Converting multi-dimensional arrays into Pandas data frame columns

Working with a multi-dimensional array, I need to add it as a new column in a DataFrame.

import pandas as pd
import numpy as np

x = np.array([[1, 2, 3], [4, 5, 6]], np.int32)
df = pd.DataFrame(['A', 'B'], columns=['First'])

Initial DataFrame:

  First 
0     A 
1     B

The desired output is:

  First  Second  
0     A   [1,2,3]
1     B   [4,5,6]

The main goal is to upload this table into a database, although I am aware that it may not be the most efficient way to store records. Any recommendations you might have are greatly appreciated.

Thank you.

pythonpandaslistdataframemultidimensional-array

Answer №1

You have the option to easily assign the array called x by breaking down its rows into a list:

df.assign(second=[*x])

   First  second
0     A  [1, 2, 3]
1     B  [4, 5, 6]

Keep in mind that, as explained in the comments, this is equivalent to:

df.assign(second=list(x))

Answer №2

Implement:

df['New Column']=x.tolist()
print(df)

  Original     New Column
0     A  [1, 2, 3]
1     B  [4, 5, 6]

Answer №3

Despite the fact that this action will increase the size of the table, I highly recommend utilizing the explode method after executing a groupby, as you can always revert it back.

Having object data type in single columns may limit the functionality of most database query operations and pandas functions.

df.assign(list=x.tolist()).explode('list')
Out[6]: 
  First list
0     A    1
0     A    2
0     A    3
1     B    4
1     B    5
1     B    6

Answer №4

 from pandas import DataFrame
 from numpy import array

 x = array([[7, 8, 9], [10, 11, 12]], np.int32)
 df = DataFrame(['C', 'D'], columns=['Primary'])
 df['secondary'] = [i*2 for i in x] or df['secondary'] = list(x*2)
 print(df)

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