16DataVisualizationWithMatplotlib.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "16DataVisualizationWithMatplotlib.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "-ANndxaqlikW"
      ],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E9pGQ4DDlfp4",
        "colab_type": "text"
      },
      "source": [
        "# 1) Matplotlib Part 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ANndxaqlikW",
        "colab_type": "text"
      },
      "source": [
        "## 1) Functional method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i3sVLvXrloxZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from numpy.random import randint"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QPSTGOe83G9J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = np.linspace(0,10,20)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UNqCYk3d3u1E",
        "colab_type": "code",
        "outputId": "8fb666d0-2a5b-4b98-defa-d7a0f884bd6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "x"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.        ,  0.52631579,  1.05263158,  1.57894737,  2.10526316,\n",
              "        2.63157895,  3.15789474,  3.68421053,  4.21052632,  4.73684211,\n",
              "        5.26315789,  5.78947368,  6.31578947,  6.84210526,  7.36842105,\n",
              "        7.89473684,  8.42105263,  8.94736842,  9.47368421, 10.        ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "78zTqQF733Qk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = randint(0,50,20)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D_Ye64Cx38sG",
        "colab_type": "code",
        "outputId": "4383ae5d-48bf-4555-db19-3f110cacea70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "y"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2, 33,  0,  3, 40, 39, 16,  6, 34, 26, 12, 16, 31, 49, 13, 21, 23,\n",
              "       10,  9, 16])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0rDyMy8s4umQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = np.sort(y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yhn3fmCx41sM",
        "colab_type": "code",
        "outputId": "21c44d54-a984-43f1-cf9c-2ffd3db145ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "y"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  2,  3,  6,  9, 10, 12, 13, 16, 16, 16, 21, 23, 26, 31, 33, 34,\n",
              "       39, 40, 49])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xc51UeS_5FNN",
        "colab_type": "code",
        "outputId": "b1a22dee-78fd-4083-8c02-3e4d66574ae4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.plot(x,y, color='m', linestyle='--', marker='*', markersize=10, lw=1.5)\n",
        "plt.xlabel('X axis')\n",
        "plt.ylabel('Y axis')\n",
        "plt.title('X vs Y axis')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wOmv0478wHosYVadVsfaGtbQ+3Zqtt5J3lPhFJBSp8/CPeN8RrLpiVbDNYMynx1BzRQ3F44r3OW72/bO7PbaoMfHSiXBpRsIuCEr8IpIWfc7Df6aVZ4Y/07W94uQKpt84PePxyV6q8YtIWvQ5Dz+lZ06kNMLkb0/OeGzSnRK/iKRF1bwqZj88u1vyT6VeObkjtMRvZiVm9oKZvWRmr5rZt5LbJ5vZ82a20szuNrPBYcUgIplVNa+K6bdMB+u+Xb1yckuYI/7dwGnufgwwBzjLzE4ErgGud/cjgS3ARSHGICIZlIgnWHPVGnCCXjlDIkGW0Tz8nBJa4vfAtuTDQcl/DpwG3Jvcfifw/rBiEJHMim+Os2v1LjA0Dz+HhTqrx8yiwGLgSOBHwOtAzN07P/rXAeP6OPZi4GKAiRMnhhmmiKTJ4JGDqZpXReW8Ss3Dz2EZ6dVjZpXA74CvAz9NlnkwswnAH9x91v6OV68ekdy2bdk21l63lmk/nkZRmWaJ54qs9upx95iZPQGcBFSaWVFy1D8eWJ+JGEQkHO1b2nnl/a+Q2Jmgo61DiX8ACHNWz4jkSB8zGwKcCTQATwAfSu42H3ggrBhEJFze4TRc0MDutbuZ+duZFI/Z95e4knvC/GgeA9yZrPNHgN+4+8Nmthz4tZl9G1gC3BZiDCISosYFjWz+w2am3TSNYScNy3Y40k+hJX53fxk4tpftq4DjwzqviGRG++Z23vzJm4z+19GM/fTYbIcjB0HFOBE5JIOGD6JucR2DRgzCzA58gOQMtWwQkYMS3xpn3f+swxNOyYQSoiVa0nCgUeIXkX7zhLNi/gpWfnEl217eduADJCep1CMi/bb6O6tpvr+ZqddPpXxOebbDkUN0wBG/mX3YzMqT979mZveZ2XHhhyYiuaTlDy00fqORkReMZPwl47MdjhyG/pR6vu7ubWb2DuAMgumXN4UblohkW+qauR3bO1gxfwVlx5Qx4+YZupg7wPUn8Xckb98H3OzuvwfUSlkkz6WumRsdGmXmvTOZed9MoqW6mDvQ9SfxrzeznwAfAR4xs+J+HiciA1hnN811N6wDoPKUSoZMHpLNkCRN+nNx91zgLOB7yZ47Y4D/E25YIpJpfa2Zu23xNhbawq7tladXMuexOZkOT9Koz5G7mVUk75YAC4EWMxtOsMCKWmWK5Jm+1sxNFSmNUPO1mkyGJSHYX8nml8nbxQSJfnHKPyV+kTyjNXMLR5+J393PTt5OdvcpydvOf1MyF6KIZErVvComfmXfhY+0Zm5+6c88/ot6PI6a2TfDC0lEsmHXml0AlEwpCUb9WjM3b/Vnds7pZvaImY0xs1nAc4B+sieSJxLtCV6/7HWen/Y8bUvaaLqticSuhNbMzWMHnNXj7ueb2UeAZcB24Hx3fyb0yEQkdLvW7WL5R5az9W9bGfsfYyk9upTosChTr5uqNXPz2AHX3DWzacCdBIn/aGA58CV33xF+eAGtuSuSfi1/aKHhYw34bmf6LdMZ9dFR2Q5J0uxw1tx9CPisuz9uwe+0vwQsAmamOUYRyaCtz26leGwxM++ZSemM0myHIxnUnxr/8e7+OIAH/i/wgXDDEpHDldprp9PuDbvZumgrAJO+OYnjnjtOSb8A9afGvzV5UbeW4Mdcnf4RWlQicthSe+2MvnA0Wx7fwvLzlxMti3L8348nUhRR350CdcDEn5y6eSpB4n8EeC/wV+BnoUYmIoelcxZO021N7Hp9F43faqT0qFJm3juTSJHabRWy/tT4PwQcAyxx90+a2SjgrnDDEpGD1VevndiTMWILg+07Gnbw2udfU6+dAtefj/2d7p4A4sn+PRuBCeGGJSIHq89eOykT99RrR6B/ib/ezCqBWwj69LwIPBtqVCJy0KrmVTHroVlYce+LpKjXjnTqz8XdzyTv/j8zexSocPeXww1LRA6Gu9PyYAuNCxrx3Q5G95G+eu1IioO6wuPujUr6IrnFO5wlJy/hlfe/Qse2DsZcPIbIUPXakb7p0r5IjuptHn4nd6f1b0ELBYsaR7zvCGbcMYO3N7ydnf/YSWKHeu1I3/a3EMsjZjYpc6GISKrUefid3J2WR1p48YQXWXLyElqfCZJ/zZU1jPnEmGBufrLXTl19HcPPHE7dojqmXDuFaIXm7EtgfzX+O4A/mdmdwLXu3p6hmESElHn4tzcx6oJRbP7jZhoXNNL2fBslk0qYcesMyo/ft1Hu7Ptnd3tsUWPipRPh0oyELQNAn4nf3e8xsz8AXyeY2fNzIJHy/PczEJ9IwehrHn7rM608GXmya3vpUaXMfWkukcGq1MqhOdBfzh6CVszFBD34U/+JSBr1NQ8/de3bSGmEaTdNU9KXw9LniN/MzgK+DzwIHJfJNswihahzzdtlZy8jsSOxz/Oahy/psr9hw5XAh9398kNJ+mY2wcyeMLPlZvaqmV2S3D7czP5sZq8lb/VXLJJUNa+Ko3959D7/ZWoevqTT/hZbf6e7v3oYrx0HLnX3WuBE4LNmVgtcDjzu7tOAx5OPRSRpwy0bgqtppnn4Eo7QCoXuvsHdX0zebwMagHHAOQQrepG8fX9YMYgMNO0t7Wz50xYAyuZoHr6Eoz/dOQ9b8vcAxwLPA6PcfUPyqSag1/XezOxi4GKAiRMnhh+kSA4YdMQgKudVUnl6JRO/PFFr3kooDrjm7mGfwKwMeBK4yt3vM7OYu1emPL/F3fdbuNSau5Lv2mPtNN3RxPhLggXORdKhrzV3Q50TZmaDgN8Cv3D3+5Kb3zKzMcnnxxC0eRYpWJ5wGi5oYNVlq9j+6vZshyMFILTEn1yY/TagocePvR4E5ifvzwceCCsGkYGgcUEjmx/ZzJE/OJKy2WXZDkcKQJg1/pOBjwHLzGxpcttXgauB35jZRcBq4NwQYxDJac0PNLP6v1cz+pOjGfsfY7MdjhSI0BK/u/+VoCt4b04P67wiA0V8W5wVF62gfG450348jeBLskj4MjKrR0T2VVRWxOwHZlM8oZhoiTpnSuao4YdIhnnCu9opDzt5GCUTS7IckRQaJX6RDFtz9RqWvGMJsb/GDryzSAiU+EUyqOXRFt742huMPH8kw04elu1wpEAp8YtkyM7Xd9JwXgND3zaUGbfM0MVcyRolfslb+1uzNtPHJ/YkeOUDr4DBrPtmES3VxVzJHiV+yVu9rVmbreMjgyOM+/w4an9Vy5ApQw7p9UTSRYlf8lbqmrXZPH7DT4KehGM/NZbh7xl+SK8lkk6hN2lLBzVpk/7obc1a3+Ndt50qT69kzmNzMnZ8T30dL5JufTVp0w+4JG/UXFnD1me3di1b2NeataM+NooNd2zY5/jxXxjfr+NHfnhkr8dPvHxir8enipRGqPlazaG+RZG0UOKXvHHANWtLgjVrdyzfwWuffW2f509YeQKzH57Ny+95GW/vJWkPCY7f8tgW1ly1Zp/n37njncHx734Zj/ee9LVmruQClXok7zT9rIkV81d022bFRu2vahnxgRHE2+LEN+87U2fw2MFEBkXYePdGGuY34Lu92/Ez755J9TnVtMfa6Wjt2Of44gnFWMR461dvseKTK7odHymJUHtPLdVnV6fxnYrsn0o9kvfaY+0MqhwEkWB0ntidIFIc3Nogo2N7kKyLyosoKu/7Tz/RHuzv7d7t+Hhb8GExqHJQcJ4+eIfvc7zWzJVcolk9khdaHm3hhWkv8Nav36LptiYSuxOUHXNoa9Y23dZEYkf2jhcJm0b8MqAl4gkaFzSy5jtrGDpzKGVzyogOizL1uqmM/8L4Q1qzNtvHi4RNNX4ZsHZv2E3D+Q3EFsYY/a+jmfY/0/SLWJEUqvFL3ml9upWtL2zlqJ8exej5o7MdjsiAoRq/5KzeeuV4h9O2uA2AkeeO5ISVJyjpixwkJX7JWT175ezZuIeXz3qZF09+kZ1v7ASgeExxNkMUGZBU6pGcldorp2RiCcs/upz4ljjTbpxGySStWiVyqJT4JWf01usGIPZUjKXvWtq1feOvNzL2U2MzHp9IvlCpR3JGzZU1REr3/kl29bpJ+ZGset2IHD4lfgnNwS5kUjK5hHGfHQd9zMhUrxuR9FCpR0KTenF29IX7zrzp2NVBtCRKvC3OotmL2L16NwDRiigd2zogpc9apCRC7d21SvoiaaDEL6FJvTg7+sLR7Fq3i9gTMWILY8SeiFE2p4xZ982iqLyII84+gtKjSqk8tZJtS7bxj8/8g8SOhHrdiIRAiV/Spq+Ls63PtLLQFu7dsQiq/1c1R5x9RNem6TdO77q/8nMru3rdTLlmCqu+soptL23r+gARkcOjxC9p06+FUJI98atO67tko143IuFSrx5Jm3hrnNcueY237nyr1+d1cVYks9SrR0K1e/1uFs1aRDwWp+LECtpebNtnpK+LsyK5QdM55ZDF2+Js/uNmAIrHFTPu8+Ooe7GOsZ8dG9T3kwuiEEEXZ0VyiBK/9Kmvefjxtjirv7ua5yY9x7JzlrGneQ8Ak781mfJjy7UQiUiOCy3xm9ntZrbRzF5J2TbczP5sZq8lb/W9P4f1bJIW3xZnzTVreG7yc7zx1TeoOLGCY58+lsHVg7sd13lxtq6+juFnDqduUR1Trp1CtEK98kVyQWgXd83sFGAb8DN3n5Xcdi2w2d2vNrPLgSp3/8qBXksXd7Nj6bylxBbGqJxXyZy/zGHHyh28cNQLDD9zOJMWTKLihIpshygi+5Hxi7vu/pSZTeqx+Rzg1OT9O4GFwAETv2RGn03Snox1m4efaE8o6YsMYJmu8Y9y9w3J+03AqAyfv6AcbK+cmitriJT00iQttXWCmqSJDHhZu7jrQY2pzzqTmV1sZvVmVr9p06YMRpY/etboe4pvC2blrLpiFbs37KZqXhWjP9X3L2M1D18kP2R6Hv9bZjbG3TeY2RhgY187uvvNwM0Q1PgzFWA+6dkrB2D3m7tZf+N6YgtjtC1qw+OODTKGvWsYxWOKmfLdKVSeWsmKC1eQ2LV3qK95+CL5I9OJ/0FgPnB18vaBDJ8/r+1vIZNuvXKAipMqmHDZBCpPrWTYPw0jOjSYcVNUVkRiZ9AUjQhqkiaSh8Kczvkr4FlghpmtM7OLCBL+mWb2GnBG8rGkSX8XMpn9yGyO+9txTLlqCsPPHN6V9DtpHr5IfgtzVs95fTx1eljnLHRV86qY/fBsXj7r5W7tEjr1t0avJmki+U1N2vLQqm+sYs1Va/ZdyOSeWqrPrs5eYCKSUX3N41fLhjyx47UdNP0sKMWUTi8NSj7qlSMivVDizwMbf7ORxXWLef2y14m3xVWjF5H9UlvmASyxO8HKS1fy5o/epOKkCmp/XUtReZFq9CKyX6rxD1CJeIIl71hC2/NtjL90PFO+O4XIIH2BE5G9tBBLnokURRh13ihqrqih+hxdsBWR/tMQMYf17LWT2JNg5ZdW0vKHFgDGXzJeSV9EDpoSfw5L7bWza80ulpyyhHXXr2Prs1uzHZqIDGAq9eSwzlk4a69by8pLVuJxp/aeWkZ+aGSWIxORgUyJP4f01Wtn+8vbu7Yt//By3jz9TeY8Nifj8YlIflCpJ4f02Wsnhfrhi8jhUuLPIZ29dqzYen1e/fBFJB2U+HOIdzixv8Tw3Q49cr/64YtIuijx54jdG3bz0hkvsfrbqxl2yjAiQ9VrR0TCocSfI9Z+by1bn9/KUT89CouYeu2ISGg0qyeLvMPZ89YeiscWM/nbkxnzqTEMPXoom363Sb12RCQ06tWTJXs27qHhwgZ2Ne5i7tK5REujBz5IROQgqFdPDok9FWP5ectpb2ln2o3Tgjq+iEiGKOOEqGevHU84q69ezdLTlhIdGqXu+TrGfmosZr1P3xQRCYMSf4hSe+1AUNNveaiFER8cQV19HWXHlGU5QhEpRCr1hKhzFs66G9Yx/KzhDK4ezNsefRvRsqhG+SKSNUr8adRXr51ti7fxtxF/69peeXqleu2ISNao1LMfPWv0B6JeOyIyECjx70fPGn1v2re00/xgMyu/uJLyuvKg184g9doRkdylUs9+dNbom25vYvSFo7u271y1k/U/Wk9sYYxtS7aBgxUbI84dQdW8Ko6+62hWzF9BYlei6xj12hGRXKHEn9oD7LoAAAdvSURBVKKvGn3r060stIVd28uPL2fb0m1UnFTBpG9OovLUSspPKCdaEvwIK7EnEfwvG4FIcYTE7oR67YhIzlDiT1FzZQ1bn91KYkcwUu+s0Xt8b60+UhphynenUHFSBdEhvf/atum2pq5eO1OumcKqr6xi20vb9vnmICKSDarxp+jsh596gTZVV43+tKo+kz5AdFiUqddNpa6+juFnDqduUR1Trp1CtEJtGUQk+9SrJ6ltcRvrfriO6T+ZzpbHtvDqB1/tNisnUhKh9p5aqs+uDjUOEZF0Ua+ePrQtaaNxQSMtD7ZQVFXE9v/cTjwWxwYbHnfV6EUk7+R1qWd/8/DjbXFe+cArLD5uMa1PtTLpvyZx4hsnUvH2im41evXDF5F8k9cj/tR5+J0XVfc072Fw9WCiZVESuxJMWjCJcZeMY1DloK7jOmv06ocvIvkoKzV+MzsL+AEQBW5196v3t/+h1viXzltKbGGMynmVHPnDI1n9rdVsfnQzJ7x+AoNHDsbd1TNHRPJWztT4zSwK/Ag4E1gHLDKzB919+eG+dl/z8GNPxqifvfeD49VzX+XYhccq6YtIQcpGjf94YKW7r3L3PcCvgXPS8cJ99srZ+wNaIqURJi2YlI7TiYgMSNlI/OOAtSmP1yW3dWNmF5tZvZnVb9q0qV8v3O95+GqbICIFLGdn9bj7ze4+193njhgxot/HVc2rovbuWiIl3d+aeuWIiASykfjXAxNSHo9PbkubeCy+t1fOkEjwLjUPX0QEyE7iXwRMM7PJZjYY+CjwYDpPoHn4IiJ9y/isHnePm9l/An8kmM55u7u/ms5zaB6+iEjf1KtHRCRP9TWPP2cv7oqISDiU+EVECowSv4hIgVHiFxEpMAPi4q6ZbQJWH+Lh1UBzGsMZCPSeC4Pec/473Pdb4+77/AJ2QCT+w2Fm9b1d1c5nes+FQe85/4X1flXqEREpMEr8IiIFphAS/83ZDiAL9J4Lg95z/gvl/eZ9jV9ERLorhBG/iIikUOIXESkweZ34zewsM/u7ma00s8uzHU+YzGyCmT1hZsvN7FUzuyTbMWWKmUXNbImZPZztWDLBzCrN7F4zW2FmDWZ2UrZjCpuZfTH5d/2Kmf3KzEqyHVO6mdntZrbRzF5J2TbczP5sZq8lb9OyklTeJv6URd3fC9QC55lZbXajClUcuNTda4ETgc/m+ftNdQnQkO0gMugHwKPufhRwDHn+3s1sHPB5YK67zyJo5/7R7EYVip8CZ/XYdjnwuLtPAx5PPj5seZv4CXFR91zk7hvc/cXk/TaCZLDPWsb5xszGA+8Dbs12LJlgZsOAU4DbANx9j7vHshtVRhQBQ8ysCCgF3sxyPGnn7k8Bm3tsPge4M3n/TuD96ThXPif+fi3qno/MbBJwLPB8diPJiBuAy4BEtgPJkMnAJuCOZHnrVjMbmu2gwuTu64HvAWuADUCru/8pu1FlzCh335C83wSMSseL5nPiL0hmVgb8FviCu2/NdjxhMrOzgY3uvjjbsWRQEXAccJO7HwtsJ01f/3NVsq59DsGH3lhgqJldmN2oMs+DufdpmX+fz4k/9EXdc42ZDSJI+r9w9/uyHU8GnAz8i5k1EpTyTjOzu7IbUujWAevcvfPb3L0EHwT57AzgDXff5O7twH3AP2U5pkx5y8zGACRvN6bjRfM58Ye+qHsuMTMjqPs2uPv3sx1PJrj7Fe4+3t0nEfz/+xd3z+uRoLs3AWvNbEZy0+nA8iyGlAlrgBPNrDT5d346eX5BO8WDwPzk/fnAA+l40Ywvtp4pmVjUPcecDHwMWGZmS5Pbvuruj2QxJgnH54BfJAc0q4BPZjmeULn782Z2L/Aiwey1JeRh6wYz+xVwKlBtZuuAbwJXA78xs4sIWtOfm5ZzqWWDiEhhyedSj4iI9EKJX0SkwCjxi4gUGCV+EZECo8QvIlJglPil4CU7m75hZsOTj6uSjyel4bX/drivIZJums4pApjZZcCR7n6xmf0EaHT372Y7LpEwaMQvErie4NehXwDeQdAUbB9mdr+ZLU72hr84ua0m2S+92swiZva0mb07+dy25O0YM3vKzJYme8q/M0PvS2QfGvGLJJnZe4BHgXe7+5/72Ge4u282syEEbUHe5e4tZvYp4D3ACwTfHD6d3H+bu5eZ2aVAibtflVwrojTZPlsk4zTiF9nrvQRtf2ftZ5/Pm9lLwHMETQCnAbj7rUAF8O/Al3s5bhHwSTNbAMxW0pdsUuIXAcxsDnAmweplX+zsiNhjn1MJOkWe5O7HEPSMKUk+V0rQARagrOexyUU2TiHoEPtTM/t4CG9DpF+U+KXgJTs+3kSwhsEa4Dp6r/EPA7a4+w4zO4rgQ6LTNcAvgG8At/RyjhrgLXe/hWC1sHxvpSw5TIlfBP4NWJNS1/8xcLSZvavHfo8CRWbWQNA18TmA5H5vB65x918Ae8ysZ8fMU4GXzGwJ8BGCdXNFskIXd0VECoxG/CIiBUaJX0SkwCjxi4gUGCV+EZECo8QvIlJglPhFRAqMEr+ISIH5/29IYDAHwaGhAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r2iyoXhk8wQh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# multiple plots on same canvas"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8DR6Wdcc8wb7",
        "colab_type": "code",
        "outputId": "4b42a166-5efd-41fb-d608-a0ef6ce7172b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "plt.subplot(1,2,1)\n",
        "plt.plot(x,y,color='r')\n",
        "plt.subplot(1,2,2)\n",
        "plt.plot(x,y,color='m')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f0b00690da0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hn4ErrhwlkfW",
        "colab_type": "text"
      },
      "source": [
        "## 2) Object Oriented Method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-4tiLp1loHG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from numpy.random import randint"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ud40Di2nLvgr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = np.linspace(0,10,20)\n",
        "y = randint(1, 50, 20)\n",
        "y = np.sort(y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p5EBBIwJecf9",
        "colab_type": "code",
        "outputId": "19098f10-e4bb-493a-99fa-0789c56799b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "x"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.        ,  0.52631579,  1.05263158,  1.57894737,  2.10526316,\n",
              "        2.63157895,  3.15789474,  3.68421053,  4.21052632,  4.73684211,\n",
              "        5.26315789,  5.78947368,  6.31578947,  6.84210526,  7.36842105,\n",
              "        7.89473684,  8.42105263,  8.94736842,  9.47368421, 10.        ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ezw09Bf1edtn",
        "colab_type": "code",
        "outputId": "633389e6-7263-4c8d-e128-76e4871a73df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "y"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 7, 12, 18, 18, 19, 19, 20, 22, 26, 28, 30, 31, 31, 35, 35, 39, 43,\n",
              "       44, 45, 47])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r6--cQvpefAO",
        "colab_type": "code",
        "outputId": "af4bce9c-4963-4a2c-813e-a67e98214dc1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        }
      },
      "source": [
        "fig = plt.figure()\n",
        "axes = fig.add_axes([0.1,0.1,1,1])\n",
        "axes.plot(x,y)\n",
        "axes.set_xlabel('X axis')\n",
        "axes.set_ylabel('Y axis')\n",
        "axes.set_title('X vs Y axis')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'X vs Y axis')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "we3MSKz8goee",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 2 sets of figures to 1 canvas"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n-B9B1KHhCN9",
        "colab_type": "code",
        "outputId": "d43540fb-2162-480a-9e97-7424952020c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        }
      },
      "source": [
        "fig = plt.figure()\n",
        "ax1 = fig.add_axes([0.1,0.1,0.8,0.8])\n",
        "ax2 = fig.add_axes([0.2,0.5,0.4,0.3])\n",
        "\n",
        "ax1.plot(x,y,color='r')\n",
        "ax1.set_xlabel('X axis')\n",
        "ax1.set_ylabel('Y axis')\n",
        "ax1.set_title('Plot 1')\n",
        "\n",
        "ax2.plot(x,y,color='m')\n",
        "ax2.set_xlabel('X axis')\n",
        "ax2.set_ylabel('Y axis')\n",
        "ax2.set_title('Plot 2')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Plot 2')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bk0cNzvHj4ra",
        "colab_type": "text"
      },
      "source": [
        "# 2) Matplotlib Part 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Eaz5YUykBDm",
        "colab_type": "text"
      },
      "source": [
        "## 1) Subplots method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NgQ6NbE5j5wh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from numpy.random import randint"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z70e7lyxj7cS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = np.linspace(0,10,20)\n",
        "y = randint(1, 50, 20)\n",
        "y = np.sort(y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "McZjB0SCGYol",
        "colab_type": "code",
        "outputId": "f544fd6a-34d6-4405-dad3-063749e9119a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "x"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.        ,  0.52631579,  1.05263158,  1.57894737,  2.10526316,\n",
              "        2.63157895,  3.15789474,  3.68421053,  4.21052632,  4.73684211,\n",
              "        5.26315789,  5.78947368,  6.31578947,  6.84210526,  7.36842105,\n",
              "        7.89473684,  8.42105263,  8.94736842,  9.47368421, 10.        ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XUOHK6wOGZzM",
        "colab_type": "code",
        "outputId": "7e9f66b3-59ef-44b0-9d5f-6a73f87c1d2f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "y"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2,  5,  7,  9, 13, 14, 17, 20, 22, 23, 27, 27, 28, 30, 33, 34, 35,\n",
              "       39, 42, 43])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0tzIQ6TKGbBx",
        "colab_type": "code",
        "outputId": "b99fbd7b-670b-43e8-92b8-f0d4213e0931",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "fig,axes = plt.subplots()\n",
        "axes.plot(x,y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f0b0058f2e8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_yVgkVK5HQcQ",
        "colab_type": "code",
        "outputId": "5aa63ac5-37f1-417d-b74c-2b682d2b497f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "fig,axes = plt.subplots(nrows=2,ncols=3)\n",
        "plt.tight_layout()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2tv9v8TnIeBw",
        "colab_type": "code",
        "outputId": "3a423c2f-85fb-46bc-aa49-cc7a2847ff42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "source": [
        "axes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f0b0047dd30>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0b0044fbe0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0b00401dd8>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f0b003c2048>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0b00376278>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0b003aa4a8>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v76QPVzjJB_l",
        "colab_type": "code",
        "outputId": "85fff6e3-a253-4529-ee23-25397efc66a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "fig,axes = plt.subplots(nrows=1,ncols=2)\n",
        "for current_ax in axes:\n",
        "  current_ax.plot(x,y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s1zuj83rJ2J1",
        "colab_type": "code",
        "outputId": "ed28c7ec-f855-4457-b781-3e3c6462c9d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "fig,axes = plt.subplots(nrows=1,ncols=2)\n",
        "axes[0].plot(x,y)\n",
        "axes[1].plot(x,y)\n",
        "axes[0].set_title('Plot 1')\n",
        "axes[1].set_title('Plot 2')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Plot 2')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ove9f3S1cXCJ",
        "colab_type": "text"
      },
      "source": [
        "## 2) Figure size, Aspect ratio and DPI"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vWm2_B95cZHS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from numpy.random import randint"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AHyIcFb3cci5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = np.linspace(0,10,20)\n",
        "y = randint(1, 50, 20)\n",
        "y = np.sort(y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OOtWP1ihdFnb",
        "colab_type": "code",
        "outputId": "387eb45c-ba47-442d-d289-643a8e463151",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        }
      },
      "source": [
        "fig = plt.figure(figsize=(3,2),dpi=100)\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "ax.plot(x,y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f0afd9bfe48>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 300x200 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RSm8mpNqjUXe",
        "colab_type": "code",
        "outputId": "99c524ec-c528-4a81-9623-58129ad6f2d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        }
      },
      "source": [
        "fig,axes = plt.subplots(nrows=1,ncols=2,figsize=(7,2))\n",
        "axes[0].plot(x,y)\n",
        "axes[1].plot(x,y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f0afd901ba8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4fP1+8kprGTUwit/cNYn54wfLEe5CdNPx6gZWbNjPgZM1mGMjeXlxKgtT4wmWkBKXqdOwUkoNBf4EDAQ0sEZrvUopFQO8CyQCxcBirfUZ95XqXhuzTvHjDw8SERokISXcKlB6akt2KSvfz0Yp+NXtqdw8KUFGJ0S3dWXzxgo8o7UeC6QDjyqlxgIrge1a62Rgu/Nnn9PQYuXp/9nPs+8dYOLQfmx94nssSImXoBLu5Nc91dxm418/zOHRd/YycmAUW5/4HrdOGSJBJXqk0z0rrXUpUOq8XaeUygMSgEXALOdibwFfAM+7pUo3ySut5dF39lJc1cBTc0fx2OyR0lDC7fy5p45V1vPo23s5XFbHg9eYefb60fK5lHCJy/rMSimVCEwCMoCBzqYDKMMxpOETtNa8s/sE/+8vh+jXO4S3/ymdGSNMRpclApC/9BTAB3tP8a8fHiQ8JIg/3j+Na0cPMLok4Ue6HFZKqSjgfeBJrXVtx/Nvaa21UuqCh58rpZYDywGGDRvWs2pdoK65jZUf5LAlu5SrR8Xx8uJUYqMufXyHEO7gLz3V2GrlJx/lsjHrFNOTYli9ZBKD+oYbXZbwM10KK6VUCI6meltr/YHz7nKl1GCtdalSajBQcaHnaq3XAGsApk6devHzqXjAqTONLH09g1Nnmnh+3hgevNosn00JQ/hLT1XUNXPX7zM4VlnPijnJrJg9Umb6Cbfo9LdKOTb3/gDkaa1f7vDQJuBe5+17gY9cX55rvfVtMSU1Tby7PJ2HZ42QoBKG8KeeWp9xkmOV9az9QRpPXzdKgkq4TVf2rK4C7gZylFL7nff9C/DvwP8opZYBx4HF7inRNbTWjqG/5DimJsYYXY4IbH7RUwBbckqYlhjDzORYo0sRfq4rswG/Bi62CzLHteW4z76TNZScbebZG0YbXYoIcP7SU0fL6zhaXs+Li8YZXYoIAAGzz74lu5TQoF7MHetTE6yE8FpbsktRCuaNH2R0KSIABERY2e363Oy/PuEhRpcjhM/T2nEOzbSkGAZEy8w/4X4BEVZ7T5yhrLaZBSmDjS5FCL9wpLyOY5UN3JgSb3QpIkAERFhtzi4lNLgXc66QgxSFcIUt2aX0UjBvnAwBCs/w+7CyO69LNWtUHNEyBChEj7XPrE03m4jr5IKJQriK34dV5vEzVNS1sCBVhiuEcIW80joKqxpYIEOAwoP8Pqy2ZJcQFtyLOWNkCFAIV9iSU0JQL8UN42RmrfAcvw4rm12z9WAZs8cMIDJMrjMpRE85ZgGWcuUIEyY5p6bwIL8Oq91FFirrWrhRZgEK4RK5JbUcr27kxgnSU8Kz/DqstuSUEB7Si9kyBCiES2zOLnUOAcosQOFZfhtWVpudTw6WMWfMQCJCZQhQiJ7SWrMlp4SrRsbSPzLU6HJEgPHbsNpdZKGqvlUOBBbCRXJOn+WkpUl6ShjCb8Nqc04pEaFBzJKrlQrhEluySwkJUtwwVoYAhef5ZVidGwK8YiC9Q4OMLkcIn9c+C3DmyFj6RsjB9cLz/DKsviusxtLQKjOWhHCR/SdrOF3TJOcCFIbxy7Dakl1KZGgQs0bHGV2KEH6hfQjwOrnEjjCI34VVm83OJ7llzB07kPAQGQIUoqfaz695dXIcfXvLEKAwht+F1bfHqqlpbJPzlgnhIu1X2V6QKsPqwjh+F1abD5QQHRbM95JjjS5FCL+wObuE0OBezL1ChgCFcfwqrFqtdj7NLeM6GQIUwiXahwCvkUvsCIP5VVh9U1BFbbNVzgUohItknThDeW2LHAgsDOc3YVVZ18I7u08QHR7MTBkCFKLHqutbWLfruPMq2zIEKIzlsyfNK69tZldhNRlFFjIKqzlW2QDA/VclEhYsQ4BCXK7KuhZ2F1mcfVXN0fJ6AG6fMoQoucSOMJjP/Aa2Wu18fLCU7445AqqoyhFO0WHBTEuKYfHUoaSZTaQk9DW4UiF8g9Vm59Pccr49VkVGkYWCCkc4RYYGMSUxhkUTE0g3m0gdIj0ljOcTYdXcZuPhdVnsPFJJn/BgpifFcNf0YaSbTYyN70NQL2V0iUL4lDabnSc37GdLTilRYcFMS+zPbVOGkJYUw/iEvoQE+c0nBMJPeH1YNbfZeHBtFl8ereRnN4/nrunDJJyE6IE2m50V6/fx8cEyfvj9MSybmUSwhJPwcl4dVs1tNh74UyZfF1Txy1sncMe0YUaXJIRPa7XaeXz9Xj7NLefHC8aybGaS0SUJ0SVeG1ZNrTaWr20PqhQWTx1qdElC+LRWq51H39nL54fK+elNY7n/Kgkq4Tu8MqyaWm0se2sP3xVW8x+3pXLblCFGlySET2ux2nj07b1sy6vgxUXjuGdGotElCXFZvC6sGlutLHszk4yian51eyr/MFmCSoieaLHaeHjdXnYcruBnN4/n7vThRpckxGXzqrBqaLHygzf3sKfYwsuLJ3LzpASjSxLCpzW32XhoXRZfHKnk57eMZ2maBJXwTV4TVg0tVu7/4x4yj1v49R0TWTRRgkqInmhus7F8bRZ/PVrJL/5hAndOlwlKwnd5RVi1WG3c98fd7D1Rw6olk7gpVS7vIURPWG32czNpX7o1hcXTZIKS8G09OrhCKTVPKXVEKVWglFrZ3dcJDepFWpKJ1RJUIsC5qqeCg3oxY4TJMZNWgkr4gW7vWSmlgoD/Aq4DTgF7lFKbtNaHuvFaPHvD6O6WIoRfcGVPATwya6QryxPCUD3Zs5oOFGitC7XWrcAGYJFryhIiIElPCXERPQmrBOBkh59POe8TQnSP9JQQF+H2E4IppZYrpTKVUpmVlZXufjsh/J70lAhEPZkNeBro+MntEOd9/4fWeg2wBkApVamUOn6J14wFqnpQk7t5e33g/TX6en3uPFBJeso7eXuNvl5fl3pKaa279e5KqWDgKDAHR0PtAe7SWud26wUdr5mptZ7a3ee7m7fXB95fo9R3yfeWnvJC3l5joNTX7T0rrbVVKfUY8CkQBLzRk6YSItBJTwlxcT06KFhrvRXY6qJahAh40lNCXJi3XXFtjdEFdMLb6wPvr1Hq8yxv//d4e33g/TUGRH3d/sxKCCGE8BRv27MSQggh/o4hYdXZ+c+UUmFKqXedj2copRI9WNtQpdROpdQhpVSuUuqJCywzSyl1Vim13/n1E0/V53z/YqVUjvO9My/wuFJKrXauv2yl1GQP1ze6w7rZr5SqVUo9ed4yHl2HSqk3lFIVSqmDHe6LUUp9rpTKd37vf5Hn3utcJl8pda876+wu6SmX1Om1fSU9BWitPfqFY5bTMcAMhAIHgLHnLfMI8Dvn7SXAux6sbzAw2Xk7GsdU4vPrmwVs9vS66/D+xUDsJR6fD3wMKCAdyDCw1iCgDBhu5DoErgYmAwc73PcSsNJ5eyXwyws8LwYodH7v77zd36j1eYl1LD3V8zp9oq8CtaeM2LPqyvnPFgFvOW9vBOYopZQnitNal2qt9zpv1wF5+N4pbxYBf9IOu4B+SqnBBtUyBzimtb7Ugatup7X+K2A57+6Ov2dvATdf4Kk3AJ9rrS1a6zPA58A8txXaPdJTnuEtfRWQPWVEWHXl/GfnltFaW4GzgMkj1XXgHCqZBGRc4OEZSqkDSqmPlVLjPFoYaOAzpVSWUmr5BR73pnPMLQHWX+QxI9chwECtdanzdhkw8ALLeNO6vBjpKdfwlb4KyJ7yiosveiOlVBTwPvCk1rr2vIf34tgFr1dKzQc+BJI9WN5MrfVppdQA4HOl1GHnVo5XUUqFAguBH17gYaPX4f+htdZKKZka60Ze3lPgA30VyD1lxJ5VV85/dm4Z5TgFTV+g2iPVOd4zBEdTva21/uD8x7XWtVrreuftrUCIUirWU/VprU87v1cAf8YxDNRRl84x5wHfB/ZqrcvPf8DodehU3j6M4/xecYFlvGVdXor0lAv4SF8FbE8ZEVZ7gGSlVJJzK2EJsOm8ZTYB7TNEbgN2aOcnc+7mHMf/A5CntX75IssMah/vV0pNx7EePdL4SqlIpVR0+23geuDgeYttAu5xzl5KB8522DX3pDu5yHCFkeuwg46/Z/cCH11gmU+B65VS/Z0zm6533udNpKd6XqOv9FXg9pSnZo6cNxtkPo4ZQceAHznvexFY6LwdDrwHFAC7AbMHa5uJY+w6G9jv/JoPPAQ85FzmMSAXx6yrXcCVHqzP7HzfA84a2tdfx/oUjivOHgNygKkG/B9H4miUvh3uM2wd4mjwUqANxxj5Mhyf2WwH8oFtQIxz2anA6x2e+wPn72IBcL+n12UX/33SUz2r0ev7KtB7Ss5gIYQQwuvJGSyEEEJ4PQkrIYQQXk/CSgghhNeTsBJCCOH1JKyEEEJ4PQkrIYQQXk/CSgghhNeTsBJCCOH1/hdvAd9bHC9UJgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 504x144 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "40Za1dhzj8Ih",
        "colab_type": "code",
        "outputId": "fbcd99aa-0490-4e0c-f705-0d0dab236321",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 157
        }
      },
      "source": [
        "fig"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 504x144 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5G_5YtAnj9wW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fig.savefig('my_pic.png',dpi=100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yA_pbwGjklbL",
        "colab_type": "code",
        "outputId": "89e5b993-2aa2-4983-f511-2be9edcbe3b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        }
      },
      "source": [
        "fig = plt.figure()\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "ax.plot(x,y)\n",
        "ax.set_xlabel('X axis')\n",
        "ax.set_ylabel('Y axis')\n",
        "ax.set_title('X vs Y')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'X vs Y')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z2Y-eaBtlZiR",
        "colab_type": "code",
        "outputId": "bdb797fb-7b90-4177-bafc-7dccbe567a18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        }
      },
      "source": [
        "# legends\n",
        "fig = plt.figure()\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "ax.plot(x,x**2,label='X vs X square')\n",
        "ax.plot(x,x**3,label='X vs X cube')\n",
        "ax.legend(loc=0)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f0afd8637f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rs51Tfv2nOpe",
        "colab_type": "text"
      },
      "source": [
        "# 3) Matplotlib Part 3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VxhxF58PnP2r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from numpy.random import randint"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7KI1eH9Vnmdb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = np.linspace(0,10,20)\n",
        "y = randint(1, 50, 20)\n",
        "y = np.sort(y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OilXWY4nwFQ4",
        "colab_type": "code",
        "outputId": "a27fc2f4-db05-45e1-be13-a9fc058ce6b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        }
      },
      "source": [
        "fig = plt.figure()\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "ax.plot(x,y,color='g',linewidth=3,ls='--',alpha=0.8,marker='o',markersize=10,markerfacecolor='yellow')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f0afd82f198>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kyeXBh8Py06D",
        "colab_type": "code",
        "outputId": "f101d98c-de1d-4603-9e0f-8e51053635f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "fig = plt.figure()\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "ax.plot(x,y,color='r',linewidth=3)\n",
        "ax.set_xlim([0,1])\n",
        "ax.set_ylim([0,10])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 10.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "baep4Kb_0FiJ",
        "colab_type": "text"
      },
      "source": [
        "# 4) Different Plots"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RvFSjzgT0JhJ",
        "colab_type": "text"
      },
      "source": [
        "## 1) Scatter Plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ECGZVzhS0MOa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xaa4F2dM0Niy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_views=[534,690,258,402,724,689,352]\n",
        "f_views=[123,342,700,305,406,648,325]\n",
        "t_views=[202,209,176,415,824,389,550]\n",
        "days=[1,2,3,4,5,6,7]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mk38-lhP-O2w",
        "colab_type": "code",
        "outputId": "fb53bd02-d53e-4490-e952-57d27d67ae1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "plt.scatter(days,y_views,label='Youtube Views',marker='o')\n",
        "plt.scatter(days,f_views,label='Facebook Views',marker='o')\n",
        "plt.scatter(days,t_views,label='Twitter Views',marker='o')\n",
        "plt.xlabel('Days')\n",
        "plt.ylabel('Views')\n",
        "plt.title('Social Media Views')\n",
        "plt.grid(color='r',linestyle='--')\n",
        "plt.legend()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f0afd6f8898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wz0vv5fDAYC8",
        "colab_type": "text"
      },
      "source": [
        "## 2) Bar plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8tzG3zdXAbOs",
        "colab_type": "code",
        "outputId": "a63f3133-ac68-460f-c4c2-e9e8914ea1eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "plt.bar(days,y_views,label='Youtube views')\n",
        "plt.bar(days,f_views,label='Facebook views')\n",
        "plt.xlabel('Days')\n",
        "plt.ylabel('Views')\n",
        "plt.title('Social Media Views')\n",
        "plt.legend()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f0afd6a69b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yd7-3VObBgZn",
        "colab_type": "text"
      },
      "source": [
        "## 3) Histogram"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mDNHLLvhBjD1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "points=[22,55,62,45,21,22,99,34,42,4,102,110,27,48,99,84]\n",
        "bins=[0,20,40,60,80,100,120]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uqQ5OYnAB2Ul",
        "colab_type": "code",
        "outputId": "2a78495e-b294-4b5c-ef96-b49ff85ecc19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.hist(points,bins)\n",
        "plt.xlabel('Bins')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Bins vs Frequency')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JapQBL0mNMuAlqVEGvCQ1qreAT3JSkluSXNNXG5KkyfV5BH8ycFiP25ckbUZvAV9VXwJu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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CSWrt5jmCe6I",
        "colab_type": "text"
      },
      "source": [
        "## 4) Pie chart"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bd3NAbZ1Chr1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "labels_1=['Facebook','Instagram','Youtube','linkedin']\n",
        "views=[300,350,400,450]\n",
        "explode_1=[0,0,0,0.2]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OKViHzfIC1-G",
        "colab_type": "code",
        "outputId": "f04985d3-8787-4f85-c2f8-6d888432812b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        }
      },
      "source": [
        "plt.pie(views,labels=labels_1,autopct='%1.1f%%',explode=explode_1,shadow=True)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mFhHbGaPrvS7W/2g9G366gfCGcJ9lO17poHROKWCZjn+6n/XfX4+z1InhM4hs\niBA4OrsJ1pSp2oFnBlKmzu93AHPn+Hw+j2HonCKZZcvBL8kO+d4NAdi890EimNjmrnBnrFmc7E6y\n+cbNVH2mCkeRA2Uqkp1JJlw7gcjGCFtu2sJhvzgMkf1bBmbCJNQQouq8D8Y0Kj9RSeUnrGTa227d\nxohzRtD631Y63+7EO8bLiIVZyUX8cOOixv37T30zHvAdXeSblAlBmg+xyW4BeymElsVWUntE4nvi\nWzNViUootty4hbLjyyg9xmoduIa5CBwTQETwTfCBQDLUe+Cuzrc68Y7z4izd358jmyIopfBUewi+\nFmTslWOJNceI7sx8ilExBtUFmQmYtW731HTr0ezH5oNfkh0KwSxCQDvg7VrdtSYTFSil2HbrNjzV\nHio+/sEsRWBWgK5VVs8nujOKSioc/t7TQXQs76BsTlmvf2t+oJmRnxppjWGYqaUOAmYss+FFlam6\ngKcGUqbO7zeAE48tKvIUGYY/M8o0PdBmkS6CDUEFvA2URZoizclwsiXddYTXhml/qZ3OVZ2su3Yd\n665dR+jNEGUnlRHbHWPtd9ey5Q9bqPlCDSJCvC1O06+b3i9vRk063+nsdTwi+HoQb60X1zAXjmIH\n3rFe1n5vLSquKBpblO6nsi+PNi5qHGjzZRxQcqzPNzETgjT7kTPdECmEIFOBmYGPAFcDmyvPrDzZ\nN8k3z25NecJ5jYsaB7QYq87vPxs48xfVo84tNozem0qadBGaunpVdke8+yDvWxYp3sMK4OvoWt2l\nw9f3A6VUBHhsIGXq/H4B5k33ep3aKLJCznRBoEDMItgQ7AbeAIaH14V3JLuT7XZrynkUjzcuaux7\nrnd/aoBhc3zFuguSHbRZZIjlgBcguiOqWxcHQQwZTOyK6YCa4HYfkW49ml7RZpEh1mClhjPCa8Lv\n2i0ml1FKxYBHB1JmbxfkCI/H8DscOv9pdlhvt4CeFIxZBBuCYeAtoLxrdddWHcS3DxRPNy5qHOjr\nUw1Uzi0uHp8JSZpeecluAT0pGLNI8TLgA+je1t1os5acRQy5ZxDFjgLURLdHL8TKAkqpbuA1u3X0\npNDMYhXWlnUjuCK4Qicf2h+lVAJ4eCBlUl2Qkya53VLqcGRlDbqGV6auXhWzW0RPCsosgg3BTqxZ\nkcro9mhbvCW+zm5NOYfiv42LGtsGWGoEMOrE4pJxmZCk2R8Rec5uDftSUGaR4j+kZkU63+58xWYt\nOccguyDTADXZo/eCZJEBBSPKBn2ahVi8ICJn9Dh3vog8PtCKRORSEanqx3U/EZGrB3r/HqwFdgCB\n0MrQ+mQ4uecQ7lVQKKVM4OxXV+QAABTzSURBVKFBFJ03zuUyhzmc1enWpNmfVFcxpwY34SBmkerz\nXwH8WkS8IlICXAdcOYi6LgUOahaHSiogTj0wDKBrdVfOvei2YfJS46LG5oEUqfP7K4Bx84pLxmRI\nlWZ/GqauXpUzQW/2ctBuiFLqbeBfwDXA94G/KqXWi8i3ReTt1HEVgIhMEpGVe8uKyGIR+Z6IXAjM\nAO4WkZUi4haRrSJSlrpujoj8p0e1M0VkuYisFZFL97nfqyLyloh8vw/ZbwDdgKf95fa39DSqhTgG\n1QU5ElCHeTx6IVaWyMXxCuj/mMUPgc8AZwA/F5HZwMXAscDxwFdE5KgDFVZK3Q2sBC5USs1ILQrq\ni6OA+cAJwI9EZKSIfAIYC8zGMp65IjK3t8LBhmAEq3UxUsVVMrwuPKh8GIVEqpX4wCCKnljtdCaH\nOxw16dakOSA5N14B/TQLpVQXcDdwp1IqCpwI3K+UiiilQlj94HTu9HxIKdWtlGoGnsMypdOwzKoB\nq+UwCegrKtbzWJvLnG0vtq0w42YkjfryD5PXGhc1DihOaZ3fPwyYOL+kpKa36F+a9KOUimO953OO\ngcyGmKmjLxL73NPbz2v3vW7f9REKKxrWT1ItkxlKqUlKqdsPdPNgQ7ADeBoYaYbNWNeqrmUH0V7Q\nDLILcgTAFI9Xz4JkCQWPTV29aqBT21lhsFOnzwPniEhRatDzk6lzO4FRIjJMRLzAgh5lQkDPyEpN\nwNGpx+fuc/+zRcQjIpVYLZYVwBPAZSJSDCAiNSJysOQaT2M9R2frs60rkl3JAQ3uFRiDSSJ0YqXD\nkax0ODKSj0WzP4bI7XZrOBCDMgul1KvAP7CWoy4H/qCUakwtUb0O68P9JFbk7b3cBvx57wAnsAS4\nSURew8r90ZO3gf9iTR/9QCm1Syn1b+A+YLmINAL3ACV96Qw2BHdjjV2MwkS1v9I+4CnfQkAl1ZuN\nixqbBlKmzu8PAFPml5RUie6DZIWkUu1Y79ecpCAiZfVFYGbAB/wMa3akq/ri6ovcle4pNsvKNt9t\nXNR43UAK1Pn9s4Errh0xcl61y6XjV2SBhFI3H7Vm9RV26zgQhbiC80OkdqP+HWvJMq3LWp9Qpuo9\nBHfhMpgI3icMczjiI51Ovcs0SzhFbrVbQ18UvFmkeBXYCAyPbo+2RTZGhsxUqkqq1Y2LGt8bSJk6\nv78EOHJ+cclIQ2SovEdsJaHU+qmrV71qt46+GBJvhGBDMAncBQQAo+XplufMmNlps6zsYHD3IEod\nDhhHeL06NWGWMCCnWxUwRMwCINgQXAe8CFSbYTMWWhl60m5N2UBEBjMLcnyJYcSrnE49VpEFlFKm\nIXKn3ToOxpAxixR7Pzju9pfaG6M7ogUdIEcl1cbGRY0Deo51fn8RMH1+cUmFQ6T3jEmatJKAl6au\nXpUzOU0PxJAyi2BDsAXLMEYD7H50d30ykszJBTBpYXBdkCmAMU13QbKGS+RXdmvoD0PKLFI8BbwD\nVCW7ktHWZ1vvV6bKbJ5AmxhkF2ROkUhilMulkx5ngahprmeAkcvsYsiZRWqw8y9YS9dLwmvC27rW\nFN5ScGWqLY2LGlcMpEyd3+8BZn20pGSYU8SVIWmaHpjww6mrV+XFYqchZxbwfnfkZqy1F46WJ1pe\niLfGN9gsK70Ig8kLchjg/Ii3KOtdkKhpcuGmJs5p2shZGzdww57dAGyNxbhwUxOnb1jPN7ZvI3aA\nRYS3tLRw+ob1fGLDBl7osia6WhMJPrt5Ews3buA/oQ+iFFy5bSvNiXjmn9RB6DbNrUWG8Te7dfSX\nIWkWAMGG4JvA4+wdv6jf/aAZMweaoStnGWQX5Di3SGK0y9XXbt6M4Bbh1jFjebB2PA/UjueFri7e\njET41Z7dLBpWzhMTJhIwHDzQvn+yuXXRKI+Fgvyrdjy31NTw4127SCpFfSjIBaVl3D2uljvbWgFY\n1hliqsfDCKf9Dack/Gjq6lV50wUesmaR4n5gG1ARb4l3ti9vf6gQlr8rU+3CSovQb+r8fjdw3Lzi\n4oDL2ruTVUSEYsN6OyaUIpH6P7wSDnOa39p/eHZpKU937r885pnOTs7wB3AbBjVuN2Ndbhq7u3Eh\ndCtFTCkMERJK8de2Ni4rH569J3YAIqa5udgwcn5tRU+GtFkEG4JR4I9YW+S9oTdCa7ve7cr/9RfC\nfY2LGgfqepMA5wwbuiB7SSrFOU0bOXHdWuYWFzPW5cJvGDhT+9hGOp3s6qX70JyIU+V0vv/7SJd1\n3YJAgGc6Q3xhyxa+WD6cf7S3sTAQoMiw/20fVerbU1evyqttB/a/ajYTbAhuA+4ARgGOlqdaXg5v\nDA/oWznXGGQX5BgnmGPcLts22TlEeLB2PMsmTqIx0s2G2KGlzfA7HPyxZgz31tZyhNfLs52dnOYP\n8P2dO7h62zZWRuyJh9SZTK46fu17g5nWtpUhbxYpXsDaGjwWkN0P734yuiP6ls2aBoUyVSsDjLRU\n5/c7gTlzi4tL3GL0FbAoKwQcDo7z+VjZHSFkmu93SXYlEozsZaxhhNPFzkTi/d93xfe/7o8te/jS\n8OH8OxhkVpGP66qr+f0eewK/J+AqWyo+RLRZAMGGoMLamfkiMA5g1/27Ho63xHMqMW2/EB5oXNQ4\n0ObtRMA7q6hociYk9YfWRIJg0pLdbZq8FO5iotvDcUU+nkzNZDzU0cHJJfuHMKkrKeGxUJCYabI1\nFmNTPMZR3g88rykWY2ciwXG+YrqViYEVdq3bhuU1bcnEsuPXvvd01itOA86DXzI0CDYEk4GZgdux\nNptNVQm1Zef9O++p/nT1JU5//uTLEJHBbEefJZAY63LbNl6xO5Hg/3buwFRgovi4P8D8khImut18\na8d2frdnN1M9Xs4tLQXgmc4Q73R3c1VFJZM9Hk73+zmraSMOhO+NGImjR7ye3+3ZzdcrKgH4hD/A\nVdu28afWFq6qOFigtfTSbZrhzqT5maxWmkYKPvjNQEkFy7kGK8fJDucwZ3HVBVWXOooc5TZLOyjK\nVEExpKJxUWO/FxHU+f0O4Ldzfb7yzw4rz9s3cj6wqrv7G59q2vgbu3UMFt0N2YdUsJzfAB1ARaIt\n0bX7X7vvypPcIw8PxChSjAeKjy7y6eXdGWR7PP7aDS17fmu3jkNBm0UvBBuC7cCvU7+WRbdH23Y9\nuOvWXN90JoYMZtXmDCBZ69Z5TDNFxDTDm+Ox85aFQnndjNdmcQCCDcGdwK+w1mAMi+2Mte+6b9et\nic5ETkYIV6bqwgqS3G/q/H4DOPGYoiJPkWH4D1pAMyg2xmLf+Z/NmzfbreNQ0WbRB8GG4Ebgp4CD\n1CrPnXfvvC3Rkdhqs7TeqG9c1BgdYJmxQOA4n+6CZIrt8fgrN7bsud5uHelAm8VBCDYENwH/HxAH\nRiZDye7tf9t+R7Q5uspmaR/iELog5ni3R3dBMkCq+3FBvnc/9qLNoh8EG4I7sAyjAxitYiqx8x87\n741sjOTESs9UvpbHBlKmzu8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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}