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cooperrc · Dec 2, 2020 · 755451e · 755451e
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diff --git a/notebooks/.ipynb_checkpoints/abbash_notebook-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/abbash_notebook-checkpoint.ipynb
diff --git a/notebooks/.ipynb_checkpoints/practice_notebook-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/practice_notebook-checkpoint.ipynb
@@ -0,0 +1,175 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# This is the Practice Notebook for Fall 2020\n",
+    "\n",
+    "Use this notebook to play around with Jupyter notebooks and the interface."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.rcParams.update({'font.size': 18})\n",
+    "plt.rcParams['lines.linewidth'] = 3\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Let's manually create an array from 0-8, then square each value. \n",
+    "\n",
+    "_Can you do it with a NumPy command?_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x=np.array([0, 1, 2, 3, 4, 5, 6, 7, 8])\n",
+    "y=x**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## We can plot the result to see the parabola using `matplotlib.pyplot`, which we shortened above to `plt`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'y')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x, y)\n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel('y')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Now, let's build a 2D array and print the result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 2 3]\n",
+      " [4 5 6]\n",
+      " [7 8 9]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "A = np.array([[1, 2, 3], \n",
+    "              [4, 5, 6],\n",
+    "              [7, 8, 9]])\n",
+    "print(A)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Then, take the transpose and print it again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 4 7]\n",
+      " [2 5 8]\n",
+      " [3 6 9]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(A.T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What's next?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}