vepriya/priya.ipynb

## priya.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "priya.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "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/36e0331594fbafc4bd04218623351aeb/priya.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YEfLpkTcNfr2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "e304fbcb-60a0-47e9-cb89-de2b22fe9635"
      },
      "source": [
        "\n",
        "import matplotlib.pyplot as plt\n",
        "# x-coordinates of left sides of bars \n",
        "x=[1,2,3,4,5,6]\n",
        "\n",
        "# heights of bars (total no. of people for each fruit)\n",
        "height = [35\t,20,\t10\t,25,\t40\t,5] \n",
        "\n",
        "# labels for bars \n",
        "fruits = ['Apple',\t'Orange',\t'Banana',\t'Kiwifruit',\t'Blueberry',\t'Grapes'] \n",
        "\n",
        "# plotting a bar chart \n",
        "plt.bar(x,height , tick_label=fruits, \n",
        "\t\twidth = 0.5, color ='blue') \n",
        "\n",
        "plt.xlabel(\"fruits\") \n",
        "\n",
        "plt.ylabel('people') \n",
        "plt.title('Nicest fruit') \n",
        "plt.show() \n"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}
	{
	"nbformat": 4,
	"nbformat_minor": 0,
	"metadata": {
	"colab": {
	"name": "priya.ipynb",
	"provenance": [],
	"collapsed_sections": [],
	"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/36e0331594fbafc4bd04218623351aeb/priya.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
	]
	},
	{
	"cell_type": "code",
	"metadata": {
	"id": "YEfLpkTcNfr2",
	"colab_type": "code",
	"colab": {
	"base_uri": "https://localhost:8080/",
	"height": 295
	},
	"outputId": "e304fbcb-60a0-47e9-cb89-de2b22fe9635"
	},
	"source": [
	"\n",
	"import matplotlib.pyplot as plt\n",
	"# x-coordinates of left sides of bars \n",
	"x=[1,2,3,4,5,6]\n",
	"\n",
	"# heights of bars (total no. of people for each fruit)\n",
	"height = [35\t,20,\t10\t,25,\t40\t,5] \n",
	"\n",
	"# labels for bars \n",
	"fruits = ['Apple',\t'Orange',\t'Banana',\t'Kiwifruit',\t'Blueberry',\t'Grapes'] \n",
	"\n",
	"# plotting a bar chart \n",
	"plt.bar(x,height , tick_label=fruits, \n",
	"\t\twidth = 0.5, color ='blue') \n",
	"\n",
	"plt.xlabel(\"fruits\") \n",
	"\n",
	"plt.ylabel('people') \n",
	"plt.title('Nicest fruit') \n",
	"plt.show() \n"
	],
	"execution_count": 51,
	"outputs": [
	{
	"output_type": "display_data",
	"data": {
	"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFAAAAABJRU5ErkJggg==\n",
	"text/plain": [
	"<Figure size 432x288 with 1 Axes>"
	]
	},
	"metadata": {
	"tags": []
	}
	}
	]
	}
	]
	}