{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sample Data: [4, 2, 5, 8, 6]\n", "Standard Deviation : 2.23606797749979\n" ] } ], "source": [ "# Write a Python program to calculate the standard deviation of the following data.\n", "# Input\n", "# Sample Data: [4, 2, 5, 8, 6] \n", "# Output\n", "# Standard Deviation : 2.23606797749979\n", "\n", "import math\n", "import sys\n", "\n", "def sd_calc(data):\n", " n = len(data)\n", "\n", " if n <= 1:\n", " return 0.0\n", "\n", " mean, sd = avg_calc(data), 0.0\n", "\n", " # calculate stan. dev.\n", " for el in data:\n", " sd += (float(el) - mean)**2\n", " sd = math.sqrt(sd / float(n-1))\n", "\n", " return sd\n", "\n", "\n", "def avg_calc(ls):\n", " n, mean = len(ls), 0.0\n", "\n", " if n <= 1:\n", " return ls[0]\n", "\n", " # calculate average\n", " for el in ls:\n", " mean = mean + float(el)\n", " mean = mean / float(n)\n", "\n", " return mean\n", "\n", "data = [4, 2, 5, 8, 6]\n", "print(\"Sample Data: \",data)\n", "print(\"Standard Deviation : \",sd_calc(data))" ] } ], "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": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }