vivek081166/matplotlib_histograms.ipynb Secret

## matplotlib_histograms.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "matplotlib_histgrams",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/vivek081166/2b1010f332ef1e8e53af9ff4d66ea4bb/matplotlib_histograms.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "EjcNRwu_fhPQ"
      },
      "cell_type": "markdown",
      "source": [
        "### Histogram"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "outputId": "860946f8-9116-447c-b673-61d3800da85b",
        "id": "WC74dGyrfhZ8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        }
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Use numpy to generate a bunch of random data in a bell curve around 5.\n",
        "n = 5 + np.random.randn(1000)\n",
        "\n",
        "m = [m for m in range(len(n))]\n",
        "plt.bar(m, n)\n",
        "plt.title(\"Raw Data\")\n",
        "plt.show()\n",
        "\n",
        "plt.hist(n, bins=20)\n",
        "plt.title(\"Histogram\")\n",
        "plt.show()\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WFmr27Nn00aX8/Hzt2bNHkrRv3z5169ZN1dXVrXfyjvRw3bp1Gjp0qHJyctS9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\nQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "AUdYWB0E4jAN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}