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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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