matplotlib.axes.Axes.hist
-
Axes.hist(self, x, bins=None, range=None, density=False, weights=None, cumulative=False, bottom=None, histtype='bar', align='mid', orientation='vertical', rwidth=None, log=False, color=None, label=None, stacked=False, *, data=None, **kwargs)[source] -
Построение гистограммы.
Вычисление и построение гистограммы для x. Возвращаемое значение — кортеж (n, bins, patches) или ([n0, n1, ...], bins, [patches0, patches1,...]) если входные данные содержат несколько наборов данных. Обратитесь к документации параметра weights, чтобы построить гистограмму уже разгруппированных данных.
Несколько наборов данных могут быть предоставлены через x как список наборов данных, возможно, разной длины ([x0, x1, ...]), или как двумерный массив NumPy, в котором каждая колонка представляет набор данных. Обратите внимание, что представление в виде массива NumPy транспонировано относительно представления в виде списка.
Маскированные массивы не поддерживаются.
Параметры bins, range, weights и density ведут себя так же, как в
numpy.histogram.Параметры: -
x(n,) array or sequence of (n,) arrays -
Вводные значения, это может быть либо один массив, либо последовательность массивов, которые необязательно должны быть одинаковой длины.
-
binsint or sequence or str, optional -
Если bins — целое число, оно определяет количество равномерно распределённых интервалов в заданном диапазоне.
Если bins — последовательность, она определяет границы интервалов, включая левую границу первого интервала и правую границу последнего интервала; в этом случае интервалы могут быть неравномерно распределёнными. Все интервалы, кроме последнего (правого), являются полуоткрытыми. Другими словами, если bins:
[1, 2, 3, 4]
то первый интервал —
[2, 3)(включая 1, но не включая 2), а второй[2, 3). Последний интервал, однако,[3, 4], который включает 4.Если bins — строка, это одна из стратегий разбиения, поддерживаемых
numpy.histogram_bin_edges: 'auto', 'fd', 'doane', 'scott', 'stone', 'rice', 'sturges' или 'sqrt'.Значение по умолчанию —
rcParams["hist.bins"](по умолчанию: 10). -
rangetuple or None, optional -
Нижняя и верхняя границы интервалов. Значения вне диапазона игнорируются. Если не указано, range по умолчанию —
(x.min(), x.max()). range не оказывает влияния, если bins — последовательность.Если bins — последовательность или задан range, автоматическое масштабирование основано на указанном диапазоне интервалов, а не на диапазоне x.
Значение по умолчанию —
None -
densitybool, optional -
Если
True, первое значение в кортеже результатов будет представлять количество, нормализованное для образования плотности вероятности, т. е. площадь (или интеграл) под гистограммой будет суммироваться до 1. Это достигается путём деления количества на число наблюдений и ширину интервала, а не на общее количество наблюдений. Если stacked такжеTrue, сумма гистограмм нормализуется до 1.Значение по умолчанию —
False. -
weights(n, ) array-like or None, optional -
Массив весов, такой же формы, как и x. Каждое значение в x вносит свой связанный вес в подсчёт интервалов (вместо 1). Если normed или density
True, веса нормализуются так, чтобы интеграл плотности в диапазоне оставался равным 1.Значение по умолчанию —
None.Этот параметр может использоваться для построения гистограммы данных, которые уже были разгруппированы, например, с помощью
np.histogram(обращаясь к каждому интервалу как к одной точке с весом, равным его количеству).counts, bins = np.histogram(data) plt.hist(bins[:-1], bins, weights=counts)
(или вы можете альтернативно использовать
bar()). -
cumulativebool or -1, optional -
Если
True, вычисляется гистограмма, где каждый интервал содержит количество в данном интервале плюс все интервалы для меньших значений. Последний интервал содержит общее количество точек данных.Если density также
True, гистограмма нормализуется таким образом, что последний интервал равен 1.Если cumulative — число меньше 0 (например, -1), направление накопления меняется. В этом случае, если density также
True, гистограмма нормализуется таким образом, что первый интервал равен 1. -
bottomarray-like, scalar, or None, default: None -
Положение нижней границы каждого интервала, т. е. интервалы строятся от
bottomдоbottom + hist(x, bins)Если скаляр, нижняя граница каждого интервала смещается на ту же величину. Если массив, каждый интервал смещается независимо, и длина bottom должна соответствовать количеству интервалов. Если None, по умолчанию 0. -
histtype{'bar', 'barstacked', 'step', 'stepfilled'}, optional -
Тип гистограммы для построения.
- 'bar' — традиционная гистограмма в виде столбцов. Если заданы несколько наборов данных, столбцы расположены рядом.
- 'barstacked' — гистограмма в виде столбцов, где несколько наборов данных накладываются друг на друга.
- 'step' генерирует график линии, который по умолчанию не заливается.
- 'stepfilled' генерирует график линии, который по умолчанию заливается.
Значение по умолчанию — 'bar'
-
align{'left', 'mid', 'right'}, optional -
Управляет тем, как строится гистограмма.
- 'left': столбцы центрированы на левых границах интервалов.
- 'mid': столбцы центрированы между границами интервалов.
- 'right': столбцы центрированы на правых границах интервалов.
Значение по умолчанию — 'mid'
-
orientation{'horizontal', 'vertical'}, optional -
Если 'horizontal',
barhбудет использоваться для гистограмм в виде столбцов, а параметр bottom будет представлять левые границы. -
rwidthscalar or None, optional -
Относительная ширина столбцов как часть ширины интервала. Если
None, ширина вычисляется автоматически.Игнорируется, если histtype — 'step' или 'stepfilled'.
Значение по умолчанию —
None -
logbool, optional -
Если
True, ось гистограммы будет задана в логарифмическом масштабе. Если log —Trueи x — одномерный массив, пустые интервалы будут отфильтрованы, и будут возвращены только непустые(n, bins, patches).Значение по умолчанию —
False -
colorcolor or array-like of colors or None, optional -
Цвет или последовательность цветов, по одному на каждый набор данных. Значение по умолчанию (
None) использует стандартную последовательность цветов линий.Значение по умолчанию —
None -
labelstr or None, optional -
Строка или последовательность строк, соответствующие нескольким наборам данных. Гистограммы с столбцами дают несколько элементов по каждому набору данных, но только первый получает метку, чтобы команда legend работала как ожидается.
По умолчанию
None -
stackedbool, optional -
Если
True, несколько наборов данных накладываются друг на друга. ЕслиFalseнесколько наборов данных располагаются рядом, если histtype — 'bar', или накладываются друг на друга, если histtype — 'step'.Значение по умолчанию —
False
Возвращаемые значения: -
narray or list of arrays -
Значения интервалов гистограммы. См. density и weights для описания возможной семантики. Если вход x — массив, то это массив длины nbins. Если вход — последовательность массивов
[data1, data2, ...], то это список массивов со значениями гистограмм для каждого массива в том же порядке. Тип данных массива n (или его элементов) всегда будет float, даже если не используются взвешивание или нормализация. -
binsarray -
Границы интервалов. Длина nbins + 1 (nbins левых границ и правая граница последнего интервала). Всегда один массив, даже когда передано несколько наборов данных.
-
patcheslist or list of lists -
Список элементов, используемых для построения гистограммы, или список таких списков, если передано несколько наборов входных данных.
Другие параметры: -
**kwargsPatch properties
См. также
-
hist2d - 2D гистограммы
Примечания
Примечание
Помимо вышеописанных аргументов, эта функция может принимать ключевой аргумент data. Если такой аргумент data задан, следующие аргументы заменяются на data[<arg>]:
- Все аргументы с следующими именами: 'weights', 'x'.
Объекты, переданные как data, должны поддерживать доступ по элементам (
data[<arg>]) и проверку на членство (<arg> in data). -
Примеры использования matplotlib.axes.Axes.hist
<img alt="Демонстрация осей" 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
<img alt="Функция гистограммы (hist) с несколькими наборами данных" src="data:image/png;base64,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<img alt="МРТ с ЭЭГ" 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