Python 在多个列上应用操作,其中有一个固定的列
我有一个如下所示的数据帧。最后一列显示所有列的值之和,即Python 在多个列上应用操作,其中有一个固定的列,python,pandas,dataframe,sum,multiple-columns,Python,Pandas,Dataframe,Sum,Multiple Columns,我有一个如下所示的数据帧。最后一列显示所有列的值之和,即A、B、D、K和T。请注意,一些列也有NaN word1,A,B,D,K,T,sum na,,63.0,,,870.0,933.0 sva,,1.0,,3.0,695.0,699.0 a,,102.0,,1.0,493.0,596.0 sa,2.0,487.0,,2.0,15.0,506.0 su,1.0,44.0,,136.0,214.0,395.0 waw,1.0,9.0,,34.0,296.0,340.0 如何计算每行的熵?i、 我
A
、B
、D
、K
和T
。请注意,一些列也有NaN
word1,A,B,D,K,T,sum
na,,63.0,,,870.0,933.0
sva,,1.0,,3.0,695.0,699.0
a,,102.0,,1.0,493.0,596.0
sa,2.0,487.0,,2.0,15.0,506.0
su,1.0,44.0,,136.0,214.0,395.0
waw,1.0,9.0,,34.0,296.0,340.0
如何计算每行的熵?i、 我应该找到如下的东西
df['A']/df['sum']*log(df['A']/df['sum']) + df['B']/df['sum']*log(df['B']/df['sum']) + ...... + df['T']/df['sum']*log(df['T']/df['sum'])
条件是,每当日志中的值变为零或NaN
时,整个值应视为零(根据定义,日志将返回错误,因为日志0未定义)
我知道使用lambda操作应用于各个列。在这里,我想不出一个纯粹的解决方案,在不同的列a
,B
,D
等上应用固定列sum
。。尽管我可以想到一个简单的循环迭代,在CSV文件上使用硬编码的列值。我认为您可以使用从a
到T
选择列,然后除以。最后用途:
安装程序
print (df['A']/df['sum']*np.log(df['A']/df['sum']))
0 NaN
1 NaN
2 NaN
3 -0.021871
4 -0.015136
5 -0.017144
dtype: float64
print (df.ix[:,'A':'T'].div(df['sum'],axis=0)*np.log(df.ix[:,'A':'T'].div(df['sum'],axis=0)))
A B D K T
0 NaN -0.181996 NaN NaN -0.065191
1 NaN -0.009370 NaN -0.023395 -0.005706
2 NaN -0.302110 NaN -0.010722 -0.156942
3 -0.021871 -0.036835 NaN -0.021871 -0.104303
4 -0.015136 -0.244472 NaN -0.367107 -0.332057
5 -0.017144 -0.096134 NaN -0.230259 -0.120651
print((df.ix[:,'A':'T'].div(df['sum'],axis=0)*np.log(df.ix[:,'A':'T'].div(df['sum'],axis=0)))
.sum(axis=1))
0 -0.247187
1 -0.038471
2 -0.469774
3 -0.184881
4 -0.958774
5 -0.464188
dtype: float64
df1 = df.iloc[:, :-1]
df2 = df1.div(df1.sum(1), axis=0)
df2.mul(np.log(df2)).sum(1)
word1
na -0.247187
sva -0.038471
a -0.469774
sa -0.184881
su -0.958774
waw -0.464188
dtype: float64
from StringIO import StringIO
import pandas as pd
text = """word1,A,B,D,K,T,sum
na,,63.0,,,870.0,933.0
sva,,1.0,,3.0,695.0,699.0
a,,102.0,,1.0,493.0,596.0
sa,2.0,487.0,,2.0,15.0,506.0
su,1.0,44.0,,136.0,214.0,395.0
waw,1.0,9.0,,34.0,296.0,340.0"""
df = pd.read_csv(StringIO(text), index_col=0)
df