matplotlib.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MatPlotLib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting matplotlib\n",
      "  Downloading matplotlib-3.6.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.8 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.8/11.8 MB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hCollecting contourpy>=1.0.1\n",
      "  Downloading contourpy-1.0.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (300 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m300.3/300.3 KB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hCollecting kiwisolver>=1.0.1\n",
      "  Using cached kiwisolver-1.4.4-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.6 MB)\n",
      "Collecting fonttools>=4.22.0\n",
      "  Using cached fonttools-4.38.0-py3-none-any.whl (965 kB)\n",
      "Requirement already satisfied: numpy>=1.19 in ./venv/lib/python3.10/site-packages (from matplotlib) (1.24.1)\n",
      "Collecting cycler>=0.10\n",
      "  Using cached cycler-0.11.0-py3-none-any.whl (6.4 kB)\n",
      "Collecting pillow>=6.2.0\n",
      "  Downloading Pillow-9.4.0-cp310-cp310-manylinux_2_28_x86_64.whl (3.4 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: packaging>=20.0 in ./venv/lib/python3.10/site-packages (from matplotlib) (23.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in ./venv/lib/python3.10/site-packages (from matplotlib) (2.8.2)\n",
      "Collecting pyparsing>=2.2.1\n",
      "  Downloading pyparsing-3.0.9-py3-none-any.whl (98 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.3/98.3 KB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: six>=1.5 in ./venv/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
      "Installing collected packages: pyparsing, pillow, kiwisolver, fonttools, cycler, contourpy, matplotlib\n",
      "Successfully installed contourpy-1.0.7 cycler-0.11.0 fonttools-4.38.0 kiwisolver-1.4.4 matplotlib-3.6.3 pillow-9.4.0 pyparsing-3.0.9\n"
     ]
    }
   ],
   "source": [
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip freeze > requirements.txt"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0, 5, 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = x ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.  ,  0.25,  1.  ,  2.25,  4.  ,  6.25,  9.  , 12.25, 16.  ,\n",
       "       20.25, 25.  ])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe84964b6a0>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cities = ['Lahore', 'Karachi', 'Multan', 'Islamabad', 'Rawalpindi']\n",
    "temp = [34, 43, 34, 24, 55]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe8459aa4d0>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1)\n",
    "plt.xlabel('cities')\n",
    "plt.ylabel('temp')\n",
    "plt.title('Hello World')\n",
    "plt.plot(cities, temp, 'g*')\n",
    "plt.subplot(1,2,2)\n",
    "plt.xlabel('cities')\n",
    "plt.ylabel('temp')\n",
    "plt.title('Hello World')\n",
    "plt.plot(x, y, 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "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": "3.10.6"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "1e0c698051b21bfdeb803ae7a60794cf1d5cfa15e417786d6dc0ab4fc7ea65f2"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}