matplotlib.axes.Axes.bxp

Axes.bxp(bxpstats, positions=None, widths=None, vert=True, patch_artist=False, shownotches=False, showmeans=False, showcaps=True, showbox=True, showfliers=True, boxprops=None, whiskerprops=None, flierprops=None, medianprops=None, capprops=None, meanprops=None, meanline=False, manage_ticks=True, zorder=None, capwidths=None)[source]

Функция рисования для построения ящиков с усами.

Постройте диаграмму ящиков с усами для каждого столбца x или каждого вектора в последовательности x. Ящик простирается от нижнего до верхнего квартиля данных, с линией в медиане. Усы простираются от ящика, чтобы показать диапазон данных. Точки выбросов - это те, которые находятся за пределами усов.

Параметры:

bxpstatsсписок словарей

Список словарей, содержащих статистику для каждого ящика. Обязательные ключи:

• med: Медиана (скаляр).
• q1, q3: Первый и третий квартили (скаляры).
• whislo, whishi: Нижняя и верхняя позиции усов (скаляры).

Необязательные ключи:

• mean: Среднее значение (скаляр). Необходимо, если showmeans=True.
• fliers: Данные за пределами усов (последовательность). Необходимо, если showfliers=True.
• cilo, cihi: Нижние и верхние доверительные интервалы относительно медианы. Необходимо, если shownotches=True.
• label: Название набора данных (строка). Если доступно, это будет использоваться в качестве метки для подписи ящика.

positionsпоследовательность, по умолчанию: [1, 2, ..., n]

Позиции ящиков. Шкалы и пределы автоматически устанавливаются для соответствия позициям.

widthsчисло или последовательность, по умолчанию: None

Ширина ящиков. По умолчанию clip(0.15*(distance between extreme positions), 0.15, 0.5).

capwidthsчисло или последовательность, по умолчанию: None

Либо скаляр, либо вектор, устанавливает ширину каждой крышки. По умолчанию 0.5*(width of the box), см. widths.

vertbool, по умолчанию: True

Если True (по умолчанию), делает ящики вертикальными. Если False, делает ящики горизонтальными.

patch_artistbool, по умолчанию: False

Если False создает ящики с помощью объекта Line2D. Если True создает ящики с помощью объекта Patch.

shownotches, showmeans, showcaps, showbox, showfliersbool

Отображать ли CI-вырезки, среднее значение (оба по умолчанию False), крышки, ящик и выбросы (все три по умолчанию True).

boxprops, whiskerprops, capprops, flierprops, medianprops, meanpropsdict, необязательно

Свойства художников для ящиков, усов, крышек, выбросов, медиан и средних значений.

meanlinebool, по умолчанию: False

Если True (и showmeans - True), попытается отобразить среднее значение как линию, охватывающую всю ширину ящика в соответствии с meanprops. Не рекомендуется, если shownotches также True. В противном случае средние значения будут показаны как точки.

manage_ticksbool, по умолчанию: True

Если True, местоположения и метки шкал будут скорректированы для соответствия позициям ящика.

zorderчисло, по умолчанию: Line2D.zorder = 2

z-порядок получившейся диаграммы ящиков.

Возвращаемое значение:

dict

Словарь, сопоставляющий каждый компонент диаграммы ящиков со списком созданных экземпляров Line2D. Этот словарь имеет следующие ключи (предполагая вертикальные диаграммы ящиков):

• boxes: основные тела диаграммы ящиков, показывающие квартили и доверительные интервалы медианы, если они включены.
• medians: горизонтальные линии в медиане каждого ящика.
• whiskers: вертикальные линии до последнего невыброшенного значения.
• caps: горизонтальные линии в конце усов.
• fliers: точки, представляющие данные за пределами усов (выбросы).
• means: точки или линии, представляющие средние значения.

Примеры

(Source code, 2x.png, png)

<img alt="" class="plot-directive" src="data:image/png;base64,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

(2x.png, png)

<img alt="" class="plot-directive" src="data:image/png;base64,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