{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
    "___\n",
    "# Matplotlib Exercises - Solutions\n",
    "\n",
    "Welcome to the exercises for reviewing matplotlib! Take your time with these, Matplotlib can be tricky to understand at first. These are relatively simple plots, but they can be hard if this is your first time with matplotlib, feel free to reference the solutions as you go along.\n",
    "\n",
    "Also don't worry if you find the matplotlib syntax frustrating, we actually won't be using it that often throughout the course, we will switch to using seaborn and pandas built-in visualization capabilities. But, those are built-off of matplotlib, which is why it is still important to get exposure to it!\n",
    "\n",
    "** * NOTE: ALL THE COMMANDS FOR PLOTTING A FIGURE SHOULD ALL GO IN THE SAME CELL. SEPARATING THEM OUT INTO MULTIPLE CELLS MAY CAUSE NOTHING TO SHOW UP. * **\n",
    "\n",
    "# Exercises\n",
    "\n",
    "Follow the instructions to recreate the plots using this data:\n",
    "\n",
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "x = np.arange(0,100)\n",
    "y = x*2\n",
    "z = x**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Import matplotlib.pyplot as plt and set %matplotlib inline if you are using the jupyter notebook. What command do you use if you aren't using the jupyter notebook?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "# plt.show() for non-notebook users"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 1\n",
    "\n",
    "** Follow along with these steps: **\n",
    "* ** Create a figure object called fig using plt.figure() **\n",
    "* ** Use add_axes to add an axis to the figure canvas at [0,0,1,1]. Call this new axis ax. **\n",
    "* ** Plot (x,y) on that axes and set the labels and titles to match the plot below:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x114ce6630>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1147775f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.plot(x,y)\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('y')\n",
    "ax.set_title('title')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 2\n",
    "** Create a figure object and put two axes on it, ax1 and ax2. Located at [0,0,1,1] and [0.2,0.5,.2,.2] respectively.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112b21518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "ax1 = fig.add_axes([0,0,1,1])\n",
    "ax2 = fig.add_axes([0.2,0.5,.2,.2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now plot (x,y) on both axes. And call your figure object to show it.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Vy4uKinzdunV+/Phxv+aaa3zYsGE+efJkr6ura/78smXL/OKLL/YRI0b4jh072r1uGPv2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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112b21518>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax1.plot(x,y)\n",
    "ax1.set_xlabel('x')\n",
    "ax1.set_ylabel('y')\n",
    "\n",
    "\n",
    "ax2.plot(x,y)\n",
    "ax2.set_xlabel('x')\n",
    "ax2.set_ylabel('y')\n",
    "\n",
    "fig # Show figure object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 3\n",
    "\n",
    "** Create the plot below by adding two axes to a figure object at [0,0,1,1] and [0.2,0.5,.4,.4]**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FCzp9+nQUcdOWS7/IcyVLruSQcisLcKOgaCOSSqVUWFjYt11QUKBUKnXNMYlE4qoxAIDc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11460a550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax2 = fig.add_axes([0.2,0.5,.4,.4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now use x,y, and z arrays to recreate the plot below. Notice the xlimits and y limits on the inserted plot:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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X9L49nClToFMnGDVKIS9hEvS5shI8J+ek8NasWePmzJnjnHNu69atrnbt2m7h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\nH51ua+eImT++AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11460a550>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.plot(x,z)\n",
    "ax.set_xlabel('X')\n",
    "ax.set_ylabel('Z')\n",
    "\n",
    "\n",
    "ax2.plot(x,y)\n",
    "ax2.set_xlabel('X')\n",
    "ax2.set_ylabel('Y')\n",
    "ax2.set_title('zoom')\n",
    "ax2.set_xlim(20,22)\n",
    "ax2.set_ylim(30,50)\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 4\n",
    "\n",
    "** Use plt.subplots(nrows=1, ncols=2) to create the plot below.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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o6c/8d4EXL/PcovLV+9xFZntRuV5ktlvMdS9huMTOrAAvTUL38OSx/cAfT615bPKH+QLw\ngXnMBf6WtSvh/w48B3x3nu95au3f9fhh6PJn/aes/RTCMeBT83rPwO8AT0zmHgPu72nul4HzwP8C\nP2TtyGrh+Rpq7iKzvahcLzLbreXaX4aSpIb5v1BKUsMseUlqmCUvSQ2z5CWpYZa8JDXMkpekhlny\nktQwS16SGvZ/GNp0aN6HtcEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11499dc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Empty canvas of 1 by 2 subplots\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now plot (x,y) and (x,z) on the axes. Play around with the linewidth and style**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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a6f9oFdzh/PNDfz6EDVHUrSiSQe+/D3/4Q3w8YADsvnty8RQwde9U4YMPwlIL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11499dc88>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axes[0].plot(x,y,color=\"blue\", lw=3, ls='--')\n",
    "axes[1].plot(x,z,color=\"red\", lw=3, ls='-')\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** See if you can resize the plot by adding the figsize() argument in plt.subplots() are copying and pasting your previous code.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x115847da0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Yxm0iBUxpJHkknbLp7dvXlU3v31952CIikuf+7d+SB9X//GfY+a9jsUQArWxn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\nJEsUbIuIiIiIZMn/A0B/LV+00DLqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1157e96a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2,figsize=(12,2))\n",
    "\n",
    "axes[0].plot(x,y,color=\"blue\", lw=5)\n",
    "axes[0].set_xlabel('x')\n",
    "axes[0].set_ylabel('y')\n",
    "\n",
    "axes[1].plot(x,z,color=\"red\", lw=3, ls='--')\n",
    "axes[1].set_xlabel('x')\n",
    "axes[1].set_ylabel('z')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Great Job!"
   ]
  }
 ],
 "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}