Скачать или смотреть How to Apply LabelEncoder to a Polars DataFrame Column?

Hello everyone! I hope this video has helped solve your questions and issues. This video is shared because a solution has been found for the question/problem. I create videos for questions that have solutions. If you have any other issues, feel free to reach out to me on Instagram:   / ky.emrah  

Below, you can find the text related to the question/problem. In the video, the question will be presented first, followed by the answers. If the video moves too fast, feel free to pause and review the answers. If you need more detailed information, you can find the necessary sources and links at the bottom of this description. I hope this video has been helpful, and even if it doesn't directly solve your problem, it will guide you to the source of the solution. I'd appreciate it if you like the video and subscribe to my channel!How to Apply LabelEncoder to a Polars DataFrame Column?

I'm trying to use scikit-learn's LabelEncoder with a Polars DataFrame to encode a categorical column. I am using the following code.
LabelEncoder
import polars as pl

from sklearn.preprocessing import LabelEncoder

df = pl.DataFrame({
"Color" : ["red","white","blue"]
})

enc = LabelEncoder()

import polars as pl

from sklearn.preprocessing import LabelEncoder

df = pl.DataFrame({
"Color" : ["red","white","blue"]
})

enc = LabelEncoder()

However, an error is raised.

ValueError: y should be a 1d array, got an array of shape () instead.

ValueError: y should be a 1d array, got an array of shape () instead.
ValueError: y should be a 1d array, got an array of shape () instead.
Next, I tried converting the column to a NumPy.
df.with_columns(
enc.fit_transform(pl.col("Color").to_numpy())
)

df.with_columns(
enc.fit_transform(pl.col("Color").to_numpy())
)

Now, a different error is raised.

AttributeError: 'Expr' object has no attribute 'to_numpy'

AttributeError: 'Expr' object has no attribute 'to_numpy'
AttributeError: 'Expr' object has no attribute 'to_numpy'
Note. I found that .cast(pl.Categorical).to_physical() could be used to obtain the desired result. Still, I'd prefer using something like transform() on my test dataset.
Note.
.cast(pl.Categorical).to_physical()
transform()
df.with_columns(
pl.col("Color").cast(pl.Categorical).to_physical().alias("Color_encoded")
)

df.with_columns(
pl.col("Color").cast(pl.Categorical).to_physical().alias("Color_encoded")
)



Tags: scikit-learn,python-polars,label-encodingSource of the question:
https://stackoverflow.com/questions/7...

Question and source license information:
https://meta.stackexchange.com/help/l...
https://stackoverflow.com/