possion distribution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set(color_codes=True)\n",
    "\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Poisson Distribution "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import poisson\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/home/.local/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Poisson Distribution '), Text(0, 0.5, 'Frequency')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_poisson = poisson.rvs(mu=3, size=10000)\n",
    "ax = sns.distplot(data_poisson,\n",
    "                  bins=50,\n",
    "                  kde=False,\n",
    "                 )\n",
    "ax.set(xlabel='Poisson Distribution ', ylabel='Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}