arrays into pandas dataframe columns

A possible solution could be transposing and renaming the columns after transforming the numpy array into a dataframe. Here is the code:

import numpy as npimport pandas as pdframe = [[0, 1, 0], [0, 0, 0], [1, 3, 3], [2, 4, 4]]numpy_data= np.array(frame)#transposing laterdf = pd.DataFrame(data=numpy_data).T #creating a list of columns using list comprehension without specifying number of columnsdf.columns = [f'mycol{i}' for i in range(0,len(df.T))] print(df)

Output:

   mycol0  mycol1  mycol2  mycol30       0       0       1       21       1       0       3       42       0       0       3       4

Same code for 11 columns:

import numpy as npimport pandas as pdframe = [[0, 1, 0], [0, 0, 0], [1, 3, 3], [2, 4, 4], [5, 2, 2], [6,7,8], [8,9,19] , [10,2,4], [2,6,5], [10,2,5], [11,2,9]]numpy_data= np.array(frame)df = pd.DataFrame(data=numpy_data).Tdf.columns = [f'mycol{i}' for i in range(0,len(df.T))]print(df)

   mycol0  mycol1  mycol2  mycol3  mycol4  mycol5  mycol6  mycol7  mycol8  mycol9  mycol100       0       0       1       2       5       6       8      10       2      10       111       1       0       3       4       2       7       9       2       6       2        22       0       0       3       4       2       8      19       4       5       5        9

You can transpose the array and add_prefix

frame = [[0, 1, 0], [0, 0, 0], [1, 3, 3], [2, 4, 4]]pd.DataFrame(np.array(frame).T).add_prefix('column')

Out:

   column0  column1  column2  column30        0        0        1        21        1        0        3        42        0        0        3        4

Works with every number of arrays

frame = [[0, 1, 0], [0, 0, 0], [1, 3, 3], [2, 4, 4], [1,0,1], [2,0,3]]pd.DataFrame(np.array(frame).T).add_prefix('column')

Out:

   column0  column1  column2  column3  column4  column50        0        0        1        2        1        21        1        0        3        4        0        02        0        0        3        4        1        3

One way may be change it to dictionary with column name by iterating each item in the list as below:

df = pd.DataFrame({'column{}'.format(index):i for index, i in enumerate(frame)})

Alternatively, other way may be to use transpose to what you already have. For column names you can exclude on creating dataframe and add later (not sure if you need numpy ):

df = pd.DataFrame(data=frame)df = df.T # transposingdf.columns = ['column{}'.format(i+1) for i in df.columns] # adding column names

Result (either way):

    column1 column2 column3 column40   0          0      1        21   1          0      3        42   0          0      3        4