Kanav-Arora/Matplotlib.ipynb

## Matplotlib.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "1yDpA0M6rZ42",
        "j9tcfINPr91p",
        "2HlRPOwVsig6",
        "wH3P_PQev-Od"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "umWh8X1apZ7H"
      },
      "source": [
        "#Matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "36kWCG4yCbd9"
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from google.colab import drive"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3HEW3MiAoG2Y"
      },
      "source": [
        "drive.mount(\"/content/drive\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zuw-TYwFoUO5"
      },
      "source": [
        "data = pd.read_csv(\"/content/drive/MyDrive/AI-ML/Assignments/Assignment-2/iris.csv\")\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XONEuYmKFCsS"
      },
      "source": [
        "## Line Plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AU14kFh9pfF6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "f14012e9-d2bc-449f-8043-657d93db2144"
      },
      "source": [
        "x = np.arange(5)\n",
        "y = x*2\n",
        "plt.plot(x,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f21a69f6c90>]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1yDpA0M6rZ42"
      },
      "source": [
        "### Adding Title and Labels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yI74tBcpqYth"
      },
      "source": [
        "plt.plot(x,y)\n",
        "plt.title(\"Matplotlib Tutorial\")\n",
        "plt.xlabel(\"x-axis\")\n",
        "plt.ylabel(\"y-axis\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j9tcfINPr91p"
      },
      "source": [
        "### Changing Line Aesthetics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wTTlji9QrxEZ"
      },
      "source": [
        "# supported values are '-', '--', '-.', ':', 'None', ' ', '', 'solid', 'dashed', 'dashdot', 'dotted'\n",
        "# for colors we can use hex values\n",
        "plt.plot(x,y,color ='r', linestyle = 'dashdot', linewidth =3)\n",
        "plt.title(\"Matplotlib Tutorial\")\n",
        "plt.xlabel(\"x-axis\")\n",
        "plt.ylabel(\"y-axis\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QBNmc3cLxtkC"
      },
      "source": [
        "Tight_Layout - used to automatically adjust subplot parameters to give specified padding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OkjtRc3XxaiC"
      },
      "source": [
        "plt.plot(x,y,color ='r', linestyle = 'dashdot', linewidth =3)\n",
        "plt.title(\"Matplotlib Tutorial\")\n",
        "plt.xlabel(\"x-axis\")\n",
        "plt.ylabel(\"y-axis\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2HlRPOwVsig6"
      },
      "source": [
        "### Adding two lines in same plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iGttDc45swgx"
      },
      "source": [
        "y1= 6*x\n",
        "plt.plot(x,y,color = 'g', linestyle=':', linewidth= 5)\n",
        "plt.plot(x,y1,color = 'r', linestyle='-', linewidth= 2)\n",
        "plt.legend(['Y','Y1'])      # add label for each plot\n",
        "plt.title(\"Line Plot\")\n",
        "plt.xlabel(\"x-label\")\n",
        "plt.ylabel(\"y-label\")\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lc5M2bMfupEL"
      },
      "source": [
        "Here the legend method is empty and instead we have added label attribute in plot methods"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZhuF5MD_ucv_"
      },
      "source": [
        "y1= 6*x\n",
        "plt.plot(x,y,color = 'g', linestyle=':', linewidth= 5, label = 'Y')\n",
        "plt.plot(x,y1,color = 'r', linestyle='-', linewidth= 2, label = 'Y1')\n",
        "plt.legend()      # add label for each plot\n",
        "plt.title(\"Line Plot\")\n",
        "plt.xlabel(\"x-label\")\n",
        "plt.ylabel(\"y-label\")\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wH3P_PQev-Od"
      },
      "source": [
        "### Marker"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Gq_6QpXwDov"
      },
      "source": [
        "plt.plot(x,y,color ='r', linestyle = 'solid', marker = '.',markerfacecolor='blue', linewidth =1)\n",
        "plt.title(\"Matplotlib Tutorial\")\n",
        "plt.xlabel(\"x-axis\")\n",
        "plt.ylabel(\"y-axis\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sw8o0HgxyIZx"
      },
      "source": [
        "### Styles"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QIj2ObpfyLep"
      },
      "source": [
        "print(plt.style.available)    # lists all the available styles"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DncCQ7RMyYrm"
      },
      "source": [
        "y1= 6*x\n",
        "plt.style.use('fivethirtyeight')\n",
        "plt.plot(x,y,color = 'g', linestyle=':', linewidth= 5)\n",
        "plt.plot(x,y1,color = 'r', linestyle='-', linewidth= 2)\n",
        "plt.legend(['Y','Y1'])      # add label for each plot\n",
        "plt.title(\"Line Plot\")\n",
        "plt.xlabel(\"x-label\")\n",
        "plt.ylabel(\"y-label\")\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FcU17hi5ylOy"
      },
      "source": [
        "plt.style.use('bmh')\n",
        "plt.plot(x,y,color = 'g', linestyle=':', linewidth= 5)\n",
        "plt.plot(x,y1,color = 'r', linestyle='-', linewidth= 2)\n",
        "plt.legend(['Y','Y1'])      # add label for each plot\n",
        "plt.title(\"Line Plot\")\n",
        "plt.xlabel(\"x-label\")\n",
        "plt.ylabel(\"y-label\")\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gNM3S9d6tB-R"
      },
      "source": [
        "### Adding sub plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9x6mF8ZItE3n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "28beb679-2621-4864-f317-1787a3005912"
      },
      "source": [
        "y1= 6*x\n",
        "plt.subplot(1,2,1)\n",
        "plt.grid(True)\n",
        "plt.plot(x,y1,color = 'g', linestyle=':', linewidth= 5)\n",
        "plt.title(\"Line Plot-1\")\n",
        "plt.xlabel(\"x-label\")\n",
        "plt.ylabel(\"y-label\")\n",
        "plt.subplot(1,2, 2)\n",
        "plt.grid(True)\n",
        "plt.plot(x,y,color = 'r', linestyle='-', linewidth= 2)\n",
        "plt.title(\"Line Plot-2\")\n",
        "plt.xlabel(\"x-label\")\n",
        "plt.ylabel(\"y-label\")\n",
        "plt.savefig('plot.png')             # used to save figure locally\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VoWq1ySVFE0P"
      },
      "source": [
        "## Bar Plot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_MQWiHAVkAU4"
      },
      "source": [
        "Here bars are overlaying each other"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "AigNsoLSFHfe",
        "outputId": "83cd158b-8246-459b-f434-2d25ad59dc88"
      },
      "source": [
        "x = np.arange(5)\n",
        "y = x*3\n",
        "y1 = x*4\n",
        "plt.bar(x, y, color = 'r', label = \"Plot-1\")\n",
        "plt.bar(x, y1, color = 'g', label = \"Plot-2\")\n",
        "plt.xlabel(\"X-Axis\")\n",
        "plt.ylabel(\"Y-Axis\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "myxR-FRyiR25",
        "outputId": "15a46f7a-57bd-4843-c403-564ebe7d6a80"
      },
      "source": [
        "x = np.array([2,4,6,8,10])\n",
        "y = x*3\n",
        "y1 = x*4\n",
        "width=0.25\n",
        "plt.bar(x-width, y, color = 'r',width=width, label = \"Plot-1\")\n",
        "plt.bar(x, y1, color = 'g',width=width, label = \"Plot-2\")\n",
        "plt.xlabel(\"X-Axis\")\n",
        "plt.ylabel(\"Y-Axis\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nOptRDyjkCSd"
      },
      "source": [
        "#### Horizontal Bars"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "xd6LDPE7kB4M",
        "outputId": "55bb8b50-e4d7-4387-896f-395fe1893160"
      },
      "source": [
        "plt.barh(y,x)\n",
        "plt.xlabel(\"X-Axis\")\n",
        "plt.ylabel(\"Y-Axis\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DJC/vmk4G7h2wpHF7ADg2yV7dz8LJNHRxeyvXAsu75eXANXPxofN2ds6qeirJO4B/ZnRF/7KqumfgssbpOOBs4K4kd3Rt76uq6wesSeP1F8DlXcfne8CfDFzP2FTVbUmuAm5nNMLtW+zi0zckuQI4ETgoyQbgg8Aq4ItJzgG+D/zhnOzLKRskqS3z+VSPJKkHBr8kNcbgl6TGGPyS1BiDX5IaY/Cred1MqPcnObBbP6BbXzLDth9LsjHJTn92kvx5kj+e+4ql58fhnBKQ5L3Ay6pqRZJ/ANZX1d9ss81uwP2M5o65oKpuHqBU6Xmzxy+NXMzom6LnAcczmiBsWycC9wCXAGduaUzy8SQf6JZfn+SWJLsl+VCS93Tt7+zurXBnkiv7PhhpR+btN3elcaqqnyf5S+AG4HXd/DDbOhO4gtHX5j+cZM9uuwuAbyb5V+ATwKlV9cxopoH/sxI4rKqeSLJ/rwcj7YQ9fun/vYHRaZxXbvtCN23CqcBXullSbwNeD1BVjwN/BtwIfLKqvjvDZ9/JaPqFsxhNQSANxh6/BCRZCryW0d3Obu3miflq9/KnGM0Muz+juZNgdGOQnwFbbgl5JPBjRlMIz+Q0RjdWeSPw/iRHVpW/ADQIg1/N62Z/vITRPQ8eSPIRYFVVLd1qmy8Af1pVV3TrewP3J9kLmADOZ3SznOuTfGWr6ZS3XBQ+tKpuTnIro9lG9wFau6OW5glP9Uij0zQPVNWN3frfA7+R5DUAXbgvA67b8oaq+ilwK6Me/GrgPVX1Q+Ac4DPb3Bh8d+DzSe5iNMvkJxq8jaLmEYdzSlJj7PFLUmMMfklqjMEvSY0x+CWpMQa/JDXG4Jekxhj8ktSY/wV49CpCMnoRMQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F-3ysxhxlVt5"
      },
      "source": [
        "## Pie Charts\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "w6KXAGEglbQN",
        "outputId": "9b4dfa13-ee97-4d6c-f102-34371a5823b7"
      },
      "source": [
        "plt.style.use(\"fivethirtyeight\")\n",
        "slices = [60,40]\n",
        "plt.pie(slices)\n",
        "plt.title(\"Pie Chart\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "Stsv2sA0mNTD",
        "outputId": "37d5f2b7-3754-4d6d-c102-2deba9835fa6"
      },
      "source": [
        "plt.style.use(\"fivethirtyeight\")\n",
        "slices = [60,40]\n",
        "labels = ['Sixty', 'Forty']\n",
        "colors = ['yellow','green','blue']      # extra elements in color list will not effect pie chart\n",
        "plt.pie(slices, labels =  labels, colors = colors, wedgeprops= {'edgecolor' :'black'})\n",
        "plt.title(\"Pie Chart\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nNRhlSx9oCMG"
      },
      "source": [
        "### Explode element"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "Bsd3ZxbtoGJ5",
        "outputId": "9cc49922-cbc1-48c1-d40c-52ce01e5c2f3"
      },
      "source": [
        "plt.style.use(\"fivethirtyeight\")\n",
        "slices = [60,40,30,20]\n",
        "labels = ['C++', 'Java', 'Python', 'HTML']\n",
        "explode = [0,0,0.1,0]\n",
        "colors = ['yellow','green','blue']\n",
        "plt.pie(slices, labels =  labels, colors = colors, explode = explode ,wedgeprops= {'edgecolor' :'black'})\n",
        "plt.title(\"Pie Chart\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H3DvSzhMopRa"
      },
      "source": [
        "### Aesthetics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "sgkmKA4IouMU",
        "outputId": "5b08c3e6-0bde-4ef0-cb61-1f67734a681b"
      },
      "source": [
        "plt.style.use(\"fivethirtyeight\")\n",
        "slices = [60,40,30,20]\n",
        "labels = ['C++', 'Java', 'Python', 'HTML']\n",
        "explode = [0,0,0.1,0]\n",
        "colors = ['yellow','green','blue']\n",
        "plt.pie(slices, labels =  labels, colors = colors, explode = explode ,wedgeprops= {'edgecolor' :'black'}, shadow = True)\n",
        "plt.title(\"Pie Chart\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "uzDt6ROKo6eM",
        "outputId": "286c0f61-2a2c-4342-9f69-52e7273bf5c1"
      },
      "source": [
        "plt.style.use(\"fivethirtyeight\")\n",
        "slices = [60,40,30,20]\n",
        "labels = ['C++', 'Java', 'Python', 'HTML']\n",
        "explode = [0,0,0.1,0]\n",
        "colors = ['yellow','green','blue']\n",
        "plt.pie(slices, labels =  labels, colors = colors, explode = explode ,wedgeprops= {'edgecolor' :'black'}, shadow = True, startangle=90)\n",
        "plt.title(\"Pie Chart\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "Z7bI2S4mpQN4",
        "outputId": "b2503525-78db-4c98-8abd-271c7933da1b"
      },
      "source": [
        "plt.style.use(\"fivethirtyeight\")\n",
        "slices = [60,40,30,20]\n",
        "labels = ['C++', 'Java', 'Python', 'HTML']\n",
        "explode = [0,0,0.1,0]\n",
        "colors = ['yellow','green','blue']\n",
        "plt.pie(slices, labels =  labels, colors = colors, explode = explode ,wedgeprops= {'edgecolor' :'black'}, shadow = True, startangle=90, autopct=\"%1.1f%%\")\n",
        "plt.title(\"Pie Chart\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tXV2-Fwvq8pX"
      },
      "source": [
        "## Scatter Plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "VhMUe8qjrEfR",
        "outputId": "f4913293-6169-4a8e-a23a-73963451c519"
      },
      "source": [
        "x = np.random.randint(10,size=10)\n",
        "y = np.random.randint(10,size=10)\n",
        "print(x)\n",
        "print(y)\n",
        "plt.title(\"Scatter Plots\")\n",
        "plt.scatter(x,y)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 2 7 1 8 8 8 0 3 6]\n",
            "[0 2 7 0 0 4 5 9 3 8]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "3uq03eNasBLQ",
        "outputId": "d93aedf7-1522-4df7-b4c6-c8d9a2557d1c"
      },
      "source": [
        "x = np.random.randint(10,size=10)\n",
        "y = np.random.randint(10,size=10)\n",
        "print(x)\n",
        "print(y)\n",
        "plt.title(\"Scatter Plots\")\n",
        "plt.scatter(x,y, s =100, c = \"green\", marker = \"X\")     # s= size\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 7 0 5 5 5 9 8 4 6]\n",
            "[1 3 5 0 0 2 2 2 7 2]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "3_DUAxeKsXoD",
        "outputId": "7ec04581-b586-4aff-cc95-d2596b4e7407"
      },
      "source": [
        "x = np.random.randint(10,size=10)\n",
        "y = np.random.randint(10,size=10)\n",
        "print(x)\n",
        "print(y)\n",
        "plt.title(\"Scatter Plots\")\n",
        "plt.scatter(x,y, s =100, c = \"green\", edgecolor =\"black\" , linewidth =1, alpha = 0.25)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2 3 9 6 4 5 3 1 6 9]\n",
            "[8 5 7 3 6 9 5 6 1 7]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}