Demonstrating matplotlib subplots, list and dict comprehension to Sam and Caitlin

demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Subplots in matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2, sharex=True, sharey=True)\n",
    "\n",
    "ax0 = axes[0]\n",
    "ax1 = axes[1]\n",
    "\n",
    "ax0.scatter([0, 1, 2], [0, 1, 2])\n",
    "\n",
    "ax1.plot([1, 2, 3], [4, 7, 8])\n",
    "\n",
    "ax0.text(1, 1, \"Hello\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# No shared x or y axis\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
    "\n",
    "ax0 = axes[0]\n",
    "ax1 = axes[1]\n",
    "\n",
    "ax0.scatter([0, 1, 2], [0, 1, 2])\n",
    "\n",
    "ax1.plot([1, 2, 3], [4, 7, 8])\n",
    "\n",
    "ax0.text(1, 1, \"Hello\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Iterating over axes\n",
    "fig, axes = plt.subplots(nrows=2, ncols=3, sharex=True, sharey=True)\n",
    "\n",
    "for ax, s in zip(fig.axes, \"abcdef\".upper()):\n",
    "    ax.text(0, 0, s=s)\n",
    "\n",
    "ax.set_xlim(-1, 1)\n",
    "ax.set_ylim(-1, 1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f2766e135f8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2766e35eb8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2766de5198>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f2766d8c470>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2766db6748>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2766d5da20>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2D array\n",
    "axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.axes._subplots.AxesSubplot at 0x7f2766e135f8>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x7f2766e35eb8>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x7f2766de5198>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x7f2766d8c470>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x7f2766db6748>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x7f2766d5da20>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1D array\n",
    "fig.axes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# List comprehension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def square(x):\n",
    "    return x * x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = [0, 1, 2, 3, 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "square_values = []\n",
    "for v in values:\n",
    "    square_values.append(square(v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 4, 9, 16]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "square_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 4, 9, 16]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[square(f) for f in values]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dictionary comprehension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "squared = {}\n",
    "for v in values:\n",
    "    squared[v] = square(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0, 1: 1, 2: 4, 3: 9, 4: 16}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0, 1: 1, 2: 4, 3: 9, 4: 16}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{v: square(v) for v in values}"
   ]
  },
  {
   "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}