python中的绑定方法与非绑定方法
1.对象的绑定方法
从上图可以看到,同样的talk(),
当实例对象调用该方法的时候,该方法是绑定到调用对象的方法。(实例对象在调用绑定到对象的方法的时候,会将实例对象传递给绑定方法的第一个参数)
当类调用的时候,talk()就是类People中的一个函数(非绑定方法,与下面第3部分分析的用@staticmethod修饰的静态方法类型一样,都是属于类中的函数,即非绑定方法)。(类中的函数,有几个参数传递几个参数)。
如上,当类People调用函数talk()时,没传递参数,发生异常。实例对象p调用方法talk()时,虽然也没有传递参数,但是会自动将实例对象作为实参传递给
talk(self)方法的第一个形参self。
注意:绑定方法都有自动传参的功能。
不是绑定的方法,例如类中的函数,静态方法都是需要有几个参数传递几个参数的。
2.类的绑定方法
在python中,使用@classmethod方法,将类中的方法绑定到类身上。下面看看代码:
In [99]: class People:
...: clsname="clsname"
...: def __init__(self,name):
...: self.name = name
...:
...: @classmethod
...: def talk(cls):
...: print(cls.clsname)
...: pass
...:
...: p = People('xiaohua')
...: print(People.talk())
...: print(People.talk)
...: print(p.talk())
...: print(p.talk)
...:
运行结果
clsname
None
<bound method type.talk of <class '__main__.People'>>
clsname
None
<bound method type.talk of <class '__main__.People'>>
运行结果如下:
使用@classmethod声明是类方法之后,方法就是一个绑定到类上的类方法,可以只用 类名. 调用,也可以使用 实例名. 调用。
使用,这两种方法调用时,都会自动将 类对象 传递到方法的第一形参上。
3.非绑定方法
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAjgAAAFoCAIAAADVcRbLAAAgAElEQVR4nO2dUZaruA5FmW7PwnO9E+gZ5H1U3zwKW8eSbYgge6+sWsTIsjCgg6iEbP/8888GAACQgH///ff1eh3+bggVAAAkoVap1+uFUAEAQBaoqAAAIDVUVAAAkBoqKgAASA0VFQAApIaKCgAAUiMrqrJ7TSJcNVua9la79j/Pqc7NQcvxdeoQAABZ6VVUZVF2tvzU7aUlTqLdM8oSTnXeHnEnIefJCUIFALnp/Y+qnCZURdZGBxvR7hxlnlV+AiPmE6rX3xcAwFXEK6oydCvMMrb8b9UoVrvHmzNO4Vk7n2w34zGEyrpl12x/v7Xu8jn9/IBQAcDlxCuqfUu91sKyFP5L9bbZ7hnFQ+ltl3Bu9fUsq5BKQzOiy3pV1BgA4HJGKyprrYVlafl/tx8W6nbPKDMRCoNSRaVddYc42tsFkF/A9CrLWBRVAADXkrWiqoew2kPeBiK0DAaCcQ70f0tbqLTxqoqqhlt/AHA5WSuq+q3V7vHWHFr09Ti37KPLKqSeUJ1XUVnGCBUAXE78e1QlnnCbrkr1ahp72q1Qo3icl9YmbEb7TJA/IiG0ypIc3V5rUr1KDA0AcDk8meILQHIA4M7wrL+nQ3kEADeHigoAAFJDRQUAAKmhogIAgNRQUQEAQGqoqAAAIDVUVAl43eOLtPqrwJZ9ns8bRuMHgCRQUaXhcUIVtTwbhArgpjy9oirTj6u4DITqZBAqgJvy9IqqZBIqfYuvbm/av2w/HntrleXn99p9ot83ilt8zic9WY95cto742nGDwDJeXpFlYd9xm9qVVNF/MvdsV7V264fB90ypaki/uXuWO+/hwXhBwDuxdMrqjx0xcCqbISZp9jSQhUtwiq6YmBVSMLMU2xpocr2OQ4AmOHpFVVJc+tvQKia7dFKy1NRTTAgVM32aKXlqagA4BlkraiKITCr2q/n7Ft/M0KV+NbfjFAhWgDPIGtFVW4rSILohxfEfbl6efttbFVOXf9xrPts1l24pr3n1t9+Vbc7KgXwGLJWVAAAANu25a2oAAAAtm2jogIAgORQUQEAQGqoqAAAIDVUVAAAkBoqKgAASA0V1eWUyFe+QsZjRL9wdMEXlCa/2lVu8n27RQ8HAXg8VFSfoPhy5dvMaT/GEqFaKF2r0nepJq1cMp8h0CoAB0+vqEqmrPSmBIUqOQtrrIWJu9hClQrxPC00DGDbtudXVCVTeirGXalm43Z+5OJZRt1nIln2M4plpeYi56du36/VLd1guo+8suytVWKs5hAA8PyKKg9llyX9yyWYXkcCcz90z2pcVVE1U3MJzluz4xafT89De1c9/BdNApA8vaLKQ/EJUjG6nEdyodqkDhVbe+rGppknGF08HWyaQqWLKoQKQPL0iqqkufVXItVAd9VC8gjVJlN2Cap4bdDtYkUSWtZPr9cDHRpRL4Bt2/JWVKV3HT3Zfj0lfgurXBL5QqFaoliHBF0W3fqzzPaDen5OZUaorP9FeeIB+GKyVlTltoIkKLsgS5Vny+/4m42L4ymNz0EIoWraH1YtoalVxRAe3f5eaxnvR/QI1cHYqpzq7n6VAoDfZK2oIEqtIvr1bfEIEAyA3GStqAAAALZto6ICAIDkUFEBAEBqqKgAACA1VFQAAJAaKioAAEgNFdUXU27+vTQA+A6oqL6eYj/BoV4FAHA5T6+oCtm2R4k8aggA4HKeXlGVb0q7Jfioof1a3SIQjwZvtounietHjHeH9jz79SNxAsAcT6+ovodSPTxQLzc7br9VrTau0Q9dFTahh7fOc5c4AaDi6RXVV1HkxyKKT6gsPxaeh7f6BSBUrAxUVB+JEwDmeHpFVb7p1t8PpVUh+e09XfZYAtBdFpXKGdwlTgCoyFpRleDHpqPtz6MsuvVnmf1g/cNmYDnJrb9scQJARdaKqiBIcUrwwxSlegnjH/xCdTC2KpK6+0nZ/y5xAkBF1ooKAABg27a8FRUAAMC2bVRUAACQHCoqAABIDRUVAACkhooKAABSQ0UFAACpoaK6jrKV/esjo1883MCIn5ofAEgLFdVFHJKvMxdH87WwPzX7Nz0Pj4hQAcCep1dUJcsTKwaEKpqvP5XfrXERKgBYwtMrqpJOqOq7f837gYf7hNaqYfvLxt0v+ONBqADgzdMrqjRYQlUrVt2l69NjL4yby4fwBuJsbmZ33O6GAMC38fSKKg2WUG29RG+1iwpGB+ActCkwoXEt/UOoACDE0yuq8vf1aaLlkdVloLIJGXsqqm7kIlSECgCiZK2oiiEwq9ovJ5TND2+dCV2LihAqS1E8MYhxESoAWELWiqrcVZAEJfhhh/0qq9FyJcY96IFQl8PbUJzNsbQfsV0A8M1kragAAAC2bctbUQEAAGzbRkUFAADJoaICAIDUUFEBAEBqqKgAACA1VFQAAJAaKqrLKZGvfIWMw5HsvtFUNwpe2/Y6J6RsgzbJEwnAd0BF9QmKT3veZk77besIjNXl0EsLVShNRxO6sE8lD6mCAXg6T6+oSsonVpSgUHnd9iohZy/hJ6pSoWx+r+zfjPZ1t60AuANPr6hKJqEqv1/N9tre67x6UlJzldXRatkvWyn41UrQr+q11t7pZ3mctcPmEACwjqdXVHkoO9XxL5eIXHUrqqaBEKrDKlFDNA26KTtkL4w9y92YX9XbkB8AOI2nV1R5KD5BKkYX1xCyZrKKKkuo6vZovvZUYEuEyjLzFFtaqHRRhVABXMLTK6qS5tZfcVRRootriJ4OTVZU29B9MGF2pVB1l0VF5Yln34h6ASwla0VVjDS9qv16SvDWX/22P0RLYGaEqrnWeQtuM5J+1D467hlC5dTgdztCBbCUrBVVua0gCcouyFLpU/kdf7OxP0Trlt2+sRahw42+g4emtolbak3LZpZvapK2b961iwqnrpy6/usuAHAyWSsqAACAbdvyVlQAAADbtlFRAQBAcqioAAAgNVRUAACQGioqAABIDRUVAACkhorq6ynrvn/2kS8Y5flWU55IAJ4FFdXXUyJCJSxDaTqa0P2PVvosqYIBeApPr6jKHZ5YcReKPZNRlQpl83tl/3tFC3AHnl5Rla8RqvL74Uzld3v5bXNYZTWK9v2q7oPvrOcteR7FFLV3+lkeZ+0QABbx9Irqqyi9Z92WlmyXliB1V+0RT/lrGnTzeMje+aw/EY/uK54N6PEDANM8vaL6KooUqlK115bCSbPvD9F87anAlgiVZeYptrRQ6aIKoQJYytMrqvI1t/42R0XV7aU7Cj+h+2BrK7BJoeoui4rKEw8ATJO1oirBn/OItj+ScpVQNR06b8FtRtKP2kfHPUOonBoMAHNkragKghSntD7sULdY9mXXuFXLhy5NxC21pmUzyzc1Sds379pFhVNXTl3/dRcAWETWigoGKF8v1QDwRLJWVBClUFYCwDOhogIAgNRQUQEAQGqoqAAAIDVUVAAAkBoqKgAASA0VFQAApIaKCgAAUvP0iqrw1SIAgHvz9IqqfI1QFfu3PIoxD9F2AIBP8PSK6qsovQfRWgaeZQCAD/H0iuqrKFJvSiU8xdAhqx0A4BM8vaIq35R2S0uomo1WL087AMC1ZK2oyqKf+bDaH0mJCFV0GQDgQ2StqAqCFKfIW3xlt1zbW34AAD5N1ooKBihICwA8kKwVFUQplEEA8EyoqAAAIDVUVAAAkJo/LRAqAADIwp8/f14VCBUAAGThR6j+/Q1CBQAAWUCoAAAgNQgVAACk5ulCVfhqEQDAvUGoAAAgNU8XKgAAuDkIFQAApObpQlW49QcAcG+yClXh96gAAGDbni9UAABwc7IKFQAAwLZtCBUAACQHoQIAgNQgVAAAkBqECgAAUoNQAQBAahAqAABIDUJ1Fa/qdeoQAx13lLKVEvMx0KXuPuOhH8xJ0w4AJ4NQXcg+UZ6XNKOeW/bXC9USD31XaBXADXm6UJVMT6xIKFSG8ULN8HOFUG1oFcD9QKj+8s5f/rtn0bt5llBZHprt3TidfpqW27YZWf7QuH/7vmt36DXW3nQuYhB+TNAqgFvxdKEKcXbF01S16HI3Tr9xcBubQlKvFR3F8rb9Up2uaDmHboNQAdwKhGpHVKhmKqquk7VCpYszN+IjD7py8gvVYdlZUSFUAA/m6UJVIv+juqai8jTqYIYrKmdINn6h8ojTjFB5Kq02qBTA3cgqVMUQmFXtTT4uVBff+hONLUK3/pIKFSoFcEOeLlQhQvfxZpw7V3naPXf5uhvl2N6uqFh3+Sy9se7jCf/OdhNUCuCeZBWqj3CXRHaXOAEAVoBQ/eW8Wmotd4kTAGARCBUAAKQGoQIAgNQgVAAAkBqECgAAUoNQAQBAahCqacr097feWF9+Oukzfp/6AKH+EphlDwDfCkI1TYkIlbDUz5g4L1kv8TzgoftMDecqAHg6TxeqsrTimaTYkYSerrSWec9jHkJCNTwKANwfhOq3Zd2lVKtq51ajaN+v8tzy8jwSSdxS24wuzdFDtwTFLUrtZ6CCRKsAvpKnC1WIUunTYbm0ZK+0BKm7ao9O5c0H9/mX9aqocZOuWUgghUOECuArQah2FClUpWqvLYWTZt8fQnnZqmDWCtVAUWW1L6zkECqAr+TpQlUi/6MqUqg8vXRH4cd/pyt6n1Cv0sZOunEuqahQKYBvJatQFSOtr2oXg+pl3Ut3fL9tOuxKSN14/a2/ZsXTFJgZodJRAcCX8XShGhu0VGKjBy2GCNW9dJx1pu7e/Yu2e+7yeYb2tO8bLf8HA7/OAcDXkFWoPkKZlrqckOgB4M4gVH8pi8qybIjyCADgDiBUAACQGoQKAABSg1ABAEBqECoAAEgNQgUAAKlBqKYp6z4oePFn8/SXqyx7AIBrQaimKRGhEpZLZGDAg/OJGN1VAADn8HShKpm+GlWCz/qLMuYkJFTDowAAjIJQ/basu5RqVe3cahTt+1X6UXjOr+taz0PajEYx+sBj+gAATuPpQhWi9B5KW+zH93Wd1G/feCTEr1VRAyFU/LMKABKAUO0oUqhK1V5bCifNvj+cLVRWpWX16j51CaECgAt5ulCVlD/zUdOVkGGhGriPpysqVAoAriWrUJVFP/NhtYtB9bLupTu+3zYdhv5X1Kx4mgIzI1TWuAAAF/J0oRobtFRiowcthgjVvXSc4kMQ1t25pgdhXIvQq2Xg1zkAgJPJKlQfoUxL3VoQBgAAhOr/lEVl2Sq6n2gAAPgOECoAAEgNQgUwQin9Fn/fVcZLOn7E7fAQM9Ou+x7Wjg10xnQlmZ8rQajgaRzShPU6dNEvPYrV4olwib0zwiWENtMzk5NDdHu928WCJwxhbB0MXf+3np8rQajggTTPNHHS+t+K5DKsgt0M5ckXJ10OO4NvbsJYSJ4UH/W/9+kvd0IVVfMw0Dv37vNzJQgVPJDzhMpjM1YAvVeNCcNJQjXjcyCkkIpEJb8ZT3c+nUOIjV0oVHnm52IQqmnKtpVFrrpfclrLpz5Y2BzXE8lvg/3Jo9NHfb7NCJU+e525xnnaRzVvID2FOFWoZioMsdaaDZ3054V/uVAlmR/zvDvz0aAI1TQlIlTCMvSEiIUs8TzgQW+vw94vVHp5s09X5xW0Xxj8V8TzNssvgZdcofuNhRPPq3ZyyL/d48EZuSc8y7Lb0l11/fyo8+40rXq6UJWgkJxKsSNZ8qy/MeY9j3kICVVrVfOEOZyHfqFyvtXnfHPcw9qQVmUTqtAo/kQc2h1R//VO6e6I+nCy9nt9GOhjYCD+ZngDvSzL6PwoodrO0iqE6rdl3aVUq2rnVqNo36/y7FrrrmBtbz2dTz91qevHQtyi1H78FaQdiZUUDieSVhfRvflWIOLxnPZNhzpyEeRaobKGnhSqpluPmdjjllm93OxVp+lmSHWEOhLRRbdkmx/R/h8naNXThSpEqfTpsFxasndosZzUb9/o/ep8+J7I7Drph4ybBP+31I9EhOSjmyxmhKqWDaGFWocOPrtxNluaUfkVzkN3M0UvT/BRM630fkGtNUlIlzMk/yzdcX5cIFTnUqRQlaq9thROmn1/CO1Xq4JZK1QDRZXVPl/JTQtVUz8O9v4UL1JYVyA9a7VlNMvMozfqYqHa747DrmlmZ703RSLWwtONWcQmGm80PwqEKkyx5cEy1su6l+4o/DhTudV4aF9VUTnpxjlZUcWjqoXqsEpfY3bfdjWsm5i6WcxaZQXT7TjGQLYdMB4z29v7rwz0zvXE4Ny5euruPj8mJ6jUlleoipHWV7WLQfWy7qU7vt82HTqz82dv/emKxxmPJ5I5rdpfLW5bJ4nMvPVfljY7Wm8nG7vJpZu5RC+xpd0YhuVZh6T7hnZus90Tdr2X90fagFAln58256jU9nyhGht076pusezLrnGrlj1x1pm6e/cv2u65y+cZ2tO+b7T8Hwyi2mZwkKg6sU4KlZWmtSbp3DSQy5qNTgkcE6qZxk3G6el+sPHPmDXJloqELC2D6J61undjs2xOnZ8Gp6nUlleoPkKZlrqcrDhQ7oXWhq2XEfy65VSFgbWeyKNDePwLUglV3UX3EonbuQkhlfLY6/bk83MxCNVfSq/cuSmiPPoCPOeeSEkiAR1WWae0rsas9BFNGe+14qX9e3BebnsSos6b/vC6rvZmB8+hixVno2cXPGN+Lgahgscyed1qGURroCihWmph3wHnUZUaHuiw1pl/t61h5r+kiAbm4THzczEIFdybmTMqdO4Nn6gnneEXJI6T5megCJsf6IzpSjI/3wBCBWfhvCVy6OK/f1WPYrV4Ilxif9JluH8sy9Izk5NDdHvVlaj/bpgzKutg8Jcgd5yfbwChghNpnmnipPW/FcllWAW7Gcp5L2Wgl8et/7UkJE+Kj/rf+/SXO6GKqnkY6J179/n5BhAqOJHzhMpjM1YAvVeNCcNJQjXjcyCkkIpEJb8ZT3c+nUOIjV0oVHnm50tAqKYp21YWubr4s3n6y1Xdjn/Znzw6fdTn24xQ6bPXmWucp31U8wbSU4hThWqmwhBrrdnQSX9e+JcLVZL5Mc+7M7/P9CkQqmlKRKiE5ZLDaMBD9BkQ1Vq/UOnlzT5dnVfQfmHwXxHP2yy/BF5yhe43Fk48r9rJIf92jwdn5J7wLMtuS3fV9fOjzrvHadXThaoEheRUih3JKpU6W6haq5onzOE89AuV860+55vjHtaGtCqbUIVG8Sfi0O6I+q93SndH1IeTtd/rw0AfAwPxN8Mb6GVZRudHCdX2NK1CqH5b1l1Ktap2bjWK9v0q/bgg59d1rechbUajGN16lJEdhpUUDieSVhfRvflWIOLxnPZNhzpyEeRaobKGnhSqpluPmdjjllm93OxVp+lmSHWEOhLRRbdkmx/R/h8P0qqnC1WIUunTYbm0ZO/QYjmp377xHE9+rYoaCKFqCl6EbrKYEapaNoQWah06+OzG2WxpRuVXOA/dzRS9PMFHzbTS+wW11iQhXc6Q/LN0x/lxgVA9kyKFqlTttaVw0uz7w9lCZVVaVi/LeFqomvpxsPeneJHCugLpWasto1lmHr1RFwvVfnccdk0zO+u9KRKxFp5uzCI20Xij+VEgVLeh2PJgGetl3Ut3FH66EjIsVF0n/orKGcaOWqgOq/Q1ZvdtV8O6iambxaxVVjDdjmMMZNsB4zGzvb3/ykDvXE8Mzp2rp+7u82PyIJXa8gpVMdL6qnYxqF7WvXTH99umQ///it5vPQIzI1TdtT32V4vb1kkiM2/9l6XNjtbbycZuculmLtFLbGk3hmF51iHpvqGd22z3hF3v5f2RNiBUyeenzbNUanu+UI0NundVt1j2Zde4VcueOMWHIKy7c00PwrgWoVfLIKptBgeJqhPrpFBZaVprks5NA7ms2eiUwDGhmmncZJye7gcb/4xZk2ypSMjSMojuWat7NzbL5tT5afA4ldryCtVHKNNSt5Y7H1g/aG3YehnBr1tOVRhY64k8OoTHvyCVUNVddC+RuJ2bEFIpj71uTz4/XwJC9ZfSK3cuRnz84YZ4zj2RkkQCOqyyTmldjVnpI5oy3mvFS/v34Lzc9iREnTf94XVd7c0OnkMXK85Gzy54xvx8CQgVnM7kdatlEK2BooRqqYV9B5xHVWp4oMNaZ/7dtoaZ/5IiGpiHx8zPl4BQAQBAahAqAABIDUIFAACpQagAACA1CBUAAKQGoQIAgNQ8XahKpq9GAQBAHIQKAABS83ShAgCAm4NQAQBAap4uVIVbfwAA9yarUBVDYFa1AwDATXi6UAEAwM3JKlQAAADbtiFUAACQHIQKAABSg1ABAEBqECoAAEgNQgUAAKlBqAAAIDU+oXpt22trtBwar+GkcV/V69QhrqQ57sf3oD8Ay/7u7QeDpnHzvBNT1FzVtKyH0PEDfA6HUImz5bwjWLg9ddBXa/m8UWaczI+7cBv9fqJJ0LK/e/uhxXPgRdXLWlWH1I0TrYKP0hMqz4G+nE+dFXcRqjEP5wlVyE/zEn7A/u7th5bugWfZaHv/+dvdL2gVfA4pVJ5zptnYPOitQ1+cut2z2u/Hg2cTnONu9tBOPzrI0Pzoca21emhPPMJJ7U1Po7C/aXtzG+t5qCfEaS/WHro3g3EeugCXYAuVPmGshDW/XL9tBjA8rqC5aZPx10OHjEWcUQMRSTQBhfaX1VfkaKuxaXP39uZy9Pyq7WvLTXYR+6XrEOBkrqqo9LLHv7V2cqzuuK/q1fWvh24ai1zjjLN21e0VGlT4DyWvgwdrNrr2T2r37LKB47DZUnfR89z1BnA+6/5H5Tl59Eml/VtrJ8fqjmv1Ff5DuWPg5A/Nv+ils5Lff2gTRJ6t1wr7u7fXc1Izf05Fz9+oK4BLmPvUn9U4eYLpBOpUhW52qFmyXd2hB4wtD6Ht1ZF0p2h4f+kwRHIPjXW79s1oXHgeeYbbfHE6zyCAc4h/j+q1ex3Yt/uTTtdV074bUigLO4MR8Yg4x+I/rPK0N+ffGrcZg8byL+LUfrrHhrB/QHu91mppTojwUA8qDgbnPAN8Dp5McQKc2AAA60CoViOunQEAIA5CBQAAqUGoAAAgNQgVAACkBqECAIDUIFQAAJAafo/qt9tTP7P3qQ8E6u/ZXE90nrN9/2nt96i23+36ONQerFVNS75HBfeB36MyPF8zyoyT+XEXbqPfTzQJWvZ3bz+0eA68qHpZq+qQunGiVfBR+D0qY9zMQjXm4TyhCvlpXsIP2N+9/dDSPfAsG23vP3+7+wWtgs/B71EFN8E57mYP7fSjgwzNjx7XWquH9sQjnNTe9DQK+5u2N7exnod6Qpz2Yu2hezMY56ELcAn8HlXlysomY/HXQ4eMRZxRAxFJNAGF9pfVV+Roq7Fpc/f25nL0/Krta8tNdhH7pesQ4GT4PareuK/q1fWvh24ai1zjjLN21e0VGlT4DyWvgwdrNrr2T2r37LKB47DZUnfR89z1BnA+/B5Vb1yrr/Afyh0DJ39o/kUvnZX8/kObIPJsvVbY3729npOa+XMqev5GXQFcAr9HZXuej7/pc8DY8hDaXh1Jd4qG95cOQyT30Fi3a9+MxoXnkWe4zRen8wwCOAd+jyoSjIhHxDkW/2GVp705/9a4zRg0ln8Rp/bTPTaE/QPa67VWS3NChId6UHEwOOcZ4HPwZIoT4MQGAFgHQrUace0MAABxECoAAEhNAqFq3rUf7rvKeEnHj7gdHmJm2nXfl3zr7HjGdCWZHwCQ9L5H9ab5P9j6Bpcws+6GXSZUHntnhEsIbaZnJieH6PZ6ORY8YQhj62Do+r/1/ACAxPE9qsOCWA69dX76aOt1CWUoT7446XLYGXxzE8ZC8qT4qP9X9dcTT6iiah4GeufefX4AQPI5ofLYjBVA71VjwnCSUM34HAgppCJRyW/G051P5xBiYxcKVZ75AYAe7v9RvS8SD+fbjFBFywgrJA9RzRtITyFOFaqZCkOstWZDJ/154V8uVKnmBwB6BIVKL28yv3uuoP3CED35Z2yWJ5clV+h+Y+HE86qdHPJv93hwRu4Jz7LstnRXfWR+AMBB7+np3ZNNpyHxVp/z+iJ64EI1m1CFRvEn4tDuiPqvd0p3R7x2NjrX14eBPgYG4m+GN9DLshyYHwBwEPkflf+K0vlWYCUpS8/qVbXDMy7PB7CGnhSqpluPmfVWX2QclsUFTfQYOEmoEs4PAPgY+jDFnhmh0pfVzVVd/DIgtqj7Gqa7maKXJ/iomVZ6v6CK0iFaT3SvjTwh5Z8fAHATFKqmfuwJpXiRwpxXoP4z35nL/I0D6I26WKhev+MRBYEQ10MXvV1jqiNiE403mh8A6BEXqs1uGXjb1bBuYnJesHdzmZU6l6uU5dMaaEY7o8Fb4tF06CkvdAzOnaun7u7zAwA9fELVrJ+WCFXtrXtZ2uxovZ1s7CaXbuYSvTzlhRXDsDzrkHTf0M5ttnvCrvfyq/rbHfdG8wMAPdy/8NsUki14ck5WVM4ya+aie0ACx4RqpnGTcXq6H2z8M2ZNsqUiIUvLILpnre7d2CybU+cHAHos/TDFzDVmqDAKrR2rVOa1RJBKqOouupdI3M5NCKmUx163J58fAOgx9IXffYtTqEQCejksm33rV000ZbzXipf278F5ue1JiDpv+sPrutqbHTyHLlacjZ5d8Iz5AYAe7p+ibxI6A7sy5nflJFRLLew74DyqUsMDHdY68+/WMvNfUkQD8/CY+QGAHgl+jwoAAMAGoQIAgNQgVAAAkBqECgAAUtN7evob57+UAQAAloJQAQBAaiK/R4VQAQDA5TiEav8d2G233Cy5rG/F+r+PAgAAsMMnVFqxxpYBAAAcOP5HtUSoKKoAAGAI34cpVlVUAAAAQeKf+uPWHwAAXMjQx9PFhynqZcseAADAAU+mAACA1CBUAACQGoQKAABSk0CoZn5ZLvRPr+H/kJ30r7UL/mN30vxEd1n9BfCBgc6YriTzAwASKVTiwROeh1B0jfVXcM0AAAPeSURBVN9dui0W0fO/a++McAmhzRz7OtpaSXg5FjxhCGPrYOj6v/X8AICkV1E1zzRx0vrfiuQyrILdDOXJFyddDjuDb27CWEieFB/1/6r+euIJVVTNw0Dv3LvPDwBIPidUHpuxAui9akwYThKqGZ8DIYVUJCr5zXi68+kcQmzsQqHKMz8A0MP9P6r3ReLhfJsRqmgZYYXkIap5A+kpxKlCNVNhiLXWbOikPy/8y4Uq1fwAQI+gUOnlTeZ3zxW0XxiiJ/+MzfLksuQK3W8snHhetZND/u0eD87IPeFZlt2W7qqPzA8AOIj8HtXWWw691ee8vogeuFDNJlShUfyJOLQ7ov7rndLdEa+djc719WGgj4GB+JvhDfSyLAfmBwAcRP5H5b+idL4VWEnK0rN6Ve3wjMvzAayhJ4Wq6dZjZr3VFxmHZXFBEz0GThKqhPMDAD6GPkyxZ0ao9GV1c1UXvwyILeq+hulupujlCT5qppXeL6iidIjWE91rI09I+ecHANwEhaqpH3tCKV6kMOcVqP/Md+Yyf+MAeqMuFqrX73hEQSDE9dBFb9eY6ojYROON5gcAesSFarNbBt52NaybmJwX7N1cZqXO5Spl+bQGmtHOaPCWeDQdesoLHYNz5+qpu/v8AEAPn1A166clQlV7616WNjtabycbu8mlm7lEL095YcUwLM86JN03tHOb7Z6w6738qv52x73R/ABAD4dQHdLEIbFeWVE5y6yZi+4BCRwTqpnGTcbp6X6w8c+YNcmWioQsLYPonrW6d2OzbE6dHwDosfTDFDPXmKHCKLR2rFKZ1xJBKqGqu+heInE7NyGkUh573Z58fgCgx9AXfvctTqESCejlsGz2rV810ZTxXite2r8H5+W2JyHqvOkPr+tqb3bwHLpYcTZ6dsEz5gcAeviEavK61TKI1kBRQrXUwr4DzqMqNTzQYa0z/24tM/8lRTQwD4+ZHwDokeD3qAAAAGwQKgAASA1CBQAAqUGoAAAgNb2np79x/ksZAABgKQgVAACkJvJ7VAgVAABcjkOo9t+B3XbLzZLL+las//soAAAAO3xCpRVrbBkAAMCB439US4SKogoAAIbwfZhiVUUFAAAQJP6pP279AQDAhQx9PF18mKJetuwBAAAc8GQKAABIDUIFAACpQagAACA1CBUAAKQGoQIAgNQgVAAAkBqECgAAUoNQAQBAahAqAABIDUIFAACpQagAACA1CBUAAKQGoQIAgNQgVAAAkBqECgAAUoNQAQBAahAqAABIDUIFAACpQagAACA1CBUAAKQGoQIAgNQgVAAAkBqECgAAUtMUqv8BjX62Hf0nf5MAAAAASUVORK5CYII=)
由上图可以看到,无论是使用 类名. 调用,还是使用 实例名. 调用静态方法,都是需要有几个形参,就传递几个实参的。
总结
1.非绑定方法:用@staticmethod修饰的静态方法(如第三部分所介绍的)、类中的函数(如第一部分所介绍的,通过 类名. 调用的没有被任何修饰器修饰的函数)
2.绑定方法:分为绑定到对象上的方法和绑定到类上的方法。
(1)绑定到对象上的方法:类中的方法,通过 对象名. 调用的方法(如第一部分所介绍的)
(2)绑定到类上的方法:用@classmethod修饰器修饰的方法(如第二部分所介绍的)
如有错误,欢迎大家指出。