python中列表、元组、字典内部功能介绍
2016-04-20 10:10
609 查看
一、列表(list)
常用功能的介绍:
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA8AAAAElCAIAAACkohzUAAAgAElEQVR4nO2dyYGEupJF8QdXsKBtwBGc6B1W9KYceMvvx/fg9SInNIeEZs7ZVaVSiLgh6SYIsfznP//5n//9vyWN7Tg2z4d/f56Pn0f2gGzH37mvGSusTHRAvPmWkXU/HZFd97OLpB5degAAgEH5999///nnnyXaQK/7+feavLfDO4djoDUyB2R8DyUKiDjfcrLu59+fGd7t+Osip8eXHgAAYFBSDfTLRfx5ncTLf/wFyz2G3AHZDou9G4mIgAjyrQzfA3/pIeTDSw8AADAyyQYaAAAAAOCJYKABAAAAACLAQAMAAAAARICBBgAAAACIAAMNAAAAABABBhoAAAAAIAIMdCPUHdz03dGMvdPYsgwAAACgEzDQjVj3U2yKY8oCAAAAQFkw0I3AQAMAAACMiWGgt0P0rre8xR4IBhoAAABgTCxXoNf9lCy5zVvscWCgAQAAAMbEsYRjO0SuN2+xR4GBBgAAABgT9xpoPHRRMNAAAAAAY+J9iBAPXQ4MNAAAAMCYhHbheLiHNndr1kh+RBIDDQAAADAmgm3sOvPQhqe1e1hhsWZgoAEAAADGRLYPdC8e+vWCPsUKr/tpmGNhsaZgoAEAAADGRPwilXU/JQY0bzHLtwzvbVpjYbG2YKABAAAAxmSsK9Cu6iTOWFisFhhoAAAAgDEZbw30txrJAmdhMSc8RAgAAAAAKqPtwvFxxJdqbJeWhcUagoEGAAAAGJOh9oHejj9zcbPpjIXF2oKBBgAAABiTgd5EaH02cDGcsbBYazDQAAAAAGPiMNDduecFA122PQAAAAAgw2Kg1/2U+N28xSS8nui7VvZ9xu/YlmXd9y2iWGMw0AAAAABjYhjo7RBdp81bTI66rcbLVn7+dzmSsFhDMNAAAAAAYyJ+kQrkBQMNAAAAMCYY6EaYO0xfPbK2f7V1UTcAAAAAtAADDQAAAAAQAQYaAAAAACACDDQAAAAAQAQYaAAAAACACDDQAAAAAAARYKABAAAAACLAQAMAAAAARICBTqfc+02yv8ARAAAAAHKBgU6lsMnFQwMAAAD0yb///vvf//53dAO97kfdN/VtR/l3A9Y4BgAAAADEYhho4ZXPvMWS+b7y2mE13y/MztyGWtZ2O7I3HQAAAABuYrkCve6nxB/mLXaH7ahqoGvaWq5CAwAAAPSGYwmH0LjlLZaKx0Dnp8IPgitchAYAAADoDPca6HE8dE0DXdk/NzggAAAAAHjxPkQ4iIeuaKAb2Nmq19cBAAAAIERoF44RPLTXYmbdrHndz/orKljFAQAAANATgm3s+vPQ3703Xs8HWgz0++FBzw4d1zo+BS7fsjvW0NVg5bDuaoTFhAcFAAAAgJrI9oHuyENvh2o430bYVWFwzcW3wK9V2+Fz3a7aXg1RrLDtgrWwmPxjAAAAAKiK+EUq635KXFzeYgZWB+uztUIDve3nrzm6Rxcdad1P8zPT+wqLyT8FAAAAgLoMdQXaYSVvG2jxEono1RTC9cveYizhAAAAAOiJodZAO6xkhivQwgbJLgZfl1d7FjgLi/EQIQAAAEBXDLULR3MD7S/9ccSXAjbzKyx2/YwL0AAAAADdMNQ+0O0NtPtY9icPDWcsLJbcPgAAAAAozFBvIiy1BjqmZfby1mcDF8MZC4tpn+CfAQAAADrCYaA7dM/L4nDQ7l3nlgIG2u52Sxlo1j8DAAAA9IbFQAstZd5iUnQLuh1/x2EuKpYfPqV9lh8E5mYe37elHNuyrPu+RRT7HQf7DAAAANAZhoHeDpFny1ssDu1FhLaDGO/6Mxy2tgeG24K7mmCWVat8ff75n/nql1Ax7DMAAABAl4hfpAIqqa+CkVLkpwcAAAAA3AYDnc66n4We78M9AwAAAHQLBhoAAAAAIAIMNAAAAABABBhoAAAAAIAIMNAAAAAAABFgoAEAAAAAIsBAAwAAAABEgIEGAAAAAIjAbaDNl/kpmx6/353HdsWRNIkbYvUA0kNNyDfIAokEWZgwkbwG2vOakK+9LvQqkVlpEjfE6gGkh5qQb5AFEgmyMGMipRroZXlb+5Hye90PSWuFxZJpErchxCr3bsdOQHqoycPz7f5IPv2IJIREIpGyMFsi3THQy3b0kd5B3lfxQ0EUFsvRnPpx61csZbFQp23MBdIPxzs9x7yX/NB8uzmSP2lEEkIiVUikoYcaIZMl0g0D3T6749gOUTcQFrvVjpkyKCPrfg7Qyhsg/YAMPKs9O98yjOTTj0hCSKQqiTTwUCNkukRKNNDFXWYBejDQTeI2jFhTT1dIX4113+edgqSQb70ZaGFa9pa9JNKgidQbUybSrSUcY9GDgQYfUxtoqMZ2THwNB6T05nuEaUn29sagiQQVwEAnFoP8YKAhA9sx801QkNKZ7xGmJdnbHWMmEtQAA51YDPKDgYa7vJYR5p1gphsKn0FPvkeYliWyF+5SMZGCQw0Z0hfjGWjjBS/OZPo+Qfsq5OoGwmKQhqqXN7KhUUYsvfkSIGtRYTFIRCa9v9Sndx6bVlQvaBHTeVxRIoWfoBe3zd1E8i0jwpE8YmuE+yOSMC1Fxa7n9/n/5YvkUi5qJ5KkopjxzV/l+6NzX5U9Rn7fIJHkjGWgX4Ir+q77aVN8O9SC71SxTs/hYpCG/nN53U9fcH2jjFh647+vb1r9VrgYJCKT3rieYt2xczv+/o5dSQ5Ht/+Wdk8B8jHk+rEzL6RtI9/KIhzJ9TQM7BCbZUT6FRc4E0mxb6t+2wv0s2Xy6NRPpMhi4QwJt015O9+6n3/neV4yiUQSM5SBtk7CtkHLmgTmP4XFIA19KFpCN6A8w4dY+nU/zfrNfWyExSANmfR2v2Hvp3ZXbUsk/wQjTiT1U6+BFrSNfCtKqQE/x4h0OUxWA73t56+gpcNBAg0SKbZYKEMkbVP/VGpkHWUUQxloO0ZCeS5KX89IWAzScE8wzuhG913LWOKoQ39wWVgMUhBK7+xpuqzWKUP8bQne74SvQIfbRr4VRDqSO1T2jPg5RqTgJ9HFXr9FmaSy0zyRMhhoUdsw0LkY1UBfVynpv70deaz/W1gM0kiIo6zv+qT/1CJYyiUsBvHIpHeX0uexQgY6lEjX5tw10ORbQe4N+L6rt3lGpG+h3Es4IC+tEymDgZa1DQOdi9EM9Ges0m5HYKC7I7uBlkhvlg5YFmExiKFzAx2XSLkMtHps8i0jEQO+kxTfE5VIGOj+aZVIUcXCBjrcNgx0LoYy0PaF+hjoLslroIXSu74ZboqwGAjo2UAnJFJOA622g3zLws0Lhx4yjkgY6P5pkkixxdKuQPtKYaDTGchAu9bPsga6SzzP07heRersu2Lpl3U/JKZKWAySEErvjLZtwspjoOWJpH3ppoEm30pyb8AP1XxzRAp/El0Mj1OIBokUX8yfIbK2YaBzMaOBtieRecVAWKwJ7wtUI8+uzgcGnU9OZTHQlt0OzH8LizXhOdLbPbDZLeMMtJFFl4O2M9DkW0GkI7lD5vW6n4VRcSYD7UvLyGKdehwSKSGR4ouFMkTStr4N9EiJNJCBtjx8rDyYc724pY9v2/F3HOaKNWGx+nxPrDMJIrH/aElwKnLpX50v+INKWKwBj5J+1fe2sxqThHXG19nBTBvRGKKUuLuEg3wri3QkN9My8bdTfCL50jKuWAcexwKJlPojPLJYMEPCbevaQA+VSCMZ6GXR18i/Gvj5n5a9+vM6m/0V8sJi9enkWvhdbIr5SihihCoypbdWZh5VWKwRj5F+WS7jpbWU3j2Nf9qqvVZpvyMbTiStXbbDRbSNfCuPdCT3J5xe059aqbuYdzIyDuyZY5zF+k6iZSGRfDV5Eik81LjLey55O6r6fXTu66XcsakfyYJVhmESaTQD/SQ23rPwVJAeakK+QRZIJMjCKInkNdA9/9adnlEyCLKD9FAT8g2yQCJBFsZJJLeBvovj7oXuyXdZsSbBbHYK1XcCmUCsvCB98TOFC+Rb8TN9BiRS8TN9BiSS6BTKGWgAAAAAgAnBQAMAAAAARICBBgAAAACIAAMNAAAAABABBhoAAAAAIAIMNAAAAABABE820E1euT7Se957ArF6ABXADxnSA6gA8KJsWj7YQDd55fpQ73nvCMTqAVQAP2RID6ACwIvCaZndQK/7MVD/afLK9X7e845YfR60Z1AB/JAhPYAK4GfbnyJUwbTMZ6C/73NxNPT9S6CzGzxN3hnZ/kWVIbH65KFidcYTVVBeVkU6BHhihvQHKjwcp+P6jGbPUapcWma+Au15DWOPBvrZY0z1d2be49li9cLzVFj38zpsrfs5VLepzvMypEdQ4fF4Hde6n4/RqmRa1jPQvdGkqV3Fp6vG+EGsHniiCsZEM+Jd6nXf61y3eGKG9Acq3CdvlxHWVq2fljPQFU9BROm0fK6BBsQCCLAdnd01S2E7hj8FgJrk7TLC2ir201IG+mlDDQb6uSAWgB9t/caYbMfwpwBQk7xdRlhbzX5ayEA/bqipaaDX/cSvdQQGGsDP+Ab6tRJy6FMAqEneLiOsrUQ/9TgusYG279RlvdD8xKHmroG+Pp5+bDZP9t2Hz/nc57WOT4HLt54lSEnCYi2LJpink0nLQRLhfqOXenWUp91DK4Ka21esSvi7gmh8e//j3Fdlh5xfOU1Vo4WG6s5zoKtmRNmf5TOqWuSQdWdII5MKMV0mWwcUFYvxSKJU+xloW9XKMZV/fiu/HjJyqJmpL9wx0NuhxvEd8OTfPN8Cv6cmR3xip09kYhm/IR0C6OXYmiArejjtKlhKncdx8oszI+Er0NIuEx7flEl/3c+/8zwv5X5Vvgqak6ajnVMs4+4Raze1XJmQdWdIo4QKgS5TpgNKikV5JJ/jen227ad/FrdW4T7X8CnM1hfSDbT1+qVvVYDQQG/7+ZNAt32Qhkwse8cwv2tR5Yl3b0ohE8tZChUyEjLQ0i7zKesb30yX/C2ojJ3WX6uehmKgy2CJq22Oi54oIYYiKvi7TKEOKDfQQo8UMtDm94wzy2yg5+sLqQbaEcPbBnrgUPaLUCzhcOIePpAuB45BSFPH7dEwSxkJBDRqBg6Nb1ID7f42BroiVk3MWMu6MyRSRoWULnO7A0oNtDhxggbaPJr+77wGesK+kGqgHeec4Qr0mHHsGplYbu3UDjNyug+Ab0wPioCBzow/oNIu8/ufr+MkGGh17ScGuiqia5+y7gzJFFFB3GVydsCoJRwSwks47BG5/D+rgZ6xL2CgHwAGeiC0IfnPPkBjoOvQqYG2PN/DFej6WNyGHmlZd4YbFFAh2GVKdMBHGOjZ+gIG+gFgoAdCFl4MdB16NND2524w0PXRdx+InBEhCwVUEBjB/B3wEQZ6tr7Q2xro2eLbBRFroJ3d4lfQ4yl6e4/niMg8sHAJG9xEsAZa0GV+dd030K6nDTDQtdkOwXRFjyxMERV8XaZYB+zBQBddAz1jX0jehcMaDPtPs8s3RjTQ7xsPIwsvFEv4YJrzgcEOtiCeQCzXKLReH732bP/QwcnPoMKL0Jgf8yxnKwNtHJd+mgfjlnSMqVC7cxtQwaGCr8sU64CSYlkNtMAau+6kSff90E5hvr5wYx9oXYLt+DsOc2mQUn5AA/29QdRf0yIQimX0DcdoYZrvde9hA+I5xHL8trGNY9odhPPs4Tf+JCosi+SiibTLZFvCYT6I/433sVnvAukVt5dljgy5XvxUbJxtBW6oOzcAFZwq+LpMsQ4oKJbXQGtBsl1+0Ye3dT/P43DmTfAUZusLN99EeE3YY3v5Mi0W+gols5nWteVd9WnvlfVhEIi1LLpg7rNWZesnOnOIJZNBKXRsHd0kG12FqEHJr5Woql8V575e6vuIqn7J1vc+/wv16S7SYxk+Q5xLB+y2RTqq1gYVHPi7TKEO6CwWMxyFHdeyLJ83dPt+ctiOfe7rsrrfEC4605n6wt1XeT8E0UIr6IMHi9WPgX6yCiBi6AxxX93r416cGFQAeJHQFzDQAoYeY57Go8XqxkA/WgUQMHqGmDfyl2Xp5ikEKagA8CKpLygG2rdNX+ML7t4tBH9t27OfQvWtV5qd6QSn8Gix9DttqFD8TAeHDLl7pma98aeFCnfPNIcK0APj9QWuQAMAAAAARICBBgAAAACIAAMNAAAAABABBhoAAAAAIAIMNAAAAABABBhoAAAAAIAIkg104qvDoWOaaEoiaaDCfKDpfKDpfKDpfJQNb6qBTn11OPRLE01JJA1UmA80nQ80nQ80nY/C4b2zhCPl1eHQN000JZE0nqPCdjS48NLooE/RtALrfkhOSlgsGTStw7qf1U4YTeejYHhvrYEe/T2gsbx/zEx9r6WJpk9LpCDzq/C9MFCzNzU56If5NVUpMlp+X1XmPythsRzNQdPCB6t9eRZN+6ktF+XCe8NAP8/19JkcOXna4NEnj1FhOxr0piYHfY6mX8qNlsL37hZ/VTWa1jtqrXNG055qy0PJ8CYa6OJjU13Wfc8oeN7aqtFE01ESqZqmj1LhIQb6UZpWoAcDjab1qGWg0bQa08ynbGO3LLkXRTZZYglFQdMSPMRAQ156MNBQj5pXoKEK08ynGOhlWbYj54yatzboATQtAgYaEsBAPwsM9GzMM59ioF+LdoRyBh8HjqoNhgBNS4GBnppSmydgoNuRQ1P75ijui5IY6LLk7afP8kgjGujvI9afB+odW2UHnuJVPr5iFJU8DiyvLVTl+6NzX5VnyX/fmCT12hNKpEhNIZavl1UibctvQwrfZPvnLSo/KKQg3jxBrKnSUd/d1FaxsBhEE9JUOmd9PlWqCO6Ng4EuQbifftQyRkt3Bnh7/Yzz6WgGet1PJd7bYR8sreUcOskvSUl6cri2cNsUI7fu5995fn7VMSvkQZpI3w9xWPnZjr+/Y1c6la7L8ukNSvzX/bQoYvz39U1NTtlB4Ta+0VKs6bdjqt80KhYWg3u4NRXPWdYqHOKHDgoZ8IbXNVo6p8M8HmkYBjPQlsjbBHO6IOcPrXoGWtI29U+lRgaTLAgTyV0aMmD9WasH2+pubYP4up+mSuYWRqKDwn08HUqsqXAkjxvwIRm/gRbNWRjozgga6KjREgPdMVZxTDUc+vju/FUz0KK2YaALI0yk8CdwB2tcZb5H/gtIX1t546AQQ/RQZSjjuSht3G4MF4P7YKDnI3wFOma0xED3jGja9C6Rc5jXWgZa1jYMdGm4At0DsaOzumjdsYbDt5oy5aCQiGyo8mnqUEX/t7AY3AcDPR8Y6BsMZqAN7Vy3/Zx0YKDDbcNAl0eQSErZSTp8V0hHZ8uzRx5JtGfJUg8KN/EPVRJNMdC9gYGeDwz0DYYz0PqTnBkGzh6uQPtKYaBLIEikLxN1+K4Qjc7WVXgiSewPkmGgKxG4pSPQFAPdGxjo+cBA32AwA20+EmTB/5SotdJqBlrWNgx0aUSJdC09S4fvCsHo7Nogw/yqfXdZuyvDQFfAOVSJNWUNdG9goOcDA32DwQy0uQTCqoNDoHU/XXvMKpI7t3SXJEeoNknb+jbQbw3G7gGyRHojz5B6zKBCZgNt2YTD8u+ODfQMmv64b6Dtvsq8fC0s1oSHaBpnoG3PnwxkoJ+i6VLIQE80nw5ooL+RVzyQbR20ppF30eS17zv1F/XkYG3htnVtoL9rH7oa02KRJtKvhChDqjGFCpLR+XWixgXHj1Trvl+fHBA4sH4N9BSa/nAPVXJNDbO9HX/HYS6fFharz2M0lc9Zulte9/M8Dk+Y2s95Ko/RdClkoCeaT4cy0M777nbR1FWu3sBci5oOSl8uG6rQX1uobb+Pzn29lDs29SP32ZSnk6s7qUQm0ueTgKbVGVoF66N+139qT5fpH3z+p963CfTSmIO2YWhNl2URj5YyTY2ix/Zyx5YuKCxWn/k1jZ2zFPHPfV1Wy/ufrf25l9F3fk3jRssCHqk6KZqOZKD97qYXFZ5B3BrizpgmkYZWAayg6Xyg6Xyg6XwkaDqSgbbc+VuW5f3DYSDbMz6jDx5zJNLoKoAJms4Hms4Hms5HkqblDLTjBox+iX+XFbPfVwjdJ5iYUuEVHbhqxHtOJFQofqbVQdPiZ1odNC1+ptVB0+JnWp3xNB3qCjQAAAAAQGsw0AAAAAAAEWCgAQAAAAAiwEADAAAAAESAgQYAAAAAiAADDQAAAAAQQbKBnut18N3RJLxoqoEKA4FYPYAKA4FYPYAKA5NqoCd7HXxvNAkvmmqgwkAgVg+gwkAgVg+gwsjcWcIx/uvgu6ZJeNtquu5nb/mECgOBWD3wQBXyUlNTxOoBVBiVW2ugG77P8v0Lauo7EE3C2+Cg31/DXf4iRoWBQKweeIoKeWmkKWL1ACoMyg0D3TT88xvoB3apdT9769GoMBCI1QMPVCEvNTVFrB5AhWFJNNDVXwefyLrvI1rsJuFtr2lnbgAVBgKxvKUqDYMPVSEvtTogYvUAKgzN5NvYbcfE16inY0zrNhuoMBAysRgGR4IOCDAIcxvo7Zh5kcd0MHP0ACoMhEgshsGhoAMCDMK4Bjr4qPJrmTQzxzgwc/QAKgxEWCyGwdas+2FRyH1XgA4IMAijGWjJo8pKmSv28t4q3/uNK59dvsC8lBPBzNH39gNTcFeFT5c5Nq0kcuXHI1bkMAhy3qE99/U7P5z7eon4e174fKpE/FsKA12J6BGJWaYAGVVQHdjPox3b9bMqwo1moL9IRpntCFncdT+VQDu3Rvwe7vfsKtso5iagqVgsuEMOFbbj7+/YlYrW/eQHZ3byDIMQjfImt3U//87zvEwMat4bCnk7Awa6AOIRiVmmIPlUUL+mPRRZcc3aow209VlU+wOqr8Nt+/mrbzs8VxEgAa+mEWLBHXKo4HbV9JesYKAboea8EuLVMAgY6OYIRyRmmaJkVEHtJVoHtK2ZKsOTDbTjY6t6rxsD9KKi+DSNEQvukEMFazm0yg8GuhEY6LGQjUjMMmXJqcK1mygPeqzXy5ylebCBdvUK65VlBrUKeIIcJRbcIYcKGOhKYKAbgYEeC9GIxCxTmKwq/Cr7rHv+LK+uKNXDDbQTDHQDAtZNLBbcIYcKGOhKYKAbgYEeC7F1Y5YpSF4VvrVtx7lv7y5V1z8/3UCLxykGtQokXPuE7ORQAQNdCQx0IzDQY3Hr2idkIrMK7+q249zX99e20O7GmXmwgY7aFoBBrQKhPblwATXIoQIGuhIY6EZgoMdCNCIxyxQmswqvnrIfr2+/r1zX7TpTG2ijjLZ5vWNmsd0E6HRQUzZTGp7g/g9CsWqDCroKHRvoB4nlKtPBm72HVyHKQOvn6X+5TXdzzfBiLeIRiVmmKLlVUPZ2aLLRw9QG2hjlrFujiJ4A7W5QW5blsml4f01LIRBksViVQQVDhX4N9LPEehEcBqszvgpiA2245XU/z+PwnH9vc834Yi0RIxKzTEGyq6B8t8UNhNEMtPJ2mi/unLqWd4RWrVKryrqgvasUNlNtNBwPDVjl8orVEFSwVqTsdN+Nas8Sa1lEw2B1BlZBeduZ+lo064vQFLnOfV1Wy0rNWE2rMrBYCSMSs0wByqigXA7Y6r1A5cNoBhoMtmPYPjURqDAQiNUDqDAQiNUDqNAbGOjBoUv1ACoMBGL1ACoMBGL1ACr0RzkD7d3N73ddfpcV6zltmp1p9UWlPWuKCsXPdIJTQKwe2oYKA50CYvXQNlTo8xS4Ag0AAAAAEAEGGgAAAAAgAgw0AAAAAEAEGGgAAAAAgAgw0AAAAAAAEWCgAQAAAAAiMA10k1euz/Ce98dAhkCHkJbzgaZQE/JtPsqG1zDQTV65PsV73p8CGQIdQlrOB5pCTci3+SgcXusSjiavXB/6Pe8e1v2Y76TIkIxMmSFNIC3nA03hSunRknyrw7qf1U64YHjta6CbvDNythdVft+DM9VZvSFDMjB1hjSBtJwPNIVlqTdakm8F+V4PrjvtlQuvzUCTQfmo/gbOKpAh+ZgzQ5pAWs4HmsKF4qMl+VaHdT+rnXPJ8OoGusl0PrGHmO/UyJC8THxqNSEt5wNNQaOoOuRbPWoZ6NLhZRu7sjy0e4AYMgQAQAKj5STUvAJdEgx0Wejw4IcMAQCQwGg5CRhokGDp8PbHiLeDfSAfCRkCACABA90psXMWBnpwvs/0Xp4GvTwiauoufH70Wu+xqR3+85ny7W+9tlRTDuo+aqNnW+eGDHHir01t0rGp/yE975E1LdXU0jLzzkEhhjKaql/11OftzrFtU3u/Oz0yjJaQiEjT9z/OfVX2P/mVe8saP2d9Cswg5GMN9IevkL9HNc1dA9f9VP7j2FdwO9SMeaeWUZXdctiHNW0ENA8qbBukQoak1Ka3bVm2g6zMSL603I6/v2PfT8Nt2Y1b8KCQSl5NrYOIdQyRDA6Sthmj1Gt8s49meUZLuEFIU+Ulfut+/p3neSn3E0I+ZxmHHhwM9H7+nfu2nz+lbf3W0Nr8p6yYNNX0Riz2WU3YNkiHDEmrTZkla26b/wzypqVQVMFBIZ18mlrLmT9hI7tzoG02g27ZQyzraAk3CGmqRns7LmqrsxQG+tEG2t8nlbxR/335miNbEu2R9Vqi5Z+ytsEdyJDk2j6j8XodoiELudIyStTwQeEG+TS1jyv6N2O6s0B6hynSFsLmHS3hBiFNMdBBMNAhIUPmwF8s0R4Jxwlh2+AOZMiN2pSbgJCRXGnpdDS2+W+Waa9T8mm6GPpthyFyVHcWSR9c+pp9tIQbhDTFQAfBQEvGLCcd2CNB2+AOZMit2vgxV4ZcaYmB7od8mn5quyxndawxjqlNKP21XtuSDslBMdAVwEDfBgOd+qNfVqzN9UXICBmSXtu6n8eGhS5BrrTEQPdDPk2/1X23wbnrRlOkf5tlY8FQxtESboCBvg0GOiSkNw2Cxe6scLUfdN33TVAMckGGSA5q43vVCwudn1xpGb0GeoZpr1Pyafrm3fEsyzeiawtLb98KWMR4nAcAABO6SURBVE+vzKMl3AADfZtkAz3L4kaBkO4rNEFHsx36D3DXdR3tn9ZnxF41Sp7I6OGpLTJkwgyJqE25adzPTz3SUhfL/cyY8eVOpz00dXXnz4IJz/oqYW0SA23ZhMPy76yjZROekm9RBlowZ0UcujqJmqYa6O/TAl0FIQGRkGa/tQ0CuqXZjr/jOPQw6Xm17ud5HJZo2q2VfU1ZsG31IUPmzBDxmWox62TuIy0Nsbbj77LB66du57KO/gKHpu7uHOp24tqE5l7/rtAHp4+WDXhMvokNtHzOkh66Nqma3lnC0cmUmIb1WQbfyXwj7C2pPz9hvXmmHPvcV8cSNaOVzubJ2lYfMsRf7agZ4qvt+tnnE7WRzX/ckZZGjcemlrN47KiDVgdN3UVDYfHWFtG2qNPIOFq2YP58+wl07utFLWWoUH/fqJXZMs/xCOm4mt5aA70Ze6QDXCFDoENIyyt93LG6C5pCTci3+UjQ9IaBJoPADxkCHUJaqsxgoNEUakK+zUeSpokGuvrDsI5L//pNiF1W7DmZ3yxuZMggCOOWNyCkZfEzFaHeR7/notG0+JnCBfKt+JlWZzxNH7+NHQAAAABADBhoAAAAAIAIMNAAAAAAABFgoAEAAAAAIsBAAwAAAABEgIEGAAAAAIgg2UDP8jp4+NFEUxJpPkgk8EOGDARiAdhJNdDTvA4evjTRlESaDxIJ/JAhA4FYAA7uLOEY+nXwYKWJpiTSfJBI4IcMGQjESmPbizR/3c/e4rIdDW4WrPvROAy31kDzPsv5aKLp0xLpfXll6tuTJJKV3qVX3gVWNpZkyEAgVhyffpSz/cp7Q7sJzLdVNce0bm5Q3DDQA2c3OGCYrELvLuo+JJKDnqVf9/PasnU/C05QZMhAIFYK636WOYNiFSezHQ3GtOrvVLeQaKB7aHpG1n3PKL6wtrwHvU8TTUmkarVVg0QaEmNWLncHnQzx0FuvR6xUMNDlD9o6DGxjtyy51+8Ia2uyaAiK0iSRADLQZA4EA3r9LGCgyx+0dRgw0MuybEdO8YW15T0o9ECTRALIgLZ+AxpBr58GDHT5g7YOAwb6tSgxl/jC2vIeFHogStPgY9RkyKx0+AT9smCgu4BePwj27R+0mwcY6PIHbR2GEQ208jCq5wHQwEOrlmqcD3YaZY0DCmsTFbs+Cf/5/+WLjK+ZqJ5IkseoY9LSX+X7o3Nfvwl17uvlGyRSJgSJJHqC3qm9VSrZQ/lqnf6UFOQbJBHKEOaFjvCKZd1ew74Txc/n2pTzH9Xb+0IGOmxXJGcaU+xroJWi9sMKTzRcTDXQgiMXYDQDbVwneaWmfV64/Nf3QEzg19PrCOZw5/iK8LeYpNi3n/yeR55ga8w+aJBIWq0hFcO1hdumvM5r3c+/8zwvmUQi3UeaSNfyrg+3wxj7HVthyNLSuKDpyV6uQJciKkOYF9oiEsvah80O9Cq27Wdwaxu9owY2wAmOIRK7IkxLWbHt+Ps7dqVZ1nMQjkiyYsYUtiqhrsJgBtoaIXO7G6s5cDoG/5jlzgP7d7Ib6G0/td+/THO3aZBI+uHvGmhJ29Q/lRr7uw04JMJEUr/gmfzUuhy+SJaW9kHKlb0Y6ELEZQjzQlNkYkUYaFMW01C4fjd7Lxz7bmBK7IowLSMmSqvBFTj55IHLnOzq94ABDbQlc7S1R45ByDV1pKzfcX8ns4HmukIRGidSBgMtahsGujSyRAp/4fXRYcwjLo3DaRn5Ow8DXYi4DGFeaIpMrBgDHexpbsub9CPciiWrhGkpLGZNW30EEo5I4oHrUrDBtecXgxlo12ojBZ+/iZiP7BUHFxaVWMIB+WmZSBkMtKxtGOjyCBJJLy6Ju3sKTZHeqDl8RxdyEZMhzAuNkYgVtYQjtHY3YS2dLAFCdkWYlqJiEgMtHJHkA9enZMsflMMZ6BfX3LDq5iTF91ieG6h2BZqBsih1E+lLHgMdbhsGuhbeRLoSMfl5nrKIlV5vAQa6OrIMYV7oAr9YfRvoKLsiHbgCxdoZ6OM4z/dj8i36xKAG+stbV2NJUUwsBU7FuuAQAz0R5RPpSrkr0L5SGOgK2BLpiiDugUeIUqQ3qsdAN8ObIcwLfWEVq2MDHWtXtG+Gm2Iv1sxAf4s2stBjGWj77ou6erFTgS+7XHMZBnpo6ieSfvy7BlrWNgx0YWSJpH4jEHfbxWflMMK0dLbBPkNhoMsQmSHMCy0RinXPQNvWQNsld73V3ZkAcrsiPlNZ9orXQAtGJPHApf3tf/CyEKMZaOtScePfDgnW63PL6te1K4+KzYgz0L7aIot1OlC+f4OOPNnWT6RASUkZ2ROOq/5wfr8G+kGJpH7kjrt9vNmOlLSMM8pdGujnZQjzQkuEYrl2tbA9RBg22s4bTvHTR4yBFp9p8kRpDEDCEUk6cBnlGljosQz0u4+aqWAEzbygH1gGdLUZ1++ZC9SVRfWW34i+2uKK9TlQfs+/v6bJqZ9ICiJlg7WF29a1gX5WIl0/dJ2w/IF8WVoaE4pncUiPBvqJGcK80BKhWHrHWvfzPA79/D8R0Udkx9M2hhd09kZ3AsjtivBMI4qFDbR4RJIVsxnt2is5hjTQGt7pKFjKKGqmrXrUV0Wf/7muIrhrCxeLOssmNFuzn4s2iaRWFK5QkEietv0+Ovf1Uu7Y1I/cZ1OexySSRHprXW71ZWkZLNX5aPOYDPnCvNAOeYiVkue+Lut+Gm7xULyCsydbDu1aO23Bes1Hq8i0K8IzFRS7Fjk2yz+tJtob3HAxa/2eg5ZiMAMNnbB5XhUBIIZEAj9kyEAgFjwKDDTEwzAJWSCRwA8ZMhCIBQ+jnIH23I+8XmffZcXolRrNwpuw6849SKSikEjFz3RwyJDiZzrBKSAWdEaNDOEKNAAAAABABBhoAAAAAIAIMNAAAAAAABFgoAEAAAAAIsBAAwAAAABEgIEGAAAAAIjANNBN3mjf5KA9gwpFIbxpELeiEN4eQIWiEN4eQAWNxLYZBrrJG+2bHLRnUKEohDcN4lYUwtsDqFAUwtsDqKCR2jbrEo4mb7RvctB+WPfD9rb3SVRY97M3aWcKb02IW1EIb33mHnsTMAOSl4eHtxNQQSOlbfY10E1eyfnQ94B+X5djnPzwKnx/1XX5q3P48DaCuBWF8NZj5LH3PbjmvR3uDkheHhrezuhfhcoktM1moIlrdSzvQZ1JhXU/e1N3pvDWhLgVhfBWZ9Cxt5zDK/5S7meHtxdGUKEqSW3TDXT1N9ovrQ7aFVoEZlOhMwM9W3hrQdzus+67a05+Zng9AanD5GNvPEUbQ3irwVAjJ7ltbGPXBT3nVgY6M9AArdiOiS9qpdA8IJOPvfEQkDlo3rOeAAa6CyYfszDQAMuyLNsx813hBNoHZPKxNx4CMgXte9YTwEB3weRjFgYahsO+FcGdyzqvdZXMal+6CMiwY2+prY2GDUheOtw5Sk4XPesJjGegla0dzHX+34+Pbbk8VayXExZzHVfvWZ8Kjk0r6eqC2hFHHLNUIbytFxhoyY4dAemVoH6quHxnrOFEFl5vKWmSXz/5fNT3DioeMsTtEw/l32owlXLBXm8k7oiBfeHPC/VEj80VlsiAMPb+CHfMUgHJI33n5A+vJdetM5Eoya+fWae20YeaaGOWZQZMZywD/YqA6YT1aGzH39+x76eRWKZxEBR7/1d70MSa48euWEVr417fvvz3resgGb4sixkmPUCW4p7Tk4RXKr1yuN9ztT1vP2kiC6+Rq/60DCS5ZdpclW8NQMa4WVPWknHiXv8rPVJEFUTDoC29tsPn3fwBYex14B1UcwekgPSdkyW8xj9f4bUPLOHwxkxtIw810jkr7wyYxFAG2jof2hLXnlOGRYgoFi7lEc+YcQW19Yw+6C6hjAwORsGAiKX/HW7bT+1i4SDjiSy87vlQkJYuu6x/dZycXDLHLcZAC3p96IMBiBi4lN7qvRUeCghjr5PgoJovIEWk75wc4bVegdiM3dIiwyub2kYfaoSeIOcMmMJQBtqOJVMcyaPHTFZMWJm9nEz0oQZxt5d1noFvMJKGN+K7L9s0Sjw1hOF1xkePiTy+mkqDzX6Z4xZ1BTo8iPtKj0FkP/1M6+t1rhfXGnvQp4y9V8KXSHMFpIz0nXM/vM46tOcoYsIrn9oGHmpkESkwAyYwqoF+32lyrGjxGSvtgn+wWGiY8Ndm8+zWLjXOIJ7QVs9gJA6v+ol3MZNgyXW/yMLrLpWU5Jayg/nn3HHDQKsk91P/6foDwtjr4b7DEwakkPSdk8NAW5+csNUlDq98aht3qJHNWWVmwGhGM9Af9+S/QpzbQDt54iCe30CLwiuUPnC4/mlpoC+Fh7t8hIEuiryfat+5a6AZex1UNdAFpO+cPAb694kzYFHhxUBjoNPZjj9L0CoYaFHKPmUQr3MF2lJMJH3gcP3T1EB/Qzecf8ZAlyW616/7eWxBH5V2BVpWzYRj75XmV6B9LRNI3zk5DbRSRJ/IosKLgcZAp+JaZis30LaRIVhMHGdRp5pgHZ4nIK6Xh3q6vSi8EdIHDtc/wvA6x8ekJFcPf+77eJvwZ44bBlolcrr5Pn8a8FH+gDD2eqi2BrqQ9J2TwUDbd5LXvxwV3kcbaDXApWbAOJIN9Pu3VEWJIg20YPaLKGa3aeFilk7lWHuaJGZ9FRbPA4POl0zED0ZqeFsZ6J7Da+//yUmufnQzfBPEzRYhy54ekQbayMyk97I0Ca90GHwV1X6KONsaCghjr5MsDk8WkCLSB5gkvI7pKSXJg63ylxxsqAnPWaVmwChSDfR3cXzF3+7m1K4s0b9cZ9qOv7/zPPWubPXZ4WK2MUV62dsinj7Rb8ffcRxJ0WyhwrIstoA4Bovfh54mhsMrl15wODGdh9dIVquFFCf59Rv3TniKuOml1v08j8M4scibufpFlJQIdRRe29mvxtOnPo8aDAhjr6cBt9cYSANSQnovU4T3ZT/VgsIfLfevDQ081EjnrFIzYAR3lnAkd44bvH8R/V1l/fzvF5B39n3ld/yIEhZ7oZTST/varGOz/DNQfku/Zd5Chd+BXSdoKeEPsTe8zuOp0luPdzM0HYd3WaRhEyf5+xsZznf4uOlVnfuqG4TYXm8cOX0IbxZeX+Cun30+UeWwnbAgIIy9P9RY2M6xVEDySx9g+PBGTUjiJA/WZKtxqKEmZs4qMQNGcGsN9O1LVaUQrv8RFuucblWYg9HDG53kSbf6rMcdOm6dQ3h7ABWKQnh7oL4KeY1ZUZt3w0B3nN0PMtAdqzAD44dXlOS/W1+Z3j44fty6hvD2ACoUhfD2QAsV5jfQ7kV+rVGv6AsXZAzqovtVYQqGD29Ekn9vEWboCsPHrW8Ibw+gQlEIbw80UCGvMStv8wbaxg4AAAAAoD0YaAAAAACACDDQAAAAAAARYKABAAAAACLAQAMAAAAARICBBgAAAACIoLSBbv1G++5oEhBUSAOx0pCdgvF2LfatAgCAUShsoJu/0b43mgQEFdJArDSSTkF7TTYAAEDPVFjC0eKN9l3TJCCokAZipRF9ChhoAAAYiBproHklp0aTgKBCGoiVRuwpYKABAGAgyhvoCbxAXnBkA4FYacSfAgYaAAAGoqyB5o32Gk0CggppIJaHdd9djwimnQIGGgAABoJt7AAgmu3IvE0IBhoAAAYCAw0AsWxH7n32MNAAADAQGGgAiOK1TR0GGgAAnksPBvr6QoXPHPrdStY2UV8+1Fdbvj869/Vb67mvl28YtSmV6Ws3lQ9/X/W0ztO2vsmpQu64xbZN1dTR/tBBh0B4pt4kl4fXcrRsscNAAwDAQPRgoD+s+/maiX+P8Ju7ya77qfzHLKG8Bm3dz7/zPC/VXcsal9Ksu9daH4pa99NuAr1tG4BMKiwl4iZp27qfqol85YO1IWOLJTxTWZJ/Sgal/1bBFWgAAHgu/RnobT9/U/N2mHO/Mc1q/1T/VKb6r0P4/mXaAMshbEfdDpFfHGVThR+ZVHCVuxU3QdtsBt2yqdoEYsnOVJzkiyi8l39joAEA4Ln0ZqD9DsYxb5uWWWKgnXbJPIpRdL2ajIi2DUAmFez/uhm3cNu030iX6pRjzCCW6EwjklwUXs+3b4KBBgCAgejNQHvnUJcbUC+TCQ202yxZLtpp/7LsQSBr2wBkUuFXWca4Bdv2PaZz2XP8QfslfKZRSS4L77deDDQAADyX4Qy0k6IGWl0xay5DkLZtADKpcKktX9zkDk+p17qCYQqxlsV/phhoAACAEgxnoMOTbBEDfXkUyz7TD3X330cmFa7VZYtbhIFWDvBnec50CrEULGeKgQYAACjBUAba/kCUTswaaGtlDtfxvr/veIWErG0DkEmFHxnjFnZ4634IVvzOIJbsTKOSHAMNAAAgwzTQyi5wVRHM346Je9U3DhDtwuHeisDtOTyhkbRNzPAqaMXzxE1ioC1bU1j+Pb5YwjONSXK5gTZK3n+zNwYaAAAGwjDQ3weT6s9movnb3JhW90JiA23ZIlffHDh07Ni2SRlfBX/pxNqE5j7wCGjKKfhoJJb0TOVJHrnE/NrJ7p86BhoAAAbCuoQjZHgyY32ky3d4z3vVfh+d+6q+TU35SFSZ7cChsMRU52dgFSxFb8Utom1RpzGsWN9DCs/Uf6LR0utV5rj2joEGAICBsK+BNl88AfVBhYFArJtgoAEAYCBsBhov0AOoMBCIlYBx4ZsQAgDAKOgGes4NvkYDFQYCsQAAAJ7Gy0D/P/j+xpiBwMKNAAAAAElFTkSuQmCC)
1.append:在列表后面追加
例如:
>>> a=[]
>>> a.append(1)
>>> a
[1]
2.clear:清空列表中的所有内容
例如:
>>> a=[1]
>>> a.clear()
>>> a
[]
3.copy:拷贝列表中的元素
4.count:统计列表中某个元素的个数
例如:
>>> a=[1,1]
>>> a.count(1)
2
5.extend:合并两个列表
例如:
>>> a=[1]
>>> b=[2,3]
>>> a.extend(b)
>>> a
[1, 2, 3]
6.index:获取列表中元素的下标值
例如:
>>> a=[1,2,3]
>>> a.index(2)
1
7.insert:在列表中插入某个元素
例如:
>>> a=[1,2,3]
>>> a.insert(2,4)
>>> a
[1, 2, 4, 3]
8.pop:删除列表中最后一个元素,并且给出元素内容提示
例如:
>>> a = [1,2,3]
>>> a.pop()
3
>>> a
[1, 2]
9.remove:删除列表中的某个元素
例如:
>>> a = [1,2,3]
>>> a.remove(1)
>>> a
[2, 3]
10.reverse:镜像列表中的元素
例如:
>>> a=[1,2,4,3]
>>> a.reverse()
>>> a
[3, 4, 2, 1]
11.sort:排序列表中的元素
例如:
>>> a=[1,2,4,3]
>>> a.sort()
>>> a
[1, 2, 3, 4]
二、元组(tuple)
常用功能介绍:
![](data:image/png;base64,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)
1.count:统计列表中某个元素的个数
例如:
>>> a = (1,1)
>>> a.count(1)
2
2.index:获取列表中元素的下标值
例如:
>>> a=(1,2,3)
>>> a.index(2)
1
三、字典(dict):
常用功能介绍:
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABBAAAADtCAIAAACJRBFzAAAgAElEQVR4nO2dvc7FsFamUwwSEqKiHTEVokh5Gm5hjiLop+AKUiFNTYcUCUQNxXS5AOpp9k2c+6HYe2fH8bK9nPg3eZ7uy+dtJ2st/7zxT4ZhGP7P//v/AwAAAAAAgAiCAQAAAAAAnCAYAAAAAADACYIBAAAAAABk/vjHPyIYAAAAAABA5p//5V8RDAAAAAAAIPO//u4fEAzDMAzDtLxer9cyHa8Yl5qhyr3ZJgIAAACAu/M//vwvEAzDMM6rNRAe5/X1er1er3Ue69yVm4r3Ni0tGgQAAAAA8oFgGMZ5lQfB06IalI/zUn4Mrby3HAWjGAAAAACexEcwTItqsUnaZI3gvtvA+/SqsxC53vVPi991ffkWAAAAAC4xjn+zzTCM86oZgaZN1gSuIbBuSD4tN1oaNM5raJsCggEAAADgQfzHf/6LsSRJOQxNm6w60hBYLwPKC4YMJX52Ub8Ue6kRDAAAAAAP4n//4/897mF4oGa4NgSuM8OQCWYYAAAAAGDPX/3PvxE2PT9NMyAYNhAMAAAAAHBAPiWpO82w7T924Rnk3l8wvFccKbeeIBgAAAAA4MefOY9VbUwzWIog3aj12kGhpmAwbtO+RePfx1KljQTeH8Tcou73CsHgPIQWAAAAAO7HX/7lX/u+w9CKZhC+MawY2erzvpSPNcMgfQbue32fUv6SgjBh4cow7h5TzTD0sMgMAAAAABLxb//274EPtykHq2mTCb+yhqhJFMNltTBYI3zPGa0qdSAcSFvwU2lKq1b7ahwAAAAAlOUPf/hDDzMMruxSTTFcuq3doN+jhxx3q1AMRZcAaQRDKsMDAAAAQBf0sodhy0bex3Bt03OKPQzvO3Dk5NoZPS3Sre2H5GW3DLDpGQAAAAAO9HFKkrBtN92L7gSnJC3Lus6je6XOQeiEtMzv2cZ5LTk8RzAAAAAAwIEOvsPg3h3cimDY7s4lGWLPXv0uSyqsFxAMAAAAAHCk+S89i1ueh7YEg7nlQJg0iN6i/VYMc+nBOYIBAAAAAA4YgqE5tTB0JxhcksFxu84pBO+WiDP3yIfbAAAAAOAMm2Cwj/MUSZtMgz103nY4L9MwjPN8/WDUxF96Flcm2Rf9mifdqVLqD7d9DOu1BoIBAAAA4Fl8BINyGJg2mR5z0/B74Pu9drmk8xsF9re1jcbFi7+irOdwZ57seKTgDIO4KVtOX/CrEAAAAADQAIEPtz2CJr8sMC3N3dLQqKkAAAAAICMIhmHQrcUpdRvrPDb5Hl+9EQIAAAAA7gSCoSW2pUG1pQsAAAAAwBsEAwAAAAAAOEEwAAAAAACAEwQDAAAAAADI/OlPf0IwAAAAAACADIIBAAAAAACcIBgAAAAAAMAJguEE47zGfowg+ZevAQAAAABKgGCI5uzYH80AAAAAAP3RuWAY56Xsd4en5cKXji/9GAAAAACgAj/BoHwBnjbZabYPIjtG4OO8Zvhc8uUR/7TwCWcAAAAA6In9DMM4r5rxcNpkV5iWooIhyWifWQYAAAAA6InjkiTleDZtsrN4BEN6UgkgJhkAAAAAoCOEPQz9aIaSgiHdhEmBqRcAAAAAgETIm5470QwFBUPKUX7RiREAAAAAgCs4T0nqQTN4R94nPpbgZpzXhAuJWJUEAAAAAL3gO1a1Pc2wnY303s8sCIbPZmfPCUr7PL4Jdr+SB/KhSQGj2OBua6YYAAAAAKAXAt9haEgzTIs5Dv8M/F0ZBtcQbQl+dzUtPpXhyu19I4ZCCExIpJ2vAAAAAADIR/jDbeO8aga3aZNZiCN23zBeKRimef3dzlGTqEoa59X+n18SIBgAAAAAoBf6mGFwjLAvCwb1yqDoRUTebQosSQIAAACAXuhjD4NjhJ1ghkF5Q7o5gf32CN8+BjY9AwAAAEAv9HFKUnXB4E/9FQq7BD5NwAQDAAAAAHRDH99hqC8Y3GXJO6U9goEPtwEAAABAP/Txpedcexhi7kxOL255HnyCgfkFAAAAAOiIvx8GQzA0qBaGwaEY3KegDhkEgywCogUD+xcAAAAAoCf2MwzKIXTaZFqOI/NpeS2LvXdAX/yZ+xMEkH3Y0vYRt2UahnGeJzMD5AIAAAAAdMRPMEyLaiibNlkchw89S4VYn1y2FMXhKCPPN6Edt2CnNbN8//97bX+DyAUAAAAA6I3wh9vA5Oyn5/JoKAAAAACArCAYTjDO64nFTKgFAAAAAOgPBAMAAAAAADhBMAAAAAAAgBMEAwAAAAAAOEEwAAAAAACAk+OH2wAAAAAAADaYYQAAAAAAACcIBgAAAAAAcCIIBvtjycZHBz6fMOarAlWp4gVc/1iIN0gCgQRJIJA6AmfdBWEPg/+zZJuciP10GSSkihdw/WMh3iAJBBIkgUDqCJx1G+QZhoCJp6UDN5z4HHNfVPFCF66HHDw83sZ5uXgbt2+RlBBIBFISCKQWbkMJzmrhNi5zSjAM09KGF2yM9VSN3mMqqnihXdf3wCc8+5wlfWi8fWa2zzYnT2qRlBBIBQKp66ZGCYEk3kebrsdZd2j7zwiG+k5QMM5rB3d5gYfWv75psylX8ex4m5bLLf7tWyQlBFKRQOq4qVFCIHUkGHBWG3dylVjB0M2T37p7ruKFblyflHGeW2p360C8tSYYlGHZWvQSSJ0GUmsQSE3djB+c1dTNXOLckqQOuLVggGJMS1MvaqAOrY3zlGFJ9LZGp4EErXGfMegDuI+zEAwAbqalrZldqENj4zxlWBK9zdFnIEFz3GcM+gDu4ywEA4CD91rQtB3qPWrX42hpnKcMyxzRC1cpGEjBpoYI6RhvINHLtAWCoQ7mN+W8dxlqVa3P07naTfs7dmJSZTI4ic71/lTfAwuW6ZD0mFBwprNcVSCFTzhR35v7Fom3hGyHW7wN62rxI46uud4iKcNSlWz/fN/rux8SS6koHUiajGLaN3+Wn3+t82icBvP7BYGUinAg6XsZan1mdLVeO56NGPfmph/BcHwdMs5ryMau/769adSOcV5dPfT+6vuX4vgynAxOonO99b5MPIR5Wl6vZTaCQ/b9L7W7FVUHkvFvZ1xo7414y8u0CIHkqPa7i4FDv5O0SL/kis5dk2y7q9+JIu0cXt475QMpMlk4QsL3ZnxQd5zX17quu0gikBKgDKQvQddT6zOic5ZuuBI97s1NL4Lh6IQhNKHqqTOiyaX+eZxXO3/7qC5lMjiHzvXy+MrusdwqQgokf4eqDiTzv17BoLg34i0r4ihHDiRFsh8pWqRdMUkFwzSvv4RChYMTVAik2GShCNHcm/mnkSPrgpOQPkKo9dnQOStquBIz7s1NH4LB3aH6VvHFtVVC2+nI43iwhDIZnEHpemfzeXSr2EWqf63B+5vwDEP43oi3jDiG6ZYXHF4OnI5+tUUK/ic62bv7aa3BvwHVAymBYFDdG4IhL9pAOvwkKBio9RlQOks54Dgx7s1NH4LhxNSmrq3aLzWTZNu2eEwxb8siwBzoXO9OdazBmQRDKJD2t3NVMBBvGXGEgrbF97ynS9MibYlSL0mCtNQOpASCQXdvCIa8KANpj35JEqRF5yztcKXBJX0PFQzfbvngRM+S4eBQUJ0MYmhcMMQFUirBYJZNvCUkYpzn5Mw4LyqQEAztUyuQopKFBUP43hAMeUEwdASCoQnSCgZxqbiqd/20oJpmWJEMFLQsGE4EUkrBYN4H8ZaEiy+GPSRskRAM7VMlkGKTnZth8KVCMKQGwdARCIYm8Oz/c33a3lklXEvA7LZznBfNIFKZDE6hdL3T2lIHnUYw6APp8KOLgoF4y4lyEWrgMCxXzhdbpPB/opMxdMhEhUCKT+aPEN29IRjykmsPA47JQMQeBsVw5cS4Nzd9CAb3Rg/nTs8kgkE4jca+rExWhc+75+r3cQGl6+X2065xcYLBiqJdofUEA/GWEbGRFqYAHG4e9yePWBknEgy+sIxM1ujQgUA6EUjxyUIRorm3tgXDgwLJ/EWPguE5zlIOV+LHvbnpRDAMktG9QyR3lbBPCDD2kO6k2zt+bRVxuKRMVoHtwVp0qB6d68fjcWNibTuxT2DfG9phowkk8yavLkki3vJyjJtpeS2LvcXA0Q1kbZH2BbnCMi5Zm0MHAunsS4fIZMEICd9b04LhWYG0T9+hYHiUs3TDlehxb276EQzDcNyFJSsv/w4tT0bfa+a7EwuHS8PJKuF9FdEPYdcPw67JEVPt81gm4aKr8XWFkTaQDvclFRdxb8Rbfo7umBa5jfYH3DGnl5mpO5kzkMSCPb2HM1nbQTQMBJIvJ08ghZsad3rPlIYjq9+/1nncpVsm8186Y+XhMYEU18soYqMCj3HWMGgqvZ1dZev0JRjgBBOf9IKCEG+QBAIJkkAgdQTOahpZMLQsQSEK6h+UhHiDJBBIkAQCqSNwVuMIguEqjqnTowaZdcluEDzVDFL8VC5c3wLEW/YnfQYEUvYnfQYEUvYnvcEj4KwOHiGDYAAAAAAAgLuAYAAAAAAAACcIBgAAAAAAcIJgAAAAAAAAJwgGAAAAAABwgmAAAAAAAAAnO8HwOaGp7EenqxTaMngB/BAhLYAXAN5QFyAJBFLz/ATD9r22kifKVim0ZfAC+CFCWgAvALyhLkASCKT2MZckTUsFw1UptGXwAvghQloAL4CfaX6Ko6gLkAQCqW0OexiqfJmbz4EfeKIXjE8WEg4Bnhgh7YEXHs7n7aS9muHbmj3HU9SFAjjj7UYQSE1jCAZ81QLP88I4r/tWcJzXJ3W18TwvQloELzwe7wBunNfH+Iq6UIT7CwYCqXE2wTAtFQZpVQptmSd6wepYe5wgHOe5TCv+xAhpD7xwnbRVRplbsXqaTzAUfAQV1IXrVKkLrUEgeWjFpxyrCpWZlhu8M5mW7h8BoCRpq4wyt4L1NJdgoKm5H1XqAnREQz5FMEBFDuuR+mRaun8EgJKkrTLK3ErW00yCgabmflSpC9ARLfkUwQAV6V8wvFeWdv0IACVJW2WUueWop+O8ukSBWjCM8yIkE18p0tTcjyifeuLtRG7QBY359LGCYTuB13uexT7V22cNzQ71i2F8A9ETZvJjkv0RS9//7X7xdtfnwjqPW/J1HnfpDl617tDyuvMZelgU2Q3G+VmvZXJ9aUdXneEcibwQU2WSVUBVMlUbonvIX6L3/6SsjTKNi1vm+yIjmxrqQk6K1wWNO9NFSGxHCWcJBVKDA4yn7mE4nsQjb7QVUq3LslJjEhKeYbAEtnNX9NZH/849MNIa9XGc19e6rrt0vyzfCe1BguM+b7ENo0XEavppWPcBoKvOcI4cXghUmTwVUJMs1IbIiZ0ZTfO+uxAOgBOzcD9r+BGoCzmpUBcOpYdcmSJC1B0lnEUbSNs/2xhgPFMwOJ1i1RoxVSO+uwchwSD/X262tj76l97wl60KtoRGUywe7Oq50Xbq870Q7Cp1mbrqDCfJ4gV/lclUAfWCwd2GCIndgsH+nfVkiQUDdSErFepCqKzY3DT3pu0o4SzKQHKnrsQjBYPD/N5K86X/JfeNETBoVCv77qPdzdm1dtBda9upz7dC9Ilta111hpPk8cKZKnO5AmoFgzpwgoLBLu14Oa1goC5kpXZdSCAYVPeGYMiMMpDC/ynNEwWDr97+3OJIhWBIjN+g7p5O+l2oMTvRDprLDJuvz/dC9RpGV53hNFm8oK4yKStg1JIkDeElSbJFdteTCgbqQmbq1oUEguHM4AfBkB5mGPrh0AW95A4JwVCGRgXDN0gOU04IhrIIo6ujpXXVGS6QwQvBKpOjAj5CMFAXslKjLmykEQzhe0Mw5EcRSEbaNqrvUwVDOOQRDGVoUTBMy0soFsFQnuM5EYJ3WW6RnQxeUAxr0lfARwgG6kJWiteFY+F5Zhh8qRAMOVAE0kY7A4wnCgbdmF+5BBUuotjD4Ow5pW2R1wWDuOPSdysN1ed7MS2KvokamZksXvBVmWwVsAXBkHUPA3UhM8Xrgp3zNcGguzcEQ25UgbRP3Ui1jhcMnzmtNm7/JA77j/ujMTzH8zTw8HfwwptQCya/EpF/VUswWOU28KmOW0SINYEeM4gyq3Md8ILDC74qk60CapIlFQwKKeCaKXU8augRqAt5KV0XAik1aU5ESNuC4UGB9KGdAUa0YNgmUvpWmfact2MXynFh+7q28A7nJl4YBs0rD6v7dA0oUi1Jsg9K2ey9TMMwzrMkI/cZ13fLPSJk/x7GaGGlVcOh6lwBvOD0gq/KZKuAimRpBcPBSNLrpmPzNs7ruizOuAk+AnUhJ8XrgoEqOBNESNOC4VmB9EvRxADj1JIkeYVpd5iLyBzPYyRapoYmfXv3grj9KtT7yqlUWf2yWOdxl9/XqeaPzCzfl7/XXG+JvFW+Ap1HiHPWVu6zVNW5AnjBgb/KZKqAzmQxzdFx+bGYeJwX46Y9d2YkWedxGOfVbTrFk1IXslCrLqjiTZ1b6N7iOso6PCuQvv8J+LQE5/YwxC3AuhXtCIYnewFUdB0h/razjSqoAi8AvKEuQBIIpDqcEQxd++oqzQiGR3sBFPQeIfbClGEYmtlFpAUvALyhLkASCKRaxAqG4ue2iTPF9uTcrEt27caPM8fV7u3RXugJIuTqk9r5xj8WXrj6pCm8AC1AXbj6pNSFYRgIpOtP2mXX9sRjVQEAAAAAQAmCAQAAAAAAnCAYAAAAAADACYIBAAAAAACcIBgAAAAAAMAJggEAAAAAAJzsBMPnhKayx8BWKfQ54NP7gU/vBz5tAbzQETirBfBCCxQ0yE8wbJ+eLnnGbZVCnwM+vR/49H7g0xbACx2Bs1oAL7RASYOYS5KmpYIbKhVaQZ9WKvQpPn0O+PR+PNCn47y2Fk94oSNwVgs80AsNUsoghz0MVT65XbTQTY2VHLtXKfTL/X1q8jF2IlOnzS0V+PR+PMWnW2PY5EtCvNAROKsFnuKFtilkEEMwPMbz01Jh8FGl0Of4dOP+ggGf3o/n+XQY57W1Th8vdATOaoEHeqFBihlkEwzTUkG7Vil0eIxgeJRP6zLOcxnX4lMPxbyQlof6tLHRD17oCJzlTUVn9CCKGuS//ulvH3is6kMEAxSjyvYUOIAXeqLPoerdwAsdoXMWzSDk4pnfYUAwQFKmBdfWBy90BUPVFsALHaFyFs0gZAPBcO9CH0naoySCub0X2OParOCF28FQtQXwQkeEnUUzCDl5uGAwjh+QapmRwLfv0kpoJdUXCmcIHyXx+b6J7QUhteZgCsHnjR5k0SsZvODN8hsh+//tfkBlTYliqNr28TC34KoXYhpVuIjHWXRGGYkO8ts2XA8WDLNR+cZ5PXr2HSXGIGGcV2HYYF19//IQJ7pC4TLeLtDlBedYUPMGjsmj3KTxwrG+OQ+v3or7nT7Byd+pCfhU7Sy4QgovRDeqcA46o3qog/zuDddDBYPc6u2cL47mpRAZ59WOGvucK1WhcJ2gYIjyAm10C6TwgniahHzExLu4aV5/+U0LUwxpCdZTpbPgCim8QNdWCDqjeiiD/OYN14NnGIQhvuoEAlEwCD88nlVwoVCIITzDEOMF2ugWSOAFx79F379nlKmYWfH5NMZZcIUUXqBrKwSdUT10QX73hgvBYFx0+HS/tFl6z6j6kjOtaiEQDPfjuhdcPhZnDtgJWgCPkaOcBVdI4QW6tkLQGdVDFeS3b7gQDMZFaRWR+arRUx33ssIhMmlVC4BguB9pBIMTBEMFAkNVtbPgCim8QNdWCDqjeqgFw60bLgSDcdHyvbgaLeT4T9RIm55pVfODYLgf+WYYThcHFznxbhuSk8ILdG2FoDOqx6UZhtuAYDAuhg8wsn86zosUIVZCWtVCIBjux3UvRB3bgmAoQOiMSGpUCVJ4ga6tEHRG9VAF+e0brnjB8Hl93rVREgsG4ZAk4XLDreodfPqjvGCw0hw3vFfgQT51pTl4wdGTjvuzkCKKq8CDfBrhrNLghaMX6NoKQWdUD2WQ37zhihYM2/7e+m3BeTS+tw9KMbY2j/M87X4oqQjhvK02W9Vb+PRHecFwyGBa6hvyST79EvSCvcrQ3bw3aLhn+VTtrMLgBfc3SY8Xq5voWc56Q2eUBXWQ37rhOrUkSV7d3wni1uT9xcMO5+M/vtcOJ7QfsWWnttA6dO3TYRhcn7p07UgKeSGcm7v0+m3Dmyf41J3e4QUzS8cpB8riKtC/Tx37AkV3eZ1VEbwgZkTXlocYZw0DnVFyooP8tg3X35/6cNtkfZUMegef3g98ej/waQvghY7AWS2AF1rgqhf+9p/+K1ow4Pn7gU/vBz69H/i0BfBCR+CsFsALLXDdC7FLkoovTPQebPub8pl1yVqO2GpPik+zgU+zP2lx8Gn2J2353vBCR4+As1q4N7zQwr2l8cIzj1UFAAAAAAAVCAYAAAAAAHCCYAAAAAAAACcIBgAAAAAAcIJgAAAAAAAAJwgGAAAAAABwEi8YPsdCtfIBwW6oYjec1RFECDQIYXk/8CkkgUB6GNGCYfvmdduHpTdHFbvhrI4gQqBBCMv7gU8hCQTS0zi1JGla8NYZqtjtrs4a5+V+D0WEJOSWEVIFwvJ+4FNIAoH0JM7tYeA73+eoYre7OWv7VOKtnuoDEZKAW0dIFQjL+4FPIQkEUgE+0yrVl2GdEQxP81UqqFjpKP61+SIQIem4Z4RUgbC8H/gUkkAgFaFXwUA3fI4qdruxs+73aERIWm78aCUhLO8HPr3OOM8Jx27K3NIWeh0C6TpVAuk8HKsKPXKzVgOSQ4QAQCamJeXLXmVuaQuFFqgSSOdBMECPMBwEP0QIAORhWlKuDlHmlrZQaIEqgXQBBAP0iDAclA/F4aXMQyFCACAD7/XkqVoNZW5pC4UWiPLpOK/+F2BFIqQHwfA99GSZht0hvJ5DUPZpzES7/yzT7zSV1zLt/8eLyfP8bPqzo2n1A25nOfP9uG5L/P2f7GqpBhmFuktV3hvEQIQ48edm3pLZZBGeV0kalmZoHSLzSqFwCrPquG0bqM5CNs7KZ6W1ClTmpkpGIJWheCBpOpiYsFRm6aQHwTAM7wqxzPO6e8BxXl21cGcF68Be82eH95BM+qVi/Prqd6CBfXZy0Fm7yzu/fFpHKyu5th0dehTix5uIuzc4CxFyJjf7NdK0EJUJSReWW591iCSH+AgWCqewqvi7cZCbAmV1nhbvIPxdgpFAHq2ocotJRiDlo0IgHXINeTGc2+U+qyfBILfI1hsb6/EPF03LG3nwqadkvK08zevPQdKoLuQsdTLtcPB4E4PciyvvDc5DhJzLzWjzg/PUEEnasFQ6VVEonGM0FNsH+1jOuOrsH5mJLxg8iiG5YCCQMlAhkI7FXxUMCfqsngSD5C3zUR0G8ygGYygw7usZXOJtWF8g6p0lJDs5HHQ35XZfH743uAIRcjq37xiABis9qcIyyqnhQuEkjoHWYefSiQoYW/Pcv0ksGAikLFQOpASCIUWfdSvBEOpf7cy+y7m+2yPoflMRjG+lsxzJTg4HlXVDG0hwASLkQm6fNVfEYnJShaWzf5YErmY0ACfx7lV6E1sB1eO8Tz39UmiGgUDKQs1ASiAYkvRZtxMMTsSmfFrWefo04OiFlKj6XYWzqg0HVYEE5yFCLuWGeM1DqrBEMLTF3m3iSCKmAgbHed8MD8vFEQz9UzaQNtIIhthexuJ2gkFXWz7ZvfcLvn82sRo4Jadf1OmS1Xl/DAkhQs7nNs7rMiEZcpAqLBEMjfIZN1mrx2LsrxiZiWvFEQw3In8g7ck3wxDFrQSD7yQCk7f158/5IpLn4RrB+FY6K8MKdbnQ/UfV9YEEpyFCNIVKbOciIRnSkyoso/cw0P3kwHGOycE3sdXZNzJznKmGYOib8oF0LP+qYEjSZ91KMPhe6givdLYft7RT6C6LkxXxrXOWGOb2OxzXe7vDRVdrPuj2LrWwbI0IuWGERORmHKPajrQlLI/OEt/oyQ5rdJx3A5+KZ9sIl+Oqs+WvXeMQLxh8uUUmI5ByUT6QAik1adL3WTcTDNJAQbaS8dt2ut1tX02DtT4KVculc9axBZ6W17JYa0SPY79xXtdlEawpDyXl1YiaU08KQ4TcM0LUT3qwmbz+oTiEpeWsaXm91nXvL0GfRhRanFv49D1WNW2ufMXgqc7m0MP8FIr9/tHYLbufqFTkFpeMQMpG+UAyULdL3twu91k9CAZxl8n+ojTB6/7vlsFhhFF9KPilkSHAOcSdNb6HUTnLigHRYUbZ6zwKwyvxLr2VVPcUZSFC/Nn2GiG+3Pb/+/7HvMnqLRhhaeW4TGY6QVNEFVqcrn06DJEmjqjO+6SBhuZlrHwW66k/t3AyAik7dQLJzCicoSKQLvVZPQiGxzFZXwMB2EOEQIMQlnvamJG8Cj6FJBBIdwDB0BxULPBDhECDEJYmdxAM+BSSQCDdgwyCQZy6sedUZl2yKjFW7RGSnHwVww2cVQUiJPuTdo7SbmkNQlhmf1IV5qz/NdWAT7M/6TMgkLI/6f1hhgEAAAAAAJwgGAAAAAAAwAmCAQAAAAAAnCAYAAAAAADACYIBAAAAAACcIBgAAAAAAMBJbcHwOQCr7HHVVQqFcxAhHYGzAN5QF7KCec+B3e5HQfNWFgzbedUlT7utUiicgwjpCJwF8Ia6kBXMew7sdj9Kmrf2DMPwkUelI6lKoXAOIqQjcNY5pjnL7Y/z2ppdpqXCm8ZxXsqb4U51oclAuo95S4LdzlGlDVFSyrwNCIY6Xw1v/1PlH93Y7DSe8TnFvLYkQjoCZ8XxrUcp79/40nAzhtnuqmSbVvXtZvd1oc1A+tK9eSvRh90KDjAC9DBDUsin9QVDH8FbgZYFwziv+zsb5zVjZSJCOgJnnWGc1zxPkC3j00xLhTZtWmp09neqC+0F0q3MW5Ie7FZ0gKGjThuipJhP6wqGKj5o2vFdYHUe+ebDiBAP4zw3pSZx1lkQDPkLLcKU29MAAA9+SURBVGuGu9WFxgLpbuYtRR92KzjA0NOs94veWP0ZBuiOKn0+WFRZDA4ZQDDkL7QtM/RGe4EEt6XJAQZtyDAgGCCew3QhVGJa8MJNQDDkL7QtM/RGe4EEd6XNAQZtyDAgGJqmwYMphqHV+vww3jtc8ELzyEdrHCaHEAz5C23LDL3RXiDBXWlzgEEbMgxfwfDZX7vO47YzfZ3H3REJzXmvV4xTJ2Tbqg6mEPLxuEp31oWZp5XMXSTVKCGhCHG6Ye+F/QET3+u7H1KdE+F1lnj8kXxS0G84JnnOX6q39oXGedYDuEJD0XDpkm2CwUgqF6t80HAys7NXlPwMEgbSMTdH2lC83arh0pnXm8psLvbWMU1hnCj0+VfbJ1zZ6AYYsRHiM2/UuDeqDfHaPtZZyra3CNsMg/GxuHFeX+v6fb2NtEqDJZzfNvf2g65/TosVNY6TBI6X5f1D1gtrzzajNl8A3IGoCNG8qt1C6HeKQgv7x26BylliHbYr0DvZNK/Bk0GOFTVwfkiwDRHUqNznhsNSl2xaXq9lNm5LfAZli6RLZnVho2HqJ5IwkIRfS15Qx9twg4ZLZ1599C6z3ThIaksYa3YX56oBhiZCFObVj3vVbYhuxKV0VuygMTeGYPjdhTEYYTYyCWJ8+Y7DCnT2wisGO7Uo9sTQF+9NLB/BkIm4CNELhmleD++98d5ldM6KEAy2W+xBhus9gXdiwPeyNzjoH9RhqUzmHhKFR5KnGy677394DUgZSMpeRh1vv+K6bbh05o2JXvWI8/jTDkduEYLBFyEq8+rHvco2RDfisrMfRGdFDxpzg2AohsOM7qNuvJ39YvWbUvw6BpUh7ez/OYIhE3ERohUMHb2W6wmds2IEQ7CmuYdcp146iAhRpQxLZTIxbI8tkLJFUjdcu4RdvnNNTOJA0vUyMo7fdt1wKc0bFb06+x68NLa5CTKEWjB4I0Rn3lOCwdOGRNQFjbOiB425QTAUxFyQqEqusbu7pXfVGVOKuxt25QoFSEVMhEQtSYL0aJwVtSRJrqhmqxzpTV0AGItqfeuA/fGmSqYRDMoWSd9wfVN2PQ5NR9pA0vUy1n+8C7K7brh05o2LXpeoOl7fp+1UL0QuSXJwqg3RCAZvGxJXFzTOihw05gbBUJx9a+kNgojO3iN3nSAYWkUXIQiGJvA7q23B8L33Q7eoaE08gRdIVk8wLMu6frY3PrxOpBcM4V5mnzIcb103XDUFwy7xuF+u0xMNC4ZQG6KvC4fyAs5SDxpzg2CoxycIriwn8E8jJ2+5fFchPd4IQTC0heishgWDe2V0KKpCDZc3WTXBsCVFMpSZYRCSqeOt64arqmDYTNetXmhYMATbkNiaFe0sZdubDQRDKeTz2H0ddNDu0uSCUYxyaO+8Bzn8EQx5iIwQBENNlM66JhikPQyyy8d5dlx3BIDrVYMdVeon1UWvRjBoWyR1w3X427nn6yGkDKSYJeeqeAsV1z5K80ZFb8Qekbft5n439icRDErzxi9J2t+kVUD02MjvrPhBY246EQwfXdVrDRgG50YZz/4Zv93lFnhaFN2zNQEWJwyaFAzPixArPISdUA1UXYnnOEuqK0JXI1dm68fOCUXnJrgkgkH9pJroVQkGbYukbbisdO1Ihip1IWEgvX8V7GVqCYaWzXs6euVk5r8umq9mE51GMOjMe1owuNoQ3YjrmItHi8cNGn2k8GkfgmHb+NHg4EfN2112Z+x0oM/uYpMkXrQnz6SQtoLfs9ipRcHwxAg5VlnHK+r2LPIkZx0r1jiv67Icn/9rkeNwQh5a2/2Wsza6A8DuqDa3LJPxKlT5pBHJwoJB3SLpkkljhzZWJlWrC8kCyZGbNNemizdFcWoaN29E9K77PbEhvTtdPXizahOdSDCozHtBMAyONkQ34jL+65XiUYNGD0l8anzp+Z3X74/XMg3Gv04Xk4A22vcLiPthXOsQAgk9e2tcL6O9ZepSaZ+gEo+JkI29x6x3Ug17aniUs4yUnwWr1uj4PfVsJHX2CWbR8ptM4ebkt2HHjL7XzJdwiidVJNsnWSbhoigavMYNJxPz9xRanmp1IVkgDcOg8VY43nI0XA2bdxi0ZjOGYi4P7H6R4HnL203j/dgI8ZlXPe6NbkOULdcnH/f/U9eHyz7dZhja57JqhptDhHQEzgJ4Q13ISu/mjV6ynuiY/t7t1gdlv6lw1afdCAaCF/wQIR2BswDeUBey0r95VYLht9om0ded+7dbuyR3lpLrPo0RDJ51MPvpklmXLOK2XccBZKPak3YOEZL9SW/wCDgLGuM5deFZdG9ec2mLVzVsMZzghXX3dmudlM6KKPK6T7uZYQAAAAAAgPIgGAAAAAAAwAmCAQAAAAAAnCAYAAAAAADACYIBAAAAAACcIBgAAAAAAMBJvGD4HAhV8mMTaqrcW8sGSQvmPQd2O4BBOgJngR8ipAXwQgvoDGId49zNGbbRgmE7GLjBR6xyby0bJC2Y9xzY7QAG6QicBX6IkBbACy1wyiDjvHZjv1NLkqal3RCpcm8tGyQtLZt3nJdmXdCy3aqAQToCZ2louf3xkGS48vAIacT1D/RCg4PtaIM0+AxOzu1haPmr4VXuLarQjwrtcxqvRfP28J6jRbtVBYOItNk44Cwf2wKDRAPvEusUjE8IJyjooRGS0PUpeIoXUkdvWmINcnfBUL+auumhxrQ5JlDRsHmb/pp9w3arAwZx0GLjgLMUXG9/xnndO36c1xIt2jiv10t5doS00vU80AtJojct8Qa5s2CoXjfGeXb1pVXuLV+hnietQuPmrR6ZLhq3W3kwyHWKNQ7PdNYJ8169Z2voU2iph27I9Zxu9wQptOLV6vxQLzQmGM4Z5M6CoTrT0tTLt4w850mTUL/xAigFjUNWTpj3avszLXXmlXRDLuLNw/WuB/OepDHBcA4EQz6mpa3Z+nw850nTgGCAx0DjkJUz5r3Y/hzWI5VDNeQi3nxc7now71kQDIXpSjC81/feoGoFI+Q2T1oOBAPcAhqHupw0byuCQT6wx/0KOzzk6jzeYg0SzzXXd27euvijN7/rk4Bg2DA/ULFMru9aBHa9G//e042dh0G3tT/yST1Zfv61zqNxksPvF1atEcpurGb5MFtt41nsp/C6wgjaz299P+jWbtuNL9NgPrbj/s0ndZlBm1t7VDRI641Dy2grYNpe5uBQ16jRX6izTCkvK7H5lN8bMn5pBqHDKGnirTmUBolvuDSuDzhLSuE3b9snA7lxdMW7y4ZlwnY7Zi7ZIr4u6Mybpa9HMAzDYJ/yMC1y9RLTOQxYa6FnWjTzaOEnDdvNUGfjvL7W9RuZohP25b1/200cC695xnl1twzBeBM6ASnDe9htmY1HE995WRdFu2lza5jKBmmwcWgZbQVM28t8+zKzUCu/iEJDMwzvEuyhlRCU8rPnjbdm0RpEXU81rlc6yyj7YnVuHKemMp470m6DP3r1dUFn3mx9PYJhGMRaIHlQjCRnj9Vxy7UjRRutsZv5p5Hj4RbEwXUzx9apsJ9dNJ863mwnTUu4yfvk1pndwhaR223bbmr11S6VDdJe49AyygqYtpdR5hZVqGJgJOsR8zcIhgMxgkHhLZVPtc7a/z5BvDWNdLvHDjXWbkMawaA0b76+HsEwOLxl1wxHXXHVho5brh0J2miV3eIEg3BHza3287B7WNfcwhAVb7aJ5FUMN7BbZCR5fh1Zn1ukskHaaxxaRlcBk/YyjhGMOLbUFxqaYRCxi0AwHIiaYQh5S+t6EbcRU1Tn5pFWAKhiymudBIJBa958fT2CYRiUMwy+JtvlxU5brh3X22id3WLGBMG1rq3zfdj3g3gWG+jjbW8xV6W+hd1ca+q/1/3D2+MEfSi3xqlskBYbh5ZRVMC0vYwjt+PlyEL1NWRaPEuoEQwHLgoGM6XS9Yd/Bde7p6jO7XMwumsJwPZf1T6B64Ihwry5+noEw5uDK6Rm6xAXr1CUdNxy7UgzJgjbLX5MsM+3MztPy+u1Lsu6fjZyOt//RsTbz2SB1yGd2w3BsOMWgiFH49Ay3gqYtpeJEAwRhYZryDfDw7IyBIOXSoJB5azwf/ZZaaO3WYwVR8L63vflCLsNqQRDlHnT9/UIhjebIHtZQfAldlqt45ZrR76XiL5UUWOCT63oJo4/d/xrjtLE29dM2unTTu2GYNhxC8GQtXFoGakCpu1lLs4wOAjUELlBQzAEqSEYtM4K/8dTaIeM2y5yeYgca7ch5wxDmGR9PYJhGIZh0mwHiR1IdNxy7bjeRuvsph4TyOcV92VtaT7LuvvogevbTrNj+vQudgsu4XQ+kdRXahaENkxlgzTXOLSMrgKm7WWUC9kjCw0O6l0jVQSDl/J7GPTOCv/HU2iXfNb5iMuR4u02pBAMWvNm7OsRDMMwCHM9zg5WVvZScisGTm05+dxZtUqoaaNDT6qxW4RgEHcJOy6HqGNeawgmS4a4ePvm4vDWLe02DOr3MPIOsrgBS+DWHmeQ5hoHJVWcpa2ASXsZ0XvC29GoQhMKhmM2ckO4/+/VeNNRLUI0BtHVU43r4we+KaqzmrqDn++g0FUDUgsGrevD5k3b1x/zQDCYUwyGeDjY125qPSFy7ObOGHpbKlXLS6oOOfikYbvpxwRv/4T7Hg2VzCu1+OIcZ1S8Df6psrvYbd23WJ7JGeu9rWRcTW4anmmQ9hoHFRWdpaqASXuZo6en5bUsi/38EYX6Ww37tcVm7/cKj3kyrv8yGud1XRaPb9LEm4JKEaI0SFSt97te76xdJlers5bagx952dEwDGfsFohedV1QmTdlX2/d5+MFg3OUJXt4i4xwLO+TnneVJ26zYT5k+GEVT+qx2+9f6zzu0i2T+a9hGITZoItNSmHz7m9fFKlij698UF/32LndvkUaMeGpV2GzxeSmvbtHGKThxkFNJWeprZa0l9mXvEzvkaNTCXgKVT+BmfCdQnxhayRc51EckIjFBiVLIFGQGt2uziAxtV7herWzPlyrzpHU8cIH/+hYaTd19Krqwve+/OZN3dcbRT9dMLiFX4opnBT43hvDZe5j3rLfVChvt7Trk5OvdsYgHXGfWg95aDZCqKdQCwSDc+n3Z9NL+vIiocZkpXfz/uadz82+n6WG3ZoeH2OQjui91kNuGo4Q6imUw5qt6MYhOY9VFSZxWrCLvF0REnEL826RW64PqWA3cxY2wdathLlhkK64Ra2HjLQbIdRTACVZBQMAAAAAAPQNggEAAAAAAJwgGAAAAAAAwAmCAQAAAAAAnCAYAAAAAADAxX8DuzMSxPszCqoAAAAASUVORK5CYII=)
1.clear:清除字典中的所有元素
例如:
>>> a = {"key1":1}
>>> a.clear()
>>> a
{}
2.copy:浅拷贝
3.fromkeys:从序列键和值设置为value来创建一个新的字典
例如:
>>> a={"key1":1,"key2":2}
>>> b=a.fromkeys(["key1","key2","key3"],10)
>>> b
{'key2': 10, 'key3': 10, 'key1': 10}
4.get:如果该键在字典中存在,获取对应的值;如果该键在字典中不存在,默认返回none,也可以设置一个默认的返回值
例如:
(1)键值在字典中存在的情况:
>>> a = {"key1":1,"key2":2,"key3":3}
>>> a.get("key1")
1
(2)键在字典中不存在的情况:
>>> a = {"key1":1,"key2":2,"key3":3}
>>> print(a.get("key4"))
None
>>> print(a.get("key4","不存在"))
不存在
5.items:获取字典中的键值对放在列表中,键值对作为已元组的形式作为列表中的元素
例如:
>>> a = {"key1":1,"key2":2,"key3":3}
>>> a.items()
dict_items([('key2', 2), ('key1', 1), ('key3', 3)])
6.keys:获取字典中的键
例如:
>>> a = {"key1":1,"key2":2,"key3":3}
>>> a.keys()
dict_keys(['key2', 'key1', 'key3'])
7.pop:删除字典中键(key)所在的键值对
例如:
>>> a = {"key1":1,"key2":2,"key3":3}
>>> a.pop("key1")
1
>>> a
{'key2': 2, 'key3': 3}
8.popitem:随机删除字典中的元素
例如:
>>> a = {"key1":1,"key2":2,"key3":3}
>>> a.popitem()
('key2', 2)
>>> a
{'key1': 1, 'key3': 3}
9.setdefault:设置字典中键对应的初始值,如果键值都存在,则显示的是原来的初始值,如果不存在,默认显示none,不默认的话,可是设置一个初始值。
例如:
(1)已存在键值对:
>>> a = {"key1":1,"key2":2,"key3":3}
>>> a.setdefault("key1")
1
(2)不存在键值对:
①不输入默认值:
>>> a = {"key1":1,"key2":2,"key3":3}
>>> a.setdefault("key4")
>>> a
{'key2': 2, 'key4': None, 'key3': 3, 'key1': 1}
②输入默认值:
>>> a = {"key1":1,"key2":2,"key3":3}
>>> a.setdefault("key4",4)
4
>>> a
{'key2': 2, 'key4': 4, 'key3': 3, 'key1': 1}
10.update:把第二个字典中的键值对更新到第一个字典中
例如:
>>> a = {"key1":1,"key2":2,"key3":3}
>>> b = {"key4":4,"key5":5}
>>> a.update(b)
>>> a
{'key2': 2, 'key4': 4, 'key3': 3, 'key1': 1, 'key5': 5}
11.values:获取字典的值
例如:
>>> a = {"key1":1,"key2":2,"key3":3}
>>> a.values()
dict_values([3, 2, 1])
三、注意:
1.列表中的元素可以被修改,可以被删除,可以添加,可以循环遍历列表中的元素,可以通过tuple()函数与元组之前的转换。
2.元组中的元素不可以被修改,不可以不删除,不可添加,可以循环遍历读取列表中的元素,可以通过list()函数与列表之间的转换
3.字典中的元素中的值通过键去修改,键是不可被修改的。
常用功能的介绍:
1.append:在列表后面追加
例如:
>>> a=[]
>>> a.append(1)
>>> a
[1]
2.clear:清空列表中的所有内容
例如:
>>> a=[1]
>>> a.clear()
>>> a
[]
3.copy:拷贝列表中的元素
4.count:统计列表中某个元素的个数
例如:
>>> a=[1,1]
>>> a.count(1)
2
5.extend:合并两个列表
例如:
>>> a=[1]
>>> b=[2,3]
>>> a.extend(b)
>>> a
[1, 2, 3]
6.index:获取列表中元素的下标值
例如:
>>> a=[1,2,3]
>>> a.index(2)
1
7.insert:在列表中插入某个元素
例如:
>>> a=[1,2,3]
>>> a.insert(2,4)
>>> a
[1, 2, 4, 3]
8.pop:删除列表中最后一个元素,并且给出元素内容提示
例如:
>>> a = [1,2,3]
>>> a.pop()
3
>>> a
[1, 2]
9.remove:删除列表中的某个元素
例如:
>>> a = [1,2,3]
>>> a.remove(1)
>>> a
[2, 3]
10.reverse:镜像列表中的元素
例如:
>>> a=[1,2,4,3]
>>> a.reverse()
>>> a
[3, 4, 2, 1]
11.sort:排序列表中的元素
例如:
>>> a=[1,2,4,3]
>>> a.sort()
>>> a
[1, 2, 3, 4]
二、元组(tuple)
常用功能介绍:
1.count:统计列表中某个元素的个数
例如:
>>> a = (1,1)
>>> a.count(1)
2
2.index:获取列表中元素的下标值
例如:
>>> a=(1,2,3)
>>> a.index(2)
1
三、字典(dict):
常用功能介绍:
1.clear:清除字典中的所有元素
例如:
>>> a = {"key1":1}
>>> a.clear()
>>> a
{}
2.copy:浅拷贝
3.fromkeys:从序列键和值设置为value来创建一个新的字典
例如:
>>> a={"key1":1,"key2":2}
>>> b=a.fromkeys(["key1","key2","key3"],10)
>>> b
{'key2': 10, 'key3': 10, 'key1': 10}
4.get:如果该键在字典中存在,获取对应的值;如果该键在字典中不存在,默认返回none,也可以设置一个默认的返回值
例如:
(1)键值在字典中存在的情况:
>>> a = {"key1":1,"key2":2,"key3":3}
>>> a.get("key1")
1
(2)键在字典中不存在的情况:
>>> a = {"key1":1,"key2":2,"key3":3}
>>> print(a.get("key4"))
None
>>> print(a.get("key4","不存在"))
不存在
5.items:获取字典中的键值对放在列表中,键值对作为已元组的形式作为列表中的元素
例如:
>>> a = {"key1":1,"key2":2,"key3":3}
>>> a.items()
dict_items([('key2', 2), ('key1', 1), ('key3', 3)])
6.keys:获取字典中的键
例如:
>>> a = {"key1":1,"key2":2,"key3":3}
>>> a.keys()
dict_keys(['key2', 'key1', 'key3'])
7.pop:删除字典中键(key)所在的键值对
例如:
>>> a = {"key1":1,"key2":2,"key3":3}
>>> a.pop("key1")
1
>>> a
{'key2': 2, 'key3': 3}
8.popitem:随机删除字典中的元素
例如:
>>> a = {"key1":1,"key2":2,"key3":3}
>>> a.popitem()
('key2', 2)
>>> a
{'key1': 1, 'key3': 3}
9.setdefault:设置字典中键对应的初始值,如果键值都存在,则显示的是原来的初始值,如果不存在,默认显示none,不默认的话,可是设置一个初始值。
例如:
(1)已存在键值对:
>>> a = {"key1":1,"key2":2,"key3":3}
>>> a.setdefault("key1")
1
(2)不存在键值对:
①不输入默认值:
>>> a = {"key1":1,"key2":2,"key3":3}
>>> a.setdefault("key4")
>>> a
{'key2': 2, 'key4': None, 'key3': 3, 'key1': 1}
②输入默认值:
>>> a = {"key1":1,"key2":2,"key3":3}
>>> a.setdefault("key4",4)
4
>>> a
{'key2': 2, 'key4': 4, 'key3': 3, 'key1': 1}
10.update:把第二个字典中的键值对更新到第一个字典中
例如:
>>> a = {"key1":1,"key2":2,"key3":3}
>>> b = {"key4":4,"key5":5}
>>> a.update(b)
>>> a
{'key2': 2, 'key4': 4, 'key3': 3, 'key1': 1, 'key5': 5}
11.values:获取字典的值
例如:
>>> a = {"key1":1,"key2":2,"key3":3}
>>> a.values()
dict_values([3, 2, 1])
三、注意:
1.列表中的元素可以被修改,可以被删除,可以添加,可以循环遍历列表中的元素,可以通过tuple()函数与元组之前的转换。
2.元组中的元素不可以被修改,不可以不删除,不可添加,可以循环遍历读取列表中的元素,可以通过list()函数与列表之间的转换
3.字典中的元素中的值通过键去修改,键是不可被修改的。
相关文章推荐
- Python学习笔记02
- Python学习(9)列表
- Python学习笔记01
- Python学习(8)字符串
- 《跟着小吴哥学python》之 04 python中的列表&元组
- Python学习(7)数字
- 下载徐小明新浪博客全部博文链接
- python中super函数用法
- mac上 python 安装lxml失败Could not find function xmlCheckVersion in library libxml2. Is libxml2 installed
- python中self的作用
- Python/Theano 加载和保存模型
- Python环境安装以及简单案例
- python selenium 爬虫页面滚动条滚动到页面底部
- Python图像库(PIL)(更新完善)
- python常用函数
- Python学习(6)循环语句
- Python学习(5)条件语句
- 【python小练】0020
- 用python实现DES加解密,并附带EBC和CBC两种分组加密模式
- 小白python学习第二周2.递归函数