csv_manipulation.py

# Convert wide format csv to long format csv
# Time Temp1 Temp2 Temp3 Temp4 Temp5
# 00   21     32   33    21    23
# 10   34     23   12    08    23
# 20   12     54   33    54    55

with open("in.csv") as f,open("out.csv","w") as out:
     headers = next(f).split()[1:]  # keep headers/Time Temp1 Temp2 Temp3 Temp4 Temp5
     for row in f:
        row = row.split()
        time = row[0]
        data = zip(headers, row[1:]) # match correct temp to row item
        for a, b in data:
            out.write("{} {} {}\n".format(time,a.lower(),b))
            print("{} {} {}".format(time,a.lower(),b))

# 00 temp1 21
# 00 temp2 32
# 00 temp3 33
# 00 temp4 21
# 00 temp5 23
# 10 temp1 34
# 10 temp2 23
# 10 temp3 12
# 10 temp4 08
# 10 temp5 23
# 20 temp1 12
# 20 temp2 54
# 20 temp3 33
# 20 temp4 54
# 20 temp5 55

# Convert wide format csv to long format csv
# Time Temp1 Temp2 Temp3 Temp4 Temp5
# 00   21     32   33    21    23
# 10   34     23   12    08    23
# 20   12     54   33    54    55
# Time Temp1 Temp2 Temp3 Temp4 Temp5
# 00   21     32   33    21    23
# 10   34     23   12    08    23
# 20   12     54   33    54    55

with open("in.csv") as f,open("out.csv","w") as out:
    headers = next(f).rstrip('\r\n').split(',')[1:]  # keep headers/Time Temp1 Temp2 Temp3 Temp4 Temp5
    for row in f:
        if ('DateTime' in row):
            headers = row.rstrip('\r\n').split(',')[1:]
            continue
        row = row.rstrip('\r\n').split(',')
        time = datetime.datetime.strptime(row[0],'%d/%m/%Y %H:%M').strftime('%Y-%m-%d %H:%M:%S')
        data = zip(headers, row[1:])  # match correct temp to row item
        for a, b in data:
            out.write("{},{},{}\n".format(a, time, b))


######################
######################

# group, id, name
# A, 12345, "eeny"
# A, 23456, "meeny"
# A, 34567, "miney mo"
# B, 99999, "foo"
# B, 88888, "bar"
# B, 77777, "foobar"

df['group_num'] = df.groupby('group')['id'].transform(lambda x: range(1, len(x)+1))
df = df.pivot(index='group', columns='group_num') 

              # id                name                 
# group_num      1      2      3     1      2         3
# group                                                
# A          12345  23456  34567  eeny  meeny  miney mo
# B          99999  88888  77777   foo    bar    foobar

df.columns = [''.join([lvl1, str(lvl2)]) for lvl1, lvl2 in df.columns]
df
         # id1    id2    id3 name1  name2     name3
# group                                            
# A      12345  23456  34567  eeny  meeny  miney mo
# B      99999  88888  77777   foo    bar    foobar