Question

我正在尝试在python中重现R aggregate()函数但没有连接。对于每一行，我只想计算给定列中具有类似值的行的出现次数。

我正在尝试从这里的一段代码中解决它： http://timotheepoisot.fr/2011/12/01/the-aggregate-function-in-python/

我实施的修改由###表示。我目前遇到的问题是第一列[0]包含字符串，代码似乎只适用于浮点数。

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，我想在其中写出行的出现次数。

以下是测试数据：

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

你可能会说，我对python还不是很擅长。欢迎任何建议。

[编辑] PS。数据已按列[0]排序。

Answer 1

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

计算python中列中具有相同值的行

1 个答案: