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Commit 30a59b5

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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "Measure_of_Spread.ipynb",
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"version": "0.3.2",
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"provenance": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "wPQ-oKmUd4VC",
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"colab_type": "text"
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},
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"source": [
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"# Range : \n",
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"\n",
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"---\n",
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"\n",
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"Range = X(lagest) - X (lowest)"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "zFat5FJ8d1-6",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 51
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},
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"outputId": "10536d99-7178-4d92-8fce-3f5f325c3c14"
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},
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"source": [
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"import numpy as np\n",
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"A=np.array([[10,14,11,7,9.5,15,19],[8,9,17,14.5,12,18,15.5], [15,7.5,11.5,10,10.5,7,11],[11.5,11,9,12,14,12,7.5]])\n",
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"A\n",
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"\n",
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"B=A.T\n",
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"B\n",
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"\n",
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"a=np.ptp(B, axis=0)\n",
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"b=np.ptp(B,axis=1)\n",
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"\n",
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"print(\"Range in Array A:\",a)\n",
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"print(\"Range in Array B:\",b)"
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],
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"execution_count": 4,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Range in Array A: [12. 10. 8. 6.5]\n",
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"Range in Array B: [ 7. 6.5 8. 7.5 4.5 11. 11.5]\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "qmZJRD0xekaC",
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"colab_type": "text"
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},
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"source": [
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"# Quartile\n",
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"\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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"metadata": {
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"id": "onAG-ZzleNRO",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 85
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},
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"outputId": "79be6256-2bf0-4d90-e955-b9f77b17d9da"
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},
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"source": [
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"A=np.array([[10,14,11,7,9.5,15,19],[8,9,17,14.5,12,18,15.5], [15,7.5,11.5,10,10.5,7,11],[11.5,11,9,12,14,12,7.5]])\n",
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"\n",
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"B=A.T\n",
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"\n",
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"a=np.percentile(B,27,axis=0, interpolation='lower')\n",
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"b=np.percentile(B,25,axis=1, interpolation='lower')\n",
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"c=np.percentile(B,75,axis=0, interpolation='lower')\n",
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"d=np.percentile(B,50,axis=0, interpolation='lower')\n",
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"\n",
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"print(a)\n",
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"\n",
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"print(b)\n",
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"\n",
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"print(c)\n",
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"\n",
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"print(d)"
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],
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"execution_count": 5,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"[9.5 9. 7.5 9. ]\n",
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"[8. 7.5 9. 7. 9.5 7. 7.5]\n",
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"[14. 15.5 11. 12. ]\n",
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"[11. 14.5 10.5 11.5]\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "7Do6eEXLgj2i",
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"colab_type": "text"
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},
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"source": [
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"# inter-qurtile range\n",
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"\n",
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"---\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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"metadata": {
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"id": "v0J_1_TVgqF_",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 34
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},
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"outputId": "0956b1cd-d0f9-4058-ccd3-d50a41a4caed"
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},
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"source": [
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"import numpy as np\n",
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"from scipy.stats import iqr\n",
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"A=np.array([[10,14,11,7,9.5,15,19],[8,9,17,14.5,12,18,15.5], [15,7.5,11.5,10,10.5,7,11],[11.5,11,9,12,14,12,7.5]])\n",
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"\n",
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"B=A.T\n",
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"\n",
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"a=iqr(B, axis=0 , rng=(25, 75), interpolation='lower')\n",
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"b=iqr(B, axis=1 , rng=(25, 75), interpolation='lower')\n",
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"\n",
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"print(a,b)"
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],
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"execution_count": 7,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"[4.5 6.5 3.5 3. ] [3.5 3.5 2.5 5. 2.5 8. 8. ]\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "TeDGcMkvg5je",
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"colab_type": "text"
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},
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"source": [
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"# Variance\n",
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"\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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"metadata": {
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"id": "RA52W0_HgrOz",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 51
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},
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"outputId": "4554289c-45b5-4a5c-d611-4092f80e7ee8"
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},
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"source": [
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"import numpy as np\n",
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"A=np.array([[10,14,11,7,9.5,15,19],[8,9,17,14.5,12,18,15.5],\n",
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" [15,7.5,11.5,10,10.5,7,11],[11.5,11,9,12,14,12,7.5]])\n",
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"\n",
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"B=A.T\n",
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"\n",
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"a = np.var(B,axis=0)\n",
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"b = np.var(B,axis=1)\n",
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"\n",
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"print(a)\n",
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"\n",
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"print(b)"
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],
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"execution_count": 8,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"[13.98979592 12.8877551 6.12244898 3.92857143]\n",
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"[ 6.546875 5.921875 8.796875 7.546875 2.875 16.5 19.0625 ]\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "H40m2neihCJC",
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"colab_type": "text"
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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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"---\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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"metadata": {
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"id": "9pETuLnmhAsc",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 68
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},
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"outputId": "1ff2c7d4-2723-4a54-cf94-5e5968bfcd17"
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},
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"source": [
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"import numpy as np\n",
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"A=np.array([[10,14,11,7,9.5,15,19],[8,9,17,14.5,12,18,15.5],\n",
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" [15,7.5,11.5,10,10.5,7,11],[11.5,11,9,12,14,12,7.5]])\n",
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"B=A.T\n",
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"a = np.std(B,axis=0)\n",
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"b = np.std(B,axis=1)\n",
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"print(a)\n",
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"\n",
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"print(b)"
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],
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"execution_count": 9,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"[3.74029356 3.58995196 2.4743583 1.98206242]\n",
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"[2.55868619 2.43349029 2.96595263 2.74715762 1.6955825 4.0620192\n",
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" 4.3660623 ]\n"
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],
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"name": "stdout"
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}
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]
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}
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]
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}
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#Range
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import numpy as np
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A=np.array([[10,14,11,7,9.5,15,19],[8,9,17,14.5,12,18,15.5], [15,7.5,11.5,10,10.5,7,11],[11.5,11,9,12,14,12,7.5]])
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A
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B=A.T
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B
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a=np.ptp(B, axis=0)
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b=np.ptp(B,axis=1)
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print(a)
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print(b)
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#Quartile
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A=np.array([[10,14,11,7,9.5,15,19],[8,9,17,14.5,12,18,15.5], [15,7.5,11.5,10,10.5,7,11],[11.5,11,9,12,14,12,7.5]])
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B=A.T
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a=np.percentile(B,27,axis=0, interpolation='lower')
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b=np.percentile(B,25,axis=1, interpolation='lower')
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c=np.percentile(B,75,axis=0, interpolation='lower')
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d=np.percentile(B,50,axis=0, interpolation='lower')
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print(a)
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print(b)
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print(c)
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print(d)
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#inter-qurtile range
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import numpy as np
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from scipy.stats import iqr
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A=np.array([[10,14,11,7,9.5,15,19],[8,9,17,14.5,12,18,15.5] [15,7.5,11.5,10,10.5,7,11],[11.5,11,9,12,14,12,7.5]])
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B=A.T
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a=iqr(B, axis=0 , rng=(25, 75), interpolation='lower')
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b=iqr(B, axis=1 , rng=(25, 75), interpolation='lower')
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print(a,b)
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#Variance
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import numpy as np
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A=np.array([[10,14,11,7,9.5,15,19],[8,9,17,14.5,12,18,15.5],
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[15,7.5,11.5,10,10.5,7,11],[11.5,11,9,12,14,12,7.5]])
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B=A.T
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a = np.var(B,axis=0)
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b = np.var(B,axis=1)
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print(a)
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print(b)
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#Standard deviation
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import numpy as np
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A=np.array([[10,14,11,7,9.5,15,19],[8,9,17,14.5,12,18,15.5],
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[15,7.5,11.5,10,10.5,7,11],[11.5,11,9,12,14,12,7.5]])
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B=A.T
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a = np.std(B,axis=0)
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b = np.std(B,axis=1)
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print(a)
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print(b)

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