Suchana34/binomialgraph.ipynb

## binomialgraph.ipynb
{
 "cells": [
   {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pmf_data = pd.DataFrame({'success' : range(0,21), 'pmf' : list(stats.binom.pmf(range(0,21), 20, 0.1) )})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for plotting the data\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Number of items returned')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.barplot(x = pmf_data.success, y = pmf_data.pmf)\n",
    "plt.ylabel('pmf')\n",
    "plt.xlabel('Number of items returned')"
   ]
  },
   {
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 11,
	"metadata": {},
	"outputs": [],
	"source": [
	"import pandas as pd"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 3,
	"metadata": {},
	"outputs": [],
	"source": [
	"pmf_data = pd.DataFrame({'success' : range(0,21), 'pmf' : list(stats.binom.pmf(range(0,21), 20, 0.1) )})"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 12,
	"metadata": {},
	"outputs": [],
	"source": [
	"# for plotting the data\n",
	"import seaborn as sn\n",
	"import matplotlib.pyplot as plt"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 6,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"Text(0.5, 0, 'Number of items returned')"
	]
	},
	"execution_count": 6,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
	"image/png": 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\n",
	"text/plain": [
	"<Figure size 432x288 with 1 Axes>"
	]
	},
	"metadata": {
	"needs_background": "light"
	},
	"output_type": "display_data"
	}
	],
	"source": [
	"sn.barplot(x = pmf_data.success, y = pmf_data.pmf)\n",
	"plt.ylabel('pmf')\n",
	"plt.xlabel('Number of items returned')"
	]
	},
	{
	"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": "3.7.1"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}