equal to the number of levels. Added in version 1.5.0. Returns: DataFrame or NoneDataFrame with the new index or None if inplace=True.
|
[">>> df = pd.DataFrame([('bird', 389.0),\n... ('bird', 24.0),\n... ('mammal', 80.5),\n... ('mammal', np.nan)],\n... index=['falcon', 'parrot', 'lion', 'monkey'],\n... columns=('class', 'max_speed'))\n>>> df\n class max_speed\nfalcon bird 389.0\nparrot bird 24.0\nlion mammal 80.5\nmonkey mammal NaN", '>>> df.reset_index()\n index class max_speed\n0 falcon bird 389.0\n1 parrot bird 24.0\n2 lion mammal 80.5\n3 monkey mammal NaN', '>>> df.reset_index(drop=True)\n class max_speed\n0 bird 389.0\n1 bird 24.0\n2 mammal 80.5\n3 mammal NaN', ">>> index = pd.MultiIndex.from_tuples([('bird', 'falcon'),\n... ('bird', 'parrot'),\n... ('mammal', 'lion'),\n... ('mammal', 'monkey')],\n... names=['class', 'name'])\n>>> columns = pd.MultiIndex.from_tuples([('speed', 'max'),\n... ('species', 'type')])\n>>> df = pd.DataFrame([(389.0, 'fly'),\n... (24.0, 'fly'),\n... (80.5, 'run'),\n... (np.nan, 'jump')],\n... index=index,\n... columns=columns)\n>>> df\n speed species\n max type\nclass name\nbird falcon 389.0 fly\n parrot 24.0 fly\nmammal lion 80.5 run\n monkey NaN jump", ">>> df.reset_index(names=['classes', 'names'])\n classes names speed species\n max type\n0 bird falcon 389.0 fly\n1 bird parrot 24.0 fly\n2 mammal lion 80.5 run\n3 mammal monkey NaN jump", ">>> df.reset_index(level='class')\n class speed species\n max type\nname\nfalcon bird 389.0 fly\nparrot bird 24.0 fly\nlion mammal 80.5 run\nmonkey mammal NaN jump", ">>> df.reset_index(level='class', col_level=1)\n speed species\n class max type\nname\nfalcon bird 389.0 fly\nparrot bird 24.0 fly\nlion mammal 80.5 run\nmonkey mammal NaN jump", ">>> df.reset_index(level='class', col_level=1, col_fill='species')\n species speed species\n class max type\nname\nfalcon bird 389.0 fly\nparrot bird 24.0 fly\nlion mammal 80.5 run\nmonkey mammal NaN jump", ">>> df.reset_index(level='class', col_level=1, col_fill='genus')\n genus speed species\n class max type\nname\nfalcon bird 389.0 fly\nparrot bird 24.0 fly\nlion mammal 80.5 run\nmonkey mammal NaN jump"]
|
pandas.DataFrame.reset_index
DataFrame.reset_index(level=None, *, drop=False, inplace=False, col_level=0, col_fill='', allow_duplicates=<no_default>, names=None)[source]# Reset the index, or a level of it. Reset the index of the DataFrame, and use the default one instead. If the DataFrame has a MultiIndex, this method can remove one or more levels.
|
[
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] |
4 |
..\pandas\reference\api\pandas.Series.cat.set_categories.html
|
pandas.Series.cat.set_categories
|
Series.cat.set_categories(*args, **kwargs)[source]# Set the categories to the specified new categories. new_categories can include new categories (which will result in unused categories) or remove old categories (which results in values set to NaN). If rename=True, the categories will simply be renamed (less or more items than in old categories will result in values set to NaN or in unused categories respectively). This method can be used to perform more than one action of adding, removing, and reordering simultaneously and is therefore faster than performing the individual steps via the more specialised methods. On the other hand this methods does not do checks (e.g., whether the old categories are included in the new categories on a reorder), which can result in surprising changes, for example when using special string dtypes, which does not considers a S1 string equal to a single char python string.
|
Parameters: new_categoriesIndex-likeThe categories in new order. orderedbool, default FalseWhether or not the categorical is treated as a ordered categorical. If not given, do not change the ordered information. renamebool, default FalseWhether or not the new_categories should be considered as a rename of the old categories or as reordered categories. Returns: Categorical with reordered categories. Raises: ValueErrorIf new_categories does not validate as categories
|
[">>> raw_cat = pd.Categorical(['a', 'b', 'c', 'A'],\n... categories=['a', 'b', 'c'], ordered=True)\n>>> ser = pd.Series(raw_cat)\n>>> ser\n0 a\n1 b\n2 c\n3 NaN\ndtype: category\nCategories (3, object): ['a' < 'b' < 'c']", ">>> ser.cat.set_categories(['A', 'B', 'C'], rename=True)\n0 A\n1 B\n2 C\n3 NaN\ndtype: category\nCategories (3, object): ['A' < 'B' < 'C']", ">>> ci = pd.CategoricalIndex(['a', 'b', 'c', 'A'],\n... categories=['a', 'b', 'c'], ordered=True)\n>>> ci\nCategoricalIndex(['a', 'b', 'c', nan], categories=['a', 'b', 'c'],\n ordered=True, dtype='category')", ">>> ci.set_categories(['A', 'b', 'c'])\nCategoricalIndex([nan, 'b', 'c', nan], categories=['A', 'b', 'c'],\n ordered=True, dtype='category')\n>>> ci.set_categories(['A', 'b', 'c'], rename=True)\nCategoricalIndex(['A', 'b', 'c', nan], categories=['A', 'b', 'c'],\n ordered=True, dtype='category')"]
|
pandas.Series.cat.set_categories
Series.cat.set_categories(*args, **kwargs)[source]# Set the categories to the specified new categories. new_categories can include new categories (which will result in unused categories) or remove old categories (which results in values set to NaN). If rename=True, the categories will simply be renamed (less or more items than in old categories will result in values set to NaN or in unused categories respectively). This method can be used to perform more than one action of adding, removing, and reordering simultaneously and is therefore faster than performing the individual steps via the more specialised methods. On the other hand this methods does not do checks (e.g., whether the old categories are included in the new categories on a reorder), which can result in surprising changes, for example when using special string dtypes, which does not considers a S1 string equal to a single char python string.
|
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] |
5 |
..\pandas\reference\api\pandas.Series.to_csv.html
|
pandas.Series.to_csv
|
Series.to_csv(path_or_buf=None, *, sep=',', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, mode='w', encoding=None, compression='infer', quoting=None, quotechar='"', lineterminator=None, chunksize=None, date_format=None, doublequote=True, escapechar=None, decimal='.', errors='strict', storage_options=None)[source]# Write object to a comma-separated values (csv) file.
|
Parameters: path_or_bufstr, path object, file-like object, or None, default NoneString, path object (implementing os.PathLike[str]), or file-like object implementing a write() function. If None, the result is returned as a string. If a non-binary file object is passed, it should be opened with newline=’’, disabling universal newlines. If a binary file object is passed, mode might need to contain a ‘b’. sepstr, default ‘,’String of length 1. Field delimiter for the output file. na_repstr, default ‘’Missing data representation. float_formatstr, Callable, default NoneFormat string for floating point numbers. If a Callable is given, it takes precedence over other numeric formatting parameters, like decimal. columnssequence, optionalColumns to write. headerbool or list of str, default TrueWrite out the column names. If a list of strings is given it is assumed to be aliases for the column names. indexbool, default TrueWrite row names (index). index_labelstr or sequence, or False, default NoneColumn label for index column(s) if desired. If None is given, and header and index are True, then the index names are used. A sequence should be given if the object uses MultiIndex. If False do not print fields for index names. Use index_label=False for easier importing in R. mode{‘w’, ‘x’, ‘a’}, default ‘w’Forwarded to either open(mode=) or fsspec.open(mode=) to control the file opening. Typical values include: ‘w’, truncate the file first. ‘x’, exclusive creation, failing if the file already exists. ‘a’, append to the end of file if it exists. encodingstr, optionalA string representing the encoding to use in the output file, defaults to ‘utf-8’. encoding is not supported if path_or_buf is a non-binary file object. compressionstr or dict, default ‘infer’For on-the-fly compression of the output data. If ‘infer’ and ‘path_or_buf’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, ‘.zst’, ‘.tar’, ‘.tar.gz’, ‘.tar.xz’ or ‘.tar.bz2’ (otherwise no compression). Set to None for no compression. Can also be a dict with key 'method' set to one of {'zip', 'gzip', 'bz2', 'zstd', 'xz', 'tar'} and other key-value pairs are forwarded to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, zstandard.ZstdCompressor, lzma.LZMAFile or tarfile.TarFile, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive: compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}. Added in version 1.5.0: Added support for .tar files. May be a dict with key ‘method’ as compression mode and other entries as additional compression options if compression mode is ‘zip’. Passing compression options as keys in dict is supported for compression modes ‘gzip’, ‘bz2’, ‘zstd’, and ‘zip’. quotingoptional constant from csv moduleDefaults to csv.QUOTE_MINIMAL. If you have set a float_format then floats are converted to strings and thus csv.QUOTE_NONNUMERIC will treat them as non-numeric. quotecharstr, default ‘"’String of length 1. Character used to quote fields. lineterminatorstr, optionalThe newline character or character sequence to use in the output file. Defaults to os.linesep, which depends on the OS in which this method is called (’\n’ for linux, ‘\r\n’ for Windows, i.e.). Changed in version 1.5.0: Previously was line_terminator, changed for consistency with read_csv and the standard library ‘csv’ module. chunksizeint or NoneRows to write at a time. date_formatstr, default NoneFormat string for datetime objects. doublequotebool, default TrueControl quoting of quotechar inside a field. escapecharstr, default NoneString of length 1. Character used to escape sep and quotechar when appropriate. decimalstr, default ‘.’Character recognized as decimal separator. E.g. use ‘,’ for European data. errorsstr, default ‘strict’Specifies how encoding and decoding errors are to be handled. See the errors argument for open() for a full list of options. storage_optionsdict, optionalExtra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here. Returns: None or strIf path_or_buf is None, returns the resulting csv format as a string. Otherwise returns None.
|
[">>> df = pd.DataFrame({'name': ['Raphael', 'Donatello'],\n... 'mask': ['red', 'purple'],\n... 'weapon': ['sai', 'bo staff']})\n>>> df.to_csv('out.csv', index=False)", ">>> df.to_csv(index=False)\n'name,mask,weapon\\nRaphael,red,sai\\nDonatello,purple,bo staff\\n'\n>>> compression_opts = dict(method='zip',\n... archive_name='out.csv') \n>>> df.to_csv('out.zip', index=False,\n... compression=compression_opts)", ">>> from pathlib import Path \n>>> filepath = Path('folder/subfolder/out.csv') \n>>> filepath.parent.mkdir(parents=True, exist_ok=True) \n>>> df.to_csv(filepath)", ">>> import os \n>>> os.makedirs('folder/subfolder', exist_ok=True) \n>>> df.to_csv('folder/subfolder/out.csv')"]
|
pandas.Series.to_csv
Series.to_csv(path_or_buf=None, *, sep=',', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, mode='w', encoding=None, compression='infer', quoting=None, quotechar='"', lineterminator=None, chunksize=None, date_format=None, doublequote=True, escapechar=None, decimal='.', errors='strict', storage_options=None)[source]# Write object to a comma-separated values (csv) file.
|
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6 |
..\pandas\reference\api\pandas.tseries.offsets.Milli.delta.html
|
pandas.tseries.offsets.Milli.delta
|
Milli.delta#
|
No parameters found
|
[]
|
pandas.tseries.offsets.Milli.delta
Milli.delta#
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] |
7 |
..\pandas\reference\api\pandas.core.groupby.SeriesGroupBy.ngroup.html
|
pandas.core.groupby.SeriesGroupBy.ngroup
|
SeriesGroupBy.ngroup(ascending=True)[source]# Number each group from 0 to the number of groups - 1. This is the enumerative complement of cumcount. Note that the numbers given to the groups match the order in which the groups would be seen when iterating over the groupby object, not the order they are first observed. Groups with missing keys (where pd.isna() is True) will be labeled with NaN and will be skipped from the count.
|
Parameters: ascendingbool, default TrueIf False, number in reverse, from number of group - 1 to 0. Returns: SeriesUnique numbers for each group.
|
['>>> df = pd.DataFrame({"color": ["red", None, "red", "blue", "blue", "red"]})\n>>> df\n color\n0 red\n1 None\n2 red\n3 blue\n4 blue\n5 red\n>>> df.groupby("color").ngroup()\n0 1.0\n1 NaN\n2 1.0\n3 0.0\n4 0.0\n5 1.0\ndtype: float64\n>>> df.groupby("color", dropna=False).ngroup()\n0 1\n1 2\n2 1\n3 0\n4 0\n5 1\ndtype: int64\n>>> df.groupby("color", dropna=False).ngroup(ascending=False)\n0 1\n1 0\n2 1\n3 2\n4 2\n5 1\ndtype: int64']
|
pandas.core.groupby.SeriesGroupBy.ngroup
SeriesGroupBy.ngroup(ascending=True)[source]# Number each group from 0 to the number of groups - 1. This is the enumerative complement of cumcount. Note that the numbers given to the groups match the order in which the groups would be seen when iterating over the groupby object, not the order they are first observed. Groups with missing keys (where pd.isna() is True) will be labeled with NaN and will be skipped from the count.
|
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] |
8 |
..\pandas\reference\api\pandas.tseries.offsets.BYearBegin.freqstr.html
|
pandas.tseries.offsets.BYearBegin.freqstr
|
BYearBegin.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.BYearBegin.freqstr
BYearBegin.freqstr# Return a string representing the frequency.
|
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] |
9 |
..\pandas\reference\api\pandas.Index.unique.html
|
pandas.Index.unique
|
Index.unique(level=None)[source]# Return unique values in the index. Unique values are returned in order of appearance, this does NOT sort.
|
Parameters: levelint or hashable, optionalOnly return values from specified level (for MultiIndex). If int, gets the level by integer position, else by level name. Returns: Index
|
[">>> idx = pd.Index([1, 1, 2, 3, 3])\n>>> idx.unique()\nIndex([1, 2, 3], dtype='int64')"]
|
pandas.Index.unique
Index.unique(level=None)[source]# Return unique values in the index. Unique values are returned in order of appearance, this does NOT sort.
|
[
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] |
10 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray.astype.html
|
pandas.api.extensions.ExtensionArray.astype
|
ExtensionArray.astype(dtype, copy=True)[source]# Cast to a NumPy array or ExtensionArray with ‘dtype’.
|
Parameters: dtypestr or dtypeTypecode or data-type to which the array is cast. copybool, default TrueWhether to copy the data, even if not necessary. If False, a copy is made only if the old dtype does not match the new dtype. Returns: np.ndarray or pandas.api.extensions.ExtensionArrayAn ExtensionArray if dtype is ExtensionDtype, otherwise a Numpy ndarray with dtype for its dtype.
|
['>>> arr = pd.array([1, 2, 3])\n>>> arr\n<IntegerArray>\n[1, 2, 3]\nLength: 3, dtype: Int64', ">>> arr1 = arr.astype('Float64')\n>>> arr1\n<FloatingArray>\n[1.0, 2.0, 3.0]\nLength: 3, dtype: Float64\n>>> arr1.dtype\nFloat64Dtype()", ">>> arr2 = arr.astype('float64')\n>>> arr2\narray([1., 2., 3.])\n>>> arr2.dtype\ndtype('float64')"]
|
pandas.api.extensions.ExtensionArray.astype
ExtensionArray.astype(dtype, copy=True)[source]# Cast to a NumPy array or ExtensionArray with ‘dtype’.
|
[
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] |
11 |
..\pandas\reference\api\pandas.DataFrame.rfloordiv.html
|
pandas.DataFrame.rfloordiv
|
DataFrame.rfloordiv(other, axis='columns', level=None, fill_value=None)[source]# Get Integer division of dataframe and other, element-wise (binary operator rfloordiv). Equivalent to other // dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, floordiv. Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **. Notes Mismatched indices will be unioned together.
|
Parameters: otherscalar, sequence, Series, dict or DataFrameAny single or multiple element data structure, or list-like object. axis{0 or ‘index’, 1 or ‘columns’}Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on. levelint or labelBroadcast across a level, matching Index values on the passed MultiIndex level. fill_valuefloat or None, default NoneFill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing. Returns: DataFrameResult of the arithmetic operation.
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[">>> df = pd.DataFrame({'angles': [0, 3, 4],\n... 'degrees': [360, 180, 360]},\n... index=['circle', 'triangle', 'rectangle'])\n>>> df\n angles degrees\ncircle 0 360\ntriangle 3 180\nrectangle 4 360", '>>> df + 1\n angles degrees\ncircle 1 361\ntriangle 4 181\nrectangle 5 361', '>>> df.add(1)\n angles degrees\ncircle 1 361\ntriangle 4 181\nrectangle 5 361', '>>> df.div(10)\n angles degrees\ncircle 0.0 36.0\ntriangle 0.3 18.0\nrectangle 0.4 36.0', '>>> df.rdiv(10)\n angles degrees\ncircle inf 0.027778\ntriangle 3.333333 0.055556\nrectangle 2.500000 0.027778', '>>> df - [1, 2]\n angles degrees\ncircle -1 358\ntriangle 2 178\nrectangle 3 358', ">>> df.sub([1, 2], axis='columns')\n angles degrees\ncircle -1 358\ntriangle 2 178\nrectangle 3 358", ">>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),\n... axis='index')\n angles degrees\ncircle -1 359\ntriangle 2 179\nrectangle 3 359", ">>> df.mul({'angles': 0, 'degrees': 2})\n angles degrees\ncircle 0 720\ntriangle 0 360\nrectangle 0 720", ">>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')\n angles degrees\ncircle 0 0\ntriangle 6 360\nrectangle 12 1080", ">>> other = pd.DataFrame({'angles': [0, 3, 4]},\n... index=['circle', 'triangle', 'rectangle'])\n>>> other\n angles\ncircle 0\ntriangle 3\nrectangle 4", '>>> df * other\n angles degrees\ncircle 0 NaN\ntriangle 9 NaN\nrectangle 16 NaN', '>>> df.mul(other, fill_value=0)\n angles degrees\ncircle 0 0.0\ntriangle 9 0.0\nrectangle 16 0.0', ">>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],\n... 'degrees': [360, 180, 360, 360, 540, 720]},\n... index=[['A', 'A', 'A', 'B', 'B', 'B'],\n... ['circle', 'triangle', 'rectangle',\n... 'square', 'pentagon', 'hexagon']])\n>>> df_multindex\n angles degrees\nA circle 0 360\n triangle 3 180\n rectangle 4 360\nB square 4 360\n pentagon 5 540\n hexagon 6 720", '>>> df.div(df_multindex, level=1, fill_value=0)\n angles degrees\nA circle NaN 1.0\n triangle 1.0 1.0\n rectangle 1.0 1.0\nB square 0.0 0.0\n pentagon 0.0 0.0\n hexagon 0.0 0.0']
|
pandas.DataFrame.rfloordiv
DataFrame.rfloordiv(other, axis='columns', level=None, fill_value=None)[source]# Get Integer division of dataframe and other, element-wise (binary operator rfloordiv). Equivalent to other // dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, floordiv. Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **. Notes Mismatched indices will be unioned together.
|
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] |
12 |
..\pandas\reference\api\pandas.Series.clip.html
|
pandas.Series.clip
|
Series.clip(lower=None, upper=None, *, axis=None, inplace=False, **kwargs)[source]# Trim values at input threshold(s). Assigns values outside boundary to boundary values. Thresholds can be singular values or array like, and in the latter case the clipping is performed element-wise in the specified axis.
|
Parameters: lowerfloat or array-like, default NoneMinimum threshold value. All values below this threshold will be set to it. A missing threshold (e.g NA) will not clip the value. upperfloat or array-like, default NoneMaximum threshold value. All values above this threshold will be set to it. A missing threshold (e.g NA) will not clip the value. axis{{0 or ‘index’, 1 or ‘columns’, None}}, default NoneAlign object with lower and upper along the given axis. For Series this parameter is unused and defaults to None. inplacebool, default FalseWhether to perform the operation in place on the data. *args, **kwargsAdditional keywords have no effect but might be accepted for compatibility with numpy. Returns: Series or DataFrame or NoneSame type as calling object with the values outside the clip boundaries replaced or None if inplace=True.
|
[">>> data = {'col_0': [9, -3, 0, -1, 5], 'col_1': [-2, -7, 6, 8, -5]}\n>>> df = pd.DataFrame(data)\n>>> df\n col_0 col_1\n0 9 -2\n1 -3 -7\n2 0 6\n3 -1 8\n4 5 -5", '>>> df.clip(-4, 6)\n col_0 col_1\n0 6 -2\n1 -3 -4\n2 0 6\n3 -1 6\n4 5 -4', '>>> df.clip([-2, -1], [4, 5])\n col_0 col_1\n0 4 -1\n1 -2 -1\n2 0 5\n3 -1 5\n4 4 -1', '>>> t = pd.Series([2, -4, -1, 6, 3])\n>>> t\n0 2\n1 -4\n2 -1\n3 6\n4 3\ndtype: int64', '>>> df.clip(t, t + 4, axis=0)\n col_0 col_1\n0 6 2\n1 -3 -4\n2 0 3\n3 6 8\n4 5 3', '>>> t = pd.Series([2, -4, np.nan, 6, 3])\n>>> t\n0 2.0\n1 -4.0\n2 NaN\n3 6.0\n4 3.0\ndtype: float64', '>>> df.clip(t, axis=0)\ncol_0 col_1\n0 9 2\n1 -3 -4\n2 0 6\n3 6 8\n4 5 3']
|
pandas.Series.clip
Series.clip(lower=None, upper=None, *, axis=None, inplace=False, **kwargs)[source]# Trim values at input threshold(s). Assigns values outside boundary to boundary values. Thresholds can be singular values or array like, and in the latter case the clipping is performed element-wise in the specified axis.
|
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] |
13 |
..\pandas\reference\api\pandas.Series.to_dict.html
|
pandas.Series.to_dict
|
Series.to_dict(*, into=<class 'dict'>)[source]# Convert Series to {label -> value} dict or dict-like object.
|
Parameters: intoclass, default dictThe collections.abc.MutableMapping subclass to use as the return object. Can be the actual class or an empty instance of the mapping type you want. If you want a collections.defaultdict, you must pass it initialized. Returns: collections.abc.MutableMappingKey-value representation of Series.
|
[">>> s = pd.Series([1, 2, 3, 4])\n>>> s.to_dict()\n{0: 1, 1: 2, 2: 3, 3: 4}\n>>> from collections import OrderedDict, defaultdict\n>>> s.to_dict(into=OrderedDict)\nOrderedDict([(0, 1), (1, 2), (2, 3), (3, 4)])\n>>> dd = defaultdict(list)\n>>> s.to_dict(into=dd)\ndefaultdict(<class 'list'>, {0: 1, 1: 2, 2: 3, 3: 4})"]
|
pandas.Series.to_dict
Series.to_dict(*, into=<class 'dict'>)[source]# Convert Series to {label -> value} dict or dict-like object.
|
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] |
14 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray.copy.html
|
pandas.api.extensions.ExtensionArray.copy
|
ExtensionArray.copy()[source]# Return a copy of the array.
|
Returns: ExtensionArray
|
['>>> arr = pd.array([1, 2, 3])\n>>> arr2 = arr.copy()\n>>> arr[0] = 2\n>>> arr2\n<IntegerArray>\n[1, 2, 3]\nLength: 3, dtype: Int64']
|
pandas.api.extensions.ExtensionArray.copy
ExtensionArray.copy()[source]# Return a copy of the array.
|
[
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] |
15 |
..\pandas\reference\api\pandas.tseries.offsets.BYearBegin.html
|
pandas.tseries.offsets.BYearBegin
|
class pandas.tseries.offsets.BYearBegin# DateOffset increments between the first 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 1A specific integer for the month of the year.
|
[">>> from pandas.tseries.offsets import BYearBegin\n>>> ts = pd.Timestamp('2020-05-24 05:01:15')\n>>> ts + BYearBegin()\nTimestamp('2021-01-01 05:01:15')\n>>> ts - BYearBegin()\nTimestamp('2020-01-01 05:01:15')\n>>> ts + BYearBegin(-1)\nTimestamp('2020-01-01 05:01:15')\n>>> ts + BYearBegin(2)\nTimestamp('2022-01-03 05:01:15')\n>>> ts + BYearBegin(month=11)\nTimestamp('2020-11-02 05:01:15')"]
|
pandas.tseries.offsets.BYearBegin
class pandas.tseries.offsets.BYearBegin# DateOffset increments between the first 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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] |
16 |
..\pandas\reference\api\pandas.Index.values.html
|
pandas.Index.values
|
property Index.values[source]# Return an array representing the data in the Index. Warning We recommend using Index.array or Index.to_numpy(), depending on whether you need a reference to the underlying data or a NumPy array.
|
Returns: array: numpy.ndarray or ExtensionArray
|
[">>> idx = pd.Index([1, 2, 3])\n>>> idx\nIndex([1, 2, 3], dtype='int64')\n>>> idx.values\narray([1, 2, 3])", '>>> idx = pd.interval_range(start=0, end=5)\n>>> idx.values\n<IntervalArray>\n[(0, 1], (1, 2], (2, 3], (3, 4], (4, 5]]\nLength: 5, dtype: interval[int64, right]']
|
pandas.Index.values
property Index.values[source]# Return an array representing the data in the Index. Warning We recommend using Index.array or Index.to_numpy(), depending on whether you need a reference to the underlying data or a NumPy array.
|
[
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] |
17 |
..\pandas\reference\api\pandas.core.groupby.SeriesGroupBy.nlargest.html
|
pandas.core.groupby.SeriesGroupBy.nlargest
|
SeriesGroupBy.nlargest(n=5, keep='first')[source]# Return the largest n elements. Notes Faster than .sort_values(ascending=False).head(n) for small n relative to the size of the Series object.
|
Parameters: nint, default 5Return this many descending sorted values. keep{‘first’, ‘last’, ‘all’}, default ‘first’When there are duplicate values that cannot all fit in a Series of n elements: first : return the first n occurrences in order of appearance. last : return the last n occurrences in reverse order of appearance. all : keep all occurrences. This can result in a Series of size larger than n. Returns: SeriesThe n largest values in the Series, sorted in decreasing order.
|
['>>> countries_population = {"Italy": 59000000, "France": 65000000,\n... "Malta": 434000, "Maldives": 434000,\n... "Brunei": 434000, "Iceland": 337000,\n... "Nauru": 11300, "Tuvalu": 11300,\n... "Anguilla": 11300, "Montserrat": 5200}\n>>> s = pd.Series(countries_population)\n>>> s\nItaly 59000000\nFrance 65000000\nMalta 434000\nMaldives 434000\nBrunei 434000\nIceland 337000\nNauru 11300\nTuvalu 11300\nAnguilla 11300\nMontserrat 5200\ndtype: int64', '>>> s.nlargest()\nFrance 65000000\nItaly 59000000\nMalta 434000\nMaldives 434000\nBrunei 434000\ndtype: int64', '>>> s.nlargest(3)\nFrance 65000000\nItaly 59000000\nMalta 434000\ndtype: int64', ">>> s.nlargest(3, keep='last')\nFrance 65000000\nItaly 59000000\nBrunei 434000\ndtype: int64", ">>> s.nlargest(3, keep='all')\nFrance 65000000\nItaly 59000000\nMalta 434000\nMaldives 434000\nBrunei 434000\ndtype: int64"]
|
pandas.core.groupby.SeriesGroupBy.nlargest
SeriesGroupBy.nlargest(n=5, keep='first')[source]# Return the largest n elements. Notes Faster than .sort_values(ascending=False).head(n) for small n relative to the size of the Series object.
|
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] |
18 |
..\pandas\reference\api\pandas.DataFrame.rmod.html
|
pandas.DataFrame.rmod
|
DataFrame.rmod(other, axis='columns', level=None, fill_value=None)[source]# Get Modulo of dataframe and other, element-wise (binary operator rmod). Equivalent to other % dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, mod. Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **. Notes Mismatched indices will be unioned together.
|
Parameters: otherscalar, sequence, Series, dict or DataFrameAny single or multiple element data structure, or list-like object. axis{0 or ‘index’, 1 or ‘columns’}Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on. levelint or labelBroadcast across a level, matching Index values on the passed MultiIndex level. fill_valuefloat or None, default NoneFill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing. Returns: DataFrameResult of the arithmetic operation.
|
[">>> df = pd.DataFrame({'angles': [0, 3, 4],\n... 'degrees': [360, 180, 360]},\n... index=['circle', 'triangle', 'rectangle'])\n>>> df\n angles degrees\ncircle 0 360\ntriangle 3 180\nrectangle 4 360", '>>> df + 1\n angles degrees\ncircle 1 361\ntriangle 4 181\nrectangle 5 361', '>>> df.add(1)\n angles degrees\ncircle 1 361\ntriangle 4 181\nrectangle 5 361', '>>> df.div(10)\n angles degrees\ncircle 0.0 36.0\ntriangle 0.3 18.0\nrectangle 0.4 36.0', '>>> df.rdiv(10)\n angles degrees\ncircle inf 0.027778\ntriangle 3.333333 0.055556\nrectangle 2.500000 0.027778', '>>> df - [1, 2]\n angles degrees\ncircle -1 358\ntriangle 2 178\nrectangle 3 358', ">>> df.sub([1, 2], axis='columns')\n angles degrees\ncircle -1 358\ntriangle 2 178\nrectangle 3 358", ">>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),\n... axis='index')\n angles degrees\ncircle -1 359\ntriangle 2 179\nrectangle 3 359", ">>> df.mul({'angles': 0, 'degrees': 2})\n angles degrees\ncircle 0 720\ntriangle 0 360\nrectangle 0 720", ">>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')\n angles degrees\ncircle 0 0\ntriangle 6 360\nrectangle 12 1080", ">>> other = pd.DataFrame({'angles': [0, 3, 4]},\n... index=['circle', 'triangle', 'rectangle'])\n>>> other\n angles\ncircle 0\ntriangle 3\nrectangle 4", '>>> df * other\n angles degrees\ncircle 0 NaN\ntriangle 9 NaN\nrectangle 16 NaN', '>>> df.mul(other, fill_value=0)\n angles degrees\ncircle 0 0.0\ntriangle 9 0.0\nrectangle 16 0.0', ">>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],\n... 'degrees': [360, 180, 360, 360, 540, 720]},\n... index=[['A', 'A', 'A', 'B', 'B', 'B'],\n... ['circle', 'triangle', 'rectangle',\n... 'square', 'pentagon', 'hexagon']])\n>>> df_multindex\n angles degrees\nA circle 0 360\n triangle 3 180\n rectangle 4 360\nB square 4 360\n pentagon 5 540\n hexagon 6 720", '>>> df.div(df_multindex, level=1, fill_value=0)\n angles degrees\nA circle NaN 1.0\n triangle 1.0 1.0\n rectangle 1.0 1.0\nB square 0.0 0.0\n pentagon 0.0 0.0\n hexagon 0.0 0.0']
|
pandas.DataFrame.rmod
DataFrame.rmod(other, axis='columns', level=None, fill_value=None)[source]# Get Modulo of dataframe and other, element-wise (binary operator rmod). Equivalent to other % dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, mod. Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **. Notes Mismatched indices will be unioned together.
|
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] |
19 |
..\pandas\reference\api\pandas.tseries.offsets.Milli.freqstr.html
|
pandas.tseries.offsets.Milli.freqstr
|
Milli.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.Milli.freqstr
Milli.freqstr# Return a string representing the frequency.
|
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] |
20 |
..\pandas\reference\api\pandas.Series.combine.html
|
pandas.Series.combine
|
Series.combine(other, func, fill_value=None)[source]# Combine the Series with a Series or scalar according to func. Combine the Series and other using func to perform elementwise selection for combined Series. fill_value is assumed when value is missing at some index from one of the two objects being combined.
|
Parameters: otherSeries or scalarThe value(s) to be combined with the Series. funcfunctionFunction that takes two scalars as inputs and returns an element. fill_valuescalar, optionalThe value to assume when an index is missing from one Series or the other. The default specifies to use the appropriate NaN value for the underlying dtype of the Series. Returns: SeriesThe result of combining the Series with the other object.
|
[">>> s1 = pd.Series({'falcon': 330.0, 'eagle': 160.0})\n>>> s1\nfalcon 330.0\neagle 160.0\ndtype: float64\n>>> s2 = pd.Series({'falcon': 345.0, 'eagle': 200.0, 'duck': 30.0})\n>>> s2\nfalcon 345.0\neagle 200.0\nduck 30.0\ndtype: float64", '>>> s1.combine(s2, max)\nduck NaN\neagle 200.0\nfalcon 345.0\ndtype: float64', '>>> s1.combine(s2, max, fill_value=0)\nduck 30.0\neagle 200.0\nfalcon 345.0\ndtype: float64']
|
pandas.Series.combine
Series.combine(other, func, fill_value=None)[source]# Combine the Series with a Series or scalar according to func. Combine the Series and other using func to perform elementwise selection for combined Series. fill_value is assumed when value is missing at some index from one of the two objects being combined.
|
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] |
21 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray.dropna.html
|
pandas.api.extensions.ExtensionArray.dropna
|
ExtensionArray.dropna()[source]# Return ExtensionArray without NA values.
|
Returns:
|
['>>> pd.array([1, 2, np.nan]).dropna()\n<IntegerArray>\n[1, 2]\nLength: 2, dtype: Int64']
|
pandas.api.extensions.ExtensionArray.dropna
ExtensionArray.dropna()[source]# Return ExtensionArray without NA values.
|
[
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] |
22 |
..\pandas\reference\api\pandas.Series.to_excel.html
|
pandas.Series.to_excel
|
Series.to_excel(excel_writer, *, sheet_name='Sheet1', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, startrow=0, startcol=0, engine=None, merge_cells=True, inf_rep='inf', freeze_panes=None, storage_options=None, engine_kwargs=None)[source]# Write object to an Excel sheet. To write a single object to an Excel .xlsx file it is only necessary to specify a target file name. To write to multiple sheets it is necessary to create an ExcelWriter object with a target file name, and specify a sheet in the file to write to. Multiple sheets may be written to by specifying unique sheet_name. With all data written to the file it is necessary to save the changes. Note that creating an ExcelWriter object with a file name that already exists will result in the contents of the existing file being erased. Notes For compatibility with to_csv(), to_excel serializes lists and dicts to strings before writing. Once a workbook has been saved it is not possible to write further data without rewriting the whole workbook.
|
Parameters: excel_writerpath-like, file-like, or ExcelWriter objectFile path or existing ExcelWriter. sheet_namestr, default ‘Sheet1’Name of sheet which will contain DataFrame. na_repstr, default ‘’Missing data representation. float_formatstr, optionalFormat string for floating point numbers. For example float_format="%.2f" will format 0.1234 to 0.12. columnssequence or list of str, optionalColumns to write. headerbool or list of str, default TrueWrite out the column names. If a list of string is given it is assumed to be aliases for the column names. indexbool, default TrueWrite row names (index). index_labelstr or sequence, optionalColumn label for index column(s) if desired. If not specified, and header and index are True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex. startrowint, default 0Upper left cell row to dump data frame. startcolint, default 0Upper left cell column to dump data frame. enginestr, optionalWrite engine to use, ‘openpyxl’ or ‘xlsxwriter’. You can also set this via the options io.excel.xlsx.writer or io.excel.xlsm.writer. merge_cellsbool, default TrueWrite MultiIndex and Hierarchical Rows as merged cells. inf_repstr, default ‘inf’Representation for infinity (there is no native representation for infinity in Excel). freeze_panestuple of int (length 2), optionalSpecifies the one-based bottommost row and rightmost column that is to be frozen. storage_optionsdict, optionalExtra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here. Added in version 1.2.0. engine_kwargsdict, optionalArbitrary keyword arguments passed to excel engine.
|
['>>> df1 = pd.DataFrame([[\'a\', \'b\'], [\'c\', \'d\']],\n... index=[\'row 1\', \'row 2\'],\n... columns=[\'col 1\', \'col 2\'])\n>>> df1.to_excel("output.xlsx")', '>>> df1.to_excel("output.xlsx",\n... sheet_name=\'Sheet_name_1\')', ">>> df2 = df1.copy()\n>>> with pd.ExcelWriter('output.xlsx') as writer: \n... df1.to_excel(writer, sheet_name='Sheet_name_1')\n... df2.to_excel(writer, sheet_name='Sheet_name_2')", ">>> with pd.ExcelWriter('output.xlsx',\n... mode='a') as writer: \n... df1.to_excel(writer, sheet_name='Sheet_name_3')", ">>> df1.to_excel('output1.xlsx', engine='xlsxwriter')"]
|
pandas.Series.to_excel
Series.to_excel(excel_writer, *, sheet_name='Sheet1', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, startrow=0, startcol=0, engine=None, merge_cells=True, inf_rep='inf', freeze_panes=None, storage_options=None, engine_kwargs=None)[source]# Write object to an Excel sheet. To write a single object to an Excel .xlsx file it is only necessary to specify a target file name. To write to multiple sheets it is necessary to create an ExcelWriter object with a target file name, and specify a sheet in the file to write to. Multiple sheets may be written to by specifying unique sheet_name. With all data written to the file it is necessary to save the changes. Note that creating an ExcelWriter object with a file name that already exists will result in the contents of the existing file being erased. Notes For compatibility with to_csv(), to_excel serializes lists and dicts to strings before writing. Once a workbook has been saved it is not possible to write further data without rewriting the whole workbook.
|
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23 |
..\pandas\reference\api\pandas.tseries.offsets.BYearBegin.is_anchored.html
|
pandas.tseries.offsets.BYearBegin.is_anchored
|
BYearBegin.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.BYearBegin.is_anchored
BYearBegin.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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] |
24 |
..\pandas\reference\api\pandas.Index.value_counts.html
|
pandas.Index.value_counts
|
Index.value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True)[source]# Return a Series containing counts of unique values. The resulting object will be in descending order so that the first element is the most frequently-occurring element. Excludes NA values by default.
|
Parameters: normalizebool, default FalseIf True then the object returned will contain the relative frequencies of the unique values. sortbool, default TrueSort by frequencies when True. Preserve the order of the data when False. ascendingbool, default FalseSort in ascending order. binsint, optionalRather than count values, group them into half-open bins, a convenience for pd.cut, only works with numeric data. dropnabool, default TrueDon’t include counts of NaN. Returns: Series
|
['>>> index = pd.Index([3, 1, 2, 3, 4, np.nan])\n>>> index.value_counts()\n3.0 2\n1.0 1\n2.0 1\n4.0 1\nName: count, dtype: int64', '>>> s = pd.Series([3, 1, 2, 3, 4, np.nan])\n>>> s.value_counts(normalize=True)\n3.0 0.4\n1.0 0.2\n2.0 0.2\n4.0 0.2\nName: proportion, dtype: float64', '>>> s.value_counts(bins=3)\n(0.996, 2.0] 2\n(2.0, 3.0] 2\n(3.0, 4.0] 1\nName: count, dtype: int64', '>>> s.value_counts(dropna=False)\n3.0 2\n1.0 1\n2.0 1\n4.0 1\nNaN 1\nName: count, dtype: int64']
|
pandas.Index.value_counts
Index.value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True)[source]# Return a Series containing counts of unique values. The resulting object will be in descending order so that the first element is the most frequently-occurring element. Excludes NA values by default.
|
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25 |
..\pandas\reference\api\pandas.core.groupby.SeriesGroupBy.nsmallest.html
|
pandas.core.groupby.SeriesGroupBy.nsmallest
|
SeriesGroupBy.nsmallest(n=5, keep='first')[source]# Return the smallest n elements. Notes Faster than .sort_values().head(n) for small n relative to the size of the Series object.
|
Parameters: nint, default 5Return this many ascending sorted values. keep{‘first’, ‘last’, ‘all’}, default ‘first’When there are duplicate values that cannot all fit in a Series of n elements: first : return the first n occurrences in order of appearance. last : return the last n occurrences in reverse order of appearance. all : keep all occurrences. This can result in a Series of size larger than n. Returns: SeriesThe n smallest values in the Series, sorted in increasing order.
|
['>>> countries_population = {"Italy": 59000000, "France": 65000000,\n... "Brunei": 434000, "Malta": 434000,\n... "Maldives": 434000, "Iceland": 337000,\n... "Nauru": 11300, "Tuvalu": 11300,\n... "Anguilla": 11300, "Montserrat": 5200}\n>>> s = pd.Series(countries_population)\n>>> s\nItaly 59000000\nFrance 65000000\nBrunei 434000\nMalta 434000\nMaldives 434000\nIceland 337000\nNauru 11300\nTuvalu 11300\nAnguilla 11300\nMontserrat 5200\ndtype: int64', '>>> s.nsmallest()\nMontserrat 5200\nNauru 11300\nTuvalu 11300\nAnguilla 11300\nIceland 337000\ndtype: int64', '>>> s.nsmallest(3)\nMontserrat 5200\nNauru 11300\nTuvalu 11300\ndtype: int64', ">>> s.nsmallest(3, keep='last')\nMontserrat 5200\nAnguilla 11300\nTuvalu 11300\ndtype: int64", ">>> s.nsmallest(3, keep='all')\nMontserrat 5200\nNauru 11300\nTuvalu 11300\nAnguilla 11300\ndtype: int64"]
|
pandas.core.groupby.SeriesGroupBy.nsmallest
SeriesGroupBy.nsmallest(n=5, keep='first')[source]# Return the smallest n elements. Notes Faster than .sort_values().head(n) for small n relative to the size of the Series object.
|
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] |
26 |
..\pandas\reference\api\pandas.DataFrame.rmul.html
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pandas.DataFrame.rmul
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DataFrame.rmul(other, axis='columns', level=None, fill_value=None)[source]# Get Multiplication of dataframe and other, element-wise (binary operator rmul). Equivalent to other * dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, mul. Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **. Notes Mismatched indices will be unioned together.
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Parameters: otherscalar, sequence, Series, dict or DataFrameAny single or multiple element data structure, or list-like object. axis{0 or ‘index’, 1 or ‘columns’}Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on. levelint or labelBroadcast across a level, matching Index values on the passed MultiIndex level. fill_valuefloat or None, default NoneFill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing. Returns: DataFrameResult of the arithmetic operation.
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[">>> df = pd.DataFrame({'angles': [0, 3, 4],\n... 'degrees': [360, 180, 360]},\n... index=['circle', 'triangle', 'rectangle'])\n>>> df\n angles degrees\ncircle 0 360\ntriangle 3 180\nrectangle 4 360", '>>> df + 1\n angles degrees\ncircle 1 361\ntriangle 4 181\nrectangle 5 361', '>>> df.add(1)\n angles degrees\ncircle 1 361\ntriangle 4 181\nrectangle 5 361', '>>> df.div(10)\n angles degrees\ncircle 0.0 36.0\ntriangle 0.3 18.0\nrectangle 0.4 36.0', '>>> df.rdiv(10)\n angles degrees\ncircle inf 0.027778\ntriangle 3.333333 0.055556\nrectangle 2.500000 0.027778', '>>> df - [1, 2]\n angles degrees\ncircle -1 358\ntriangle 2 178\nrectangle 3 358', ">>> df.sub([1, 2], axis='columns')\n angles degrees\ncircle -1 358\ntriangle 2 178\nrectangle 3 358", ">>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),\n... axis='index')\n angles degrees\ncircle -1 359\ntriangle 2 179\nrectangle 3 359", ">>> df.mul({'angles': 0, 'degrees': 2})\n angles degrees\ncircle 0 720\ntriangle 0 360\nrectangle 0 720", ">>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')\n angles degrees\ncircle 0 0\ntriangle 6 360\nrectangle 12 1080", ">>> other = pd.DataFrame({'angles': [0, 3, 4]},\n... index=['circle', 'triangle', 'rectangle'])\n>>> other\n angles\ncircle 0\ntriangle 3\nrectangle 4", '>>> df * other\n angles degrees\ncircle 0 NaN\ntriangle 9 NaN\nrectangle 16 NaN', '>>> df.mul(other, fill_value=0)\n angles degrees\ncircle 0 0.0\ntriangle 9 0.0\nrectangle 16 0.0', ">>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],\n... 'degrees': [360, 180, 360, 360, 540, 720]},\n... index=[['A', 'A', 'A', 'B', 'B', 'B'],\n... ['circle', 'triangle', 'rectangle',\n... 'square', 'pentagon', 'hexagon']])\n>>> df_multindex\n angles degrees\nA circle 0 360\n triangle 3 180\n rectangle 4 360\nB square 4 360\n pentagon 5 540\n hexagon 6 720", '>>> df.div(df_multindex, level=1, fill_value=0)\n angles degrees\nA circle NaN 1.0\n triangle 1.0 1.0\n rectangle 1.0 1.0\nB square 0.0 0.0\n pentagon 0.0 0.0\n hexagon 0.0 0.0']
|
pandas.DataFrame.rmul
DataFrame.rmul(other, axis='columns', level=None, fill_value=None)[source]# Get Multiplication of dataframe and other, element-wise (binary operator rmul). Equivalent to other * dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, mul. Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **. Notes Mismatched indices will be unioned together.
|
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] |
27 |
..\pandas\reference\api\pandas.Series.combine_first.html
|
pandas.Series.combine_first
|
Series.combine_first(other)[source]# Update null elements with value in the same location in ‘other’. Combine two Series objects by filling null values in one Series with non-null values from the other Series. Result index will be the union of the two indexes.
|
Parameters: otherSeriesThe value(s) to be used for filling null values. Returns: SeriesThe result of combining the provided Series with the other object.
|
['>>> s1 = pd.Series([1, np.nan])\n>>> s2 = pd.Series([3, 4, 5])\n>>> s1.combine_first(s2)\n0 1.0\n1 4.0\n2 5.0\ndtype: float64', ">>> s1 = pd.Series({'falcon': np.nan, 'eagle': 160.0})\n>>> s2 = pd.Series({'eagle': 200.0, 'duck': 30.0})\n>>> s1.combine_first(s2)\nduck 30.0\neagle 160.0\nfalcon NaN\ndtype: float64"]
|
pandas.Series.combine_first
Series.combine_first(other)[source]# Update null elements with value in the same location in ‘other’. Combine two Series objects by filling null values in one Series with non-null values from the other Series. Result index will be the union of the two indexes.
|
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] |
28 |
..\pandas\reference\api\pandas.tseries.offsets.BYearBegin.is_month_end.html
|
pandas.tseries.offsets.BYearBegin.is_month_end
|
BYearBegin.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.BYearBegin.is_month_end
BYearBegin.is_month_end(ts)# Return boolean whether a timestamp occurs on the month end.
|
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] |
29 |
..\pandas\reference\api\pandas.Series.to_frame.html
|
pandas.Series.to_frame
|
Series.to_frame(name=<no_default>)[source]# Convert Series to DataFrame.
|
Parameters: nameobject, optionalThe passed name should substitute for the series name (if it has one). Returns: DataFrameDataFrame representation of Series.
|
['>>> s = pd.Series(["a", "b", "c"],\n... name="vals")\n>>> s.to_frame()\n vals\n0 a\n1 b\n2 c']
|
pandas.Series.to_frame
Series.to_frame(name=<no_default>)[source]# Convert Series to DataFrame.
|
[
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30 |
..\pandas\reference\api\pandas.Index.view.html
|
pandas.Index.view
|
Index.view(cls=None)[source]#
|
No parameters found
|
[]
|
pandas.Index.view
Index.view(cls=None)[source]#
|
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] |
31 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray.dtype.html
|
pandas.api.extensions.ExtensionArray.dtype
|
property ExtensionArray.dtype[source]# An instance of ExtensionDtype.
|
No parameters found
|
['>>> pd.array([1, 2, 3]).dtype\nInt64Dtype()']
|
pandas.api.extensions.ExtensionArray.dtype
property ExtensionArray.dtype[source]# An instance of ExtensionDtype.
|
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32 |
..\pandas\reference\api\pandas.tseries.offsets.Milli.html
|
pandas.tseries.offsets.Milli
|
class pandas.tseries.offsets.Milli# Offset n milliseconds. Examples You can use the parameter n to represent a shift of n milliseconds. Attributes base Returns a copy of the calling offset object with n=1 and all other attributes equal. delta freqstr Return a string representing the frequency. kwds Return a dict of extra parameters for the offset. n name Return a string representing the base frequency. nanos Return an integer of the total number of nanoseconds. normalize rule_code
|
Parameters: nint, default 1The number of milliseconds represented.
|
[">>> from pandas.tseries.offsets import Milli\n>>> ts = pd.Timestamp(2022, 12, 9, 15)\n>>> ts\nTimestamp('2022-12-09 15:00:00')", ">>> ts + Milli(n=10)\nTimestamp('2022-12-09 15:00:00.010000')", ">>> ts - Milli(n=10)\nTimestamp('2022-12-09 14:59:59.990000')", ">>> ts + Milli(n=-10)\nTimestamp('2022-12-09 14:59:59.990000')"]
|
pandas.tseries.offsets.Milli
class pandas.tseries.offsets.Milli# Offset n milliseconds. Examples You can use the parameter n to represent a shift of n milliseconds. Attributes base Returns a copy of the calling offset object with n=1 and all other attributes equal. delta freqstr Return a string representing the frequency. kwds Return a dict of extra parameters for the offset. n name Return a string representing the base frequency. nanos Return an integer of the total number of nanoseconds. normalize rule_code
|
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] |
33 |
..\pandas\reference\api\pandas.core.groupby.SeriesGroupBy.nth.html
|
pandas.core.groupby.SeriesGroupBy.nth
|
property SeriesGroupBy.nth[source]# Take the nth row from each group if n is an int, otherwise a subset of rows. Can be either a call or an index. dropna is not available with index notation. Index notation accepts a comma separated list of integers and slices. If dropna, will take the nth non-null row, dropna is either ‘all’ or ‘any’; this is equivalent to calling dropna(how=dropna) before the groupby.
|
Parameters: nint, slice or list of ints and slicesA single nth value for the row or a list of nth values or slices. Changed in version 1.4.0: Added slice and lists containing slices. Added index notation. dropna{‘any’, ‘all’, None}, default NoneApply the specified dropna operation before counting which row is the nth row. Only supported if n is an int. Returns: Series or DataFrameN-th value within each group.
|
[">>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2],\n... 'B': [np.nan, 2, 3, 4, 5]}, columns=['A', 'B'])\n>>> g = df.groupby('A')\n>>> g.nth(0)\n A B\n0 1 NaN\n2 2 3.0\n>>> g.nth(1)\n A B\n1 1 2.0\n4 2 5.0\n>>> g.nth(-1)\n A B\n3 1 4.0\n4 2 5.0\n>>> g.nth([0, 1])\n A B\n0 1 NaN\n1 1 2.0\n2 2 3.0\n4 2 5.0\n>>> g.nth(slice(None, -1))\n A B\n0 1 NaN\n1 1 2.0\n2 2 3.0", '>>> g.nth[0, 1]\n A B\n0 1 NaN\n1 1 2.0\n2 2 3.0\n4 2 5.0\n>>> g.nth[:-1]\n A B\n0 1 NaN\n1 1 2.0\n2 2 3.0', ">>> g.nth(0, dropna='any')\n A B\n1 1 2.0\n2 2 3.0", ">>> g.nth(3, dropna='any')\nEmpty DataFrame\nColumns: [A, B]\nIndex: []"]
|
pandas.core.groupby.SeriesGroupBy.nth
property SeriesGroupBy.nth[source]# Take the nth row from each group if n is an int, otherwise a subset of rows. Can be either a call or an index. dropna is not available with index notation. Index notation accepts a comma separated list of integers and slices. If dropna, will take the nth non-null row, dropna is either ‘all’ or ‘any’; this is equivalent to calling dropna(how=dropna) before the groupby.
|
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] |
34 |
..\pandas\reference\api\pandas.DataFrame.rolling.html
|
pandas.DataFrame.rolling
|
DataFrame.rolling(window, min_periods=None, center=False, win_type=None, on=None, axis=<no_default>, closed=None, step=None, method='single')[source]# Provide rolling window calculations. Notes See Windowing Operations for further usage details and examples.
|
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. Deprecated since version 2.1.0: The axis keyword is deprecated. For axis=1, transpose the DataFrame first instead. 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', the 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'). stepint, default None Added in version 1.5.0. Evaluate the window at every step result, equivalent to slicing as [::step]. window must be an integer. Using a step argument other than None or 1 will produce a result with a different shape than the input. methodstr {‘single’, ‘table’}, default ‘single’ Added in version 1.3.0. 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.Window or pandas.api.typing.RollingAn instance of Window is returned if win_type is passed. Otherwise, an instance of Rolling is returned.
|
[">>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})\n>>> df\n B\n0 0.0\n1 1.0\n2 2.0\n3 NaN\n4 4.0", '>>> df.rolling(2).sum()\n B\n0 NaN\n1 1.0\n2 3.0\n3 NaN\n4 NaN', ">>> df_time = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},\n... index=[pd.Timestamp('20130101 09:00:00'),\n... pd.Timestamp('20130101 09:00:02'),\n... pd.Timestamp('20130101 09:00:03'),\n... pd.Timestamp('20130101 09:00:05'),\n... pd.Timestamp('20130101 09:00:06')])", '>>> df_time\n B\n2013-01-01 09:00:00 0.0\n2013-01-01 09:00:02 1.0\n2013-01-01 09:00:03 2.0\n2013-01-01 09:00:05 NaN\n2013-01-01 09:00:06 4.0', ">>> df_time.rolling('2s').sum()\n B\n2013-01-01 09:00:00 0.0\n2013-01-01 09:00:02 1.0\n2013-01-01 09:00:03 3.0\n2013-01-01 09:00:05 NaN\n2013-01-01 09:00:06 4.0", '>>> indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=2)\n>>> df.rolling(window=indexer, min_periods=1).sum()\n B\n0 1.0\n1 3.0\n2 2.0\n3 4.0\n4 4.0', '>>> df.rolling(2, min_periods=1).sum()\n B\n0 0.0\n1 1.0\n2 3.0\n3 2.0\n4 4.0', '>>> df.rolling(3, min_periods=1, center=True).sum()\n B\n0 1.0\n1 3.0\n2 3.0\n3 6.0\n4 4.0', '>>> df.rolling(3, min_periods=1, center=False).sum()\n B\n0 0.0\n1 1.0\n2 3.0\n3 3.0\n4 6.0', '>>> df.rolling(2, min_periods=1, step=2).sum()\n B\n0 0.0\n2 3.0\n4 4.0', ">>> df.rolling(2, win_type='gaussian').sum(std=3)\n B\n0 NaN\n1 0.986207\n2 2.958621\n3 NaN\n4 NaN", ">>> df = pd.DataFrame({\n... 'A': [pd.to_datetime('2020-01-01'),\n... pd.to_datetime('2020-01-01'),\n... pd.to_datetime('2020-01-02'),],\n... 'B': [1, 2, 3], },\n... index=pd.date_range('2020', periods=3))", '>>> df\n A B\n2020-01-01 2020-01-01 1\n2020-01-02 2020-01-01 2\n2020-01-03 2020-01-02 3', ">>> df.rolling('2D', on='A').sum()\n A B\n2020-01-01 2020-01-01 1.0\n2020-01-02 2020-01-01 3.0\n2020-01-03 2020-01-02 6.0"]
|
pandas.DataFrame.rolling
DataFrame.rolling(window, min_periods=None, center=False, win_type=None, on=None, axis=<no_default>, closed=None, step=None, method='single')[source]# Provide rolling window calculations. Notes See Windowing Operations for further usage details and examples.
|
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] |
35 |
..\pandas\reference\api\pandas.Series.compare.html
|
pandas.Series.compare
|
Series.compare(other, align_axis=1, keep_shape=False, keep_equal=False, result_names=('self', 'other'))[source]# Compare to another Series and show the differences. Notes Matching NaNs will not appear as a difference.
|
Parameters: otherSeriesObject to compare with. align_axis{0 or ‘index’, 1 or ‘columns’}, default 1Determine which axis to align the comparison on. 0, or ‘index’Resulting differences are stacked verticallywith rows drawn alternately from self and other. 1, or ‘columns’Resulting differences are aligned horizontallywith columns drawn alternately from self and other. keep_shapebool, default FalseIf true, all rows and columns are kept. Otherwise, only the ones with different values are kept. keep_equalbool, default FalseIf true, the result keeps values that are equal. Otherwise, equal values are shown as NaNs. result_namestuple, default (‘self’, ‘other’)Set the dataframes names in the comparison. Added in version 1.5.0. Returns: Series or DataFrameIf axis is 0 or ‘index’ the result will be a Series. The resulting index will be a MultiIndex with ‘self’ and ‘other’ stacked alternately at the inner level. If axis is 1 or ‘columns’ the result will be a DataFrame. It will have two columns namely ‘self’ and ‘other’.
|
['>>> s1 = pd.Series(["a", "b", "c", "d", "e"])\n>>> s2 = pd.Series(["a", "a", "c", "b", "e"])', '>>> s1.compare(s2)\n self other\n1 b a\n3 d b', '>>> s1.compare(s2, align_axis=0)\n1 self b\n other a\n3 self d\n other b\ndtype: object', '>>> s1.compare(s2, keep_shape=True)\n self other\n0 NaN NaN\n1 b a\n2 NaN NaN\n3 d b\n4 NaN NaN', '>>> s1.compare(s2, keep_shape=True, keep_equal=True)\n self other\n0 a a\n1 b a\n2 c c\n3 d b\n4 e e']
|
pandas.Series.compare
Series.compare(other, align_axis=1, keep_shape=False, keep_equal=False, result_names=('self', 'other'))[source]# Compare to another Series and show the differences. Notes Matching NaNs will not appear as a difference.
|
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] |
36 |
..\pandas\reference\api\pandas.Series.to_hdf.html
|
pandas.Series.to_hdf
|
Series.to_hdf(path_or_buf, *, key, mode='a', complevel=None, complib=None, append=False, format=None, index=True, min_itemsize=None, nan_rep=None, dropna=None, data_columns=None, errors='strict', encoding='UTF-8')[source]# Write the contained data to an HDF5 file using HDFStore. Hierarchical Data Format (HDF) is self-describing, allowing an application to interpret the structure and contents of a file with no outside information. One HDF file can hold a mix of related objects which can be accessed as a group or as individual objects. In order to add another DataFrame or Series to an existing HDF file please use append mode and a different a key. Warning One can store a subclass of DataFrame or Series to HDF5, but the type of the subclass is lost upon storing. For more information see the user guide.
|
Parameters: path_or_bufstr or pandas.HDFStoreFile path or HDFStore object. keystrIdentifier for the group in the store. mode{‘a’, ‘w’, ‘r+’}, default ‘a’Mode to open file: ‘w’: write, a new file is created (an existing file with the same name would be deleted). ‘a’: append, an existing file is opened for reading and writing, and if the file does not exist it is created. ‘r+’: similar to ‘a’, but the file must already exist. complevel{0-9}, default NoneSpecifies a compression level for data. A value of 0 or None disables compression. complib{‘zlib’, ‘lzo’, ‘bzip2’, ‘blosc’}, default ‘zlib’Specifies the compression library to be used. These additional compressors for Blosc are supported (default if no compressor specified: ‘blosc:blosclz’): {‘blosc:blosclz’, ‘blosc:lz4’, ‘blosc:lz4hc’, ‘blosc:snappy’, ‘blosc:zlib’, ‘blosc:zstd’}. Specifying a compression library which is not available issues a ValueError. appendbool, default FalseFor Table formats, append the input data to the existing. format{‘fixed’, ‘table’, None}, default ‘fixed’Possible values: ‘fixed’: Fixed format. Fast writing/reading. Not-appendable, nor searchable. ‘table’: Table format. Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data. If None, pd.get_option(‘io.hdf.default_format’) is checked, followed by fallback to “fixed”. indexbool, default TrueWrite DataFrame index as a column. min_itemsizedict or int, optionalMap column names to minimum string sizes for columns. nan_repAny, optionalHow to represent null values as str. Not allowed with append=True. dropnabool, default False, optionalRemove missing values. data_columnslist of columns or True, optionalList of columns to create as indexed data columns for on-disk queries, or True to use all columns. By default only the axes of the object are indexed. See Query via data columns. for more information. Applicable only to format=’table’. errorsstr, default ‘strict’Specifies how encoding and decoding errors are to be handled. See the errors argument for open() for a full list of options. encodingstr, default “UTF-8”
|
[">>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]},\n... index=['a', 'b', 'c']) \n>>> df.to_hdf('data.h5', key='df', mode='w')", ">>> s = pd.Series([1, 2, 3, 4]) \n>>> s.to_hdf('data.h5', key='s')", ">>> pd.read_hdf('data.h5', 'df') \nA B\na 1 4\nb 2 5\nc 3 6\n>>> pd.read_hdf('data.h5', 's') \n0 1\n1 2\n2 3\n3 4\ndtype: int64"]
|
pandas.Series.to_hdf
Series.to_hdf(path_or_buf, *, key, mode='a', complevel=None, complib=None, append=False, format=None, index=True, min_itemsize=None, nan_rep=None, dropna=None, data_columns=None, errors='strict', encoding='UTF-8')[source]# Write the contained data to an HDF5 file using HDFStore. Hierarchical Data Format (HDF) is self-describing, allowing an application to interpret the structure and contents of a file with no outside information. One HDF file can hold a mix of related objects which can be accessed as a group or as individual objects. In order to add another DataFrame or Series to an existing HDF file please use append mode and a different a key. Warning One can store a subclass of DataFrame or Series to HDF5, but the type of the subclass is lost upon storing. For more information see the user guide.
|
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] |
37 |
..\pandas\reference\api\pandas.core.groupby.SeriesGroupBy.nunique.html
|
pandas.core.groupby.SeriesGroupBy.nunique
|
SeriesGroupBy.nunique(dropna=True)[source]# Return number of unique elements in the group.
|
Returns: SeriesNumber of unique values within each group.
|
[">>> lst = ['a', 'a', 'b', 'b']\n>>> ser = pd.Series([1, 2, 3, 3], index=lst)\n>>> ser\na 1\na 2\nb 3\nb 3\ndtype: int64\n>>> ser.groupby(level=0).nunique()\na 2\nb 1\ndtype: int64", ">>> ser = pd.Series([1, 2, 3, 3], index=pd.DatetimeIndex(\n... ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))\n>>> ser\n2023-01-01 1\n2023-01-15 2\n2023-02-01 3\n2023-02-15 3\ndtype: int64\n>>> ser.resample('MS').nunique()\n2023-01-01 2\n2023-02-01 1\nFreq: MS, dtype: int64"]
|
pandas.core.groupby.SeriesGroupBy.nunique
SeriesGroupBy.nunique(dropna=True)[source]# Return number of unique elements in the group.
|
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] |
38 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray.duplicated.html
|
pandas.api.extensions.ExtensionArray.duplicated
|
ExtensionArray.duplicated(keep='first')[source]# Return boolean ndarray denoting duplicate values.
|
Parameters: keep{‘first’, ‘last’, False}, default ‘first’ first : Mark duplicates as True except for the first occurrence. last : Mark duplicates as True except for the last occurrence. False : Mark all duplicates as True. Returns: ndarray[bool]
|
['>>> pd.array([1, 1, 2, 3, 3], dtype="Int64").duplicated()\narray([False, True, False, False, True])']
|
pandas.api.extensions.ExtensionArray.duplicated
ExtensionArray.duplicated(keep='first')[source]# Return boolean ndarray denoting duplicate values.
|
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] |
39 |
..\pandas\reference\api\pandas.tseries.offsets.BYearBegin.is_month_start.html
|
pandas.tseries.offsets.BYearBegin.is_month_start
|
BYearBegin.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.BYearBegin.is_month_start
BYearBegin.is_month_start(ts)# Return boolean whether a timestamp occurs on the month start.
|
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] |
40 |
..\pandas\reference\api\pandas.DataFrame.round.html
|
pandas.DataFrame.round
|
DataFrame.round(decimals=0, *args, **kwargs)[source]# Round a DataFrame to a variable number of decimal places.
|
Parameters: decimalsint, dict, SeriesNumber of decimal places to round each column to. If an int is given, round each column to the same number of places. Otherwise dict and Series round to variable numbers of places. Column names should be in the keys if decimals is a dict-like, or in the index if decimals is a Series. Any columns not included in decimals will be left as is. Elements of decimals which are not columns of the input will be ignored. *argsAdditional keywords have no effect but might be accepted for compatibility with numpy. **kwargsAdditional keywords have no effect but might be accepted for compatibility with numpy. Returns: DataFrameA DataFrame with the affected columns rounded to the specified number of decimal places.
|
[">>> df = pd.DataFrame([(.21, .32), (.01, .67), (.66, .03), (.21, .18)],\n... columns=['dogs', 'cats'])\n>>> df\n dogs cats\n0 0.21 0.32\n1 0.01 0.67\n2 0.66 0.03\n3 0.21 0.18", '>>> df.round(1)\n dogs cats\n0 0.2 0.3\n1 0.0 0.7\n2 0.7 0.0\n3 0.2 0.2', ">>> df.round({'dogs': 1, 'cats': 0})\n dogs cats\n0 0.2 0.0\n1 0.0 1.0\n2 0.7 0.0\n3 0.2 0.0", ">>> decimals = pd.Series([0, 1], index=['cats', 'dogs'])\n>>> df.round(decimals)\n dogs cats\n0 0.2 0.0\n1 0.0 1.0\n2 0.7 0.0\n3 0.2 0.0"]
|
pandas.DataFrame.round
DataFrame.round(decimals=0, *args, **kwargs)[source]# Round a DataFrame to a variable number of decimal places.
|
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] |
41 |
..\pandas\reference\api\pandas.Index.where.html
|
pandas.Index.where
|
final Index.where(cond, other=None)[source]# Replace values where the condition is False. The replacement is taken from other.
|
Parameters: condbool array-like with the same length as selfCondition to select the values on. otherscalar, or array-like, default NoneReplacement if the condition is False. Returns: pandas.IndexA copy of self with values replaced from other where the condition is False.
|
[">>> idx = pd.Index(['car', 'bike', 'train', 'tractor'])\n>>> idx\nIndex(['car', 'bike', 'train', 'tractor'], dtype='object')\n>>> idx.where(idx.isin(['car', 'train']), 'other')\nIndex(['car', 'other', 'train', 'other'], dtype='object')"]
|
pandas.Index.where
final Index.where(cond, other=None)[source]# Replace values where the condition is False. The replacement is taken from other.
|
[
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] |
42 |
..\pandas\reference\api\pandas.tseries.offsets.Milli.is_anchored.html
|
pandas.tseries.offsets.Milli.is_anchored
|
Milli.is_anchored()# Return False. Deprecated since version 2.2.0: is_anchored is deprecated and will be removed in a future version. Use False instead.
|
No parameters found
|
['>>> pd.offsets.Hour().is_anchored()\nFalse\n>>> pd.offsets.Hour(2).is_anchored()\nFalse']
|
pandas.tseries.offsets.Milli.is_anchored
Milli.is_anchored()# Return False. Deprecated since version 2.2.0: is_anchored is deprecated and will be removed in a future version. Use False instead.
|
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] |
43 |
..\pandas\reference\api\pandas.Series.convert_dtypes.html
|
pandas.Series.convert_dtypes
|
Series.convert_dtypes(infer_objects=True, convert_string=True, convert_integer=True, convert_boolean=True, convert_floating=True, dtype_backend='numpy_nullable')[source]# Convert columns to the best possible dtypes using dtypes supporting pd.NA. Notes By default, convert_dtypes will attempt to convert a Series (or each Series in a DataFrame) to dtypes that support pd.NA. By using the options convert_string, convert_integer, convert_boolean and convert_floating, it is possible to turn off individual conversions to StringDtype, the integer extension types, BooleanDtype or floating extension types, respectively. For object-dtyped columns, if infer_objects is True, use the inference rules as during normal Series/DataFrame construction. Then, if possible, convert to StringDtype, BooleanDtype or an appropriate integer or floating extension type, otherwise leave as object. If the dtype is integer, convert to an appropriate integer extension type. If the dtype is numeric, and consists of all integers, convert to an appropriate integer extension type. Otherwise, convert to an appropriate floating extension type. In the future, as new dtypes are added that support pd.NA, the results of this method will change to support those new dtypes.
|
Parameters: infer_objectsbool, default TrueWhether object dtypes should be converted to the best possible types. convert_stringbool, default TrueWhether object dtypes should be converted to StringDtype(). convert_integerbool, default TrueWhether, if possible, conversion can be done to integer extension types. convert_booleanbool, defaults TrueWhether object dtypes should be converted to BooleanDtypes(). convert_floatingbool, defaults TrueWhether, if possible, conversion can be done to floating extension types. If convert_integer is also True, preference will be give to integer dtypes if the floats can be faithfully casted to integers. dtype_backend{‘numpy_nullable’, ‘pyarrow’}, default ‘numpy_nullable’Back-end data type applied to the resultant DataFrame (still experimental). Behaviour is as follows: "numpy_nullable": returns nullable-dtype-backed DataFrame (default). "pyarrow": returns pyarrow-backed nullable ArrowDtype DataFrame. Added in version 2.0. Returns: Series or DataFrameCopy of input object with new dtype.
|
['>>> df = pd.DataFrame(\n... {\n... "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")),\n... "b": pd.Series(["x", "y", "z"], dtype=np.dtype("O")),\n... "c": pd.Series([True, False, np.nan], dtype=np.dtype("O")),\n... "d": pd.Series(["h", "i", np.nan], dtype=np.dtype("O")),\n... "e": pd.Series([10, np.nan, 20], dtype=np.dtype("float")),\n... "f": pd.Series([np.nan, 100.5, 200], dtype=np.dtype("float")),\n... }\n... )', '>>> df\n a b c d e f\n0 1 x True h 10.0 NaN\n1 2 y False i NaN 100.5\n2 3 z NaN NaN 20.0 200.0', '>>> df.dtypes\na int32\nb object\nc object\nd object\ne float64\nf float64\ndtype: object', '>>> dfn = df.convert_dtypes()\n>>> dfn\n a b c d e f\n0 1 x True h 10 <NA>\n1 2 y False i <NA> 100.5\n2 3 z <NA> <NA> 20 200.0', '>>> dfn.dtypes\na Int32\nb string[python]\nc boolean\nd string[python]\ne Int64\nf Float64\ndtype: object', '>>> s = pd.Series(["a", "b", np.nan])\n>>> s\n0 a\n1 b\n2 NaN\ndtype: object', '>>> s.convert_dtypes()\n0 a\n1 b\n2 <NA>\ndtype: string']
|
pandas.Series.convert_dtypes
Series.convert_dtypes(infer_objects=True, convert_string=True, convert_integer=True, convert_boolean=True, convert_floating=True, dtype_backend='numpy_nullable')[source]# Convert columns to the best possible dtypes using dtypes supporting pd.NA. Notes By default, convert_dtypes will attempt to convert a Series (or each Series in a DataFrame) to dtypes that support pd.NA. By using the options convert_string, convert_integer, convert_boolean and convert_floating, it is possible to turn off individual conversions to StringDtype, the integer extension types, BooleanDtype or floating extension types, respectively. For object-dtyped columns, if infer_objects is True, use the inference rules as during normal Series/DataFrame construction. Then, if possible, convert to StringDtype, BooleanDtype or an appropriate integer or floating extension type, otherwise leave as object. If the dtype is integer, convert to an appropriate integer extension type. If the dtype is numeric, and consists of all integers, convert to an appropriate integer extension type. Otherwise, convert to an appropriate floating extension type. In the future, as new dtypes are added that support pd.NA, the results of this method will change to support those new dtypes.
|
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] |
44 |
..\pandas\reference\api\pandas.Series.to_json.html
|
pandas.Series.to_json
|
Series.to_json(path_or_buf=None, *, orient=None, date_format=None, double_precision=10, force_ascii=True, date_unit='ms', default_handler=None, lines=False, compression='infer', index=None, indent=None, storage_options=None, mode='w')[source]# Convert the object to a JSON string. Note NaN’s and None will be converted to null and datetime objects will be converted to UNIX timestamps. Notes The behavior of indent=0 varies from the stdlib, which does not indent the output but does insert newlines. Currently, indent=0 and the default indent=None are equivalent in pandas, though this may change in a future release. orient='table' contains a ‘pandas_version’ field under ‘schema’. This stores the version of pandas used in the latest revision of the schema.
|
Parameters: path_or_bufstr, path object, file-like object, or None, default NoneString, path object (implementing os.PathLike[str]), or file-like object implementing a write() function. If None, the result is returned as a string. orientstrIndication of expected JSON string format. Series: default is ‘index’ allowed values are: {‘split’, ‘records’, ‘index’, ‘table’}. DataFrame: default is ‘columns’ allowed values are: {‘split’, ‘records’, ‘index’, ‘columns’, ‘values’, ‘table’}. The format of the JSON string: ‘split’ : dict like {‘index’ -> [index], ‘columns’ -> [columns], ‘data’ -> [values]} ‘records’ : list like [{column -> value}, … , {column -> value}] ‘index’ : dict like {index -> {column -> value}} ‘columns’ : dict like {column -> {index -> value}} ‘values’ : just the values array ‘table’ : dict like {‘schema’: {schema}, ‘data’: {data}} Describing the data, where data component is like orient='records'. date_format{None, ‘epoch’, ‘iso’}Type of date conversion. ‘epoch’ = epoch milliseconds, ‘iso’ = ISO8601. The default depends on the orient. For orient='table', the default is ‘iso’. For all other orients, the default is ‘epoch’. double_precisionint, default 10The number of decimal places to use when encoding floating point values. The possible maximal value is 15. Passing double_precision greater than 15 will raise a ValueError. force_asciibool, default TrueForce encoded string to be ASCII. date_unitstr, default ‘ms’ (milliseconds)The time unit to encode to, governs timestamp and ISO8601 precision. One of ‘s’, ‘ms’, ‘us’, ‘ns’ for second, millisecond, microsecond, and nanosecond respectively. default_handlercallable, default NoneHandler to call if object cannot otherwise be converted to a suitable format for JSON. Should receive a single argument which is the object to convert and return a serialisable object. linesbool, default FalseIf ‘orient’ is ‘records’ write out line-delimited json format. Will throw ValueError if incorrect ‘orient’ since others are not list-like. compressionstr or dict, default ‘infer’For on-the-fly compression of the output data. If ‘infer’ and ‘path_or_buf’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, ‘.zst’, ‘.tar’, ‘.tar.gz’, ‘.tar.xz’ or ‘.tar.bz2’ (otherwise no compression). Set to None for no compression. Can also be a dict with key 'method' set to one of {'zip', 'gzip', 'bz2', 'zstd', 'xz', 'tar'} and other key-value pairs are forwarded to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, zstandard.ZstdCompressor, lzma.LZMAFile or tarfile.TarFile, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive: compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}. Added in version 1.5.0: Added support for .tar files. Changed in version 1.4.0: Zstandard support. indexbool or None, default NoneThe index is only used when ‘orient’ is ‘split’, ‘index’, ‘column’, or ‘table’. Of these, ‘index’ and ‘column’ do not support index=False. indentint, optionalLength of whitespace used to indent each record. storage_optionsdict, optionalExtra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here. modestr, default ‘w’ (writing)Specify the IO mode for output when supplying a path_or_buf. Accepted args are ‘w’ (writing) and ‘a’ (append) only. mode=’a’ is only supported when lines is True and orient is ‘records’. Returns: None or strIf path_or_buf is None, returns the resulting json format as a string. Otherwise returns None.
|
['>>> from json import loads, dumps\n>>> df = pd.DataFrame(\n... [["a", "b"], ["c", "d"]],\n... index=["row 1", "row 2"],\n... columns=["col 1", "col 2"],\n... )', '>>> result = df.to_json(orient="split")\n>>> parsed = loads(result)\n>>> dumps(parsed, indent=4) \n{\n "columns": [\n "col 1",\n "col 2"\n ],\n "index": [\n "row 1",\n "row 2"\n ],\n "data": [\n [\n "a",\n "b"\n ],\n [\n "c",\n "d"\n ]\n ]\n}', '>>> result = df.to_json(orient="records")\n>>> parsed = loads(result)\n>>> dumps(parsed, indent=4) \n[\n {\n "col 1": "a",\n "col 2": "b"\n },\n {\n "col 1": "c",\n "col 2": "d"\n }\n]', '>>> result = df.to_json(orient="index")\n>>> parsed = loads(result)\n>>> dumps(parsed, indent=4) \n{\n "row 1": {\n "col 1": "a",\n "col 2": "b"\n },\n "row 2": {\n "col 1": "c",\n "col 2": "d"\n }\n}', '>>> result = df.to_json(orient="columns")\n>>> parsed = loads(result)\n>>> dumps(parsed, indent=4) \n{\n "col 1": {\n "row 1": "a",\n "row 2": "c"\n },\n "col 2": {\n "row 1": "b",\n "row 2": "d"\n }\n}', '>>> result = df.to_json(orient="values")\n>>> parsed = loads(result)\n>>> dumps(parsed, indent=4) \n[\n [\n "a",\n "b"\n ],\n [\n "c",\n "d"\n ]\n]', '>>> result = df.to_json(orient="table")\n>>> parsed = loads(result)\n>>> dumps(parsed, indent=4) \n{\n "schema": {\n "fields": [\n {\n "name": "index",\n "type": "string"\n },\n {\n "name": "col 1",\n "type": "string"\n },\n {\n "name": "col 2",\n "type": "string"\n }\n ],\n "primaryKey": [\n "index"\n ],\n "pandas_version": "1.4.0"\n },\n "data": [\n {\n "index": "row 1",\n "col 1": "a",\n "col 2": "b"\n },\n {\n "index": "row 2",\n "col 1": "c",\n "col 2": "d"\n }\n ]\n}']
|
pandas.Series.to_json
Series.to_json(path_or_buf=None, *, orient=None, date_format=None, double_precision=10, force_ascii=True, date_unit='ms', default_handler=None, lines=False, compression='infer', index=None, indent=None, storage_options=None, mode='w')[source]# Convert the object to a JSON string. Note NaN’s and None will be converted to null and datetime objects will be converted to UNIX timestamps. Notes The behavior of indent=0 varies from the stdlib, which does not indent the output but does insert newlines. Currently, indent=0 and the default indent=None are equivalent in pandas, though this may change in a future release. orient='table' contains a ‘pandas_version’ field under ‘schema’. This stores the version of pandas used in the latest revision of the schema.
|
[
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] |
45 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray.equals.html
|
pandas.api.extensions.ExtensionArray.equals
|
ExtensionArray.equals(other)[source]# Return if another array is equivalent to this array. Equivalent means that both arrays have the same shape and dtype, and all values compare equal. Missing values in the same location are considered equal (in contrast with normal equality).
|
Parameters: otherExtensionArrayArray to compare to this Array. Returns: booleanWhether the arrays are equivalent.
|
['>>> arr1 = pd.array([1, 2, np.nan])\n>>> arr2 = pd.array([1, 2, np.nan])\n>>> arr1.equals(arr2)\nTrue']
|
pandas.api.extensions.ExtensionArray.equals
ExtensionArray.equals(other)[source]# Return if another array is equivalent to this array. Equivalent means that both arrays have the same shape and dtype, and all values compare equal. Missing values in the same location are considered equal (in contrast with normal equality).
|
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] |
46 |
..\pandas\reference\api\pandas.core.groupby.SeriesGroupBy.ohlc.html
|
pandas.core.groupby.SeriesGroupBy.ohlc
|
SeriesGroupBy.ohlc()[source]# Compute open, high, low and close values of a group, excluding missing values. For multiple groupings, the result index will be a MultiIndex
|
Returns: DataFrameOpen, high, low and close values within each group.
|
[">>> lst = ['SPX', 'CAC', 'SPX', 'CAC', 'SPX', 'CAC', 'SPX', 'CAC',]\n>>> ser = pd.Series([3.4, 9.0, 7.2, 5.2, 8.8, 9.4, 0.1, 0.5], index=lst)\n>>> ser\nSPX 3.4\nCAC 9.0\nSPX 7.2\nCAC 5.2\nSPX 8.8\nCAC 9.4\nSPX 0.1\nCAC 0.5\ndtype: float64\n>>> ser.groupby(level=0).ohlc()\n open high low close\nCAC 9.0 9.4 0.5 0.5\nSPX 3.4 8.8 0.1 0.1", ">>> data = {2022: [1.2, 2.3, 8.9, 4.5, 4.4, 3, 2 , 1],\n... 2023: [3.4, 9.0, 7.2, 5.2, 8.8, 9.4, 8.2, 1.0]}\n>>> df = pd.DataFrame(data, index=['SPX', 'CAC', 'SPX', 'CAC',\n... 'SPX', 'CAC', 'SPX', 'CAC'])\n>>> df\n 2022 2023\nSPX 1.2 3.4\nCAC 2.3 9.0\nSPX 8.9 7.2\nCAC 4.5 5.2\nSPX 4.4 8.8\nCAC 3.0 9.4\nSPX 2.0 8.2\nCAC 1.0 1.0\n>>> df.groupby(level=0).ohlc()\n 2022 2023\n open high low close open high low close\nCAC 2.3 4.5 1.0 1.0 9.0 9.4 1.0 1.0\nSPX 1.2 8.9 1.2 2.0 3.4 8.8 3.4 8.2", ">>> ser = pd.Series([1, 3, 2, 4, 3, 5],\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').ohlc()\n open high low close\n2023-01-01 1 3 1 2\n2023-02-01 4 5 3 5"]
|
pandas.core.groupby.SeriesGroupBy.ohlc
SeriesGroupBy.ohlc()[source]# Compute open, high, low and close values of a group, excluding missing values. For multiple groupings, the result index will be a MultiIndex
|
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] |
47 |
..\pandas\reference\api\pandas.tseries.offsets.BYearBegin.is_on_offset.html
|
pandas.tseries.offsets.BYearBegin.is_on_offset
|
BYearBegin.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.BYearBegin.is_on_offset
BYearBegin.is_on_offset(dt)# Return boolean whether a timestamp intersects with this frequency.
|
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] |
48 |
..\pandas\reference\api\pandas.core.groupby.SeriesGroupBy.pct_change.html
|
pandas.core.groupby.SeriesGroupBy.pct_change
|
SeriesGroupBy.pct_change(periods=1, fill_method=<no_default>, limit=<no_default>, freq=None, axis=<no_default>)[source]# Calculate pct_change of each value to previous entry in group.
|
Returns: Series or DataFramePercentage changes 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).pct_change()\na NaN\na 1.000000\nb NaN\nb 0.333333\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").pct_change()\n b c\n tuna NaN NaN\n salmon 1.5 1.000\n catfish NaN NaN\ngoldfish 0.2 0.125']
|
pandas.core.groupby.SeriesGroupBy.pct_change
SeriesGroupBy.pct_change(periods=1, fill_method=<no_default>, limit=<no_default>, freq=None, axis=<no_default>)[source]# Calculate pct_change of each value to previous entry in group.
|
[
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49 |
..\pandas\reference\api\pandas.Series.to_latex.html
|
pandas.Series.to_latex
|
Series.to_latex(buf=None, *, columns=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, bold_rows=False, column_format=None, longtable=None, escape=None, encoding=None, decimal='.', multicolumn=None, multicolumn_format=None, multirow=None, caption=None, label=None, position=None)[source]# Render object to a LaTeX tabular, longtable, or nested table. Requires \usepackage{{booktabs}}. The output can be copy/pasted into a main LaTeX document or read from an external file with \input{{table.tex}}. Changed in version 2.0.0: Refactored to use the Styler implementation via jinja2 templating. Notes As of v2.0.0 this method has changed to use the Styler implementation as part of Styler.to_latex() via jinja2 templating. This means that jinja2 is a requirement, and needs to be installed, for this method to function. It is advised that users switch to using Styler, since that implementation is more frequently updated and contains much more flexibility with the output.
|
Parameters: bufstr, Path or StringIO-like, optional, default NoneBuffer to write to. If None, the output is returned as a string. columnslist of label, optionalThe subset of columns to write. Writes all columns by default. headerbool or list of str, default TrueWrite out the column names. If a list of strings is given, it is assumed to be aliases for the column names. indexbool, default TrueWrite row names (index). na_repstr, default ‘NaN’Missing data representation. formatterslist of functions or dict of {{str: function}}, optionalFormatter functions to apply to columns’ elements by position or name. The result of each function must be a unicode string. List must be of length equal to the number of columns. float_formatone-parameter function or str, optional, default NoneFormatter for floating point numbers. For example float_format="%.2f" and float_format="{{:0.2f}}".format will both result in 0.1234 being formatted as 0.12. sparsifybool, optionalSet to False for a DataFrame with a hierarchical index to print every multiindex key at each row. By default, the value will be read from the config module. index_namesbool, default TruePrints the names of the indexes. bold_rowsbool, default FalseMake the row labels bold in the output. column_formatstr, optionalThe columns format as specified in LaTeX table format e.g. ‘rcl’ for 3 columns. By default, ‘l’ will be used for all columns except columns of numbers, which default to ‘r’. longtablebool, optionalUse a longtable environment instead of tabular. Requires adding a usepackage{{longtable}} to your LaTeX preamble. By default, the value will be read from the pandas config module, and set to True if the option styler.latex.environment is “longtable”. Changed in version 2.0.0: The pandas option affecting this argument has changed. escapebool, optionalBy default, the value will be read from the pandas config module and set to True if the option styler.format.escape is “latex”. When set to False prevents from escaping latex special characters in column names. Changed in version 2.0.0: The pandas option affecting this argument has changed, as has the default value to False. encodingstr, optionalA string representing the encoding to use in the output file, defaults to ‘utf-8’. decimalstr, default ‘.’Character recognized as decimal separator, e.g. ‘,’ in Europe. multicolumnbool, default TrueUse multicolumn to enhance MultiIndex columns. The default will be read from the config module, and is set as the option styler.sparse.columns. Changed in version 2.0.0: The pandas option affecting this argument has changed. multicolumn_formatstr, default ‘r’The alignment for multicolumns, similar to column_format The default will be read from the config module, and is set as the option styler.latex.multicol_align. Changed in version 2.0.0: The pandas option affecting this argument has changed, as has the default value to “r”. multirowbool, default TrueUse multirow to enhance MultiIndex rows. Requires adding a usepackage{{multirow}} to your LaTeX preamble. Will print centered labels (instead of top-aligned) across the contained rows, separating groups via clines. The default will be read from the pandas config module, and is set as the option styler.sparse.index. Changed in version 2.0.0: The pandas option affecting this argument has changed, as has the default value to True. captionstr or tuple, optionalTuple (full_caption, short_caption), which results in \caption[short_caption]{{full_caption}}; if a single string is passed, no short caption will be set. labelstr, optionalThe LaTeX label to be placed inside \label{{}} in the output. This is used with \ref{{}} in the main .tex file. positionstr, optionalThe LaTeX positional argument for tables, to be placed after \begin{{}} in the output. Returns: str or NoneIf buf is None, returns the result as a string. Otherwise returns None.
|
['>>> df = pd.DataFrame(dict(name=[\'Raphael\', \'Donatello\'],\n... age=[26, 45],\n... height=[181.23, 177.65]))\n>>> print(df.to_latex(index=False,\n... formatters={"name": str.upper},\n... float_format="{:.1f}".format,\n... )) \n\\begin{tabular}{lrr}\n\\toprule\nname & age & height \\\\\n\\midrule\nRAPHAEL & 26 & 181.2 \\\\\nDONATELLO & 45 & 177.7 \\\\\n\\bottomrule\n\\end{tabular}']
|
pandas.Series.to_latex
Series.to_latex(buf=None, *, columns=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, bold_rows=False, column_format=None, longtable=None, escape=None, encoding=None, decimal='.', multicolumn=None, multicolumn_format=None, multirow=None, caption=None, label=None, position=None)[source]# Render object to a LaTeX tabular, longtable, or nested table. Requires \usepackage{{booktabs}}. The output can be copy/pasted into a main LaTeX document or read from an external file with \input{{table.tex}}. Changed in version 2.0.0: Refactored to use the Styler implementation via jinja2 templating. Notes As of v2.0.0 this method has changed to use the Styler implementation as part of Styler.to_latex() via jinja2 templating. This means that jinja2 is a requirement, and needs to be installed, for this method to function. It is advised that users switch to using Styler, since that implementation is more frequently updated and contains much more flexibility with the output.
|
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] |
50 |
..\pandas\reference\api\pandas.DataFrame.rpow.html
|
pandas.DataFrame.rpow
|
DataFrame.rpow(other, axis='columns', level=None, fill_value=None)[source]# Get Exponential power of dataframe and other, element-wise (binary operator rpow). Equivalent to other ** dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, pow. Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **. Notes Mismatched indices will be unioned together.
|
Parameters: otherscalar, sequence, Series, dict or DataFrameAny single or multiple element data structure, or list-like object. axis{0 or ‘index’, 1 or ‘columns’}Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on. levelint or labelBroadcast across a level, matching Index values on the passed MultiIndex level. fill_valuefloat or None, default NoneFill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing. Returns: DataFrameResult of the arithmetic operation.
|
[">>> df = pd.DataFrame({'angles': [0, 3, 4],\n... 'degrees': [360, 180, 360]},\n... index=['circle', 'triangle', 'rectangle'])\n>>> df\n angles degrees\ncircle 0 360\ntriangle 3 180\nrectangle 4 360", '>>> df + 1\n angles degrees\ncircle 1 361\ntriangle 4 181\nrectangle 5 361', '>>> df.add(1)\n angles degrees\ncircle 1 361\ntriangle 4 181\nrectangle 5 361', '>>> df.div(10)\n angles degrees\ncircle 0.0 36.0\ntriangle 0.3 18.0\nrectangle 0.4 36.0', '>>> df.rdiv(10)\n angles degrees\ncircle inf 0.027778\ntriangle 3.333333 0.055556\nrectangle 2.500000 0.027778', '>>> df - [1, 2]\n angles degrees\ncircle -1 358\ntriangle 2 178\nrectangle 3 358', ">>> df.sub([1, 2], axis='columns')\n angles degrees\ncircle -1 358\ntriangle 2 178\nrectangle 3 358", ">>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),\n... axis='index')\n angles degrees\ncircle -1 359\ntriangle 2 179\nrectangle 3 359", ">>> df.mul({'angles': 0, 'degrees': 2})\n angles degrees\ncircle 0 720\ntriangle 0 360\nrectangle 0 720", ">>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')\n angles degrees\ncircle 0 0\ntriangle 6 360\nrectangle 12 1080", ">>> other = pd.DataFrame({'angles': [0, 3, 4]},\n... index=['circle', 'triangle', 'rectangle'])\n>>> other\n angles\ncircle 0\ntriangle 3\nrectangle 4", '>>> df * other\n angles degrees\ncircle 0 NaN\ntriangle 9 NaN\nrectangle 16 NaN', '>>> df.mul(other, fill_value=0)\n angles degrees\ncircle 0 0.0\ntriangle 9 0.0\nrectangle 16 0.0', ">>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],\n... 'degrees': [360, 180, 360, 360, 540, 720]},\n... index=[['A', 'A', 'A', 'B', 'B', 'B'],\n... ['circle', 'triangle', 'rectangle',\n... 'square', 'pentagon', 'hexagon']])\n>>> df_multindex\n angles degrees\nA circle 0 360\n triangle 3 180\n rectangle 4 360\nB square 4 360\n pentagon 5 540\n hexagon 6 720", '>>> df.div(df_multindex, level=1, fill_value=0)\n angles degrees\nA circle NaN 1.0\n triangle 1.0 1.0\n rectangle 1.0 1.0\nB square 0.0 0.0\n pentagon 0.0 0.0\n hexagon 0.0 0.0']
|
pandas.DataFrame.rpow
DataFrame.rpow(other, axis='columns', level=None, fill_value=None)[source]# Get Exponential power of dataframe and other, element-wise (binary operator rpow). Equivalent to other ** dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, pow. Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **. Notes Mismatched indices will be unioned together.
|
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] |
51 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray.factorize.html
|
pandas.api.extensions.ExtensionArray.factorize
|
ExtensionArray.factorize(use_na_sentinel=True)[source]# Encode the extension array as an enumerated type. Notes pandas.factorize() offers a sort keyword as well.
|
Parameters: use_na_sentinelbool, default TrueIf True, the sentinel -1 will be used for NaN values. If False, NaN values will be encoded as non-negative integers and will not drop the NaN from the uniques of the values. Added in version 1.5.0. Returns: codesndarrayAn integer NumPy array that’s an indexer into the original ExtensionArray. uniquesExtensionArrayAn ExtensionArray containing the unique values of self. Note uniques will not contain an entry for the NA value of the ExtensionArray if there are any missing values present in self.
|
['>>> idx1 = pd.PeriodIndex(["2014-01", "2014-01", "2014-02", "2014-02",\n... "2014-03", "2014-03"], freq="M")\n>>> arr, idx = idx1.factorize()\n>>> arr\narray([0, 0, 1, 1, 2, 2])\n>>> idx\nPeriodIndex([\'2014-01\', \'2014-02\', \'2014-03\'], dtype=\'period[M]\')']
|
pandas.api.extensions.ExtensionArray.factorize
ExtensionArray.factorize(use_na_sentinel=True)[source]# Encode the extension array as an enumerated type. Notes pandas.factorize() offers a sort keyword as well.
|
[
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] |
52 |
..\pandas\reference\api\pandas.tseries.offsets.Milli.is_month_end.html
|
pandas.tseries.offsets.Milli.is_month_end
|
Milli.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.Milli.is_month_end
Milli.is_month_end(ts)# Return boolean whether a timestamp occurs on the month end.
|
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] |
53 |
..\pandas\reference\api\pandas.Series.copy.html
|
pandas.Series.copy
|
Series.copy(deep=True)[source]# Make a copy of this object’s indices and data. When deep=True (default), a new object will be created with a copy of the calling object’s data and indices. Modifications to the data or indices of the copy will not be reflected in the original object (see notes below). When deep=False, a new object will be created without copying the calling object’s data or index (only references to the data and index are copied). Any changes to the data of the original will be reflected in the shallow copy (and vice versa). Note The deep=False behaviour as described above will change in pandas 3.0. Copy-on-Write will be enabled by default, which means that the “shallow” copy is that is returned with deep=False will still avoid making an eager copy, but changes to the data of the original will no longer be reflected in the shallow copy (or vice versa). Instead, it makes use of a lazy (deferred) copy mechanism that will copy the data only when any changes to the original or shallow copy is made. You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True Notes When deep=True, data is copied but actual Python objects will not be copied recursively, only the reference to the object. This is in contrast to copy.deepcopy in the Standard Library, which recursively copies object data (see examples below). While Index objects are copied when deep=True, the underlying numpy array is not copied for performance reasons. Since Index is immutable, the underlying data can be safely shared and a copy is not needed. Since pandas is not thread safe, see the gotchas when copying in a threading environment. When copy_on_write in pandas config is set to True, the copy_on_write config takes effect even when deep=False. This means that any changes to the copied data would make a new copy of the data upon write (and vice versa). Changes made to either the original or copied variable would not be reflected in the counterpart. See Copy_on_Write for more information.
|
Parameters: deepbool, default TrueMake a deep copy, including a copy of the data and the indices. With deep=False neither the indices nor the data are copied. Returns: Series or DataFrameObject type matches caller.
|
['>>> s = pd.Series([1, 2], index=["a", "b"])\n>>> s\na 1\nb 2\ndtype: int64', '>>> s_copy = s.copy()\n>>> s_copy\na 1\nb 2\ndtype: int64', '>>> s = pd.Series([1, 2], index=["a", "b"])\n>>> deep = s.copy()\n>>> shallow = s.copy(deep=False)', '>>> s is shallow\nFalse\n>>> s.values is shallow.values and s.index is shallow.index\nTrue', '>>> s is deep\nFalse\n>>> s.values is deep.values or s.index is deep.index\nFalse', '>>> s.iloc[0] = 3\n>>> shallow.iloc[1] = 4\n>>> s\na 3\nb 4\ndtype: int64\n>>> shallow\na 3\nb 4\ndtype: int64\n>>> deep\na 1\nb 2\ndtype: int64', '>>> s = pd.Series([[1, 2], [3, 4]])\n>>> deep = s.copy()\n>>> s[0][0] = 10\n>>> s\n0 [10, 2]\n1 [3, 4]\ndtype: object\n>>> deep\n0 [10, 2]\n1 [3, 4]\ndtype: object', '>>> with pd.option_context("mode.copy_on_write", True):\n... s = pd.Series([1, 2], index=["a", "b"])\n... copy = s.copy(deep=False)\n... s.iloc[0] = 100\n... s\na 100\nb 2\ndtype: int64\n>>> copy\na 1\nb 2\ndtype: int64']
|
pandas.Series.copy
Series.copy(deep=True)[source]# Make a copy of this object’s indices and data. When deep=True (default), a new object will be created with a copy of the calling object’s data and indices. Modifications to the data or indices of the copy will not be reflected in the original object (see notes below). When deep=False, a new object will be created without copying the calling object’s data or index (only references to the data and index are copied). Any changes to the data of the original will be reflected in the shallow copy (and vice versa). Note The deep=False behaviour as described above will change in pandas 3.0. Copy-on-Write will be enabled by default, which means that the “shallow” copy is that is returned with deep=False will still avoid making an eager copy, but changes to the data of the original will no longer be reflected in the shallow copy (or vice versa). Instead, it makes use of a lazy (deferred) copy mechanism that will copy the data only when any changes to the original or shallow copy is made. You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True Notes When deep=True, data is copied but actual Python objects will not be copied recursively, only the reference to the object. This is in contrast to copy.deepcopy in the Standard Library, which recursively copies object data (see examples below). While Index objects are copied when deep=True, the underlying numpy array is not copied for performance reasons. Since Index is immutable, the underlying data can be safely shared and a copy is not needed. Since pandas is not thread safe, see the gotchas when copying in a threading environment. When copy_on_write in pandas config is set to True, the copy_on_write config takes effect even when deep=False. This means that any changes to the copied data would make a new copy of the data upon write (and vice versa). Changes made to either the original or copied variable would not be reflected in the counterpart. See Copy_on_Write for more information.
|
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] |
54 |
..\pandas\reference\api\pandas.IndexSlice.html
|
pandas.IndexSlice
|
pandas.IndexSlice = <pandas.core.indexing._IndexSlice object># Create an object to more easily perform multi-index slicing. Notes See Defined Levels for further info on slicing a MultiIndex.
|
No parameters found
|
[">>> midx = pd.MultiIndex.from_product([['A0','A1'], ['B0','B1','B2','B3']])\n>>> columns = ['foo', 'bar']\n>>> dfmi = pd.DataFrame(np.arange(16).reshape((len(midx), len(columns))),\n... index=midx, columns=columns)", ">>> dfmi.loc[(slice(None), slice('B0', 'B1')), :]\n foo bar\n A0 B0 0 1\n B1 2 3\n A1 B0 8 9\n B1 10 11", ">>> idx = pd.IndexSlice\n>>> dfmi.loc[idx[:, 'B0':'B1'], :]\n foo bar\n A0 B0 0 1\n B1 2 3\n A1 B0 8 9\n B1 10 11"]
|
pandas.IndexSlice
pandas.IndexSlice = <pandas.core.indexing._IndexSlice object># Create an object to more easily perform multi-index slicing. Notes See Defined Levels for further info on slicing a MultiIndex.
|
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] |
55 |
..\pandas\reference\api\pandas.tseries.offsets.BYearBegin.is_quarter_end.html
|
pandas.tseries.offsets.BYearBegin.is_quarter_end
|
BYearBegin.is_quarter_end(ts)# Return boolean whether a timestamp occurs on the quarter end.
|
No parameters found
|
['>>> ts = pd.Timestamp(2022, 1, 1)\n>>> freq = pd.offsets.Hour(5)\n>>> freq.is_quarter_end(ts)\nFalse']
|
pandas.tseries.offsets.BYearBegin.is_quarter_end
BYearBegin.is_quarter_end(ts)# Return boolean whether a timestamp occurs on the quarter end.
|
[
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] |
56 |
..\pandas\reference\api\pandas.core.groupby.SeriesGroupBy.pipe.html
|
pandas.core.groupby.SeriesGroupBy.pipe
|
SeriesGroupBy.pipe(func, *args, **kwargs)[source]# Apply a func with arguments to this GroupBy object and return its result. Use .pipe when you want to improve readability by chaining together functions that expect Series, DataFrames, GroupBy or Resampler objects. Instead of writing You can write which is much more readable. Notes See more here
|
Parameters: funccallable or tuple of (callable, str)Function to apply to this GroupBy object or, alternatively, a (callable, data_keyword) tuple where data_keyword is a string indicating the keyword of callable that expects the GroupBy object. argsiterable, optionalPositional arguments passed into func. kwargsdict, optionalA dictionary of keyword arguments passed into func. Returns: the return type of func.
|
['>>> h = lambda x, arg2, arg3: x + 1 - arg2 * arg3\n>>> g = lambda x, arg1: x * 5 / arg1\n>>> f = lambda x: x ** 4\n>>> df = pd.DataFrame([["a", 4], ["b", 5]], columns=["group", "value"])\n>>> h(g(f(df.groupby(\'group\')), arg1=1), arg2=2, arg3=3)', ">>> (df.groupby('group')\n... .pipe(f)\n... .pipe(g, arg1=1)\n... .pipe(h, arg2=2, arg3=3))", ">>> df = pd.DataFrame({'A': 'a b a b'.split(), 'B': [1, 2, 3, 4]})\n>>> df\n A B\n0 a 1\n1 b 2\n2 a 3\n3 b 4", ">>> df.groupby('A').pipe(lambda x: x.max() - x.min())\n B\nA\na 2\nb 2"]
|
pandas.core.groupby.SeriesGroupBy.pipe
SeriesGroupBy.pipe(func, *args, **kwargs)[source]# Apply a func with arguments to this GroupBy object and return its result. Use .pipe when you want to improve readability by chaining together functions that expect Series, DataFrames, GroupBy or Resampler objects. Instead of writing You can write which is much more readable. Notes See more here
|
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] |
57 |
..\pandas\reference\api\pandas.Series.to_list.html
|
pandas.Series.to_list
|
Series.to_list()[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
|
['>>> s = pd.Series([1, 2, 3])\n>>> s.to_list()\n[1, 2, 3]', ">>> idx = pd.Index([1, 2, 3])\n>>> idx\nIndex([1, 2, 3], dtype='int64')", '>>> idx.to_list()\n[1, 2, 3]']
|
pandas.Series.to_list
Series.to_list()[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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] |
58 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray.fillna.html
|
pandas.api.extensions.ExtensionArray.fillna
|
ExtensionArray.fillna(value=None, method=None, limit=None, copy=True)[source]# Fill NA/NaN values using the specified method.
|
Parameters: valuescalar, array-likeIf a scalar value is passed it is used to fill all missing values. Alternatively, an array-like “value” can be given. It’s expected that the array-like have the same length as ‘self’. method{‘backfill’, ‘bfill’, ‘pad’, ‘ffill’, None}, default NoneMethod to use for filling holes in reindexed Series: pad / ffill: propagate last valid observation forward to next valid. backfill / bfill: use NEXT valid observation to fill gap. Deprecated since version 2.1.0. limitint, default NoneIf method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Deprecated since version 2.1.0. copybool, default TrueWhether to make a copy of the data before filling. If False, then the original should be modified and no new memory should be allocated. For ExtensionArray subclasses that cannot do this, it is at the author’s discretion whether to ignore “copy=False” or to raise. The base class implementation ignores the keyword in pad/backfill cases. Returns: ExtensionArrayWith NA/NaN filled.
|
['>>> arr = pd.array([np.nan, np.nan, 2, 3, np.nan, np.nan])\n>>> arr.fillna(0)\n<IntegerArray>\n[0, 0, 2, 3, 0, 0]\nLength: 6, dtype: Int64']
|
pandas.api.extensions.ExtensionArray.fillna
ExtensionArray.fillna(value=None, method=None, limit=None, copy=True)[source]# Fill NA/NaN values using the specified method.
|
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59 |
..\pandas\reference\api\pandas.Series.corr.html
|
pandas.Series.corr
|
Series.corr(other, method='pearson', min_periods=None)[source]# Compute correlation with other Series, excluding missing values. The two Series objects are not required to be the same length and will be aligned internally before the correlation function is applied. Notes Pearson, Kendall and Spearman correlation are currently computed using pairwise complete observations. Pearson correlation coefficient Kendall rank correlation coefficient Spearman’s rank correlation coefficient Automatic data alignment: as with all pandas operations, automatic data alignment is performed for this method. corr() automatically considers values with matching indices.
|
Parameters: otherSeriesSeries with which to compute the correlation. method{‘pearson’, ‘kendall’, ‘spearman’} or callableMethod used to compute correlation: pearson : Standard correlation coefficient kendall : Kendall Tau correlation coefficient spearman : Spearman rank correlation callable: Callable with input two 1d ndarrays and returning a float. Warning Note that the returned matrix from corr will have 1 along the diagonals and will be symmetric regardless of the callable’s behavior. min_periodsint, optionalMinimum number of observations needed to have a valid result. Returns: floatCorrelation with other.
|
['>>> def histogram_intersection(a, b):\n... v = np.minimum(a, b).sum().round(decimals=1)\n... return v\n>>> s1 = pd.Series([.2, .0, .6, .2])\n>>> s2 = pd.Series([.3, .6, .0, .1])\n>>> s1.corr(s2, method=histogram_intersection)\n0.3', '>>> s1 = pd.Series([1, 2, 3], index=[0, 1, 2])\n>>> s2 = pd.Series([1, 2, 3], index=[2, 1, 0])\n>>> s1.corr(s2)\n-1.0']
|
pandas.Series.corr
Series.corr(other, method='pearson', min_periods=None)[source]# Compute correlation with other Series, excluding missing values. The two Series objects are not required to be the same length and will be aligned internally before the correlation function is applied. Notes Pearson, Kendall and Spearman correlation are currently computed using pairwise complete observations. Pearson correlation coefficient Kendall rank correlation coefficient Spearman’s rank correlation coefficient Automatic data alignment: as with all pandas operations, automatic data alignment is performed for this method. corr() automatically considers values with matching indices.
|
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] |
60 |
..\pandas\reference\api\pandas.tseries.offsets.BYearBegin.is_quarter_start.html
|
pandas.tseries.offsets.BYearBegin.is_quarter_start
|
BYearBegin.is_quarter_start(ts)# Return boolean whether a timestamp occurs on the quarter start.
|
No parameters found
|
['>>> ts = pd.Timestamp(2022, 1, 1)\n>>> freq = pd.offsets.Hour(5)\n>>> freq.is_quarter_start(ts)\nTrue']
|
pandas.tseries.offsets.BYearBegin.is_quarter_start
BYearBegin.is_quarter_start(ts)# Return boolean whether a timestamp occurs on the quarter start.
|
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] |
61 |
..\pandas\reference\api\pandas.infer_freq.html
|
pandas.infer_freq
|
pandas.infer_freq(index)[source]# Infer the most likely frequency given the input index.
|
Parameters: indexDatetimeIndex, TimedeltaIndex, Series or array-likeIf passed a Series will use the values of the series (NOT THE INDEX). Returns: str or NoneNone if no discernible frequency. Raises: TypeErrorIf the index is not datetime-like. ValueErrorIf there are fewer than three values.
|
[">>> idx = pd.date_range(start='2020/12/01', end='2020/12/30', periods=30)\n>>> pd.infer_freq(idx)\n'D'"]
|
pandas.infer_freq
pandas.infer_freq(index)[source]# Infer the most likely frequency given the input index.
|
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] |
62 |
..\pandas\reference\api\pandas.core.groupby.SeriesGroupBy.plot.html
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pandas.core.groupby.SeriesGroupBy.plot
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property SeriesGroupBy.plot[source]# Make plots of Series or DataFrame. Uses the backend specified by the option plotting.backend. By default, matplotlib is used. Notes See matplotlib documentation online for more on this subject If kind = ‘bar’ or ‘barh’, you can specify relative alignments for bar plot layout by position keyword. From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5 (center)
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Parameters: dataSeries or DataFrameThe object for which the method is called. xlabel or position, default NoneOnly used if data is a DataFrame. ylabel, position or list of label, positions, default NoneAllows plotting of one column versus another. Only used if data is a DataFrame. kindstrThe kind of plot to produce: ‘line’ : line plot (default) ‘bar’ : vertical bar plot ‘barh’ : horizontal bar plot ‘hist’ : histogram ‘box’ : boxplot ‘kde’ : Kernel Density Estimation plot ‘density’ : same as ‘kde’ ‘area’ : area plot ‘pie’ : pie plot ‘scatter’ : scatter plot (DataFrame only) ‘hexbin’ : hexbin plot (DataFrame only) axmatplotlib axes object, default NoneAn axes of the current figure. subplotsbool or sequence of iterables, default FalseWhether to group columns into subplots: False : No subplots will be used True : Make separate subplots for each column. sequence of iterables of column labels: Create a subplot for each group of columns. For example [(‘a’, ‘c’), (‘b’, ‘d’)] will create 2 subplots: one with columns ‘a’ and ‘c’, and one with columns ‘b’ and ‘d’. Remaining columns that aren’t specified will be plotted in additional subplots (one per column). Added in version 1.5.0. sharexbool, default True if ax is None else FalseIn case subplots=True, share x axis and set some x axis labels to invisible; defaults to True if ax is None otherwise False if an ax is passed in; Be aware, that passing in both an ax and sharex=True will alter all x axis labels for all axis in a figure. shareybool, default FalseIn case subplots=True, share y axis and set some y axis labels to invisible. layouttuple, optional(rows, columns) for the layout of subplots. figsizea tuple (width, height) in inchesSize of a figure object. use_indexbool, default TrueUse index as ticks for x axis. titlestr or listTitle to use for the plot. If a string is passed, print the string at the top of the figure. If a list is passed and subplots is True, print each item in the list above the corresponding subplot. gridbool, default None (matlab style default)Axis grid lines. legendbool or {‘reverse’}Place legend on axis subplots. stylelist or dictThe matplotlib line style per column. logxbool or ‘sym’, default FalseUse log scaling or symlog scaling on x axis. logybool or ‘sym’ default FalseUse log scaling or symlog scaling on y axis. loglogbool or ‘sym’, default FalseUse log scaling or symlog scaling on both x and y axes. xtickssequenceValues to use for the xticks. ytickssequenceValues to use for the yticks. xlim2-tuple/listSet the x limits of the current axes. ylim2-tuple/listSet the y limits of the current axes. xlabellabel, optionalName to use for the xlabel on x-axis. Default uses index name as xlabel, or the x-column name for planar plots. Changed in version 2.0.0: Now applicable to histograms. ylabellabel, optionalName to use for the ylabel on y-axis. Default will show no ylabel, or the y-column name for planar plots. Changed in version 2.0.0: Now applicable to histograms. rotfloat, default NoneRotation for ticks (xticks for vertical, yticks for horizontal plots). fontsizefloat, default NoneFont size for xticks and yticks. colormapstr or matplotlib colormap object, default NoneColormap to select colors from. If string, load colormap with that name from matplotlib. colorbarbool, optionalIf True, plot colorbar (only relevant for ‘scatter’ and ‘hexbin’ plots). positionfloatSpecify relative alignments for bar plot layout. From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5 (center). tablebool, Series or DataFrame, default FalseIf True, draw a table using the data in the DataFrame and the data will be transposed to meet matplotlib’s default layout. If a Series or DataFrame is passed, use passed data to draw a table. yerrDataFrame, Series, array-like, dict and strSee Plotting with Error Bars for detail. xerrDataFrame, Series, array-like, dict and strEquivalent to yerr. stackedbool, default False in line and bar plots, and True in area plotIf True, create stacked plot. secondary_ybool or sequence, default FalseWhether to plot on the secondary y-axis if a list/tuple, which columns to plot on secondary y-axis. mark_rightbool, default TrueWhen using a secondary_y axis, automatically mark the column labels with “(right)” in the legend. include_boolbool, default is FalseIf True, boolean values can be plotted. backendstr, default NoneBackend to use instead of the backend specified in the option plotting.backend. For instance, ‘matplotlib’. Alternatively, to specify the plotting.backend for the whole session, set pd.options.plotting.backend. **kwargsOptions to pass to matplotlib plotting method. Returns: matplotlib.axes.Axes or numpy.ndarray of themIf the backend is not the default matplotlib one, the return value will be the object returned by the backend.
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['>>> ser = pd.Series([1, 2, 3, 3])\n>>> plot = ser.plot(kind=\'hist\', title="My plot")', '>>> df = pd.DataFrame({\'length\': [1.5, 0.5, 1.2, 0.9, 3],\n... \'width\': [0.7, 0.2, 0.15, 0.2, 1.1]},\n... index=[\'pig\', \'rabbit\', \'duck\', \'chicken\', \'horse\'])\n>>> plot = df.plot(title="DataFrame Plot")', '>>> lst = [-1, -2, -3, 1, 2, 3]\n>>> ser = pd.Series([1, 2, 2, 4, 6, 6], index=lst)\n>>> plot = ser.groupby(lambda x: x > 0).plot(title="SeriesGroupBy Plot")', '>>> df = pd.DataFrame({"col1" : [1, 2, 3, 4],\n... "col2" : ["A", "B", "A", "B"]})\n>>> plot = df.groupby("col2").plot(kind="bar", title="DataFrameGroupBy Plot")']
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pandas.core.groupby.SeriesGroupBy.plot
property SeriesGroupBy.plot[source]# Make plots of Series or DataFrame. Uses the backend specified by the option plotting.backend. By default, matplotlib is used. Notes See matplotlib documentation online for more on this subject If kind = ‘bar’ or ‘barh’, you can specify relative alignments for bar plot layout by position keyword. From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5 (center)
|
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] |
63 |
..\pandas\reference\api\pandas.tseries.offsets.Milli.is_month_start.html
|
pandas.tseries.offsets.Milli.is_month_start
|
Milli.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.Milli.is_month_start
Milli.is_month_start(ts)# Return boolean whether a timestamp occurs on the month start.
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] |
64 |
..\pandas\reference\api\pandas.Series.to_markdown.html
|
pandas.Series.to_markdown
|
Series.to_markdown(buf=None, mode='wt', index=True, storage_options=None, **kwargs)[source]# Print Series in Markdown-friendly format. Notes Requires the tabulate package.
|
Parameters: bufstr, Path or StringIO-like, optional, default NoneBuffer to write to. If None, the output is returned as a string. modestr, optionalMode in which file is opened, “wt” by default. indexbool, optional, default TrueAdd index (row) labels. storage_optionsdict, optionalExtra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here. **kwargsThese parameters will be passed to tabulate. Returns: strSeries in Markdown-friendly format.
|
['>>> s = pd.Series(["elk", "pig", "dog", "quetzal"], name="animal")\n>>> print(s.to_markdown())\n| | animal |\n|---:|:---------|\n| 0 | elk |\n| 1 | pig |\n| 2 | dog |\n| 3 | quetzal |', '>>> print(s.to_markdown(tablefmt="grid"))\n+----+----------+\n| | animal |\n+====+==========+\n| 0 | elk |\n+----+----------+\n| 1 | pig |\n+----+----------+\n| 2 | dog |\n+----+----------+\n| 3 | quetzal |\n+----+----------+']
|
pandas.Series.to_markdown
Series.to_markdown(buf=None, mode='wt', index=True, storage_options=None, **kwargs)[source]# Print Series in Markdown-friendly format. Notes Requires the tabulate package.
|
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] |
65 |
..\pandas\reference\api\pandas.DataFrame.rsub.html
|
pandas.DataFrame.rsub
|
DataFrame.rsub(other, axis='columns', level=None, fill_value=None)[source]# Get Subtraction of dataframe and other, element-wise (binary operator rsub). Equivalent to other - dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, sub. Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **. Notes Mismatched indices will be unioned together.
|
Parameters: otherscalar, sequence, Series, dict or DataFrameAny single or multiple element data structure, or list-like object. axis{0 or ‘index’, 1 or ‘columns’}Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on. levelint or labelBroadcast across a level, matching Index values on the passed MultiIndex level. fill_valuefloat or None, default NoneFill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing. Returns: DataFrameResult of the arithmetic operation.
|
[">>> df = pd.DataFrame({'angles': [0, 3, 4],\n... 'degrees': [360, 180, 360]},\n... index=['circle', 'triangle', 'rectangle'])\n>>> df\n angles degrees\ncircle 0 360\ntriangle 3 180\nrectangle 4 360", '>>> df + 1\n angles degrees\ncircle 1 361\ntriangle 4 181\nrectangle 5 361', '>>> df.add(1)\n angles degrees\ncircle 1 361\ntriangle 4 181\nrectangle 5 361', '>>> df.div(10)\n angles degrees\ncircle 0.0 36.0\ntriangle 0.3 18.0\nrectangle 0.4 36.0', '>>> df.rdiv(10)\n angles degrees\ncircle inf 0.027778\ntriangle 3.333333 0.055556\nrectangle 2.500000 0.027778', '>>> df - [1, 2]\n angles degrees\ncircle -1 358\ntriangle 2 178\nrectangle 3 358', ">>> df.sub([1, 2], axis='columns')\n angles degrees\ncircle -1 358\ntriangle 2 178\nrectangle 3 358", ">>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),\n... axis='index')\n angles degrees\ncircle -1 359\ntriangle 2 179\nrectangle 3 359", ">>> df.mul({'angles': 0, 'degrees': 2})\n angles degrees\ncircle 0 720\ntriangle 0 360\nrectangle 0 720", ">>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')\n angles degrees\ncircle 0 0\ntriangle 6 360\nrectangle 12 1080", ">>> other = pd.DataFrame({'angles': [0, 3, 4]},\n... index=['circle', 'triangle', 'rectangle'])\n>>> other\n angles\ncircle 0\ntriangle 3\nrectangle 4", '>>> df * other\n angles degrees\ncircle 0 NaN\ntriangle 9 NaN\nrectangle 16 NaN', '>>> df.mul(other, fill_value=0)\n angles degrees\ncircle 0 0.0\ntriangle 9 0.0\nrectangle 16 0.0', ">>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],\n... 'degrees': [360, 180, 360, 360, 540, 720]},\n... index=[['A', 'A', 'A', 'B', 'B', 'B'],\n... ['circle', 'triangle', 'rectangle',\n... 'square', 'pentagon', 'hexagon']])\n>>> df_multindex\n angles degrees\nA circle 0 360\n triangle 3 180\n rectangle 4 360\nB square 4 360\n pentagon 5 540\n hexagon 6 720", '>>> df.div(df_multindex, level=1, fill_value=0)\n angles degrees\nA circle NaN 1.0\n triangle 1.0 1.0\n rectangle 1.0 1.0\nB square 0.0 0.0\n pentagon 0.0 0.0\n hexagon 0.0 0.0']
|
pandas.DataFrame.rsub
DataFrame.rsub(other, axis='columns', level=None, fill_value=None)[source]# Get Subtraction of dataframe and other, element-wise (binary operator rsub). Equivalent to other - dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, sub. Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **. Notes Mismatched indices will be unioned together.
|
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66 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray.html
|
pandas.api.extensions.ExtensionArray
|
class pandas.api.extensions.ExtensionArray[source]# Abstract base class for custom 1-D array types. pandas will recognize instances of this class as proper arrays with a custom type and will not attempt to coerce them to objects. They may be stored directly inside a DataFrame or Series. Attributes dtype An instance of ExtensionDtype. nbytes The number of bytes needed to store this object in memory. ndim Extension Arrays are only allowed to be 1-dimensional. shape Return a tuple of the array dimensions. Methods argsort(*[, ascending, kind, na_position]) Return the indices that would sort this array. astype(dtype[, copy]) Cast to a NumPy array or ExtensionArray with 'dtype'. copy() Return a copy of the array. dropna() Return ExtensionArray without NA values. duplicated([keep]) Return boolean ndarray denoting duplicate values. factorize([use_na_sentinel]) Encode the extension array as an enumerated type. fillna([value, method, limit, copy]) Fill NA/NaN values using the specified method. equals(other) Return if another array is equivalent to this array. insert(loc, item) Insert an item at the given position. interpolate(*, method, axis, index, limit, ...) See DataFrame.interpolate.__doc__. isin(values) Pointwise comparison for set containment in the given values. isna() A 1-D array indicating if each value is missing. ravel([order]) Return a flattened view on this array. repeat(repeats[, axis]) Repeat elements of a ExtensionArray. searchsorted(value[, side, sorter]) Find indices where elements should be inserted to maintain order. shift([periods, fill_value]) Shift values by desired number. take(indices, *[, allow_fill, fill_value]) Take elements from an array. tolist() Return a list of the values. unique() Compute the ExtensionArray of unique values. view([dtype]) Return a view on the array. _accumulate(name, *[, skipna]) Return an ExtensionArray performing an accumulation operation. _concat_same_type(to_concat) Concatenate multiple array of this dtype. _explode() Transform each element of list-like to a row. _formatter([boxed]) Formatting function for scalar values. _from_factorized(values, original) Reconstruct an ExtensionArray after factorization. _from_sequence(scalars, *[, dtype, copy]) Construct a new ExtensionArray from a sequence of scalars. _from_sequence_of_strings(strings, *[, ...]) Construct a new ExtensionArray from a sequence of strings. _hash_pandas_object(*, encoding, hash_key, ...) Hook for hash_pandas_object. _pad_or_backfill(*, method[, limit, ...]) Pad or backfill values, used by Series/DataFrame ffill and bfill. _reduce(name, *[, skipna, keepdims]) Return a scalar result of performing the reduction operation. _values_for_argsort() Return values for sorting. _values_for_factorize() Return an array and missing value suitable for factorization. Notes The interface includes the following abstract methods that must be implemented by subclasses: _from_sequence _from_factorized __getitem__ __len__ __eq__ dtype nbytes isna take copy _concat_same_type interpolate A default repr displaying the type, (truncated) data, length, and dtype is provided. It can be customized or replaced by by overriding: __repr__ : A default repr for the ExtensionArray. _formatter : Print scalars inside a Series or DataFrame. Some methods require casting the ExtensionArray to an ndarray of Python objects with self.astype(object), which may be expensive. When performance is a concern, we highly recommend overriding the following methods: fillna _pad_or_backfill dropna unique factorize / _values_for_factorize argsort, argmax, argmin / _values_for_argsort searchsorted map The remaining methods implemented on this class should be performant, as they only compose abstract methods. Still, a more efficient implementation may be available, and these methods can be overridden. One can implement methods to handle array accumulations or reductions. _accumulate _reduce One can implement methods to handle parsing from strings that will be used in methods such as pandas.io.parsers.read_csv. _from_sequence_of_strings This class does not inherit from ‘abc.ABCMeta’ for performance reasons. Methods and properties required by the interface raise pandas.errors.AbstractMethodError and no register method is provided for registering virtual subclasses. ExtensionArrays are limited to 1 dimension. They may be backed by none, one, or many NumPy arrays. For example, pandas.Categorical is an extension array backed by two arrays, one for codes and one for categories. An array of IPv6 address may be backed by a NumPy structured array with two fields, one for the lower 64 bits and one for the upper 64 bits. Or they may be backed by some other storage type, like Python lists. Pandas makes no assumptions on how the data are stored, just that it can be converted to a NumPy array. The ExtensionArray interface does not impose any rules on how this data is stored. However, currently, the backing data cannot be stored in attributes called .values or ._values to ensure full compatibility with pandas internals. But other names as .data, ._data, ._items, … can be freely used. If implementing NumPy’s __array_ufunc__ interface, pandas expects that You defer by returning NotImplemented when any Series are present in inputs. Pandas will extract the arrays and call the ufunc again. You define a _HANDLED_TYPES tuple as an attribute on the class. Pandas inspect this to determine whether the ufunc is valid for the types present. See NumPy universal functions for more. By default, ExtensionArrays are not hashable. Immutable subclasses may override this behavior.
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No parameters found
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[]
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pandas.api.extensions.ExtensionArray
class pandas.api.extensions.ExtensionArray[source]# Abstract base class for custom 1-D array types. pandas will recognize instances of this class as proper arrays with a custom type and will not attempt to coerce them to objects. They may be stored directly inside a DataFrame or Series. Attributes dtype An instance of ExtensionDtype. nbytes The number of bytes needed to store this object in memory. ndim Extension Arrays are only allowed to be 1-dimensional. shape Return a tuple of the array dimensions. Methods argsort(*[, ascending, kind, na_position]) Return the indices that would sort this array. astype(dtype[, copy]) Cast to a NumPy array or ExtensionArray with 'dtype'. copy() Return a copy of the array. dropna() Return ExtensionArray without NA values. duplicated([keep]) Return boolean ndarray denoting duplicate values. factorize([use_na_sentinel]) Encode the extension array as an enumerated type. fillna([value, method, limit, copy]) Fill NA/NaN values using the specified method. equals(other) Return if another array is equivalent to this array. insert(loc, item) Insert an item at the given position. interpolate(*, method, axis, index, limit, ...) See DataFrame.interpolate.__doc__. isin(values) Pointwise comparison for set containment in the given values. isna() A 1-D array indicating if each value is missing. ravel([order]) Return a flattened view on this array. repeat(repeats[, axis]) Repeat elements of a ExtensionArray. searchsorted(value[, side, sorter]) Find indices where elements should be inserted to maintain order. shift([periods, fill_value]) Shift values by desired number. take(indices, *[, allow_fill, fill_value]) Take elements from an array. tolist() Return a list of the values. unique() Compute the ExtensionArray of unique values. view([dtype]) Return a view on the array. _accumulate(name, *[, skipna]) Return an ExtensionArray performing an accumulation operation. _concat_same_type(to_concat) Concatenate multiple array of this dtype. _explode() Transform each element of list-like to a row. _formatter([boxed]) Formatting function for scalar values. _from_factorized(values, original) Reconstruct an ExtensionArray after factorization. _from_sequence(scalars, *[, dtype, copy]) Construct a new ExtensionArray from a sequence of scalars. _from_sequence_of_strings(strings, *[, ...]) Construct a new ExtensionArray from a sequence of strings. _hash_pandas_object(*, encoding, hash_key, ...) Hook for hash_pandas_object. _pad_or_backfill(*, method[, limit, ...]) Pad or backfill values, used by Series/DataFrame ffill and bfill. _reduce(name, *[, skipna, keepdims]) Return a scalar result of performing the reduction operation. _values_for_argsort() Return values for sorting. _values_for_factorize() Return an array and missing value suitable for factorization. Notes The interface includes the following abstract methods that must be implemented by subclasses: _from_sequence _from_factorized __getitem__ __len__ __eq__ dtype nbytes isna take copy _concat_same_type interpolate A default repr displaying the type, (truncated) data, length, and dtype is provided. It can be customized or replaced by by overriding: __repr__ : A default repr for the ExtensionArray. _formatter : Print scalars inside a Series or DataFrame. Some methods require casting the ExtensionArray to an ndarray of Python objects with self.astype(object), which may be expensive. When performance is a concern, we highly recommend overriding the following methods: fillna _pad_or_backfill dropna unique factorize / _values_for_factorize argsort, argmax, argmin / _values_for_argsort searchsorted map The remaining methods implemented on this class should be performant, as they only compose abstract methods. Still, a more efficient implementation may be available, and these methods can be overridden. One can implement methods to handle array accumulations or reductions. _accumulate _reduce One can implement methods to handle parsing from strings that will be used in methods such as pandas.io.parsers.read_csv. _from_sequence_of_strings This class does not inherit from ‘abc.ABCMeta’ for performance reasons. Methods and properties required by the interface raise pandas.errors.AbstractMethodError and no register method is provided for registering virtual subclasses. ExtensionArrays are limited to 1 dimension. They may be backed by none, one, or many NumPy arrays. For example, pandas.Categorical is an extension array backed by two arrays, one for codes and one for categories. An array of IPv6 address may be backed by a NumPy structured array with two fields, one for the lower 64 bits and one for the upper 64 bits. Or they may be backed by some other storage type, like Python lists. Pandas makes no assumptions on how the data are stored, just that it can be converted to a NumPy array. The ExtensionArray interface does not impose any rules on how this data is stored. However, currently, the backing data cannot be stored in attributes called .values or ._values to ensure full compatibility with pandas internals. But other names as .data, ._data, ._items, … can be freely used. If implementing NumPy’s __array_ufunc__ interface, pandas expects that You defer by returning NotImplemented when any Series are present in inputs. Pandas will extract the arrays and call the ufunc again. You define a _HANDLED_TYPES tuple as an attribute on the class. Pandas inspect this to determine whether the ufunc is valid for the types present. See NumPy universal functions for more. By default, ExtensionArrays are not hashable. Immutable subclasses may override this behavior.
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] |
67 |
..\pandas\reference\api\pandas.Series.count.html
|
pandas.Series.count
|
Series.count()[source]# Return number of non-NA/null observations in the Series.
|
Returns: intNumber of non-null values in the Series.
|
['>>> s = pd.Series([0.0, 1.0, np.nan])\n>>> s.count()\n2']
|
pandas.Series.count
Series.count()[source]# Return number of non-NA/null observations in the Series.
|
[
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] |
68 |
..\pandas\reference\api\pandas.tseries.offsets.BYearBegin.is_year_end.html
|
pandas.tseries.offsets.BYearBegin.is_year_end
|
BYearBegin.is_year_end(ts)# Return boolean whether a timestamp occurs on the year end.
|
No parameters found
|
['>>> ts = pd.Timestamp(2022, 1, 1)\n>>> freq = pd.offsets.Hour(5)\n>>> freq.is_year_end(ts)\nFalse']
|
pandas.tseries.offsets.BYearBegin.is_year_end
BYearBegin.is_year_end(ts)# Return boolean whether a timestamp occurs on the year end.
|
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] |
69 |
..\pandas\reference\api\pandas.core.groupby.SeriesGroupBy.prod.html
|
pandas.core.groupby.SeriesGroupBy.prod
|
SeriesGroupBy.prod(numeric_only=False, min_count=0)[source]# Compute prod 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. Returns: Series or DataFrameComputed prod 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).prod()\na 2\nb 12\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").prod()\n b c\na\n1 16 10\n2 30 72']
|
pandas.core.groupby.SeriesGroupBy.prod
SeriesGroupBy.prod(numeric_only=False, min_count=0)[source]# Compute prod of group values.
|
[
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70 |
..\pandas\reference\api\pandas.Series.cov.html
|
pandas.Series.cov
|
Series.cov(other, min_periods=None, ddof=1)[source]# Compute covariance with Series, excluding missing values. The two Series objects are not required to be the same length and will be aligned internally before the covariance is calculated.
|
Parameters: otherSeriesSeries with which to compute the covariance. min_periodsint, optionalMinimum number of observations needed to have a valid result. ddofint, default 1Delta degrees of freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. Returns: floatCovariance between Series and other normalized by N-1 (unbiased estimator).
|
['>>> s1 = pd.Series([0.90010907, 0.13484424, 0.62036035])\n>>> s2 = pd.Series([0.12528585, 0.26962463, 0.51111198])\n>>> s1.cov(s2)\n-0.01685762652715874']
|
pandas.Series.cov
Series.cov(other, min_periods=None, ddof=1)[source]# Compute covariance with Series, excluding missing values. The two Series objects are not required to be the same length and will be aligned internally before the covariance is calculated.
|
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] |
71 |
..\pandas\reference\api\pandas.DataFrame.rtruediv.html
|
pandas.DataFrame.rtruediv
|
DataFrame.rtruediv(other, axis='columns', level=None, fill_value=None)[source]# Get Floating division of dataframe and other, element-wise (binary operator rtruediv). Equivalent to other / dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, truediv. Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **. Notes Mismatched indices will be unioned together.
|
Parameters: otherscalar, sequence, Series, dict or DataFrameAny single or multiple element data structure, or list-like object. axis{0 or ‘index’, 1 or ‘columns’}Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on. levelint or labelBroadcast across a level, matching Index values on the passed MultiIndex level. fill_valuefloat or None, default NoneFill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing. Returns: DataFrameResult of the arithmetic operation.
|
[">>> df = pd.DataFrame({'angles': [0, 3, 4],\n... 'degrees': [360, 180, 360]},\n... index=['circle', 'triangle', 'rectangle'])\n>>> df\n angles degrees\ncircle 0 360\ntriangle 3 180\nrectangle 4 360", '>>> df + 1\n angles degrees\ncircle 1 361\ntriangle 4 181\nrectangle 5 361', '>>> df.add(1)\n angles degrees\ncircle 1 361\ntriangle 4 181\nrectangle 5 361', '>>> df.div(10)\n angles degrees\ncircle 0.0 36.0\ntriangle 0.3 18.0\nrectangle 0.4 36.0', '>>> df.rdiv(10)\n angles degrees\ncircle inf 0.027778\ntriangle 3.333333 0.055556\nrectangle 2.500000 0.027778', '>>> df - [1, 2]\n angles degrees\ncircle -1 358\ntriangle 2 178\nrectangle 3 358', ">>> df.sub([1, 2], axis='columns')\n angles degrees\ncircle -1 358\ntriangle 2 178\nrectangle 3 358", ">>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),\n... axis='index')\n angles degrees\ncircle -1 359\ntriangle 2 179\nrectangle 3 359", ">>> df.mul({'angles': 0, 'degrees': 2})\n angles degrees\ncircle 0 720\ntriangle 0 360\nrectangle 0 720", ">>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')\n angles degrees\ncircle 0 0\ntriangle 6 360\nrectangle 12 1080", ">>> other = pd.DataFrame({'angles': [0, 3, 4]},\n... index=['circle', 'triangle', 'rectangle'])\n>>> other\n angles\ncircle 0\ntriangle 3\nrectangle 4", '>>> df * other\n angles degrees\ncircle 0 NaN\ntriangle 9 NaN\nrectangle 16 NaN', '>>> df.mul(other, fill_value=0)\n angles degrees\ncircle 0 0.0\ntriangle 9 0.0\nrectangle 16 0.0', ">>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],\n... 'degrees': [360, 180, 360, 360, 540, 720]},\n... index=[['A', 'A', 'A', 'B', 'B', 'B'],\n... ['circle', 'triangle', 'rectangle',\n... 'square', 'pentagon', 'hexagon']])\n>>> df_multindex\n angles degrees\nA circle 0 360\n triangle 3 180\n rectangle 4 360\nB square 4 360\n pentagon 5 540\n hexagon 6 720", '>>> df.div(df_multindex, level=1, fill_value=0)\n angles degrees\nA circle NaN 1.0\n triangle 1.0 1.0\n rectangle 1.0 1.0\nB square 0.0 0.0\n pentagon 0.0 0.0\n hexagon 0.0 0.0']
|
pandas.DataFrame.rtruediv
DataFrame.rtruediv(other, axis='columns', level=None, fill_value=None)[source]# Get Floating division of dataframe and other, element-wise (binary operator rtruediv). Equivalent to other / dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, truediv. Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **. Notes Mismatched indices will be unioned together.
|
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] |
72 |
..\pandas\reference\api\pandas.Int16Dtype.html
|
pandas.Int16Dtype
|
class pandas.Int16Dtype[source]# An ExtensionDtype for int16 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.Int16Dtype
class pandas.Int16Dtype[source]# An ExtensionDtype for int16 integer data. Uses pandas.NA as its missing value, rather than numpy.nan. Attributes None Methods None
|
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] |
72 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray.insert.html
|
pandas.api.extensions.ExtensionArray.insert
|
ExtensionArray.insert(loc, item)[source]# Insert an item at the given position. Notes This method should be both type and dtype-preserving. If the item cannot be held in an array of this type/dtype, either ValueError or TypeError should be raised. The default implementation relies on _from_sequence to raise on invalid items.
|
Parameters: locint itemscalar-like Returns: same type as self
|
['>>> arr = pd.array([1, 2, 3])\n>>> arr.insert(2, -1)\n<IntegerArray>\n[1, 2, -1, 3]\nLength: 4, dtype: Int64']
|
pandas.api.extensions.ExtensionArray.insert
ExtensionArray.insert(loc, item)[source]# Insert an item at the given position. Notes This method should be both type and dtype-preserving. If the item cannot be held in an array of this type/dtype, either ValueError or TypeError should be raised. The default implementation relies on _from_sequence to raise on invalid items.
|
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] |
74 |
..\pandas\reference\api\pandas.Series.to_numpy.html
|
pandas.Series.to_numpy
|
Series.to_numpy(dtype=None, copy=False, na_value=<no_default>, **kwargs)[source]# A NumPy ndarray representing the values in this Series or Index. Notes The returned array will be the same up to equality (values equal in self will be equal in the returned array; likewise for values that are not equal). When self contains an ExtensionArray, the dtype may be different. For example, for a category-dtype Series, to_numpy() will return a NumPy array and the categorical dtype will be lost. For NumPy dtypes, this will be a reference to the actual data stored in this Series or Index (assuming copy=False). Modifying the result in place will modify the data stored in the Series or Index (not that we recommend doing that). For extension types, to_numpy() may require copying data and coercing the result to a NumPy type (possibly object), which may be expensive. When you need a no-copy reference to the underlying data, Series.array should be used instead. This table lays out the different dtypes and default return types of to_numpy() for various dtypes within pandas. dtype array type category[T] ndarray[T] (same dtype as input) period ndarray[object] (Periods) interval ndarray[object] (Intervals) IntegerNA ndarray[object] datetime64[ns] datetime64[ns] datetime64[ns, tz] ndarray[object] (Timestamps)
|
Parameters: dtypestr or numpy.dtype, optionalThe dtype to pass to numpy.asarray(). copybool, default FalseWhether to ensure that the returned value is not a view on another array. Note that copy=False does not ensure that to_numpy() is no-copy. Rather, copy=True ensure that a copy is made, even if not strictly necessary. na_valueAny, optionalThe value to use for missing values. The default value depends on dtype and the type of the array. **kwargsAdditional keywords passed through to the to_numpy method of the underlying array (for extension arrays). Returns: numpy.ndarray
|
[">>> ser = pd.Series(pd.Categorical(['a', 'b', 'a']))\n>>> ser.to_numpy()\narray(['a', 'b', 'a'], dtype=object)", '>>> ser = pd.Series(pd.date_range(\'2000\', periods=2, tz="CET"))\n>>> ser.to_numpy(dtype=object)\narray([Timestamp(\'2000-01-01 00:00:00+0100\', tz=\'CET\'),\n Timestamp(\'2000-01-02 00:00:00+0100\', tz=\'CET\')],\n dtype=object)', '>>> ser.to_numpy(dtype="datetime64[ns]")\n... \narray([\'1999-12-31T23:00:00.000000000\', \'2000-01-01T23:00:00...\'],\n dtype=\'datetime64[ns]\')']
|
pandas.Series.to_numpy
Series.to_numpy(dtype=None, copy=False, na_value=<no_default>, **kwargs)[source]# A NumPy ndarray representing the values in this Series or Index. Notes The returned array will be the same up to equality (values equal in self will be equal in the returned array; likewise for values that are not equal). When self contains an ExtensionArray, the dtype may be different. For example, for a category-dtype Series, to_numpy() will return a NumPy array and the categorical dtype will be lost. For NumPy dtypes, this will be a reference to the actual data stored in this Series or Index (assuming copy=False). Modifying the result in place will modify the data stored in the Series or Index (not that we recommend doing that). For extension types, to_numpy() may require copying data and coercing the result to a NumPy type (possibly object), which may be expensive. When you need a no-copy reference to the underlying data, Series.array should be used instead. This table lays out the different dtypes and default return types of to_numpy() for various dtypes within pandas. dtype array type category[T] ndarray[T] (same dtype as input) period ndarray[object] (Periods) interval ndarray[object] (Intervals) IntegerNA ndarray[object] datetime64[ns] datetime64[ns] datetime64[ns, tz] ndarray[object] (Timestamps)
|
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] |
75 |
..\pandas\reference\api\pandas.tseries.offsets.Milli.is_on_offset.html
|
pandas.tseries.offsets.Milli.is_on_offset
|
Milli.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.Milli.is_on_offset
Milli.is_on_offset(dt)# Return boolean whether a timestamp intersects with this frequency.
|
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] |
76 |
..\pandas\reference\api\pandas.tseries.offsets.BYearBegin.is_year_start.html
|
pandas.tseries.offsets.BYearBegin.is_year_start
|
BYearBegin.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.BYearBegin.is_year_start
BYearBegin.is_year_start(ts)# Return boolean whether a timestamp occurs on the year start.
|
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] |
77 |
..\pandas\reference\api\pandas.core.groupby.SeriesGroupBy.quantile.html
|
pandas.core.groupby.SeriesGroupBy.quantile
|
SeriesGroupBy.quantile(q=0.5, interpolation='linear', numeric_only=False)[source]# Return group values at the given quantile, a la numpy.percentile.
|
Parameters: qfloat or array-like, default 0.5 (50% quantile)Value(s) between 0 and 1 providing the quantile(s) to compute. interpolation{‘linear’, ‘lower’, ‘higher’, ‘midpoint’, ‘nearest’}Method to use when the desired quantile falls between two points. 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 DataFrameReturn type determined by caller of GroupBy object.
|
[">>> df = pd.DataFrame([\n... ['a', 1], ['a', 2], ['a', 3],\n... ['b', 1], ['b', 3], ['b', 5]\n... ], columns=['key', 'val'])\n>>> df.groupby('key').quantile()\n val\nkey\na 2.0\nb 3.0"]
|
pandas.core.groupby.SeriesGroupBy.quantile
SeriesGroupBy.quantile(q=0.5, interpolation='linear', numeric_only=False)[source]# Return group values at the given quantile, a la numpy.percentile.
|
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] |
78 |
..\pandas\reference\api\pandas.Series.cummax.html
|
pandas.Series.cummax
|
Series.cummax(axis=None, skipna=True, *args, **kwargs)[source]# Return cumulative maximum over a DataFrame or Series axis. Returns a DataFrame or Series of the same size containing the cumulative maximum.
|
Parameters: axis{0 or ‘index’, 1 or ‘columns’}, default 0The index or the name of the axis. 0 is equivalent to None or ‘index’. For Series this parameter is unused and defaults to 0. skipnabool, default TrueExclude NA/null values. If an entire row/column is NA, the result will be NA. *args, **kwargsAdditional keywords have no effect but might be accepted for compatibility with NumPy. Returns: scalar or SeriesReturn cumulative maximum of scalar or Series.
|
['>>> s = pd.Series([2, np.nan, 5, -1, 0])\n>>> s\n0 2.0\n1 NaN\n2 5.0\n3 -1.0\n4 0.0\ndtype: float64', '>>> s.cummax()\n0 2.0\n1 NaN\n2 5.0\n3 5.0\n4 5.0\ndtype: float64', '>>> s.cummax(skipna=False)\n0 2.0\n1 NaN\n2 NaN\n3 NaN\n4 NaN\ndtype: float64', ">>> df = pd.DataFrame([[2.0, 1.0],\n... [3.0, np.nan],\n... [1.0, 0.0]],\n... columns=list('AB'))\n>>> df\n A B\n0 2.0 1.0\n1 3.0 NaN\n2 1.0 0.0", '>>> df.cummax()\n A B\n0 2.0 1.0\n1 3.0 NaN\n2 3.0 1.0', '>>> df.cummax(axis=1)\n A B\n0 2.0 2.0\n1 3.0 NaN\n2 1.0 1.0']
|
pandas.Series.cummax
Series.cummax(axis=None, skipna=True, *args, **kwargs)[source]# Return cumulative maximum over a DataFrame or Series axis. Returns a DataFrame or Series of the same size containing the cumulative maximum.
|
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] |
79 |
..\pandas\reference\api\pandas.DataFrame.sample.html
|
pandas.DataFrame.sample
|
DataFrame.sample(n=None, frac=None, replace=False, weights=None, random_state=None, axis=None, ignore_index=False)[source]# Return a random sample of items from an axis of object. You can use random_state for reproducibility. Notes If frac > 1, replacement should be set to True.
|
Parameters: nint, optionalNumber of items from axis to return. Cannot be used with frac. Default = 1 if frac = None. fracfloat, optionalFraction of axis items to return. Cannot be used with n. replacebool, default FalseAllow or disallow sampling of the same row more than once. weightsstr or ndarray-like, optionalDefault ‘None’ results in equal probability weighting. If passed a Series, will align with target object on index. Index values in weights not found in sampled object will be ignored and index values in sampled object not in weights will be assigned weights of zero. If called on a DataFrame, will accept the name of a column when axis = 0. Unless weights are a Series, weights must be same length as axis being sampled. If weights do not sum to 1, they will be normalized to sum to 1. Missing values in the weights column will be treated as zero. Infinite values not allowed. 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 axis{0 or ‘index’, 1 or ‘columns’, None}, default NoneAxis to sample. Accepts axis number or name. Default is stat axis for given data type. For Series this parameter is unused and defaults to None. ignore_indexbool, default FalseIf True, the resulting index will be labeled 0, 1, …, n - 1. Added in version 1.3.0. Returns: Series or DataFrameA new object of same type as caller containing n items randomly sampled from the caller object.
|
[">>> df = pd.DataFrame({'num_legs': [2, 4, 8, 0],\n... 'num_wings': [2, 0, 0, 0],\n... 'num_specimen_seen': [10, 2, 1, 8]},\n... index=['falcon', 'dog', 'spider', 'fish'])\n>>> df\n num_legs num_wings num_specimen_seen\nfalcon 2 2 10\ndog 4 0 2\nspider 8 0 1\nfish 0 0 8", ">>> df['num_legs'].sample(n=3, random_state=1)\nfish 0\nspider 8\nfalcon 2\nName: num_legs, dtype: int64", '>>> df.sample(frac=0.5, replace=True, random_state=1)\n num_legs num_wings num_specimen_seen\ndog 4 0 2\nfish 0 0 8', '>>> df.sample(frac=2, replace=True, random_state=1)\n num_legs num_wings num_specimen_seen\ndog 4 0 2\nfish 0 0 8\nfalcon 2 2 10\nfalcon 2 2 10\nfish 0 0 8\ndog 4 0 2\nfish 0 0 8\ndog 4 0 2', ">>> df.sample(n=2, weights='num_specimen_seen', random_state=1)\n num_legs num_wings num_specimen_seen\nfalcon 2 2 10\nfish 0 0 8"]
|
pandas.DataFrame.sample
DataFrame.sample(n=None, frac=None, replace=False, weights=None, random_state=None, axis=None, ignore_index=False)[source]# Return a random sample of items from an axis of object. You can use random_state for reproducibility. Notes If frac > 1, replacement should be set to True.
|
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] |
80 |
..\pandas\reference\api\pandas.Series.to_period.html
|
pandas.Series.to_period
|
Series.to_period(freq=None, copy=None)[source]# Convert Series from DatetimeIndex to PeriodIndex.
|
Parameters: freqstr, default NoneFrequency associated with the PeriodIndex. 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: SeriesSeries with index converted to PeriodIndex.
|
[">>> idx = pd.DatetimeIndex(['2023', '2024', '2025'])\n>>> s = pd.Series([1, 2, 3], index=idx)\n>>> s = s.to_period()\n>>> s\n2023 1\n2024 2\n2025 3\nFreq: Y-DEC, dtype: int64", ">>> s.index\nPeriodIndex(['2023', '2024', '2025'], dtype='period[Y-DEC]')"]
|
pandas.Series.to_period
Series.to_period(freq=None, copy=None)[source]# Convert Series from DatetimeIndex to PeriodIndex.
|
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] |
81 |
..\pandas\reference\api\pandas.Series.cummin.html
|
pandas.Series.cummin
|
Series.cummin(axis=None, skipna=True, *args, **kwargs)[source]# Return cumulative minimum over a DataFrame or Series axis. Returns a DataFrame or Series of the same size containing the cumulative minimum.
|
Parameters: axis{0 or ‘index’, 1 or ‘columns’}, default 0The index or the name of the axis. 0 is equivalent to None or ‘index’. For Series this parameter is unused and defaults to 0. skipnabool, default TrueExclude NA/null values. If an entire row/column is NA, the result will be NA. *args, **kwargsAdditional keywords have no effect but might be accepted for compatibility with NumPy. Returns: scalar or SeriesReturn cumulative minimum of scalar or Series.
|
['>>> s = pd.Series([2, np.nan, 5, -1, 0])\n>>> s\n0 2.0\n1 NaN\n2 5.0\n3 -1.0\n4 0.0\ndtype: float64', '>>> s.cummin()\n0 2.0\n1 NaN\n2 2.0\n3 -1.0\n4 -1.0\ndtype: float64', '>>> s.cummin(skipna=False)\n0 2.0\n1 NaN\n2 NaN\n3 NaN\n4 NaN\ndtype: float64', ">>> df = pd.DataFrame([[2.0, 1.0],\n... [3.0, np.nan],\n... [1.0, 0.0]],\n... columns=list('AB'))\n>>> df\n A B\n0 2.0 1.0\n1 3.0 NaN\n2 1.0 0.0", '>>> df.cummin()\n A B\n0 2.0 1.0\n1 2.0 NaN\n2 1.0 0.0', '>>> df.cummin(axis=1)\n A B\n0 2.0 1.0\n1 3.0 NaN\n2 1.0 0.0']
|
pandas.Series.cummin
Series.cummin(axis=None, skipna=True, *args, **kwargs)[source]# Return cumulative minimum over a DataFrame or Series axis. Returns a DataFrame or Series of the same size containing the cumulative minimum.
|
[
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] |
82 |
..\pandas\reference\api\pandas.tseries.offsets.Milli.is_quarter_end.html
|
pandas.tseries.offsets.Milli.is_quarter_end
|
Milli.is_quarter_end(ts)# Return boolean whether a timestamp occurs on the quarter end.
|
No parameters found
|
['>>> ts = pd.Timestamp(2022, 1, 1)\n>>> freq = pd.offsets.Hour(5)\n>>> freq.is_quarter_end(ts)\nFalse']
|
pandas.tseries.offsets.Milli.is_quarter_end
Milli.is_quarter_end(ts)# Return boolean whether a timestamp occurs on the quarter end.
|
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] |
83 |
..\pandas\reference\api\pandas.Int32Dtype.html
|
pandas.Int32Dtype
|
class pandas.Int32Dtype[source]# An ExtensionDtype for int32 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.Int32Dtype
class pandas.Int32Dtype[source]# An ExtensionDtype for int32 integer data. Uses pandas.NA as its missing value, rather than numpy.nan. Attributes None Methods None
|
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] |
84 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray.interpolate.html
|
pandas.api.extensions.ExtensionArray.interpolate
|
ExtensionArray.interpolate(*, method, axis, index, limit, limit_direction, limit_area, copy, **kwargs)[source]# See DataFrame.interpolate.__doc__.
|
No parameters found
|
['>>> arr = pd.arrays.NumpyExtensionArray(np.array([0, 1, np.nan, 3]))\n>>> arr.interpolate(method="linear",\n... limit=3,\n... limit_direction="forward",\n... index=pd.Index([1, 2, 3, 4]),\n... fill_value=1,\n... copy=False,\n... axis=0,\n... limit_area="inside"\n... )\n<NumpyExtensionArray>\n[0.0, 1.0, 2.0, 3.0]\nLength: 4, dtype: float64']
|
pandas.api.extensions.ExtensionArray.interpolate
ExtensionArray.interpolate(*, method, axis, index, limit, limit_direction, limit_area, copy, **kwargs)[source]# See DataFrame.interpolate.__doc__.
|
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] |
85 |
..\pandas\reference\api\pandas.core.groupby.SeriesGroupBy.rank.html
|
pandas.core.groupby.SeriesGroupBy.rank
|
SeriesGroupBy.rank(method='average', ascending=True, na_option='keep', pct=False, axis=<no_default>)[source]# Provide the rank of values within each group.
|
Parameters: method{‘average’, ‘min’, ‘max’, ‘first’, ‘dense’}, default ‘average’ average: average rank of group. min: lowest rank in group. max: highest rank in group. first: ranks assigned in order they appear in the array. dense: like ‘min’, but rank always increases by 1 between groups. ascendingbool, default TrueFalse for ranks by high (1) to low (N). na_option{‘keep’, ‘top’, ‘bottom’}, default ‘keep’ keep: leave NA values where they are. top: smallest rank if ascending. bottom: smallest rank if descending. pctbool, default FalseCompute percentage rank of data within each group. axisint, default 0The axis of the object over which to compute the rank. Deprecated since version 2.1.0: For axis=1, operate on the underlying object instead. Otherwise the axis keyword is not necessary. Returns: DataFrame with ranking of values within each group
|
['>>> df = pd.DataFrame(\n... {\n... "group": ["a", "a", "a", "a", "a", "b", "b", "b", "b", "b"],\n... "value": [2, 4, 2, 3, 5, 1, 2, 4, 1, 5],\n... }\n... )\n>>> df\n group value\n0 a 2\n1 a 4\n2 a 2\n3 a 3\n4 a 5\n5 b 1\n6 b 2\n7 b 4\n8 b 1\n9 b 5\n>>> for method in [\'average\', \'min\', \'max\', \'dense\', \'first\']:\n... df[f\'{method}_rank\'] = df.groupby(\'group\')[\'value\'].rank(method)\n>>> df\n group value average_rank min_rank max_rank dense_rank first_rank\n0 a 2 1.5 1.0 2.0 1.0 1.0\n1 a 4 4.0 4.0 4.0 3.0 4.0\n2 a 2 1.5 1.0 2.0 1.0 2.0\n3 a 3 3.0 3.0 3.0 2.0 3.0\n4 a 5 5.0 5.0 5.0 4.0 5.0\n5 b 1 1.5 1.0 2.0 1.0 1.0\n6 b 2 3.0 3.0 3.0 2.0 3.0\n7 b 4 4.0 4.0 4.0 3.0 4.0\n8 b 1 1.5 1.0 2.0 1.0 2.0\n9 b 5 5.0 5.0 5.0 4.0 5.0']
|
pandas.core.groupby.SeriesGroupBy.rank
SeriesGroupBy.rank(method='average', ascending=True, na_option='keep', pct=False, axis=<no_default>)[source]# Provide the rank of values within each group.
|
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] |
86 |
..\pandas\reference\api\pandas.tseries.offsets.BYearBegin.kwds.html
|
pandas.tseries.offsets.BYearBegin.kwds
|
BYearBegin.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.BYearBegin.kwds
BYearBegin.kwds# Return a dict of extra parameters for the offset.
|
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87 |
..\pandas\reference\api\pandas.Series.to_pickle.html
|
pandas.Series.to_pickle
|
Series.to_pickle(path, *, compression='infer', protocol=5, storage_options=None)[source]# Pickle (serialize) object to file.
|
Parameters: pathstr, path object, or file-like objectString, path object (implementing os.PathLike[str]), or file-like object implementing a binary write() function. File path where the pickled object will be stored. compressionstr or dict, default ‘infer’For on-the-fly compression of the output data. If ‘infer’ and ‘path’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, ‘.zst’, ‘.tar’, ‘.tar.gz’, ‘.tar.xz’ or ‘.tar.bz2’ (otherwise no compression). Set to None for no compression. Can also be a dict with key 'method' set to one of {'zip', 'gzip', 'bz2', 'zstd', 'xz', 'tar'} and other key-value pairs are forwarded to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, zstandard.ZstdCompressor, lzma.LZMAFile or tarfile.TarFile, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive: compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}. Added in version 1.5.0: Added support for .tar files. protocolintInt which indicates which protocol should be used by the pickler, default HIGHEST_PROTOCOL (see [1] paragraph 12.1.2). The possible values are 0, 1, 2, 3, 4, 5. A negative value for the protocol parameter is equivalent to setting its value to HIGHEST_PROTOCOL. [1] https://docs.python.org/3/library/pickle.html. storage_optionsdict, optionalExtra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.
|
['>>> original_df = pd.DataFrame({"foo": range(5), "bar": range(5, 10)}) \n>>> original_df \n foo bar\n0 0 5\n1 1 6\n2 2 7\n3 3 8\n4 4 9\n>>> original_df.to_pickle("./dummy.pkl")', '>>> unpickled_df = pd.read_pickle("./dummy.pkl") \n>>> unpickled_df \n foo bar\n0 0 5\n1 1 6\n2 2 7\n3 3 8\n4 4 9']
|
pandas.Series.to_pickle
Series.to_pickle(path, *, compression='infer', protocol=5, storage_options=None)[source]# Pickle (serialize) object to file.
|
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] |
88 |
..\pandas\reference\api\pandas.DataFrame.select_dtypes.html
|
pandas.DataFrame.select_dtypes
|
DataFrame.select_dtypes(include=None, exclude=None)[source]# Return a subset of the DataFrame’s columns based on the column dtypes. Notes To select all numeric types, use np.number or 'number' To select strings you must use the object dtype, but note that this will return all object dtype columns See the numpy dtype hierarchy To select datetimes, use np.datetime64, 'datetime' or 'datetime64' To select timedeltas, use np.timedelta64, 'timedelta' or 'timedelta64' To select Pandas categorical dtypes, use 'category' To select Pandas datetimetz dtypes, use 'datetimetz' or 'datetime64[ns, tz]'
|
Parameters: include, excludescalar or list-likeA selection of dtypes or strings to be included/excluded. At least one of these parameters must be supplied. Returns: DataFrameThe subset of the frame including the dtypes in include and excluding the dtypes in exclude. Raises: ValueError If both of include and exclude are empty If include and exclude have overlapping elements If any kind of string dtype is passed in.
|
[">>> df = pd.DataFrame({'a': [1, 2] * 3,\n... 'b': [True, False] * 3,\n... 'c': [1.0, 2.0] * 3})\n>>> df\n a b c\n0 1 True 1.0\n1 2 False 2.0\n2 1 True 1.0\n3 2 False 2.0\n4 1 True 1.0\n5 2 False 2.0", ">>> df.select_dtypes(include='bool')\n b\n0 True\n1 False\n2 True\n3 False\n4 True\n5 False", ">>> df.select_dtypes(include=['float64'])\n c\n0 1.0\n1 2.0\n2 1.0\n3 2.0\n4 1.0\n5 2.0", ">>> df.select_dtypes(exclude=['int64'])\n b c\n0 True 1.0\n1 False 2.0\n2 True 1.0\n3 False 2.0\n4 True 1.0\n5 False 2.0"]
|
pandas.DataFrame.select_dtypes
DataFrame.select_dtypes(include=None, exclude=None)[source]# Return a subset of the DataFrame’s columns based on the column dtypes. Notes To select all numeric types, use np.number or 'number' To select strings you must use the object dtype, but note that this will return all object dtype columns See the numpy dtype hierarchy To select datetimes, use np.datetime64, 'datetime' or 'datetime64' To select timedeltas, use np.timedelta64, 'timedelta' or 'timedelta64' To select Pandas categorical dtypes, use 'category' To select Pandas datetimetz dtypes, use 'datetimetz' or 'datetime64[ns, tz]'
|
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] |
89 |
..\pandas\reference\api\pandas.Series.cumprod.html
|
pandas.Series.cumprod
|
Series.cumprod(axis=None, skipna=True, *args, **kwargs)[source]# Return cumulative product over a DataFrame or Series axis. Returns a DataFrame or Series of the same size containing the cumulative product.
|
Parameters: axis{0 or ‘index’, 1 or ‘columns’}, default 0The index or the name of the axis. 0 is equivalent to None or ‘index’. For Series this parameter is unused and defaults to 0. skipnabool, default TrueExclude NA/null values. If an entire row/column is NA, the result will be NA. *args, **kwargsAdditional keywords have no effect but might be accepted for compatibility with NumPy. Returns: scalar or SeriesReturn cumulative product of scalar or Series.
|
['>>> s = pd.Series([2, np.nan, 5, -1, 0])\n>>> s\n0 2.0\n1 NaN\n2 5.0\n3 -1.0\n4 0.0\ndtype: float64', '>>> s.cumprod()\n0 2.0\n1 NaN\n2 10.0\n3 -10.0\n4 -0.0\ndtype: float64', '>>> s.cumprod(skipna=False)\n0 2.0\n1 NaN\n2 NaN\n3 NaN\n4 NaN\ndtype: float64', ">>> df = pd.DataFrame([[2.0, 1.0],\n... [3.0, np.nan],\n... [1.0, 0.0]],\n... columns=list('AB'))\n>>> df\n A B\n0 2.0 1.0\n1 3.0 NaN\n2 1.0 0.0", '>>> df.cumprod()\n A B\n0 2.0 1.0\n1 6.0 NaN\n2 6.0 0.0', '>>> df.cumprod(axis=1)\n A B\n0 2.0 2.0\n1 3.0 NaN\n2 1.0 0.0']
|
pandas.Series.cumprod
Series.cumprod(axis=None, skipna=True, *args, **kwargs)[source]# Return cumulative product over a DataFrame or Series axis. Returns a DataFrame or Series of the same size containing the cumulative product.
|
[
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] |
90 |
..\pandas\reference\api\pandas.tseries.offsets.Milli.is_quarter_start.html
|
pandas.tseries.offsets.Milli.is_quarter_start
|
Milli.is_quarter_start(ts)# Return boolean whether a timestamp occurs on the quarter start.
|
No parameters found
|
['>>> ts = pd.Timestamp(2022, 1, 1)\n>>> freq = pd.offsets.Hour(5)\n>>> freq.is_quarter_start(ts)\nTrue']
|
pandas.tseries.offsets.Milli.is_quarter_start
Milli.is_quarter_start(ts)# Return boolean whether a timestamp occurs on the quarter start.
|
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] |
91 |
..\pandas\reference\api\pandas.DataFrame.sem.html
|
pandas.DataFrame.sem
|
DataFrame.sem(axis=0, skipna=True, ddof=1, numeric_only=False, **kwargs)[source]# Return unbiased standard error of the mean over requested axis. Normalized by N-1 by default. This can be changed using the ddof argument
|
Parameters: axis{index (0), columns (1)}For Series this parameter is unused and defaults to 0. Warning The behavior of DataFrame.sem with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis). skipnabool, default TrueExclude NA/null values. If an entire row/column is NA, the result will be NA. ddofint, default 1Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. numeric_onlybool, default FalseInclude only float, int, boolean columns. Not implemented for Series. Returns: Series or DataFrame (if level specified)
|
['>>> s = pd.Series([1, 2, 3])\n>>> s.sem().round(6)\n0.57735', ">>> df = pd.DataFrame({'a': [1, 2], 'b': [2, 3]}, index=['tiger', 'zebra'])\n>>> df\n a b\ntiger 1 2\nzebra 2 3\n>>> df.sem()\na 0.5\nb 0.5\ndtype: float64", '>>> df.sem(axis=1)\ntiger 0.5\nzebra 0.5\ndtype: float64', ">>> df = pd.DataFrame({'a': [1, 2], 'b': ['T', 'Z']},\n... index=['tiger', 'zebra'])\n>>> df.sem(numeric_only=True)\na 0.5\ndtype: float64"]
|
pandas.DataFrame.sem
DataFrame.sem(axis=0, skipna=True, ddof=1, numeric_only=False, **kwargs)[source]# Return unbiased standard error of the mean over requested axis. Normalized by N-1 by default. This can be changed using the ddof argument
|
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] |
92 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray.isin.html
|
pandas.api.extensions.ExtensionArray.isin
|
ExtensionArray.isin(values)[source]# Pointwise comparison for set containment in the given values. Roughly equivalent to np.array([x in values for x in self])
|
Parameters: valuesnp.ndarray or ExtensionArray Returns: np.ndarray[bool]
|
['>>> arr = pd.array([1, 2, 3])\n>>> arr.isin([1])\n<BooleanArray>\n[True, False, False]\nLength: 3, dtype: boolean']
|
pandas.api.extensions.ExtensionArray.isin
ExtensionArray.isin(values)[source]# Pointwise comparison for set containment in the given values. Roughly equivalent to np.array([x in values for x in self])
|
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..\pandas\reference\api\pandas.tseries.offsets.BYearBegin.month.html
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pandas.tseries.offsets.BYearBegin.month
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BYearBegin.month#
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pandas.tseries.offsets.BYearBegin.month
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] |
94 |
..\pandas\reference\api\pandas.Int64Dtype.html
|
pandas.Int64Dtype
|
class pandas.Int64Dtype[source]# An ExtensionDtype for int64 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.Int64Dtype
class pandas.Int64Dtype[source]# An ExtensionDtype for int64 integer data. Uses pandas.NA as its missing value, rather than numpy.nan. Attributes None Methods None
|
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] |
95 |
..\pandas\reference\api\pandas.core.groupby.SeriesGroupBy.resample.html
|
pandas.core.groupby.SeriesGroupBy.resample
|
SeriesGroupBy.resample(rule, *args, include_groups=True, **kwargs)[source]# Provide resampling when using a TimeGrouper. Given a grouper, the function resamples it according to a string “string” -> “frequency”. See the frequency aliases documentation for more details.
|
Parameters: rulestr or DateOffsetThe offset string or object representing target grouper conversion. *argsPossible arguments are how, fill_method, limit, kind and on, and other arguments of TimeGrouper. include_groupsbool, default TrueWhen True, will attempt to include the groupings in the operation in the case that they are columns of the DataFrame. If this raises a TypeError, the result will be computed with the groupings excluded. When False, the groupings will be excluded when applying func. Added in version 2.2.0. Deprecated since version 2.2.0: Setting include_groups to True is deprecated. Only the value False will be allowed in a future version of pandas. **kwargsPossible arguments are how, fill_method, limit, kind and on, and other arguments of TimeGrouper. Returns: pandas.api.typing.DatetimeIndexResamplerGroupby, pandas.api.typing.PeriodIndexResamplerGroupby, or pandas.api.typing.TimedeltaIndexResamplerGroupbyReturn a new groupby object, with type depending on the data being resampled.
|
[">>> idx = pd.date_range('1/1/2000', periods=4, freq='min')\n>>> df = pd.DataFrame(data=4 * [range(2)],\n... index=idx,\n... columns=['a', 'b'])\n>>> df.iloc[2, 0] = 5\n>>> df\n a b\n2000-01-01 00:00:00 0 1\n2000-01-01 00:01:00 0 1\n2000-01-01 00:02:00 5 1\n2000-01-01 00:03:00 0 1", ">>> df.groupby('a').resample('3min', include_groups=False).sum()\n b\na\n0 2000-01-01 00:00:00 2\n 2000-01-01 00:03:00 1\n5 2000-01-01 00:00:00 1", ">>> df.groupby('a').resample('30s', include_groups=False).sum()\n b\na\n0 2000-01-01 00:00:00 1\n 2000-01-01 00:00:30 0\n 2000-01-01 00:01:00 1\n 2000-01-01 00:01:30 0\n 2000-01-01 00:02:00 0\n 2000-01-01 00:02:30 0\n 2000-01-01 00:03:00 1\n5 2000-01-01 00:02:00 1", ">>> df.groupby('a').resample('ME', include_groups=False).sum()\n b\na\n0 2000-01-31 3\n5 2000-01-31 1", ">>> (\n... df.groupby('a')\n... .resample('3min', closed='right', include_groups=False)\n... .sum()\n... )\n b\na\n0 1999-12-31 23:57:00 1\n 2000-01-01 00:00:00 2\n5 2000-01-01 00:00:00 1", ">>> (\n... df.groupby('a')\n... .resample('3min', closed='right', label='right', include_groups=False)\n... .sum()\n... )\n b\na\n0 2000-01-01 00:00:00 1\n 2000-01-01 00:03:00 2\n5 2000-01-01 00:03:00 1"]
|
pandas.core.groupby.SeriesGroupBy.resample
SeriesGroupBy.resample(rule, *args, include_groups=True, **kwargs)[source]# Provide resampling when using a TimeGrouper. Given a grouper, the function resamples it according to a string “string” -> “frequency”. See the frequency aliases documentation for more details.
|
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] |
96 |
..\pandas\reference\api\pandas.Series.to_sql.html
|
pandas.Series.to_sql
|
Series.to_sql(name, con, *, schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None, method=None)[source]# Write records stored in a DataFrame to a SQL database. Databases supported by SQLAlchemy [1] are supported. Tables can be newly created, appended to, or overwritten. Notes Timezone aware datetime columns will be written as Timestamp with timezone type with SQLAlchemy if supported by the database. Otherwise, the datetimes will be stored as timezone unaware timestamps local to the original timezone. Not all datastores support method="multi". Oracle, for example, does not support multi-value insert. References [1] https://docs.sqlalchemy.org [2] https://www.python.org/dev/peps/pep-0249/
|
Parameters: namestrName of SQL table. consqlalchemy.engine.(Engine or Connection) or sqlite3.ConnectionUsing SQLAlchemy makes it possible to use any DB supported by that library. Legacy support is provided for sqlite3.Connection objects. The user is responsible for engine disposal and connection closure for the SQLAlchemy connectable. See here. If passing a sqlalchemy.engine.Connection which is already in a transaction, the transaction will not be committed. If passing a sqlite3.Connection, it will not be possible to roll back the record insertion. schemastr, optionalSpecify the schema (if database flavor supports this). If None, use default schema. if_exists{‘fail’, ‘replace’, ‘append’}, default ‘fail’How to behave if the table already exists. fail: Raise a ValueError. replace: Drop the table before inserting new values. append: Insert new values to the existing table. indexbool, default TrueWrite DataFrame index as a column. Uses index_label as the column name in the table. Creates a table index for this column. index_labelstr or sequence, default NoneColumn label for index column(s). If None is given (default) and index is True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex. chunksizeint, optionalSpecify the number of rows in each batch to be written at a time. By default, all rows will be written at once. dtypedict or scalar, optionalSpecifying the datatype for columns. If a dictionary is used, the keys should be the column names and the values should be the SQLAlchemy types or strings for the sqlite3 legacy mode. If a scalar is provided, it will be applied to all columns. method{None, ‘multi’, callable}, optionalControls the SQL insertion clause used: None : Uses standard SQL INSERT clause (one per row). ‘multi’: Pass multiple values in a single INSERT clause. callable with signature (pd_table, conn, keys, data_iter). Details and a sample callable implementation can be found in the section insert method. Returns: None or intNumber of rows affected by to_sql. None is returned if the callable passed into method does not return an integer number of rows. The number of returned rows affected is the sum of the rowcount attribute of sqlite3.Cursor or SQLAlchemy connectable which may not reflect the exact number of written rows as stipulated in the sqlite3 or SQLAlchemy. Added in version 1.4.0. Raises: ValueErrorWhen the table already exists and if_exists is ‘fail’ (the default).
|
[">>> from sqlalchemy import create_engine\n>>> engine = create_engine('sqlite://', echo=False)", ">>> df = pd.DataFrame({'name' : ['User 1', 'User 2', 'User 3']})\n>>> df\n name\n0 User 1\n1 User 2\n2 User 3", '>>> df.to_sql(name=\'users\', con=engine)\n3\n>>> from sqlalchemy import text\n>>> with engine.connect() as conn:\n... conn.execute(text("SELECT * FROM users")).fetchall()\n[(0, \'User 1\'), (1, \'User 2\'), (2, \'User 3\')]', ">>> with engine.begin() as connection:\n... df1 = pd.DataFrame({'name' : ['User 4', 'User 5']})\n... df1.to_sql(name='users', con=connection, if_exists='append')\n2", '>>> df2 = pd.DataFrame({\'name\' : [\'User 6\', \'User 7\']})\n>>> df2.to_sql(name=\'users\', con=engine, if_exists=\'append\')\n2\n>>> with engine.connect() as conn:\n... conn.execute(text("SELECT * FROM users")).fetchall()\n[(0, \'User 1\'), (1, \'User 2\'), (2, \'User 3\'),\n (0, \'User 4\'), (1, \'User 5\'), (0, \'User 6\'),\n (1, \'User 7\')]', '>>> df2.to_sql(name=\'users\', con=engine, if_exists=\'replace\',\n... index_label=\'id\')\n2\n>>> with engine.connect() as conn:\n... conn.execute(text("SELECT * FROM users")).fetchall()\n[(0, \'User 6\'), (1, \'User 7\')]', '>>> from sqlalchemy.dialects.postgresql import insert\n>>> def insert_on_conflict_nothing(table, conn, keys, data_iter):\n... # "a" is the primary key in "conflict_table"\n... data = [dict(zip(keys, row)) for row in data_iter]\n... stmt = insert(table.table).values(data).on_conflict_do_nothing(index_elements=["a"])\n... result = conn.execute(stmt)\n... return result.rowcount\n>>> df_conflict.to_sql(name="conflict_table", con=conn, if_exists="append", method=insert_on_conflict_nothing) \n0', '>>> from sqlalchemy.dialects.mysql import insert\n>>> def insert_on_conflict_update(table, conn, keys, data_iter):\n... # update columns "b" and "c" on primary key conflict\n... data = [dict(zip(keys, row)) for row in data_iter]\n... stmt = (\n... insert(table.table)\n... .values(data)\n... )\n... stmt = stmt.on_duplicate_key_update(b=stmt.inserted.b, c=stmt.inserted.c)\n... result = conn.execute(stmt)\n... return result.rowcount\n>>> df_conflict.to_sql(name="conflict_table", con=conn, if_exists="append", method=insert_on_conflict_update) \n2', '>>> df = pd.DataFrame({"A": [1, None, 2]})\n>>> df\n A\n0 1.0\n1 NaN\n2 2.0', '>>> from sqlalchemy.types import Integer\n>>> df.to_sql(name=\'integers\', con=engine, index=False,\n... dtype={"A": Integer()})\n3', '>>> with engine.connect() as conn:\n... conn.execute(text("SELECT * FROM integers")).fetchall()\n[(1,), (None,), (2,)]']
|
pandas.Series.to_sql
Series.to_sql(name, con, *, schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None, method=None)[source]# Write records stored in a DataFrame to a SQL database. Databases supported by SQLAlchemy [1] are supported. Tables can be newly created, appended to, or overwritten. Notes Timezone aware datetime columns will be written as Timestamp with timezone type with SQLAlchemy if supported by the database. Otherwise, the datetimes will be stored as timezone unaware timestamps local to the original timezone. Not all datastores support method="multi". Oracle, for example, does not support multi-value insert. References [1] https://docs.sqlalchemy.org [2] https://www.python.org/dev/peps/pep-0249/
|
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] |
97 |
..\pandas\reference\api\pandas.Series.cumsum.html
|
pandas.Series.cumsum
|
Series.cumsum(axis=None, skipna=True, *args, **kwargs)[source]# Return cumulative sum over a DataFrame or Series axis. Returns a DataFrame or Series of the same size containing the cumulative sum.
|
Parameters: axis{0 or ‘index’, 1 or ‘columns’}, default 0The index or the name of the axis. 0 is equivalent to None or ‘index’. For Series this parameter is unused and defaults to 0. skipnabool, default TrueExclude NA/null values. If an entire row/column is NA, the result will be NA. *args, **kwargsAdditional keywords have no effect but might be accepted for compatibility with NumPy. Returns: scalar or SeriesReturn cumulative sum of scalar or Series.
|
['>>> s = pd.Series([2, np.nan, 5, -1, 0])\n>>> s\n0 2.0\n1 NaN\n2 5.0\n3 -1.0\n4 0.0\ndtype: float64', '>>> s.cumsum()\n0 2.0\n1 NaN\n2 7.0\n3 6.0\n4 6.0\ndtype: float64', '>>> s.cumsum(skipna=False)\n0 2.0\n1 NaN\n2 NaN\n3 NaN\n4 NaN\ndtype: float64', ">>> df = pd.DataFrame([[2.0, 1.0],\n... [3.0, np.nan],\n... [1.0, 0.0]],\n... columns=list('AB'))\n>>> df\n A B\n0 2.0 1.0\n1 3.0 NaN\n2 1.0 0.0", '>>> df.cumsum()\n A B\n0 2.0 1.0\n1 5.0 NaN\n2 6.0 1.0', '>>> df.cumsum(axis=1)\n A B\n0 2.0 3.0\n1 3.0 NaN\n2 1.0 1.0']
|
pandas.Series.cumsum
Series.cumsum(axis=None, skipna=True, *args, **kwargs)[source]# Return cumulative sum over a DataFrame or Series axis. Returns a DataFrame or Series of the same size containing the cumulative sum.
|
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98 |
..\pandas\reference\api\pandas.tseries.offsets.Milli.is_year_end.html
|
pandas.tseries.offsets.Milli.is_year_end
|
Milli.is_year_end(ts)# Return boolean whether a timestamp occurs on the year end.
|
No parameters found
|
['>>> ts = pd.Timestamp(2022, 1, 1)\n>>> freq = pd.offsets.Hour(5)\n>>> freq.is_year_end(ts)\nFalse']
|
pandas.tseries.offsets.Milli.is_year_end
Milli.is_year_end(ts)# Return boolean whether a timestamp occurs on the year end.
|
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99 |
..\pandas\reference\api\pandas.api.extensions.ExtensionArray.isna.html
|
pandas.api.extensions.ExtensionArray.isna
|
ExtensionArray.isna()[source]# A 1-D array indicating if each value is missing. Notes If returning an ExtensionArray, then na_values._is_boolean should be True na_values should implement ExtensionArray._reduce() na_values.any and na_values.all should be implemented
|
Returns: numpy.ndarray or pandas.api.extensions.ExtensionArrayIn most cases, this should return a NumPy ndarray. For exceptional cases like SparseArray, where returning an ndarray would be expensive, an ExtensionArray may be returned.
|
['>>> arr = pd.array([1, 2, np.nan, np.nan])\n>>> arr.isna()\narray([False, False, True, True])']
|
pandas.api.extensions.ExtensionArray.isna
ExtensionArray.isna()[source]# A 1-D array indicating if each value is missing. Notes If returning an ExtensionArray, then na_values._is_boolean should be True na_values should implement ExtensionArray._reduce() na_values.any and na_values.all should be implemented
|
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