Resolved: How do I make a new column every 4 elements in a row excel file?

input from excel 0.282836353 0.287834898 0.284861334 0.284880417 0.242768564 0.241888021    0.240441747 0.244627766 0.245007869 0.244790578 0.243485108 0.244743911 0.267488959 0.264294548 0.267708093 0.268158158

output: 0.282836353 0.287834898 0.284861334 0.284880417 
        0.242768564 0.241888021 0.240441747 0.244627766 
        0.245007869 0.244790578 0.243485108 0.244743911
        0.267488959 0.264294548 0.267708093 0.268158158

import openpyxl<br />
excelFile = openpyxl.load_workbook("nameoffile.xlsx")<br />
sheet = excelFile["Sheet1"]<br />
nRows = sheet.max_row<br />
nCols = sheet.max_column<br />
r = 2<br />
groups = 4 # how many to be grouped together<br />
parsedList = []</p>
<p>while r <= nRows:
    rList,rListSum = [],[]
    x = 0
    c = 2
    while c <= nCols:
        if x < groups:
            cell = sheet.cell(row=r,column=c)
            rList.append(cell.value)
        x += 1
        if x == groups:
            rListSum.append(rList)  
    c += 1
    parsedList.append(rListSum)
r += 1
[/code]

</p>



my current output is nothing. something seems to be wrong with my loop.<br> 



<h2>Answer:</h2>



The phrase "multiple rows of 60 numbers" in the question is not clear.<br>
Does each row have a string of 60 numbers (e.g. "0.1 0.2 0.3 ...") or 60 columns (e.g. A1, B1, ...)?<br> 



I assumed the latter one and you can simply do what you want by using <code>NumPy</code> and <code>Pandas</code>.<br> 


<p>[code]import numpy as np<br />
import pandas as pd</p>
<p>df = pd.DataFrame(np.random.rand(5,60))

0   1   2   3   4   5   6   7   8   9   ... 50  51  52  53  54  55  56  57  58  59
0   0.609861    0.778792    0.223185    0.945089    0.630081    0.629745    0.893291    0.557107    0.044485    0.267008    ... 0.423123    0.236365    0.216665    0.173979    0.436419    0.206685    0.188097    0.042587    0.316459    0.252943
1   0.451599    0.234676    0.540550    0.576964    0.067314    0.180313    0.952212    0.725722    0.368086    0.351682    ... 0.209218    0.284543    0.504832    0.306373    0.830000    0.878597    0.549021    0.430474    0.162942    0.194625
2   0.346460    0.803657    0.270074    0.375594    0.080673    0.051869    0.119898    0.632888    0.497251    0.689102    ... 0.586911    0.725564    0.662852    0.814569    0.753149    0.363969    0.811467    0.144431    0.523725    0.725145
3   0.846687    0.488381    0.803212    0.405574    0.137206    0.963724    0.200077    0.576446    0.449057    0.125634    ... 0.295565    0.153730    0.259037    0.725119    0.427521    0.750190    0.222516    0.960042    0.786858    0.446018
4   0.563980    0.809230    0.420195    0.776374    0.506152    0.429394    0.802265    0.261096    0.924650    0.778620    ... 0.282234    0.333429    0.714918    0.404162    0.730436    0.939195    0.657210    0.368771    0.096418    0.189417

df_new = pd.DataFrame(df.to_numpy().reshape(-1, 4))

    0   1   2   3
0   0.609861    0.778792    0.223185    0.945089
1   0.630081    0.629745    0.893291    0.557107
2   0.044485    0.267008    0.433519    0.093443
3   0.057903    0.786750    0.474591    0.579831
4   0.491986    0.686017    0.930706    0.144237
... ... ... ... ...
70  0.958695    0.789661    0.835077    0.146602
71  0.936193    0.436480    0.558998    0.770272
72  0.411825    0.388468    0.282234    0.333429
73  0.714918    0.404162    0.730436    0.939195
74  0.657210    0.368771    0.096418    0.189417