CODEWITHSUNDEEP
pythonarrayspandas
Dmitry
Dmitry

Reputation: 737

How to convert a pandas column containing float array to int array

I have a pandas dataframe. One of it's columns contains variable-length float arrays. I need to convert them to arrays of uint8 because actually these arrays contain grayscale images with values from 0 to 255. Currently arrays dimention is 1. I understand that it's possible to iterate over rows and do conversion in this cycle. But hope there is an out of box solution cause this task seems to be common. I also tryed the following df['grayscale255'] = df['grayscale255'].astype('uint8'), but it doesn't work becuase

TypeError: only size-1 arrays can be converted to Python scalars

Data snippet: enter image description here

Upvotes: 0

Views: 2026

Answers (2)

Henry Ecker
Henry Ecker

Reputation: 35686

Use apply + astype

df['grayscale255'] = df['grayscale255'].apply(lambda x: x.astype('uint8'))

Or apply np.ubyte:

df['grayscale255'] = df['grayscale255'].apply(np.ubyte)

df:

   random_len         grayscale255
0           4    [72, 195, 17, 79]
1           3        [70, 97, 198]
2           4  [161, 129, 163, 48]
3           2            [152, 22]
4           3        [40, 23, 175]

Sample Data:

import numpy as np
import pandas as pd

np.random.seed(5)

df = pd.DataFrame({'random_len': np.random.randint(1, 5, size=5)})
df['grayscale255'] = df['random_len'].apply(
    lambda x: np.random.random(size=x) * 200
)

   random_len                                       grayscale255
0           4  [72.74737942011788, 195.8889998322892, 17.9642...
1           3  [70.82760858300028, 97.32759937167242, 198.164...
2           4  [161.6563366066534, 129.89177664881987, 163.89...
3           2           [152.87452173223647, 22.180152347812744]
4           3  [40.830949566118434, 23.81907149565208, 175.58...

Upvotes: 2

shivesh kumar
shivesh kumar

Reputation: 85

df['grayscale255'] = df['grayscale255'].apply('uint8')

Try this

Upvotes: -1

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