NumPy_Statistics-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "53dddbf4",
   "metadata": {},
   "source": [
    "# NumPy: Statistics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5d7fd6ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "07af3725",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1],\n",
       "       [2, 3]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q1\n",
    "f = np.arange(4).reshape((2,2))\n",
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "64fc9c61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "50f21230",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fe9cf775",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q2\n",
    "f.min(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "435f0164",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.max(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "19f909f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5],\n",
       "       [ 6,  7,  8,  9, 10, 11]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q3\n",
    "a = np.arange(12).reshape(2,6)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eef72980",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 5])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = a.max(axis=1) - a.min(axis=1)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "81372562",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4., 10.])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q4\n",
    "np.percentile(a, 80, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ac583421",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.5"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q5\n",
    "np.median(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a8171ad3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q6\n",
    "w = np.arange(5)\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "edd049ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(w) # Simple average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "12bf562d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.6666666666666665"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = np.arange(1,6)\n",
    "np.average(w, weights=weights) # weighted average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "66156bea",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Q7\n",
    "a = np.arange(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "913bf7e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "96306e81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.707825127659933"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4952816b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.9166666666666665"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d0aad239",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Q8\n",
    "x = np.arange(3)\n",
    "y = np.flip(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e065e2c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5d130f58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 0])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "55794522",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1., -1.],\n",
       "       [-1.,  1.]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cov(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3f604e1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Q9\n",
    "p = np.array([0,1,3])\n",
    "q = np.array([2,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0be64386",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 3])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "12fe2038",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 5])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "29147015",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([19])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.correlate(p,q) # cross correlation: also handles non-linear and non-stationary relationships"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "489af541",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.92857143],\n",
       "       [0.92857143, 1.        ]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q10\n",
    "np.corrcoef(p,q) # pearson correl: only linear and stationary relations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ce23c306",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5],\n",
       "       [6, 7, 8]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q12\n",
    "a = np.arange(9).reshape((3,3))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "993c0b64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.2, 4.2, 7.2])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(a, axis=1, weights=[1./4, 2./4, 2./4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "00fe31fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 6, 1, 4, 1, 2, 2, 7])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#q13\n",
    "x = np.array([0, 1, 6, 1, 4, 1, 2, 2, 7])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a667d3e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 2, 0, 1, 0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.bincount(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "819b138c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Q14\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e99a09d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "nums = np.array([0.5, 0.7, 1.0, 1.2, 1.3, 2.1])\n",
    "bins = np.array([0, 1, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "eb351946",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.5, 0.7, 1. , 1.2, 1.3, 2.1])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8b274fea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6bd0da14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2, 3, 1], dtype=int64), array([0, 1, 2, 3]))"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.histogram(nums, bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "01008969",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2., 3., 1.]), array([0, 1, 2, 3]), <BarContainer object of 3 artists>)"
      ]
     },
     "execution_count": 47,
     "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": [
    "plt.hist(nums, bins=bins)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}