Splitting information in a specific column python? - python

Here is an example of what my file looks like:
Type Variant_class ACC_NUM dbsnp genomic_coordinates_hg18 genomic_coordinates_hg19 HGVS_cdna HGVS_protein gene disease sequence_context_hg18 sequence_context_hg19 codon_change codon_number intron_number site location location_reference_point author journal vol page year pmid entrezid sift_score sift_prediction mutpred_score
1 DM CM920001 rs1800433 null chr12:9232351:- NM_000014.4 NP_000005.2:p.C972Y A2M Chronicobstructivepulmonarydisease null CACAAAATCTTCTCCAGATGCCCTATGGCT[G/A]TGGAGAGCAGAATATGGTCCTCTTTGCTCC TGT-TAT 972 null null 2 null Poller HUMGENET 88 313 1992 1370808 2 0 DAMAGING 0.594315245478036
1 DM CM004784 rs74315453 null chr22:43089410:- NM_017436.4 NP_059132.1:p.M183K A4GALT Pksynthasedeficiency(pphenotype) null TGCTCTCCGACGCCTCCAGGATCGCACTCA[T/A]GTGGAAGTTCGGCGGCATCTACCTGGACAC ATG-AAG 183 null null 2 null Steffensen JBC 275 16723 2000 10747952 53947 0 DAMAGING 0.787878787878788
1 DM CM1210274 null null chr22:43089327:- NM_017436.4 NP_059132.1:p.Q211E A4GALT NORpolyagglutination null CTGCGGAACCTGACCAACGTGCTGGGCACC[C/G]AGTCCCGCTACGTCCTCAACGGCGCGTTCC CAG-GAG 211 null null null null Suchanowska JBC 287 38220 2012 22965229 53947 0.79 TOLERATED null
What I want to do is split the information in column 13 by the - mark. In my example file above, this column contains the data ATG-AAG and CAG-GAG. I would like to separate it with a tab separation.
I've tried my code below:
with open('disease_mut_split2.txt') as inf:
with open('disease_mut_splitfinal.txt', 'w') as outf:
for line in inf:
outf.write('\t'.join(line.split('-')))
However, this also splits the - in the 6 column, which I do not want. Is there any way I can specify the column to split with the code I have?


If you know it's always going to be at column 13, just use a slice:
'{}\t{}'.format(line[:13], line[14:])
Alternatively, if you always know it's going to be the first thing you can limit the # of splits:
>>> x = 'this has - a few - dashes - in it'
>>> x.split('-', maxsplit=1)
['this has ', ' a few - dashes - in it']
If by column you mean that your data is a csv file (tab separated files work the same way), then Python's csv module will aid you:
with open('infile.txt') as f, open('outfile.txt', 'w') as outfile:
reader = csv.reader(f, delimiter='\t')
writer = csv.writer(outfile, delimiter='\t')
writer.writerow(next(reader, None)) # Write out the header row
for row in reader:
# Note: Python lists begin with [0],
# so the 13th column will have an index of 12
row[12] = row[12].replace('-', ' ')
writer.writerow(row)


Assuming what you're doing is in fact parsing/formatting a csv file Wayne Werner's csv module approach is probably the most robust way to solve this.
As an alternative, you might consider using re.sub from the re module. The exact regex to use will depend on the data. If, for instance that column is always 3 nucleotides, - and 3 nucleotides, something like this might work:
re.sub(r'(?<=[ACTG]{3})-(?=[ACTG]{3})', '\t', line))
The regex uses lookbehind and lookahead to replace a - between two sets of 3 nucleotides, so assuming that sort of pattern doesn't appear elsewhere in your file should work well.
EDIT: Changed to re.sub For some reason the original code just had me in a split mindset!

Related

How to read space delimited data, two row types, no fixed width and plenty of missing values?

There's lots of good information out there on how to read space-delimited data with missing values if the data is fixed-width.
http://jonathansoma.com/lede/foundations-2017/pandas/opening-fixed-width-files/
Reading space delimited file in Python/Pandas with missing values
ASCII table with consecutive white-spaces as separators and missing data python pandas
I'm currently trying to read Japan's Meteorological Agency typhoon history data which is supposed to have this format, but doesn't actually:
# Header rows:
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80
::::+::::|::::+::::|::::+::::|::::+::::|::::+::::|::::+::::|::::+::::|::::+::::|
AAAAA BBBB CCC DDDD EEEE F G HHHHHHHHHHHHHHHHHHHH IIIIIIII
# Data rows:
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80
::::+::::|::::+::::|::::+::::|::::+::::|::::+::::|::::+::::|::::+::::|::::+::::|
AAAAAAAA BBB C DDD EEEE FFFF GGG HIIII JJJJ KLLLL MMMM P
It is very similar to NOAA's hurricane best track data, except that it comma delimited, and missing values were given -999 or NaN, which simplified reading the data. Additionally, Japan's data doesn't actually follow the advertised format. For example, column FFFF in the data rows don't always have width 4. Sometimes it has width 3.
I must say that I'm at a complete loss as how to process this data into a dataframe. I've investigated the pd.read_fwf method, and it initially looked promising until I discovered the malformed columns and the two different row types.
My question:
How can I approach cleaning this data and getting it into a dataframe? I'd just find a different dataset, but honestly I can't find any comprehensive typhoon data anywhere else.

I went a little deep for you here, because I'm assuming you're doing this in the name of science and if I can help someone trying to understand climate change then its a good cause.
After looking the data over I've noticed the issue is relating to the data being stored in a de-normalized structure. There are 2 ways you can approach this issue off the top of my head. Re-Writing the file to another file to load into pandas or dask is what I'll show, since thats probably the easiest way to think about it (but certainly not the most efficient for those that will inevitably roast me in the comments)
Think of this like its Two Separate Tables, with a 1-to-Many relationship. 1 table for Typhoons and another for the data belonging to a given typhoon.
A decent, but not really efficient way would be to rewrite it to a better nested structure, like JSON. Then load the data in using that. Note the 2 distinct types of columns.
Step 1: map the data out
There are really 2 tables in one table here. Each typhoon is going to show up as a row that appears like this:
66666 9119 150 0045 9119 0 6 MIRREILE 19920701
While the records for that typhoon are going to follow that row (think of this as a separate row:
20080100 002 3 178 1107 994 035 00000 0000 30600 0200
Load the File in, reading it as raw lines. By using the .readlines() method, we can read each individual line in as an item in a list.
# load the file as raw input
with open('./test.txt') as f:
lines = f.readlines()
Now that we have that read in, we're going to need to perform some logic to separate some lines from others. It appears the every time there is a Typhoon record, the line is preceded with a '66666', so lets key off that. So, given we look at each individual line in a horribly inefficient loop, we can write some if/else logic to have a look:
if row[:5] == '66666':
# do stuff
else:
# do other stuff
Thats going to be a pretty solid way to separate that logic for now, which will be useful to guide splitting that up. Now, we need to write a loop that will check that for each row:
# initialize list of dicts
collection = []
def write_typhoon(row: str, collection: Dict) -> Dict:
if row[:5] == '66666':
# do stuff
else:
# do other stuff
# read through lines list from the .readlines(), looping sequentially
for line in lines:
write_typhoon(line, collection)
Lastly, we're going to need to write some logic to now extract that data out in some manner within the if/then loop inside the write_typhoon() function. I didn't care to do a whole lot of thinking here, and opted for the simplest I could make it: defining the fwf metadata myself. because "yolo":
def write_typhoon(row: str, collection: Dict) -> Dict:
if row[:5] == '66666':
typhoon = {
"AA":row[:5],
"BB":row[6:11],
"CC":row[12:15],
"DD":row[16:20],
"EE":row[21:25],
"FF":row[26:27],
"GG":row[28:29],
"HH":row[30:50],
"II":row[51:],
"data":[]
}
# clean that whitespace
for key, value in typhoon.items():
if key != 'data':
typhoon[key] = value.strip()
collection.append(typhoon)
else:
sub_data = {
"A":row[:9],
"B":row[9:12],
"C":row[13:14],
"D":row[15:18],
"E":row[19:23],
"F":row[24:32],
"G":row[33:40],
"H":row[41:42],
"I":row[42:46],
"J":row[47:51],
"K":row[52:53],
"L":row[54:57],
"M":row[58:70],
"P":row[71:]
}
# clean that whitespace
for key, value in sub_data.items():
sub_data[key] = value.strip()
collection[-1]['data'].append(sub_data)
return collection
Okay that took me longer than I'm willing to admit. I wont lie. Gave me PTSD flashbacks from writing COBOL programs...
Anyway, now we have a nice, nested data structure in native python types. The fun can begin!
Step 2: Load this into a usable format
To analyze it, I'm assuming you'll want it in pandas (or maybe Dask if its too big). Here is what I was able to come up with along that front:
import pandas as pd
df = pd.json_normalize(
collection,
record_path='data',
meta=["AA","BB","CC","DD","EE","FF","GG","HH","II"]
)
A great reference for that can be found in the answers for this question (particularly the second one, not the selected one)
Put it all together now:
from typing import Dict
import pandas as pd
# load the file as raw input
with open('./test.txt') as f:
lines = f.readlines()
# initialize list of dicts
collection = []
def write_typhoon(row: str, collection: Dict) -> Dict:
if row[:5] == '66666':
typhoon = {
"AA":row[:5],
"BB":row[6:11],
"CC":row[12:15],
"DD":row[16:20],
"EE":row[21:25],
"FF":row[26:27],
"GG":row[28:29],
"HH":row[30:50],
"II":row[51:],
"data":[]
}
for key, value in typhoon.items():
if key != 'data':
typhoon[key] = value.strip()
collection.append(typhoon)
else:
sub_data = {
"A":row[:9],
"B":row[9:12],
"C":row[13:14],
"D":row[15:18],
"E":row[19:23],
"F":row[24:32],
"G":row[33:40],
"H":row[41:42],
"I":row[42:46],
"J":row[47:51],
"K":row[52:53],
"L":row[54:57],
"M":row[58:70],
"P":row[71:]
}
for key, value in sub_data.items():
sub_data[key] = value.strip()
collection[-1]['data'].append(sub_data)
return collection
# read through file sequentially
for line in lines:
write_typhoon(line, collection)
# load to pandas df using json_normalize
df = pd.json_normalize(
collection,
record_path='data',
meta=["AA","BB","CC","DD","EE","FF","GG","HH","II"]
)
print(df.head(20)) # lets see what we've got!

There's someone who might have had the same problem and created a library for it, you can check it out here:
https://github.com/miniufo/besttracks
It also includes a quickstart notebook with loading the same dataset.

Here is how I ended up doing it. The key was realizing there are two types of rows in the data, but within each type the columns are fixed width:
header_fmt = "AAAAA BBBB CCC DDDD EEEE F G HHHHHHHHHHHHHHHHHHHH IIIIIIII"
track_fmt = "AAAAAAAA BBB C DDD EEEE FFFF GGG HIIII JJJJ KLLLL MMMM P"
So, here's how it went. I wrote these two functions to help me reformat the text file int CSV format:
def get_idxs(string, char):
idxs = []
for i in range(len(string)):
if string[i - 1].isalpha() and string[i] == char:
idxs.append(i)
return idxs
def replace(string, idx, replacement):
string = list(string)
try:
for i in idx: string[i] = replacement
except TypeError:
string[idx] = replacement
return ''.join(string)
# test it out
header_fmt = "AAAAA BBBB CCC DDDD EEEE F G HHHHHHHHHHHHHHHHHHHH IIIIIIII"
track_fmt = "AAAAAAAA BBB C DDD EEEE FFFF GGG HIIII JJJJ KLLLL MMMM P"
header_idxs = get_idxs(header_fmt, ' ')
track_idxs = get_idxs(track_fmt, ' ')
print(replace(header_fmt, header_idxs, ','))
print(replace(track_fmt, track_idxs, ','))
Testing the function on the format strings, we see commas were put in the appropriate places:
AAAAA,BBBB, CCC,DDDD,EEEE,F,G,HHHHHHHHHHHHHHHHHHHH, IIIIIIII
AAAAAAAA,BBB,C,DDD,EEEE,FFFF, GGG, HIIII,JJJJ,KLLLL,MMMM, P
So next apply those functions to the .txt and create a .csv file with the output:
from contextlib import ExitStack
from tqdm.notebook import tqdm
with ExitStack() as stack:
read_file = stack.enter_context(open('data/bst_all.txt', 'r'))
write_file = stack.enter_context(open('data/bst_all_clean.txt', 'a'))
for line in tqdm(read_file.readlines()):
if ' ' in line[:8]: # line is header data
write_file.write(replace(line, header_idxs, ',') + '\n')
else: # line is track data
write_file.write(replace(line, track_idxs, ',') + '\n')
The next task is to add the header data to ALL rows, so that all rows have the same format:
header_cols = ['indicator', 'international_id', 'n_tracks', 'cyclone_id', 'international_id_dup',
'final_flag', 'delta_t_fin', 'name', 'last_revision']
track_cols = ['date', 'indicator', 'grade', 'latitude', 'longitude', 'pressure', 'max_wind_speed',
'dir_long50', 'long50', 'short50', 'dir_long30', 'long30', 'short30', 'jp_landfall']
data = pd.read_csv('data/bst_all_clean.txt', names=track_cols, skipinitialspace=True)
data.date = data.date.astype('string')
# Get headers. Header rows have variable 'indicator' which is 5 characters long.
headers = data[data.date.apply(len) <= 5]
data[['storm_id', 'records', 'name']] = headers.iloc[:, [1, 2, 7]]
# Rearrange columns; bring identifiers to the first three columns.
cols = list(data.columns[-3:]) + list(data.columns[:-3])
data = data[cols]
# front fill NaN's for header data
data[['storm_id', 'records', 'name']] = data[['storm_id', 'records', 'name']].fillna(method='pad')
# delete now extraneous header rows
data = data.drop(headers.index)
And that yields some nicely formatted data, like this:
storm_id records name date indicator grade latitude longitude
15 5102.0 37.0 GEORGIA 51031900 2 2 67.0 1614
16 5102.0 37.0 GEORGIA 51031906 2 2 70.0 1625
17 5102.0 37.0 GEORGIA 51031912 2 2 73.0 1635

Alignment of column names and its corresponding rows in Python

I have a CSV file which is very messy in terms of column and row alignment. In the first cell, all column names are stated, but they do not align with the rows beneath. So when I load this CSV in python using pandas
I do not get a clean dataframe
In the below picture, there is an example of how it should look like with the columns separated and matching the rows.
Some details:
Few lines of raw CSV file:
Columns:
VMName;"Cluster";"time";"AvgValue";"MinValue";"MaxValue";"MetricId";"MemoryMB";"CpuMHz";"NumCpu"
Rows:
ITLT4301;1;"1-5-2018";976439;35059255;53842;6545371441;3235864;95200029;"MemActive";"4096";"0";"0"
Code:
df = pd.read_csv(file_location, sep=";")
Output when loading the dataframe in python:
VMName;"Cluster";"time";"AvgValue";"MinValue";"MaxValue";"MetricId";"MemoryMB";"CpuMHz";"NumCpu",,,
ITLT4301;1;"1-5-2018";976439,35059255 53842,6545371441 3235864,"95200029 MemActive"" 4096"" 0"" 0"""
Desired output:
VMName Cluster time AvgValue MinValue MaxValue MetricId MemoryMB CpuMHz
ITLT4301 1 1-5-201 976439 35059255 53842 6545371441 95200029 MemActive
NumCpu
4096
Hopefully this clears up the topic and problem a bit. Desired output is a well-organized data frame where the columns match the rows based on separater sign ";"

Your input data file is not a standard csv file. The correct way would be to fix the previous step in order to get a normal csv file instead of a mess of double quotes preventing any decent csv parser to correctly extract data.
As a workaround, it is possible to remove the initial and terminating double quote, remove any doubled double quote, and split every line on semi-column ignoring any remaining double quote. Optionnaly, you could also try to just remove any double quote and split the lines on ';'. It really depends on what values you expect.
A possible code could be:
def split_line(line):
'''split a line on ; after stripping white spaces, the initial and terminating "
doubles double quotes are also removed'''
return line.strip()[1:-1].replace('""', '').split(';')
with open('file.dat') as fd:
cols = split_line(next(fd)) # extract column names from header line
data = [split_line(line) for line in fd] # process data lines
df = pd.DataFrame(data, columns=cols) # build a dataframe from that
With that input:
"VMName;""Cluster"";""time"";""AvgValue"";""MinValue"";""MaxValue"";""MetricId"";""MemoryMB"";""CpuMHz"";""NumCpu"""
"ITLT4301;1;""1-5-2018"";976439" 35059255;53842 6545371441;3235864 "95200029;""MemActive"";""4096"";""0"";""0"""
"ITLT4301;1;""1-5-2018"";98" 9443749608104;29 3435452286154;673 "067568681366;""CpuUsageMHz"";""0"";""5600"";""2"""
It gives:
VMName Cluster time AvgValue MinValue \
0 ITLT4301 1 1-5-2018 976439" 35059255 53842 6545371441
1 ITLT4301 1 1-5-2018 98" 9443749608104 29 3435452286154
MaxValue MetricId MemoryMB CpuMHz NumCpu
0 3235864 "95200029 MemActive 4096 0 0
1 673 "067568681366 CpuUsageMHz 0 5600 2

Finding the row number for the header row in a CSV file / Pandas Dataframe

I am trying to get an index or row number for the row that holds the headers in my CSV file.
The issue is, the header row can move up and down depending on the output of the report from our system (I have no control to change this)
code:
ht = pd.read_csv(file.csv)
test = ht.get_loc('Code') #Code being header im using to locate the header row
csv1 = read_csv(file.csv, header=test)
df1 = df1.append(csv1) #Appending as have many files
If I was to print test, I would expect a number around 4 or 5, and that's what I am feeding into the second read "read_csv"
The error I'm getting is that it's expecting 1 header column, but I have 26 columns. I am just trying to use the first header string to get the row number
Thanks
:-)
Edit:
CSV format
This file contains the data around the volume of items blablalbla
the deadlines for delivery of items a - z is 5 days
the deadlines for delivery of items aa through zz are 3 days
the deadlines for delivery of items aaa through zzz are 1 days
code,type,arrived_date,est_del_date
a/wrwgwr12/001,kids,12-dec-18,17-dec-18
aa/gjghgj35/030,pet,15-dec-18,18-dec-18
as you will see the "The deadlines" rows are the same, this can be 3 or 5 based on the code ids, thus the header row can change up or down.
I also did not write out all 26 column headers, not sure that matters.
Wanted DF format
index | code | type | arrived_date | est_del_date
1 | a/wrwgwr12/001 | kids | 12-dec-18 | 17-dec-18
2 | aa/gjghgj35/030 | Pet | 15-dec-18 | 18-dec-18
Hope this makes sense..
Thanks,

You can use the csv module to find the first row which contains a delimiter, then feed the index of this row as the skiprows parameter to pd.read_csv:
from io import StringIO
import csv
import pandas as pd
x = """This file contains the data around the volume of items blablalbla
the deadlines for delivery of items a - z is 5 days
the deadlines for delivery of items aa through zz are 3 days
the deadlines for delivery of items aaa through zzz are 1 days
code,type,arrived_date,est_del_date
a/wrwgwr12/001,kids,12-dec-18,17-dec-18
aa/gjghgj35/030,pet,15-dec-18,18-dec-18"""
# replace StringIO(x) with open('file.csv', 'r')
with StringIO(x) as fin:
reader = csv.reader(fin)
idx = next(idx for idx, row in enumerate(reader) if len(row) > 1) # 4
# replace StringIO(x) with 'file.csv'
df = pd.read_csv(StringIO(x), skiprows=idx)
print(df)
code type arrived_date est_del_date
0 a/wrwgwr12/001 kids 12-dec-18 17-dec-18
1 aa/gjghgj35/030 pet 15-dec-18 18-dec-18

Python data wrangling issues

I'm currently stumped by some basic issues with a small data set. Here are the first three lines to illustrate the format of the data:
"Sport","Entry","Contest_Date_EST","Place","Points","Winnings_Non_Ticket","Winnings_Ticket","Contest_Entries","Entry_Fee","Prize_Pool","Places_Paid"
"NBA","NBA 3K Crossover #3 [3,000 Guaranteed] (Early Only) (1/15)","2015-03-01 13:00:00",35,283.25,"13.33","0.00",171,"20.00","3,000.00",35
"NBA","NBA 1,500 Layup #4 [1,500 Guaranteed] (Early Only) (1/25)","2015-03-01 13:00:00",148,283.25,"3.00","0.00",862,"2.00","1,500.00",200
The issues I am having after using read_csv to create a DataFrame:
The presence of commas in certain categorical values (such as Prize_Pool) results in python considering these entries as strings. I need to convert these to floats in order to make certain calculations. I've used python's replace() function to get rid of the commas, but that's as far as I've gotten.
The category Contest_Date_EST contains timestamps, but some are repeated. I'd like to subset the entire dataset into one that has only unique timestamps. It would be nice to have a choice in which repeated entry or entries are removed, but at the moment I'd just like to be able to filter the data with unique timestamps.

Use thousands=',' argument for numbers that contain a comma
In [1]: from pandas import read_csv
In [2]: d = read_csv('data.csv', thousands=',')
You can check Prize_Pool is numerical
In [3]: type(d.ix[0, 'Prize_Pool'])
Out[3]: numpy.float64
To drop rows - take first observed, you can also take last
In [7]: d.drop_duplicates('Contest_Date_EST', take_last=False)
Out[7]:
Sport Entry \
0 NBA NBA 3K Crossover #3 [3,000 Guaranteed] (Early ...
Contest_Date_EST Place Points Winnings_Non_Ticket Winnings_Ticket \
0 2015-03-01 13:00:00 35 283.25 13.33 0
Contest_Entries Entry_Fee Prize_Pool Places_Paid
0 171 20 3000 35

Edit: Just realized you're using pandas - should have looked at that.
I'll leave this here for now in case it's applicable but if it gets
downvoted I'll take it down by virtue of peer pressure :)
I'll try and update it to use pandas later tonight
Seems like itertools.groupby() is the tool for this job;
Something like this?
import csv
import itertools
class CsvImport():
def Run(self, filename):
# Get the formatted rows from CSV file
rows = self.readCsv(filename)
for key in rows.keys():
print "\nKey: " + key
i = 1
for value in rows[key]:
print "\nValue {index} : {value}".format(index = i, value = value)
i += 1
def readCsv(self, fileName):
with open(fileName, 'rU') as csvfile:
reader = csv.DictReader(csvfile)
# Keys may or may not be pulled in with extra space by DictReader()
# The next line simply creates a small dict of stripped keys to original padded keys
keys = { key.strip(): key for (key) in reader.fieldnames }
# Format each row into the final string
groupedRows = {}
for k, g in itertools.groupby(reader, lambda x : x["Contest_Date_EST"]):
groupedRows[k] = [self.normalizeRow(v.values()) for v in g]
return groupedRows;
def normalizeRow(self, row):
row[1] = float(row[1].replace(',','')) # "Prize_Pool"
# and so on
return row
if __name__ == "__main__":
CsvImport().Run("./Test1.csv")
Output:
More info:
https://docs.python.org/2/library/itertools.html
Hope this helps :)

Extracting Groups

Using Python 3.2 I was hoping to solve the below issue. My data consist of hundreds of rows (signifying a project) and 21 columns. The first of which is a unique project ID and the other 20 columns is the group of people, or person, that led the project. person_1 is always filled and if there is a name in person_3 that means 3 people are working together. If there is a name in person_18 that means 18 people are working together.
I have an excel spreadsheet that is setup the following way:
unique ID person_1 person _2 person_3 person_4 ... person_20
12 Tom Sally Mike
16 Joe Mike
5 Joe Sally
1 Sally Mike Tom
6 Sally Tom Mike
2 Jared Joe Mike John ... Carl
I want to do a few things:
1) Make a column that will give me a unique 'Group Name' which will be, using unique ID 1 as my example, Sally/Mike/Tom. So it will be the names separated by '/'.
2) How can I treat, from my example, Sally/Mike/Tom the same as Sally/Tom/Mike. Meaning, I would like another column that makes the group name in alphabetical order (no matter the actual permutation), still separated by '/'.
3) This question is similar to (2). However, I want the person listed in person_1 to matter. Meaning Joe/Tom/Mike is different from Tom/Joe/Mike but not different than Joe/Mike/Tom. So there will be another column that keeps person_1 at the start of the group name but alphabetizes person_2 through person_20 if applicable (i.e., if the project has more than 1 person on it).
Thanks for the help and suggestions

The previous answer gave a clear statement of method, but perhaps you are stuck on either the string processing or the csv processing. Both are demonstrated in the following code. The relevant string methods are sorted and join. '/'.join tells join to use / as separator between joined items. The + operator between lists in tname and writerow statements concatenates the lists. A csv.reader is an iterator that delivers one list per row, and a csv.writer converts a list to a row and writes it out. You will want to add error testing to the file opens, etc. The data file used to test this code is shown after the code.
import csv
fi = open('xgroup.csv')
fo = open('xgroup3.csv', 'w')
w = csv.writer(fo)
r = csv.reader(fi)
li = 0
print "Opened reader and writer"
for row in r:
gname = '/'.join(row[1:])
sname = '/'.join(sorted(row[1:]))
tname = '/'.join([row[1]]+sorted(row[2:]))
w.writerow([row[0], gname, sname, tname]+row[1:])
li += 1
fi.close()
fo.close()
print "Closed reader and writer after",li,"lines"
File xgroup.csv is shown next.
unique-ID,person_1,person,_2,person_3,person_4,...,person_20
12,Tom,Sally,Mike
16,Joe,Mike
5,Joe,Sally
1,Sally,Mike,Tom
6,Sally,Tom,Mike
2,Jared,Joe,Mike,John,...,Carl
Upon reading data as above, the program prints Opened reader and writer and Closed reader and writer after 7 lines and produces output in file xgroup3.csv as shown next.
unique-ID,person_1/person/_2/person_3/person_4/.../person_20,.../_2/person/person_1/person_20/person_3/person_4,person_1/.../_2/person/person_20/person_3/person_4,person_1,person,_2,person_3,person_4,...,person_20
12,Tom/Sally/Mike,Mike/Sally/Tom,Tom/Mike/Sally,Tom,Sally,Mike
16,Joe/Mike,Joe/Mike,Joe/Mike,Joe,Mike
5,Joe/Sally,Joe/Sally,Joe/Sally,Joe,Sally
1,Sally/Mike/Tom,Mike/Sally/Tom,Sally/Mike/Tom,Sally,Mike,Tom
6,Sally/Tom/Mike,Mike/Sally/Tom,Sally/Mike/Tom,Sally,Tom,Mike
2,Jared/Joe/Mike/John/.../Carl,.../Carl/Jared/Joe/John/Mike,Jared/.../Carl/Joe/John/Mike,Jared,Joe,Mike,John,...,Carl
Note, given a data line like
5,Joe,Sally,,,,,
instead of
5,Joe,Sally
the program as above produces
5,Joe/Sally/////,/////Joe/Sally,Joe//////Sally,Joe,Sally,,,,,
instead of
5,Joe/Sally,Joe/Sally,Joe/Sally,Joe,Sally
If that's a problem, filter out empty entries. For example, if
row=['5', 'Joe', 'Sally', '', '', '', '', ''], then
'/'.join(row[1:]) produces
'Joe/Sally/////', while
'/'.join(filter(lambda x: x, row[1:])) and
'/'.join(x for x in row[1:] if x) and
'/'.join(filter(len, row[1:])) produce
'Joe/Sally' .

You could do the following:
Export your file to a .csv file from Excel
Open that input file using python's csv module, using csv.reader
Open another file (output) to write to it using csv.writer
Iterate over each row in your reader, do your treatment, and write that using your writer
Import the output file in Excel

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