2021-03-29 python数据处理系统学习（十一）统计相关函数
内容导读
互联网集市收集整理的这篇技术教程文章主要介绍了2021-03-29 python数据处理系统学习（十一）统计相关函数，小编现在分享给大家，供广大互联网技能从业者学习和参考。文章包含5226字，纯文字阅读大概需要8分钟。
内容图文
2021-03-29 python数据处理系统学习（十一）统计基础的相关函数
axis=0表示对行作用得到列的性质，axis=1表示对列作用得到行的性质。
先读入数据，
import numpy as np
data =np.genfromtxt(r'C:\Users\wangyu\Desktop\毕业论文\python练习1.txt',delimiter=' ')
data.shape
Out[6]: (13, 4)
读取结果如下：
1、求和
data.sum(axis=0)#对行进行作用，返回4列总和
Out[7]: array([1.120000e+03, 1.241450e+01, 2.709128e+07, 1.906630e+05])
data.sum(axis=1)#对列进行作用，返回13行总和
Out[8]:
array([-82959690.6960142 , 11620329.51070258, 13974025.23977928,
42850879.00860883, -682932.54615708, 1269070.3025655 ,
12168307.98312435, 5124024.47889916, 7935968.65946828,
9321502.0170493 , 135926.29829416, 3564250.70368717,
2961414.42229263])
沿着指定轴的元素累加和所组成的数组（cumsum）：
data.cumsum(axis=0)
Out[11]:
array([[ 1.00000000e+02, 9.63985802e-01, -8.29709267e+07,
1.11350000e+04],
[ 2.00000000e+02, 1.92468839e+00, -7.13937981e+07,
5.42350000e+04],
[ 2.60000000e+02, 2.90446767e+00, -5.74478889e+07,
8.22900000e+04],
[ 3.20000000e+02, 3.82307649e+00, -1.45996328e+07,
8.48520000e+04],
[ 4.00000000e+02, 4.77971941e+00, -1.52857353e+07,
8.79410000e+04],
[ 5.00000000e+02, 5.72628491e+00, -1.40284929e+07,
9.96680000e+04],
[ 6.00000000e+02, 6.69940927e+00, -1.88096090e+06,
1.20343000e+05],
[ 7.00000000e+02, 7.64230843e+00, 3.21282664e+06,
1.50479000e+05],
[ 8.00000000e+02, 8.61877671e+00, 1.11387353e+07,
1.60438000e+05],
[ 8.80000000e+02, 9.55682601e+00, 2.04557804e+07,
1.64814000e+05],
[ 9.40000000e+02, 1.05095202e+01, 2.05878797e+07,
1.68580000e+05],
[ 1.02000000e+03, 1.14802073e+01, 2.41500295e+07,
1.70600000e+05],
[ 1.12000000e+03, 1.24145000e+01, 2.70912800e+07,
1.90663000e+05]])
data.cumsum(axis=1)
Out[12]:
array([[ 1.00000000e+02, 1.00963986e+02, -8.29708257e+07,
-8.29596907e+07],
[ 1.00000000e+02, 1.00960703e+02, 1.15772295e+07,
1.16203295e+07],
[ 6.00000000e+01, 6.09797793e+01, 1.39459702e+07,
1.39740252e+07],
[ 6.00000000e+01, 6.09186088e+01, 4.28483170e+07,
4.28508790e+07],
[ 8.00000000e+01, 8.09566429e+01, -6.86021546e+05,
-6.82932546e+05],
[ 1.00000000e+02, 1.00946566e+02, 1.25734330e+06,
1.26907030e+06],
[ 1.00000000e+02, 1.00973124e+02, 1.21476330e+07,
1.21683080e+07],
[ 1.00000000e+02, 1.00942899e+02, 5.09388848e+06,
5.12402448e+06],
[ 1.00000000e+02, 1.00976468e+02, 7.92600966e+06,
7.93596866e+06],
[ 8.00000000e+01, 8.09380493e+01, 9.31712602e+06,
9.32150202e+06],
[ 6.00000000e+01, 6.09526942e+01, 1.32160298e+05,
1.35926298e+05],
[ 8.00000000e+01, 8.09706872e+01, 3.56223070e+06,
3.56425070e+06],
[ 1.00000000e+02, 1.00934293e+02, 2.94135142e+06,
2.96141442e+06]])
2、平均数
data.mean(axis=0)#对行进行作用，返回4列平均数
Out[9]: array([8.61538462e+01, 9.54961536e-01, 2.08394461e+06, 1.46663846e+04])
data.mean(axis=1)#对列进行作用，返回13行平均数
Out[10]:
array([-20739922.67400355, 2905082.37767565, 3493506.30994482,
10712719.75215221, -170733.13653927, 317267.57564138,
3042076.99578109, 1281006.11972479, 1983992.16486707,
2330375.50426233, 33981.57457354, 891062.67592179,
740353.60557316])
3、累积求积
data.cumprod()
Out[13]:
array([ 1.00000000e+002, 9.63985802e+001, -7.99827953e+009,
-8.90608425e+013, -8.90608425e+015, -8.55609815e+015,
-9.90550481e+022, -4.26927257e+027, -2.56156354e+029,
-2.50976689e+029, -3.50009814e+036, -9.81952532e+040,
-5.89171519e+042, -5.41218154e+042, -2.31902541e+050,
-5.94134309e+053, -4.75307447e+055, -4.54699506e+055,
3.11970469e+061, 9.63676779e+064, 9.63676779e+066,
9.12183194e+066, 1.14683535e+073, 1.34489381e+077,
1.34489381e+079, 1.30874892e+079, 1.58980694e+086,
3.28692585e+090, 3.28692585e+092, 3.09923962e+092,
1.57868681e+099, 4.75753058e+103, 4.75753058e+105,
4.64557771e+105, 3.68204247e+112, 3.66694610e+116,
2.93355688e+118, 2.75182098e+118, 2.56388402e+125,
1.12195565e+129, 6.73173387e+130, 6.41328352e+130,
8.47190556e+135, 3.19051963e+139, 2.55241571e+141,
2.47759718e+141, 8.82557213e+147, 1.78276557e+151,
1.78276557e+153, 1.66562474e+153, 4.89901958e+159,
9.82890298e+163])
4、最大值
data.max()
Out[14]: 42848256.09
data.max(axis=0)
Out[15]: array([1.00000000e+02, 9.79779283e-01, 4.28482561e+07, 4.31000000e+04])
data.max(axis=1)
Out[16]:
array([1.11350000e+04, 1.15771286e+07, 1.39459093e+07, 4.28482561e+07,
3.08900000e+03, 1.25724236e+06, 1.21475320e+07, 5.09378754e+06,
7.92590868e+06, 9.31704508e+06, 1.32099346e+05, 3.56214973e+06,
2.94125049e+06])
5、计算分位数
np.percentile(data,50)
Out[17]: 100.0
np.median(data)
Out[18]: 100.0
可以看出这两个公式表示的意思一样
np.percentile(data,[10,20,30,40,50])
Out[19]:
array([ 0.94326579, 0.96532608, 60. , 88. ,
100. ])
6、极差
np.ptp(data)
Out[22]: 125819182.75
data.max()-data.min()
Out[23]: 125819182.75
np.ptp(data,axis=0)
Out[24]: array([4.00000000e+01, 6.11704630e-02, 1.25819183e+08, 4.10800000e+04])
7、判断大小
data>100
Out[25]:
array([[False, False, False, True],
[False, False, True, True],
[False, False, True, True],
[False, False, True, True],
[False, False, False, True],
[False, False, True, True],
[False, False, True, True],
[False, False, True, True],
[False, False, True, True],
[False, False, True, True],
[False, False, True, True],
[False, False, True, True],
[False, False, True, True]])
np.sum(data>100)
Out[26]: 24
内容总结
以上是互联网集市为您收集整理的2021-03-29 python数据处理系统学习（十一）统计相关函数全部内容，希望文章能够帮你解决2021-03-29 python数据处理系统学习（十一）统计相关函数所遇到的程序开发问题。 如果觉得互联网集市技术教程内容还不错，欢迎将互联网集市网站推荐给程序员好友。
内容备注
版权声明：本文内容由互联网用户自发贡献，该文观点与技术仅代表作者本人。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容， 请发送邮件至 gblab@vip.qq.com 举报，一经查实，本站将立刻删除。
内容手机端
扫描二维码推送至手机访问。
来源：【匿名】