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Update PortfolioRebalancing.ipynb with scikit-learn note #793

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Update PortfolioRebalancing.ipynb with scikit-learn note #793

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4 changes: 1 addition & 3 deletions documentation/source/general/tutorial/QAOAtutorial/PortfolioRebalancing.ipynb
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Expand Up @@ -211,7 +211,7 @@
"source": [
"## Example implementation\n",
"\n",
"Let us now look at what these building blocks look like in working code example. We start off by defining the relevant parameters for a portfolio rebalancing instance, namely the number of assets and lots, the asset covariance matrix, the previous portfolio positions, the risk return factor, the normalized asset returns and the trading cost."
"Let us now look at what these building blocks look like in working code example. We start off by defining the relevant parameters for a portfolio rebalancing instance, namely the number of assets and lots, the asset covariance matrix, the previous portfolio positions, the risk return factor, the normalized asset returns and the trading cost. We will use scikit-learn (``pip install scikit-learn``) to normalize our data."

@purva-thakre purva-thakre Aug 10, 2026

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Thanks for creating the PR!

I wonder if it makes more sense to add a note box for this instead.

BTW have you gone through all the code blocks in this page and made sure everything works as expected? I cannot replicate the steps that made me recreate the original issue anymore because it was some time ago.

@micpap25 micpap25 Aug 10, 2026

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@purva-thakre See #795, the tutorial is broken regardless of these changes unfortunately.

]
},
{
Expand All @@ -222,7 +222,6 @@
"outputs": [],
"source": [
"# imports\n",
"import sklearn\n",
"from sklearn import preprocessing # Add explicit import for preprocessing\n",
"import numpy as np\n",
"\n",
Expand Down Expand Up @@ -294,7 +293,6 @@
" 0.010873,\n",
"]\n",
"mu = [data1[i] for i in range(len(data1)) if i % 2 == 0]\n",
"from sklearn import preprocessing\n",
"\n",
"mu_array = np.array(mu)\n",
"# this is the normalized asset return array\n",
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