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Pandas line plot without transposing the dataframe

I have a pandas dataframe which looks like this -
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Marks
0 30 31 29 15 30 30 30 50 30 30 30 26 Student1
1 45 45 45 45 41 45 35 45 45 45 37 45 Student2
2 21 11 21 21 21 21 21 21 21 21 17 21 Student3
3 30 30 33 30 30 30 50 30 30 30 22 30 Student4
4 39 34 34 34 34 34 23 34 40 34 34 34 Student5
5 41 41 41 28 41 56 41 41 41 41 41 41 Student6
If I transpose the data like below, I am able to plot a line graph
Marks Student1 Student2 Student3 Student4 Student5 Student6
0 Jan 30 45 21 30 39 41
1 Feb 31 45 11 30 34 41
2 Mar 29 45 21 33 34 41
3 Apr 15 45 21 30 34 28
4 May 30 41 21 30 34 41
5 Jun 30 45 21 30 34 56
6 Jul 30 35 21 50 23 41
7 Aug 50 45 21 30 34 41
8 Sep 30 45 21 30 40 41
9 Oct 30 45 21 30 34 41
10 Nov 30 37 17 22 34 41
11 Dec 26 45 21 30 34 41
However, my original data is huge, and transposing it is taking too long. Is there some other way to achieve this?
Please note - this is just a dummy dataframe I created for the sake of simplicity, my original data is quite complex and huge.

If you're data is huge, you're probably not going to be able to see anything on the line plot anyways...
import matplotlib.pyplot as plt
import pandas as pd
from io import StringIO
import numpy as np
df = pd.read_table(StringIO(""" Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Marks
0 30 31 29 15 30 30 30 50 30 30 30 26 Student1
1 45 45 45 45 41 45 35 45 45 45 37 45 Student2
2 21 11 21 21 21 21 21 21 21 21 17 21 Student3
3 30 30 33 30 30 30 50 30 30 30 22 30 Student4
4 39 34 34 34 34 34 23 34 40 34 34 34 Student5
5 41 41 41 28 41 56 41 41 41 41 41 41 Student6"""), sep='\s+')
x = df.columns.tolist()[:-1]
y = df.iloc[:, :-1].values
for i, j in enumerate(y):
plt.plot(x, j, label=df['Marks'].iloc[i])
plt.ylim(bottom=0)
plt.legend(loc='upper right')

Split several columns by "space" pandas

I want to split my data frame by "space" for all columns. I can do it for 1 column. How to apply it to the whole data? (maybe with loop)
df =
0 1 2 4
11 22 12 22 13 22 14 22
15 16 17 18 33 44 22 55
19 20 21 22 66 55 33 66
23 24 25 26 22 44 66 44
I am splitting in like:
df[0].str.split(' ', 1, expand=True)
Output is:
0 1
11 22
15 16
19 20
23 24

You can stack and unstack:
df.stack().str.split(' ', expand=True).unstack()
Output:
0 1
0 1 2 4 0 1 2 4
0 11 12 13 14 22 22 22 22
1 15 17 33 22 16 18 44 55
2 19 21 66 33 20 22 55 66
3 23 25 22 66 24 26 44 44

Why does OpenCV matchShape() match "X" with "N"?

I am comparing the contours of letters and have several cases of unexpected results. The most confusing to me is how X and N are being identified as best matches.
In the images below, yellow represents the unknown shape and blue represents candidate shapes. The white numbers are the result returned by cv.matchShapes using CONTOURS_MATCH_I3. (I've tried the other matching methods and just get similar odd results but with a different set of letters.)
Below shows X matching N better than X
Below shows N matching X better than N
At the end of the post are the raw data and below is a chart of the the data.
I can't come up with a rotation, scale, or skew to show that this is an optical illusion. I'm not suggesting there is an issue in matchShapes but rather an issue in my understanding of Hu moments.
I'd appreciate if someone would take a moment (pun intended) and explain how cv.matchShapes is producing these results.
--- edited ----
The images below are the result of using poly-filled shapes. I am still baffled how these letters match better than the correct ones.
target_letter
33 23
32 24
30 24
28 26
28 30
29 31
29 32
31 34
31 35
33 37
33 38
36 41
36 42
38 44
38 47
35 50
35 51
33 53
33 54
30 57
30 58
28 60
28 61
27 62
27 67
29 69
34 69
38 65
38 64
40 62
40 61
42 59
42 58
46 54
47 54
49 56
49 57
51 59
51 60
53 62
53 63
56 66
56 67
58 69
63 69
65 67
65 60
63 58
63 57
60 54
60 53
58 51
58 50
55 47
55 44
57 42
57 41
61 37
61 36
64 33
64 32
65 31
65 25
64 24
62 24
61 23
60 24
58 24
55 27
55 28
52 31
52 32
50 34
50 35
47 38
45 36
45 35
41 31
41 30
40 29
40 28
38 26
38 25
37 24
35 24
34 23
candidateLetter N
10 3
9 4
7 4
6 5
5 5
5 6
4 7
4 9
3 10
3 44
4 45
4 47
6 49
12 49
13 48
13 47
14 46
14 23
15 22
17 24
17 25
21 29
21 30
24 33
24 34
27 37
27 38
31 42
31 43
34 46
34 47
35 48
36 48
37 49
43 49
45 47
45 6
43 4
38 4
36 6
36 8
35 9
35 27
36 28
36 29
34 31
33 30
33 29
31 27
31 26
27 22
27 21
24 18
24 17
21 14
21 13
18 10
18 9
13 4
11 4
candidateLetter X
10 2
9 3
7 3
6 4
6 6
5 7
5 8
6 9
6 11
8 13
8 14
10 16
10 17
14 21
14 22
16 24
16 25
13 28
13 29
10 32
10 33
7 36
7 37
5 39
5 40
4 41
4 46
6 48
11 48
15 44
15 43
17 41
17 40
19 38
19 37
21 35
21 34
23 32
26 35
26 36
28 38
28 39
30 41
30 42
33 45
33 46
35 48
40 48
42 46
42 39
40 37
40 36
37 33
37 32
34 29
34 28
32 26
32 23
34 21
34 20
37 17
37 16
41 12
41 11
42 10
42 4
41 3
39 3
38 2
37 3
35 3
32 6
32 7
29 10
29 11
27 13
27 14
24 17
21 14
21 13
18 10
18 9
17 8
17 7
15 5
15 4
14 3
12 3
11 2

Grid of integers

I need to make a grid with the numbers generated by the code, but I'm not understanding how to align them in columns.
Is there a parameter of print or something else that could help me out?
#main()
a=0
b=0
for i in range(1, 13):
a=a+1
print(" ")
b=b+1
for f in range(1,13):
print(f*b, end=" ")
My output at the moment:

I would recommend using python's f-strings:
for i in range(1, 13):
print(''.join(f"{i*j: 4}" for j in range(1,13)))
Here's the output:
1 2 3 4 5 6 7 8 9 10 11 12
2 4 6 8 10 12 14 16 18 20 22 24
3 6 9 12 15 18 21 24 27 30 33 36
4 8 12 16 20 24 28 32 36 40 44 48
5 10 15 20 25 30 35 40 45 50 55 60
6 12 18 24 30 36 42 48 54 60 66 72
7 14 21 28 35 42 49 56 63 70 77 84
8 16 24 32 40 48 56 64 72 80 88 96
9 18 27 36 45 54 63 72 81 90 99 108
10 20 30 40 50 60 70 80 90 100 110 120
11 22 33 44 55 66 77 88 99 110 121 132
12 24 36 48 60 72 84 96 108 120 132 144
The most common form is to use almost any arbitrary expression within the curly braces. This can include dictionary values, function calls and so on. The above usage specifies formatting after the colon. The space before the 4 indicates that the fill character should be a space, and the 4 indicates that the whole expression should take up 4 characters total. For more info, check out the documentation.

Considering the width of each grid cell is stored as w, which for above snippet suffices as 4, a regularly spaced grid can be printed using
w = 4
a, b = 0, 0
for i in range(1, 13):
a, b = a+1, b+1
for f in range(1, 13):
print(('{:'+str(w)+'}').format(f*b), end='')
print('')
Its output is
1 2 3 4 5 6 7 8 9 10 11 12
2 4 6 8 10 12 14 16 18 20 22 24
3 6 9 12 15 18 21 24 27 30 33 36
4 8 12 16 20 24 28 32 36 40 44 48
5 10 15 20 25 30 35 40 45 50 55 60
6 12 18 24 30 36 42 48 54 60 66 72
7 14 21 28 35 42 49 56 63 70 77 84
8 16 24 32 40 48 56 64 72 80 88 96
9 18 27 36 45 54 63 72 81 90 99 108
10 20 30 40 50 60 70 80 90 100 110 120
11 22 33 44 55 66 77 88 99 110 121 132
12 24 36 48 60 72 84 96 108 120 132 144

You can reference keyword argument values passed to the str.format() method in the format string by name via {name}. Here's an example of doing that where the value referenced is computed (as opposed to being a constant):
mx = 12
w = len(str(mx*mx)) + 1
for b in range(1, mx+1):
for f in range(1, mx+1):
print(('{:{w}}').format(f*b, w=w), end='')
print('')
Output:
1 2 3 4 5 6 7 8 9 10 11 12
2 4 6 8 10 12 14 16 18 20 22 24
3 6 9 12 15 18 21 24 27 30 33 36
4 8 12 16 20 24 28 32 36 40 44 48
5 10 15 20 25 30 35 40 45 50 55 60
6 12 18 24 30 36 42 48 54 60 66 72
7 14 21 28 35 42 49 56 63 70 77 84
8 16 24 32 40 48 56 64 72 80 88 96
9 18 27 36 45 54 63 72 81 90 99 108
10 20 30 40 50 60 70 80 90 100 110 120
11 22 33 44 55 66 77 88 99 110 121 132
12 24 36 48 60 72 84 96 108 120 132 144

Reading csv file with delimiter | using pandas

def main():
l=[]
for i in range(1981,2018):
df = pd.read_csv("ftp://ftp.cpc.ncep.noaa.gov/htdocs/degree_days/weighted/daily_data/"+ str(i)+"/Population.Heating.txt")
print(df[12:])
I am trying to download and read the "CONUS" row in Population.Heating.txt from 1981 to 2017.
My code seems to get the CONUS parts, but How can I actually read it like csv format with |?
Thank you!

Try this:
def main():
l=[]
url = "ftp://ftp.cpc.ncep.noaa.gov/htdocs/degree_days/weighted/daily_data/{}/Population.Heating.txt"
for i in range(1981,2018):
df = pd.read_csv(url.format(i), sep='\|', skiprows=3, engine='python')
print(df[12:])
Demo:
In [14]: url = "ftp://ftp.cpc.ncep.noaa.gov/htdocs/degree_days/weighted/daily_data/{}/Population.Heating.txt"
In [15]: i = 2017
In [16]: df = pd.read_csv(url.format(i), sep='\|', skiprows=3, engine='python')
In [17]: df
Out[17]:
Region 20170101 20170102 20170103 20170104 20170105 20170106 20170107 20170108 20170109 ... 20171222 20171223 \
0 1 30 36 31 25 37 39 47 51 55 ... 40 32
1 2 28 32 28 23 39 41 46 49 51 ... 31 25
2 3 34 30 26 43 52 58 57 54 44 ... 29 32
3 4 37 34 37 57 60 62 59 54 43 ... 39 45
4 5 15 11 9 10 20 21 27 36 33 ... 12 7
5 6 16 9 7 22 31 38 45 44 35 ... 9 9
6 7 8 5 9 23 23 34 37 32 17 ... 9 19
7 8 30 32 34 33 36 42 42 31 23 ... 36 33
8 9 25 25 24 23 22 25 23 15 17 ... 23 20
9 CONUS 24 23 21 26 33 38 40 39 34 ... 23 22
20171224 20171225 20171226 20171227 20171228 20171229 20171230 20171231
0 32 34 43 53 59 59 57 59
1 30 33 43 49 54 53 50 55
2 41 47 58 62 60 54 54 60
3 47 55 61 64 57 54 62 68
4 12 20 21 22 27 26 24 29
5 22 33 31 35 37 33 32 39
6 19 24 23 28 28 23 19 27
7 34 30 32 29 26 24 27 30
8 18 17 17 15 13 11 12 15
9 26 30 34 37 38 35 34 40
[10 rows x 366 columns]

def main():
l=[]
for i in range(1981,2018):
l.append( pd.read_csv("ftp://ftp.cpc.ncep.noaa.gov/htdocs/degree_days/weighted/daily_data/"+ str(i)+"/Population.Heating.txt"
, sep='|', skiprows=3))
Files look like:
Product: Daily Heating Degree Days
Regions: Regions::CensusDivisions
Weights: Population
[... data ...]
so you need to skip 3 rows. Afterwards you have several 'df' in your list 'l' for further processing.