discrete distribution .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set(color_codes=True)\n",
    "\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import bernoulli"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_bernouli = bernoulli.rvs(size=1000, p =0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fa9417c29d0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.displot(data_bernouli,bins=100, kde=False )\n",
    "plt.title(\"Discrete Distribution Graph\")\n",
    "ax.set(xlabel= 'bernoulli 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
}