我是Python新手,我想做以下事情。我有一个csv文件(input.csv),其中包含标题行和4列。这个csv文件的一部分如下所示:
gene-name p-value stepup(p-value) fold-change
IFIT1 6.79175E-005 0.0874312 96.0464
IFITM1 0.00304362 0.290752 86.3192
IFIT1 0.000439152 0.145488 81.499
IFIT3 5.87135E-005 0.0838258 77.1737
RSAD2 6.7615E-006 0.0685623 141.898
RSAD2 3.98875E-005 0.0760279 136.772
IFITM1 0.00176673 0.230063 72.0445
我想只保留折叠更改值最高的行,并删除包含相同基因名称且折叠更改值较低的所有其他行。例如,在这种情况下,我需要一个以下格式的csv输出文件:
gene-name p-value stepup(p-value) fold-change
IFIT1 6.79175E-005 0.0874312 96.0464
IFITM1 0.00304362 0.290752 86.3192
RSAD2 6.7615E-006 0.0685623 141.898
IFIT3 5.87135E-005 0.0838258 77.1737
如果你能帮我解决这个问题,我将不胜感激 非常感谢你。
答案 0 :(得分:1)
愚蠢的解决方案:走完文件中的每一行,做一个手动比较。假设:
::
fi = open('inputfile.csv','r') # read
header = fi.readline()
# capture the header line ("gene-name p-value stepup(p-value) fold-change")
out_a = [] # we will store the results in here
for line in fi: # we can read a line this way too
temp_a = line.strip('\r\n').split(' ')
# strip the newlines, split the line into an array
try:
pos = [gene[0] for gene in out_a].index(temp_a[0])
# try to see if the gene is already been seen before
# [0] is the first column (gene-name)
# return the position in out_a where the existing gene is
except ValueError: # python throws this if a value is not found
out_a.append(temp_a)
# add it to the list initially
else: # we found an existing gene
if float(temp_a[3]) > float(out_a[pos][3]):
# new line has higher fold-change (column 4)
out_a[pos] = temp_a
# so we replace
fi.close() # we're done with our input file
fo = open('outfile.csv','w') # prepare to write to output
fo.write(header) # don't forget about our header
for result in out_a:
# iterate through out_a and write each line to fo
fo.write(' '.join(result) + '\n')
# result is a list [XXXX,...,1234]
# we ' '.join(result) to turn it back into a line
# don't forget the '\n' which makes each result on a line
fo.close()
这样做的一个优点是它可以保留输入文件中首次遇到的基因顺序。
答案 1 :(得分:0)
尝试使用pandas:
import pandas as pd
df = pd.read_csv('YOUR_PATH_HERE')
print(df.loc[(df['gene-name'] != df.loc[(df['fold-change'] == df['fold-change'].max())]['gene-name'].tolist()[0])])
代码很长,因为我选择在一行中完成,但代码正在做的是这个。我抓住最高gene-name
的{{1}},然后使用fold-change
运算符说,“抓住!=
与{{1}不同的所有内容我们刚才做的计算。
细分:
gene-name
输出:
gene-name
编辑:
获取预期输出使用# gets the max value in fold-change
max_value = df['fold-change'].max()
# gets the gene name of that max value
gene_name_max = df.loc[df['fold-change'] == max_value]['gene-name']
# reassigning so you see the progression of grabbing the name
gene_name_max = gene_name_max.values[0]
# the final output
df.loc[(df['gene-name'] != gene_name_max)]
:
gene-name p-value stepup(p-value) fold-change
0 IFIT1 0.000068 0.087431 96.0464
1 IFITM1 0.003044 0.290752 86.3192
2 IFIT1 0.000439 0.145488 81.4990
3 IFIT3 0.000059 0.083826 77.1737
6 IFITM1 0.001767 0.230063 72.0445