Remy-小野兔 发表于 2021-8-4 13:17:13

小白numpy库使用疑问

本帖最后由 Remy-小野兔 于 2021-8-4 13:19 编辑

scores = np.loadtxt("cj.csv",delimiter=",",skiprows=1,encoding="utf-8",dtype=np.str)
scores = np.NAN
新手想问一下,问什么采用np.NAN替换空值后最后得到的是na不是nan呢?

大马强 发表于 2021-8-4 13:33:15

你可以看看他们两个相不相等

大马强 发表于 2021-8-4 13:34:54

我记得nan是个浮点数来着,好像怎么运算都为nan

Remy-小野兔 发表于 2021-8-4 14:18:31

?可是都是nan也是不相等呀,你用np.nan == np.nan,它都会给你报false的。而且nan表示是不是数值,而na代表缺失值不是吗?

大马强 发表于 2021-8-4 15:49:11

试试 np.nan

nahongyan1997 发表于 2021-8-4 16:08:37

哪来的代码啊,好像语法就是错的啊。

Remy-小野兔 发表于 2021-8-4 16:36:22

大马强 发表于 2021-8-4 15:49
试试 np.nan

试过了,还是一样

Remy-小野兔 发表于 2021-8-4 16:37:42

nahongyan1997 发表于 2021-8-4 16:08
哪来的代码啊,好像语法就是错的啊。

如果语法错的话它应该报错呀,可是数组还是读了出来,只是空值本来想让它变nan的结果变成na

nahongyan1997 发表于 2021-8-5 08:46:32

本帖最后由 nahongyan1997 于 2021-8-5 08:48 编辑

你的

scores=np.nan 并没有替换掉空值,而是替换掉了元组的第一个元素。
元祖的数据不可以改变,所以你那个位置原本就是 na ,根本就没变。

毕竟我没有你那个文件所以答案可能是错,但是第一句话肯定是对的,
如果答案还是不对的的话你可以把 csv 文件给我我可以给你看看。

阿奇_o 发表于 2021-8-5 15:45:24

你的代码并不完整。。
首先,先确认你的 scores 里是否有 '' 空值, scores# --> 有吗?
其次,因为 scores 的 dtype 是 np.str 字符串类型,而 np.NaN / np.NAN / np.nan是 浮点数类型。
所以,若的确过滤出有 空值(缺失值),那么 scores = np.NaN或 scores = np.NaN
# --> 这样替换 会先将浮点数 np.NaN 转为 字符串的 'nan' ,并赋值替换。
# 这样"成功替换"后,打印scores显示的结果是:原来的 '', 变成了 'nan'

你要替换 'na', 也是同理,但要 先确保过滤出来的 的确是它们。

Remy-小野兔 发表于 2021-8-6 17:39:23

阿奇_o 发表于 2021-8-5 15:45
你的代码并不完整。。
首先,先确认你的 scores 里是否有 '' 空值, scores# --> 有吗?
...

因为是练习创的数据,是设置了空值‘’的。但是我实际的问题是我预想替换后显示'nan',但是它显示了'na'运行结果我在上面的图上是有的,最开始不替换它直接显示的是‘’,但是通过np.nan替换后它显示是‘na’,那么说明结果的确是被替换了,只是替换成了'na',不是我想要的'nan'。最后我换了另外一个练习文件,用同样的代码是成功将读取的文件里的‘’替换为'nan'的。我不明白,同样的代码,对这个文件为什么就不可以,两个文件encoding都是utf-8的

阿奇_o 发表于 2021-8-6 20:37:14

Remy-小野兔 发表于 2021-8-6 17:39
因为是练习创的数据,是设置了空值‘’的。但是我实际的问题是我预想替换后显示'nan',但是它显示了'na' ...

你用的是哪个版本的numpy, 缺失值np.nan的字符显示应该是 'nan',怎么会是'na'?

其次,程序是"死脑筋"的,"你看见的"不等于就是"程序处理的",排除疑问需要一步步验证……

Remy-小野兔 发表于 2021-8-6 21:29:20

阿奇_o 发表于 2021-8-6 20:37
你用的是哪个版本的numpy, 缺失值np.nan的字符显示应该是 'nan',怎么会是'na'?

其次,程序是"死脑筋 ...

Anaconda3中的jupyter notebook ,配置是python3,另外不替换值读取的结果和替换后的如图所示,我实在是弄不清楚

阿奇_o 发表于 2021-8-6 23:17:38

Remy-小野兔 发表于 2021-8-6 21:29
Anaconda3中的jupyter notebook ,配置是python3,另外不替换值读取的结果和替换后的如图所示,我实在是弄 ...

这个'na',还是'nan',倒不是关键。关键是,若要进行运算,进行分组统计的时候,要注意 缺失值的类型和行为。
另外,我的numpy是1.17。
scores = np.loadtxt('testdata.csv', dtype=np.str, delimiter=',', skiprows=1)
scores
array([['1', 'alice', '88'],
       ['2', 'bill', ''],
       ['3', 'cat', ''],
       ['4', 'david', '100']], dtype='<U5')
np.NAN
nan
scores = np.NAN
scores
array([['1', 'alice', '88'],
       ['2', 'bill', 'nan'],
       ['3', 'cat', 'nan'],
       ['4', 'david', '100']], dtype='<U5')
type(scores)
numpy.str_
scores is np.NAN
False
np.__version__
'1.17.1'

Remy-小野兔 发表于 2021-8-7 08:19:36

阿奇_o 发表于 2021-8-6 23:17
这个'na',还是'nan',倒不是关键。关键是,若要进行运算,进行分组统计的时候,要注意 缺失值的类型和行 ...

因为nan是浮点数,na不是浮点数,我后面直接改数据为浮点数的时候就不能一步完成了。我看了我的numpy版本是1.15.1,我更换版本试试

feng_striver 发表于 2021-10-27 17:04:20

将cvs中数据的数值类型改为数值应该就可以了

阴阳神万物主 发表于 2021-11-3 10:43:29

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Zt3KmH7lqjezdLOnNK7yzbpBunPq/MrJX65nfX6c5Vn4W2sr4TROTygb41Am74o5YGG0tCDQHNNbon93llTlmjxdvi7l0Mip5DQOcYELsQ4FbthkWaNnqw+qW0rb6eon6DR6ugZJeagniOPhUCVn2lNZcrNCee0ye92OaHry7TsmW/1Jzrh2h6qUced602FE7QJamWjDFK6TdcUx99q8vvokHv/qHz49y1G1Q44RKlWh1+hyv2yR3B96dtYu7xVKtolCVj0pW/rvNeer8T86ZdKplzvYZfkCrLt9K/lXqBhk9dpA219uf/92p7iIiIiBgz+2QI2PNSheb/+yplZq3UXauOquj2l3Xrsjp9fOqcdOZLffjSRn3L72H1MaRXIeCMmo/U64VFazQud6OW7w/hqgohhoDT6zcl/KUbgwsB52NAdUxCQL1W5w+SZQ1SbslWHWvy/dzdqP0rZyjDWBq6sKrH5+lTISAGvvlQnvKuyZAxRtlLNmnW1RdpXOEa7W90y+Nu1I5fj1easTRxaUOHxx3UC3fm6fohKb7HlSr7oiGaXrJDjW7v73BHSa4GWZaGFm6LWAw4PzH3v5febmLu3laka9ON0q8t1Jr9je3jaTr2qu4blSaTnquyhshtDxERERFja58MAZK8k+qslbpmyit67P+x9+7RUZWH/v671l4r63zJWuQIpI3itxCqcEQQUjz8qJTLaSg0zULwUtITqXwt5we0aHuIXcFflUOhLS1H5WuFGj2WUBs9cimEVayaKAdrG5GDioJIgHCNGgImceDEyUBdn98fMwkhs+c+s+f2PGs9a1XmsvfMZNK8z373u/0GxWe15jsbNHbxe3IlakejIdoQ8PLLPQPz/+e2Ov3+cFfAy+AFfDwzAoLGgClTvuZ8CKibr0HG6O/vfsHm9hatmWhkBs1XXYjnIQTEwT0VKjRGVs6Nuu+1NtvXaiauUUuAz9DKK9E6m6PqB5eNsj2KHq29B+aBjtL7D8x9P0tmvFafDvxzOHFNS5y2h4iIiIjJNuNDwNyNnbY371gawWJ6ThHTjIC/6fy5Fr22oV7T/+kZTV9xRGfCrQGsEZCaIeDgSo2xjPLm/tH29uoSI2NKVB3ieQgBcdAXAsavPh34syi0eZ9CPM7dskYTjZFVUh2XWQFXDszdctXNV4ExMqOW9Ryl9x+Yu7S1LE/Gulm/PBL4tRdW+M8+iW57iIiIiJhsMz4E3Puy/c2ZFwIuc7GxQaVTNuhrK46HNzOAEJCipwbY6zpzTIfffkk/GEUIcEzfYDjQ6wkVAgK/D77ZBF+6Tw1x2M++A3O3+7TWTLRkTK7u2OQ9Sh/+wLxNzYcP6/XHvqFBYYaA2LaHiIiIiE5JCIj7HsZAnEKA5NbmHz6jsVO2qerDMO5OCEjhxQLdcruatPM3FZcXDMzpr/whRRpZkIUhIAmLBbrd7gSGgCNaOSa8zzEc/Qfm/kfpgw3M297aon/rWTDQUr+r8nXD6OuVG3YIiGx7iIiIiJgcCQFx38MYiFsI6H6fNqisJoxVEAgBKXr5QJeanp2roTlG1oDRmrv6Jb3dfPn8dE4NcNCEhYDwP8dwtBuY9z5KX1LdYj8wb3tNyyYMkGVyNHRGhX73+jHvooa9XkO4ISCs7SEiIiJiUiUExH0PYyCSEHCxS63nAi8K2B0CSp84G/q5CAFBI4Dd/Z0IAd2rsOdNXqP3XP63EwIcNGEh4B0tHZ7oENDrKH1hhar9Bua+Rf6s4Zr/gs1aBhGHgFDbQ0RERMRkSwiI+x7GQNghoEO/nf+Mxk55Vksb/mZ7D0JAcAKHAP8IYHf/xIcA35Rxa7Iet7lsm9tNCHDUhIWArSrLNTLDl+qdOOxnoIG5292ixyd7j9IXFg66cn+2linXGBUs2hX0tUcSAoJuDxERERGTLiEg7nsYA2GHgEbdO2WDxk55RvO3212+z63/XLxBY6ds1KoDYWyXEBA0AtjdP/EhoFolJsDg0ichwEETFQJ2LVJBsEF4hAYemF/elumzP3sqCv3+ze41RBYCAm8PEREREZMvISDuexgDEcwIqJq3QWOn/ElbbDrAxcbdmvVPG/SVxe+pLZztRhoC2hv1o5nPaOy0zfppg12ISH38Q0DgCGB3/8SHgDrNHxRkRoCrST8f3ysEVJepbKv9c8UcAtZFs6I9IaD346wxK3sup3dZ37n01kStOX3lbaerZ2qAZWQNmKnq0+HvZ9CBec9R+itfR8uaiTJBYkTb9vJeVw3Yo4qylToSw/YQERERMfmmWQi4qJd/UqOxU2r0rzuDXxjPvbNeX52yQXOfu2AzuOvU7xdt0NgZL6m2PVH7GgURrBFwsfFtlX/rGU3/yfva3+F7he7PtL/+Lyr/1gZ95Y6dejXc1xZhCPif2hc0dsoGr0tTKqWEzZUD++ARwP/+zqwRcLq6xLdGwGq9ecZ7Kba25rf10uq5Gn3NdRp9fa6MmaYnXS7VzZ+plXbXgHdHGwLadKihRHm/MjK/ylNJwyG1RfT4zAoBbc/eqlxjNP7nTXL53X5aq8cbmdxperLvoN0XAkZNnqyhN1do8yHvYo9thzar4uY8GWuAZlb3PS//fT10o/dIuglwJD7QZ7Zu6iDd/YIr8H3sgoarQRXDfWsEbPZ9zq4zOvb671Qx4zpdM3qkCozRl+5rkPv0GpXM3Rrb9hAREREx6aZBCOieBh/EKwajZ7XmO31u7z3I9c0U6H17WOfRO0GkVw1wn9OWX2zXN7/1jO+1PKOJc7arYsMJnQneSa4kq2cEhI4AV97f4asGbK/QpCH9leMbFOb0H6Kiuev0Vptb7oO/1tcHWDJWP425t04tAZ4nshDgmwUQ0HAXtcuUEOB9Hd2DctP3dA3fQLf37VcM3HsGwr7P8pp+soyRsfrpmqK5WvdWm+12L88ImKBfBjziftnu6f2XDfQ5tai6JNfmqgFvad1tN+iqft4j+Mbqp6tumKFl25vkcrv08uKhyjFGOV/8ph7/IA7bQ0RERMSkmgYhIIuI4+UDIyJr1wgILwJcvr/TISA+Rn9qQCxmSgiIUY6IIyIiImIKSghIJWxmK3QbaK2DyLCZLWE3ayILOLbu22FHAEly1S3RlJ4QMEVL6lxRbTdZIcAYG4MsRBiR1SX2z88AmBCAiIiIiCkpIQDAQZIRAjB5dq8tMOqBfTZrCyAiIiIiJkdCAICDEAKyRZu1BUywFfYREREREZ2TEADgIIQARERERERMtoQAAAchBCAiIiIiYrIlBAA4CCEAERERERGTLSEAwEEIAYiIiIiImGwJAQAOQghARERERMRkSwgAcBBCACIiIiIiJltCAICDEAIQERERETHZEgIAHIQQgFd6Wi/M/4pm/e5sCuxLKntAy6dO0MoGVwrsCyIiImL6SwgAcBBCAF7WpYaK4RpQUq3TV/z7Pr0/bbjqC4frL7/cq86k72e8je71uRoqNHxAiZ5sIgYgIiIixiohAMBBCAHY7enqEg0YXqEGV5/b3v+lXi8sVH1hoeoLS/TB8eTva1yN4fWdri5RXt5M1bSkwOtARERETGMJAQAOQghAt9t3dDtnuCpsp7p/pNMrZ+u/ritUfeEk7d+X/P2Nr7G8Ppe2luUpz28WBSIiIiJGIiEAwEEIAeh279HS4ZYK5tfJFeR+Hy+/RfXXzdHh1vht+8Iby/XX76xXh++/m+Z1H5nvtu/AfJ/2Twp2u1sX3tui/d8t1s7hvvsMH63Xv/uITp08H3Rfon59LdUqyc3VtCdPp8BniYiIiJieEgIAHIQQgKfXTJRlTdbjQae3n9eJRSP16qI/xm2NgI5XK/V60Ux9sPfKAXrnq0u0q7BQ9bc+0RMI+tr56hL9ef42Xej7nBu/p/8qmqn9LxyS67xbbneHPn1zi976p+tUf90/af/eQDEgtte3p6JQJq9MW/ueVoGIiIiIYUkIAHAQQkAQa2ZqeMWe5O9HQt2lRQVGuXdsCjIb4Lzan52nnTfNU9NH8dluxx8X67WiOWr8wGZgvm+l/lxYqPp5GwM/x76V2rOyz8/t+Y3ac12hXlu+1//+Hz2rN0cWqv6mxTp5PgGvr+VxTbYsjVl5MAU+U0RERMT0kxAA4CCEgMAeWTlGhRkeAlyb7lCuGaT5dYHvc37LIv35zpU6HdcIME9HA03VjzYE+B73yh1P284kOLl4pOoLR+qtFxPx+lr0+GRLprBCe1Lgc0VERERMNwkBAA5CCAjkHlUUmowPAVvLcmVyy7TVoe15p+4HiQBud/Qh4Phv1HBdoer/8Sf6yOYxZ1ZOUn1hofZsTMxra1kzUcYUqmJP8j9XRERExHSTEADgIIQAf9uaX9fq4gGyTKaHgAbd9yUjM3GNWhzYXncECDn9PtoQ4Har8+xJtZ+1jwzeGQEJvPxhw336kjGauKYlBT5bRERExPSSEADgIJGHAJeati/T7KIh6p9jZIyRMTnqP6RIc9e9pba4/1KIYXuuJm1fNkM39M/xPc5Sv2uKNLvi3/Xv371XVX0Xx6su8d0viH5Tv6tV0vc+JdW+27yzCmwf/8GLeuKJJ/TvFbM1esJS7XG36a11t/Xsq9XvKt0wY5leOB36vZlxQ3/l9Lwvk7ToufeCrv7fd98Hza9L8C927zn4/1X0vfDOwY8hBATUt0bAzu9tDfO9iULXk5rmyPuJiIiImHkSAgAcJLIQcFqbygbLsgZr5rrX1dzm+3fXGR3YcIcKjKXhS+N5BD2W7e3RQzfmKO/mCm0+cMY7+HOd0YHNFZowwAo9hXtPhQrDnhHQpkP/d5pyjdH41f6XkDu9bqr+zhqu+15ru/zv//ULzfrGaA2wjEzhfXqy4kZ9cUKFNh/y3qft0GZV3Jwnk1eiatsY4FLD0huVYw3WzHVv6oyr+/XN13DL0uCyTaGvax/Ra4xWXwSYsFgnwj0HP44hoPPsSX285SdqKLpBf/7RNrX7LRQYT31RqCcEISIiImK4EgIAHCSiEFA3X4OM0d/f/YLN7S1aM9HIDJqvunj9Qohle1vLlGuVqNrucm6nq1WSZ1RSHWTbkQ6SXZt0R65RwaJdfrcdWTkmwFFi32uwLA3//mv+sxt8z2mNWamDfW5rqZmpPJOnsq0uv+f13parkuoQU9QTHgLO6+zTcyKLAG53nELAPu2fVKj6wkLVF96kv/50uz7pSMRr7C0hABERETFaCQEADhJRCDi4UmMso7y5f7S9vbrEyJgSVcfrF0IM2zv768lBF8FrebxYkx4+GXjbUQyS6+YPsgkTR7RyTOBV+atLjMygu/VCgOvP180fZDN7wXvJv8Dn9vtOSRizUkeC7XOCQ8D515fqz//wTR18L8LHxvPUgI6P1LrrGb078ybV3zRT77/RkZDX6pUQgIiIiBithAAAB4nHYoGuM8d0+O2X9INRcQ4BsWxv1yLvqQP3veKdNh/pdqIZJO9apAKTq7Ktvf7tyEqNCTJLorrEbt2BXvrWLZj2pMtv34YvfSfgvoQVZRJ+TnsyZwT09SMdvWuk6q8r0QeHE/Wz6dSaC4iIiIiZJyEAwEGiCgGuJu38TcXlBfxy+it/SJFGFiQoBES1Pd859MbIWP103dcW6t+2vBl+FIjqaLn3SH3uHZt6FqQLfFqA15AhYGuZcvvuhy8OBDu1wRsCQl3KzomrBiR3jYArbPiJdhUW6tVFf1RnIl4rVw1AREREjFpCAICDRBYCXGp6dq6G5hhZA0Zr7uqX9Hbz5QXw4n5qQBy252raqdVzi/TF7isO5HxRkxb9Tm+1hdh2lNPmDy4bJZN7hza53Ap1WkDPawgWAmz24+yvJ4e+uoEJJwS4tbUsN+gpFPExBa4a4HbL7d6oPYWFqh+5WCcT8Dpb1kwM6z1HRERERH8JAQAOEkkIcG0tU54xypu8Ru/ZHFmPdwiI7/badOil1Zpb9EXlGCNrcJk2Bbs0X7Tnzx9cplHGUkm1K+RpAT2vIVgIqJkpK4oZAWG/x9UlskzwWBEvOzZ+T/9VNC90DIghBFz46Jg+DbgooC8EFM5TU9xfn0vVJZZMwSLtSvD7iIiIiJiJEgIAHCT8EHBEK8cYGWuyHm+xv098Q0As22tRXfXWgAvltb12n4YHWYTQ7XbHsJCed7+tyY/rgzUTQ54vHioEHFk5RsZYmlnT6999V1MYs/JI7O+za6vK8pw7r707Bhw9eT7w/aIMAZ0vLtbOwkK9UvJrnbN9XAJDQMvjmmwZjVp20JH3ERERETHTJAQAOEj4IcC3InqQQWt8Q0As29ujisJRWnYw8PPXzR8kM3yp3gm0/ZY1mmgbAvbolw/+p84G2feWxyfLsgZrcEHoI+3BQ0CLHp9s2cSQrSrL9caGwOf2t2hr9dawzv0/uHKMrCDBJd52/HGxXgsWA47/Wn8NEQJcT9+hN55uveLfzqyc5L1c4D/+RB/ZPe7PldpVWKhX5mzoWcMhXnpPCSlRtUPvISIiImKmSQgAcJDwQ0Cd5g8KcoTe1aSfj+81MK8uu7x6vqtBS2/MkTE5unFpQ5iDsBi25/YOsPNKqnXa9rld2nRHrnLLtgbZvnew7XekfE+FSkLNEvAdHQ5nmrg3ZuSpbKvL7zZX3XwVGKOC+XV+79nBlWNkmULd1+D/OLfbrdPVJbo53NkMrjrNL7DfTqL0xoA5avzALgYc1vvfKFT9yEU6cd7m8R2v6p2vztHh1iv/vfOPi/RqYaF2PfiGzXN+pKZ5N6m+8B+1789BZiNEY0u1SnItjVnJbABERETEaCUEADhIJGsEnK4u8Z2zv1pvnvEOQNua39ZLq+dq9DXXafT1uTJmmp50uVQ3f6ZWHvE99vnbvKv3m8hmDES9PXf3ANvSgAmL9LvXm9Xm+3fXmQN66d8mK88arqVBF3VzaWtZnkzuRP38zTNyud1qO7RZ8yfcppqQR329oaFg0a6Qr7G6xMgMmqjJNw7VzHW+qxq4zujNdTM12DLKuXGpGmyvdHBa1TMHyLIGa+bql3TA9/64zhzQ5ooJumZMoMfZ62q4T4Uh35P42vFqpV4vmqkP9voPzDv3/lJ/GV6onTN/oqa3z3pX+T9/Vud2PaI3b75Z/73xI5vn/EgnFk/QKzfN1P4XDsl13i23+7xch3bp0Pcm6JXCm9Tw671xvmKA9+fEGrNSBx163xAREREzUUIAgINEfNWA7RWaNKR/z8A+p/8QFc1d512F/+Cv9fUBlozVT2Purbs8Lb3XjICh94Q3XT2m7bndalj6dc2o+Hc98W8L9bUbLj/e5PTXDTMq9Nx79kfSr9D1np687Tr1s4yMyVH/G27Tk+E8zu1Sdcn/1qJdoV9jz6kBbW9p3W03eC+PaIxy+t+gGcu2qynoYN6lpu3LNOOGq3z76H1/JlWEepz9czVUDFfO8IqIAkKsXnhjuf76nfXqsLmt8+RfdehHpXrthuu8U/6Hj9ZrpT/Skfc6gjznebXXPaI9xWP16nWFqi8s1Cs3jNXrd/x/IR4XnaerS5SXV6LqYAtPIiIiImJICQEADhJZCMCwbHlck/93eKvHh7xqgKOe1gvzh2tAwFMqsLeuhgoN73ejlgY4PQMRERERw5cQAOAghID42NZ8WM1t3v99cNmosFePT60Q4JY3BnxFs353NgX2JZU9oOVTJ2glEQARERExLhICAByEEBAHj/xSN1tG1oAJWvTvP9bkoWXaGs70etc+PTDKyOTeqmfbUuB1ICIiIiImSUIAgIMQAuJgS43mfDFHxljqd12Y6whUl/gWT+xlSXXyXwsiIiIiYhIkBAA4CCEAERERERGTLSEAwEEIAYiIiIiImGwJAQAOQghARERERMRkSwgAcBBCACIiIiIiJltCAICDEAIQERERETHZEgIAHIQQgIiIiIhUuxhdAAAgAElEQVSIyZYQAOAghABEREREREy2hAAAByEEICIiIiJisiUEADgIIQAREREREZMtIQDAQQgBiIiIiIiYbAkBAA5CCEBERERExGRLCABwEEIAIiIiIiImW0IAgIMQAhARERERMdkSAgAcpDsEdHZ2Jv3Lj4iIiIiI2SkhAMBBjh07pn379un48ePEAERERERETIqEAAAH6ezs7JkVgIiIiIiImAwJAQAO09nZ2TMzABERERER0WkJAQAAAAAAAABZBCEAAAAAAAAAIIsgBAAAAAAAAABkEYQAAAAAAAAAgCyCEAAAAAAAAACQRRACAAAAAAAAALIIQgAAAAAAAABAFkEIAAAAAAAAAMgiCAEAAAAAAAAAWQQhAAAAAAAAACCLIAQAAAAAAAAAZBGEAAAAAAAAAIAsghAAAAAAAAAAkEUQAgAAAAAAAACyCEIAAAAAAAAAQBZBCAAAAAAAAADIIggBkAS26FRxvhptbKp6Py5b+OQB++dvLC7XJ3HZAgAAAAAAQHpCCIAksEWnkjEgP7pCTYSALKJV9Qtu1u3PnU/2jgAApAhH9LOv36JVez3J3hEAAEgyhABIAoQASDQe7a0coYGlNWoNcq+aUiNjbBxWqXevuOe7+umoHBmTo1EP7lXi/4R2envRkibvS02p/edsjEprErm/kFzS5XsULdG9Ps/eSo0YWKr1zZn3jgAAQPgQAjKFS0fV9tgsNc0c7J0C/81hOrb0YXWcTcX/o480BHym/6lfoVP/Z5gO+6b4H575NZ3a8KrclyLYLCEga2itKdXAEZUKddCrpjTMgeDBBzWiZ/BYpFWn4rKbqbO9aEnr9+VdVQ4jBGQ06fI9ipYYXl9rTany8mZrY3vidg8AAFIbQkAm0P4nNZcVqLHsbp07/Kk+l/T5hf0699AYNX5zkloOpVoMiCQEnNMnDw3Xke+vUPtp72tT1yc6X/99HZuer8a7V+hCuDGAEJAVePZWakTOCFWGMfU17BCgVtVX3qKBlpExw1T5buhHxIbT24uWdH5f0ikEdOrsyZM6efJNPbf4Fv3LxmTvT7qQLt+jaInl9Xm0ozxPeSFmTQEAQOZCCEh7zunsDwvUWPptnfMr++d07sdD1HjrIrVHcuQ8Atoe+qo+Phrpo8IPAZ/XlauxuEgfHfK/zfOnb+twcb6O/KzOGwhCQQjIAt7VgyMsXb1gV1jTZMMPAV72LhkiY01VlUNH0ZzeXrSk5/uSTiHgD7r7C19Q/xxOZ4iGRPx8dr6xRGOLf6MW33/7n2bUd2Du/XkLfLvUeeA5VZaM7PmcTU5/jSxZoRdDTOGP+vW116g0N1fT15MCAACyEUJAmvP56/+iw8X5OrbhhP0djq5QUxxX47+SZp1ZcHNCQ8CFqpvVWFygE384Z3NrnZpL89VYukhh/f1DCMh4WtdOkhXBH8SRhQCPts3JVe6cbQ6da+z09qIlXd+XdAoBPnYtUD4hIELi//PZWr9AIwaO91twz1N/j/KNkZnwaMCj7J76ezTstk3q7PucNbM1cOB4VW4/og6PJHXq7JvPae4IS8YKNsMpttf3buUwmbxy7UjtXzIAAJAACAFpTYfOLilQY/HXdeZMoPvs00f/HMFgORKOrlBTcWJDgPv5GWosztfRx/7b5tb39fFdEVwSkBCQ4TRo8dUmoj+Iww8BHjWvL1VeXqlqHDl45vT2oiWd35c0DAHvVmpYOCFg42yNyLx58FEQ/5/P1m3lunbgVK1ttPkt4/t8TLAP6N1Klfb9bDw1KrWMhizZa7PB9ZqeawIM1uPw+tqrNNWyVLSqKconAACAdIUQkM6c/61OFOer8fb79WmQu7U9VKDG4iFqbuj1j5c+1YVNs9VcZ/+Yv3W8rdYl9wYYNH8mz8EndPK2AjUmOARIHl08+7Eu2p7a4JsRsOAxucN5KkJARuPZNke5Jl8LdoX/mHBDwPnnynTdlErVOzQod3p70ZLe70vmhoBTq4o0jBAQ959PbwQIstp+tCHA9zhr0lrbmQQ7ynNlTK7Kd1z57/F5fe2qmmrZXCkFAAAyHUJAOtOwyLuK/o+fC3qOvHd6fb6OPd2oy0fRvZ7qGwJ8pxJ4b/cfNH/ywOXHBjL0aQjxuXygd42AL+vUzo7wHhBpCLi0Ty3fG6zG6cN14vlEnFoB8WRHea5Mbrl2hL5rD5GuEQCZRKaGAO/rIgTEF+/U/RCX3Is2BJx6WOMtI3P1YjXYPmRYQteGaF87KUMXUwQAgGAQAtKYi3+Y6R18P7Al+B3ryv3u97ed83TYLgRI6lmAMMSg2RsYEj0joC8eXTz7tj557Fs68q1xat75UXgLBUqRh4D9lTraHTjuWqELUe0vOMNeLRliZCatjegUGEJANpN5IaDz7Jt69BsDZRlCQDzpjgAhp99HGwIkeTo+1Icd9pHBOyMggZc/3LtEQ4zRpLUpviopAADEFUJAGtN9pL9p3b7gd7QJAd1H/u1DQPdzp1gI6D1bYVaxPt51Sn+L+PHpOCPAo+YXV+iOcUMvryZtctR/6DjNe+qA36JTan9LW55+Wk8/vVI/uH2Gxg3ud/l8YU+zXlxRouv7WTLGUr/r52lba4yPi3Y/VaNS02el7Z4/oPuusG1CTF31Pld+JOcFyKkQEOn7kuH4fs7WVN6hm255UO9K6jzwlO4c2V85xshY/TR43A+0KcRK6bGTwiHA930b2T9HxhhZ/QZrSuWLat4bIATUlPZZsd5Gv+9PlN+/Y6/q6Ss+v04deOrOXvs6QCNLVoSYru79TpR0f+YmR/2HTtHizY3+63uE89rC/j0RKd5z8AcOnB3eOfgxhICA+NYIyJu9Mf7r/HTjWa/pUfz+BACA9IYQkMb0hIBQU/EzJQT4+FtHoz7941IdvzVfR77/sM53hfnAtFwjoFXbyq+VZV2r2U+9qbPdo0ZPh448O0dXG0sjHuw7zfR53Xf77br99ts148Y8me6jg569WjW+UOMrt+t4p++SU8Yot/vE02gfF+1+SpI6dXzddOUaowmP+v+l3frU1/V31ggteSPEcNn3B3ikR0ETHwKifV8ymFPP677bizU818gMW6LNa6eqoPBOPfXmWXVK6jy+XfcMCzxNOn6kaAjw7NWq8XkyeVP16Dsd8kjydBzR9spbdP2oEaGvGhDRdyGK799fV+v2GTd5r10/bInWV45SwS2V2n7ce5/O49tVOT5PJuACdh7tfXCUcqxrNfupd7wr5Hs6dGT7Ao2wLF1bvi1FrmvviwDXlttEzwDEMQR4Oj7Um88t1i0D+6lw3iYltov5olDKfRkAACCREALSmO4QcPSRvwa/Y4aFgB7an9NJ32KBn4Vz/3QMAb7LhV11T73Nje1aO8nI5C/QrkCP7xkU1KumtFClvf4yb984W3kmR+MftlktOtLHxbKfnm2ak2t09WL/Yd+pVUXhHaVK1RAQ6+eXwdSUGhnL0sDS9X6DHE9NqawIF36MnFQMAR7tWnC1jBmmJTaXi2td7x20xy8EKMrvn+9n17I04t43/Ge1+J7TKlqlvr9dvL8/8lRuc7067225Kq1J9hR1jxrXTo0sAkhxCgG9Z2P8vb5SsUXHEz5tiBAAAJCNEALSmM//9O2o1wjIiBAgyfXYKDUWD9Hp18M4XJKOIaBplYoso7x5L9veXFNqZEypAv755vvDNH/YCI2v3Bv+daYjfVyM+7lrQb7NgPiUVhWFORhM1RAQ6+eXwdSUGpn8e1Rv98MV7mXyYiIFQ0B7laZaRtbUKvtp4OG8L1F8F6L5/gX9/Lqf028BOu8lPgOv5eEbBBetUqJOhw+H839eqML/NVa/OBDhA+N5akDnWb2/8z+0eHyeTN54rQg1KyomCAEAANkIISCdeed+72J2P/ytLga5m+0pBBkSArojx+GfBXghvUnHEBAAT8eHOvn+Tt03OrwQEPF5s9E+Ltr9bFisq/teHuvUKhWFe7Q8ynNck7VYYNjvSwZTUxrk5ytLQ4B3JkSQRdsSFAKi+f4F/fy8d5AxRtPX9yoFvn0b8eDBgLuSGnEsmTMC+tKq9dNzZawirbKZvBUfoltjBQAA0htCQDpzaYtOTc9X4+3369Mgd2v/2RA1Fg9Rc++Zn2kTAj6Tp+WTwIsC2s12CEQ6hwBPs/7yH5WXF5zL6a8vDB2nG68OLwREvIJ4tI+Ldj99Rwpz52zrmX0Q9mkBklL+qgFRvy+ZCyHAH+9l4vyvF9/rDokJAVF8/0KGgB3lyu27H744EGz/vSGg10yCpC0WmNw1Aq6gYbGuNld+PnGFqwYAAGQlhIC0pkNnlxSosXiyWj4MdJ/39fFd+Wos/Re1X+r1z2kRAjxq/9mX1VhcoOPPnrC/S8aHAI+aN81TYY6RNfAmzXt0p94/e3mKaLinBiQ+BMS4n5KaVoyWyZ2jbR4potMCfOwoz5XJLVegMZQdiQ8Bsb8vmQohwB9vCAhyPfeEhYDIv38hQ4DNfpx/YmqYA/pUuaZ9Clw1QFLP1P0If7+FS/vaSSn0ngMAgFMQAtKcz32nBwS8csCHD+tYcb6OPvbfV/67LwSc+EOHzYM8+uSBgpAh4OIfZtqHgKO/UcuuC0EeGW4I8EUMu/338ekj/6DG4gKd3G73OvruV/qFAM+OcuUZo7ypa9VocygoVUJAzPspSU0rNNpYKq3xRHZaQPc+RLHAXKJDQFzelwyFEOCPNwQM0ZK9Ae+QsBAQ6fcvZAjYOFtWFDMCUpHWmtkaODDQVRB6EUMI6Dx7UmcDLgPQfanHRPyu8Kim1HLgKh0AAJBqEALSng61PfRlNU6fqbN+s/rO6dyPh6jx1rvVdr7PTb7TCg7/9E/6vM9Nnx9apmPTQ4cANSzS4eJ/0IfvXPnPF6rKQ8wSiGBGwE+HqLF4tD6yW7SpfYtO3Zqvxm/fr08v2dzel0hDwKV9avneYDVOH64Tz4e4RGNCOKVVRUbGmqqqADM2UyMExGE/ez2PNbVKx9ZOivx8Vc8OledFdp5rYkNAvN6XzIQQ4I9n/fTgA+VEhoAIv3+hQsCpVUUyxtLsjb3+0XcVjaJVyVwKMDq6Y8D6YNfxizIEdAdDu6sseElgCPAtUDl6RcIWIAAAgBSFEJAJXNqnM9//shpvu1Othz/V55I+v7Bf5x4ao8ZvTlLLIbs/XHzT7qePUXP9Ke85+Jc+VedfVujE3ffqzLqb1VhcoCPz5+j0f+4JsN06Nd+ar8M/fFwXLngkfabP/nK/TiyvDbp4YURrBLT/Sc1lBTry/RVqP+19bbr0qTrffkKnygrUeOsMnbF9fTZEGgL2V3oXYyzOV+NdKxRsjkNi8P3xF+SP7dQIAXHYTx/tVVNlWdfq2quju3Rc06oiWUEG3nb7lbiBYPzel/B5Vz8dlSNjcjTqwQiuEpEE0iMEOPx+nlqloiCLtrVXTZUV6n1pX6tJtt/dd/Xw8i3q24T9nj/M71/wENCuqqmWTQTbofLcIFdF8D12R82OiNb6cIrWbeW6NlgM8H1+wUJA+9pJfufie2eCmMBH5X0BJfj7Fh3eU0JKlfQrNgIAgOMQAjKFSx+pY8N3dGzmYO/A9ZvDdGzpw+o4G+RP10tHdW7513Tkm77B7rdu0vFfPqP/6ZIuPP1PavrRYn28qUbn3joZ8Ck+P/2cTt/9pcvbXP6cOkMenY/wqgHdr222bzvFBTo8+2s68X+9+xo2aTcjYJcW5Ac5ouxp1q8m9BpI1pT7LzLmSAiIw3524zs6FfU0Vc8uLbja6OoFu8IatCU2BMTxfQmXgw9qRM951kVK5QOvaRECHH8/fQPovHLt6PsD7NmrJSMsWX1X4vfDO9j2iwnhnJsewffPG7HyVO63o5Jn1wJdbey/h02rimSZYVqy1/41tNaUanwKn6zujQFTtdbuXB81acVo02uthT501uueL/n/PvBsm6NcY3TNvW/YbVE1pXky5mot2BXnFNVeo9JcS0WJuxwBAACkMIQASAJxvHxgJKThGgGtNaW+c8wf1Tsd3j8CO8++r52PztNNg6/XTcNzZcx0rfd4tGvBbL+BiueNezXEGOV/90VFchXqSB8X63722rK2zcnV1YujP1vVs3eJhlkj9GAYY4lErxEQv/cl7C2qvvIWDbRSacE1Gzwf6KHRRib/u3rR5gfMU3+P8o3RsCWJPAofzoyAJLyfrTUqzTPKGbVY249735zO49tVecv1uvPBBRpmvAtPzrh9tf5q+wQe7SjPk8mdpF+90yGP7/ELbrlTG0Me9Q3/+1dTamTyJ2nqqELNfuoddXgkeTr0zlOzda1llDPqQdmP9VtVM3ugLOtazX50p474vheejiPaXnmLBo8N9LjUobV+gUYMHK9VNjvq2fugRuUY5Y1frOd87788HTqyc6W+UVCg2bYLDbRqW/m1svLGq3L7Ee97KY86juzUo7OvlWXyNH5VvL8L3p+TwKcjAABApkMIgCSwRae6p9z3MeCihxHyyQP2zx9y3YOUw6PmFys1ZWh/5fiOTOb0H6px857SgU5JTU9o2kBLxuqnsf+6yzttNNTltgIdhY32cdHuZ4DnqSn9kmLoAJI82ls5QjkjKkMOKBy5akBc3pfI2LtkSNC1CZKKzc9Z78/Ae6S5t4laQyH8NQIcfz87D+ipO0d6LzVpLPW7vkQr6luldys1qmCc7qhco6efflXHAj3e06j1d16vfpaRMTnqP/JOrbc9gu33wLC/fz0zOq7YV6Oc/iNVsuJFBTuV3vu9WKGSkQN8++j9XkypDPW41KHzjSUaW/wbtdjc5mn+ix6dN06D+1nen+Gc/ho6bp6eOhAsq/q/J1a/ARo5+QchHhcdrTWlyssLYwFEAADIWAgBAJA6tFdp6pfisXp1q+oXjNDA0hoF+zs38SEgGXiP6ibsmuMZQ7ghIIvezwi+fyGvGgApi2dvpUb0G6UHU33qBQAAJBRCAAAkld6XzWpaMTqOq1e3qn7Bzbr9ucDLo/kffQ5n9kMq473uOUf6+hBktkvwEJD572e03z9CQLpyRD/7+i22pzUAAEB2QQgAgORx6mGNt4ysgbdo8ZoHNLXQZpE0CJvzz5XpuimVqs/QQavTZPz7Ge33r3uNh9zbtCn+s9YBAADAAQgBAJ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如上图所示,不晓得这样你能不能理解{:10_256:}


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查看完整版本: 小白numpy库使用疑问