.ipynb_checkpoints/Matplotlib-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "29d6b6ce",
   "metadata": {},
   "source": [
    "# Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fde9163",
   "metadata": {},
   "source": [
    "#### Data Visualiztion\n",
    "\n",
    "<ul>\n",
    "    <li>Data visualization is about viewing or visualizing data in the form of graphical plots, chast, figures, and animations.</li>\n",
    "    <li>It is an effective way of communicating information to others.</li>\n",
    "    <li>Libraries: \n",
    "\n",
    "* [Altair](https://github.com/altair-viz/altair) - Declarative statistical visualization library for Python.\n",
    "* [Bokeh](https://github.com/bokeh/bokeh) - Interactive Web Plotting for Python.\n",
    "* [bqplot](https://github.com/bloomberg/bqplot) - Interactive Plotting Library for the Jupyter Notebook.\n",
    "* [Cartopy](https://github.com/SciTools/cartopy) - A cartographic python library with matplotlib support.\n",
    "* [Dash](https://plot.ly/products/dash/) - Built on top of Flask, React and Plotly aimed at analytical web applications.\n",
    "* [diagrams](https://github.com/mingrammer/diagrams) - Diagram as Code.\n",
    "* [Matplotlib](http://matplotlib.org/) - A Python 2D plotting library.\n",
    "* [plotnine](https://github.com/has2k1/plotnine) - A grammar of graphics for Python based on ggplot2.\n",
    "* [Pygal](http://www.pygal.org/en/latest/) - A Python SVG Charts Creator.\n",
    "* [PyGraphviz](https://pypi.org/project/pygraphviz/) - Python interface to [Graphviz](http://www.graphviz.org/).\n",
    "* [PyQtGraph](http://www.pyqtgraph.org/) - Interactive and realtime 2D/3D/Image plotting and science/engineering widgets.\n",
    "* [Seaborn](https://github.com/mwaskom/seaborn) - Statistical data visualization using Matplotlib.\n",
    "* [VisPy](https://github.com/vispy/vispy) - High-performance scientific visualization based on OpenGL.\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f00d3def",
   "metadata": {},
   "source": [
    "### Matplotlib\n",
    "Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+.\n",
    "\n",
    "<ul>\n",
    "    <li>Orgainzed in hierarchy</li>\n",
    "    <li>top-level <b>matplotlib.pypplot</b> module is present</li>\n",
    "    <li>pyplot is used for few activity such as figure creation, </li>\n",
    "    <li>Through the created figure, one or more axes/subplot object are created</li>\n",
    "    <li>Axes object can further use for doing many plotting actions.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4cbfc477",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import library\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "76846d29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in c:\\users\\mritu\\anaconda3\\lib\\site-packages (3.8.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\mritu\\anaconda3\\lib\\site-packages (from matplotlib) (1.0.5)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\mritu\\anaconda3\\lib\\site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\mritu\\anaconda3\\lib\\site-packages (from matplotlib) (4.25.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\mritu\\anaconda3\\lib\\site-packages (from matplotlib) (1.4.4)\n",
      "Requirement already satisfied: numpy<2,>=1.21 in c:\\users\\mritu\\anaconda3\\lib\\site-packages (from matplotlib) (1.24.3)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\mritu\\anaconda3\\lib\\site-packages (from matplotlib) (23.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\mritu\\anaconda3\\lib\\site-packages (from matplotlib) (9.4.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\mritu\\anaconda3\\lib\\site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\mritu\\anaconda3\\lib\\site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\mritu\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "#install matplotlib\n",
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "448eab16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.8.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get matplotlib version\n",
    "matplotlib.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "73b4f98c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in c:\\users\\mritu\\anaconda3\\lib\\site-packages (3.8.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\mritu\\anaconda3\\lib\\site-packages (from matplotlib) (1.0.5)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\mritu\\anaconda3\\lib\\site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\mritu\\anaconda3\\lib\\site-packages (from matplotlib) (4.25.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\mritu\\anaconda3\\lib\\site-packages (from matplotlib) (1.4.4)\n",
      "Requirement already satisfied: numpy<2,>=1.21 in c:\\users\\mritu\\anaconda3\\lib\\site-packages (from matplotlib) (1.24.3)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\mritu\\anaconda3\\lib\\site-packages (from matplotlib) (23.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\mritu\\anaconda3\\lib\\site-packages (from matplotlib) (9.4.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\mritu\\anaconda3\\lib\\site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\mritu\\anaconda3\\lib\\site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\mritu\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "#upgrade matplotlib\n",
    "!pip install --upgrade matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "410c299c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.8.0'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get matplotlib version after upgrade\n",
    "matplotlib.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5bbdbda",
   "metadata": {},
   "source": [
    "### Parts of Matplotlib \n",
    "\n",
    "<ol>\n",
    "    <li>Figure: Whole area chosen for plotting</li>\n",
    "    <li>Axes: Area where data is plotted</li>\n",
    "    <li>Axis: Number-line link objects, which define graph limits (x, y axis)</li>\n",
    "    <li>Artist: Every element on the figure is an artist (Major and Minor tick table)</li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4713601",
   "metadata": {},
   "source": [
    "### Figure\n",
    "\n",
    "<ul>\n",
    "    <li>It refer to the whole area or page on which everything is drawn.</li>\n",
    "    <li>It include Axes, Axis and other Artist element</li>\n",
    "    <li>Figure is created using <b>figure</b> function of pyplot module</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c0339f50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create figure using plt.figure() return figure object\n",
    "fig = plt.figure()\n",
    "\n",
    "# Viewing figure to display figure need to tell explicitly pyplot to display it\n",
    "# below command will return the object to display figure it required at least one axes.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbdb34e3",
   "metadata": {},
   "source": [
    "### Axes\n",
    "\n",
    "<ul>\n",
    "    <li>Axes is the region of the figure, available for plotting data</li>\n",
    "    <li>Axes object is associated with only one Figure</li>\n",
    "    <li>Figure can contain one or more number of Axes element</li>\n",
    "    <li>Axes contain two Axis object in case of 2D plots and three object in case of 3D plots</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8ef16b5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Axes can be added by using methods add_subplot(nrows, ncols, index) return axes object\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "548a7ad5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Adjusting Figure Size => plt.figure(figsize=(x, y))\n",
    "fig = plt.figure(figsize=(3, 3))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "858077c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#setting title and axis label as parameter\n",
    "fig = plt.figure(figsize=(10,3))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.set(title=\"Figure\"\n",
    "      , xlabel='x-asix'\n",
    "      , ylabel='y-axis'\n",
    "      , xlim=(0,5)\n",
    "      , ylim=(0,10)\n",
    "      , xticks=[0.1, 0.9, 2, 3, 4, 5]\n",
    "      , xticklabels=['pointone', 'pointnine', 'two', 'three', 'four', 'five'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b0f903cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#setting title and axis label as method\n",
    "fig = plt.figure(figsize=(3,3))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_title(\"Figure\")\n",
    "ax.set_xlabel(\"X-Axis\")\n",
    "ax.set_ylabel('Y-Axis')\n",
    "ax.set_xlim([0,5])\n",
    "ax.set_ylim([0,10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6523c16c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#setting title and axis label eplicitly\n",
    "fig = plt.figure(figsize=(3,3))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "plt.title('Figure')\n",
    "plt.xlabel('X-Axis')\n",
    "plt.ylabel('Y-Axis')\n",
    "plt.xlim(0,5)\n",
    "plt.ylim(0,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cbd61951",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot data points in graph => plt.plot(x, y) will use\n",
    "x = list(range(0,5))\n",
    "y = list(range(0,10, 2))\n",
    "\n",
    "fig = plt.figure(figsize=(3,3))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.set(title=\"Figure\"\n",
    "      , xlabel='x-asix'\n",
    "      , ylabel='y-axis'\n",
    "      , xlim=(0,5)\n",
    "      , ylim=(0,10))\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2779d09b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#adding legend in graph => plt.legend(x, y, label='legend'); plt.legend() will use\n",
    "x = list(range(0,5))\n",
    "y = list(range(0,10, 2))\n",
    "\n",
    "fig = plt.figure(figsize=(3,3))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.set(title=\"Figure\"\n",
    "      , xlabel='x-asix'\n",
    "      , ylabel='y-axis'\n",
    "      , xlim=(0,5)\n",
    "      , ylim=(0,10))\n",
    "plt.plot(x,y, label='legend')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "431ca18f",
   "metadata": {},
   "source": [
    "### Types Of Plot\n",
    "\n",
    "1. Line plot\n",
    "2. Scatter plot\n",
    "3. Bar plot\n",
    "4. Pie plot\n",
    "5. Histogram\n",
    "6. Box plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a176c0d",
   "metadata": {},
   "source": [
    "### Line Plot\n",
    "\n",
    "* Line Plot is used to visualize a trend in data.\n",
    "* Line Plot is also used to compare two variables.\n",
    "* Line Plots are simple and effective in communicating.\n",
    "* plot function is used for drawing Line plots.\n",
    "* Syntax: plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bf465e13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [1, 5, 8, 12, 15, 19, 22, 26, 29]\n",
    "y = [29.3, 30.1, 30.4, 31.5, 32.3, 32.6, 31.8, 32.4, 32.7]\n",
    "\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.set(title='Line Plot Graph'\n",
    "       , xlabel='X-Axis'\n",
    "       , ylabel='Y-Axis'\n",
    "       , xlim=(0, 30)\n",
    "       , ylim=(25, 35))\n",
    "ax.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79884193",
   "metadata": {},
   "source": [
    "### Parameter for Plot \n",
    "\n",
    "* color - Sets the color of the line.\n",
    "* linestyle - Sets the line style, e.g., solid, dashed, etc.\n",
    "* linewidth - Sets the thickness of a line.\n",
    "* marker - Chooses a marker for data points, e.g., circle, triangle, etc.\n",
    "* markersize - Sets the size of the chosen marker.\n",
    "* label - Names the line, which will come in legend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fcaecb5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6,3))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.set(title='Line Plot Graph'\n",
    "       , xlabel='X-Axis'\n",
    "       , ylabel='Y-Axis'\n",
    "       , xlim=(0, 30)\n",
    "       , ylim=(25, 35))\n",
    "ax.plot(x\n",
    "        , y\n",
    "        , color='blue'\n",
    "        , linestyle='dotted'\n",
    "        , linewidth=1\n",
    "        , marker='*'\n",
    "        , markersize=10\n",
    "        , label='label')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79d99deb",
   "metadata": {},
   "source": [
    "### Multiple line with single plot function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e30de308",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2c779862bd0>,\n",
       " <matplotlib.lines.Line2D at 0x2c779484650>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=[1,4,6,8,2]\n",
    "y=[10,10,1,5,0]\n",
    "\n",
    "fig = plt.figure(figsize=(8,4))\n",
    "ax = fig.add_subplot()\n",
    "ax.plot(x, y, 'g<', x,y, 'r--')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96901b3b",
   "metadata": {},
   "source": [
    "### Scatter Graph\n",
    "\n",
    "* It similar to line graph\n",
    "* Used to show how one variable is related to another\n",
    "* It consist of data point, if it in linear then it higly corelated\n",
    "* It only mark the data point.\n",
    "* Syntax: plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc74a964",
   "metadata": {},
   "source": [
    "### Parameter of Scatter Graph\n",
    "\n",
    "* c: Sets color of markers.\n",
    "* s: Sets size of markers.\n",
    "* marker: Selects a marker. e.g: circle, triangle, etc\n",
    "* edgecolor: Sets the color of lines on edges of markers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "367ecba1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2c77b3e1b50>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=[1,4,6,8,2]\n",
    "y=[10,10,1,5,0]\n",
    "\n",
    "fig = plt.figure(figsize=(8,4))\n",
    "ax = fig.add_subplot()\n",
    "ax.scatter(x\n",
    "           , y\n",
    "           , c='red'\n",
    "           , s=500\n",
    "           , marker='*'\n",
    "           , edgecolor='blue'\n",
    "           , label='scatter')\n",
    "ax.set_title('Scatter')\n",
    "ax.set_xlabel('X-Axis')\n",
    "ax.set_ylabel('Y-Axis')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a709c26c",
   "metadata": {},
   "source": [
    "### Bar Chart\n",
    "\n",
    "* It mostly used to compare categories\n",
    "* bar is used for vertical bar plots\n",
    "* barh is used for horizontal bar plots\n",
    "* Syntax: bar(x, height) or bar(y,width)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6997ba0b",
   "metadata": {},
   "source": [
    "### Parameter of Bar Graph\n",
    "\n",
    "* color: Sets the color of bars.\n",
    "* edgecolor: Sets the color of the border line of bars.\n",
    "* label: Sets label to a bar, appearing in legend.\n",
    "* color: color of bar\n",
    "* align: Aligns the bars w.r.t x-coordinates\n",
    "\n",
    "##### bar(x,width)\n",
    "* width: Sets the width of bars\n",
    "\n",
    "#### barh(y,height)\n",
    "* height: Sets the height of bars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4adeb973",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#vertical bar graph\n",
    "x = [1, 2, 3]\n",
    "y = [10,20,30]\n",
    "\n",
    "fig = plt.figure(figsize=(8,4))\n",
    "ax = fig.add_subplot()\n",
    "\n",
    "ax.set(title='Bar Graph'\n",
    "       , xlabel='X-Axis'\n",
    "       , ylabel='Y-Axis'\n",
    "       , xticks=x\n",
    "       , label='label'\n",
    "       , xticklabels=['first', 'second', 'third'])\n",
    "ax.bar(x\n",
    "       , y\n",
    "       , color='green'\n",
    "       , edgecolor='red'\n",
    "       , width=0.5\n",
    "       , align='edge'\n",
    "       , label='label'\n",
    "       , linewidth=5)\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cfb71a8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#horizontal bar graph\n",
    "x = [1, 2, 3]\n",
    "y = [10,20,30]\n",
    "\n",
    "fig = plt.figure(figsize=(8,4))\n",
    "ax = fig.add_subplot()\n",
    "\n",
    "ax.set(title='Bar Graph'\n",
    "       , xlabel='X-Axis'\n",
    "       , ylabel='Y-Axis'\n",
    "       , xticks=x\n",
    "       , label='label'\n",
    "       , xticklabels=['first', 'second', 'third'])\n",
    "plt.barh(x\n",
    "       , y\n",
    "       , color='green'\n",
    "       , edgecolor='red'\n",
    "       , height=0.5\n",
    "       , align='edge'\n",
    "       , label='label')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96cc1109",
   "metadata": {},
   "source": [
    "### Error Bar\n",
    "\n",
    "* It is used to plot y versus x as lines and/or markers with attached errorbars.\n",
    "\n",
    "### Parameter of Error Bar\n",
    "\n",
    "* ecolor: it is the color of the errorbar lines.\n",
    "* elinewidth: it is the linewidth of the errorbar lines.\n",
    "* capsize: it is the length of the error bar caps in points.\n",
    "* barsabove: It contains boolean value True for plotting errorsbars above the plot symbols. Its default value is False.\n",
    "* Syntax: errorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d92ba5a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ErrorbarContainer object of 3 artists>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = [1, 3, 5, 7]\n",
    "b = [11, 2, 4, 19]\n",
    "c = [1, 1, 1, 1]\n",
    "d = [4, 3, 2, 1]\n",
    "\n",
    "fig=plt.figure(figsize=(4,4))\n",
    "ax=fig.add_subplot()\n",
    "plt.bar(a, b) \n",
    "plt.errorbar(a, b, xerr=c, yerr=d, fmt=\"^\", color=\"r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96fce55f",
   "metadata": {},
   "source": [
    "### Pie Plot\n",
    "\n",
    "* It is effective in showing the proportion of categories.\n",
    "* It is best suited for comparing fewer categories.\n",
    "* It is used to highlight proportion of one or a group of categories.\n",
    "* Syntax: pie(x), x: size of portions, passed as fraction or number"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b561126",
   "metadata": {},
   "source": [
    "### Parameter of Pie\n",
    "\n",
    "* colors: Sets the colors of portions.\n",
    "* labels: Sets the labels of portions.\n",
    "* startangle: Sets the start angle at which portion drawing starts.\n",
    "* autopct: Sets the percentage display format of an area, covering portions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bd7647fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x2c77bbad2d0>,\n",
       "  <matplotlib.patches.Wedge at 0x2c77b56d310>,\n",
       "  <matplotlib.patches.Wedge at 0x2c77b56fe90>,\n",
       "  <matplotlib.patches.Wedge at 0x2c77b56de10>,\n",
       "  <matplotlib.patches.Wedge at 0x2c77b537e50>],\n",
       " [Text(1.075962358309037, 0.22870287165240302, 'first'),\n",
       "  Text(0.7360436312779136, 0.817459340184711, 'second'),\n",
       "  Text(-0.33991877217145816, 1.046162142464278, 'third'),\n",
       "  Text(-1.0759623315431446, -0.2287029975759841, 'fourth'),\n",
       "  Text(0.5500001932481627, -0.9526278325909777, 'fifth')],\n",
       " [Text(0.5868885590776565, 0.12474702090131072, '6.7%'),\n",
       "  Text(0.4014783443334074, 0.4458869128280241, '13.3%'),\n",
       "  Text(-0.18541023936624987, 0.5706338958896061, '20.0%'),\n",
       "  Text(-0.5868885444780788, -0.12474708958690041, '26.7%'),\n",
       "  Text(0.3000001054080887, -0.5196151814132605, '33.3%')])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=[1,2,3,4,5]\n",
    "\n",
    "fig=plt.figure(figsize=(4,4))\n",
    "ax=fig.add_subplot()\n",
    "ax.set(title='Pie')\n",
    "\n",
    "plt.pie(x\n",
    "       , colors=['brown', 'red', 'green', 'yellow', 'blue']\n",
    "       , labels=['first', 'second', 'third', 'fourth', 'fifth']\n",
    "       , startangle=0\n",
    "       , autopct='%1.1f%%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb8e9680",
   "metadata": {},
   "source": [
    "### Histogram Chart\n",
    "\n",
    "* It used to visualize the spread of data of a distribution\n",
    "* Syntax: hist(x), x is the data values\n",
    "\n",
    "### Parameter of Histogram\n",
    "\n",
    "* color: Sets the color of bars.\n",
    "* bins: Sets the number of bins to be used.\n",
    "* density: Sets to True where bins display fraction and not the count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7f802e86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.5, 0. , 0.5, 0. , 0. , 0.5, 0. , 0.5, 0. , 0.5]),\n",
       " array([1. , 1.4, 1.8, 2.2, 2.6, 3. , 3.4, 3.8, 4.2, 4.6, 5. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=[1,2,3,4,5]\n",
    "\n",
    "fig=plt.figure(figsize=(20,4))\n",
    "ax=fig.add_subplot()\n",
    "\n",
    "ax.set(title='Histogram'\n",
    "      , xlabel='X-Axis'\n",
    "      , ylabel='Y-Axis')\n",
    "\n",
    "ax.hist(x\n",
    "       , color='red'\n",
    "       , bins=10\n",
    "       , density=True\n",
    "       , orientation='vertical')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68d5f560",
   "metadata": {},
   "source": [
    "### Box Plot\n",
    "\n",
    "It is a type of chart that depicts a group of numerical data through their quartiles. It is a simple way to visualize the shape of our data. It makes comparing characteristics of data between categories very easy.\n",
    "\n",
    "* Box plots are also used to visualize the spread of data.\n",
    "* Box plots are used to compare distributions.\n",
    "* Box plots can also be used to detect outliers.\n",
    "* Syntax: boxplot(x)\n",
    "\n",
    "### Parameter of Box Plot\n",
    "\n",
    "* labels: Sets the labels for box plots.\n",
    "* notch: Sets to True if notches need to be created around the median.\n",
    "* bootstrap: Number set to indicate that notches around the median are bootstrapped.\n",
    "* vert: Sets to False for plotting Box plots horizontally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e855fdfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x2c779aa7390>,\n",
       "  <matplotlib.lines.Line2D at 0x2c779aa6a90>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x2c779aa51d0>,\n",
       "  <matplotlib.lines.Line2D at 0x2c779aa5190>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x2c77b4aabd0>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x2c779aa46d0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x2c779aab810>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=[1,2,3,4,5]\n",
    "\n",
    "fig=plt.figure(figsize=(20,10))\n",
    "ax=fig.add_subplot()\n",
    "\n",
    "ax.set(title='Histogram'\n",
    "      , xlabel='X-Axis'\n",
    "      , ylabel='Y-Axis')\n",
    "\n",
    "ax.boxplot(x\n",
    "          , labels=['start']\n",
    "          , notch=False\n",
    "          , bootstrap=10000\n",
    "          , vert=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2d2f5f2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(100)\n",
    "x = 50 + 10*np.random.randn(1000)\n",
    "y = 70 + 25*np.random.randn(1000)\n",
    "z = 30 + 5*np.random.randn(1000)\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set(title=\"Box plot with outlier\",\n",
    "      xlabel='x-Axis', ylabel='Y-Axis')\n",
    "ax.boxplot([x, y, z], labels=['A', 'B', 'C'], notch=True, bootstrap=10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f26089c",
   "metadata": {},
   "source": [
    "### Matplotlib Styles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7f6946fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Solarize_Light2',\n",
       " '_classic_test_patch',\n",
       " '_mpl-gallery',\n",
       " '_mpl-gallery-nogrid',\n",
       " 'bmh',\n",
       " 'classic',\n",
       " 'dark_background',\n",
       " 'fast',\n",
       " 'fivethirtyeight',\n",
       " 'ggplot',\n",
       " 'grayscale',\n",
       " 'seaborn-v0_8',\n",
       " 'seaborn-v0_8-bright',\n",
       " 'seaborn-v0_8-colorblind',\n",
       " 'seaborn-v0_8-dark',\n",
       " 'seaborn-v0_8-dark-palette',\n",
       " 'seaborn-v0_8-darkgrid',\n",
       " 'seaborn-v0_8-deep',\n",
       " 'seaborn-v0_8-muted',\n",
       " 'seaborn-v0_8-notebook',\n",
       " 'seaborn-v0_8-paper',\n",
       " 'seaborn-v0_8-pastel',\n",
       " 'seaborn-v0_8-poster',\n",
       " 'seaborn-v0_8-talk',\n",
       " 'seaborn-v0_8-ticks',\n",
       " 'seaborn-v0_8-white',\n",
       " 'seaborn-v0_8-whitegrid',\n",
       " 'tableau-colorblind10']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.style.available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ab5554e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1e508864",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<contextlib._GeneratorContextManager at 0x2c77bbe2390>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.style.context('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1c062df5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [1, 5, 8, 12, 15, 19, 22, 26, 29]\n",
    "y = [29.3, 30.1, 30.4, 31.5, 32.3, 32.6, 31.8, 32.4, 32.7]\n",
    "\n",
    "with plt.style.context(['dark_background', 'ggplot']):\n",
    "    fig = plt.figure(figsize=(8,6))\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.set(title='ggplot'\n",
    "           , xlabel='X-Axis'\n",
    "           , ylabel='Y-Axis'\n",
    "           , xlim=(0, 30)\n",
    "           , ylim=(25, 35))    \n",
    "    ax.plot(x, y, color='green', linestyle='--', linewidth=3)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "673321a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sepal_len=[6.01,6.94,7.59]\n",
    "sepal_wd=[4.42,3.77,3.97]\n",
    "petal_len=[2.46,5.26,6.55]\n",
    "petal_wd=[1.24,2.33,3.03]\n",
    "species=['setosa','versicolor','virginica']\n",
    "species_index1=[0.8,1.8,2.8]\n",
    "species_index2=[1.0,2.0,3.0]\n",
    "species_index3=[1.2,2.2,3.2]\n",
    "species_index4=[1.4,2.4,3.4]\n",
    "\n",
    "with plt.style.context('ggplot'):\n",
    "    fig = plt.figure(figsize=(9,7))\n",
    "    ax = fig.add_subplot()\n",
    "    ax.bar(species_index1\n",
    "           , sepal_len\n",
    "           , width=0.2\n",
    "           , label='Sepal Width'\n",
    "          )\n",
    "    ax.bar(species_index2\n",
    "           , sepal_wd\n",
    "           , width=0.2\n",
    "           , label='Sepal Width'\n",
    "          )\n",
    "    ax.bar(species_index3\n",
    "           , petal_len\n",
    "           , width=0.2\n",
    "           , label='Petal Length'\n",
    "          )\n",
    "    ax.bar(species_index4\n",
    "           , petal_wd\n",
    "           , width=0.2\n",
    "           , label='Petal Width'\n",
    "          )\n",
    "    \n",
    "    ax.set(xlabel='Species'\n",
    "           , ylabel='Iris Measurements (cm)'\n",
    "           , title='Mean Measurements of Iris Species'\n",
    "           , xlim=(0.5,3.7)\n",
    "           , ylim=(0,10)\n",
    "           , xticks=species_index2\n",
    "           , xticklabels=species\n",
    "          )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c48c947d",
   "metadata": {},
   "source": [
    "### Custom Style\n",
    "\n",
    "* A style sheet is a text file having extension .mplstyle.\n",
    "* All custom style sheets are placed in a folder, stylelib, present in the config directory of matplotlib.\n",
    "* Create a file mystyle.mplstyle with the below-shown contents and save it in the folder <matplotlib_configdir/stylelib/.\n",
    "* Reload the matplotlib library with the subsequent expression.\n",
    "* Use the below expression for knowing the Config folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f40f9265",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mritu\\.matplotlib\n"
     ]
    }
   ],
   "source": [
    "#Reload the matplotlib library with the subsequent expression.\n",
    "print(matplotlib.get_configdir())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "954b7f01",
   "metadata": {},
   "source": [
    "### Create a file mystyle.mplstyle with the below-shown contents and save it in the folder <matplotlib_configdir/stylelib/.\n",
    "\n",
    "* axes.titlesize : 24\n",
    "* axes.labelsize : 20\n",
    "* lines.linewidth : 8\n",
    "* lines.markersize : 10\n",
    "* xtick.labelsize : 16\n",
    "* ytick.labelsize : 16\n",
    "\n",
    "Reload the matplotlib library with the subsequent expression.\n",
    "```matplotlib.style.reload_library()```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6cecbb4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-v0_8', 'seaborn-v0_8-bright', 'seaborn-v0_8-colorblind', 'seaborn-v0_8-dark', 'seaborn-v0_8-dark-palette', 'seaborn-v0_8-darkgrid', 'seaborn-v0_8-deep', 'seaborn-v0_8-muted', 'seaborn-v0_8-notebook', 'seaborn-v0_8-paper', 'seaborn-v0_8-pastel', 'seaborn-v0_8-poster', 'seaborn-v0_8-talk', 'seaborn-v0_8-ticks', 'seaborn-v0_8-white', 'seaborn-v0_8-whitegrid', 'tableau-colorblind10']\n"
     ]
    }
   ],
   "source": [
    "print(plt.style.available)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e78d3f6",
   "metadata": {},
   "source": [
    "### matplotlibrc file ###\n",
    "\n",
    "* matplotlib uses all the settings specified in matplotlibrc file.\n",
    "* These settings are known as rc settings or rc parameters.\n",
    "* For customization, rc settings can be altered in the file or interactively.\n",
    "* The location of active matplotlibrc file used by matplotlib can be found with below expression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b6178aae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\mritu\\\\anaconda3\\\\Lib\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\matplotlibrc'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.matplotlib_fname()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b71e318",
   "metadata": {},
   "source": [
    "### Matplotlib rcParams\n",
    "\n",
    "* All rc settings, present in matplotlibrc file are stored in a dictionary named matplotlib.rcParams.\n",
    "* Any settings can be changed by editing values of this dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "718eb46b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams['lines.linewidth'] = 2\n",
    "mpl.rcParams['lines.color'] = 'r'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "942a8b45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Solarize_Light2',\n",
       " '_classic_test_patch',\n",
       " '_mpl-gallery',\n",
       " '_mpl-gallery-nogrid',\n",
       " 'bmh',\n",
       " 'classic',\n",
       " 'dark_background',\n",
       " 'fast',\n",
       " 'fivethirtyeight',\n",
       " 'ggplot',\n",
       " 'grayscale',\n",
       " 'seaborn-v0_8',\n",
       " 'seaborn-v0_8-bright',\n",
       " 'seaborn-v0_8-colorblind',\n",
       " 'seaborn-v0_8-dark',\n",
       " 'seaborn-v0_8-dark-palette',\n",
       " 'seaborn-v0_8-darkgrid',\n",
       " 'seaborn-v0_8-deep',\n",
       " 'seaborn-v0_8-muted',\n",
       " 'seaborn-v0_8-notebook',\n",
       " 'seaborn-v0_8-paper',\n",
       " 'seaborn-v0_8-pastel',\n",
       " 'seaborn-v0_8-poster',\n",
       " 'seaborn-v0_8-talk',\n",
       " 'seaborn-v0_8-ticks',\n",
       " 'seaborn-v0_8-white',\n",
       " 'seaborn-v0_8-whitegrid',\n",
       " 'tableau-colorblind10']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.style.available"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01a78d8c",
   "metadata": {},
   "source": [
    "### Subplots\n",
    "\n",
    "It provides a way to plot multiple plots on a single figure. Given the number of rows and columns, it returns a tuple (fig, ax), giving a single figure fig with an array of axes ax.\n",
    "\n",
    "* subplot creates the Axes object at index position and returns it.\n",
    "\n",
    "```python\n",
    "# 'index' is the position in a virtual grid with 'nrows' and 'ncols'\n",
    "# 'index' number varies from 1 to `nrows*ncols`.\n",
    "subplot(nrows, ncols, index)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "a3df9eb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,8))\n",
    "ax1 = plt.subplot(2, 2, 1, title='Plot1')\n",
    "ax2 = plt.subplot(2, 2, 2, title='Plot2')\n",
    "ax3 = plt.subplot(2, 2, 3, title='Plot3')\n",
    "ax4 = plt.subplot(2, 2, 4, title='Plot4')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "9e1b2745",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,8))\n",
    "\n",
    "ax1 = plt.subplot(2, 2, (1,2), title='Plot1')\n",
    "ax1.set_xticks([])\n",
    "ax1.set_yticks([])\n",
    "\n",
    "ax2 = plt.subplot(2, 2, 3, title='Plot2')\n",
    "ax2.set_xticks([])\n",
    "ax2.set_yticks([])\n",
    "\n",
    "ax3 = plt.subplot(2, 2, 4, title='Plot3')\n",
    "ax3.set_xticks([])\n",
    "ax3.set_yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b9ce8d6",
   "metadata": {},
   "source": [
    "### Subplots Using 'GridSpec'\n",
    "\n",
    "* GridSpec class of matplotlib.gridspec can also be used to create Subplots.\n",
    "* Initially, a grid with given number of rows and columns is set up.\n",
    "* Later while creating a subplot, the number of rows and columns of grid, spanned by the subplot are provided as inputs to subplot function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "80cc6c4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "gd = gridspec.GridSpec(2,2)\n",
    "\n",
    "ax1 = plt.subplot(gd[0,:],title='Plot1')\n",
    "\n",
    "ax2 = plt.subplot(gd[1,0])\n",
    "\n",
    "ax3 = plt.subplot(gd[1,-1])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "f4d09a5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,8))\n",
    "gd = gridspec.GridSpec(3,2)\n",
    "\n",
    "ax1 = plt.subplot(gd[0,:],title='Plot1')\n",
    "ax1.set_xticks([])\n",
    "ax1.set_yticks([])\n",
    "\n",
    "ax2 = plt.subplot(gd[1,0],title='Plot1')\n",
    "ax2.set_xticks([])\n",
    "ax2.set_yticks([])\n",
    "\n",
    "ax3 = plt.subplot(gd[2,0],title='Plot1')\n",
    "ax3.set_xticks([])\n",
    "ax3.set_yticks([])\n",
    "\n",
    "ax4 = plt.subplot(gd[1:,1],title='Plot1')\n",
    "ax4.set_xticks([])\n",
    "ax4.set_yticks([])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "90c83517",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(1500)\n",
    "x=np.random.rand(10)\n",
    "y=np.random.rand(10)\n",
    "z=np.sqrt(x**2+y**2)\n",
    "\n",
    "fig=plt.figure(figsize=(9,7))\n",
    "fig.suptitle('Main title for all subplot')\n",
    "\n",
    "axes1=plt.subplot(2,2,1,title='Scatter plot with Diamond Markers')\n",
    "axes1.scatter(x\n",
    "              , y\n",
    "              , s=80\n",
    "              , c=z\n",
    "              , marker='d'\n",
    "             )\n",
    "axes1.set(xticks=(0.0,0.5,1.0,1.5)\n",
    "          , yticks=(-0.2,0.2,0.6,1.0)\n",
    "         )\n",
    "\n",
    "axes2=plt.subplot(2,2,2,title='Scatter plot with Circle Markers')\n",
    "axes2.scatter(x\n",
    "              , y\n",
    "              , s=80\n",
    "              , c=z\n",
    "              , marker='o'\n",
    "             )\n",
    "axes2.set(xticks=(0.0,0.5,1.0,1.5)\n",
    "          , yticks=(-0.2,0.2,0.6,1.0)\n",
    "         )\n",
    "\n",
    "axes3=plt.subplot(2,2,3,title='Scatter plot with Plus Markers')\n",
    "axes3.scatter(x\n",
    "              , y\n",
    "              , s=80\n",
    "              , c=z\n",
    "              , marker='*'\n",
    "             )\n",
    "axes3.set(xticks=(0.0,0.5,1.0,1.5)\n",
    "          , yticks=(-0.2,0.2,0.6,1.0)\n",
    "         )\n",
    "\n",
    "axes4=plt.subplot(2,2,4,title='Scatter plot with Upper Triangle')\n",
    "axes4.scatter(x\n",
    "              , y\n",
    "              , s=80\n",
    "              , c=z\n",
    "              , marker='^'\n",
    "             )\n",
    "axes4.set(xticks=(0.0,0.5,1.0,1.5)\n",
    "          , yticks=(-0.2,0.2,0.6,1.0)\n",
    "         )\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "c8253812",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "gs = gridspec.GridSpec(3, 3)\n",
    "ax1 = plt.subplot(gs[:2, :2])\n",
    "ax2 = plt.subplot(gs[0, 2])\n",
    "ax3 = plt.subplot(gs[1, 2])\n",
    "ax4 = plt.subplot(gs[-1, 0])\n",
    "ax5 = plt.subplot(gs[-1, 1:])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcf149a5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}