我正在尝试在 python 中重现 R aggregate()
函数,但不进行连接。对于每一行,我只想计算给定列中具有相似值的行的出现次数。
我正在尝试根据此处的一段代码来解决这个问题: http://timotheepoisot.fr/2011/12/01/the-aggregate-function-in-python/
我实现的修改由###
表示。我当前遇到的问题是第一列 [0] 包含字符串,并且代码似乎仅适用于 float 。
import numpy as np
import scipy as sp
def MSD(vec):
return [np.mean(vec),np.std(vec)]
def aggregate(df,by=0,to=1,func=np.sum):
Dat = []
# ColBy = df.T[by]
ColBy = int(df.T[by][3:]) ### my attempt to read only the numbers in the first column's character strings
ColTo = df.T[to]
UniqueBy = np.sort(np.unique(ColBy))
for ub in UniqueBy:
uTo = ColTo[ColBy==ub]
Out = func(uTo)
# Dat.append(np.concatenate(([ub],Out)))
Dat.append([ub],Out) ### because I do not want to concatenate
return Dat
test_df = np.loadtxt('in_test.txt')
Agr = aggregate(test_df,0,3,MSD)
sp.savetxt("out_test.txt", Agr)
这是错误消息:
Traceback (most recent call last):
File "count_same_reads.py", line 30, in <module>
test_df = np.loadtxt('in_test.txt')
File "/usr/lib/python2.7/dist-packages/numpy/lib/npyio.py", line 796, in loadtxt
items = [conv(val) for (conv, val) in zip(converters, vals)]
ValueError: could not convert string to float: Tag19184
我的数据以制表符分隔,主要包含字符串,但第 3 列除外,我想在其中写入行出现的次数。
这是测试数据:
Tag19184 CTAAC hffef 1 a 36 - chr1 10006 0 36M 36
Tag19184 CTAAC hffef 1 a 36 - chr1 10012 0 36M 36
Tag19184 CTAAC hffef 1 a 36 - chr1 10018 0 36M 36
Tag19184 CTAAC hffef 1 a 36 - chr1 10024 0 36M 36
Tag19184 CTAAC hffef 1 a 36 - chr1 10030 0 36M 36
Tag19184 CTAAC hffef 1 a 36 - chr1 10036 0 36M 36
Tag19184 CTAAC hffef 1 a 36 - chr1 10042 0 36M 36
Tag20198 CTAAC hffef 1 a 36 - chr1 10048 0 36M 36
Tag20198 CTAAC hffef 1 a 36 - chr1 10054 0 36M 36
Tag45093 CTAAC hffef 1 a 36 - chr1 10060 0 36M 36
结果应如下所示:
Tag19184 CTAAC hffef 7 a 36 - chr1 10006 0 36M 36
Tag19184 CTAAC hffef 7 a 36 - chr1 10012 0 36M 36
Tag19184 CTAAC hffef 7 a 36 - chr1 10018 0 36M 36
Tag19184 CTAAC hffef 7 a 36 - chr1 10024 0 36M 36
Tag19184 CTAAC hffef 7 a 36 - chr1 10030 0 36M 36
Tag19184 CTAAC hffef 7 a 36 - chr1 10036 0 36M 36
Tag19184 CTAAC hffef 7 a 36 - chr1 10042 0 36M 36
Tag20198 CTAAC hffef 2 a 36 - chr1 10048 0 36M 36
Tag20198 CTAAC hffef 2 a 36 - chr1 10054 0 36M 36
Tag45093 CTAAC hffef 1 a 36 - chr1 10060 0 36M 36
正如你可能知道的那样,我还不太擅长 python。欢迎任何建议。
[编辑] PS。数据已按列 [0] 排序。
最佳答案
我会建议pandas
,特别是在基因组数据的情况下,数据的大小可能非常大:
In [44]:
#you can read you data by pandas.read_csv()
import pandas as pd
print df
v0 v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11
0 Tag19184 CTAAC hffef 1 a 36 - chr1 10006 0 36M 36
1 Tag19184 CTAAC hffef 1 a 36 - chr1 10012 0 36M 36
2 Tag19184 CTAAC hffef 1 a 36 - chr1 10018 0 36M 36
3 Tag19184 CTAAC hffef 1 a 36 - chr1 10024 0 36M 36
4 Tag19184 CTAAC hffef 1 a 36 - chr1 10030 0 36M 36
5 Tag19184 CTAAC hffef 1 a 36 - chr1 10036 0 36M 36
6 Tag19184 CTAAC hffef 1 a 36 - chr1 10042 0 36M 36
7 Tag20198 CTAAC hffef 1 a 36 - chr1 10048 0 36M 36
8 Tag20198 CTAAC hffef 1 a 36 - chr1 10054 0 36M 36
9 Tag45093 CTAAC hffef 1 a 36 - chr1 10060 0 36M 36
In [45]:
#if we want to group by the first 3 fields
df.groupby(['v0','v1','v2']).transform(sum).v3
Out[45]:
0 7
1 7
2 7
3 7
4 7
5 7
6 7
7 2
8 2
9 1
Name: v3, dtype: int64
In [46]:
#all it takes is just one line
df['v3']=df.groupby(['v0','v1','v2']).transform(sum).v3
print df
v0 v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11
0 Tag19184 CTAAC hffef 7 a 36 - chr1 10006 0 36M 36
1 Tag19184 CTAAC hffef 7 a 36 - chr1 10012 0 36M 36
2 Tag19184 CTAAC hffef 7 a 36 - chr1 10018 0 36M 36
3 Tag19184 CTAAC hffef 7 a 36 - chr1 10024 0 36M 36
4 Tag19184 CTAAC hffef 7 a 36 - chr1 10030 0 36M 36
5 Tag19184 CTAAC hffef 7 a 36 - chr1 10036 0 36M 36
6 Tag19184 CTAAC hffef 7 a 36 - chr1 10042 0 36M 36
7 Tag20198 CTAAC hffef 2 a 36 - chr1 10048 0 36M 36
8 Tag20198 CTAAC hffef 2 a 36 - chr1 10054 0 36M 36
9 Tag45093 CTAAC hffef 1 a 36 - chr1 10060 0 36M 36
关于python - 计算Python中列中具有相同值的行,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/24581967/