pb111/Data Visualization with Matplotlib.ipynb

## Data Visualization with Matplotlib.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization with Matplotlib\n",
    "\n",
    "\n",
    "This project is all about Matplotlib, the basic data visualization tool of Python programming language. I have discussed Matplotlib object hierarchy, various plot types with Matplotlib and customization techniques associated with Matplotlib. \n",
    "\n",
    "\n",
    "This project is divided into various sections based on contents which are listed below:- \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "\n",
    "\n",
    "1.\tIntroduction\n",
    "\n",
    "2.\tOverview of Python Data Visualization Tools\n",
    "\n",
    "3.\tIntroduction to Matplotlib\n",
    "\n",
    "4.\tImport Matplotlib\n",
    "\n",
    "5.\tDisplaying Plots in Matplotlib\n",
    "\n",
    "6.\tMatplotlib Object Hierarchy\n",
    "\n",
    "7.\tMatplotlib interfaces\n",
    "\n",
    "8.\tPyplot API\n",
    "\n",
    "9.\tObject-Oriented API\n",
    "\n",
    "10.\tFigure and Subplots\n",
    "\n",
    "11.\tFirst plot with Matplotlib\n",
    "\n",
    "12.\tMultiline Plots\n",
    "\n",
    "13.\tParts of a Plot\n",
    "\n",
    "14.\tSaving the Plot\n",
    "\n",
    "15.\tLine Plot\n",
    "\n",
    "16.\tScatter Plot\n",
    "\n",
    "17.\tHistogram\n",
    "\n",
    "18.\tBar Chart\n",
    "\n",
    "19.\tHorizontal Bar Chart\n",
    "\n",
    "20.\tError Bar Chart\n",
    "\n",
    "21.\tMultiple Bar Chart\n",
    "\n",
    "22.\tStacked Bar Chart\n",
    "\n",
    "23.\tBack-to-back Bar Chart\n",
    "\n",
    "24.\tPie Chart\n",
    "\n",
    "25.\tBox Plot\n",
    "\n",
    "26.\tArea Chart\n",
    "\n",
    "27.\tContour Plot\n",
    "\n",
    "28.\tImage Plot\n",
    "\n",
    "29.\tPolar Chart\n",
    "\n",
    "30.\t3D Plotting with Matplotlib\n",
    "\n",
    "31.\tStyles with Matplotlib Plots\n",
    "\n",
    "32.\tAdding a grid\n",
    "\n",
    "33.\tHandling axes\n",
    "\n",
    "34.\tHandling X and Y ticks\n",
    "\n",
    "35.\tAdding labels\n",
    "\n",
    "36.\tAdding a title\n",
    "\n",
    "37.\tAdding a legend\n",
    "\n",
    "38.\tControl colours\n",
    "\n",
    "39.\tControl line styles\n",
    " \n",
    "40.\tSummary\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Introduction\n",
    "\n",
    "\n",
    "When we want to convey some information to others, there are several ways to do so. The process of conveying the information with the help of plots and graphics is called **Data Visualization**. The plots and graphics take numerical data as input and display output in the form of charts, figures and tables. It helps to analyze and visualize the data clearly and make concrete decisions. It makes complex data more accessible and understandable. The goal of data visualization is to communicate information in a clear and efficient manner.\n",
    "\n",
    "\n",
    "In this project, I shed some light on **Matplotlib**, which is the basic data visualization tool of Python programming language. Python has different data visualization tools available which are suitable for different purposes. First of all, I will list these data visualization tools and then I will discuss Matplotlib.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Overview of Python Visualization Tools\n",
    "\n",
    "\n",
    "\n",
    "Python is the preferred language of choice for data scientists. Python have multiple options for data visualization. It has several tools which can help us to visualize the data more effectively. These Python data visualization tools are as follows:-\n",
    "\n",
    "\n",
    "\n",
    "•\tMatplotlib\n",
    "\n",
    "•\tSeaborn\n",
    "\n",
    "•\tpandas\n",
    "\n",
    "•\tBokeh\n",
    "\n",
    "•\tPlotly\n",
    "\n",
    "•\tggplot\n",
    "\n",
    "•\tpygal\n",
    "\n",
    "\n",
    "\n",
    "In the following sections, I discuss Matplotlib as the data visualization tool. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Introduction to Matplotlib\n",
    "\n",
    "\n",
    "**Matplotlib** is the basic plotting library of Python programming language. It is the most prominent tool among Python visualization packages. Matplotlib is highly efficient in performing wide range of tasks. It can produce publication quality figures in a variety of formats.  It can export visualizations to all of the common formats like PDF, SVG, JPG, PNG, BMP and GIF. It can create popular visualization types – line plot, scatter plot, histogram, bar chart, error charts, pie chart, box plot, and many more types of plot. Matplotlib also supports 3D plotting. Many Python libraries are built on top of Matplotlib. For example, pandas and Seaborn are built on Matplotlib. They allow to access Matplotlib’s methods with less code. \n",
    "\n",
    "\n",
    "The project **Matplotlib** was started by John Hunter in 2002. Matplotlib was originally started to visualize Electrocorticography (ECoG) data of epilepsy patients during post-doctoral research in Neurobiology. The open-source tool Matplotlib emerged as the most widely used plotting library for the Python programming language. It was used for data visualization during landing of the Phoenix spacecraft in 2008.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## 4. Import Matplotlib\n",
    "\n",
    "Before, we need to actually start using Matplotlib, we need to import it. We can import Matplotlib as follows:-\n",
    "\n",
    "`import matplotlib`\n",
    "\n",
    "\n",
    "Most of the time, we have to work with **pyplot** interface of Matplotlib. So, I will import **pyplot** interface of Matplotlib as follows:-\n",
    "\n",
    "\n",
    "`import matplotlib.pyplot`\n",
    "\n",
    "\n",
    "To make things even simpler, we will use standard shorthand for Matplotlib imports as follows:-\n",
    "\n",
    "\n",
    "`import matplotlib.pyplot as plt`\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dependencies\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Matplotlib\n",
    "\n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Displaying Plots in Matplotlib\n",
    "\n",
    "\n",
    "Viewing the Matplotlib plot is context based. The best usage of Matplotlib differs depending on how we are using it. \n",
    "There are three applicable contexts for viewing the plots. The three applicable contexts are using plotting from a script, plotting from an IPython shell or plotting from a Jupyter notebook.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting from a script\n",
    "\n",
    "\n",
    "\n",
    "If we are using Matplotlib from within a script, then the **plt.show()** command is of great use. It starts an event loop, \n",
    "looks for all currently active figure objects, and opens one or more interactive windows that display the figure or figures.\n",
    "\n",
    "\n",
    "The **plt.show()** command should be used only once per Python session. It should be used only at the end of the script. Multiple **plt.show()** commands can lead to unpredictable results and should mostly be avoided.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting from an IPython shell\n",
    "\n",
    "\n",
    "We can use Matplotlib interactively within an IPython shell. IPython works well with Matplotlib if we specify Matplotlib mode. To enable this mode, we can use the **%matplotlib** magic command after starting ipython. Any plt plot command will cause a figure window to open and further commands can be run to update the plot.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting from a Jupyter notebook\n",
    "\n",
    "\n",
    "The Jupyter Notebook (formerly known as the IPython Notebook) is a data analysis and visualization tool that provides multiple tools under one roof.  It provides code execution, graphical plots, rich text and media display, mathematics formula and much more facilities into a single executable document.\n",
    "\n",
    "\n",
    "Interactive plotting within a Jupyter Notebook can be done with the **%matplotlib** command. There are two possible options to work with graphics in Jupyter Notebook. These are as follows:-\n",
    "\n",
    "\n",
    "•\t**%matplotlib notebook** – This command will produce interactive plots embedded within the notebook.\n",
    "\n",
    "•\t**%matplotlib inline** – It will output static images of the plot embedded in the notebook.\n",
    "\n",
    "\n",
    "After this command (it needs to be done only once per kernel per session), any cell within the notebook that creates a plot will embed a PNG image of the graphic.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "\n",
    "x1 = np.linspace(0, 10, 100)\n",
    "\n",
    "\n",
    "# create a plot figure\n",
    "fig = plt.figure()\n",
    "\n",
    "plt.plot(x1, np.sin(x1), '-')\n",
    "plt.plot(x1, np.cos(x1), '--');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Matplotlib Object Hierarchy\n",
    "\n",
    "\n",
    "There is an Object Hierarchy within Matplotlib. In Matplotlib, a plot is a hierarchy of nested Python objects. \n",
    "A**hierarch** means that there is a tree-like structure of Matplotlib objects underlying each plot.\n",
    "\n",
    "\n",
    "A **Figure** object is the outermost container for a Matplotlib plot. The **Figure** object contain multiple **Axes** objects. So, the **Figure** is the final graphic that may contain one or more **Axes**. The **Axes** represent an individual plot.\n",
    "\n",
    "\n",
    "So, we can think of the **Figure** object as a box-like container containing one or more **Axes**. The **Axes** object contain smaller objects such as tick marks, lines, legends, title and text-boxes.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.\tMatplotlib API Overview\n",
    "\n",
    "\n",
    "\n",
    "Matplotlib has two APIs to work with. A MATLAB-style state-based interface and a more powerful object-oriented (OO) interface. \n",
    "The former MATLAB-style state-based interface is called **pyplot interface** and the latter is called **Object-Oriented** interface.\n",
    "\n",
    "\n",
    "There is a third interface also called **pylab** interface. It merges pyplot (for plotting) and NumPy (for mathematical functions) together in an environment closer to MATLAB. This is considered bad practice nowadays. So, the use of **pylab** is strongly discouraged and hence, I will not discuss it any further.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Pyplot API \n",
    "\n",
    "\n",
    "**Matplotlib.pyplot** provides a MATLAB-style, procedural, state-machine interface to the underlying object-oriented library in Matplotlib. **Pyplot** is a collection of command style functions that make Matplotlib work like MATLAB. Each pyplot function makes some change to a figure - e.g., creates a figure, creates a plotting area in a figure etc. \n",
    "\n",
    "\n",
    "**Matplotlib.pyplot** is stateful because the underlying engine keeps track of the current figure and plotting area information and plotting functions change that information. To make it clearer, we did not use any object references during our plotting we just issued a pyplot command, and the changes appeared in the figure.\n",
    "\n",
    "\n",
    "We can get a reference to the current figure and axes using the following commands-\n",
    "\n",
    "\n",
    "`plt.gcf ( )`   # get current figure\n",
    "\n",
    "`plt.gca ( )`   # get current axes \n",
    "\n",
    " \n",
    "**Matplotlib.pyplot** is a collection of commands and functions that make Matplotlib behave like MATLAB (for plotting). \n",
    "The MATLAB-style tools are contained in the pyplot (plt) interface. \n",
    "\n",
    "This is really helpful for interactive plotting, because we can issue a command and see the result immediately. But, it is not suitable for more complicated cases. For these cases, we have another interface called **Object-Oriented** interface, described later.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code produces sine and cosine curves using Pyplot API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "# create a plot figure\n",
    "plt.figure()\n",
    "\n",
    "\n",
    "# create the first of two panels and set current axis\n",
    "plt.subplot(2, 1, 1)   # (rows, columns, panel number)\n",
    "plt.plot(x1, np.sin(x1))\n",
    "\n",
    "\n",
    "# create the second of two panels and set current axis\n",
    "plt.subplot(2, 1, 2)   # (rows, columns, panel number)\n",
    "plt.plot(x1, np.cos(x1));\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure(432x288)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get current figure information\n",
    "\n",
    "print(plt.gcf())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "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": [
    "# get current axis information\n",
    "\n",
    "print(plt.gca())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization with Pyplot\n",
    "\n",
    "\n",
    "Generating visualization with Pyplot is very easy. The x-axis values ranges from 0-3 and the y-axis from 1-4. If we provide a single list or array to the plot() command, matplotlib assumes it is a sequence of y values, and automatically generates the x values. Since python ranges start with 0, the default x vector has the same length as y but starts with 0. Hence the x data are [0,1,2,3] and y data are [1,2,3,4]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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([1, 2, 3, 4])\n",
    "plt.ylabel('Numbers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot() - A versatile command\n",
    "\n",
    "\n",
    "**plot()** is a versatile command. It will take an arbitrary number of arguments. For example, to plot x versus y, \n",
    "we can issue the following command:-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "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([1, 2, 3, 4], [1, 4, 9, 16])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### State-machine interface\n",
    "\n",
    "Pyplot provides the state-machine interface to the underlying object-oriented plotting library. The state-machine implicitly and automatically creates figures and axes to achieve the desired plot. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "x = np.linspace(0, 2, 100)\n",
    "\n",
    "plt.plot(x, x, label='linear')\n",
    "plt.plot(x, x**2, label='quadratic')\n",
    "plt.plot(x, x**3, label='cubic')\n",
    "\n",
    "plt.xlabel('x label')\n",
    "plt.ylabel('y label')\n",
    "\n",
    "plt.title(\"Simple Plot\")\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Formatting the style of plot\n",
    "\n",
    "\n",
    "For every x, y pair of arguments, there is an optional third argument which is the format string that indicates the color and line type of the plot. The letters and symbols of the format string are from MATLAB. We can concatenate a color string with a line style string. The default format string is 'b-', which is a solid blue line. For example, to plot the above line with red circles, we would issue the following command:-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD8CAYAAABw1c+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAEoFJREFUeJzt3X+MZeV93/H3ZwG33TUpOIwJ5sdu2iIsx6oxGa1roVrYDgQoMk5kJaCpS1JLk0R2ZauVGicrxa0jJFdVnKolMpoANW4n2K1tEtRgm1XiClvyD2a3i4EsDhSxsFnKrrMOmE5UC+fbP+5ZeRjf2bl7z929s/O8X9LVOec5zznne4T4zNnnnnNuqgpJUju2TLsASdKpZfBLUmMMfklqjMEvSY0x+CWpMQa/JDVm3eBPcnGSLyfZn+SxJB/s2l+TZHeSJ7rpuWtsf0vX54kkt0z6BCRJJybr3cef5ALggqram+RsYA/wbuCXgKNV9bEkHwbOrapfX7Xta4AlYBaobtufrqrvTvxMJEkjWfeKv6qeq6q93fz3gP3AhcCNwN1dt7sZ/DFY7WeB3VV1tAv73cC1kyhckjSeM0+kc5IdwJuBbwDnV9VzMPjjkOS1Qza5EHh2xfLBrm3YvueBeYBt27b99Otf//oTKU2SmrZnz57vVNXMKH1HDv4krwY+B3yoql5MMtJmQ9qGji1V1QKwADA7O1tLS0ujliZJzUtyYNS+I93Vk+QsBqG/WFWf75qf78b/j30PcHjIpgeBi1csXwQcGrU4SdLkjXJXT4A7gf1V9fEVq+4Djt2lcwvwR0M2/xJwTZJzu7t+runaJElTMsoV/5XAe4F3JNnXfa4HPgZcneQJ4OpumSSzSe4AqKqjwG8DD3Wfj3ZtkqQpWfd2zmlwjF+STkySPVU1O0pfn9yVpMYY/JLUGINfkhpj8EtSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TGGPyS1BiDX5IaY/BLUmMMfklqjMEvSY0x+CWpMQa/JDXG4Jekxhj8ktSYM9frkOQu4AbgcFW9sWv7DHBZ1+Uc4K+q6vIh2z4NfA/4AfDyqD8LJkk6edYNfuCTwG3Ap441VNUvHptP8jvAC8fZ/u1V9Z1xC5QkTda6wV9VDybZMWxdkgC/ALxjsmVJkk6WvmP8/xh4vqqeWGN9AQ8k2ZNkvuexJEkTMMpQz/HcDNxznPVXVtWhJK8Fdid5vKoeHNax+8MwD3DJJZf0LEuStJaxr/iTnAn8PPCZtfpU1aFuehi4F9h5nL4LVTVbVbMzMzPjliVJWkefoZ6fAR6vqoPDVibZluTsY/PANcCjPY4nSZqAdYM/yT3A14DLkhxM8r5u1U2sGuZJ8rok93eL5wNfTfIw8E3gj6vqi5MrXZI0jlHu6rl5jfZfGtJ2CLi+m38KeFPP+iRJE+aTu5LUGINfkhpj8EtSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TGGPySYHERduyALVsG08XFaVekk6jv2zklne4WF2F+HpaXB8sHDgyWAebmpleXThqv+KXW7dr1w9A/Znl50K5NyeCXWvfMMyfWrtOewS+1bq0fPvIHkTYtg19q3a23wtatr2zbunXQrk3J4JdaNzcHCwuwfTskg+nCgl/sbmLe1SNpEPIGfTO84pekxhj8ktQYg1+SGjPKj63fleRwkkdXtP2bJH+RZF/3uX6Nba9N8u0kTyb58CQLlySNZ5Qr/k8C1w5p/92qurz73L96ZZIzgN8DrgPeANyc5A19ipUk9bdu8FfVg8DRMfa9E3iyqp6qqu8DnwZuHGM/kqQJ6jPG/4Ek3+qGgs4dsv5C4NkVywe7tqGSzCdZSrJ05MiRHmVJko5n3OD/BPD3gcuB54DfGdInQ9pqrR1W1UJVzVbV7MzMzJhlSZLWM1bwV9XzVfWDqvob4PcZDOusdhC4eMXyRcChcY4nSZqcsYI/yQUrFn8OeHRIt4eAS5P8ZJJXATcB941zPEnS5Kz7yoYk9wBXAeclOQh8BLgqyeUMhm6eBn6l6/s64I6qur6qXk7yAeBLwBnAXVX12Ek5C0nSyFK15rD71MzOztbS0tK0y5Ck00aSPVU1O0pfn9yVpMYY/JLUGINfkhpj8EtSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TGGPyS1BiDX5IaY/BLUmMMfklqjMEvSY0x+CWpMQa/JDXG4Jekxhj8ktSYdYM/yV1JDid5dEXbv0/yeJJvJbk3yTlrbPt0kkeS7EvibylK0gYwyhX/J4FrV7XtBt5YVf8Q+HPgN46z/dur6vJRfwtSknRyrRv8VfUgcHRV2wNV9XK3+HXgopNQmyTpJJjEGP8/B76wxroCHkiyJ8n88XaSZD7JUpKlI0eOTKAsSdIwvYI/yS7gZWBxjS5XVtUVwHXA+5O8ba19VdVCVc1W1ezMzEyfsiRJxzF28Ce5BbgBmKuqGtanqg5108PAvcDOcY8nSZqMsYI/ybXArwPvqqrlNfpsS3L2sXngGuDRYX0lSafOKLdz3gN8DbgsycEk7wNuA84Gdne3at7e9X1dkvu7Tc8HvprkYeCbwB9X1RdPyllIkkZ25nodqurmIc13rtH3EHB9N/8U8KZe1UmSJs4ndyWpMQa/JDXG4Jekxhj8ktQYg1+SGmPwS1JjDH5JaozBL0mNMfglqTEGvyQ1xuCXpMYY/JLUGINfkhpj8EtSYwx+SWqMwS9JjTH4JakxBr8kNWak4E9yV5LDSR5d0faaJLuTPNFNz11j21u6Pk8kuWVShUuSxjPqFf8ngWtXtX0Y+JOquhT4k275FZK8BvgI8BZgJ/CRtf5ASJJOjZGCv6oeBI6uar4RuLubvxt495BNfxbYXVVHq+q7wG5+9A+IJOkU6jPGf35VPQfQTV87pM+FwLMrlg92bT8iyXySpSRLR44c6VGWJOl4TvaXuxnSVsM6VtVCVc1W1ezMzMxJLkuS2tUn+J9PcgFANz08pM9B4OIVyxcBh3ocU5LUU5/gvw84dpfOLcAfDenzJeCaJOd2X+pe07VJkqZk1Ns57wG+BlyW5GCS9wEfA65O8gRwdbdMktkkdwBU1VHgt4GHus9HuzZJ0pSkauiQ+1TNzs7W0tLStMuQpNNGkj1VNTtKX5/claTGGPyS1BiDXxrF4iLs2AFbtgymi4vTrkga25nTLkDa8BYXYX4elpcHywcODJYB5uamV5c0Jq/4pfXs2vXD0D9meXnQLp2GDH5pPc88c2Lt0gZn8EvrueSSE2uXNjiDX1rPrbfC1q2vbNu6ddAunYYMfmk9c3OwsADbt0MymC4s+MWuTlve1SONYm7OoNem4RW/JDXG4Jekxhj8ktQYg1+SGmPwS1JjDH5JaozBL0mNMfglqTFjB3+Sy5LsW/F5McmHVvW5KskLK/r8Vv+SJUl9jP3kblV9G7gcIMkZwF8A9w7p+pWqumHc40iSJmtSQz3vBP53VR2Y0P4kSSfJpIL/JuCeNda9NcnDSb6Q5KfW2kGS+SRLSZaOHDkyobIkSav1Dv4krwLeBfz3Iav3Atur6k3AfwL+cK39VNVCVc1W1ezMzEzfsiRJa5jEFf91wN6qen71iqp6sape6ubvB85Kct4EjilJGtMkgv9m1hjmSfITSdLN7+yO95cTOKYkaUy93sefZCtwNfArK9p+FaCqbgfeA/xakpeBvwZuqqrqc0xJUj+9gr+qloEfX9V2+4r524Db+hxDkjRZPrkrSY0x+CWpMQa/JDXG4Jekxhj8ktQYg1+SGmPwS1JjDH5JaozBL0mNMfglqTEGvyQ1xuCXpMYY/JLUGINfkhpj8EtSYwx+SWqMwS9JjTH4JakxvYM/ydNJHkmyL8nSkPVJ8h+TPJnkW0mu6HtMSdL4ev3m7gpvr6rvrLHuOuDS7vMW4BPdVJI0BadiqOdG4FM18HXgnCQXnILjSpKGmETwF/BAkj1J5oesvxB4dsXywa7tFZLMJ1lKsnTkyJEJlCVJGmYSwX9lVV3BYEjn/Unetmp9hmxTP9JQtVBVs1U1OzMzM4GyJEnD9A7+qjrUTQ8D9wI7V3U5CFy8Yvki4FDf40qSxtMr+JNsS3L2sXngGuDRVd3uA/5Zd3fPPwJeqKrn+hxXkjS+vnf1nA/cm+TYvv6gqr6Y5FcBqup24H7geuBJYBn45Z7HlCT10Cv4q+op4E1D2m9fMV/A+/scR5I0OT65K0mNMfglqTEGvyZjcRF27IAtWwbTxcVpVyRpDZN6ZYNatrgI8/OwvDxYPnBgsAwwNze9uiQN5RW/+tu164ehf8zy8qBd0oZj8Ku/Z545sXZJU2Xwq79LLjmxdklTZfCrv1tvha1bX9m2deugXdKGY/Crv7k5WFiA7dshGUwXFvxiV9qgvKtHkzE3Z9BLpwmv+CWpMQa/JDXG4Jekxhj8ktQYg1+SGmPwS1JjDH5JaozBL0mNGTv4k1yc5MtJ9id5LMkHh/S5KskLSfZ1n9/qV64kqa8+T+6+DPyrqtqb5GxgT5LdVfVnq/p9papu6HEcSdIEjX3FX1XPVdXebv57wH7gwkkVJkk6OSYyxp9kB/Bm4BtDVr81ycNJvpDkpyZxPEnS+Hq/pC3Jq4HPAR+qqhdXrd4LbK+ql5JcD/whcOka+5kH5gEu8T3uknTS9LriT3IWg9BfrKrPr15fVS9W1Uvd/P3AWUnOG7avqlqoqtmqmp2ZmelTliTpOPrc1RPgTmB/VX18jT4/0fUjyc7ueH857jElSf31Geq5Engv8EiSfV3bbwKXAFTV7cB7gF9L8jLw18BNVVU9jilJ6mns4K+qrwJZp89twG3jHkOSNHk+uStJjTH4JakxBr8kNcbgl6TGGPyS1BiDX5IaY/BLUmMMfklqjMF/qiwuwo4dsGXLYLq4OO2KJDWq99s5NYLFRZifh+XlwfKBA4NlgLm56dUlqUle8Z8Ku3b9MPSPWV4etEvSKWbwnwrPPHNi7ZJ0Ehn8p8JaPyzjD85ImgKD/1S49VbYuvWVbVu3Dtol6RQz+E+FuTlYWIDt2yEZTBcW/GJX0lR4V8+pMjdn0EvaELzil6TGGPyS1BiDX5Ia0yv4k1yb5NtJnkzy4SHr/1aSz3Trv5FkR5/jSZL6Gzv4k5wB/B5wHfAG4OYkb1jV7X3Ad6vqHwC/C/y7cY8nSZqMPlf8O4Enq+qpqvo+8GngxlV9bgTu7uY/C7wzSXocU5LUU5/bOS8Enl2xfBB4y1p9qurlJC8APw58Z/XOkswD3ZvL+H9JHu1R20Z2HkPOfxPx/E5vnt/p67JRO/YJ/mFX7jVGn0Fj1QKwAJBkqapme9S2YW3mcwPP73Tn+Z2+kiyN2rfPUM9B4OIVyxcBh9bqk+RM4O8CR3scU5LUU5/gfwi4NMlPJnkVcBNw36o+9wG3dPPvAf60qoZe8UuSTo2xh3q6MfsPAF8CzgDuqqrHknwUWKqq+4A7gf+S5EkGV/o3jbj7hXHrOg1s5nMDz+905/mdvkY+t3gBLklt8cldSWqMwS9JjdlQwb/eKyBOZ0nuSnJ4sz6fkOTiJF9Osj/JY0k+OO2aJinJ307yzSQPd+f3b6dd06QlOSPJ/0ryP6Zdy6QleTrJI0n2nchtj6eLJOck+WySx7v/B9963P4bZYy/ewXEnwNXM7gN9CHg5qr6s6kWNiFJ3ga8BHyqqt447XomLckFwAVVtTfJ2cAe4N2b6L9fgG1V9VKSs4CvAh+sqq9PubSJSfIvgVngx6rqhmnXM0lJngZmq2pTPryV5G7gK1V1R3eX5daq+qu1+m+kK/5RXgFx2qqqB9nEzzBU1XNVtbeb/x6wn8GT25tCDbzULZ7VfTbGVdMEJLkI+CfAHdOuRScmyY8Bb2NwFyVV9f3jhT5srOAf9gqITRMcLenewvpm4BvTrWSyuqGQfcBhYHdVbabz+w/Avwb+ZtqFnCQFPJBkT/d6mM3k7wFHgP/cDdXdkWTb8TbYSME/8usdtHEleTXwOeBDVfXitOuZpKr6QVVdzuAp9Z1JNsWQXZIbgMNVtWfatZxEV1bVFQzeJvz+buh1szgTuAL4RFW9Gfi/wHG/I91IwT/KKyC0gXVj358DFqvq89Ou52Tp/hn9P4Frp1zKpFwJvKsbB/808I4k/3W6JU1WVR3qpoeBexkMLW8WB4GDK/4F+lkGfwjWtJGCf5RXQGiD6r78vBPYX1Ufn3Y9k5ZkJsk53fzfAX4GeHy6VU1GVf1GVV1UVTsY/H/3p1X1T6dc1sQk2dbdcEA3BHINsGnurquq/wM8m+TY2znfCRz3poo+b+ecqLVeATHlsiYmyT3AVcB5SQ4CH6mqO6db1URdCbwXeKQbBwf4zaq6f4o1TdIFwN3d3WdbgP9WVZvutsdN6nzg3u6nQM4E/qCqvjjdkibuXwCL3UXzU8AvH6/zhrmdU5J0amykoR5J0ilg8EtSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TG/H9sTSG9ra8nVQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'ro')\n",
    "plt.axis([0, 6, 0, 20])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **axis()** command in the example above takes a list of [xmin, xmax, ymin, ymax] and specifies the viewport of the axes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working with NumPy arrays\n",
    "\n",
    "\n",
    "Generally, we have to work with NumPy arrays. All sequences are converted to numpy arrays internally. The below example illustrates plotting several lines with different format styles in one command using arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# evenly sampled time at 200ms intervals\n",
    "t = np.arange(0., 5., 0.2)\n",
    "\n",
    "# red dashes, blue squares and green triangles\n",
    "plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.\tObject-Oriented API\n",
    "\n",
    "\n",
    "The **Object-Oriented API** is available for more complex plotting situations. It allows us to exercise more control over the figure. In Pyplot API, we depend on some notion of an \"active\" figure or axes. But, in the **Object-Oriented API** the plotting functions are methods of explicit Figure and Axes objects.\n",
    "\n",
    "\n",
    "**Figure** is the top level container for all the plot elements. We can think of the **Figure** object as a box-like container containing one or more **Axes**. \n",
    "\n",
    "\n",
    "The **Axes** represent an individual plot. The **Axes** object contain smaller objects such as axis, tick marks, lines, legends, title and text-boxes.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code produces sine and cosine curves using Object-Oriented API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fXbv0OL3ERH0sPeUU3xEFj4iuKc2aZTt/o4mIrjdNnaqdNc86K7LXD/TOU+NXgQJaKfDjj7pDz4TesGHw8ccwYIAl9WjinG4QK1gwust/LbGbbLnkEq3tHToUpk/3HU2wrF0LDz8MjRppHbWJLqedpuW/ixZBv36+ozk2S+wm2/r3h6pVdTFp+3bf0QRDRob25klI0MOpE+w7NCo1b36k/HfBAt/R/J3dNibb8uaFt9/W3ZA2sgyNl17SczcHD7Zj7qLdv/+tu+BbtdKeMtHEErvJkbp14V//0rNmx43zHU1sW7ZMNyBdf73O35roVriwtndYvRoef9x3NP/LErvJsR494Lzz9NCHn3/2HU1s2rcPbr9dk8Xrr1sr6ljRuLGuNb3yCkyb5juaIyyxmxxLStI+Jvv36/xwrPTTiCY9euiI/a23dHHOxI7+/aF6dV1ripbj9Cyxm5CoXFnnh2fM0Plhk3UzZ+oxdx06wNVX+47GnKy8eXUqcts23bwXDb3bLbGbkLn3XrjuOp1vXLrUdzSx4ffftRV1lSp6hq+JTbVq6ch94kRtu+GbJXYTMof7aRQrBrfeGn2VAtHmcCvYLVtg9GjIl893RCYnHnkELrtM59xXr/YbiyV2E1LFi+t8+6pVeqObf/baazBpEjzzjPVYD4KEBK2SyZsXbrtN15y8xeLv0iaoLr0UunbV6o4JE3xHE52WLdPdpU2a6AjPBMPpp+tT66JF0C3spz//M0vsJiz69NEa97Zt4aeffEcTXf78E1q21NJG210aPM2awYMP6vGdUzw1MLdbyoRF7tzaS1wEWrSAAwd8RxQ9OnaElSt1yqpECd/RmHAYOBBq19byXx9npVpiN2FToQIMH669NKwLpHrrLf3VowdcfrnvaEy45MmjA5v0dC0kiHQXSEvsJqyaN9fH0kGDbL7922+1Vr1xY+jVy3c0JtwqVYL//Afmzo38wMYSuwm7gQN1vr1NG62WiUc7d8JNN+nxgu++q4eUmOBr0UIb5L38MowZE7nrWmI3YZcnD3zwgR5+fcMNmuTiyaFD2tRr7VoYO9bm1ePN889Dw4Zwzz3w3XeRuaYldhMRZcvqnOMPP8RfP5mnn4bJk7VtwEUX+Y7GRFru3PD++3rqUvPmsGNH+K9pid1ETKNGOi0zcaIe+RYPJk6E3r21bYD1rI9fpUppW+u0NO23H25J4b+EMUc8/LBu3ujZU/uj3HST74jC57vvdAqmbl3dZWqteOPbhRfCunWR6d5pI3YTUc5pk6Tzz9eTZ775xndE4bFpEzRtCvnzazVQcrLviEw0iFRLZkvsJuKSk3WKomRJ7Qa5YYPviEJr92645hrtzf3xx1CmjO+ITLyxxG68OO00TXp792oP8t9/9x1RaKSna7uAJUt0TrV2bd8RmXhkid14U726djf84Qcd4e7Z4zuinBGB++/XH1hDhtihGcYfS+zGq0aNtLZ7/ny48cbY7SkjogvDb76pC8Pt2/uOyMQzS+zGuxtu0K3X06bBHXdEvq9GKDz5pLZNeOghrVs3xicrdzRR4e67dePGI49ARoZuv86d23dUJyYC/fpB3766s/DFF62s0fhnid1EjYcf1h4qnTtrffv772s7gmgloocpDByopZtWq26ihU3FmKjy4IMwdCh8+KGWQu7a5TuiY8vI0IXSgQO1Y+OIEdbYy0QPS+wm6tx/v/ZxnzlTd+ulpfmO6H/t2aMlja+/Dt27wyuv2ClIJrrY7Wii0l13wSefaEfE886DxYt9R6Q2bNAfNhMmwAsvQP/+Nv1ioo8ldhO1rrgCvv4akpK07emwYTqv7csXX2jflx9/hI8+0oVeY6KRJXYT1WrU0Br388/XqpPbbotM29OjHTigR9ldcom2Xp03D666KrIxGHMyLLGbqFeypNa49++vlTLnnqu7OyNh+XJo0ECv3aYNLFwI1apF5trGZJcldhMTEhN1ofLzz7WJ2DXXQLNm8NNP4bne1q3QsSOcc47Oq0+cqFNBBQuG53rGhFKOErtz7mbn3HLn3CHnXEqogjLmn5x/PixdqmWGM2fq6Pm+++D770Pz+lu36oajSpW0Lv2++2DFCrj++tC8vjGRkNMR+3dAc+DzEMRiTJbkzg1du+rB2G3awMiRULWqtib44IOTbyaWnq4Lo3feqS12e/bUI+yWLdNmXqeeGpa3YUzY5GjnqYisBHBW72U8KFNGR9W9e8PgwVpXPmmSTtVcfrlWsNSooV0kCxbUHwiJiXoIxrp1sGYNzJmjI/8dO/RQjHvv1Q1H1av7fnfGZJ+1FDAxr0QJnT7p3VtH3hMnag38hx+e+GvLlNH2BVdcoZUuBQqEP15jwu2Eid05NwMoeYz/1UNEJmf1Qs65dkA7gHLlymU5QGOyKilJ2wA3aqQj+N27YeVKWL1ap2cOHNDOkSVKQIUKUL68HjJsD5wmaJyEYMeHc24O0EVEUrPy+SkpKZKamqVPNcYYk8k5t1BETlioYuWOxhgTMDktd7zBOZcGNAA+ds5NC01YxhhjsiskUzEnfVHntgLrs/nlxYFtIQwnFth7jg/2nuNDTt7zGSJywgJcL4k9J5xzqVmZYwoSe8/xwd5zfIjEe7Y5dmOMCRhL7MYYEzCxmNjf8B2AB/ae44O95/gQ9vccc3Psxhhjji8WR+zGGGOOwxK7McYETEwldudcE+fcaufcGudcN9/xhJtzrqxzbrZzbmVm3/vOvmOKBOdconNusXPuI9+xRIJzrrBzbrxzblXmv3UD3zGFm3Pu4cx7+jvn3BjnXLLvmELNOTfcObfFOffdUR8r6pyb7pz7IfO/RcJx7ZhJ7M65RGAIcBVQHbjVORf05qrpwKMiUg2oDzwQB+8ZoDOw0ncQETQI+FREqgLnEPD37pwrDTwIpIhIDSARaOk3qrAYATT5y8e6ATNFpDIwM/PPIRcziR2oB6wRkbUicgAYCzTzHFNYicgmEVmU+ftd6Dd8ab9RhZdzrgzQFHjTdyyR4JwrCFwEDAMQkQMist1vVBGRBOR1ziUB+YBfPMcTciLyOfD7Xz7cDBiZ+fuRQFjO5oqlxF4a2HjUn9MIeJI7mnOuPFALmO83krB7GXgMOOQ7kAipCGwF3sqcfnrTOXeK76DCSUR+Bp4HNgCbgB0i8pnfqCKmhIhsAh24AaeF4yKxlNiP1TU7Lmo1nXP5gQ+Ah0Rkp+94wsU5dw2wRUQW+o4lgpKA2sCrIlIL2EOYHs+jRea8cjOgAnA6cIpz7g6/UQVLLCX2NKDsUX8uQwAf3/7KOZcLTeqjRWSC73jCrCFwnXNuHTrV1tg5N8pvSGGXBqSJyOEnsfFoog+yy4CfRGSriBwEJgDne44pUjY750oBZP53SzguEkuJfQFQ2TlXwTmXG11smeI5prByepjsMGCliLzoO55wE5HuIlJGRMqj/76zRCTQIzkR+RXY6JyrkvmhS4EVHkOKhA1Afedcvsx7/FICvmB8lClA68zftwayfArdyYiZM09FJN051xGYhq6iDxeR5Z7DCreGQCtgmXNuSebHnhCRTzzGZEKvEzA6c8CyFrjLczxhJSLznXPjgUVo5ddiAthawDk3BrgEKJ55bsVTwDPAOOdcW/QH3M1huba1FDDGmGCJpakYY4wxWWCJ3RhjAsYSuzHGBIyXxdPixYtL+fLlfVzaGGNi1sKFC7dl5czTkCR259xw4PDmkhon+vzy5cuTmpoaiksbY0zccM6tz8rnhWoqZgR/b3ZjjDHGg5CM2EXk88xeJmH17bfw669QrJj+KlEC8uYN91WN8WfnTvj5Z9i0Sf98yimQPz+UKwcFCviNzUSviM2xO+faAe0AypUrl63XePVVeO21I39OTISaNaFBA7joIrj2Wr3xjYlVy5fDzJkwezZ88QX89tuxP885qFwZatWCSy6BG2+EU08482riRcg2KGWO2D/Kyhx7SkqKZGeOPS0N1q3Tm/2332DtWpg3D+bPh927NanfdBO0aQMXX6w3vzHRbtcuGDMG3ngDFma2P6tYUe/hatWgdGkoVUrv5z179F7//ntYtEg/f+NGHeQ0bqz3/i23QFLM7Ck3J8M5t1BEUk74ebGU2P9JRgZ8/TW8/TaMG6ePrw0awNNPw6WXWoI30WnXLnjxRXjhBf19zZpw771w3XVwxhlZew0RnaIcNw7eew9+/FF/KHTrBnfeCXnyhPc9mMjKamIPRB17YiJceCH85z86B//aazqKufxyaNQIli3zHaExR6Snw5AhUKkS9OoFV1wBc+fC0qXQqVPWkzrooOWcc6BfPx3FT5oERYtCu3Y62p86NWxvw0SxkCT2zGY3c4Eqzrm0zAY3XuTNC/fdB2vWwL//DStWQO3a8NRTsH+/r6iMUStXQsOG0LGjJt5582D8eKhfP+dPlgkJ0KwZfPONJvTcueHqq6FFC/gl8A2uzdFCkthF5FYRKSUiuTLbrg4LxevmRJ48+s2zYgW0bKnTMrVr62OrMZGWkaFTLrVq6aBjzBhdID3vvNBfyzlo0kSfAPr0gcmToUYNmBLoJtfmaIGYijme4sXhnXfgk0/gjz90ZPTOO76jMvFk+3adN+/SRRPu8uU62Aj32k+ePNCzp05FVqigo/kuXeDgwfBe1/gX+MR+2FVXweLFUK+eLip16AAHDviOygTdihV6z332GQwdChMnQsmSkY2hcmX46iu95194Qcsjt4Tl3B4TLeImsYNuaJoxA7p21Zr4q6/WChpjwmHqVJ1q2blTp13uv99fhVZysi7Yjh2rZZINGuhiqwmmuErsoPW9AwfCyJEwZ46OXjZv9h2VCZoxY3T6pXJlSE2FCy7wHZFq0UJ/yOzcCeefr2XCJnjiLrEfdued8OGHsHq13uA//ug7IhMUQ4fC7bdr9cucOVCmjO+I/lf9+lpeWaSI7vOYPt13RCbU4jaxg867z54NO3Zovfvatb4jMrFu4EB44AFtbzF1KhQs6DuiY6tUSUfr//d/Guu0ab4jMqEU14kddGFr5kzdqt2okbYsMCY7Bg2Cxx/XipcPPoj+BnWnnqr3ftWqWjHz6ae+IzKhEveJHXTn3vTpOu/YqBFs2OA7IhNrXn8dHnoImjfX1hax0quleHFN7tWqaXKfPdt3RCYULLFnql1bk/sff8CVV/5zVz1j/mrUKGjfHpo21UXTXLl8R3RyihXTarFKlTS5L17sOyKTU5bYj5KSorv01q7Vioa9e31HZKLdjBlw1136pDd+vG7jj0XFiuk8e+HCuolqzRrfEZmcsMT+FxdfDKNHa9XArbdqwyZjjuXbb3XqpVo13XiUnOw7opwpU0Y3UmVkaGMyKwOOXZbYj+Gmm2DwYB29P/SQ72hMNEpL0w1uBQtqu4pChXxHFBpVq+r7+fVXuOEG2LfPd0QmOyyx/4OOHbWvxpAhukvVmMP27NESwV27NAlGW516TtWrpwvAc+dC27ba893EFkvsx/HMM7og1qkTzJrlOxoTDUR0Tn3pUt2ef/bZviMKj5tugr594d13oX9/39GYk2WJ/TgSE/XGrlJFb3RbUDL9+sH778Ozz+oGtyB74gm44w7tEDlpku9ozMmwxH4CBQtq64GEBLj+en0MN/FpyhT417802XXp4jua8HNOTyWrW1dbcKxe7Tsik1WW2LOgYkWtT16xQo8csznH+PPDD9CqlZbEvvFG/Jyjm5ysu2jz5NEKoF27fEdkssISexZdfrmeRvPuu7qgauLH3r06FZeUpLXq0d4qINTKltWDsletgrvvtoFNLLDEfhK6d4drroGHH7Z2p/HkgQf0FKJRo07uoOkgadxYiwnGj9eeOCa6WWI/CQkJWgZWrpz2tba2A8E3fDi89ZYuIAZ9sfREunTRdabHHoMFC3xHY47HEvtJKlJEH0s3b7bH0qBbvlxH65deCk895Tsa/5zTH3Snnw633KJnuZroZIk9G1JStO/2lCm6Q9UEz59/6lNZwYI6BZOY6Dui6FCkiNbvp6XZ5qVoZok9mzp31kZhXbvCwoW+ozGh9sgjOmJ/553IHz4d7erXhwEDYMIE25UdrSyxZ9Phx9ISJfRghd27fUdkQuX997W/+mOPaTMs83ePPKJrDo8+qj8ATXSxxJ5nCKzeAAAQr0lEQVQDxYppJ8gff7RmYUGxcaPuVahXT7fUm2NLSNBF5YIFtQuqNQuLLpbYc+iii7QMctgw3chhYldGhu6wTE/X/QqxdmBGpJUoocl92TLo1s13NOZolthDoFcvXVC9915dVDKx6YUXYM4cXRA/80zf0cSGq6/WJnmDBtmZqdHEEnsI5MqlI7z9+6F1azh0yHdE5mQtWqS16jfeCG3a+I4mtgwcCDVqaNdL29sRHSyxh0jlyjpqmTXLSiBjzd692tjr1FN10TRe+sCESnKyloT+9hvcd5+VQEYDS+wh1LatHsDQrZtVCsSS7t1h5UoYMUIXxM3JO+ccXWz+4ANN8sYvS+whdLjNaYEC2gnwwAHfEZkTmTVLn7Q6ddJGbyb7Hn0ULrxQTx9bv953NPHNEnuIlSihyX3xYnj6ad/RmOPZvl3n06tU0QZXJmcSE7WXkoitNflmiT0Mrr9eE8aAATBvnu9ozD958EH45RfdXZovn+9ogqF8eXj5Zfjvf22tySdL7GEyaJAecty6tfYdMdFl4kRN6D166AlBJnTuukvbW3fvrj3cTeRZYg+TggV188b33+sNbqLHli1avVG7tpY4mtA6vNZ0yilHNnyZyLLEHkaNG+ui3ODBMHu272gM6Pzv/ffDjh06H2y7S8OjZEltELZggU5JmsgKSWJ3zjVxzq12zq1xztnm4qM884zWuLdpAzt3+o7GvPuudiXs2xfOOst3NMF2883aR+bpp2HJEt/RxJccJ3bnXCIwBLgKqA7c6pyrntPXDYp8+XRkmJam5WDGn59/1lK888/X7oQm/F55BYoX1ymZ/ft9RxM/QjFirwesEZG1InIAGAs0C8HrBkb9+tq3/c034ZNPfEcTn0Tgnnt0b8GIEXZwRqQULar3/bJl0Lu372jiRygSe2lg41F/Tsv82P9wzrVzzqU651K3bt0agsvGlt699dH/nnvg9999RxN/hg3TJlXPPqtTYyZymjbVSplnn4X5831HEx9CkdiP1Vnjb90iROQNEUkRkZRTTz01BJeNLXny6JTM1q1aP20iZ906ePhhaNQIOnTwHU18euklKF1ap2Ss/Df8QpHY04CyR/25DPBLCF43cA6X140ebb3bI+XQIT10/PCJVwlWB+ZFoUL69//997p3wIRXKG7zBUBl51wF51xuoCUwJQSvG0hPPKEJvn17rac24fXKK1pq+uKLuivS+HPZZfDAA7ozdc4c39EEW44Tu4ikAx2BacBKYJyIWG/Df5Arl07J7NxpLU7DbfVqePxxPZuzbVvf0RjQefYzz9Q59127fEcTXCF5MBWRT0Tk/0TkTBHpF4rXDLKzztI66kmTdFrGhF56urZzyJtXqzKsx3p0OOUUGDlSuz9a+W/42IyjJ488Ag0bal21HacXegMHagXG0KFw+um+ozFHa9gQunTRtgNW/hseTjzMBaSkpEhqamrErxtt1qyBc8+FBg1g2jRb2AuVpUu1sdcNN8B77/mOxhzLvn36b7RtG3z3nR1wklXOuYUiknKiz7NU4lGlSnqA8owZMGSI72iCYd8+PeauWDEdrZvolJys3TV/+01799haU2hZYvesXTs96f2xx6zFaSj07KkjwOHDbRQY7c49Vzfuvf8+jB3rO5pgscTumXO6uHfKKXqc3sGDviOKXYfLGu+/XythTPTr2lWnIjt0gI0bT/z5JmsssUeBUqXg9dchNRX69PEdTWzasUM7aFaqBM895zsak1VJSTolc/CgHacXSpbYo8SNN2pi6tcPvvzSdzSxp0MH7d749tv69GNix5lnHjmz4MUXfUcTDJbYo8jgwbo7slUrHYGarBk9WvusP/mkdtI0seeuu6B5c92ZvXSp72hinyX2KFKgAIwapXONHTv6jiY2/PSTzqk3bKhJwcQm53Q6snhxuO02axSWU5bYo0yDBlrZMWqU7Uo9kfR0LW10Tv++kpJ8R2Ryonhx3ZW6YoUdhJJTltijUM+eOgJt3143MZlj69MHvv4aXnvNGnwFxeWXa+nv669bB9ScsMQehZKSdM44Vy5o0cKOFDuWWbM0sbduredqmuDo2xfq1dNDadav9x1NbLLEHqXKldNNNosWQffuvqOJLps3w+23Q5UqtmM3iHLlgjFjtPTxtttsb0d2WGKPYtdfD5066ekzH37oO5rocOiQVg1t3w7jxllpY1BVrAhvvKFTbXYwx8mzxB7lBg7UgznuvBPWrvUdjX/9+sH06VoaWrOm72hMOLVoofsTnntOW1ybrLPEHuWSk2H8eK38uOkm2LvXd0T+TJsGTz2lj+f33OM7GhMJL74IKSm6ec8GNllniT0GVKig264XL9apmXi0bp0m9Bo19BHdDs6ID3nyaJOwhAQb2JwMS+wxomlTLYMcNkwPKIgn+/Zpy4WMDJgwwebV40358jqwWbJEu6Fai98Ts8QeQ3r1giuv1AOB46WfjIieDbtokfaBqVTJd0TGh6ZN4emndSOa9ZM5MUvsMSQxUftWV6igfTU2bPAdUfg995wm9F694LrrfEdjfOrRQ6djHnsMPvvMdzTRzRJ7jClcGKZMgQMHNNHt2eM7ovCZMgW6dYNbbtEGXya+OQdvvaWHwbdoAatX+44oellij0FVqujIfdky3aiTkeE7otA7/N7q1NFvZlssNQD588PkyZA7tx6msmWL74iikyX2GNWkCQwapDd5p07BWlDasEG/aQsW1PrlfPl8R2SiSYUKumHv11/h2mutE+SxWGKPYR076nzjq6/CM8/4jiY0fvtNf2jt3g1Tp0Lp0r4jMtGoXj1tO7BggZbBBvGpNScssce4AQN0yuKJJ3TKIpb9+Sdcc41uRJk8Gc4+23dEJpo1a6Y7kCdP1jJIO1bvCOtgHeMSErRZ2ObNuhszb15o2dJ3VCfvcK36N9/ohpSLL/YdkYkFHTvC1q1aCpkvnyZ6W4+xxB4IuXPrXPTVV+vBE7lyaZKMFfv2afnmp5/Cm2/q743Jql69tDrshRd089qAAZbcbSomIE45BT76CM47T0fssdI0af9+/SE0daruqG3b1ndEJtY4p/sd2reHZ5/VNtdBKibIDkvsAVKggCbIOnV0I8fIkb4jOr7du7U18SefaP8Xa+xlsss57c1/332a3Dt0iO85d5uKCZiCBbWtbfPm2hFvyxbo2tV3VH+3ebNuE1+yRPvf3H2374hMrEtI0AqxIkW0SmzHDh3c5MrlO7LIs8QeQAUK6LRM69ZaDvnLL/qoGi2HPf/wg/a82bxZKxqaNvUdkQkK53SOvVAhnZLZtEkX44sX9x1ZZNlUTEDlyaPnpnbuDC+/rIl061bfUR1ZB9i1C2bPtqRuwqNbN+0IOXcu1K0L337rOyL4/ns9rHvjxvBfyxJ7gCUkaFIfMQK++koPLEhN9RNLerqOoK69Vtuwzp+vm0yMCZc77oDPP9e+Sg0aaKL3tag6erSufS1aFJkDQyyxx4HWrTWxg97gvXrpzR4p338PjRrpvOe99+o5lhUrRu76Jn7Vq6e7U2vV0uMlb74Ztm2L3PV37dJKrzvugHPP1TWlSOzRsMQeJ+rU0ROYWraE3r318XThwvBe88ABPaP07LO1qdfbb2v1S3JyeK9rzNFOPx3++18dWEyZoqdwvfdeeEfvItqor2pV3RHeo4dOPZYtG75rHs0SexwpWlQfR6dM0fn2unW1HcGaNaG9TkaGfuOce66e+tSsGaxcCa1ahfY6xmRVYiI8/rhORZYqpQOcBg3Cc2BNaqrOpd96K5QsCfPmQd++kS1eyFFid87d7Jxb7pw75JxLCVVQJryuvRZWrNAFpkmToFo1rSFPTc3ZKGbvXp1LrFnzSFuDDz/UJF+qVGhiNyYnzj5b7/Phw3UR88ILtbBg8mRdB8ouEZgxAy677MjT8CuvaIsML2tJIpLtX0A1oAowB0jJ6tfVqVNHTHTYtEnkgQdEkpNFQKRmTZHnnhNZuFAkPf3EX799u8jUqSJt2ogUKKCvUb26yNixWft6Y3zZvVukf3+R0qX1vi1bVuTxx0VmzBDZu/fEX3/woMhXX4k88ohI+fL6GqVKiQwcKLJjR3hiBlIlCznWSQgmmpxzc4AuIpKlmouUlBRJ9VWeYY5p+3YdWQ8frqMM0Hr4evWgTBk47TQoVkx7cmzfrgtQixYdOcWmYEFtDXDbbdC4sVbkGBML0tO1DPfVV2HWLP1zcjKccw6ccYb+KlxY21/s26d94Jct06fe/ft1A9Tll+tJXy1baqlxuDjnForICWdHIpbYnXPtgHYA5cqVq7N+/focX9eEx8aN8MUX+mvhQr2RN2/WxVDnNIkXKaJTLnXravK/6CLtLGlMLNu9WxdaZ8yA776D9ev14Jf9+/X/58mja1U1a+q0Tp06eihMoUKRiS9kid05NwMoeYz/1UNEJmd+zhxsxB5oItovPW9eG42b+HLoEBw8qF1UfXeNzGpiP+E6rYhcFpqQTCxzTjtIGhNvEhLCO70SDjb2MsaYgMlpueMNzrk0oAHwsXNuWmjCMsYYk10hWTw96Ys6txXI7uppcSCCm4Kjgr3n+GDvOT7k5D2fISKnnuiTvCT2nHDOpWZl8SBI7D3HB3vP8SES79nm2I0xJmAssRtjTMDEYmJ/w3cAHth7jg/2nuND2N9zzM2xG2OMOb5YHLEbY4w5jphK7M65Js651c65Nc65br7jCTfnXFnn3Gzn3MrM9sidfccUCc65ROfcYufcR75jiQTnXGHn3Hjn3KrMf+sGvmMKN+fcw5n39HfOuTHOucAdv+KcG+6c2+Kc++6ojxV1zk13zv2Q+d8i4bh2zCR251wiMAS4CqgO3Oqcq+43qrBLBx4VkWpAfeCBOHjPAJ2Blb6DiKBBwKciUhU4h4C/d+dcaeBBtNV3DSARaOk3qrAYATT5y8e6ATNFpDIwM/PPIRcziR2oB6wRkbUicgAYCzTzHFNYicgmEVmU+ftd6Dd8ab9RhZdzrgzQFHjTdyyR4JwrCFwEDAMQkQMist1vVBGRBOR1ziUB+YBfPMcTciLyOfD7Xz7cDBiZ+fuRwPXhuHYsJfbSwMaj/pxGwJPc0Zxz5YFawHy/kYTdy8BjwCHfgURIRWAr8Fbm9NObzrlAt1sTkZ+B54ENwCZgh4h85jeqiCkhIptAB27AaeG4SCwl9mM1zIyLkh7nXH7gA+AhEdnpO55wcc5dA2wRkTAfsx1VkoDawKsiUgvYQ5gez6NF5rxyM6ACcDpwinPuDr9RBUssJfY04OgzvssQwMe3v3LO5UKT+mgRmeA7njBrCFznnFuHTrU1ds6N8htS2KUBaSJy+ElsPJrog+wy4CcR2SoiB4EJwPmeY4qUzc65UgCZ/90SjovEUmJfAFR2zlVwzuVGF1umeI4prJxzDp17XSkiL/qOJ9xEpLuIlBGR8ui/7ywRCfRITkR+BTY656pkfuhSYIXHkCJhA1DfOZcv8x6/lIAvGB9lCtA68/etgcnhuMgJD9qIFiKS7pzrCExDV9GHi8hyz2GFW0OgFbDMObck82NPiMgnHmMyodcJGJ05YFkL3OU5nrASkfnOufHAIrTyazEB3IHqnBsDXAIUz2xv/hTwDDDOOdcW/QF3c1iubTtPjTEmWGJpKsYYY0wWWGI3xpiAscRujDEBY4ndGGMCxhK7McYEjCV2Y4wJGEvsxhgTMJbYjTEmYP4f3DdIcXGyliEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First create a grid of plots\n",
    "# ax will be an array of two Axes objects\n",
    "fig, ax = plt.subplots(2)\n",
    "\n",
    "\n",
    "# Call plot() method on the appropriate object\n",
    "ax[0].plot(x1, np.sin(x1), 'b-')\n",
    "ax[1].plot(x1, np.cos(x1), 'b-');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Objects and Reference\n",
    "\n",
    "\n",
    "The main idea with the **Object Oriented API** is to have objects that one can apply functions and actions on. The real advantage of this approach becomes apparent when more than one figure is created or when a figure contains more than one \n",
    "subplot.\n",
    "\n",
    "\n",
    "We create a reference to the figure instance in the **fig** variable. Then, we ceate a new axis instance **axes** using the \n",
    "**add_axes** method in the Figure class instance fig as follows:-\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nPhGpb8ECOPVUWLcubDx41lmxE0me0UhDRIJJk+DII8MajLlzVRjSIJWGSKmrrYWrroLzzoNDD4X586Fbt9ipJE8lVhpmNsHMVpvZm/Uea29mM8xsaebP3ZI6vohk4dNPoX9/uOkmGDYMnnsO9tgjdirJY0mONO4j3HujviuBme7eBZiZ+VpEYnjnHejZE559FsaOhXHjoGXL2KkkzyVWGu4+G/hoi4cHAJWZzyuBU5M6vohsw9NPQ48eYcJ75kz413+NnUgKRNpzGnvWu+/4h4DGwSJpcoff/z7cy7u8POwnpW3NpRHydiLczIaaWbWZVa9ZsyZ2HJHCt3EjDBoEI0fCGWeEBXvf/nbsVFJg0i6NVWbWESDz5+qtvdDdx7t7hbtXlJWVpRZQpCitWBFGFA8+CDfcENZgtG4dO5UUoLRLYyrhhk5k/nwi5eOLlJ65c6GiItw86Yknwipvs9ippEAlecntJKAKONDMVpjZhcBNQB8zWwr0yXwtIkmZMAF694Y2beDll8PltSJNkNg2Iu5+7laeOjapY4pIxubNcPnlcMcd0KcPPPQQtG8fO5UUgbydCBeRHbRuXdjO/I474LLLYPp0FYbkjDYsFCkmb7wBAwbABx/AfffB4MHb/RaRxtBIQ6RYTJkChx8OmzaFW7KqMCQBKg2RQldXB7/9LZx+Ohx0UFiw17Nn7FRSpHR6SqSQbdgAQ4bA5MlwwQXwpz9Bq1axU0kRU2mIFKq//z3MXyxeDLfeCpdeqvUXkjiVhkghmjULzjwz3Avjqaegb9/YiaREaE5DpJC4w513hrUXe+wB8+apMCRVKg2RQlFTA0OHwiWXwIknhhXeXbrETiUlRqUhUghWrYJjjoF77gl7Rz3+OOy6a+xUUoI0pyGS7xYsgFNPDSu9H34YzjordiIpYRppiOSzSZPgyCOhWbOwW60KQyJTaYjko9pauPJKOO88OPRQmD8funWLnUpEpSGSd5Ytg2OPhZtvhmHD4LnnwpVSInlApSGSL+rq4K674Ac/gNdeC/fCGDcOWraMnUzkK5oIF8kHy5bBhRfC88+Hbc3vvhv23Td2KpFv0EhDJKb6o4tXXw2X1D71lApD8pZGGiKxaHQhBUgjDZG0aXQhBUwjDZE0aXQhBU4jDZE0aHQhRUIjDZGkaXQhRUQjDZGkaHQhRUgjDZEkaHQhRUojDZFc0uhCipxGGiK5otGFlACNNESaSqMLKSEaaYg0hUYXUmI00hDZERpdSInSSEOksTS6kBKmkYZItjS6ENFIQyQrGl2IABppiGybRhci/yTKSMPMlgPrgVpgs7tXxMghsk0aXYh8Q8zTU73dfW3E44s0rK4Oxo6FX/8amjcPo4uf/hTMYicTiU5zGiL1aXQhsk2x5jQceNbMFpjZ0IZeYGZDzazazKrXrFmTcjwpORs2wI03au5CZDtijTSOcPeVZrYHMMPM3nb32fVf4O7jgfEAFRUVHiOklICNG8OpqN/9DtauhQED4I9/VFmIbEWUkYa7r8z8uRqYAvSIkUNKWE0NjBsHBxwAl18O3brByy/D44+rMES2IfXSMLPWZtb2y8+BvsCbaeeQElVbC5WV8C//AhdfDOXlMGsWzJgBPXvGTieS92KMNPYEXjSzRcA84El3fzpCDikldXXw3/8NBx8MQ4bAbrvB9Onw4otw9NGx04kUjNTnNNx9GfDDtI8rJcodnnwS/v3fYeFC+N73YPJkOO00XUIrsgO0IlyK18yZ8OMfwymnwPr18MAD8PrrcPrpKgyRHaTSkOJTVQXHHAPHHQcrVsD48bBkCZx/flisJyI7TKUhxeO11+Dkk8PoYvFiGDMGli6Fiy6CFi1ipxMpCioNKXxLlsCZZ8Ihh8DcuWHNxbJlMGIEtGoVO51IUdE2IlK4li2D66+HiRNhl11g1Ci47DJo1y52MpGipdKQwrNiBdxwA9x7L+y0E/zqV2FzwQ4dYicTKXoqDSkcq1eHU09jx4Z1F8OGwdVXQ8eOsZOJlAyVhuS/jz+GW26B228Pe0UNHhxORZWXx04mUnJUGpK/1q8PRXHLLfDpp3DOOXDddXDggbGTiZQslYbkny13nu3fH/7jP8K25SISlS65lfxRUxPK4sudZ7t3h1degSeeUGGI5AmVhsRXf+fZn/8cOncOd8579lnooV3zRfKJSkPiWbcO7rzz651n27cPd8ubMweOOip2OhFpgOY0JF01NaEYKith2jT44otwGko7z4oUBJWGJM893He7shImTQqT23vuCZdcAhdcAD/UTvkihUKlIclZuRIefDCUxeLF0LJluAf34MFw/PFhNbeIFBT9Xyu5tXFjuM92ZWW4hWpdHRx+eLgq6uyzwx3zRKRgqTSk6dzDbVPvvx8eeQQ++wz22w+uuiqcfuraNXZCEckRlYbsuL//PRTF/feHHWdbt4aBA8Ppp6OOgma6OE+k2Kg0pHE++wwefTScfpo9O1zt1Ls3XHttuI1qmzaxE4pIglQasn21teF+25WVMGVKmLfo0iVsTz5oUDgVJSIlQaUhW/fWW6EoJk4MV0K1axdOPQ0eDD17ak2FSAlSacg/W7curKWorITqamjeHE44Idxv+5RTdPtUkRKn0pCGV2n/8Idw661w3nlhIZ6ICCqN0qVV2iKyA1QapcIdli+Hqip4+eUwsf3WW1qlLSKNop8QxWrjxjAnUVX19ceqVeG5XXYJE9mXXKJV2iLSKCqNYrDlKKKqChYuhM2bw/MHHAB9+8Jhh4UtPb7/fY0oRGSH6CdHIdreKKJHD7jiilAQhx0GZWVx84pI0VBp5Lv6o4gvRxIaRYhIJPrpkm80ihCRPKbSiGnLUURVFSxapFGEiOQt/QRKk0YRIlLgopSGmfUDbgeaA/e4+00xcuwwd/j887DlRmM+Pv30679DowgRKUCp/5Qys+bAXUAfYAUw38ymuvtbaWcBwqmgjz7K7od+/dfV1Gz979x1V9h9968/unQJf5aVQbduGkWISMGK8attD+Cv7r4MwMweAgYAuS+N9evDDYKy/e1/Sy1ahB/27duHPw84ICyKq18IW360bx++T0SkCMUojX2A9+t9vQLoueWLzGwoMBRgvx29X8PGjTB8ePh8a7/9b+ujTRtt/y0iUk+M0mjop7B/4wH38cB4gIqKim88n5UOHeAf/9Bv/yIiORKjNFYA+9b7uhOwMpEjNWumbb1FRHKoWYRjzge6mFlnM2sJnANMjZBDREQaKfWRhrtvNrPhwDOES24nuPvitHOIiEjjRVkY4O7Tgekxji0iIjsuxukpEREpUCoNERHJmkpDRESyptIQEZGsqTRERCRr5r5ji63TZGZrgHeb8Fd0ANbmKE4hKdX3DXrveu+lp6nv/dvuvt2dVAuiNJrKzKrdvSJ2jrSV6vsGvXe999KT1nvX6SkREcmaSkNERLJWKqUxPnaASEr1fYPee6nSe09YScxpiIhIbpTKSENERHJApSEiIlkr6tIws35m9hcz+6uZXRk7T1rMbIKZrTazN2NnSZuZ7Wtms8xsiZktNrMRsTOlxcxamdk8M1uUee/Xx86UJjNrbmavmdm02FnSZGbLzewNM1toZtWJH69Y5zTMrDnwDtCHcLfA+cC57v5W1GApMLNewAbgfnc/OHaeNJlZR6Cju79qZm2BBcCpJfLf3YDW7r7BzFoALwIj3P3lyNFSYWa/AiqAXd395Nh50mJmy4EKd09lUWMxjzR6AH9192XuXgM8BAyInCkV7j4b+Ch2jhjc/UN3fzXz+XpgCbBP3FTp8GBD5ssWmY/i/K1wC2bWCTgJuCd2lmJXzKWxD/B+va9XUCI/PCQws3KgO/BK3CTpyZyiWQisBma4e6m89zHASKAudpAIHHjWzBaY2dCkD1bMpWENPFYSv3UJmFkbYDJwqbt/FjtPWty91t27AZ2AHmZW9KcnzexkYLW7L4idJZIj3P0Q4ATgF5nT04kp5tJYAexb7+tOwMpIWSRFmfP5k4EH3f2x2HlicPdPgOeBfpGjpOEIoH/m3P5DwDFmNjFupPS4+8rMn6uBKYRT84kp5tKYD3Qxs85m1hI4B5gaOZMkLDMZfC+wxN1vjZ0nTWZWZmbtMp/vDBwHvB03VfLc/Sp37+Tu5YT/z//s7udHjpUKM2udueADM2sN9AUSvWqyaEvD3TcDw4FnCJOhj7j74rip0mFmk4Aq4EAzW2FmF8bOlKIjgEGE3zYXZj5OjB0qJR2BWWb2OuGXphnuXlKXn5agPYEXzWwRMA940t2fTvKARXvJrYiI5F7RjjRERCT3VBoiIpI1lYaIiGRNpSEiIllTaYiISNZUGiI5ZmbdzKwqs9Ps62Z2duxMIrmiS25FcszMuhL2D1xqZnsTdtr9bmaVtkhB00hDpAnM7NDMaKJVZnXuYqCluy+Fr7Z4WA2URQ0qkiMaaYg0kZndALQCdgZWuPvv6j3XA6gEDnL3UtyBVYqMSkOkiTJ7m80HNgE/dvfazOMdCZsGDi6VGyFJ8dPpKZGmaw+0AdoSRhyY2a7Ak8A1KgwpJhppiDSRmU0lbMndmbBp4K+Ap4D/cfcxMbOJ5NpOsQOIFDIzuwDY7O7/L3Nf+rmE7bl7Abub2ZDMS4e4+8JIMUVyRiMNERHJmuY0REQkayoNERHJmkpDRESyptIQEZGsqTRERCRrKg0REcmaSkNERLL2/wHzqsxR7sebYQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "x2 = np.linspace(0, 5, 10)\n",
    "y2 = x2 ** 2\n",
    "\n",
    "axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "\n",
    "axes.plot(x2, y2, 'r')\n",
    "\n",
    "axes.set_xlabel('x2')\n",
    "axes.set_ylabel('y2')\n",
    "axes.set_title('title');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure and Axes\n",
    "\n",
    "\n",
    "I start by creating a figure and an axes. A figure and axes can be created as follows:\n",
    "\n",
    "\n",
    "`fig = plt.figure()`\n",
    "\n",
    "`ax = plt.axes()`\n",
    "\n",
    "\n",
    "\n",
    "In Matplotlib, the **figure** (an instance of the class plt.Figure) is a single container that contains all the objects representing axes, graphics, text and labels. The **axes** (an instance of the class plt.Axes) is a bounding box with \n",
    "ticks and labels. It will contain the plot elements that make up the visualization. I have used the variable name fig \n",
    "to refer to a figure instance, and ax to refer to an axes instance or group of axes instances.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "ax = plt.axes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10. Figure and Subplots\n",
    "\n",
    "\n",
    "\n",
    "Plots in Matplotlib reside within a Figure object. As described earlier, we can create a new figure with plt.figure() \n",
    "as follows:-\n",
    "\n",
    "\n",
    "`fig = plt.figure()`\n",
    "\n",
    "\n",
    "Now, I create one or more subplots using fig.add_subplot() as follows:-\n",
    "\n",
    "\n",
    "`ax1 = fig.add_subplot(2, 2, 1)`\n",
    "\n",
    "\n",
    "The above command means that there are four plots in total (2 * 2 = 4). I select the first of four subplots (numbered from 1).\n",
    "\n",
    "\n",
    "I create the next three subplots using the fig.add_subplot() commands as follows:-\n",
    "\n",
    "\n",
    "`ax2 = fig.add_subplot(2, 2, 2)`\n",
    "\n",
    "`ax3 = fig.add_subplot(2, 2, 3)`\n",
    "\n",
    "`ax4 = fig.add_subplot(2, 2, 4)`"
   ]
  },
  {
   "attachments": {
    "Subplots.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above command result in creation of subplots. The diagrammatic representation of subplots are as follows:-\n",
    "\n",
    "\n",
    "![Subplots.png](attachment:Subplots.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating empty matplotlib figure with four subplots\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "ax1 = fig.add_subplot(2, 2, 1)\n",
    "ax2 = fig.add_subplot(2, 2, 2)\n",
    "ax3 = fig.add_subplot(2, 2, 3)\n",
    "ax4 = fig.add_subplot(2, 2, 4)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concise representation of Subplots\n",
    "\n",
    "\n",
    "There is a concise form by which we can represent Subplots. Matplotlib includes a convenience method plt.subplots() that \n",
    "creates a new figure and returns the subplot objects. We can create subplots as follows:-\n",
    "\n",
    "`fig, axes = plt.subplots()`\n",
    "\n",
    "\n",
    "The above command creates a figure and one subplot. It is short and concise representation of the following commands:-\n",
    "\n",
    "\n",
    "`fig = plt.figure()`\n",
    "\n",
    "`ax1 = fig.add_subplot(1, 1, 1)`\n",
    "\n",
    "\n",
    "We get a reference to both figure and axis in a single step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAADYBJREFUeJzt3HGI33d9x/Hny8ROprWO5QRJou1YuhrKoO7oOoRZ0Y20fyT/FEmguEppwK0OZhE6HCr1rylDELJptolT0Fr9Qw+J5A9X6RAjudJZmpTALTpzROhZu/5TtGZ774/fT++4XHLf3v3uLt77+YDA7/v7fX6/e+fD3TO/fH/3+6WqkCRtf6/a6gEkSZvD4EtSEwZfkpow+JLUhMGXpCYMviQ1sWrwk3wuyXNJnrnC7Uny6SRzSZ5O8rbJjylJWq8hz/A/Dxy4yu13AfvGf44C/7T+sSRJk7Zq8KvqCeBnV1lyCPhCjZwC3pDkTZMaUJI0GTsn8Bi7gQtLjufH1/1k+cIkRxn9L4DXvva1f3TLLbdM4MtLUh9PPvnkT6tqai33nUTws8J1K35eQ1UdB44DTE9P1+zs7AS+vCT1keS/13rfSfyWzjywd8nxHuDiBB5XkjRBkwj+DPDe8W/r3AG8WFWXnc6RJG2tVU/pJPkycCewK8k88FHg1QBV9RngBHA3MAe8BLxvo4aVJK3dqsGvqiOr3F7AX01sIknShvCdtpLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDUxKPhJDiQ5l2QuycMr3P7mJI8neSrJ00nunvyokqT1WDX4SXYAx4C7gP3AkST7ly37O+CxqroNOAz846QHlSStz5Bn+LcDc1V1vqpeBh4FDi1bU8Drx5dvAC5ObkRJ0iQMCf5u4MKS4/nxdUt9DLg3yTxwAvjASg+U5GiS2SSzCwsLaxhXkrRWQ4KfFa6rZcdHgM9X1R7gbuCLSS577Ko6XlXTVTU9NTX1yqeVJK3ZkODPA3uXHO/h8lM29wOPAVTV94DXALsmMaAkaTKGBP80sC/JTUmuY/Si7MyyNT8G3gWQ5K2Mgu85G0m6hqwa/Kq6BDwInASeZfTbOGeSPJLk4HjZQ8ADSX4AfBm4r6qWn/aRJG2hnUMWVdUJRi/GLr3uI0sunwXePtnRJEmT5DttJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNDAp+kgNJziWZS/LwFda8J8nZJGeSfGmyY0qS1mvnaguS7ACOAX8GzAOnk8xU1dkla/YBfwu8vapeSPLGjRpYkrQ2Q57h3w7MVdX5qnoZeBQ4tGzNA8CxqnoBoKqem+yYkqT1GhL83cCFJcfz4+uWuhm4Ocl3k5xKcmClB0pyNMlsktmFhYW1TSxJWpMhwc8K19Wy453APuBO4AjwL0necNmdqo5X1XRVTU9NTb3SWSVJ6zAk+PPA3iXHe4CLK6z5RlX9sqp+CJxj9A+AJOkaMST4p4F9SW5Kch1wGJhZtubrwDsBkuxidIrn/CQHlSStz6rBr6pLwIPASeBZ4LGqOpPkkSQHx8tOAs8nOQs8Dnyoqp7fqKElSa9cqpafjt8c09PTNTs7uyVfW5J+UyV5sqqm13Jf32krSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSE4OCn+RAknNJ5pI8fJV19ySpJNOTG1GSNAmrBj/JDuAYcBewHziSZP8K664H/hr4/qSHlCSt35Bn+LcDc1V1vqpeBh4FDq2w7uPAJ4CfT3A+SdKEDAn+buDCkuP58XW/luQ2YG9VffNqD5TkaJLZJLMLCwuveFhJ0toNCX5WuK5+fWPyKuBTwEOrPVBVHa+q6aqanpqaGj6lJGndhgR/Hti75HgPcHHJ8fXArcB3kvwIuAOY8YVbSbq2DAn+aWBfkpuSXAccBmZ+dWNVvVhVu6rqxqq6ETgFHKyq2Q2ZWJK0JqsGv6ouAQ8CJ4Fngceq6kySR5Ic3OgBJUmTsXPIoqo6AZxYdt1HrrD2zvWPJUmaNN9pK0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqYlDwkxxIci7JXJKHV7j9g0nOJnk6ybeTvGXyo0qS1mPV4CfZARwD7gL2A0eS7F+27Clguqr+EPga8IlJDypJWp8hz/BvB+aq6nxVvQw8ChxauqCqHq+ql8aHp4A9kx1TkrReQ4K/G7iw5Hh+fN2V3A98a6UbkhxNMptkdmFhYfiUkqR1GxL8rHBdrbgwuReYBj650u1VdbyqpqtqempqaviUkqR12zlgzTywd8nxHuDi8kVJ3g18GHhHVf1iMuNJkiZlyDP808C+JDcluQ44DMwsXZDkNuCzwMGqem7yY0qS1mvV4FfVJeBB4CTwLPBYVZ1J8kiSg+NlnwReB3w1yX8mmbnCw0mStsiQUzpU1QngxLLrPrLk8rsnPJckacJ8p60kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNDAp+kgNJziWZS/LwCrf/VpKvjG//fpIbJz2oJGl9Vg1+kh3AMeAuYD9wJMn+ZcvuB16oqt8HPgX8/aQHlSStz5Bn+LcDc1V1vqpeBh4FDi1bcwj4t/HlrwHvSpLJjSlJWq+dA9bsBi4sOZ4H/vhKa6rqUpIXgd8Ffrp0UZKjwNHx4S+SPLOWobehXSzbq8bci0XuxSL3YtEfrPWOQ4K/0jP1WsMaquo4cBwgyWxVTQ/4+tuee7HIvVjkXixyLxYlmV3rfYec0pkH9i453gNcvNKaJDuBG4CfrXUoSdLkDQn+aWBfkpuSXAccBmaWrZkB/mJ8+R7g36vqsmf4kqSts+opnfE5+QeBk8AO4HNVdSbJI8BsVc0A/wp8Mckco2f2hwd87ePrmHu7cS8WuReL3ItF7sWiNe9FfCIuST34TltJasLgS1ITGx58P5Zh0YC9+GCSs0meTvLtJG/Zijk3w2p7sWTdPUkqybb9lbwhe5HkPePvjTNJvrTZM26WAT8jb07yeJKnxj8nd2/FnBstyeeSPHel9ypl5NPjfXo6ydsGPXBVbdgfRi/y/hfwe8B1wA+A/cvW/CXwmfHlw8BXNnKmrfozcC/eCfz2+PL7O+/FeN31wBPAKWB6q+fewu+LfcBTwO+Mj9+41XNv4V4cB94/vrwf+NFWz71Be/GnwNuAZ65w+93Atxi9B+oO4PtDHnejn+H7sQyLVt2Lqnq8ql4aH55i9J6H7WjI9wXAx4FPAD/fzOE22ZC9eAA4VlUvAFTVc5s842YZshcFvH58+QYuf0/QtlBVT3D19zIdAr5QI6eANyR502qPu9HBX+ljGXZfaU1VXQJ+9bEM282QvVjqfkb/gm9Hq+5FktuAvVX1zc0cbAsM+b64Gbg5yXeTnEpyYNOm21xD9uJjwL1J5oETwAc2Z7RrzivtCTDsoxXWY2Ify7ANDP57JrkXmAbesaETbZ2r7kWSVzH61NX7NmugLTTk+2Ino9M6dzL6X99/JLm1qv5ng2fbbEP24gjw+ar6hyR/wuj9P7dW1f9t/HjXlDV1c6Of4fuxDIuG7AVJ3g18GDhYVb/YpNk222p7cT1wK/CdJD9idI5yZpu+cDv0Z+QbVfXLqvohcI7RPwDbzZC9uB94DKCqvge8htEHq3UzqCfLbXTw/ViGRavuxfg0xmcZxX67nqeFVfaiql6sql1VdWNV3cjo9YyDVbXmD426hg35Gfk6oxf0SbKL0Sme85s65eYYshc/Bt4FkOStjIK/sKlTXhtmgPeOf1vnDuDFqvrJanfa0FM6tXEfy/AbZ+BefBJ4HfDV8evWP66qg1s29AYZuBctDNyLk8CfJzkL/C/woap6fuum3hgD9+Ih4J+T/A2jUxj3bccniEm+zOgU3q7x6xUfBV4NUFWfYfT6xd3AHPAS8L5Bj7sN90qStALfaStJTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ18f+GmWq6NWLIwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More than one Subplot\n",
    "\n",
    "\n",
    "We can use\n",
    "\n",
    "\n",
    "`fig, axes = plt.subplots(nrows = 2, ncols = 2)`\n",
    "\n",
    "\n",
    "to create four Subplots with grid(2, 2) in one figure object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a subplot with 2 rows and 2 columns\n",
    "\n",
    "fig, axes = plt.subplots(nrows = 2, ncols = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": [
    "# Create a subplot with 2 rows and 1 column\n",
    "\n",
    "fig, axes = plt.subplots(2, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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J/4ifAVaBrwOv3Ka6/wL8N/CN6Z/l7XrM69aO+Yux2WMO8LfAaeBbwKFt/DnvA74y/aX5BvAHI9T8FPA94GdMznjuBN4GvG3m8R6d9vStsf6dFznXi5ztRc31Imd7EXM9z9n2qxUkqQmvtJWkJgx8SWrCwJekJgx8SWrCwJekJgx8SWrCwJekJv4PcgCmcLyIQvoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a subplot with 1 row and 2 columns\n",
    "\n",
    "fig, axes = plt.subplots(1, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 11. First plot with Matplotlib\n",
    "\n",
    "\n",
    "Now, I will start producing plots. Here is the first example:-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "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([1, 3, 2, 4], 'b-')\n",
    "\n",
    "plt.show( )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`plt.plot([1, 3, 2, 4], 'b-')`\n",
    "\n",
    "This code line is the actual plotting command. Only a list of values has been plotted that represent the vertical coordinates of the points to be plotted. Matplotlib will use an implicit horizontal values list, from 0 (the first value) to N-1 (where N is the number of items in the list)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Specify both Lists\n",
    "\n",
    "\n",
    "Also, we can explicitly specify both the lists as follows:- \n",
    "\n",
    "\n",
    "`x3 = range(6)`\n",
    "\n",
    "\n",
    "`plt.plot(x3, [xi**2 for xi in x3])` \n",
    "\n",
    "\n",
    "`plt.show()`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "x3 = np.arange(0.0, 6.0, 0.01) \n",
    "\n",
    "plt.plot(x3, [xi**2 for xi in x3], 'b-') \n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 12.\tMultiline Plots\n",
    "\n",
    "Multiline Plots mean plotting more than one plot on the same figure. We can plot more than one plot on the same figure.  \n",
    "It can be achieved by plotting all the lines before calling show(). It can be done as follows:-\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAHvRJREFUeJzt3Xl0XOWdp/Hnp83YkjfJBq+yLGMM3hdh5JBgOpA0AYPBGGISkpB0jrvTk3TWhqTnzOTMzJnTWTqsWcDsSUhCmiTddDpJJ50eQgi2sWxsbNaAbYwX8CJbtiRrrXf+uLekUqkklVXLrVv1/ZyjkyrVFfVeXenJ6/feKplzDhERCb+ioAcgIiLpoaCLiOQJBV1EJE8o6CIieUJBFxHJEwq6iEieUNBFRPKEgi4ikicUdBGRPFGSzSebMGGCq6mpyeZTioiE3tatW4865yYOtV1Wg15TU0NDQ0M2n1JEJPTM7M1kttOSi4hInlDQRUTyhIIuIpInFHQRkTyhoIuI5Ikhg25mD5nZYTPbFfO5b5rZK2b2gpn9wszGZXaYIiIylGRm6I8AV8R97nfAfOfcQuA14CtpHpeIiJyhIYPunHsaaIz73G+dc13+3U3AtAyMTUQk/JoOwG++AqdPZPyp0vHCok8Ajw/0oJmtB9YDVFdXp+HpRERCoOkAPHMHbHsUXARmXgJzPpDRp0wp6Gb234Eu4LGBtnHObQA2ANTV1ekvUotIfosP+ZKb4T1fhHGZn9AOO+hm9jFgFXCZc06hFpHCdvKgF/Ktj/SG/N1fgPEzsjaEYQXdzK4AbgNWOuda0zskEZEQiQ/54g97M/IshjxqyKCb2Y+BS4EJZrYf+CreVS0jgN+ZGcAm59zfZHCcIiK55eRBeOZOP+TdgYY8asigO+duSvDpBzMwFhGR3HfyUMyMvBsWf8gPeU3QI8vu2+eKiITWyUPwpzuh4eGcC3mUgi4iMpjYkEe6ekNeOTPokfWjoIuIJHLqbX+N/GHo7szpkEcp6CIisfqF/CZ4z5dyOuRRCrqICHgh/9Nd0PBQ6EIepaCLSGGLD/mim+CSL0JlbdAjO2MKuogUplPv+CF/0A/5Om+NvGpW0CMbNgVdRApLHoY8SkEXkcLQfNgL+ZYHobsdFq6DS76UFyGPUtBFJL8VQMijFHQRyU/9Qv5BuOTv8zLkUQq6iOSX5iPw7F3w3AMFE/IoBV1E8kM05FsehK42WHCjF/IJ5wY9sqxR0EUk3JqPwLN3w5YHCjbkUQq6iIRTv5Df4Id8dtAjC4yCLiLh0nLUC/lz9yvkcRR0EQmH+JDPX+uFfOJ5QY8sZyjoIpLbWo7Cs/d4Ie9s7Z2RK+T9KOgikptajvXOyDtbYcFauORWhXwQCrqI5JaWY7DxHti8ISbkfw8T5wQ9spynoItIbogP+fzrYeWtCvkZUNBFJFgtx2Djt+G5DdDRopCnQEEXkWC0NvonO6MhX+OtkZ99ftAjCy0FXUSyq7XRm5Fvvs8L+bzrYOVtCnkaDBl0M3sIWAUcds7N9z9XCTwO1AB7gRudc8czN0wRCb2EIb8Vzr4g6JHljaIktnkEuCLuc18Gfu+cmw383r8vItJfayP8/v/AnQvhj7fD7PfBp56FGx5WzNNsyBm6c+5pM6uJ+/Rq4FL/9qPAU8BtaRyXiIRdayNs/I4/Iz/lzcgvuRXOmRv0yPLWcNfQz3HOHQJwzh0ys7PTOCYRCbPWRtj0Xdh0rxfyudd6a+QKecZl/KSoma0H1gNUV1dn+ulEJCgJQ34rnDMv6JEVjOEG/R0zm+zPzicDhwfa0Dm3AdgAUFdX54b5fCKSq04fh43fhc33QvtJmLvan5Er5Nk23KA/CXwM+Jr/v/+athGJSDgo5DknmcsWf4x3AnSCme0HvooX8p+a2V8B+4AbMjlIEckhp4/Dpu95H+0n4YJrvJBPmh/0yApeMle53DTAQ5eleSwikssU8pynV4qKyOBOn4gJeRNccLUf8gVBj0ziKOgikphCHjoKuoj0dfqEd6Jz43e9kJ+/ygv55IVBj0yGoKCLiEchDz0FXaTQtTV5Lwba9B3v9vmrvBcETV4U9MjkDCnoIoVKIc87CrpIoWlr8t4wa+O3vdtzroJLb1PI84CCLlIoEoV85a0wZXHQI5M0UdBF8l3byZiQn4A5V3onOxXyvKOgi+Sr+JCf9wFvaWXKkqBHJhmioIvkm7aT8Nx98KxCXmgUdJF80S/kV3hLK1OXBj0yyRIFXSTs2k7Ccxu8pZXTx2H2X8KlX1bIC5CCLhJW7ad618h7Qn4bTF0W9MgkIAq6SNi0n/Jm5M/eo5BLHwq6SFj0C/n7YeWXYZpCLh4FXSTXtZ+C5+73Q96okMuAFHSRXBUf8nPf553snFYX9MgkRynoIrmmvRm23A9/ulshlzOioIvkin4hv9xbWpl+YdAjk5BQ0EWC1t4MWx6AZ++G1mMKuQybgi4SlI4WL+R/ussL+azLvKWV6cuDHpmElIIukm09Ib8bWo8q5JI2CrpItnS0wJYH/Rn5UZj1Xm9ppfqioEcmeUJBF8k0hVyyJKWgm9nngU8CDtgJfNw515aOgYmEXkcLNDzkhbzlCNT+hbe0Ul0f9MgkTw076GY2Ffg7YK5z7rSZ/RRYBzySprGJhFNHKzQ8qJBL1qW65FICjDSzTmAUcDD1IYmEVEerPyO/0w/5pd7SyowVQY9MCsSwg+6cO2Bm/wTsA04Dv3XO/TZtIxMJC4VcckQqSy7jgdXATOAE8M9mdrNz7odx260H1gNUV1enMFSRHNPRClsfhmfuhJbDMHOlt7Qy411Bj0wKVCpLLpcDe5xzRwDM7OfAu4A+QXfObQA2ANTV1bkUnk8kNyQM+aMKuQQulaDvA+rNbBTekstlQENaRiWSizpPQ8PD3tJK8zsw8xJY+QjUXBz0yESA1NbQN5vZE8A2oAt4Hn8mLpJXEoV87cMKueSclK5ycc59FfhqmsYikls6T8PWR+CZO7yQ17wH1j4ENe8OemQiCemVoiLxFHIJKQVdJKrzNGx91A/5217Ir38QZr4n6JGJJEVBF4kP+Yx3w/UPKOQSOgq6FK7ONtj2KPzxdj/kFyvkEmoKuhSeaMifuQNOHVLIJW8o6FI4Ottg2/fhmdu9kFe/C9Zs8NbKzYIenUjKFHTJfwq5FAgFXfJXZxs8/wNvjfzUQaheAdfd570wSCGXPKSgS/5JGPJ7FXLJewq65I+udm9pJRry6fVw3fe8N89SyKUAKOgSftGQP3MHnDygkEvBUtAlvLrae5dWTh6A6RfB6u94f2BCIZcCpKBL+HS1w/M/9EO+XyEX8SnoEh7xIZ+2HFbf4/0RZoVcREGXEOjqgO0/hKe/pZCLDEJBl9wVDfkfb4emt2DahXDN3TDrvQq5SAIKuuSerg7Y/hj88Vu9Ib/6LoVcZAgKuuSO+JBPrYOr74RZlynkIklQ0CV4XR2w40feGnnTPoVcZJgUdAlOopCvugPOVchFhkNBl+zr7oTtP4I//hOc2AdTlynkImmgoEv2JAr5VbfDuZcr5CJpoKBL5nV3wo4fw9Pf9EI+ZalCLpIBCrpkTqKQX/ktmP0+hVwkAxR0Sb/uTtjxEz/kb8KUJQq5SBakFHQzGwc8AMwHHPAJ59zGdAxMQihhyL8Js9+vkItkQaoz9LuA3zjn1ppZGTAqDWOSsOnuhBce90J+fK9CLhKQYQfdzMYAlwC3ADjnOoCO9AxLQiE+5JMXw02Pw3l/qZCLxHHOYRn+vUhlhl4LHAEeNrNFwFbgs865lrSMTHJXd1dMyPco5CIJHDxxmk27j/kfjXz7Q0tYOG1cRp8zlaCXAEuBzzjnNpvZXcCXgf8Ru5GZrQfWA1RXV6fwdBK4fiFfBDf9BM67QiGXghcf8H2NrQCMHVnKRTMrMTL/O5JK0PcD+51zm/37T+AFvQ/n3AZgA0BdXZ1L4fkkKN1dsPOn8IdvKOQivkNNfsDfaGTTnmO8eaxvwG95Vw31tVWcP2k0RUXZ+T0ZdtCdc2+b2VtmNsc59ypwGfBS+oYmgYsP+aSFsO7HMOcDCrkUnIECPuasEi6qreKjK2qor63kgkljshbweKle5fIZ4DH/CpfdwMdTH5IErrsLdv4zPP0NaNytkEtBerupLWYJ5Rh7Bwj4+ZPGUBxQwOOlFHTn3HagLk1jkaD1C/kCWPcjmHOlQi55b7CAL59Zxc31M6ivreKCybkT8Hh6pah4Id/1hLe00viGQi4F4e2mNjbv6T2Jueeod4He6LNKuCgkAY+noBey+JCfswA++Bicf5VCLnnnnZNtfa5C6RvwSj58UXXoAh5PQS9E3V2w62fe0sqx13tDPudKKCoKenQiadEb8EY27z7G7jwMeDwFvZBEumHnEzEhnw8f/CHMuUohl9A7fLKNjYkCPqKE5TMruWm5F/C5U/In4PEU9EIQ6fZm5H/4ukIueePwyTY27WnsWUbZfaTwAh5PQc9nPSH/Bhz7M5w9D278AZy/SiGX0Bko4BV+wNddON0L+OQxlBQX5s+3gp6PIt2w6+f+jDwa8u/D+Vcr5BIah0+1sXl3b8DfUMCHpKDnk34hn6uQS2gcOdXe5zrw2IBfWDOeG+u8gM+booAPREHPB5FuePEXXsiPvuaF/IZH4YJrFHLJWUdOtfe5Dvz1w80AlJcVc+HMSgV8GBT0MOsJ+Tfg6KsKueS0o83tbN7dyMbdRxMGfO2yadTXVjFfAR82BT2M4kM+8QK44RG4YLVCLjkjGvDoEsqfYwJeV1PJ9UunsWKWAp5OCnqYRLrhpX/xQn7kFYVccsrR5nae29PIxjf6BnxUWTEX1lSyZuk06msrmT91LKUKeEYo6GEQicBLv4gJ+fmw9mGYe61CLoE51tzO5pjLCF97pzfgdTWVXLd0KitqqxTwLFLQc1kk4s/Iv66QS+CORWfgAwT82iVTqa+tYoECHhgFPRf1hPwbcORlmDAH1j7kh7w46NFJgYgGPHoVyqvvnAJgZGkxdTXjWb14KitmKeC5REHPJZEIvPyv8NTXFXLJusaWDp7bc8xfA+8f8GsWT6G+toqF0xTwXKWg54JEIb/+QZh3nUIuGRMN+Cb/SpRX3k4c8AVTx1JWooCHgYIepEgEXn7SWyM//BJMOE8hl4w53tLR5yRmfMCvXjSF+tpKFkwdp4CHlIIeBIVcsmCggJ9VWkTdjEq+9P7J/hq4Ap4vFPRsikTglX/zllYOv6iQS1qdaO0N+MY3EgfcWwNXwPOVgp4N8SGvmg1rHoD5axRyGbbYgG/a3cgrb5/EOS/gy2aM54vvO48VsxTwQqKgZ1IkAq/80ltaeWeXQi4pOdHa4V9G6EX8ZT/gI0qKqKsZzxcuP4/6Wd5VKCNK9PNViBT0TOgX8nNhzf0w/3qFXJLW1Nrpvxth/4AvmzGez18enYEr4OJR0NMpEoFX/91bWnlnp0IuZ6SptZPn9vaexHzpUP+A19dWsWi6Ai6JKejpEB/yyllw3QYv5MX6FktiAwW8rKSIZdXj+dxl51FfW8mi6eM4q1QBl6GlXBszKwYagAPOuVWpDylEnINX/h3+8DV4Oxry+2D+WoVc+mk63cmW6EnMPcd48aACLumVjup8FngZGJOG/1Y49At5rUIu/TSd7qRhr/92snEBX1o9js9eNpv62ioWK+CSJinVx8ymAVcB/xf4QlpGlMucg1d/BU/9Y2/Ir70XFtygkAsn22Jm4LsbefFgExEFXLIo1QrdCdwKjE7DWHJXT8i/Bm+/oJAL4AW8ZwYeG/DiIpZUj+Mz7/UCvqRaAZfsGHaNzGwVcNg5t9XMLh1ku/XAeoDq6urhPl0wnINXf+3PyF+A8TPh2u/BghsV8gIUDXj0MsJdBxRwyS3mnBveF5r9I/ARoAs4C28N/efOuZsH+pq6ujrX0NAwrOfLqkQhX3mrQl5gTrV10rD3uPdS+riAL64eR31tFfW1lSytHq+AS0aZ2VbnXN1Q2w27Ts65rwBf8Z/sUuBLg8U8FJyD137jhfzQDhhfA6u/Cws/qJAXgNiAb9p9jJ1xAf/0e2cr4JLTVCkYJOQ3QnFp0KOTDGlu72LL3t6TmLsONNEdcZQWG0umj+fTf3Guv4QynpFlCrjkvrQE3Tn3FPBUOv5bWeUcvPYffsi3+yH/jj8jV8jzzVAB/2+XzlLAJdQKc4YeH/JxMxTyPNTc3tXnJObOmIAvnj6Ov/UDvlQBlzxRWEF3Dv78Wy/kB5/3Qn7Nt2HROoU8D7T0zMAVcClMhRF0hTwvtbR30fBm70nMF/b3BnzRtHF8aqUf8BnjGFVWGD/qUtjy+6fcOfjz7/yQb4Nx1XDNPbDoJoU8hFrau9j65nE2Rq9C2d9EV8RRUuTNwBVwKXT5+VOvkOeFaMBjZ+DRgC+aPo6/XllLfW0Vy2aMV8BFyLegOwev/6cX8gNbYWw1XH23F/KSsqBHJ0No7fBn4G8o4CLDkR+/FQp5KEUDHr2McMdbJ3oCvnDaWNZf0hvw8hH58aMqkknh/i1xDl7/vR/yBj/kd8GiDynkOeh0R3efJZQd+0/Q2a2Ai6RLOH9r+oV8Oqy6ExZ/WCHPIQMFvNgP+Cff4wW8TgEXSYtw/RY5B2/83nsb2/1bFPIcc7qjm237egO+/a3EAV82YzwVCrhI2oXjtyo+5GOmwao7YPHNCnmABgv4gqlj+at311JfW0ldTaUCLpIF4fgt++XnYevDMSH/MJSMCHpUBaets5ttMScxt791go7uCMVFxvypY/nEu2eyorZKARcJSDh+6+ZdB5MWwJKbFfIsauv0Z+Bv9A14kcGCaeP4+MU11M/y1sBHn6Xr+0WCFo6g1670PiSjegLuvxfK9n0xAZ86VgEXyXHhCLpkRFtnN8/vO9HzUvqEAa+toq5GARcJAwW9gEQDHj2J+fxbJ+jo8gI+f+pYbrm4hhW1VSyrGc8YBVwkdBT0PNbW2c32t070vJS+X8DfVdNzFYoCLhJ+CnoeiQY8OgPftq834POmjOVjK2awYlaVAi6SpxT0EGvv6mZ7zBp4ooDX+5cRjh2pgIvkOwU9RKIBj16Fsm3fcdq7IpjBvClj+Gh97wxcARcpPAp6Dmvv6mbHW009a+DxAf9IvTcDv3CmAi4iCnpOiQY8uga+9c3egM+dPIab62eworaKC2sqGTtKAReRvhT0ALV3dfPC/t4ZeKKA19dWsVwBF5EkKOhZ1NEVYcf+E95L6fd4AW/r9AJ+waQxfPgibw1cAReR4VDQM6ijK8IL+/3rwBME/EPLZ1BfW8nymZWMG6V3jRSR1Aw76GY2Hfg+MAmIABucc3ela2BhFA149N0IG95spK0zAsAFk8dw0/Jq6muruEgBF5EMSGWG3gV80Tm3zcxGA1vN7HfOuZfSNLac19EVYeeB3ssIG/Ye53RnN9A34MtrKhlfroCLSGYNO+jOuUPAIf/2KTN7GZgK5G3QBwv4+ZNG88ELp/fMwBVwEcm2tKyhm1kNsATYnI7/Xq7o7I7wwv7eywgVcBHJZSkH3cwqgJ8Bn3POnUzw+HpgPUB1dXWqT5dR8QHf+uZxWjviA17J8plVVCrgIpJjUgq6mZXixfwx59zPE23jnNsAbACoq6tzqTxfunV2R9h5oKn3JObexj4Bv7FOAReR8EjlKhcDHgReds7dnr4hZc5gAZ9zzmhuWDbNO4k5s5KqCv2pOxEJl1Rm6BcDHwF2mtl2/3P/4Jz7VerDSo/O7gi7DjTFnMRspEUBF5E8lcpVLs8AlsaxpKyrZwbeP+DnnVPB9TEBn6CAi0ieCfUrRbu6I+w6eLLnJOaWPQq4iBSuUAV9sIDPPruCNUu9gF9Uq4CLSOEJRdCf3HGQX2zbz5a9x2lu7wL6Bnz5zEomjlbARaSwhSLor759kreOn+baJVP8F/JUKeAiInHMuexdGl5XV+caGhrO+OsiEUdRUU6dfxURyRoz2+qcqxtqu1DM0BVzEckFERehtbOV5s5mWjpbONVxipbOlqTu33bhbSycuDCj4wtF0EVEUhFxEU53ne4b3I4WTnX69zv8ACe639Eb6JbOFhxDr2qUl5ZTXlpORWmF91FWgWXhKm8FXURyVjTEzR3NNHc294Q4ejsa3kT3Y2fIyYZ4VMmongBXlFZQXlrOOaPO6bkd/XxFaQXlZTHB9r+mvLScUSWjKC4qzsJ3pz8FXUTSzjlHa1dr3+D6UT6T+8mGeGTJSEaXju6JbHlpOWePOrtnllxeWs7ostED3q8oqwg0xOmioItID+ecNyOOCWxPZAeaJcfdj95ONsTxgZ04cmL/AJeVe8H2Z8nlpeU9AS8vKQ99iNNFQRfJA31CPNj6cMx6cPz95o5mWrpaiLjIkM83smRknzXi8rJyJoyckDC4A82Qy0vLKSlSgtJJ302RAEVDnDC4Z3AFRUvnmYc4Gt+qkVV9lh56Hou/H3NbIc5NOioiwxAb4qSCGztjjrufTIjPKj6r3yy3enT1gMGNXx+uKK1gVOkoSotKs/DdkaAo6FJQnHO0dbcNHtwklyi6XfeQzxcNcezVEdMrpicV4uh9hViSpaBLKMSGOFFwz+QStmRCPKJ4RJ9L0WJDnNTyhL+urBBLNinoklHOOdq72wcM7oCXrcU91tLRQpfrGvL5RhSP6DfTnVoxtd/Md6D14ej90mKFWMJHQZeEnHN0RDrOfFkiZvvhhDh2qWFqxdTESxEDXcKmEEuBU9DzTDTEyQR3qPtdkaFDXFZU1m+mO6ViyqCvrOt30q60QiEWSQMFPYd0dHcMuPQw5JUTMQE/kxDHLj9MKp/EuePOHfAqiUT3y4rLsvCdEZFkKOhpEA1x/Am6ZNeHo/c7I51DPldJUQmjS0f3ieqkUZOoGDf0ybrYWbJCLJJ/Cjrond2dCV/iPNSyRHycOyIdQz5XNMSxyxDnjDqH2rLafm/uk/C+/zUKsYgMJJRB7wnxUO8xMchlbEmH2Ep6T7z5yxBnjzqbmaUzB32zn/gTd2VFZZjpfd1FJHNCEfT7dtzHk2882RPw9u72Ib8mNsQJ331tkDf7iQ20QiwiYRGKoE8cNZF5E+YNuT4cG/ARxSMUYhEpKKEI+prZa1gze03QwxARyWlFqXyxmV1hZq+a2etm9uV0DUpERM7csINuZsXAd4APAHOBm8xsbroGJiIiZyaVGfpy4HXn3G7nXAfwE2B1eoYlIiJnKpWgTwXeirm/3/+ciIgEIJWgJ7qEpN8fETSz9WbWYGYNR44cSeHpRERkMKkEfT8wPeb+NOBg/EbOuQ3OuTrnXN3EiRNTeDoRERlMKkHfAsw2s5lmVgasA55Mz7BERORMDfs6dOdcl5l9GvgPoBh4yDn3YtpGJiIiZ8Sc67fsnbknMzsCvDnML58AHE3jcIKkfck9+bIfoH3JVansywzn3JBr1lkNeirMrME5Vxf0ONJB+5J78mU/QPuSq7KxLym9UlRERHKHgi4ikifCFPQNQQ8gjbQvuSdf9gO0L7kq4/sSmjV0EREZXJhm6CIiMoicCrqZPWRmh81s1wCPm5nd7b9d7wtmtjTbY0xWEvtyqZk1mdl2/+N/ZnuMyTCz6Wb2/8zsZTN70cw+m2CbUByXJPclLMflLDN7zsx2+PvyvxJsM8LMHvePy2Yzq8n+SIeW5L7cYmZHYo7LJ4MYazLMrNjMnjezXyZ4LLPHxDmXMx/AJcBSYNcAj18J/BrvfWTqgc1BjzmFfbkU+GXQ40xiPyYDS/3bo4HXgLlhPC5J7ktYjosBFf7tUmAzUB+3zd8C9/q31wGPBz3uFPblFuDbQY81yf35AvCjRD9HmT4mOTVDd849DTQOsslq4PvOswkYZ2aTszO6M5PEvoSCc+6Qc26bf/sU8DL931UzFMclyX0JBf973ezfLfU/4k+IrQYe9W8/AVxmOfh3GZPcl1Aws2nAVcADA2yS0WOSU0FPQr69Ze8K/5+ZvzazeUEPZij+Pw+X4M2gYoXuuAyyLxCS4+L/0347cBj4nXNuwOPinOsCmoCq7I4yOUnsC8D1/pLeE2Y2PcHjueBO4FYgMsDjGT0mYQt6Um/ZGxLb8F7Ouwi4B/iXgMczKDOrAH4GfM45dzL+4QRfkrPHZYh9Cc1xcc51O+cW473T6XIzmx+3SWiOSxL78m9AjXNuIfCf9M5yc4aZrQIOO+e2DrZZgs+l7ZiELehJvWVvGDjnTkb/memc+xVQamYTAh5WQmZWihfAx5xzP0+wSWiOy1D7EqbjEuWcOwE8BVwR91DPcTGzEmAsOb4MONC+OOeOOefa/bv3A8uyPLRkXAxcY2Z78f6C23vN7Idx22T0mIQt6E8CH/WvqqgHmpxzh4Ie1HCY2aTo2pmZLcc7FseCHVV//hgfBF52zt0+wGahOC7J7EuIjstEMxvn3x4JXA68ErfZk8DH/Ntrgf9y/tm4XJLMvsSdk7kG7/xHTnHOfcU5N805V4N3wvO/nHM3x22W0WMy7LfPzQQz+zHeVQYTzGw/8FW8EyQ45+4FfoV3RcXrQCvw8WBGOrQk9mUt8Ckz6wJOA+ty8ZcNb9bxEWCnv8YJ8A9ANYTuuCSzL2E5LpOBR837Y+1FwE+dc780s/8NNDjnnsT7P68fmNnreLPAdcENd1DJ7Mvfmdk1QBfevtwS2GjPUDaPiV4pKiKSJ8K25CIiIgNQ0EVE8oSCLiKSJxR0EZE8oaCLiOQJBV1EJE8o6CIieUJBFxHJE/8f0HIpMQlrQi8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x4 = range(1, 5)\n",
    "\n",
    "plt.plot(x4, [xi*1.5 for xi in x4])\n",
    "\n",
    "plt.plot(x4, [xi*3 for xi in x4])\n",
    "\n",
    "plt.plot(x4, [xi/3.0 for xi in x4])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {
    "Parts%20of%20a%20plot.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 13.\tParts of a Plot\n",
    "\n",
    "\n",
    "There are different parts of a plot. These are title, legend, grid, axis and labels etc. These are denoted in the following figure:-\n",
    "\n",
    "\n",
    "![Parts%20of%20a%20plot.png](attachment:Parts%20of%20a%20plot.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.\tSaving the Plot\n",
    "\n",
    "\n",
    "We can save the figures in a wide variety of formats. We can save them using the **savefig()** command as follows:-\n",
    "\n",
    "\n",
    "`fig.savefig(‘fig1.png’)`\n",
    "\n",
    "\n",
    "\n",
    "We can explore the contents of the file using the IPython **Image** object.\n",
    "\n",
    "\n",
    "\n",
    "`from IPython.display import Image`\n",
    "\n",
    "\n",
    "`Image(‘fig1.png’)`\n",
    "\n",
    "\n",
    "In **savefig()** command, the file format is inferred from the extension of the given filename. Depending on the backend, \n",
    "many different file formats are available. The list of supported file types can be found by using the get_supported_filetypes() method of the figure canvas object as follows:-\n",
    "\n",
    "\n",
    "`fig.canvas.get_supported_filetypes()` \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the figure\n",
    "\n",
    "fig.savefig('plot1.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore the contents of figure\n",
    "\n",
    "from IPython.display import Image\n",
    "\n",
    "Image('plot1.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ps': 'Postscript',\n",
       " 'eps': 'Encapsulated Postscript',\n",
       " 'pdf': 'Portable Document Format',\n",
       " 'pgf': 'PGF code for LaTeX',\n",
       " 'png': 'Portable Network Graphics',\n",
       " 'raw': 'Raw RGBA bitmap',\n",
       " 'rgba': 'Raw RGBA bitmap',\n",
       " 'svg': 'Scalable Vector Graphics',\n",
       " 'svgz': 'Scalable Vector Graphics',\n",
       " 'jpg': 'Joint Photographic Experts Group',\n",
       " 'jpeg': 'Joint Photographic Experts Group',\n",
       " 'tif': 'Tagged Image File Format',\n",
       " 'tiff': 'Tagged Image File Format'}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore supported file formats\n",
    "\n",
    "\n",
    "fig.canvas.get_supported_filetypes() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.\tLine Plot\n",
    "\n",
    "\n",
    "We can use the following commands to draw the simple sinusoid line plot:-\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Create figure and axes first\n",
    "fig = plt.figure()\n",
    "\n",
    "ax = plt.axes()\n",
    "\n",
    "# Declare a variable x5\n",
    "x5 = np.linspace(0, 10, 1000)\n",
    "\n",
    "\n",
    "# Plot the sinusoid function\n",
    "ax.plot(x5, np.sin(x5), 'b-'); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we can use the Pyplot interface and let the figure and axes be created for us in the background."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Plot the sinusoid function \n",
    "\n",
    "plt.plot(x5, np.sin(x5));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can draw multiple lines in a single figure in OO API as follows:-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig = plt.figure()\n",
    "\n",
    "ax = plt.axes()\n",
    "\n",
    "x6 = np.linspace(0, 10, 1000)\n",
    "\n",
    "ax.plot(x6, np.sin(x6), 'b-')\n",
    "\n",
    "ax.plot(x6, np.cos(x6), 'r-'); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, if we want to create a single figure with multiple lines, we can call the plot function multiple times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "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(x6, np.sin(x6), 'b-')\n",
    "\n",
    "plt.plot(x6, np.cos(x6), 'r-');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 16.\tScatter Plot\n",
    "\n",
    "Another commonly used plot type is the scatter plot. Here the points are represented individually with a dot or a circle.\n",
    "\n",
    "\n",
    "### Scatter Plot with plt.plot()\n",
    "\n",
    "We have used plt.plot/ax.plot to produce line plots. We can use the same functions to produce the scatter plots as follows:-\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "x7 = np.linspace(0, 10, 30)\n",
    "\n",
    "y7 = np.sin(x7)\n",
    "\n",
    "plt.plot(x7, y7, 'o', color = 'black');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can customize the plot by combining the character codes together with line and color codes to plot points along with a line connecting them as follows:-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "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(x7, y7, '-ok');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Plot with plt.scatter()\n",
    "\n",
    "\n",
    "An alternative approach of creating scatter plot is to use plt.scatter() function. It is more powerful method of creating scatter plots than to use the plt.plot() function. We can use plt.scatter() function as follows:-\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "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.scatter(x7, y7, marker='o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plt.plot() Vs plt.scatter() functions\n",
    "\n",
    "\n",
    "For smaller datasets, it does not matter whether we use plt.plot() or plt.scatter() functions to create scatter-plots.\n",
    "\n",
    "\n",
    "But for larger datasets, plt.plot() function is more efficient than plt.scatter() function. In plt.plot() function, the points are clones of each other. So the work of determining the appearance of the points is done only once for the entire set of data. In plt.scatter() function, the renderer must do the extra work of constructing each point individually.\n",
    "\n",
    "\n",
    "So, for large datasets, the difference between plt.plot() and plt.scatter() functions can lead to vastly different performance.  Hence, plt.plot() function should be preferred over plt.scatter() function for large datasets.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 17.\tHistogram\n",
    "\n",
    "\n",
    "Histogram charts are a graphical display of frequencies. They are represented as bars. They show what portion of the \n",
    "dataset falls into each category, usually specified as non-overlapping intervals. These categories are called bins.\n",
    "\n",
    "The **plt.hist()** function can be used to plot a simple histogram as follows:-\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "data1 = np.random.randn(1000)\n",
    "\n",
    "plt.hist(data1); \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **plt.hist()** function has many parameters to tune both the calculation and the plot display. \n",
    "We can see these parameters in action as follows:-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "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.hist(data1, bins=30, density=True, alpha=0.5, histtype='stepfilled', color='steelblue')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare Histograms of several distributions\n",
    "\n",
    "\n",
    "We can use the combination of histtype=’stepfilled’ along with transparency level alpha to compare histograms of several distributions.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "x1 = np.random.normal(0, 4, 1000)\n",
    "x2 = np.random.normal(-2, 2, 1000)\n",
    "x3 = np.random.normal(1, 5, 1000)\n",
    "\n",
    "kwargs = dict(histtype='stepfilled', alpha = 0.5, density = True, bins = 40)\n",
    "\n",
    "plt.hist(x1, **kwargs)\n",
    "plt.hist(x2, **kwargs)\n",
    "plt.hist(x3, **kwargs)\n",
    "\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Histogram with OO API\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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O0EtS4/4XCJma9jvGPcoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plots in Matplotlib reside within a figure object - Use plt.figure() to create new figure object\n",
    "fig = plt.figure()\n",
    "\n",
    "# Create subplots using add_subplot() function\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "# Plot histogram\n",
    "ax.hist(data1, bins=10)\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Two-Dimensional Histograms\n",
    "\n",
    "\n",
    "Just as we create histograms in one dimension, we can also create histograms in two-dimensions by dividing points among two-dimensional bins.\n",
    "\n",
    "We can use Matplotlib’s plt.hist2d() function to plot a two-dimensional histogram as follows:-\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": [
    "mean = [0, 0]\n",
    "cov = [[1, 1], [1, 2]]\n",
    "x8, y8 = np.random.multivariate_normal(mean, cov, 10000).T\n",
    "\n",
    "plt.hist2d(x8, y8, bins = 30, cmap = 'Blues')\n",
    "\n",
    "cb = plt.colorbar()\n",
    "\n",
    "cb.set_label('counts in bin')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 18.\tBar Chart\n",
    "\n",
    "\n",
    "Bar charts display rectangular bars either in vertical or horizontal form. Their length is proportional to the values they represent. They are used to compare two or more values.\n",
    "\n",
    "\n",
    "We can plot a bar chart using plt.bar() function. We can plot a bar chart as follows:-\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "data2 = [5. , 25. , 50. , 20.]\n",
    "\n",
    "plt.bar(range(len(data2)), data2)\n",
    "\n",
    "plt.show() \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The thickness of the Bar Chart\n",
    "\n",
    "By default, a bar will have a thickness of 0.8 units. If we draw a bar of unit length, we have a gap of 0.2 between them. \n",
    "We can change the thickness of the bar chart by setting width parameter to 1.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "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.bar(range(len(data2)), data2, width = 1)\n",
    "\n",
    "plt.show() \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 19.\tHorizontal Bar Chart\n",
    "\n",
    "\n",
    "We can produce Horizontal Bar Chart using the plt.barh() function. It is the strict equivalent of plt.bar() function.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "data2 = [5. , 25. , 50. , 20.]\n",
    "\n",
    "plt.barh(range(len(data2)), data2)\n",
    "\n",
    "plt.show() \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 20.\tError Bar Chart\n",
    "\n",
    "\n",
    "\n",
    "In experimental design, the measurements lack perfect precision. So, we have to repeat the measurements. It results in \n",
    "obtaining a set of values. The representation of the distribution of data values is done by plotting a single data point \n",
    "(known as mean value of dataset) and an error bar to represent the overall distribution of data.\n",
    "\n",
    "\n",
    "We can use Matplotlib's **errorbar()** function to represent the distribution of data values. It can be done as follows:-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "x9 = np.arange(0, 4, 0.2)\n",
    "\n",
    "y9 = np.exp(-x9)\n",
    "\n",
    "e1 = 0.1 * np.abs(np.random.randn(len(y9)))\n",
    "\n",
    "plt.errorbar(x9, y9, yerr = e1, fmt = '.-')\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have plotted x versus y with error deltas as vertical error bars, as specified by the **yerr** keyword argument. There is an \n",
    "equivalent argument, **xerr**, to draw horizontal error bars."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also specify both xerr and yerr together as follows:-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "e2 = 0.1 * np.abs(np.random.randn(len(y9)))\n",
    "\n",
    "plt.errorbar(x9, y9, yerr=e1, xerr=e2, fmt = '.-', capsize=0)\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that, for each point we have four different errors. One for each direction: -x, +x, -y and +y."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Asymmetrical Error Bars\n",
    "\n",
    "\n",
    "The error bars described in the previous section, are called **symmetrical error bars**. Their negative error is equal in value\n",
    "to the positive error. So error bar is symmetrical to the point where it is drawn. \n",
    "\n",
    "There is another type of error bar, which is **asymmetrical error bar**. To draw **asymmetrical error bars**, we have to pass \n",
    "two lists(or a 2D array) of values to yerr and/or xerr - the first list is for negative errors while the second list is for positive errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BonU7vS5LRDwSjfDPAzY1Wy4PP9fEzMYBA5xzz0ShP4nAOSf0Ys43J9AjNciXZy3m0UUbtB9AJAFFI/ytleea0sTMfMBvge8d9YPMZphZqZmVVlRURKE0ac2Q3HTmfHMCZxbm8MM5y7nt78uprW/0uiwR6UTRCP9yYECz5f7AlmbLGcBo4GUz+wgoAea2ttPXOfegc67YOVecm5sbhdKkLd2Tg8z66incMHEIj7+1kStmLaJi70GvyxKRThKN8H8bKDSzQWaWBEwH5n7yonOuyjmX45wrcM4VAIuAKc650ij0LRHw+4zvTxrOvZeNY9nmKqb+4XWWb67yuiwR6QQRh79zrh64EXgBWAU86ZxbYWZ3mNmUSD9fOt6Usf14+vrTAbjovjcpr6xm8+4Dui6QSBcWleP8nXPznHPDnHNDnHM/Cz93u3NubittJ2qrP/aMzstk7k1nMDgnjc27a0IXhntIF4YT6ap0hq80yUnvxuQxfZuWa+obebJ00xHeISLxSuEvh5gw9NMLwxkw++1N/Ogfy9l/UGcFi3QlCn85RFF+FsP7ZNA/K4XHrjmVq04v4JGFG5h0z6ss/FAnhYl0FQp/OUxGcpC8HimcPjSHH08ZxewZJfjMuOyhRdyuXwEiXYLCX47q1ME9ee5bZ3L1hAIeXaRfASJdgcJf2iU1KcCPvjCK2TNO068AkS5A4S/HZPygbJ7/1lmH/Ap488MdXpclIsdI4S/HLCXJ3/QrwG/G5Q8t5odzYudXgO4hLHJ0Cn85buMHZfPct87iaxMG8ZfF+hUgEk8CXhcgsWf2dae1u21Kkp/bvzCSyWP6cMtT73P5Q4vJSg2SmuRnyYZKivKzOrBSETle2vKXqDilIPQr4PNj+lJZXcfm3TVMe2Ahb63XUUEisUjhL1GTkuRnRL/uTcv1jY7rHl3C88u36oYxIjFG4S9RVTK4Z9PlIYJ+I7VbgOv/8g4X3fcmpR/t8rY4EWmiOX+JqqL8LJ66/nQWrdtJyeCejO2fydNLyvnNi2u5+P6FfHZkb74/aThDe6V7XapIQlP4S9QV5WcdsqN3+viBTDmpHw+/vp77X1nH5373KtNOGcC3zyukV/fkw97/yWGax7LjWUSOjaZ9pFOkJgW48dxCXrllIl8pyefJtzdx9t0v85sX17IvRs4PEEkkCn/pVD3Tu/HjKaN46btnc+7wXtw7/wMm3r2ARxd+RF2DbiIv0lkU/uKJgpw0Zl5xMn+/4XQG56bzw3+s4LO/fZXnlm1lz4E63UZSpIMp/MVT4wZmMXtGCbOuLCbgM77x2Dus2rY3dBvJWbqNpEhHUfiL58yMz4zszXPfOpNJo3o3PV9T18is19ZxsL7Bw+pEuiaFv8SMgN/HtWcNwZrdRvK55duYcNcCfj//Ayr313pan0hXokM9JaYU5Wcxok8Ge2rquWfaSVTXNfDQa+v59YtrmflyGRed3J+vnTGIIbmxe56ADlWVeKDwl5iTkRwkIzlIUUE2AGcW5rJm217++Po6niot57HFGzlveC+uOXMwJYOzsU9+KohIu0Vl2sfMJpnZGjMrM7NbW3n9u2a20syWmtl8M8uPRr9d1ezrTtNWYwsn9MnglxeP5Y1bz+Xm8wp5d9NuLntoEV/4w+vMeXezDhMVOUYRh7+Z+YGZwGRgJHCZmY1s0exdoNg5dyLwNPDLSPuVxJSb0Y3vnj+MN289lzu/NIYDtQ18e/Z7nPmLBdz38odUVdext0aHioocTTS2/McDZc65dc65WuAJYGrzBs65Bc656vDiIqB/FPqVBJYc9HP5qQN58Ttn839XncLg3DR+8fxqxt/5Eiu3JvahorqTmbRHNMI/D9jUbLk8/Fxbvg48F4V+RfD5jHOG9+Kv15bw7M1nHHLBuJq6Rv53QRk79x30sEKR2BSN8G9tb1urF283sy8DxcDdbbw+w8xKzay0oqIiCqVJIhnVL5M7po4+5FDR+au3c+qd87nmkbeZt2wrNXU6Z0AEonO0TzkwoNlyf2BLy0Zm9hngv4CznXOtboo55x4EHgQoLi7W3T/kmB1yqOj0caQm+fn7u5uZ8+5mXlq1nYzkAJ8/sR8XnpxHcX6WjhSShBWN8H8bKDSzQcBmYDpwefMGZjYOeACY5JzbHoU+RdrUdKho+LLSI/p25z8nDeeNsh1NA8Hjb21kYHYqXxyXx4Xj8ijISfO4apHOFXH4O+fqzexG4AXADzzsnFthZncApc65uYSmedKBp8JbWhudc1Mi7Vukvfw+46xhuZw1LJeffLGeF5Zv42/vlvP7f3/AvfM/oCg/iy+Ny+PzJ/blw4r9TTej0Q3opauKyklezrl5wLwWz93e7PFnotGPSDSkdwtwUVF/Lirqz9aqA8x5dwt/e6ec/56znB/PXUF9Y2jGMTng47FrSzQASJeka/tIQuubmcI3Jg7hX985i2duOoOxA3o0vVZT38h3Zr/HH19fz0c79ntYpUj0KfxFCF1ZdHReJrddMKLpBvR+n9HoHD95ZiUTf/Uy5/36ZX4+bxWL1+2kXmcUd1mJcp6Eru0j0kxRfhbDmx0tVJSfxcad1cxf/TH/Xr2dh99YzwOvrqNHapCJw3I5d0Rvzh6WS2ZK0OvSRY6Jwl+khZZHCw3smcrVEwZx9YRB7K2p47UPdjB/1XYWrNnOnPe2EPAZpxRkc96IXpw3ojd7a+rYU1PPkg2V2l8gMUvhL11OR14ULyM5yAVj+nLBmL40NDre21TJ/FXbmb9qOz99dhU/fXZVU9vpDy7kwa8Ucc7w3kf4RBFvKPxFjpPfZxTlZ1OUn833Jw1n065qfvzPFcxfFTqVpa7BcfWfShnaK51TB2Vz6uCenDoom97dkz2uXEThL3KY4/3lMCA7lRsmDmXB6u00OkgK+Li0aADlu6v5x3tbeGzxRgAKeqZy6qCejB+UzamDs+mflRrN8iXOddbNgBT+IlHU2g5jgPqGRlZt3cvi9TtZtG4Xz6/YxuzS0PUQ83qkhH8ZZHPqoJ7s3HeQRet3xe1JZrqTWXxQ+ItEWcsdxhC6P/GY/pmM6Z/JNWcOprHRsebjvSxet5PF63fxytoK/vbu5kM+J+AzbrtgOBee3J8eqUmd/a8hXZzCX8QDPp8xom93RvTtzlUTBuGc48OKffzi+TW8uPJjAOobHXc8s4o7nlnFgOwUxuRlMjovkxPzejA6r3uHDQjack8MCn+RGGBmDO2VwfVnD2H+qo9pdNAt4OMHk4dTU9/IsvIqlm2uYt6ybU3vGZid+umA0D+T0f0yyUwN6lBTj8XL4KnwF4khbe0z+MTu6lqWb97Dss1VLN9cxdLNu3l22dam1/t078a2PaErpk9/cCG/uXQsk0f3JeDXyfxyKIW/SIxpbZ/BJ3qkJnFGYQ5nFOY0PffJgLB0827mvLu5KfzrGhw3Pf4eSf6lDM5NY2ivdAp7ZVDYO53CXunk90wjKaBBIVEp/EXiXPMB4dRBPbnk/jebDjWdcdZg6hoaKft4H0vLq3h22VZc+DZJAZ9RkJNGYa/QYDC0dwaFvdLZc6COvQc1bdTVKfxFupCjTRsdqG3gw4p9lG3fxwfb9/LBx/tYvW0vL6zYRmOLe+ddcv+bnDe8N0UFWQzMTg399Uyle3LHXscoXubM453CX6SLOdK0UUqSn9HhncTN1dQ1sH7HfmYuKOOZpaF9CI0OXiur4MVVHx/SNis1GB4I0hiYnRIeGNIY2DOVPt2TtcM5Tij8RYTkoJ8Rfbtz9YRBzFu2lUYHyUEfj11TQmHvdDbtqmbjzmo27qpmw65qNu2qZmn5buYt20pDs58MAZ813Qzn0vsXcmFRHicPzKJPZjL9MlPok5lM9+SA7p0cAxT+ItKkrWmjUf0yGdUv87D29Q2NbK2qYUN4YJjz3mbeWr8LgAbneKq0nKdKyw95T2qSnz6ZyfTNTKZvZgp9M5Oblvt0T6GyupZq7XPocAp/ETnEkaaNWgr4fQzITmVAduj6RCf0yWja4Zwc9PHnr40nLyuVbVUH2FpVw7aqGrbsrmHbntDyG2U7+HhPzWH7GwAuuu9NhuSmkd8zjZz0JHLSu4X+MrqRk55Ebni5R2rwkF8SSzZU6h7M7aDwF4myeN9RGUn9bf1yyOuR0uZ76hsa2bGvli1VB/jTGx8x9/0tTa81OthWVcPyzVXs3F97yBTTJ4J+o2daN3Iykgj6fby7cTcQmoK69sxBjB2QRVZqkKy0JHqkBOmRmnTEQ1wTZZ+Fwl9EoupYfjlA6NdDn/DUj3PwzNItTb8cfnXJ2KbPaWx07D5Qx459B9mx9yAV+w6yY19t0/KOfQdZuXVP0+fWNzrue2Vdq32mJfnpkZpEVlqQrNSk0OPUIAfqGli1dS+O0Elyt39+JMUF2WQkB0L/Xt0C+HxH3l8RL4OHwl9EYsaRDlX1+YzstCSy05IY1juj1fcv2VD56bRTwMc9l40jr0cKu6vrqKyuZXd1bfhxHbura6msrqWyuo5Nu6qprK6j6kBd02fVNTh++I8Vh3y+GaQnBeieEiQjOUD35GB4YAg9t/9g/SGDx/c+ewIn9s8kvVuA1KQA6d0CpHXzk5oUwN/GINJZg4fCX0SiKtJpr2P95dDc0c5zOJq3P9rFpQ8sxIVPkrtt8nB6d09mb009e8KhvOdAXdPy3po6tlbVsHZ7HXsOhF77ZGKqrsFx13Or2+wrJegnrVuA9PBgkN4tQH1jIyu37gXgilmLeOyakg4bAKIS/mY2CbgH8AOznHN3tXi9G/BnoAjYCUxzzn0Ujb5FpGvxcvA4pSCbEREMHks+2sXF4cGjW8DHT6aOYkB2GvsP1rO/tp79BxvYf7CefQfrqa6tZ194OfS4ni27DjR9Vl19I4vW7Yzd8DczPzATOB8oB942s7nOuZXNmn0dqHTODTWz6cAvgGmR9i1dU7zvMJX4FtEvj0gHj2bTVsGAj5LBPY+5hvaKxlWdxgNlzrl1zrla4Algaos2U4FHwo+fBs4zneUhIl1QRnKQvB4pEU1b9c9K6dApH4hO+OcBm5otl4efa7WNc64eqAI6bkgTEYlTkQwexyIac/6tbcG3PBi3PW0wsxnADICBAwdGXpmIyDFKlGnHaGz5lwMDmi33B7a01cbMAkAmsKvlBznnHnTOFTvninNzc6NQmoiItCYaW/5vA4VmNgjYDEwHLm/RZi7wVWAhcDHwb+dcKyd0i0ikW56JsuUqkYk4/J1z9WZ2I/ACoUM9H3bOrTCzO4BS59xc4I/Ao2ZWRmiLf3qk/YqIxKJ4GXyjcpy/c24eMK/Fc7c3e1wDXBKNvkREurLOGjx0A08RkQSk8BcRSUAKfxGRBKTwFxFJQAp/EZEEpPAXEUlACn8RkQSk8BcRSUAKfxGRBKTwFxFJQAp/EZEEpPAXEUlACn8RkQSk8BcRSUAKfxGRBKTwFxFJQAp/EZEEpPAXEUlAUbmNo4hIrIiXe+h6TVv+IiIJSOEvIpKAFP4iIglI4S8ikoAiCn8zyzazF83sg/A/s1ppc5KZLTSzFWa21MymRdKniIhELtIt/1uB+c65QmB+eLmlauBK59woYBLwOzPrEWG/IiISgUjDfyrwSPjxI8AXWzZwzq11zn0QfrwF2A7kRtiviIhEINLw7+2c2woQ/mevIzU2s/FAEvBhhP2KiEgEjnqSl5m9BPRp5aX/OpaOzKwv8CjwVedcYxttZgAzAAYOHHgsHy8iIsfAnHPH/2azNcBE59zWcLi/7Jw7oZV23YGXgZ87555q52dXABuOuzjIAXZE8P6Opvoio/oio/oiE8v15Tvnjjq1HunlHeYCXwXuCv/zHy0bmFkS8Hfgz+0NfoD2FH8kZlbqnCuO5DM6kuqLjOqLjOqLTKzX1x6RzvnfBZxvZh8A54eXMbNiM5sVbnMpcBZwlZm9F/47KcJ+RUQkAhFt+TvndgLntfJ8KXBN+PFfgL9E0o+IiERXVz7D90GvCzgK1RcZ1RcZ1ReZWK/vqCLa4SsiIvGpK2/5i4hIG+I6/M1skpmtMbMyMzvs0hJm1s3MZodfX2xmBTFW31VmVtFsR/g1nVzfw2a23cyWt/G6mdm94fqXmtnJMVbfRDOrarb+bu/k+gaY2QIujqr5AAADXElEQVQzWxW+dtW3Wmnj2TpsZ32erUMzSzazt8zs/XB9/9NKG8++w+2sz9PvcEScc3H5B/gJnSk8mNBZw+8DI1u0uQG4P/x4OjA7xuq7CviDh+vwLOBkYHkbr18APAcYUAIsjrH6JgLPeLj++gInhx9nAGtb+W/s2TpsZ32ercPwOkkPPw4Ci4GSFm28/A63pz5Pv8OR/MXzlv94oMw5t845Vws8QehaQ801v/bQ08B5ZmYxVJ+nnHOvAruO0GQqofMznHNuEdAjfDJfp2hHfZ5yzm11zr0TfrwXWAXktWjm2TpsZ32eCa+TfeHFYPiv5U5Iz77D7awvbsVz+OcBm5otl3P4/9hNbZxz9UAV0LNTqmtffQAXhacDnjazAZ1TWru199/BS6eFf5Y/Z2ajvCoiPB0xjtDWYXMxsQ6PUB94uA7NzG9m7xG64OOLzrk2158H3+H21Aex/R1uUzyHf2ujf8tRuT1tOkp7+v4nUOCcOxF4iU+3cGKFl+uvPd4hdCr7WOD3wBwvijCzdOD/Ad92zu1p+XIrb+nUdXiU+jxdh865BufcSUB/YLyZjW7RxNP11476Yv073KZ4Dv9yoPko2x/Y0lYbMwsAmXTeNMJR63PO7XTOHQwvPgQUdVJt7dWedewZ59yeT36WO+fmAUEzy+nMGswsSChYH3PO/a2VJp6uw6PVFwvrMNz3bkLX/5rU4iUvv8NN2qovDr7DbYrn8H8bKDSzQRa6ftB0Qtcaau6Taw8BXAz824X30sRCfS3mfqcQmpONJXOBK8NHrJQAVS58Ce9YYGZ9Ppn/tdDlwn3Azk7s34A/Aqucc79po5ln67A99Xm5Ds0s18I3djKzFOAzwOoWzTz7Drenvjj4Drcp0gu7ecY5V29mNwIvEDqy5mHn3AozuwModc7NJfQ//qNmVkZoa2F6jNV3s5lNAerD9V3VWfUBmNnjhI72yDGzcuBHhHZq4Zy7H5hH6GiVMkJ3ZLs6xuq7GPiGmdUDB4DpnTi4A0wAvgIsC88LA9wGDGxWo5frsD31ebkO+wKPmJmf0KDzpHPumVj5DrezPk+/w5HQGb4iIgkonqd9RETkOCn8RUQSkMJfRCQBKfxFRBKQwl9EJAEp/EVEEpDCX0QkASn8RUQS0P8HlZEdzznpQqUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(x9, y9, yerr=[e1, e2], fmt='.-')\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 21. Multiple Bar Chart\n",
    "\n",
    "\n",
    "When we want to compare several quantities while changing one variable, we might want to plot a Multiple Bar Chart. \n",
    "A Multiple Bar Chart is a bar chart where each variable is reflected by bars of different colours.\n",
    "\n",
    "It can be plotted by adjusting the thickness and positions of the bars as follows:-\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "data3 = [[15., 25., 40., 30.],\n",
    "         [11., 23., 51., 17.],\n",
    "         [16., 22., 52., 19.]]\n",
    "\n",
    "z1 = np.arange(4)\n",
    "\n",
    "plt.bar(z1 + 0.00, data3[0], color = 'r', width = 0.25)\n",
    "plt.bar(z1 + 0.25, data3[1], color = 'g', width = 0.25)\n",
    "plt.bar(z1 + 0.50, data3[2], color = 'b', width = 0.25)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data3 variable contains three series of four values. The code snippet shows three bar charts of four bars. The bars will have a thickness of 0.25 units. Each bar chart will be shifted 0.25 units from the previous one. Color has been added for clear presentation and understanding."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 22. Stacked Bar Chart\n",
    "\n",
    "\n",
    "We can draw stacked bar chart by using a special parameter called **bottom** from the plt.bar() function. It can be done as follows:- "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "A = [15., 30., 45., 22.]\n",
    "\n",
    "B = [15., 25., 50., 20.]\n",
    "\n",
    "z2 = range(4)\n",
    "\n",
    "plt.bar(z2, A, color = 'b')\n",
    "plt.bar(z2, B, color = 'r', bottom = A)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The optional **bottom** parameter of the plt.bar() function allows us to specify a starting position for a bar. Instead of running from zero to a value, it will go from the bottom to value. The first call to plt.bar() plots the blue bars. The second call to plt.bar() plots the red bars, with the bottom of the red bars being at the top of the blue bars."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 23. Back-to-Back Bar Charts\n",
    "\n",
    "\n",
    "We can plot two bar charts back to back at the same time using a simple trick. The idea is to have two bar charts, using a simple trick, that is, the length/height of one bar can be negative.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "U1 = np.array([15., 35., 45., 32.])\n",
    "U2 = np.array([12., 30., 50., 25.])\n",
    "\n",
    "z1 = np.arange(4)\n",
    "\n",
    "plt.barh(z1, U1, color = 'r')\n",
    "plt.barh(z1, -U2, color = 'b')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 24. Pie Chart\n",
    "\n",
    "\n",
    "Pie charts are circular representations, divided into sectors. The sectors are also called **wedges**. The arc length of each sector is proportional to the quantity we are describing. It is an effective way to represent information when we are interested mainly in comparing the wedge against the whole pie, instead of wedges against each other.\n",
    "\n",
    "Matplotlib provides the **pie()** function to plot pie charts from an array X. Wedges are created proportionally, so that each value x of array X generates a wedge proportional to x/sum(X)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "\n",
    "x10 = [35, 25, 20, 20]\n",
    "\n",
    "labels = ['Computer', 'Electronics', 'Mechanical', 'Chemical']\n",
    "\n",
    "plt.pie(x10, labels=labels);\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploded Pie Chart\n",
    "\n",
    "\n",
    "We can plot an exploded Pie chart with the addition of keyword argument **explode**. It is an array of the same length as that of X. Each of its values specify the radius fraction with which to offset the wedge from the center of the pie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "\n",
    "x11 = [30, 25, 20, 15, 10]\n",
    "\n",
    "labels = ['Computer', 'Electronics', 'Mechanical', 'Chemical', 'Agriculture']\n",
    "\n",
    "explode = [0.2, 0.1, 0.1, 0.05, 0]\n",
    "\n",
    "plt.pie(x11, labels=labels, explode=explode, autopct='%1.1f%%');\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 25. Boxplot\n",
    "\n",
    "\n",
    "Boxplot allows us to compare distributions of values by showing the median, quartiles, maximum and minimum of a set of values.\n",
    "\n",
    "We can plot a boxplot with the **boxplot()** function as follows:-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD8CAYAAABjAo9vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAACX5JREFUeJzt3V+Ipfddx/HP1934By24MQOVpnEFpWwZxMJQsF2QaC+iCKIgmAsRHNgrlwpeiMyFVwFFECQKsphQL8qIqKGilTaFlTDYFicllI0jIoXiopBps6LFC5P69WJXSeJmN5nn2TnZb14vGNg555nf892b9zz8znPOVHcHgDm+ZdMDALAuYQcYRtgBhhF2gGGEHWAYYQcYRtgBhhF2gGGEHWCYs5s46UMPPdTnz5/fxKkB7lvPP//817p7627HbSTs58+fz+Hh4SZODXDfqqqvvpXjbMUADCPsAMMIO8Awwg4wjLADDCPskGR/fz/b29s5c+ZMtre3s7+/v+mR4MQW3+5YVd+e5Lkk33ZrvT/t7t9Yui6clv39/ezt7eWpp57KxYsXc3BwkN3d3STJ448/vuHp4O2rpX8ar6oqyXd29zeq6oEkB0k+3t1feLOf2dnZafex806xvb2dJ598Mo8++uj/PXb16tVcvnw5165d2+Bk8HpV9Xx379ztuMVX7H3zN8M3bn37wK0vf0iV+8bR0VEuXrz4uscuXryYo6OjDU0Ey6yyx15VZ6rqhSQvJXm2u7+4xrpwGi5cuJCDg4PXPXZwcJALFy5saCJYZpWwd/c3u/uHkzyc5MNVtf3GY6rqUlUdVtXh8fHxGqeFVezt7WV3dzdXr17NK6+8kqtXr2Z3dzd7e3ubHg1OZNXPiunuf6uqv0nyWJJrb3juSpIryc099jXPC0v87wukly9fztHRUS5cuJAnnnjCC6fct9Z48XQrySu3ov4dST6b5Le6+y/f7Ge8eArw9p3ai6dJvjfJH1XVmdzc2vmTO0UdgHtrjbtivpzkQyvMAsAKvPMUYBhhBxhG2AGGEXaAYYQdYBhhBxhG2AGGEXaAYYQdYBhhBxhG2AGGEXaAYYQdYBhhBxhG2AGGEXaAYYQdYBhhBxhG2AGGEXaAYYQdYBhhBxhG2AGGEXaAYYQdYBhhBxhG2AGGEXaAYYQdYJjFYa+q91fV1ao6qqoXq+rjawwGwMmcXWGNV5P8and/qarek+T5qnq2u/9+hbUBeJsWX7F3979295du/fs/khwled/SdQE4mVX32KvqfJIPJfnibZ67VFWHVXV4fHy85mkBeI3Vwl5V35Xkz5L8Snf/+xuf7+4r3b3T3TtbW1trnRaAN1gl7FX1QG5G/ZPd/edrrAnAyaxxV0wleSrJUXf/zvKRAFhijSv2jyb5hSQ/VlUv3Pr6yRXWBeAEFt/u2N0HSWqFWQBYgXeeAgwj7ADDCDvAMGt8pAC8Y928aeve6+5TOQ+8FcLOaG83uFUl0tz3bMUADCPsAMMIO8Awwg4wjLADDCPsAMMIO8Awwg4wjLADDCPsAMMIO8Awwg4wjLADDCPsAMMIO8Awwg4wjLADDCPsAMMIO8Awwg4wjLADDCPsAMMIO8Awq4S9qp6uqpeq6toa6wFwcmtdsX8iyWMrrQXAAquEvbufS/LyGmsBsIw9doBhTi3sVXWpqg6r6vD4+Pi0TgvwrnNqYe/uK9290907W1tbp3VagHcdWzEAw6x1u+N+ks8n+UBVXa+q3TXWBeDtO7vGIt39+BrrALCcrRiAYYQdYJhVtmLgNDz44IO5cePGPT9PVd3T9c+dO5eXX/Z+Pu4dYee+cePGjXT3psdY7F7/4gBbMQDDCDvAMMIOMIywAwwj7ADDCDvAMMIOMIywAwwj7ADDCDvAMMIOMIywAwwj7ADDCDvAMMIOMIywAwwj7ADDCDvAMMIOMIywAwwj7ADDCDvAMMIOMIywAwxzdtMDwFt15usfydmXP7rpMRY78/WPbHoEhlsl7FX1WJLfTXImyR9292+usS681je/52/T3ZseY7GqSu7//wbvYIu3YqrqTJLfT/ITST6Y5PGq+uDSdQE4mTX22D+c5J+6+yvd/V9J/jjJT6+wLgAnsEbY35fkn1/z/fVbjwGwAWuEvW7z2P/bQayqS1V1WFWHx8fHK5wWgNtZI+zXk7z/Nd8/nORf3nhQd1/p7p3u3tna2lrhtADczhph/7skP1hV319V35rk55P8xQrrAnACi2937O5Xq+qXk3wmN293fLq7X1w8GQAnssp97N396SSfXmMtAJbxkQIAwwg7wDDCDjCMsAMMI+wAwwg7wDDCDjCMsAMMI+wAwwg7wDDCDjCMsAMMI+wAwwg7wDDCDjCMsAMMs8of2oDTUnW7v51+fzl37tymR2A4Yee+0d33/BxVdSrngXvJVgzAMMIOMIywAwwj7ADDCDvAMMIOMIywAwwj7ADDCDvAMMIOMIywAwyzKOxV9XNV9WJV/XdV7aw1FAAnt/SK/VqSn03y3AqzALCCRZ/u2N1HyYyPUgWY4tT22KvqUlUdVtXh8fHxaZ0W4F3nrlfsVfW5JO+9zVN73f2pt3qi7r6S5EqS7Ozs+MBrgHvkrmHv7o+dxiAArMPtjgDDLL3d8Weq6nqSH0nyV1X1mXXGAuCklt4V80ySZ1aaBYAV2IoBGEbYAYYRdoBhhB1gGGEHGEbYAYYRdoBhhB1gGGEHGEbYAYYRdoBhhB1gGGEHGEbYAYYRdoBhhB1gGGEHGEbYAYYRdoBhhB1gGGEHGEbYAYYRdoBhhB1gGGEHGEbYAYYRdoBhhB1gmEVhr6rfrqp/qKovV9UzVfXdaw0GwMksvWJ/Nsl2d/9Qkn9M8uvLRwJgiUVh7+7Pdvert779QpKHl48EwBJr7rH/UpK/XnE9AE7g7N0OqKrPJXnvbZ7a6+5P3TpmL8mrST55h3UuJbmUJI888siJhgXg7u4a9u7+2J2er6pfTPJTSX68u/sO61xJciVJdnZ23vQ4AJa5a9jvpKoeS/JrSX60u/9znZEAWGLpHvvvJXlPkmer6oWq+oMVZgJggUVX7N39A2sNAsA6vPMUYBhhBxhG2AGGWbTHDu90VXUqP3OHO33h1Ak7owku70a2YgCGEXaAYYQdYBhhBxhG2AGGEXaAYYQdYBhhBximNvEGjqo6TvLVUz8x3N1DSb626SHgTXxfd2/d7aCNhB3eqarqsLt3Nj0HLGErBmAYYQcYRtjh9a5segBYyh47wDCu2AGGEXZIUlVPV9VLVXVt07PAUsION30iyWObHgLWIOyQpLufS/LypueANQg7wDDCDjCMsAMMI+wAwwg7JKmq/SSfT/KBqrpeVbubnglOyjtPAYZxxQ4wjLADDCPsAMMIO8Awwg4wjLADDCPsAMMIO8Aw/wPP/dhANwPrTgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data3 = np.random.randn(100)\n",
    "\n",
    "plt.boxplot(data3)\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **boxplot()** function takes a set of values and computes the mean, median and other statistical quantities. The following points describe the preceeding boxplot:\n",
    "\n",
    "\n",
    "•\tThe red bar is the median of the distribution. \n",
    "\n",
    "•\tThe blue box includes 50 percent of the data from the lower quartile to the upper quartile. Thus, the box is centered on the median of the data. \n",
    "\n",
    "•\tThe lower whisker extends to the lowest value within 1.5 IQR from the lower quartile. \n",
    "\n",
    "•\tThe upper whisker extends to the highest value within 1.5 IQR from the upper quartile. \n",
    "\n",
    "•\tValues further from the whiskers are shown with a cross marker.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customized Boxplot\n",
    "\n",
    "\n",
    "To show more than one boxplot in a single graph, calling plt.boxplot() once for each boxplot is not going to work. It will simply draw the boxplots over each other. We can draw several boxplots with just one single call to plt.boxplot() as follows:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "data4 = np.random.randn(100, 5)\n",
    "\n",
    "plt.boxplot(data4)\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 26. Area Chart\n",
    "\n",
    "\n",
    "An **Area Chart** is very similar to a **Line Chart**. The area between the x-axis and the line is filled in with color or shading. It represents the evolution of a numerical variable following another numerical variable.\n",
    "\n",
    "We can create an Area Chart as follows:-\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Create some data\n",
    "x12 = range(1, 6)\n",
    "y12 = [1, 4, 6, 8, 4]\n",
    "\n",
    "# Area plot\n",
    "plt.fill_between(x12, y12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have created a basic Area chart. I could also use the stackplot function to create the Area chart as follows:-\n",
    "\n",
    "`plt.stackplot(x12, y12)`\n",
    "\n",
    "The fill_between() function is more convenient for future customization.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 27. Contour Plot\n",
    "\n",
    "\n",
    "**Contour plots** are useful to display three-dimensional data in two dimensions using contours or color-coded regions.\n",
    "**Contour lines** are also known as **level lines** or **isolines**. **Contour lines** for a function of two variables are \n",
    "curves where the function has constant values. They have specific names beginning with iso- according to the nature of the variables being mapped.\n",
    "\n",
    "\n",
    "There are lot of applications of **Contour lines** in several fields such as meteorology(for temperature, pressure, rain, wind\n",
    "speed), geography, magnetism, engineering, social sciences and so on.\n",
    "\n",
    "\n",
    "The density of the lines indicates the **slope** of the function. The **gradient** of the function is always perpendicular to the contour lines. When the lines are close together, the length of the gradient is large and the variation is steep.\n",
    "\n",
    "\n",
    "A **Contour plot** can be created with the **plt.contour()** function as follows:-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Create a matrix\n",
    "matrix1 = np.random.rand(10, 20)\n",
    "\n",
    "cp = plt.contour(matrix1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **contour()** function draws contour lines. It takes a 2D array as input.Here, it is a matrix of 10 x 20 random elements.\n",
    "\n",
    "The number of level lines to draw is chosen automatically, but we can also specify it as an additional parameter, N.\n",
    "\n",
    "`plt.contour(matrix, N)`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is also a similar function that draws a filled contours plot, **contourf()**. We can use this function as follows:-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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": [
    "csf = plt.contourf(matrix1)\n",
    "\n",
    "plt.colorbar()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**contourf()** fills the spaces between the contours lines with the same color progression used in the **contour()**\n",
    "plot. Contour colors can be changed using a **colormap**. A **colormap** is a set of colors used as a lookup table by Matplotlib\n",
    "when it needs to select more colors. I also added a **colorbar()** call to draw a color bar next to the plot to identify the \n",
    "ranges the colors are assigned to."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Labeling the level lines is important in order to provide information about what levels were chosen to display. \n",
    "**clabel()** does this by taking as input a contour instance.\n",
    "\n",
    "I draw several ellipses and then call **clabel()** to display the selected levels. The output of the code is shown below:-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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