Use ColumnTransformer to apply different preprocessing to different columns

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Use ColumnTransformer to apply different preprocessing to different columns:
- select from DataFrame columns by name
- passthrough or drop unspecified columns
Requires scikit-learn 0.20+

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So glad you are making videos again! I just finished a Udemy intro to ML via a course and this is great reinforcement. This and pandas is data science gold!

karakol

Thank you for the quick and easy explanation!

SidVanam

Amazing explanation! Thank you from NYC!

hsoley

Thanks Kevin. God bless you. I really love your videos, & I hope you'll also bring some topics about pyspark.

goitomyacobb.

Thanks a million for this video! Beautifully explained.

vedangsharmapixels

Hey! Loved it. Have you created any other ML (using scikit-learn) playlist other than the one already on your channel? I was searching for boosting related content.

pradhyumnchoudhary

Thanks Kevin for the awesome content and tips you share with us. Was eagerly waiting for your new series :)

prernaaggarwal

this video is exactly what I want . thank you so much

shannonli

Thanks Kevin, very useful, love it !

carriemu

Hi Kevin,

How to add custom transformation functions to ColumnTransformer or Pipeline? Say I have a custom function to deal with missing values or Outliers. How can I add them in ColumnTransformer or pipeline? It seems like these two accept only those transformers which are available in Sklearn.

anandvyavahare

Hi... I am very lucky that i came across with your channel.Its really of great help to me.I have read somewhere that we cannot apply onehot encoder without labelencoder as onehot encoder requires integer inputs for processing categorical data.Please clarify the same.

powergear

Eagerly waiting for this😃.
I will surely try to prepare notes of this playlist, Sir
Very helpful🙏

RavitejaGMusic

Kevin one question. i have understanding that if we have reminder set to default which is drop if i fit_transform column transformer with 10 columns i only specified three columns as you have done model will only account three columns and it will drop rest three. However now after pipeline and model creation does pipeline expects to feed 10 columns or just 3?

Gannu

How to take mean of positive numbers only in the series or in dataframe?

Al-Ahdal

Thank you so much for the awesome content you share us

yousifahmed

I use d ColumnTransformer class . It’s more straightforward

okonkwo.ify

Thanks for introducing this as I've always found the labeling & ohe procedures cumbersome, silly and error-prone.

tomparatube

Hi Kevin, I am using StandardScaler or MinMaxScaler on my numeric data but when I try to use get_feature_names. I am getting an error saying:
"Transformer minmaxscaler (type MinMaxScaler) does not provide get_feature_names" How to handle that?
I can see that my data after make_column_transformer() is a sparse matrix. How Can I convert it back to a pandas dataframe?

supriyajyoti

Hi. Could you please clarify what does the function make_column_transform does with the 2 values which are NaN in the Embarked column when you take the raw data? Rows 61 and 829 have an NaN value, after running the function it assigns a 0, 0, 0 to both of them meaning they don't exist. Is there a way to bypass them with the "most presetn value" kind of what it does for Age placing the average?

jasonjason

Hi awesome video

I have a small question. I have completed basic python (list, tuple, dict, functions, etc) do I need to know about other things as well in my machine learning journey with respect to python? Like decorators n oops concepts as well?? Please do reply

sunayanak

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