stats/2-Measure_of_Spread.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wPQ-oKmUd4VC"
   },
   "source": [
    "# Range : \n",
    "\n",
    "---\n",
    "\n",
    "Range = X(lagest) - X (lowest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "zFat5FJ8d1-6",
    "outputId": "10536d99-7178-4d92-8fce-3f5f325c3c14"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "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",
    "A\n",
    "\n",
    "B=A.T\n",
    "B\n",
    "\n",
    "a=np.ptp(B, axis=0)\n",
    "b=np.ptp(B,axis=1)\n",
    "\n",
    "print(\"Range in Array A:\",a)\n",
    "print(\"Range in Array B:\",b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qmZJRD0xekaC"
   },
   "source": [
    "# Quartile\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "onAG-ZzleNRO",
    "outputId": "79be6256-2bf0-4d90-e955-b9f77b17d9da"
   },
   "outputs": [],
   "source": [
    "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",
    "\n",
    "B=A.T\n",
    "\n",
    "a=np.percentile(B,27,axis=0, interpolation='lower')\n",
    "b=np.percentile(B,25,axis=1, interpolation='lower')\n",
    "c=np.percentile(B,75,axis=0, interpolation='lower')\n",
    "d=np.percentile(B,50,axis=0, interpolation='lower')\n",
    "\n",
    "print(a)\n",
    "\n",
    "print(b)\n",
    "\n",
    "print(c)\n",
    "\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7Do6eEXLgj2i"
   },
   "source": [
    "# inter-qurtile range\n",
    "\n",
    "---\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "v0J_1_TVgqF_",
    "outputId": "0956b1cd-d0f9-4058-ccd3-d50a41a4caed"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import iqr\n",
    "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",
    "\n",
    "B=A.T\n",
    "\n",
    "a=iqr(B, axis=0 , rng=(25, 75), interpolation='lower')\n",
    "b=iqr(B, axis=1 , rng=(25, 75), interpolation='lower')\n",
    "\n",
    "print(a,b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TeDGcMkvg5je"
   },
   "source": [
    "# Variance\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "RA52W0_HgrOz",
    "outputId": "4554289c-45b5-4a5c-d611-4092f80e7ee8"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "A=np.array([[10,14,11,7,9.5,15,19],[8,9,17,14.5,12,18,15.5],\n",
    "  [15,7.5,11.5,10,10.5,7,11],[11.5,11,9,12,14,12,7.5]])\n",
    "\n",
    "B=A.T\n",
    "\n",
    "a = np.var(B,axis=0)\n",
    "b = np.var(B,axis=1)\n",
    "\n",
    "print(a)\n",
    "\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "H40m2neihCJC"
   },
   "source": [
    "# Standard deviation\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "9pETuLnmhAsc",
    "outputId": "1ff2c7d4-2723-4a54-cf94-5e5968bfcd17"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "A=np.array([[10,14,11,7,9.5,15,19],[8,9,17,14.5,12,18,15.5],\n",
    "  [15,7.5,11.5,10,10.5,7,11],[11.5,11,9,12,14,12,7.5]])\n",
    "B=A.T\n",
    "a = np.std(B,axis=0)\n",
    "b = np.std(B,axis=1)\n",
    "print(a)\n",
    "\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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