Concatenate columns of a panda frame into a 1D array

Question

I have a pd dataframe consisting of hundreds of columns. I want to contenate all the columns into a 1D array. For instance, supppose the dataframe is something like this:

pd df:
set1  set2  set3  ... set175
3      5     6    ...  9
4      8     0    ...  22
.      .     .    ...  .
.      .     .    ...  .

And, I want something like this after the concatenation:

concatenated to 1D array:
[3,4,...,5,8,...,6,0,...,9,22]

I may also want to concatenate only some of the columns say from columns #1 to 3:

concatenated to column 1-3:
[3,4,...,5,8,...,6,0]

What is a convenient way to do this? Should I convert the pd df into a numpy array?

So far, I have found the solutions that concatenate a panda dataframe using the column headers which is not practical for hundreds of columns. In another approch, columns of multiple dataframes are concatenated using pd.concatenate(). But, I want to concatenate the columns of a single dataframe. This issue is a minor part of a complex processing I am currently working on. So, I would appreciate a straightforward answer.

Asked By: inteface

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Source

Answer 1

# stack and take the values

# choosing all columns and rows
df.T.stack().values

array([3, 4, 5, 8, 6, 0, '...', '...', 9, 22], dtype=object)

# choosing only two columns
df.T.iloc[:,1:3].stack().values

array([4, 8, 0, '...', 22], dtype=object)

Answered By: Naveed

Concatenate columns of a panda frame into a 1D array

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