vepriya/untitled0.ipynb

## untitled0.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled0.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/vepriya/2123c60e27761954e7fdc0304bc4b10d/untitled0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_W4UolqJIJll",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "28bb9dd2-5ad9-493e-b29d-2f5ca13ee02f"
      },
      "source": [
        "import matplotlib.pyplot as plt \n",
        "x = [0,1,2,3,4,5,6] \n",
        "\n",
        "y = [ 1,2,4,1,5,2,6] \n",
        "\n",
        "# plotting the points \n",
        "plt.plot(x, y, color='red', linestyle='dashed', linewidth = 2, \n",
        "\t\tmarker='o', markerfacecolor='green', markersize=13) \n",
        "\n",
        "# setting x and y axis range \n",
        "plt.ylim(0,8) \n",
        "plt.xlim(1,8) \n",
        "\n",
        "plt.xlabel('X - axis') \n",
        "plt.ylabel('Y - axis') \n",
        "\n",
        "\n",
        "plt.title('Customizations!') \n",
        "\n",
        "\n",
        "plt.show() \n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gQOjTJyT7a6+NO5ot84c/hOZocdaoZ5NZqEZq3x7uvz90YW1LO+wQ5pFcdllY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\nP+nC4VHsv3XRAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}
	{
	"nbformat": 4,
	"nbformat_minor": 0,
	"metadata": {
	"colab": {
	"name": "Untitled0.ipynb",
	"provenance": [],
	"include_colab_link": true
	},
	"kernelspec": {
	"name": "python3",
	"display_name": "Python 3"
	}
	},
	"cells": [
	{
	"cell_type": "markdown",
	"metadata": {
	"id": "view-in-github",
	"colab_type": "text"
	},
	"source": [
	"<a href=\"https://colab.research.google.com/gist/vepriya/2123c60e27761954e7fdc0304bc4b10d/untitled0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
	]
	},
	{
	"cell_type": "code",
	"metadata": {
	"id": "_W4UolqJIJll",
	"colab_type": "code",
	"colab": {
	"base_uri": "https://localhost:8080/",
	"height": 295
	},
	"outputId": "28bb9dd2-5ad9-493e-b29d-2f5ca13ee02f"
	},
	"source": [
	"import matplotlib.pyplot as plt \n",
	"x = [0,1,2,3,4,5,6] \n",
	"\n",
	"y = [ 1,2,4,1,5,2,6] \n",
	"\n",
	"# plotting the points \n",
	"plt.plot(x, y, color='red', linestyle='dashed', linewidth = 2, \n",
	"\t\tmarker='o', markerfacecolor='green', markersize=13) \n",
	"\n",
	"# setting x and y axis range \n",
	"plt.ylim(0,8) \n",
	"plt.xlim(1,8) \n",
	"\n",
	"plt.xlabel('X - axis') \n",
	"plt.ylabel('Y - axis') \n",
	"\n",
	"\n",
	"plt.title('Customizations!') \n",
	"\n",
	"\n",
	"plt.show() \n"
	],
	"execution_count": 16,
	"outputs": [
	{
	"output_type": "display_data",
	"data": {
	"image/png": 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gQOjTJyT7a6+NO5ot84c/hOZocdaoZ5NZqEZq3x7uvz90YW1LO+wQ5pFcdllY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\nP+nC4VHsv3XRAAAAAElFTkSuQmCC\n",
	"text/plain": [
	"<Figure size 432x288 with 1 Axes>"
	]
	},
	"metadata": {
	"tags": []
	}
	}
	]
	}
	]
	}