Q:

Using the proper delimiters with pandas

I'm using pandas to read a csv file, sort it for the specified values in "regex", and then spit out a new csv file with the sorted data. My output looks great, except that it looks like this and is all listed as being in one row of a csv file
row 1                           row 2
" 1 2 3 4 5 6 7 8 9 10 11 12"   "1 2 3 4 5 6 7 8 9 10 11 12"

when really I want it like:
row 1 row 2 row 3 row 4
1     2     3     4

What is the proper delimiter method for separating the data into individual rows? I've tried quite a few combinations but nothing has worked yet.
at the end of this file I'll attach a snippet of the actual data in my csv file.
File = 'file'
out_file ='out_file'

df = pd.read_csv(File)
dffilter = df.filter(regex='201411')
dffilter.to_csv(blank)

 "20141107T204206Z 28.00 82.82 239.93 1.849e-01 5.590e-02 1.4210e+02 -9.0000e+00 6.686 1 13.88 -9.00 0.01580 1 8 -9.0 -9.0
20141107T204334Z 28.00 83.06 240.19 2.067e-01 5.547e-02 1.1299e+02 -9.0000e+00 6.840 97 13.93 -9.00 0.02789 1 8 -9.0 -9.0

And so on.

A:

you can try this 
# read all columns except 1st as datasets
df = pd.read_csv('csvFile.csv', header=None).iloc[:, 1:]

# read 1st column as columns of the data and save them into a list
headers = pd.read_csv('csvFile.csv', header=None).iloc[:, 0].tolist()

# transpose the data
df = df.transpose()

# rename columns
df.columns = headers

df.reset_index(drop=True)
print(df)

    20141107T204206Z  20141107T204334Z
0            28.0000          28.00000
1            82.8200          83.06000
2           239.9300         240.19000
3             0.1850           0.20700
4             0.0559           0.05550
5           142.0000         113.00000
6            -9.0000          -9.00000
7             6.6860           6.84000
8             1.0000          97.00000
9            13.8800          13.93000
10           -9.0000          -9.00000
11            0.0158           0.02789
12            1.0000           1.00000
13            8.0000           8.00000
14           -9.0000          -9.00000
15           -9.0000          -9.00000

