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matplotlib_histgrams
{
"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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v1m233aYbbrhB77zzjumaAGQAAiP9OT3G6TwXbG8Xf//739e0adN0+eWXa+vWrZo8ebLW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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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jRh05ckRZWVmy2+2e291ud6f2ExMTKZst1Ot2Dofd6zZWxxp0YB3M1Z3/N3wfdGAdrLUG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\nAAQZAAADEGQAAAzwvxFWUP3KaKGlAAAAAElFTkSuQmCC\n",
"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": []
}
]
}
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