I'll check it out. Thank you.
On Wed, Aug 19, 2020 at 9:46 AM Sole Galli via scikit-learn <
scikit-learn@python.org> wrote:
> Did you have a look at the package feature-engine? It has its own imputers
> and encoders that allow you to select the columns to transform and returns
> a dataframe. It a
Did you have a look at the package feature-engine? It has its own imputers and
encoders that allow you to select the columns to transform and returns a
dataframe. It also has a sklear wrapper that wraps sklearn transformers so that
they return a dataframe instead of a numpy array.
Cheers.
Sole
On Tue, Aug 18, 2020 at 6:53 PM Kevin Markham wrote:
> Hi Ram,
>
> > For a column with numbers written like "one", "two" and missing values
> "?", I had to do two things: Change them to numbers (1, 2), and then,
> instead of the missing values, add the most common element, or mean or
> whatever.
Hi Ram,
> For a column with numbers written like "one", "two" and missing values
"?", I had to do two things: Change them to numbers (1, 2), and then,
instead of the missing values, add the most common element, or mean or
whatever. When I tried to use LabelEncoder to do the first part, it
complain
On Mon, Aug 17, 2020 at 8:55 PM Kevin Markham wrote:
> Hi Ram,
>
> These are great questions!
>
Thank you for the detailed answers.
>
> > The task was to remove these irregularities. So for the "?" items,
> replace them with mean, and for the "one", "two" etc. replace with a
> numerical value.
Hi Ram,
These are great questions!
> The task was to remove these irregularities. So for the "?" items,
replace them with mean, and for the "one", "two" etc. replace with a
numerical value.
If your primary task is "data cleaning", then pandas is usually the optimal
tool. If "preprocessing your d
Hey guys,
This is a bit of a complicated question.
I was helping my friend do a task with Pandas/sklearn for her data science
class. I figured it'll be a breeze, since I'm fancy-pancy Python
programmer. Oh wow, it was so not.
I was trying to do things that felt simple to me, but there were so ma
