保持行具有特定列的最大值

Question

I am new to Python and I want to do the following. I have a csv file (input.csv) which contains a header row and 4 columns. A part of this csv file is depicted below:

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

I want to keep only the rows with the highest value of fold-change and remove all other rows containing the same gene name with lower value of fold-change. For example, in this case I need a csv output file of the following format:

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

I would be grateful to you if you provided me a solution to this problem.
Thank you very much.

Answer 1

The dumb solution: walk each line in the file, do a manual compare. Assumptions:

• each column is separated by a single space
• The number of the result rows are expected to fit into memory, since we have to finish our entire search and compare before flushing the results to a file
• no presorting so this scales (speed) poorly since it does a full walk of result list on every input line.
• You want to keep the first line you see for a gene if it somehow has the same fold change later on.

::

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()

One advantage of this is it preserves the first-encountered order of the genes from the input file.

Answer 2

try using 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])])

The code is long because I chose to do it in one line, but what the code is doing is this. I grab the gene-name of the highest fold-change , I then use the != operator to say, "grab me everything where the gene-name is not the same as the gene-name of the calculation we just did.

broken down:

# 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)]

output:

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

EDIT:

to get the expected output use groupby :

import pandas as pd

df = pd.read_csv('YOUR_PATH_HERE')
df.groupby(['gene-name'], sort=False)['fold-change'].max()

# output below
gene-name
IFIT1      96.0464
IFITM1     86.3192
IFIT3      77.1737
RSAD2     141.8980