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# cbook

## `matplotlib.cbook`

A collection of utility functions and classes. Originally, many (but not all) were from the Python Cookbook – hence the name cbook.

This module is safe to import from anywhere within matplotlib; it imports matplotlib only at runtime.

class `matplotlib.cbook.``Bunch`(**kwds)

  Bases: `object`

  Often we want to just collect a bunch of stuff together, naming each item of the bunch; a dictionary’s OK for that, but a small do- nothing class is even handier, and prettier to use. Whenever you want to group a few variables:

      >>> point = Bunch(datum=2, squared=4, coord=12)
      >>> point.datum

      By: Alex Martelli
      From: https://code.activestate.com/recipes/121294/

class `matplotlib.cbook.``CallbackRegistry`

  Bases: `object`

  Handle registering and disconnecting for a set of signals and callbacks:

      >>> def oneat(x):
      ...    print('eat', x)
      >>> def ondrink(x):
      ...    print('drink', x)

      >>> from matplotlib.cbook import CallbackRegistry
      >>> callbacks = CallbackRegistry()

      >>> id_eat = callbacks.connect('eat', oneat)
      >>> id_drink = callbacks.connect('drink', ondrink)

      >>> callbacks.process('drink', 123)
      drink 123
      >>> callbacks.process('eat', 456)
      eat 456
      >>> callbacks.process('be merry', 456) # nothing will be called
      >>> callbacks.disconnect(id_eat)
      >>> callbacks.process('eat', 456)      # nothing will be called

  In practice, one should always disconnect all callbacks when they are no longer needed to avoid dangling references (and thus memory leaks). However, real code in matplotlib rarely does so, and due to its design, it is rather difficult to place this kind of code. To get around this, and prevent this class of memory leaks, we instead store weak references to bound methods only, so when the destination object needs to die, the CallbackRegistry won’t keep it alive. The Python stdlib weakref module can not create weak references to bound methods directly, so we need to create a proxy object to handle weak references to bound methods (or regular free functions). This technique was shared by Peter Parente on his “Mindtrove” blog.

  `connect`(s, func)

    register func to be called when a signal s is generated func will be called

  `disconnect`(cid)

    disconnect the callback registered with callback id cid

  `process`(s, *args, **kwargs)

    process signal `s`. All of the functions registered to receive callbacks on `s` will be called with `**args` and `**kwargs`

class `matplotlib.cbook.``GetRealpathAndStat`

  Bases: `object`

class `matplotlib.cbook.``Grouper`(init=())

  Bases: `object`

  This class provides a lightweight way to group arbitrary objects together into disjoint sets when a full-blown graph data structure would be overkill.

  Objects can be joined using `join()`, tested for connectedness using `joined()`, and all disjoint sets can be retreived by using the object as an iterator.

  The objects being joined must be hashable and weak-referenceable.

  For example:

      >>> from matplotlib.cbook import Grouper
      >>> class Foo(object):
      ...     def __init__(self, s):
      ...         self.s = s
      ...     def __repr__(self):
      ...         return self.s
      ...
      >>> a, b, c, d, e, f = [Foo(x) for x in 'abcdef']
      >>> grp = Grouper()
      >>> grp.join(a, b)
      >>> grp.join(b, c)
      >>> grp.join(d, e)
      >>> sorted(map(tuple, grp))
      [(a, b, c), (d, e)]
      >>> grp.joined(a, b)
      True
      >>> grp.joined(a, c)
      True
      >>> grp.joined(a, d)
      False

  `clean`()

    Clean dead weak references from the dictionary

  `get_siblings`(a)

    Returns all of the items joined with a, including itself.

  `join`(a, *args)

    Join given arguments into the same set. Accepts one or more arguments.

  `joined`(a, b)

    Returns True if a and b are members of the same set.

  `remove`(a)

exception `matplotlib.cbook.``IgnoredKeywordWarning`

  Bases: `UserWarning`

  A class for issuing warnings about keyword arguments that will be ignored by matplotlib

class `matplotlib.cbook.``Locked`(path)

  Bases: `object`

  Context manager to handle locks.

  Based on code from conda.

  (c) 2012-2013 Continuum Analytics, Inc. / https://www.continuum.io/ All Rights Reserved

  conda is distributed under the terms of the BSD 3-clause license. Consult LICENSE_CONDA or https://opensource.org/licenses/BSD-3-Clause.

  `LOCKFN` = ‘.matplotlib_lock’
  exception `TimeoutError`

    Bases: `RuntimeError`

class `matplotlib.cbook.``Null`(*args, **kwargs)

  Bases: `object`

  Deprecated since version 2.1: The Null class was deprecated in version 2.1.

  Null objects always and reliably “do nothing.”

class `matplotlib.cbook.``RingBuffer`(*args, **kwargs)

  Bases: `object`

  Deprecated since version 2.1: The RingBuffer class was deprecated in version 2.1.

  class that implements a not-yet-full buffer

  `append`(x)

    append an element at the end of the buffer

  `get`()

    Return a list of elements from the oldest to the newest.

class `matplotlib.cbook.``Sorter`(*args, **kwargs)

  Bases: `object`

  Deprecated since version 2.1: sorted(…, key=itemgetter(…))

  Sort by attribute or item

  Example usage:

      sort = Sorter()

      list = [(1, 2), (4, 8), (0, 3)]
      dict = [{'a': 3, 'b': 4}, {'a': 5, 'b': 2}, {'a': 0, 'b': 0},
              {'a': 9, 'b': 9}]

      sort(list)       # default sort
      sort(list, 1)    # sort by index 1
      sort(dict, 'a')  # sort a list of dicts by key 'a'

  `byAttribute`(data, attributename, inplace=1)
  `byItem`(data, itemindex=None, inplace=1)
  `sort`(data, itemindex=None, inplace=1)

class `matplotlib.cbook.``Stack`(default=None)

  Bases: `object`

  Implement a stack where elements can be pushed on and you can move back and forth. But no pop. Should mimic home / back / forward in a browser

  `back`()

    move the position back and return the current element

  `bubble`(o)

    raise o to the top of the stack and return o. o must be in the stack

  `clear`()

    empty the stack

  `empty`()
  `forward`()

    move the position forward and return the current element

  `home`()

    push the first element onto the top of the stack

  `push`(o)

    push object onto stack at current position - all elements occurring later than the current position are discarded

  `remove`(o)

    remove element o from the stack

class `matplotlib.cbook.``Xlator`(*args, **kwargs)

  Bases: `dict`

  Deprecated since version 2.1: The Xlator class was deprecated in version 2.1.

  All-in-one multiple-string-substitution class

  Example usage:

      text = "Larry Wall is the creator of Perl"
      adict = {
      "Larry Wall" : "Guido van Rossum",
      "creator" : "Benevolent Dictator for Life",
      "Perl" : "Python",
      }

      print(multiple_replace(adict, text))

      xlat = Xlator(adict)
      print(xlat.xlat(text))

  `xlat`(text)

    Translate text, returns the modified text.

`matplotlib.cbook.``align_iterators`(func, *iterables)

  This generator takes a bunch of iterables that are ordered by func It sends out ordered tuples:

      (func(row), [rows from all iterators matching func(row)])

  It is used by `matplotlib.mlab.recs_join()` to join record arrays

`matplotlib.cbook.``allequal`(seq)

  Deprecated since version 2.1: The allequal function was deprecated in version 2.1.

  Return True if all elements of seq compare equal. If seq is 0 or 1 length, return True

`matplotlib.cbook.``allpairs`(x)

  Deprecated since version 2.1: The allpairs function was deprecated in version 2.1.

  return all possible pairs in sequence x

`matplotlib.cbook.``alltrue`(seq)

  Deprecated since version 2.1: The alltrue function was deprecated in version 2.1.

  Return True if all elements of seq evaluate to True. If seq is empty, return False.

`matplotlib.cbook.``boxplot_stats`(X, whis=1.5, bootstrap=None, labels=None, autorange=False)

  Returns list of dictionaries of statistics used to draw a series of box and whisker plots. The `Returns` section enumerates the required keys of the dictionary. Users can skip this function and pass a user-defined set of dictionaries to the new `axes.bxp` method instead of relying on MPL to do the calculations.

  Parameters: | X : array-like Data that will be represented in the boxplots. Should have 2 or fewer dimensions. whis : float, string, or sequence (default = 1.5) As a float, determines the reach of the whiskers to the beyond the first and third quartiles. In other words, where IQR is the interquartile range (`Q3-Q1`), the upper whisker will extend to last datum less than `Q3 + whis*IQR`). Similarly, the lower whisker will extend to the first datum greater than `Q1 - whis*IQR`. Beyond the whiskers, data are considered outliers and are plotted as individual points. This can be set this to an ascending sequence of percentile (e.g., [5, 95]) to set the whiskers at specific percentiles of the data. Finally, `whis` can be the string `'range'` to force the whiskers to the minimum and maximum of the data. In the edge case that the 25th and 75th percentiles are equivalent, `whis` can be automatically set to `'range'` via the `autorange` option. bootstrap : int, optional Number of times the confidence intervals around the median should be bootstrapped (percentile method). labels : array-like, optional Labels for each dataset. Length must be compatible with dimensions of `X`. autorange : bool, optional (False) When `True` and the data are distributed such that the 25th and 75th percentiles are equal, `whis` is set to `'range'` such that the whisker ends are at the minimum and maximum of the data.
  > Returns: | bxpstats : list of dict A list of dictionaries containing the results for each column of data. Keys of each dictionary are the following:
  > Key | Value Description
  > --- | ---
  > label | tick label for the boxplot
  > mean | arithemetic mean value
  > med | 50th percentile
  > q1 | first quartile (25th percentile)
  > q3 | third quartile (75th percentile)
  > cilo | lower notch around the median
  > cihi | upper notch around the median
  > whislo | end of the lower whisker
  > whishi | end of the upper whisker
  > fliers | outliers

  Notes

  Non-bootstrapping approach to confidence interval uses Gaussian- based asymptotic approximation:

  General approach from: McGill, R., Tukey, J.W., and Larsen, W.A. (1978) “Variations of Boxplots”, The American Statistician, 32:12-16.

class `matplotlib.cbook.``converter`(*args, **kwargs)

  Bases: `object`

  Deprecated since version 2.1: The converter class was deprecated in version 2.1.

  Base class for handling string -> python type with support for missing values

  `is_missing`(s)

`matplotlib.cbook.``dedent`(s)

  Remove excess indentation from docstring s.

  Discards any leading blank lines, then removes up to n whitespace characters from each line, where n is the number of leading whitespace characters in the first line. It differs from textwrap.dedent in its deletion of leading blank lines and its use of the first non-blank line to determine the indentation.

  It is also faster in most cases.

`matplotlib.cbook.``delete_masked_points`(*args)

  Find all masked and/or non-finite points in a set of arguments, and return the arguments with only the unmasked points remaining.

  Arguments can be in any of 5 categories:

  1. 1-D masked arrays
  2. 1-D ndarrays
  3. ndarrays with more than one dimension
  4. other non-string iterables
  5. anything else

  The first argument must be in one of the first four categories; any argument with a length differing from that of the first argument (and hence anything in category 5) then will be passed through unchanged.

  Masks are obtained from all arguments of the correct length in categories 1, 2, and 4; a point is bad if masked in a masked array or if it is a nan or inf. No attempt is made to extract a mask from categories 2, 3, and 4 if `np.isfinite()` does not yield a Boolean array.

  All input arguments that are not passed unchanged are returned as ndarrays after removing the points or rows corresponding to masks in any of the arguments.

  A vastly simpler version of this function was originally written as a helper for Axes.scatter().

`matplotlib.cbook.``dict_delall`(d, keys)

  Deprecated since version 2.1: The dict_delall function was deprecated in version 2.1.

  delete all of the keys from the `dict` d

`matplotlib.cbook.``exception_to_str`(s=None)

  Deprecated since version 2.1: The exception_to_str function was deprecated in version 2.1.

`matplotlib.cbook.``file_requires_unicode`(x)

  Returns `True` if the given writable file-like object requires Unicode to be written to it.

`matplotlib.cbook.``finddir`(o, match, case=False)

  Deprecated since version 2.1: The finddir function was deprecated in version 2.1.

  return all attributes of o which match string in match. if case is True require an exact case match.

`matplotlib.cbook.``flatten`(seq, scalarp=<function is_scalar_or_string>)

  Returns a generator of flattened nested containers

  For example:

      >>> from matplotlib.cbook import flatten
      >>> l = (('John', ['Hunter']), (1, 23), [[([42, (5, 23)], )]])
      >>> print(list(flatten(l)))
      ['John', 'Hunter', 1, 23, 42, 5, 23]

  By: Composite of Holger Krekel and Luther Blissett From: https://code.activestate.com/recipes/121294/ and Recipe 1.12 in cookbook

`matplotlib.cbook.``get_label`(y, default_name)

`matplotlib.cbook.``get_recursive_filelist`(args)

  Recurse all the files and dirs in args ignoring symbolic links and return the files as a list of strings

`matplotlib.cbook.``get_sample_data`(fname, asfileobj=True)

  Return a sample data file. fname is a path relative to the `mpl-data/sample_data` directory. If asfileobj is `True` return a file object, otherwise just a file path.

  Set the rc parameter examples.directory to the directory where we should look, if sample_data files are stored in a location different than default (which is ‘mpl-data/sample_data` at the same level of ‘matplotlib` Python module files).

  If the filename ends in .gz, the file is implicitly ungzipped.

`matplotlib.cbook.``get_split_ind`(seq, N)

  seq is a list of words. Return the index into seq such that:

      len(' '.join(seq[:ind])<=N

  .

`matplotlib.cbook.``index_of`(y)

  A helper function to get the index of an input to plot against if x values are not explicitly given.

  Tries to get `y.index` (works if this is a pd.Series), if that fails, return np.arange(y.shape[0]).

  This will be extended in the future to deal with more types of labeled data.

  Parameters: | y : scalar or array-like The proposed y-value
  Returns: | x, y : ndarray The x and y values to plot.

`matplotlib.cbook.``is_hashable`(obj)

  Returns true if obj can be hashed

`matplotlib.cbook.``is_math_text`(s)

`matplotlib.cbook.``is_numlike`(obj)

  return true if obj looks like a number

`matplotlib.cbook.``is_scalar`(obj)

  Deprecated since version 2.1: The is_scalar function was deprecated in version 2.1.

  return true if obj is not string like and is not iterable

`matplotlib.cbook.``is_scalar_or_string`(val)

  Return whether the given object is a scalar or string like.

`matplotlib.cbook.``is_sequence_of_strings`(obj)

  Deprecated since version 2.1: The is_sequence_of_strings function was deprecated in version 2.1.

  Returns true if obj is iterable and contains strings

`matplotlib.cbook.``is_string_like`(obj)

  Deprecated since version 2.1: The is_string_like function was deprecated in version 2.1.

  Return True if obj looks like a string

`matplotlib.cbook.``is_writable_file_like`(obj)

  return true if obj looks like a file object with a write method

`matplotlib.cbook.``issubclass_safe`(x, klass)

  Deprecated since version 2.1: The issubclass_safe function was deprecated in version 2.1.

  return issubclass(x, klass) and return False on a TypeError

`matplotlib.cbook.``iterable`(obj)

  return true if obj is iterable

`matplotlib.cbook.``listFiles`(root, patterns=’*’, recurse=1, return_folders=0)

  Recursively list files

  from Parmar and Martelli in the Python Cookbook

`matplotlib.cbook.``local_over_kwdict`(local_var, kwargs, *keys)

  Enforces the priority of a local variable over potentially conflicting argument(s) from a kwargs dict. The following possible output values are considered in order of priority:

  > local_var > kwargs[keys[0]] > … > kwargs[keys[-1]]

  The first of these whose value is not None will be returned. If all are None then None will be returned. Each key in keys will be removed from the kwargs dict in place.

  Parameters: | local_var: any object The local variable (highest priority) kwargs: dict Dictionary of keyword arguments; modified in place keys: str(s) Name(s) of keyword arguments to process, in descending order of priority
  Returns: | out: any object Either local_var or one of kwargs[key] for key in keys
  Raises: | IgnoredKeywordWarning For each key in keys that is removed from kwargs but not used as the output value

class `matplotlib.cbook.``maxdict`(maxsize)

  Bases: `dict`

  A dictionary with a maximum size; this doesn’t override all the relevant methods to constrain the size, just setitem, so use with caution

`matplotlib.cbook.``mkdirs`(newdir, mode=511)

  make directory newdir recursively, and set mode. Equivalent to

      > mkdir -p NEWDIR
      > chmod MODE NEWDIR

`matplotlib.cbook.``normalize_kwargs`(kw, alias_mapping=None, required=(), forbidden=(), allowed=None)

  Helper function to normalize kwarg inputs

  The order they are resolved are:

  > 1. aliasing
  > 2. required
  > 3. forbidden
  > 4. allowed

  This order means that only the canonical names need appear in `allowed`, `forbidden`, `required`

  Parameters: | alias_mapping, dict, optional A mapping between a canonical name to a list of aliases, in order of precedence from lowest to highest. If the canonical value is not in the list it is assumed to have the highest priority. required : iterable, optional A tuple of fields that must be in kwargs. forbidden : iterable, optional A list of keys which may not be in kwargs allowed : tuple, optional A tuple of allowed fields. If this not None, then raise if `kw` contains any keys not in the union of `required` and `allowed`. To allow only the required fields pass in `()` for `allowed`
  Raises: | TypeError To match what python raises if invalid args/kwargs are passed to a callable.

`matplotlib.cbook.``onetrue`(seq)

  Deprecated since version 2.1: The onetrue function was deprecated in version 2.1.

  Return True if one element of seq is True. It seq is empty, return False.

`matplotlib.cbook.``pieces`(seq, num=2)

  Deprecated since version 2.1: The pieces function was deprecated in version 2.1.

  Break up the seq into num tuples

`matplotlib.cbook.``print_cycles`(objects, outstream=<_io.TextIOWrapper name=’<stdout>’ mode=’w’ encoding=’UTF-8’>, show_progress=False)
  objects
    A list of objects to find cycles in. It is often useful to pass in gc.garbage to find the cycles that are preventing some objects from being garbage collected.
  outstream
    The stream for output.
  show_progress
    If True, print the number of objects reached as they are found.

`matplotlib.cbook.``pts_to_midstep`(x, *args)

  Convert continuous line to mid-steps.

  Given a set of `N` points convert to `2N` points which when connected linearly give a step function which changes values at the middle of the intervals.

  Parameters: | x : array The x location of the steps. y1, …, yp : array y arrays to be turned into steps; all must be the same length as `x`.
  Returns: | out : array The x and y values converted to steps in the same order as the input; can be unpacked as `x_out, y1_out, ..., yp_out`. If the input is length `N`, each of these arrays will be length `2N`.

  Examples

  >> x_s, y1_s, y2_s = pts_to_midstep(x, y1, y2)

`matplotlib.cbook.``pts_to_poststep`(x, *args)

  Convert continuous line to post-steps.

  Given a set of `N` points convert to `2N + 1` points, which when connected linearly give a step function which changes values at the end of the intervals.

  Parameters: | x : array The x location of the steps. y1, …, yp : array y arrays to be turned into steps; all must be the same length as `x`.
  Returns: | out : array The x and y values converted to steps in the same order as the input; can be unpacked as `x_out, y1_out, ..., yp_out`. If the input is length `N`, each of these arrays will be length `2N + 1`.

  Examples

  >> x_s, y1_s, y2_s = pts_to_poststep(x, y1, y2)

`matplotlib.cbook.``pts_to_prestep`(x, *args)

  Convert continuous line to pre-steps.

  Given a set of `N` points, convert to `2N - 1` points, which when connected linearly give a step function which changes values at the beginning of the intervals.

  Parameters: | x : array The x location of the steps. y1, …, yp : array y arrays to be turned into steps; all must be the same length as `x`.
  Returns: | out : array The x and y values converted to steps in the same order as the input; can be unpacked as `x_out, y1_out, ..., yp_out`. If the input is length `N`, each of these arrays will be length `2N + 1`.

  Examples

  >> x_s, y1_s, y2_s = pts_to_prestep(x, y1, y2)

`matplotlib.cbook.``recursive_remove`(path)

  Deprecated since version 2.1: The recursive_remove function was deprecated in version 2.1. Use shutil.rmtree instead.

`matplotlib.cbook.``report_memory`(i=0)

  return the memory consumed by process

`matplotlib.cbook.``restrict_dict`(d, keys)

  Return a dictionary that contains those keys that appear in both d and keys, with values from d.

`matplotlib.cbook.``reverse_dict`(d)

  Deprecated since version 2.1: The reverse_dict function was deprecated in version 2.1.

  reverse the dictionary – may lose data if values are not unique!

`matplotlib.cbook.``safe_first_element`(obj)

`matplotlib.cbook.``safe_masked_invalid`(x, copy=False)

`matplotlib.cbook.``safezip`(*args)

  make sure args are equal len before zipping

`matplotlib.cbook.``sanitize_sequence`(data)

  Converts dictview object to list

class `matplotlib.cbook.``silent_list`(type, seq=None)

  Bases: `list`

  override repr when returning a list of matplotlib artists to prevent long, meaningless output. This is meant to be used for a homogeneous list of a given type

`matplotlib.cbook.``simple_linear_interpolation`(a, steps)

`matplotlib.cbook.``soundex`(name, len=4)

  Deprecated since version 2.1: The soundex function was deprecated in version 2.1.

  soundex module conforming to Odell-Russell algorithm

`matplotlib.cbook.``strip_math`(s)

  remove latex formatting from mathtext

`matplotlib.cbook.``to_filehandle`(fname, flag=’rU’, return_opened=False)

  fname can be a filename or a file handle. Support for gzipped files is automatic, if the filename ends in .gz. flag is a read/write flag for `file()`

class `matplotlib.cbook.``todate`(*args, **kwargs)

  Bases: `matplotlib.cbook.converter`

  Deprecated since version 2.1: The todate class was deprecated in version 2.1.

  convert to a date or None

class `matplotlib.cbook.``todatetime`(*args, **kwargs)

  Bases: `matplotlib.cbook.converter`

  Deprecated since version 2.1: The todatetime class was deprecated in version 2.1.

  convert to a datetime or None

class `matplotlib.cbook.``tofloat`(*args, **kwargs)

  Bases: `matplotlib.cbook.converter`

  Deprecated since version 2.1: The tofloat class was deprecated in version 2.1.

  convert to a float or None

class `matplotlib.cbook.``toint`(*args, **kwargs)

  Bases: `matplotlib.cbook.converter`

  Deprecated since version 2.1: The toint class was deprecated in version 2.1.

  convert to an int or None

class `matplotlib.cbook.``tostr`(*args, **kwargs)

  Bases: `matplotlib.cbook.converter`

  Deprecated since version 2.1: The tostr class was deprecated in version 2.1.

  convert to string or None

`matplotlib.cbook.``unicode_safe`(s)

`matplotlib.cbook.``unique`(x)

  Deprecated since version 2.1: The unique function was deprecated in version 2.1.

  Return a list of unique elements of x

`matplotlib.cbook.``unmasked_index_ranges`(mask, compressed=True)

  Deprecated since version 2.1: The unmasked_index_ranges function was deprecated in version 2.1.

  Find index ranges where mask is False.

  mask will be flattened if it is not already 1-D.

  Returns Nx2 `numpy.ndarray` with each row the start and stop indices for slices of the compressed `numpy.ndarray` corresponding to each of N uninterrupted runs of unmasked values. If optional argument compressed is False, it returns the start and stop indices into the original `numpy.ndarray`, not the compressed `numpy.ndarray`. Returns None if there are no unmasked values.

  Example:

      y = ma.array(np.arange(5), mask = [0,0,1,0,0])
      ii = unmasked_index_ranges(ma.getmaskarray(y))
      # returns array [[0,2,] [2,4,]]

      y.compressed()[ii[1,0]:ii[1,1]]
      # returns array [3,4,]

      ii = unmasked_index_ranges(ma.getmaskarray(y), compressed=False)
      # returns array [[0, 2], [3, 5]]

      y.filled()[ii[1,0]:ii[1,1]]
      # returns array [3,4,]

  Prior to the transforms refactoring, this was used to support masked arrays in Line2D.

`matplotlib.cbook.``violin_stats`(X, method, points=100)

  Returns a list of dictionaries of data which can be used to draw a series of violin plots. See the `Returns` section below to view the required keys of the dictionary. Users can skip this function and pass a user-defined set of dictionaries to the `axes.vplot` method instead of using MPL to do the calculations.

  Parameters: | X : array-like Sample data that will be used to produce the gaussian kernel density estimates. Must have 2 or fewer dimensions. method : callable The method used to calculate the kernel density estimate for each column of data. When called via `method(v, coords)`, it should return a vector of the values of the KDE evaluated at the values specified in coords. points : scalar, default = 100 Defines the number of points to evaluate each of the gaussian kernel density estimates at.
  Returns: | A list of dictionaries containing the results for each column of data. The dictionaries contain at least the following: coords: A list of scalars containing the coordinates this particular kernel density estimate was evaluated at. vals: A list of scalars containing the values of the kernel density estimate at each of the coordinates given in `coords`. mean: The mean value for this column of data. median: The median value for this column of data. min: The minimum value for this column of data. max: The maximum value for this column of data.

`matplotlib.cbook.``wrap`(prefix, text, cols)

  Deprecated since version 2.1: The wrap function was deprecated in version 2.1. Use textwrap.TextWrapper instead.

  wrap text with prefix at length cols