pandas.core.groupby.DataFrameGroupBy.boxplot

DataFrameGroupBy.boxplot(subplots=True, column=None, fontsize=None, rot=0, grid=True, ax=None, figsize=None, layout=None, sharex=False, sharey=True, backend=None, **kwargs)[source]

Построение диаграмм размаха (box plots) из данных DataFrameGroupBy.

Параметры

группированный_данные:Группированная таблица DataFrame

subplots:bool

• False - не будут использоваться подграфики

• True - создать подграфик для каждой группы.

column:Имя столбца или список имён, или вектор

Может быть любым допустимым входом для groupby.

fontsize:int или str

rot:Угол поворота подписей

grid:Установка в True отобразит сетку

ax:Объект осей Matplotlib, по умолчанию None

figsize:Кортеж (ширина, высота) в дюймах

layout:Кортеж (необязательно)

Макет графика: (строки, столбцы).

sharex:bool, по умолчанию False

Будут ли оси x общими для подграфиков.

sharey:bool, по умолчанию True

Будут ли оси y общими для подграфиков.

backend:str, по умолчанию None

Используемый бэкенд вместо указанного в plotting.backend. Например, ‘matplotlib’. Альтернативно, для указания plotting.backend для всей сессии, установите pd.options.plotting.backend.

Добавлен в версии 1.0.0.

**kwargs

Все другие ключевые аргументы для построения графиков, передаваемые в функцию boxplot matplotlib.

Возвращает

Словарь ключ/значение = ключ группы/значение возвращаемое DataFrame.boxplot

или значение возвращаемое DataFrame.boxplot в случае subplots=figures=False

Примеры

Можно создать диаграммы размаха для сгруппированных данных и отобразить их как отдельные подграфики:

>>> import itertools
>>> tuples = [t for t in itertools.product(range(1000), range(4))]
>>> index = pd.MultiIndex.from_tuples(tuples, names=['lvl0', 'lvl1'])
>>> data = np.random.randn(len(index),4)
>>> df = pd.DataFrame(data, columns=list('ABCD'), index=index)
>>> grouped = df.groupby(level='lvl1')
>>> grouped.boxplot(rot=45, fontsize=12, figsize=(8,10))  

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Xw9AOYSVAh2zUyrSE9OmiTxAEOaqbZdrGw0XalrEfEBvJtrgfE2TYYtfH9CFUOu9/q4GeYAgDONMZISFUzyTcDGrQ9e56lfmtVF7/NKh61x5vOFDWBronWRV2n7DoXuaaA5NG4A2seoQniYVz5pDy1q1z2u52PeeoH5xAeiPro2a8u5ODk2d2LBgimdWzAt0qUrRUp+mvqQTpJJuktg7kXIUYuwT/C3ocpCdKuFJv0j0n24BHebK4vVrh351VIdAWx3dyKHlzh+HDqpnl6OaOeVQtax+ykHHsT8ldsmOcko4AXRYXDo2NuUgR3USl6ABXeZqgLycFehUKqDlklZALadHZT2LA33t08lo7AGRT/JJXMJFh06evZXE4WRtVNaz+CspLvc2Z882x+sXK1wzVrLsSnipwUMK+YgWVMaHP//g2ayvpJCizJKsFFL3sOWP483kcpF7lF3/2ZUPu19J0StLxzs0sTIhN5/II4xqhU+fQx/Uen9YMIcmR25U1mNFUfgQc+XQFgAdFElVVkkRzAo0OQWeIFmjNv76k3IY4OuT0gagwzSda1psANpUrW7YCuglHXV5TaUaQNrs0G0Tt9eGR6MXTmplQt4ecBToeXpmZpOWUYXtwkBvGlGK5j7nwklPwl0Emm6w5O9N30SQ6qSUqfU5dDjbwmlBabMQ0OTDJq9tE2zzjihs3brajrxwWrBT6CjQ9LVtnm1exx5jQF04LdgecBRo+to2z/Lq3HM5ZmhlAmgjkZtnm9czoJFDL7i2TXAtx+sBPAPagms5Futy0Gq6Na9tDgM9SffK0T40raabYG2bYg8AQBsYwMudQvraNs+FOADaD6AnqOmoa5tHd6wAaONAz3OHqvksHEAD6BlpetEnGXGxHIAG0PPk0BM49JiL5QA0gB7b5Vj6ajvyXWAAGkBPWNNN4dCvL5YD0ADalciR7wID0ADapsihywGgVxW5Ke4Cc+C5HADaF6CnuNrOzudyAGgvgSbXpbgeGkCvKnJrvGMFQHscuVHb9wAaQMOhATSAXmAEV3LojjcAGkCP6nK4cE/hrRIAGkCPItqJewo/8hRAA+glIzep7uwNQAPo1QAd1ucAQM8A9Cx3qALo4CD2AHoGoNfwqHhrgf7tlRQXcQ1+Af36lRS+aPJXUox6nYOPkZvElX+9kuJRXy6XtLkUcGizDo1Hxc+j5kn2HUCbBdqCu769AHrb9qrT9gKg4dCrKApVsFEUGgd6Fa9bAtAA+rcux9J3ffsD9PcOCEBr/c7yd30DaACNyAFoAI3IAWgADaABNIAG0JgWAA2gMSyIHIAG0IgcgAbQABpAA2gA/eUBQerCWkRuvshZbuHmz2ELDgGRW40wLYgcgMa0IHK26rz4ADYcAiIHQRAEQRAEQRC0hIrbBkFA5FajfV7XR+ogISKHyPUROe7JQ0SHljaAzA6/PeZJc5DCvdD5GjmTK5bgH9fhp/7pva8ll+8EfyiybBPSbGLfNbLuNm6FztvIGVXNa9ZcCQNcZHbf7ORWf4RtHREXvX1aZ4+MZ5FTofM3cgZdJojERyyqtH3TXvSatyI4slZ/+T1UN9q3GMjofbIVdeRQ6LyNnFlt6qx4O9Wsji5a1cGZH/q53YhUZrrFyfA0a/WXi3CjS0a/8IbBXaRHd0LnceRMKoz5cHIKxpuzzu+/5VUcFH0a1kmpZ1Xhtup+ApHprL8DGcPvh0HLT+6EzuPIGdUtrS9BXkWxFFphvfXzUte9QWzFh+a6V9f3H/MSyaMeGdfnvORyHzoTOo8jZ1Qn1uZVGwZvevlYeMuZKPrFs/jj8ZDfGCDcskb5y+Uju2ieU2pewuAkLu6EzufIGVw3+2q9FG1BaP78OM+LW/Ye6g0SRqw+FMX9o9Ks+AevU19lVzsVOl8jZ3Zawq4601YbFZX9h9CvK+6yEkJqd7DCp9Mcs/Oc21700PkaOcPVusipyWReHR5sSxmhPZ3bG+kIrj0ZcuNY6LyNnFEdOG0DdjjPebmlHkZIOwIqGYuEzt/ImdQb/fqWt+RInhNq3jMBGQuEztfIGc0FJ7g6ZfkLXMhkLBQ6TyNnul5fgQqEztlzCoIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCJpF10awOkYcNCJXV6U84QJ8y1Tn1/bBOwTi2zq8R+2JJQiEXVJ32ecSgdDTexUiCPbpxBADPXUMQFuopkYMNFTs7+KBMNindnh0K/RtlZx/wKDt00Y0CIKGwu29q1AUWqeLrPeIghbRYRhzvIvQMu1reUMUdLWlPnsQmrqwaaojoqCtAxzaMu14d++FDa9vKztF7ZnlCIRdElwJPvNtnSWr6g5OAEEQBEEQBEEQBEEQBEGQ9eLQIERuxsiZBfrV/xBR/wJ5gBkOgSNyM0YOQANoAI1pAdCeAP3G+B7TAqBXA3QuADSAXg/QbXUC0AB6NUAXdXcA0AB6NUB3sgDQAHotQIeXKgoANIBejUPvsuAX0OdhpyeCoui704LI6UZuYoM+lvfLpeNve/gMHHoNDh09N+A/MC0Aeg1A39pe7zw6YloA9Dq6HEGAotAToMM4FWkcrh/oGED7AHSYsyRJWB6uHugQC6cPQMds24+wZfHqgUYm6AXQaaJGGBq1ABpAux85eVAjHCSABtBwaAANoJFDA2gAbbTLsUt2PnQ5ALQXQAdhnInMhz40gPYD6AA3yWJaEDkADaAROX+AnuJ6AgC9VFk5YvI8A3qS6wkA9DIjjJo8z4CepBcKoJcZYdTkeQb0JLtVAHqZEUZNnmdAT3I9AYBeZoRRkweHBtBwaOTQNtbqq+8PIYf+v4Uy/XoCS2v11feHwrwUlSjR5fjTZ+jXE1jqM6tf2wC0X5ng6qsPpBx+1eqr7w+hKIRDr8qh0bazEmh/c2hqn8QJh77WVSlPhUdAj2q0WNnlIPaHyH0SJ3Low3vUnlgy9oBmefiO4RGKpCqrpAjcApreHyJ7vDErmFzvVTjugOZ5+I5xh3axD00fgJ6FjzmnrAC6YyOBjtmpd+iT4RuHkUMbWRx9eS5Hsb+Lx8gDSqVyN5k5DLSjXQ764jiFQ7twgX/J+Uc48oBEqdytFA4DLQ9qWlzrQ9O7HFPk0A5c4B9u7131LApfvliBCfVDMP1XFjykkI8l35kgRZklWSmkW6+kkJn6kUn9IdKyqqoy1Z+6sus/u/Jh7yspFNFhGPPNTA5twSU2Cb/2A1x54pZD0zPgUZdiLJKsTa4tb0fn0EPjRqbLLZz0HFqqeZXe7RSSY+/OTuFhrEPH7JSJjNDlsGEDt1ZA197l0OTYO+HQ2SlqzywfeUDk7apRFZlhoJ9pk2NA03cKyUmLEzuFZ8mquhu99U3drvrZ90uXBFr10mcEutj8UPf8WWj2zIg7hWSHdmencD6fGVWRGQa64jKVvJoP6A3/U5tFvvcESYszO4UzljZjKjLjDj3UAfM79N///Zvi0MGij9P9vMr865fw7TEGYyqylebQf/3nLxKP5OvCKEnL51XmX5cZ34Aek8Ca7nIoo6odA3rhDv7nVQYO/aSJl+Xw35I3Eu2UUc1/LQcNaDseovb6O3gGdMX7amz4oORxtHbBYlfb0YC24xE9APqTSp709pjw0mge9/Up0bA8z1nz5RlhH9B2PERtRUBPY4+cK3vkWl/75yHotwtGN9DsAnr44nWuQp/XOl98mslbF9B0e1SjM1WRMW7WJsaWNo44NDn200zeOh2a1k1lXDWBOVsGaKPTYtah+0yp+af5kSlpOzS5Fb7CHJpG046XvUOXfAegv6kwjNOKen+ypZFzFOjBJo6Cs5JxcTRrEysEeooBALRdmSCABtDWZYJjNpsANIB2JYde87QgcgAaQCNyABpAA2hMC4AG0JgWRA5AA2hEDkADaAANoAE0gMa0AOi1A31tBKtjTAuAXgnQdX5tH7zDtADodQB9Gz5yiWkB0CvKoU8M0wKgVwR0U2NaAPR6gG75AdMCoP/H3hmwt4ljURTYeimWwRDDEBOHGOPUzSTZ7v//dYtpOtN0OzHoAUZwbr+m7dcZVdx3uDzJjjUZoBcqf/tdk4MVbj/dys5WEA8wxBT6OJIC5waRU/pZQs6Q0FNJ6CTzHykLQE8F6ChPd5QFoCcD9MY7nCpFlAWgJwG0+vUbsCkLQBu+y0FZABqgKQvOATRA4xxAAzRAAzRAAzRlAWiApiw4B9AAjXMADdAADdAADdCUBaABmrLgHEADNM4BNEADNGUBaICmLDgH0ACNcwAN0AAN0AAN0JQFoAGasuAcQAM0zgE0QAN0F9ptMi+gLAA9FaBdlfsADdCTAdqxrBygAXpKPTRAAzRAUxaABmiAxrlBgeZgBY6kMP5IChKahKbloCwADdAAjXP9KzkeM/94TCgLQE8C6AVHUgD0xFoOygLQAE1ZcA6gARrnANpgoJPPP+vu0927PydmOJe0uwaAni7QyaePlRjgnJO0vAYDgP4wZ5LhU0JnCm2zshOgP7//Z+5v799P4rMJUfD+Ii5fw/iBTrqNmSFSwhJnZUdAv7tSx23vQyLtWcRdz/tpXr6G8QP9Yc7c6dzywpTQmELrrOwDaKs90MPfyZfKNQmgP8iZ9jEjTwmNKbTOypEA3fZ5L7+T5we05XYcM+1TwpICbZkD9OdOBxhgCjMAuvOUAGiAvi7QHZsK0AAN0AAN0AAN0AAN0AAN0AAN0AAN0AAN0AAN0AAN0AAN0AAN0AAN0AAN0AAN0AAN0AAN0AYA7ezWttpHAA3Q0wC6VOtia78ANEBPA+iHtKy+2CVAA/QkgA5C6/wBd+6ogb7/+uXfP+uPn//w5es9QAP0D6n9+av9MGqgv/zrQ30ZKdAf3oc6N+I1gP7lIi5ew7WBjg811i/TTujWaHUC9IX7sP2NeA2g24bJqID+7Ukht1+//fGP+vb1VnguR5NjPz78b5oM8O0CWt96OWPlQ+eaWTcC71tOYfwth/h53/mTt/1D4joJPYJea/iuZxSLwqePFoXi533nT9757HK0bWD7996Abbuk7227EST0bHc55pfQvLDCtt2kgLbOL32/8NI3QE8FaEdUFoAG6JEBbQE0QAM0QAM0QAM0QAM0QAM0QAM0QAM0QAM0QAM0QAM0QAM0QAM0QAM0QAM0QAM0QAM0QAM0QAM0QAM0QAM0QP+ke/nJ8eJvrZNO4d7Qw+vvpWd933PW9/9N6E56UrfcVPEU7toeN+71HgUjvXDxPTV6oJ3o7pdreFely47ITRVP4cIAvxnBGyAKWl/45WnLL1x8T40/oR2n9ZPyveSmiqfQeoBOgO76wttft9w5q+09NX6gLzSGDUyVDiCfQusBOgHaGXzaPYwwRBQYBrQ1V6BxDqABGqBlOubK21IWgJ4K0Hn2CtAAPR2gHSsBaICeUg8N0AAN0JQFoAEaoHGuF5W7H/oF6N8erKBxjEG/AwwxhU6OpMC5gbT1foiEJqFpOSgLQAM0QOPcINodl97qWFAWgJ4G0PVqxgsoC0BPpeWgLAAN0JQF5wAaoHEOoAEaoAEaoAGasgA0QFMWnANogMY5gAZogAZogAZoygLQAE1ZcA6gARrnABqgAZqyADRAUxacA2iAxjmABmicA2iABmjKAtAATVlwDqABGucAGqABuguV+yxV+Y6yAPQ0gL5RL4WbpU+UBaAnAXRyPjmxtPeUBaAn1EP7K8oC0NMB+jE+UBaAng7QYVpSFoA2F+j3R1I4W899+wsOVuBICvOPpHDjB3KGhJ5My3GyV5QFoCcD9C7NHcoC0FMB+lGp4nQ67SgLQE8C6MLjSAqAnlQPTVkAGqApC84BNEDjHEADNEADNEADNGUBaICmLDgH0ACNcwAN0AA9OqCjRa0///Pn998sIoAGaIOBXni/aAHQAG1+Qi8OCxIaoK8O9A8c/+oYdHG0XGuaZQFoo4DurGEAaIAeU0L/1THoJbSzDFSwdEQ9i/5DImo6wAiBrpxLdZ17u/pV6qWrCKA7DFgnjFWq4lCrLvKHxKLpAJ0CHXVyH+Z2/t/qp9YI9QA75fm+76ldr8uXmQG9jO3VamXHS0lC6z8koqYDdAr0ouv7sPUI8ihovOc6M6D9+KYa4Cb2RXMUd+Gu1UPOeOLb6KMBsjCsGoYwzLQTOvXrKfipzhQa3xIzA9oO6gECe05AdxMFqecHvpfqR0Ec1lMIY8nT8eKeK0DPBGjJavgs5R2raR89pT3C+V5wz3fG6Jybd8shJeM6QItWw3UUeA/VdT94+lGwerslVgDdHU3VonCz2mguCt/IqJeVoWMW0KLVcN0w2HXLYcfaFx2pegQV9ZolZgEtzRlHHFTW0mCuH3gAAAXkSURBVK4z3l6aBbT40VQl9FqtJQl93oeOJfvQjbLk2kBvfDsNisFyprrJq7JIGoZgVfO4WZsFtB3U6aa/eJD30OJ+sVGWXBvo10Ph5vFpqJyRL+n8bT3C1jcMaFVHgdIGWr7LIfa+UZaMoeWI1KpxznSwSSEDOtjUUWdaQosDNtjUDYPsuoUr+iZZMo4jKTbmAL306i7cM62H9urVsKcdsJ2sHeaQ0E5UHmyDWo6lVz96jQM6q+/DTNs5+XJavkVlQg9dPQU9222xKBTuusl76Lhe7ceG9dDijkEOtHjDsxrgXP3x7XK8O5KiPBWh/bbNcflghcCLqx+Bez3F/rOvqp9xr/9K50dSPHtxmqax96w7o+f4UH09xNoDVCOElXOhYITj2o7t9XHkR1JUWjf9ON0OnnvShI6l2wVXSWhH3CnJtysDv3bOD3qN+FEsCp9V05ZjkFc1rF63C67UQ0s7Jfl2pXpbAGk7Z0YPfb7zgqYJPcyrGlav2wXXAVq8PyS3XvzmJBN2OYp8WRxzz22aM4O8qmH1ul1gKNDyh6M4oU3Yh17kKlb5qfFa/foJHWzqF89Ne2FFvOHZaIvhUg99HsHXds6YVwotp/G23fV7aEPfnNTB2wyl74JZ2nUbr++cMT108wnJY0IM9DBT6HyXYwT7Q2LnRrsPLSilOCbEZRlmCh7OaQ4ws+9Y6WSAWX5P4ZSdA2iABmjKAtAATVlwDqABGucAGqABGqABGqApC0ADNGXBOYAGaJwDaIAGaIAGaICmLAAN0JQF5wAaoHEOoAEaoAEaoAGasswJ6NEe5gHQAK3

Опция subplots=False отображает ящики с усами на одном рисунке.

>>> grouped.boxplot(subplots=False, rot=45, fontsize=12)