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‎Basics of python.ipynb

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‎Data Structure Overview.ipynb

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‎Dictionary methods .ipynb

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‎Getting Started with Python.ipynb

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‎Mean Median & Mode.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "cd8aeb1d",
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"metadata": {},
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"source": [
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"________________________________________________________________________________________________________________________\n",
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"\n",
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"\n",
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"* Yuvraj Sutar \n",
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"* [email protected]\n",
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"_ _________________________________________________________________________________________________________________________\n",
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"_ _________________________________________________________________________________________________________________________\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "04ba84a1",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"from scipy import stats\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "0738972a",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Data \n",
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"a=[20,21,23,22,23,20,21,23,24,23,23,22,21,800,1]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "5830bf37",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Q1: 21.0\n"
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]
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}
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],
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"source": [
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"#percentile...\n",
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"\n",
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"import numpy as np \n",
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"Q1 = np.percentile(a,22)\n",
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"print('Q1:',Q1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "29371334",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"72.46666666666667\n"
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]
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}
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],
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"source": [
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"#mean.....\n",
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"\n",
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"import numpy\n",
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"x = numpy.mean(a)\n",
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"print(x)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "1344f0bd",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"ModeResult(mode=array([23]), count=array([5]))\n"
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]
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}
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],
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"source": [
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"#Mode.....\n",
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"\n",
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"x = stats.mode(a)\n",
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"print(x)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "81dcecc8",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Q1: 22.0\n"
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]
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}
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],
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"source": [
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"#Median.....\n",
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"\n",
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"Q1 = np.median(a) \n",
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"print('Q1:',Q1)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "9e6ca76b",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"minimum 1\n"
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]
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}
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],
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"source": [
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"#minimum....\n",
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"\n",
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"m2=np.min(a)\n",
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"print(\"minimum \",m2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "038bdbb6",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"maximum 800\n"
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]
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}
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],
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"source": [
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"#maximum....\n",
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"\n",
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"m1=np.max(a)\n",
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"print(\"maximum \",m1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "e8896058",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"37836.11555555555\n"
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]
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}
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],
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"source": [
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"#variance...\n",
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"\n",
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"x = numpy.var(a)\n",
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"print(x)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "702e9526",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"799\n"
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]
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}
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],
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"source": [
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"#range...\n",
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"\n",
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"x = numpy.ptp(a)\n",
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"print(x)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "99f88cbc",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[ 1. 21. 22. 23. 800.]\n"
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]
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}
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],
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"source": [
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"#quantile...\n",
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"\n",
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"import numpy\n",
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"x = numpy.quantile(a, [0,0.25,0.5,0.75,1])\n",
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"print(x)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "5778ca0c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2.0\n"
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]
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}
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],
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"source": [
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"#interquartile range...\n",
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"\n",
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"from scipy import stats\n",
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"a = [20,21,23,22,23,20,21,23,24,23,23,22,21,800,1]\n",
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"x = stats.iqr(a)\n",
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"print(x)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "7d271475",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"194.51507796455152\n"
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]
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}
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],
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"source": [
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"#Standard Deviation...\n",
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"\n",
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"import numpy\n",
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"a = [20,21,23,22,23,20,21,23,24,23,23,22,21,800,1]\n",
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"x = numpy.std(a)\n",
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"print(x)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "486b0e0d",
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"metadata": {},
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"source": [
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".............................................................................................................................................................................................................................................................."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "3fcedaa0",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Data \n",
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"b=[20,21,23,22,23,20,21,23,24,23,23,22,21,1]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "f5bda976",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"20.5\n"
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]
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}
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],
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"source": [
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"#mean.....\n",
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"\n",
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"import numpy\n",
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"x = numpy.mean(b)\n",
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"print(x)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "9a1cecc1",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"ModeResult(mode=array([23]), count=array([5]))\n"
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]
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}
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],
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"source": [
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"#Mode.....\n",
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"\n",
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"x = stats.mode(b)\n",
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"print(x)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"id": "a3c2fab0",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Q1: 22.0\n"
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]
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}
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],
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"source": [
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"#Median.....\n",
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"\n",
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"Q1 = np.median(b) \n",
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"print('Q1:',Q1)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e72d5b5c",
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"metadata": {},
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"source": [
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"* Yuvraj Sutar \n",
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"* [email protected]"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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