matplotlib.axes.Axes.bxp

Axes.bxp(self, 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) [source]

Функция для построения диаграмм размаха.

Постройте диаграмму размаха для каждого столбца x или каждого вектора в последовательности x. Коробки охватывают значения данных от нижнего до верхнего квартиля, с линией на медиане. Усы простираются от коробки, показывая диапазон данных. Точки-выбросы — это те, что находятся за пределами усов.

Параметры:

bxpstatslist of dicts Список словарей, содержащих статистические данные для каждой диаграммы размаха. Требуемые ключи: med: Медиана (скалярное число с плавающей точкой). q1: Первый квартиль (25-й перцентиль) (скалярное число с плавающей точкой). q3: Третий квартиль (75-й перцентиль) (скалярное число с плавающей точкой). whislo: Нижняя граница нижнего уса (скалярное число с плавающей точкой). whishi: Верхняя граница верхнего уса (скалярное число с плавающей точкой). Необязательные ключи: mean: Среднее значение (скалярное число с плавающей точкой). Необходимо, если showmeans=True. fliers: Данные за пределами усов (последовательность чисел с плавающей точкой). Необходимо, если showfliers=True. cilo и cihi: Нижние и верхние доверительные интервалы относительно медианы. Необходимо, если shownotches=True. label: Название набора данных (строка). Если доступно, оно будет использоваться как метка для диаграммы размаха. positionsarray-like, default = [1, 2, ..., n] Устанавливает позиции ящиков. Разметки и пределы автоматически устанавливаются для соответствия позициям. widthsarray-like, default = None Скаляр или вектор, устанавливает ширину каждого ящика. По умолчанию 0.15*(distance between extreme positions), ограничено снизу 0,15 и сверху 0,5. vertbool, default = True Если True (по умолчанию), делает ящики вертикальными. Если False, делает ящики горизонтальными. patch_artistbool, default = False Если False, создаёт ящики с помощью Line2D. Если True, создаёт ящики с помощью Patch. shownotchesbool, default = False Если False (по умолчанию), создаёт прямоугольные ящики. Если True, создаёт ящики с выемками. showmeansbool, default = False Если True, включит отображение средних значений. showcapsbool, default = True Если True, включит отображение концов усов. showboxbool, default = True Если True, включит отображение ящиков. showfliersbool, default = True Если True, включит отображение выбросов. boxpropsdict or None (default) Если задано, установит стиль отображения ящиков. whiskerpropsdict or None (default) Если задано, установит стиль отображения усов. cappropsdict or None (default) Если задано, установит стиль отображения концов усов. flierpropsdict or None (default) Если задано, установит стиль отображения выбросов. medianpropsdict or None (default) Если задано, установит стиль отображения медиан. meanpropsdict or None (default) Если задано, установит стиль отображения средних значений. meanlinebool, default = False Если True (и showmeans True), попытается отобразить среднее значение как линию, охватывающую всю ширину ящика в соответствии с meanprops. Не рекомендуется, если shownotches тоже True. В противном случае среднее значение будет отображаться в виде точек. manage_ticksbool, default = True Если True, положение и метки отметок будут скорректированы для соответствия положениям ящиков. zorderscalar, default = None Порядок отрисовки (zorder) получившейся диаграммы размаха.

Возвращает:

resultdict Словарь, сопоставляющий каждый компонент диаграммы размаха списку созданных объектов Line2D. Этот словарь имеет следующие ключи (для вертикальных диаграмм размаха): boxes: основной блок диаграммы размаха, отображающий квартили и доверительные интервалы медианы, если включено. medians: горизонтальные линии на медиане каждого ящика. whiskers: вертикальные линии, простирающиеся до самых крайних значений данных, не являющихся выбросами. caps: горизонтальные линии на концах усов. fliers: точки, представляющие данные, выходящие за пределы усов (выбросы). means: точки или линии, представляющие средние значения.

Примеры

(Исходный код, png, pdf)

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

(png, pdf)

<img alt="../../_images/bxp_01_00.png" 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ArSQnPP81G+xYKG99U2/Nupkvl4fmwLJQudecTvK7Qm6CsXEupvHvmWJR06d5VIOVwLKuIvjiVNLjWj1qGo7BhKFZ5b+tV2xKqItUlks0lCZbXt/aWJnglnuqSaXOOAimXzYUzQqIx3WBZs0K7jiU4KxRvlLah8JQxxyfHGNes8KeDkZ88jcJHNdL4me6hkFRdIhW/aNtmorv9q1diDkZYiasevkwjxwRvlocz6z/PPJyU8afdsXbb3atue1dwBW+iyLfNRFhusPYcLG4f4Npz0NaZs0Cqc22b+ZcjMRMpNyi7NZlmPr0pWD2BNqQtERHeTGHvkpp/3Jy7pC5dGf8lV61zaeU6BwVSk5HJrFCEEWs92UypxdaTg1dnSI1WAstkxPq4Hy3Z+zp5GCGtJ/eMpZVrIwdLOiTH6loPWNOsp3p1hcwnVUs6B2xpwxOPY5GqqqSNfqo7FunjvmLnkxkxE3hYF225O9oca6HWyfdxD1wfN0eB9MBV2MhBjkUaoEiO9Y4y6EZ4+xdpgCI5Fikxy8CsUDylJu7Zo0wToqtjkVLqiPbsZbCORSoC2ObTEjSfJG+m8M6xCEUA6qxQfNuM1jJ+Kn37F2nPgdbTTR/RhQY10kZ5Ukh83xq+qdwzyVqqY/GsQZM2+ql/VCRpoUWzq6pCLUgb5SMEi7bnoLBUeS9oUTmW2WPV+UhdVfFwW9LSsLiK5enqTDklN6yGjjU+E5fg20g9B4XlHIvz1zj4fto0g3VZsFSv1O0/L6MHayNFYGlNx32FTe4pMZv6CO+uoc+uxIx7fAg6ASDNYBUcDzooRHr91A2FdvRzRvijLwyYnfGzAWeTM1cd6yzm6GOfFU4cq8IqIo5FtLkkk3e/6Jv96uTRD/2mSPT1rc5+Z6vOG/2eq/Leyw5YQe+jIOZVRJsjVMzCEJZy2swVuxI59ZEQPekuV6nRqwEW0ebuyoM7s5fuBpt3vI3CPkQp52NFH/2Ba9vpQczRqwFWfDYX9iEmARahUfgNN1FHn2GwrF7qlq3kvSs6//S/kiJgTStms1IsZ8VMZvQZBmtScRiyYVHilVQBi1YxA1iJNVIFLNKJuIqA5Z5wj97MVGf1N29G068WswhW0LN0YgkkZHUmU471aX2mr+zr+vrz5KvP47/gWBEEYq/OlMSeOpAtsNbH/p5VnPva1OamGikHVq0w12zaZasWXSDCFbMsgfXm+/P6+hu/n/DX+AW/SaOpPqkGVs33+d21RALJKFh/fv1zBpb10g+s4ng9K2AVFlxqVtqeuFfQ+0vA5lQE6yV5fxnOAsFa+MeYwBq9zC08R9wVwCqIv7+02ZxKx3ELgGXbXMRgPb+M1J6XiRcsms0BLBGwvNwuB2AVVguKP3qfQbeWebCEtEChNarNJ5ZFgCU66NZSD9boN1OjeMB6XnfMP6d4/LbuXzEbPZsazVrOB93cgOUz6C6NuakEyweSWMHyr5jNrhQ24qoAFmUq6fMTCzkDy8f9gsGSciUFwCJNJQEWl5MtgGWNap/kXUkBsEhTSXXAcj2bIU6wPAe0OMFyP5kiTrDkNUohWK6nycQJlrz5JxEs97N0AFYWHCtxsOBYEWppR1KuwHLvxwJYMSfvo0+mRjLBmpWkkgUryeQdjhUmP+rk2xwcK2uORZICYMGx0uxYCoMFx4JjwbEAFsACWBgKAVY8jmXN8DzKDSHryRLB8n4DYvNPOFbqwNLemRItSflQR9tr4/0GxBAGWOkDKy7zkTnmuq4EsFKXY8kEK9B8pIK1fCV6jmX8/k8vBX/cSTf6XQnHGr3y0N/j8TgIrPH471eeGmkyG/m8O1cjumP95x9e+m/wx510o/8pAdarX3w09gdr7Nfml1eazEYB787RCGDBseBYGQVrKcsY/dtUHHfp+G5f934DYlUP5Fjpq2PFdZeO1C0RZLB4Z4UfKBO82Brl7i6dIPOROv9cvhLKDZkGS4EdpAALYAEsgAWwABZupgBYcCw4VvbAoqwnx7clAmCpClbwerIPddhBCrBWG758qANYAGs1sMjp0qvFQ0HaAAtgSQLLoe8AC2C5Plmn2qJgWedjfZdncwqAVWfDLJ+PJesuHXHzcTayTvSbNNLGYUdFclxJgTrWMG8n+o1NedylE7KeLG4+AWD5H25rzj+5rhSvY5HM53pYFz+D1OdKlYzeTEEyH+f4aR3H3Z59tRg0/+S6UrxgDWN7MsVQ3eO4JYMl/1l0XFeKFyyS+ZCi/7tSRh8gQDMfIlhcV4oXLFog0TXKEljqdi3AAlgAC2ABLIDlfB/t76baAAtgya5jEfZjASyABbAAFsACWOqCJeXJFMp2raQnUwAst7yfpePeP5BNsCQ9Swdg8TqWe/8AHAtgCcpr28zi9oEpWN/HmcyxlrfNkDYqACy+9+Fc5V1Y5i1msWud3xrfRoW8OZY/WPI3KqTQseLbqJBDx3JuH5jvHyhms2v9vzUNgWQKrHx1LcCKbSjMU9fmO3o4FhwLYAEsgIXBAEMhHAuOBbAAFsDCUIihEI4FxwJYAAtgASyAhRwLORYci1OXjp0ElzlzrKSjzzBYBceup0LOwEo6+sw71my3Zj4dK7noM5+8k5SZ5D256AEWwMKsUKWuzXf0cCw4FsCKpWvDZunZBkti0ABLbJaebbAkRg+wxGbpeXAsKdEDLFkhIXqABbAAFsACWMtSuJIjoWvzHT0cC44FsAAWwELXAiyABbAAFsACWOhagAWwABbAAlgAC2ABLIAFsAAWwMozWO5nM+Spa/MdfaRyP00mT12b7+jhWHAsFXMsiRtHVjiIIKmuzXf0qiTvKxxEkIHkXcnoVXIs0kEEmXEspaJXq9yQ7pAQPcACWBgKle3afEcPx4JjASyABbAwGGAohGPBsQAWwAJYGAoxFMKx4FgAC2ABLIAFsJBjIceCY8GxABbAAlgAC2ABLIAFsAAWwMKsELNCOBYcC2ABLIAFsAAWwAJYAAtgASyABbAAFsDKH1jelZz0nHARbdfmO/oE3kd6TrhIwrHyE33s7yM9J1wkAVZ+os/F+0hbjpWH6AEWwAJYAAsfKLoWYAEsgAWwABY+UE07Yq2NjKnFjhB94mqxDKqF6BNXsXXki36L8vuSfKOjVhHRZ22wTnkjRA+wABbAAlgAS0DtvXbmGiF6CIIgCIIgKJPaZQ3RJluMser9B8FW51eb+ubbpuCF9PL9cVH94FMZfbTaujJEFwe2GufnHxqbYo12ytufb2+6+4IXahVOjUZb9eDTGX2kujRuH05F+/at+b86uxZq1Ni095UfCV9IW2ND1YNPZfTR6rijXey/E+/by/6+kEdfszOCodhdq500FA8+ndFHq0FXa5fXRNMMw2BlsTum7tgBuWsfthUPPp3RR6pb/VzT+luCEd/Xandb5R2xNDl9XRtb8KmMPlr17K09xpF4xEWjp/pgEFvw+RsK2+Xujan994S+rV7Flr4eqx58CqOPVge6HWqvIzzjvukzseSkZk64azfdbcELWRPut0XVg09h9NHqbWOaWwoV/OwaYeez4LXO+1aJcE3wQhGWCOMMPn3RQxAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRCUAfkd7bTDPiB6iCzfo51y0bX5jj5Sze8Ot7vyiK1p1w9lff9Ysw4+GGjF0039pGl19OFA79zedYzG9OyoNdY80QfnF9vVB/MnFM/M7/ts/rm1qe93NesO8m652m8j+pzq5TyLedf2T+52mhfaHWueX2vd6uFtT6+Z/7rdvOl0BvXCfn/WtR3zxeC+UC+bP+J0u1k71te09t7dTsU4NLu22r+9MIaIPqd6Odpp3rVvtxYGg7J1JF6nb74ye+nQOtPgbH/+O2u+YDVN699rbWPX+j19mPxT/4v5982ipn15QPQ51a67ay+Mk159+nrDPh7jamC+urN60xwIjquzrrVeGObf9k60G2aY0jua9v6kbP+51Zg0RPQYCgvWS7Mrr48f9CtX136wevNosWtnL8yu3WVrNVM72qH+vlDrn0xP6Ul71+Y7+pjS11/ZhaY1pwf4DKtay+rqhcEgsGs39Mrkq1f35v/u/9/eufamrWxh2If9YWI44hLnBEhcsXMhQCChhKRFKGqyFakfKu3dSs2Hfqq0//+fOLaBJiSBeoYx9nieV1UvUlz7XTxea80F25jQ2u0+UT092ql3NS30gtDW37WmlV7QOPSHp8/a17WhdS69s5Z7eBb8fPu27hsTWrvdJzxFuHi007TXOA/v2X614Vd2g/u2GA6464sB9/rQHgyaNe8uGBeVfX90aU5o7XaPEEIIIYQQQhsoGhhZpXCsd3XNJ5+wusOD5ZhbAdbNDp/8tmOOSbReB+0r3z9pBUlp5NWKF4uo1mdbjLrXXu3q06wUnvntauNu6NTD/UuF/IdgVgqL/XKjGO6J2f3sR7OpKKYeHlofKuclZ1A83j0+mke1cT997106zrX3flr2b2Zg1e4+udV7Z3x/Mhx2cx+CBVj+YetC3DrdavnD9L7ZBRgZ3Yipc3331EaV/bHjHDZK43DFtetdzMAKdyQdejmtEq9CsACrHKQ079DZC1+z2W20gSWuWvdFvyHeO67fvF6ErRyufH0Qu8Gv4C+fyzOwwh1J70QOwXozBAuwwtJ43ncuo31Y4hBg4qp5155Ow01wO0cj//M6sPy8gvVmCBZghdvaz+vOqBduw2qdAkzsEnAcvnF7truyPX/zdtn/03Emi1IYxHYJrFHFhhC/AGviM/cgpZJXbhV6QVQHR7e3Za/0snlvPzXvC7D6xdubbu5D8AKscfPqeLdw3YGYuGpXa+eFIKqT84Z/5z5NNzTC6YY/l6Yb5mDd3DVyNd2wIgTLYDnDslcrjkhbG4nZQQRYCLAQQgghhBBCCG2qUud0J2c67ZRwn7o6Iofq4D51nYpO3u7ZTvyvatjtPlHtiNytV0lYsts9YL3VHRWOCiVrwdLjHrBe66EYNBPFB0vB0uQesF5HVlT2x/sV8WAlWLrcJ6jDatPE0JaKlbAQlCrF0iahtds9Geu1CmI/+nP/zR2Cec9Y2twD1ksdifH84o8sBEube8AiY5GxzOoyduixcgzWmkmZdeOinXyMClN0bxBYClFaOymjdFBqYJnm3hywVKK0dlJG6UNMCyzj3BsDlkqU1jcMSVuy270pYClFKRjiRLffvr4vraYDloHuTQFr/TB45aTMXlRB9t6clDGoFBro3hSw1k/crfw85hVE7p7NXvNuoPvE1I32he3oGuIo5fVuzQufCNL1ajIPBtGxDGu3+0QVPf5zxXUoDHFKxV50UE+uy5hPysh8HlqmCO12n1bGehCNIEoNuWHw5XzT9aVUBVHoMrQsatjtPmGt2jhS8ubZ1pO4/YKDZp+Hl/S4SNMyrN3uU2ne2+IqyrZXQuLxq21RjW6/qsxBqqN0Pfes3e5TyFj1+TW3RV2qZZsPcSQOWr/wlWyXYbf7VDJWENooRUuF9lL0IsM9qTZDeSUkuUVoW9ynUgoV8vpAnEehPRcDqUvQvXaroRTa4T6N5t0X3mQ48YQv4Xnv19h5L/kr1zH3bLf7VDKW4hBnfpDkwpfKPZvoko4t7lMAqyAuomx7ITdxpzBFmMX9WLa4T6EUHolxdFNILXwFTWUtMFyTm1dU2pGkZ1HDbvcpZSyV7RwP87nnxHckJbzn3Rb3afRYKnk9aE1ODs8OT2TnnqM/pT7EhCdIbXGfQil0LufjIpk5GaUJa6Wyk/CSjh3uzclY4YR1X/TlJqyVGuUsZiyl6fp03ZvSYwWh7Ysfoi8VWrUF3wz2WEpgpes+BbCUtkO2RVH89cdfwe9tydDuSIY24SUdRfcKpTBd9+lkLPmmstQIuPojIKsh1b4qFIPE57FU3Kswkq77FJp3pWFwP+IqJKsv1Yl2B9eD7o7k1wnqQdGtO5uFVov7g28fZ/r5PVAt3B3Yjf79LUH35n6ZIsi2IzGSmrhbcCVHluI924+Kbj+ZjCW3meXr/57pe5hHvkV//ZqcewVLWQHLefCDlODLTPaJH38s9EPIVJBmcKamXJfRnxfdfjJgSS20LIH1M3QRHyyl5t1ssNalBJ0ZKwhtdCap0K4vujq+/iWxNLwE1qMjD1bsxmxRdJ8qbfd1zc0yWGPXHc1Twsh1x4n2WAUxP5NEMfgNwhuCFbgPdLx3HP05lvu4x78+7lg9llwpXIHwV2Oad1eIX4wI4cb7rxRp/PzrTJ91FV315j1i5PH7TLVo5e8xHiOKNP78/vHj0c/34cx7DBqXwPqYabBW3LOjZylhFJMRN/ocoqoWn0al+vm7y1PPWMvtUohIIWZVc5deOuLKJp99UYhRP1dcnjlgKfXh0T07CvrwUbx7dnbQ0pkkEF6XUHWBJdMuzTLWntiLmbGWzvT101XxUwyEVyRUc3osxT481EiMZH7cFc/OpKvoagLrq1wfPrMTN1Mvz1GEiNTinengdwhne1So1IfLhXbOyOjXmUbxG7Ok5rGe9eGP34/l+nA5ROlgqgAABhFJREFU97MzPT4+1qtB8ikOXPdfTWfK+HTD2okifWCpnyla7nY0g/UsMcgbkXZ/UCqVDg5KEzEpRf860HKm/M1jKYKleqbVRVfLY4y2AFYyB2X++VhrUoLmKOk+E2BldR5LqQ/fIEqaz6TlXTqApTljtdyFFgOPSK18hvbFjz6Zt8L9NsFqrXrRcMsCsFaaz6n7bYLlPt2oi/WJ2f27znzGbnRlsJ6Zt8L9dsFypc1n7UbfACzXscl91sFSu9HzApbB7rMPlrtZxOKHNlbZ2TZYabtvAdamoY1XdvIKVryBFWAp/WCcspNXsFa4f1FzrQdLZTAV70wmgKXRvQtYGw+mcgOWTveAtflgKjdg6XSfQbDeeDfDFsFK7CDlN1NkzY