Reshaping a single column in to multiple column using Python

[sahar]

I have an excel file containing a single column (Row's number is not fixed). Using Python 3, I want to,

1. Import my excel file/data in python,
   
2. Read/select the data column (first column), and
   
3. Reshape this column into multiple columns having 10 rows in each column and finally
   
4. Writing output to a new excel file.
   

I am a Matlab user and know how to do this in Matlab using Matlab’s reshape command

newVar = reshape(myColumn, 10, [])

.

Looking for someone to help me out achieving this in Python 3.

[jefsummers]

We will help you, but you need to show effort and let us know where you get stuck. Of course, there is not just one way, so my suggestions are how I would approach the problem. As a start, use Pandas to import into a dataframe. Use the iloc function to select your range in the desired column, and copy that data to the new column.

See Pandas documentation

[sahar]

Ok, I have tried but could not get the desired result.

import pandas as pd
import numpy as np
df =  pd.read_excel('sample.xlsx')
first_column = pd.DataFrame(df.iloc[:,0])
arr = np.array(first_column)
newArr = arr.reshape(arr, (10, -1))

Code shows the error:

newArr = arr.reshape(10, -1)

TypeError: only integer scalar arrays can be converted to a scalar index

[deanhystad]

reshape is a function, not a method of nparray. The call is "new_array = numpy.reshape(arr, (10, -1))".

[sahar]

reshape is a function, not a method of nparray. The call is "new_array = numpy.reshape(arr, (10, -1))".

Hi, thanks for your explanation. I have tried this but no results.

import pandas as pd
import numpy as np
df =  pd.read_excel('sample.xlsx')
myCol = pd.DataFrame(df.iloc[:,0])
arr = np.array (myCol)
newArr = np.reshape(arr, (10, -1), order='F')
np.savetxt("newMatrix.csv", newArr, delimiter=",")

[sahar]

I have found a strange thing. If i add one more row in my column, the same code performs as intended.
Image
Please note now I have 31 rows. To me it seems that something has to do with indexing.

[jefsummers]

The default in pandas read_excel() function is that the first row is used as a header. If you start with data then add the parameter header=None

[deanhystad]

Always helps to read the documentation, especially when trying something new.

https://pandas.pydata.org/pandas-docs/st...excel.html

headerint, list of int, default 0
Row (0-indexed) to use for the column labels of the parsed DataFrame. If a list of integers is passed those row positions will be combined into a MultiIndex. Use None if there is no header.

https://numpy.org/doc/stable/reference/g...shape.html

numpy.reshape(a, newshape, order='C')[source]