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I have a few columns (or say even one column) that I need to do imputation ( I don't want to do imputation on all the columns).

Once imputation is done, I would like to do standard scalar on more columns (including the above had gone through the imputation). I also have other transformations apart from this and would like to preserve the order of columns.

Is there a way to achieve this when using columntransformer and pipeline.

I see that within a columntransformer you can use pipeline, for the imputation and standard scalar (pipeline does the impute and scalar). But then, both the impute and scaling will be performed on all the columns.

I need them to be performed on subset of columns only. Is there a way to achieve the above ?

Update:

Here is an example of the issue

  1. I wanted to perform target encoding for some columns (say c1, c2,c3) and I also want to perform imputation for a column (c4) and I now wanted to perform standardscalar (once the target encoding and imputation are performed) for all these and more columns (c1,c2,c3,c4,c5,c6,c7,c8).

  2. Note I also have more columns that I need to perform, say OHE, but those columns are disjoint from the above c1...c8.

I am not sure how to use column transformer and pipeline, such that when I perform different transformations with some of the columns being same among the transformations (as in the above example). Is there a way to do this as part of the pipeline. Thank you.

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    I normally, and in several questions here (I'll suggest as duplicates if I get around to finding them), purpose having a pipeline for every preprocessing combination; so here you'd have one that imputes them scales, and another that just scales, and maybe some others, in one ColumnTransformer. That won't preserve column order, but get_feature_names_out should give you the ability to reorder the columns as desired. Commented Aug 25, 2022 at 0:11
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    BTW @'ing won't create a notification if the user hasn't interacted with your post yet. Commented Aug 25, 2022 at 0:12
  • Thanks but I think both pipeline and column transformer preserve the column order, right ? Commented Aug 25, 2022 at 0:19
  • Also if I have multiple pipelines that operate on a same subset of columns, column transformer would create duplicate columns, as it performs in parallel, so not sure how that solves the issue. I was assuming you propose having multiple pipelines in same transformers, where each pipeline can have few columns in common. Commented Aug 25, 2022 at 0:23
  • ColumnTransformers don't preserve column order; the output order is predictable, but generally not the same as the input. // I'm not suggesting that two different pipelines operate on a shared column. Rather, columns are groups together by exactly which preprocessing steps you will perform on them. Commented Aug 25, 2022 at 12:33

2 Answers 2

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TL;DR: for your use case, jump to the last section

One ColumnTransformer, many Pipelines

This is what I was suggesting in the comments and I've advanced elsewhere on this site. We use a single ColumnTransformer, each of whose transformers is a Pipeline. There is one pipeline for each combination of preprocessing steps you would like to perform. This has the advantage of being able to specify columns by name for each transformer. Downsides include having lots of different copies of the scaler, so if you wanted to hyperparameter-tune something about a transformer you'd have to change it in many places; also, if you have a lot of different unique preprocessing step combinations, this would take a lot of code to specify, but there are some ways to partially mitigate that.

pipe_target_encode = Pipeline([ ("te", TargetEncoder()), ("sc", StandardScaler()), ]) pipe_impute = Pipeline([ ("imp", SimpleImputer()), ("sc", StandardScaler()), ]) ColumnTransformer([ ("target_enc", pipe_target_encode, ["c1", "c2", "c3"]), ("impute", pipe_impute, ["c4"]), ("scale", StandardScaler(), ["c5", "c6", "c7", "c8"]), ("ohe", OneHotEncoder(), ["c9", "c10"]), ])

One Pipeline, many ColumnTransformers

This one will be more readily possible when dataframes-out is accomplished, but if you can keep track of column ordering it can be done now.

target_enc = ColumnTransformer( [("target_enc", TargetEncoder(), [3])], # c4 remainder="passthrough", ) impute = ColumnTransformer( [("impute", SimpleImputer(), [1, 2, 3])], # c4 is now first; c1, c2, c3 remainder="passthrough", ) scale = ColumnTransformer( [("scale", StandardScaler(), [0, 1, 2, 3, 4, 5, 6, 7])], #c1-3 are first, then c4, then c5-8 remainder="passthrough", ) ohe = ColumnTransformer( [("ohe", OneHotEncoder(), [8, 9])], remainder="passthrough", ) pipe = Pipeline([ ("target_enc", target_enc), ("impute", impute), ("scale", scale), ("ohe", ohe), ])

The columns will be output in order 8-dummies, 9-dummies, 1, 2, 3, 4, 5, 6, 7. You could move the steps around to try to get the columns into the better order, but since OHE will produce an apriori-unknown number of columns, it might be tough to get the column indices right.

Hybrid

The best for this particular case, it's the cleanest and most semantically correct. Because your transformers operate in a hierarchical way, we can get away with all column specifications being strings; if we had to specify things in a ColumnTransformer after any sklearn step, we'd have input arrays and would have to resort to index specification as above (again, until pandas-out is a thing).

step1 = ColumnTransformer( [ ("target_enc", TargetEncoder(), ["c1", "c2", "c3"]), ("impute", SimpleImputer(), ["c4"]), ], remainder="passthrough", ) num_pipe = Pipeline([ ("prep", step1), ("scale", StandardScaler()) ]) preproc = ColumnTransformer([ ("num", num_pipe, ["c1", "c2", "c3", "c4", "c5", "c6", "c7", "c8"]), ("ohe", OneHotEncoder(), ["c9", "c10"]), ])

Run this snippet for the Hybrid approach's diagram, and see the overflow answer for diagrams of the other two approaches.

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<div id="sk-5d6832b8-0986-4e65-92ae-040d8a1ce75c" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, Pipeline(steps=[(&#x27;prep&#x27;, ColumnTransformer(remainder=&#x27;passthrough&#x27;, transformers=[(&#x27;target_enc&#x27;, TargetEncoder(), [&#x27;c1&#x27;, &#x27;c2&#x27;, &#x27;c3&#x27;]), (&#x27;impute&#x27;, SimpleImputer(), [&#x27;c4&#x27;])])), (&#x27;scale&#x27;, StandardScaler())]), [&#x27;c1&#x27;, &#x27;c2&#x27;, &#x27;c3&#x27;, &#x27;c4&#x27;, &#x27;c5&#x27;, &#x27;c6&#x27;, &#x27;c7&#x27;, &#x27;c8&#x27;]), (&#x27;ohe&#x27;, OneHotEncoder(), [&#x27;c9&#x27;, &#x27;c10&#x27;])])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="8b1cfddf-93ac-47a5-8d5f-46f4b91f1dbd" type="checkbox" ><label for="8b1cfddf-93ac-47a5-8d5f-46f4b91f1dbd" class="sk-toggleable__label sk-toggleable__label-arrow">ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, Pipeline(steps=[(&#x27;prep&#x27;, ColumnTransformer(remainder=&#x27;passthrough&#x27;, transformers=[(&#x27;target_enc&#x27;, TargetEncoder(), [&#x27;c1&#x27;, &#x27;c2&#x27;, &#x27;c3&#x27;]), (&#x27;impute&#x27;, SimpleImputer(), [&#x27;c4&#x27;])])), (&#x27;scale&#x27;, StandardScaler())]), [&#x27;c1&#x27;, &#x27;c2&#x27;, &#x27;c3&#x27;, &#x27;c4&#x27;, &#x27;c5&#x27;, &#x27;c6&#x27;, &#x27;c7&#x27;, &#x27;c8&#x27;]), (&#x27;ohe&#x27;, OneHotEncoder(), [&#x27;c9&#x27;, &#x27;c10&#x27;])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="b42df77f-364c-47b4-81dc-2591b43c0201" type="checkbox" checked ><label for="b42df77f-364c-47b4-81dc-2591b43c0201" class="sk-toggleable__label sk-toggleable__label-arrow">num</label><div class="sk-toggleable__content"><pre>[&#x27;c1&#x27;, &#x27;c2&#x27;, &#x27;c3&#x27;, &#x27;c4&#x27;, &#x27;c5&#x27;, &#x27;c6&#x27;, &#x27;c7&#x27;, &#x27;c8&#x27;]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="27c515f2-9c65-498d-ad8b-1e7942530ec9" type="checkbox" ><label for="27c515f2-9c65-498d-ad8b-1e7942530ec9" class="sk-toggleable__label sk-toggleable__label-arrow">prep: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;, transformers=[(&#x27;target_enc&#x27;, TargetEncoder(), [&#x27;c1&#x27;, &#x27;c2&#x27;, &#x27;c3&#x27;]), (&#x27;impute&#x27;, SimpleImputer(), [&#x27;c4&#x27;])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="7d4f168c-5ce5-484d-bb05-ba4986ba97d5" type="checkbox" checked ><label for="7d4f168c-5ce5-484d-bb05-ba4986ba97d5" class="sk-toggleable__label sk-toggleable__label-arrow">target_enc</label><div class="sk-toggleable__content"><pre>[&#x27;c1&#x27;, &#x27;c2&#x27;, &#x27;c3&#x27;]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="f645cf10-e9b2-49d8-8822-a37d9c67b337" type="checkbox" ><label for="f645cf10-e9b2-49d8-8822-a37d9c67b337" class="sk-toggleable__label sk-toggleable__label-arrow">TargetEncoder</label><div class="sk-toggleable__content"><pre>TargetEncoder()</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="aac2d6a2-5b8c-4907-8bc9-4a6d99244755" type="checkbox" checked ><label for="aac2d6a2-5b8c-4907-8bc9-4a6d99244755" class="sk-toggleable__label sk-toggleable__label-arrow">impute</label><div class="sk-toggleable__content"><pre>[&#x27;c4&#x27;]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="e085f2bd-c170-490d-a432-685977ff2741" type="checkbox" ><label for="e085f2bd-c170-490d-a432-685977ff2741" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer()</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="c9b092a5-aa9f-46e1-a7e3-5c97c4d6001e" type="checkbox" ><label for="c9b092a5-aa9f-46e1-a7e3-5c97c4d6001e" class="sk-toggleable__label sk-toggleable__label-arrow">remainder</label><div class="sk-toggleable__content"><pre></pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="b89a30e1-bcd2-4677-8071-99e6857ffb5f" type="checkbox" ><label for="b89a30e1-bcd2-4677-8071-99e6857ffb5f" class="sk-toggleable__label sk-toggleable__label-arrow">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="591ec259-41a7-403e-bc5d-4f45fa612895" type="checkbox" ><label for="591ec259-41a7-403e-bc5d-4f45fa612895" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="410b46ee-6c96-4ddd-84bc-73577e0c2538" type="checkbox" checked ><label for="410b46ee-6c96-4ddd-84bc-73577e0c2538" class="sk-toggleable__label sk-toggleable__label-arrow">ohe</label><div class="sk-toggleable__content"><pre>[&#x27;c9&#x27;, &#x27;c10&#x27;]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="5c4492e0-ba44-446b-a82d-36caa3f1cccb" type="checkbox" ><label for="5c4492e0-ba44-446b-a82d-36caa3f1cccb" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder()</pre></div></div></div></div></div></div></div></div></div></div>

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5 Comments

Thank you so much for the great answers, diagrams and the detail. This is very helpful, can't appreciate you enough.
Thanks a ton for addressing the important column label/names specification issue too. You think in the hybrid approach because the column transformer is operating in a hierarchical fashion, we can just specify the column in the outer (preproc) and inner transformer (step1)?
@tjt I didn't try it, but I think that works, yes: the ColumnTransformer slices up the frame to pass to its transformers, but I think preserves them as frames. It's the actual transformers that will convert to numpy arrays.
Thanks, Ben. This has been a source of immense frustration for me; I imagine the upcoming Pandas functionality will help greatly. It would also be great if there were a class to do what Pipeline does, but allowing you to specify specific columns to mutate each transformation, while carrying over the rest untouched (unlike ColumnTransformer, which simply concatenates separate arrays, which have each gone through their own pre-specified transformation process).
@User356 the pandas-out functionality is now here, in v1.2. Setting that, and assuming you don't modify the column names anywhere, I think a pipeline of column transformers can do what you want with little headache. I guess I should update this answer...
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diagrams of the other two approaches; including them made the main answer too long (don't upvote!!!)

One-CT:

<style>#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c {color: black;background-color: white;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c pre{padding: 0;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c div.sk-toggleable {background-color: white;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c div.sk-estimator:hover {background-color: #d4ebff;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c div.sk-item {z-index: 1;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c div.sk-parallel-item:only-child::after {width: 0;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-f2196474-2b94-440b-a0e4-8a58641f5c1c div.sk-text-repr-fallback {display: none;}</style>
<div id="sk-f2196474-2b94-440b-a0e4-8a58641f5c1c" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>ColumnTransformer(transformers=[(&#x27;target_enc&#x27;, Pipeline(steps=[(&#x27;te&#x27;, TargetEncoder()), (&#x27;sc&#x27;, StandardScaler())]), [&#x27;c1&#x27;, &#x27;c2&#x27;, &#x27;c3&#x27;]), (&#x27;impute&#x27;, Pipeline(steps=[(&#x27;imp&#x27;, SimpleImputer()), (&#x27;sc&#x27;, StandardScaler())]), [&#x27;c4&#x27;]), (&#x27;scale&#x27;, StandardScaler(), [&#x27;c5&#x27;, &#x27;c6&#x27;, &#x27;c7&#x27;, &#x27;c8&#x27;]), (&#x27;ohe&#x27;, OneHotEncoder(), [&#x27;c9&#x27;, &#x27;c10&#x27;])])</pre></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="5c66e59e-6733-434f-a958-0d47b122de49" type="checkbox" ><label for="5c66e59e-6733-434f-a958-0d47b122de49" class="sk-toggleable__label sk-toggleable__label-arrow">ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[(&#x27;target_enc&#x27;, Pipeline(steps=[(&#x27;te&#x27;, TargetEncoder()), (&#x27;sc&#x27;, StandardScaler())]), [&#x27;c1&#x27;, &#x27;c2&#x27;, &#x27;c3&#x27;]), (&#x27;impute&#x27;, Pipeline(steps=[(&#x27;imp&#x27;, SimpleImputer()), (&#x27;sc&#x27;, StandardScaler())]), [&#x27;c4&#x27;]), (&#x27;scale&#x27;, StandardScaler(), [&#x27;c5&#x27;, &#x27;c6&#x27;, &#x27;c7&#x27;, &#x27;c8&#x27;]), (&#x27;ohe&#x27;, OneHotEncoder(), [&#x27;c9&#x27;, &#x27;c10&#x27;])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="6450180d-933f-4a27-ac8e-386be31a20cd" type="checkbox" checked ><label for="6450180d-933f-4a27-ac8e-386be31a20cd" class="sk-toggleable__label sk-toggleable__label-arrow">target_enc</label><div class="sk-toggleable__content"><pre>[&#x27;c1&#x27;, &#x27;c2&#x27;, &#x27;c3&#x27;]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="a7af0193-5262-49d3-8bed-147e9e16b24e" type="checkbox" ><label for="a7af0193-5262-49d3-8bed-147e9e16b24e" class="sk-toggleable__label sk-toggleable__label-arrow">TargetEncoder</label><div class="sk-toggleable__content"><pre>TargetEncoder()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="7864972f-3048-465f-8061-7d223cec014a" type="checkbox" ><label for="7864972f-3048-465f-8061-7d223cec014a" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="504bbeeb-bec2-4606-b2dc-ab9248dd1b8c" type="checkbox" checked><label for="504bbeeb-bec2-4606-b2dc-ab9248dd1b8c" class="sk-toggleable__label sk-toggleable__label-arrow">impute</label><div class="sk-toggleable__content"><pre>[&#x27;c4&#x27;]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="3a78a6b3-d021-472c-af14-baa968c7790f" type="checkbox" ><label for="3a78a6b3-d021-472c-af14-baa968c7790f" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="4804b7a2-b134-4fb9-8a8d-5867c6748f8c" type="checkbox" ><label for="4804b7a2-b134-4fb9-8a8d-5867c6748f8c" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="578d2847-6c63-402e-836c-1f5ffc7b62ec" type="checkbox" checked><label for="578d2847-6c63-402e-836c-1f5ffc7b62ec" class="sk-toggleable__label sk-toggleable__label-arrow">scale</label><div class="sk-toggleable__content"><pre>[&#x27;c5&#x27;, &#x27;c6&#x27;, &#x27;c7&#x27;, &#x27;c8&#x27;]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="e4f0d0c1-192d-4e5d-9d25-6409c59d8836" type="checkbox" ><label for="e4f0d0c1-192d-4e5d-9d25-6409c59d8836" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="ddba4409-5b82-4102-86cc-64091d5f0bef" type="checkbox" checked ><label for="ddba4409-5b82-4102-86cc-64091d5f0bef" class="sk-toggleable__label sk-toggleable__label-arrow">ohe</label><div class="sk-toggleable__content"><pre>[&#x27;c9&#x27;, &#x27;c10&#x27;]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="c58c238f-8ab1-462b-acb1-f972a9f20c93" type="checkbox" ><label for="c58c238f-8ab1-462b-acb1-f972a9f20c93" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder()</pre></div></div></div></div></div></div></div></div></div></div>

One Pipeline:

<style>#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 {color: black;background-color: white;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 pre{padding: 0;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 div.sk-toggleable {background-color: white;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 div.sk-estimator:hover {background-color: #d4ebff;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 div.sk-item {z-index: 1;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 div.sk-parallel-item:only-child::after {width: 0;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-ced6b6e1-e96b-4741-ab14-62a08208d157 div.sk-text-repr-fallback {display: none;}</style>
<div id="sk-ced6b6e1-e96b-4741-ab14-62a08208d157" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;target_enc&#x27;, ColumnTransformer(remainder=&#x27;passthrough&#x27;, transformers=[(&#x27;target_enc&#x27;, TargetEncoder(), [3])])), (&#x27;impute&#x27;, ColumnTransformer(remainder=&#x27;passthrough&#x27;, transformers=[(&#x27;impute&#x27;, SimpleImputer(), [1, 2, 3])])), (&#x27;scale&#x27;, ColumnTransformer(remainder=&#x27;passthrough&#x27;, transformers=[(&#x27;scale&#x27;, StandardScaler(), [0, 1, 2, 3, 4, 5, 6, 7])])), (&#x27;ohe&#x27;, ColumnTransformer(remainder=&#x27;passthrough&#x27;, transformers=[(&#x27;ohe&#x27;, OneHotEncoder(), [8, 9])]))])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="a320204f-f77e-4b4b-94f2-9ac588617232" type="checkbox" ><label for="a320204f-f77e-4b4b-94f2-9ac588617232" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;target_enc&#x27;, ColumnTransformer(remainder=&#x27;passthrough&#x27;, transformers=[(&#x27;target_enc&#x27;, TargetEncoder(), [3])])), (&#x27;impute&#x27;, ColumnTransformer(remainder=&#x27;passthrough&#x27;, transformers=[(&#x27;impute&#x27;, SimpleImputer(), [1, 2, 3])])), (&#x27;scale&#x27;, ColumnTransformer(remainder=&#x27;passthrough&#x27;, transformers=[(&#x27;scale&#x27;, StandardScaler(), [0, 1, 2, 3, 4, 5, 6, 7])])), (&#x27;ohe&#x27;, ColumnTransformer(remainder=&#x27;passthrough&#x27;, transformers=[(&#x27;ohe&#x27;, OneHotEncoder(), [8, 9])]))])</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="59c766af-6da9-43a9-bc67-11b61fa0053e" type="checkbox"><label for="59c766af-6da9-43a9-bc67-11b61fa0053e" class="sk-toggleable__label sk-toggleable__label-arrow">target_enc: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;, transformers=[(&#x27;target_enc&#x27;, TargetEncoder(), [3])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="9aa47181-e273-4438-b52d-1f73f17c3631" type="checkbox" checked><label for="9aa47181-e273-4438-b52d-1f73f17c3631" class="sk-toggleable__label sk-toggleable__label-arrow">target_enc</label><div class="sk-toggleable__content"><pre>[3]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="0c01315d-281f-473b-89ab-a9dd2ae92de6" type="checkbox" ><label for="0c01315d-281f-473b-89ab-a9dd2ae92de6" class="sk-toggleable__label sk-toggleable__label-arrow">TargetEncoder</label><div class="sk-toggleable__content"><pre>TargetEncoder()</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="05d4f3b3-a06a-432d-a0eb-fa4fce393acc" type="checkbox" ><label for="05d4f3b3-a06a-432d-a0eb-fa4fce393acc" class="sk-toggleable__label sk-toggleable__label-arrow">remainder</label><div class="sk-toggleable__content"><pre></pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="949c9196-8d6f-4ab7-bcc3-1bd80b4a4504" type="checkbox" ><label for="949c9196-8d6f-4ab7-bcc3-1bd80b4a4504" class="sk-toggleable__label sk-toggleable__label-arrow">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="9b01bc09-48f8-49c8-b358-2f40440d8d2a" type="checkbox" ><label for="9b01bc09-48f8-49c8-b358-2f40440d8d2a" class="sk-toggleable__label sk-toggleable__label-arrow">impute: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;, transformers=[(&#x27;impute&#x27;, SimpleImputer(), [1, 2, 3])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="1346daa4-bdaa-43e8-9fb7-94ab3f7a77e3" type="checkbox" checked ><label for="1346daa4-bdaa-43e8-9fb7-94ab3f7a77e3" class="sk-toggleable__label sk-toggleable__label-arrow">impute</label><div class="sk-toggleable__content"><pre>[1, 2, 3]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="2dba1089-b7bc-4ee4-ba38-c32763f59d50" type="checkbox" ><label for="2dba1089-b7bc-4ee4-ba38-c32763f59d50" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer()</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="1a8bb098-c884-4442-b74e-033446b9f4cf" type="checkbox" ><label for="1a8bb098-c884-4442-b74e-033446b9f4cf" class="sk-toggleable__label sk-toggleable__label-arrow">remainder</label><div class="sk-toggleable__content"><pre></pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="5a27f77c-7e94-442d-9340-fd8dd912a7db" type="checkbox" ><label for="5a27f77c-7e94-442d-9340-fd8dd912a7db" class="sk-toggleable__label sk-toggleable__label-arrow">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="618a09c4-5356-4e78-9020-03ced5e4f29a" type="checkbox" ><label for="618a09c4-5356-4e78-9020-03ced5e4f29a" class="sk-toggleable__label sk-toggleable__label-arrow">scale: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;, transformers=[(&#x27;scale&#x27;, StandardScaler(), [0, 1, 2, 3, 4, 5, 6, 7])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="1527c9af-9be6-4387-8b7b-98aaa9ac884c" type="checkbox" checked ><label for="1527c9af-9be6-4387-8b7b-98aaa9ac884c" class="sk-toggleable__label sk-toggleable__label-arrow">scale</label><div class="sk-toggleable__content"><pre>[0, 1, 2, 3, 4, 5, 6, 7]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="79361708-dafe-43f3-844b-3ed2b38835c5" type="checkbox" ><label for="79361708-dafe-43f3-844b-3ed2b38835c5" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="6caca512-3692-4cc2-ae22-8682ccbe59d4" type="checkbox" ><label for="6caca512-3692-4cc2-ae22-8682ccbe59d4" class="sk-toggleable__label sk-toggleable__label-arrow">remainder</label><div class="sk-toggleable__content"><pre></pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="a5b86925-e8e9-4032-8e43-b33c6b2ed682" type="checkbox" ><label for="a5b86925-e8e9-4032-8e43-b33c6b2ed682" class="sk-toggleable__label sk-toggleable__label-arrow">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="695005d9-65af-4b32-b25f-0c17268a1ca7" type="checkbox" ><label for="695005d9-65af-4b32-b25f-0c17268a1ca7" class="sk-toggleable__label sk-toggleable__label-arrow">ohe: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;, transformers=[(&#x27;ohe&#x27;, OneHotEncoder(), [8, 9])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="28b9c156-f9f8-416f-a520-97479ba04b4b" type="checkbox" checked ><label for="28b9c156-f9f8-416f-a520-97479ba04b4b" class="sk-toggleable__label sk-toggleable__label-arrow">ohe</label><div class="sk-toggleable__content"><pre>[8, 9]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="a8779d0e-d6ca-441a-b2fe-39651ace8ef7" type="checkbox" ><label for="a8779d0e-d6ca-441a-b2fe-39651ace8ef7" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder()</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="ee45dee4-3c58-4287-8901-8f943bdf388b" type="checkbox" ><label for="ee45dee4-3c58-4287-8901-8f943bdf388b" class="sk-toggleable__label sk-toggleable__label-arrow">remainder</label><div class="sk-toggleable__content"><pre></pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="b916f616-68f0-4628-9486-6a896308652b" type="checkbox" ><label for="b916f616-68f0-4628-9486-6a896308652b" class="sk-toggleable__label sk-toggleable__label-arrow">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</pre></div></div></div></div></div></div></div></div></div></div></div></div>

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