Convert Two Numpy Array to Dataframe
I Want to Convert Two Numpy Array to One Dataframe Containing Two Columns. the First Numpy Array 'Images' Is of Shape 102, 1024. the Second Numpy Array 'Label'...
I want to convert two numpy array to one DataFrame containing two columns.
The first numpy array 'images' is of shape 102, 1024.
The second numpy array 'label' is of shape (1020, )
My core code is:
images=np.array(images)
label=np.array(label)
l=np.array([images,label])
dataset=pd.DataFrame(l)
But it turns out to be an error saying that:
ValueError: could not broadcast input array from shape (1020,1024) into shape (1020)
What should I do to convert these two numpy array into two columns in one dataframe?
3 Answers
You can't stack them easily, especially if you want them as different columns, because you can't insert a 2D array in one column of a DataFrame, so you need to convert it to something else, for example a list.
So something like this would work:
import pandas as pd
import numpy as np
images = np.array(images)
label = np.array(label)
dataset = pd.DataFrame({'label': label, 'images': list(images)}, columns=['label', 'images'])
This will create a DataFrame with 1020 rows and 2 columns, where each item in the second column contains 1D arrays of length 1024.
Coming from engineering, I like the visual side of creating matrices.
matrix_aux = np.vstack([label,images])
matrix = np.transpose(matrix_aux)
df_lab_img = pd.DataFrame(matrix)
Takes a little bit more of code but leaves you with the Numpy array too.
You can also use hstack
import pandas as pd
import numpy as np
dataset = pd.DataFrame(np.hstack((images, label.reshape(-1, 1))))
Marcus Vance is a cybersecurity auditor and technology writer dedicated to educating the public about online safety, data privacy regulations, enterprise security, and emerging cyber threats.
