Xjyuk

here is my dataframe -:

 Dec-18 Jan-19 Feb-19 Mar-19 Apr-19 May-19
Saturday 2540.0 2441.0 3832.0 4093.0 1455.0 2552.0
Sunday 1313.0 1891.0 2968.0 2260.0 1454.0 1798.0
Monday 1360.0 1558.0 2967.0 2156.0 1564.0 1752.0
Tuesday 1089.0 2105.0 2476.0 1577.0 1744.0 1457.0
Wednesday 1329.0 1658.0 2073.0 2403.0 1231.0 874.0
Thursday 798.0 1195.0 2183.0 1287.0 1460.0 1269.0

i have tried some pandas ops but i am not able to do that

this is what i want to do

 items
Saturday 2540.0 
Sunday 1313.0 
Monday 1360.0 
Tuesday 1089.0 
Wednesday 1329.0 
Thursday 798.0 
Saturday 2441.0 
Sunday 1891.0 
Monday 1558.0 
Tuesday 2105.0 
Wednesday 1658.0 
Thursday 1195.0 ............ and so on 

i want to set those rows into rows in downside

how to do that?

thanks in advance

df.reset_index().melt(id_vars='index').drop('variable',1)

Output:

 index value
0 Saturday 2540.0
1 Sunday 1313.0
2 Monday 1360.0
3 Tuesday 1089.0
4 Wednesday 1329.0
5 Thursday 798.0
6 Saturday 2441.0
7 Sunday 1891.0
8 Monday 1558.0
9 Tuesday 2105.0
10 Wednesday 1658.0
11 Thursday 1195.0
12 Saturday 3832.0
13 Sunday 2968.0
14 Monday 2967.0
15 Tuesday 2476.0
16 Wednesday 2073.0
17 Thursday 2183.0
18 Saturday 4093.0
19 Sunday 2260.0
20 Monday 2156.0
21 Tuesday 1577.0
22 Wednesday 2403.0
23 Thursday 1287.0
24 Saturday 1455.0
25 Sunday 1454.0
26 Monday 1564.0
27 Tuesday 1744.0
28 Wednesday 1231.0
29 Thursday 1460.0
30 Saturday 2552.0
31 Sunday 1798.0
32 Monday 1752.0
33 Tuesday 1457.0
34 Wednesday 874.0
35 Thursday 1269.0

Note: just noted a commented suggesting to do the same thing, I will delete my post if requested :)

Create it with numpy by reshaping the data.

import pandas as pd
import numpy as np

pd.DataFrame(df.to_numpy().flatten('F'), 
 index=np.tile(df.index, df.shape[1]), 
 columns=['items'])

Output:

 items
Saturday 2540.0
Sunday 1313.0
Monday 1360.0
Tuesday 1089.0
Wednesday 1329.0
Thursday 798.0
Saturday 2441.0
...
Sunday 1798.0
Monday 1752.0
Tuesday 1457.0
Wednesday 874.0
Thursday 1269.0

You can do:

df = df.stack().sort_index(level=1).reset_index(level = 1, drop=True).to_frame('items')

It is interesting that this method got overlooked even though it is the fastest:

import time
start = time.time()
df.stack().sort_index(level=1).reset_index(level = 1, drop=True).to_frame('items')
end = time.time()
print("time taken ".format(end-start))

yields: time taken 0.006181955337524414

while this:

start = time.time()
df.reset_index().melt(id_vars='days').drop('variable',1)
end = time.time()
print("time taken ".format(end-start))

yields: time taken 0.010072708129882812

Any my output format matches OP's requested exactly.