Matplotlib-Exercises.ipynb

{
 "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 \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": 1,
   "metadata": {},
   "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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "#plt.show()"
   ]
  },
  {
   "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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'title')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0485c471d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create empty canvas\n",
    "fig = plt.figure()\n",
    "#Add set of axis to fig\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "#plot on the set of axes\n",
    "ax.plot(x,y,'b')\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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0485cb6ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0485cb6ef0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax1.plot(x,y,'r')\n",
    "ax2.plot(x,y,'r')\n",
    "fig"
   ]
  },
  {
   "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": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f047dcfd2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = plt.figure()\n",
    "axes1 = fig2.add_axes([0,0,1,1])\n",
    "axes2 = fig2.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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f047dcfd2b0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Larger Figure Axes \n",
    "axes1.plot(x, z, 'b')\n",
    "axes1.set_xlabel('X')\n",
    "axes1.set_ylabel('Z')\n",
    "axes1.set_title('Title')\n",
    "# Insert Figure Axes 2\n",
    "axes2.plot(x, y, 'b')\n",
    "axes2.set_xlabel('X')\n",
    "axes2.set_ylabel('Y')\n",
    "axes2.set_title('Title')\n",
    "axes2.set_ylim([30, 50])\n",
    "axes2.set_xlim([20, 22])\n",
    "fig2"
   ]
  },
  {
   "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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J/4ifAVaBrwOv3Ka6/wL8N/CN6Z/l7XrM69aO+Yux2WMO8LfAaeBbwKFt/DnvA74y/aX5BvAHI9T8FPA94GdMznjuBN4GvG3m8R6d9vStsf6dFznXi5ztRc31Imd7EXM9z9n2qxUkqQmvtJWkJgx8SWrCwJekJgx8SWrCwJekJgx8SWrCwJekJv4PcgCmcLyIQvoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f047dfac908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,2)"
   ]
  },
  {
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f047de79588>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f047dfa77b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1)\n",
    "plt.plot(x,y,color='blue', lw=3, ls ='--')\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(x,z,color='red', lw=3, ls = '-')"
   ]
  },
  {
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f047ddb6438>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f047de49550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,2))\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(x,y,color='blue', lw=3, ls ='-')\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(x,z,color='red', lw=3, ls = '--')"
   ]
  },
  {
   "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": "3.6.4"
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 "nbformat": 4,
 "nbformat_minor": 1
}