add numpy exercises

fabridamicelli · Oct 15, 2024 · cfea811 · cfea811
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diff --git a/README.md b/README.md
@@ -1,2 +1,3 @@
 # Python Course 🐍
 A hands-on introductory course, covering the basics of the Python language with emphasis on scientific and data analysis tasks.
+Check it out [here](https://fabridamicelli.github.io/python-course/)!
diff --git a/chapters/01_getting_started.ipynb b/chapters/01_getting_started.ipynb
@@ -27,7 +27,7 @@
     "- Manage virtual environments\n",
     "- Install third party libraries\n",
     "\n",
-    "We we'll use here [`uv`](https://docs.astral.sh/uv) which is a high level tool that does all 3 for us."
+    "We will use here [`uv`](https://docs.astral.sh/uv) which is a high level tool that does all 3 for us."
    ]
   },
   {

diff --git a/chapters/08_numpy.ipynb b/chapters/08_numpy.ipynb
@@ -16,7 +16,20 @@
    "id": "ec86658f-670c-4043-86f7-f2661e486756",
    "metadata": {},
    "source": [
-    "Take a look at this intro in [Wes MacKinney's book](https://wesmckinney.com/book/numpy-basics)"
+    "We will follow here this excellent chapter from [Wes MacKinney's book](https://wesmckinney.com/book/numpy-basics)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "022c4714-d3cc-4b4f-b755-bade698ffa86",
+   "metadata": {},
+   "source": [
+    "In particular the subtitles:\n",
+    "\n",
+    "- Creating ndarray\n",
+    "- Arithmetic with numpy arrays\n",
+    "- Basic indexing and slicing\n",
+    "- Boolean indexing"
    ]
   },
   {
@@ -227,40 +240,79 @@
     "For example the `random` submodule exposes several useful functions for stochastic modelling and statistical analysis."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "84c6a8d7-2192-4cf9-adf2-a9f110d5b01f",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "3f77af0d-4398-404f-b379-214220dddbdd",
+   "id": "123df306-ae3f-4ebf-b283-7e7d9f7705d9",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
+       "       17, 18, 19, 20, 21, 22, 23, 24])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#| code-fold: true\n",
-    "import matplotlib.pyplot as plt"
+    "arr = np.arange(25)\n",
+    "arr"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "832538ec-db42-4f9f-befc-aee257286bd8",
+   "execution_count": 6,
+   "id": "a0840103-6335-4077-afa0-3c843a4d3aec",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "array([[ 0,  1,  2,  3,  4],\n",
+       "       [ 5,  6,  7,  8,  9],\n",
+       "       [10, 11, 12, 13, 14],\n",
+       "       [15, 16, 17, 18, 19],\n",
+       "       [20, 21, 22, 23, 24]])"
       ]
      },
+     "execution_count": 6,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "plt.hist(\n",
-    "    # Draw samples from normal distribution \n",
-    "    np.random.normal(size=10_000),\n",
-    "    bins=100\n",
-    ");"
+    "arr.reshape(5,5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0db2d668-2302-4288-8a98-1aee524cc44e",
+   "metadata": {},
+   "source": [
+    "1) Reshape `arr` to be a square matrix, call it `arr_sq`.\n",
+    "2) Extract the values at this coordinates [(0, 3), (2, 4), (3, 2)].\n",
+    "3) Extract one index operation the elements 1, 4, 9, 11, 21.\n",
+    "4) Create a boolean mask that will extract the odd numbers from the `arr_sq`.\n",
+    "5) Find the indices of the even elements off `arr_sq`.\n",
+    "6) Flatten `arr_sq` into 1 dimension.\n",
+    "7) Get the third column of `arr_sq`.\n",
+    "8) Get the second and the fourth rows of `arr_sq`. \n",
+    "9) Compute the dot product between `arr_sq` and `arr_sq` transposed.\n",
+    "10) Extract the upper right triangle values (including the diagonal.\n",
+    "11) Create a square matrix of 25 elements filled only with the value 100 along the main diagonal\n",
+    "12) Create a square matrix of 25 elements filled only with the value 1 the *counter-diagonal*."
    ]
   }
  ],

diff --git a/docs/01_getting_started.html b/docs/01_getting_started.html
@@ -383,7 +383,7 @@ <h2 data-number="1.1" class="anchored" data-anchor-id="introduction"><span class
 <li>Manage virtual environments</li>
 <li>Install third party libraries</li>
 </ul>
-<p>We we’ll use here <a href="https://docs.astral.sh/uv"><code>uv</code></a> which is a high level tool that does all 3 for us.</p>
+<p>We will use here <a href="https://docs.astral.sh/uv"><code>uv</code></a> which is a high level tool that does all 3 for us.</p>
 </section>
 <section id="install-uv" class="level2" data-number="1.2">
 <h2 data-number="1.2" class="anchored" data-anchor-id="install-uv"><span class="header-section-number">1.2</span> Install <code>uv</code></h2>

diff --git a/docs/081_pandas.html b/docs/081_pandas.html
@@ -308,7 +308,13 @@
 <div id="quarto-sidebar-glass" class="quarto-sidebar-collapse-item" data-bs-toggle="collapse" data-bs-target=".quarto-sidebar-collapse-item"></div>
 <!-- margin-sidebar -->
     <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
-
+        <nav id="TOC" role="doc-toc" class="toc-active">
+    <h2 id="toc-title">Table of contents</h2>
+
+  <ul>
+  <li><a href="#exercises" id="toc-exercises" class="nav-link active" data-scroll-target="#exercises"><span class="header-section-number">15.1</span> Exercises</a></li>
+  </ul>
+</nav>
     </div>
 <!-- main -->
 <main class="content" id="quarto-document-content">
@@ -332,13 +338,25 @@ <h1 class="title"><span class="chapter-number">15</span>&nbsp; <span class="chap
 </header>
 
 
-<p>Here’s the introductory <a href="https://wesmckinney.com/book/pandas-basics">chapter of the book</a>, by the original author of the library pandas.</p>
+<p>Here’s the introductory <a href="https://wesmckinney.com/book/pandas-basics">chapter of the book</a>, by the original author of the library pandas Wes McKinney. In particular, we’ll look at subtitles:</p>
+<ul>
+<li><a href="https://wesmckinney.com/book/pandas-basics#pandas_construction">5.1. Introduction to pandas Data Structures</a></li>
+<li><a href="https://wesmckinney.com/book/pandas-basics#pandas_series">5.2 Essential Functionality</a></li>
+</ul>
 <p>These are useful <a href="https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf">cheatsheets</a>.</p>
 <p>Check out the pandas getting-started documentation <a href="https://pandas.pydata.org/docs/getting_started/index.html#getting-started">here</a></p>
 <p>Here’s short introduction by the very author of the library:</p>
 <div class="quarto-video ratio ratio-16x9"><iframe data-external="1" src="https://www.youtube.com/embed/_T8LGqJtuGc" title="" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen=""></iframe></div>
+<section id="exercises" class="level2" data-number="15.1">
+<h2 data-number="15.1" class="anchored" data-anchor-id="exercises"><span class="header-section-number">15.1</span> Exercises</h2>
+<p>Series from dict Missing values</p>
+<p>Series from dict Display first 7 elements Display last 7 elements Read in data, assign column names Read in only X rows Compute mean of 1 column Add two columns (numbers) Add two columns (strings) Add a column to existing dataframe Add an indicator column to existing dataframe</p>
+<p>Find an interesting dataset, download and explore a bit.</p>
+<p>Discard rows with missing values Fill missing values Drop entire rows (by index) Drop entire cols Sort elements (index, values)</p>
+<p>Summary metric on specific cols Substract mean of a col from other columns</p>
 
 
+</section>
 
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