Python 仅对非零值运行df.descripe()
我有一个数据帧Python 仅对非零值运行df.descripe(),python,pandas,dataframe,statistics,Python,Pandas,Dataframe,Statistics,我有一个数据帧每日,看起来像这样 import pandas as pd daily time_stamp 22 72 79 86 87 88 90 2013-10-01 0.000000 0.000 8.128000 0.254 0.000000 0.000000 0.000000 2013-10-01 0.000000 0.000 8.128000
每日,看起来像这样
import pandas as pd
daily
time_stamp 22 72 79 86 87 88 90
2013-10-01 0.000000 0.000 8.128000 0.254 0.000000 0.000000 0.000000
2013-10-01 0.000000 0.000 8.128000 0.254 0.000000 0.000000 0.000000
2013-10-02 0.000000 0.000 0.000000 0.000 0.000000 0.000000 0.000000
2013-10-04 0.000000 0.000 0.000000 0.000 2.540000 0.762000 0.000000
2013-10-08 2.286000 0.000 0.000000 1.016 1.016000 0.254000 0.000000
2013-10-11 2.794000 0.000 0.000000 0.000 3.810000 1.016000 0.762000
2013-10-12 1.524000 0.000 0.000000 2.286 5.588000 0.254000 26.41600
2013-10-13 0.762000 0.000 8.890000 0.000 2.540000 1.270000 4.572000
2013-10-14 1.524000 0.000 0.000000 0.000 2.540000 4.064000 0.000000
2013-10-15 0.000000 0.000 0.000000 0.000 0.000000 0.000000 0.000000
2013-10-16 0.000000 3.810 1.524000 3.048 0.508000 0.762000 5.080000
2013-10-17 0.000000 0.000 0.254000 0.000 0.000000 0.000000 0.508000
2013-10-18 8.128000 0.762 4.826000 0.508 7.366000 4.572000 1.524000
2013-10-19 8.382000 0.254 0.000000 0.000 6.858000 16.510000 2.032000
2013-10-20 0.000000 0.000 0.000000 0.000 4.064000 5.842000 0.000000
2013-10-21 0.000000 0.508 0.000000 0.000 1.016000 0.000000 0.000000
2013-10-22 2.794000 2.540 1.016000 0.000 0.508000 15.748000 0.000000
我想对大于0的值进行汇总统计,以便descripe()
问题是如果我使用命令dailyrf=daily[(daily>0.)。any(1)]
当我执行dailyrf.descripe()时,带零的行仍然包括在内。或者,当我执行dailyrf=daily[(daily>0.).all(1)]
时,它只返回所有行中值大于0的行
我还尝试了daily[daily==0]='NaN'
,这给了我一条警告信息:“试图在数据帧的切片副本上设置一个值。
尝试改用.loc[row\u indexer,col\u indexer]=value
请参阅文档中的注意事项:
这与ipykernel包是分开的,因此我们可以避免在“之前”进行导入
这也不是一个解决方案,因为descripe
函数返回:
22 72 79 86 87 88 90 93 95 96 97
count 720 684 721 719 718 720 720 721 720 720 719
unique 103 80 73 64 80 108 112 108 86 113 98
top NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
freq 470 494 560 510 539 483 486 441 570 474 476
我真正想要的是每列中大于0的所有值的平均值、标准偏差等 使用mask
应该非常简单
df.mask(df == 0).describe()
22 72 79 86 87 88 90
count 8.000000 5.000000 7.000000 6.000000 12.000000 11.000000 7.00000
mean 3.524250 1.574800 4.680857 1.227667 3.196167 4.641273 5.84200
std 3.000573 1.538745 3.752722 1.174092 2.391229 5.992560 9.24574
min 0.762000 0.254000 0.254000 0.254000 0.508000 0.254000 0.50800
25% 1.524000 0.508000 1.270000 0.317500 1.016000 0.762000 1.14300
50% 2.540000 0.762000 4.826000 0.762000 2.540000 1.270000 2.03200
75% 4.127500 2.540000 8.128000 1.968500 4.445000 5.207000 4.82600
max 8.382000 3.810000 8.890000 3.048000 7.366000 16.510000 26.41600
所有满足df==0
的值都被屏蔽,并且description
在计算统计数据时不会考虑这些值。要修复代码,请注意NaN!='NaN'
df[df==0] = np.nan
df.describe()
Out[696]:
22 72 79 86 87 88 90
count 8.000000 5.000000 7.000000 6.000000 12.000000 11.000000 7.00000
mean 3.524250 1.574800 4.680857 1.227667 3.196167 4.641273 5.84200
std 3.000573 1.538745 3.752722 1.174092 2.391229 5.992560 9.24574
min 0.762000 0.254000 0.254000 0.254000 0.508000 0.254000 0.50800
25% 1.524000 0.508000 1.270000 0.317500 1.016000 0.762000 1.14300
50% 2.540000 0.762000 4.826000 0.762000 2.540000 1.270000 2.03200
75% 4.127500 2.540000 8.128000 1.968500 4.445000 5.207000 4.82600
max 8.382000 3.810000 8.890000 3.048000 7.366000 16.510000 26.41600