Try_use)_jup.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "04e843f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello world\n"
     ]
    }
   ],
   "source": [
    "print(\"hello world\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bf2d4e4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5+2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "49592121",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Counter' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                  Traceback (most recent call last)",
      "Input \u001b[1;32mIn [3]\u001b[0m, in \u001b[0;36m<cell line: 3>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m num_friends \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m100.0\u001b[39m,\u001b[38;5;241m49\u001b[39m,\u001b[38;5;241m41\u001b[39m,\u001b[38;5;241m40\u001b[39m,\u001b[38;5;241m25\u001b[39m,\u001b[38;5;241m21\u001b[39m,\u001b[38;5;241m21\u001b[39m,\u001b[38;5;241m19\u001b[39m,\u001b[38;5;241m19\u001b[39m,\u001b[38;5;241m18\u001b[39m,\u001b[38;5;241m18\u001b[39m,\u001b[38;5;241m16\u001b[39m,\u001b[38;5;241m15\u001b[39m,\u001b[38;5;241m15\u001b[39m,\u001b[38;5;241m15\u001b[39m,\u001b[38;5;241m15\u001b[39m,\u001b[38;5;241m14\u001b[39m,\u001b[38;5;241m14\u001b[39m,\u001b[38;5;241m13\u001b[39m,\u001b[38;5;241m13\u001b[39m,\u001b[38;5;241m13\u001b[39m,\u001b[38;5;241m13\u001b[39m,\u001b[38;5;241m12\u001b[39m,\u001b[38;5;241m12\u001b[39m,\u001b[38;5;241m11\u001b[39m,\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m9\u001b[39m,\u001b[38;5;241m9\u001b[39m,\u001b[38;5;241m9\u001b[39m,\u001b[38;5;241m9\u001b[39m,\u001b[38;5;241m9\u001b[39m,\u001b[38;5;241m9\u001b[39m,\u001b[38;5;241m9\u001b[39m,\u001b[38;5;241m9\u001b[39m,\u001b[38;5;241m9\u001b[39m,\u001b[38;5;241m9\u001b[39m,\u001b[38;5;241m9\u001b[39m,\u001b[38;5;241m9\u001b[39m,\u001b[38;5;241m9\u001b[39m,\u001b[38;5;241m9\u001b[39m,\u001b[38;5;241m9\u001b[39m,\u001b[38;5;241m9\u001b[39m,\u001b[38;5;241m9\u001b[39m,\u001b[38;5;241m9\u001b[39m,\u001b[38;5;241m8\u001b[39m,\u001b[38;5;241m8\u001b[39m,\u001b[38;5;241m8\u001b[39m,\u001b[38;5;241m8\u001b[39m,\u001b[38;5;241m8\u001b[39m,\u001b[38;5;241m8\u001b[39m,\u001b[38;5;241m8\u001b[39m,\u001b[38;5;241m8\u001b[39m,\u001b[38;5;241m8\u001b[39m,\u001b[38;5;241m8\u001b[39m,\u001b[38;5;241m8\u001b[39m,\u001b[38;5;241m8\u001b[39m,\u001b[38;5;241m8\u001b[39m,\u001b[38;5;241m7\u001b[39m,\u001b[38;5;241m7\u001b[39m,\u001b[38;5;241m7\u001b[39m,\u001b[38;5;241m7\u001b[39m,\u001b[38;5;241m7\u001b[39m,\u001b[38;5;241m7\u001b[39m,\u001b[38;5;241m7\u001b[39m,\u001b[38;5;241m7\u001b[39m,\u001b[38;5;241m7\u001b[39m,\u001b[38;5;241m7\u001b[39m,\u001b[38;5;241m7\u001b[39m,\u001b[38;5;241m7\u001b[39m,\u001b[38;5;241m7\u001b[39m,\u001b[38;5;241m7\u001b[39m,\u001b[38;5;241m7\u001b[39m,\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m----> 3\u001b[0m friends_counts \u001b[38;5;241m=\u001b[39m \u001b[43mCounter\u001b[49m(num_friends)\n\u001b[0;32m      4\u001b[0m xs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m103\u001b[39m)\n\u001b[0;32m      5\u001b[0m ys \u001b[38;5;241m=\u001b[39m [friends_counts[x] \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m xs]\n",
      "\u001b[1;31mNameError\u001b[0m: name 'Counter' is not defined"
     ]
    }
   ],
   "source": [
    "num_friends = [100.0,49,41,40,25,21,21,19,19,18,18,16,15,15,15,15,14,14,13,13,13,13,12,12,11,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,8,8,8,8,8,8,8,8,8,8,8,8,8,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]\n",
    "\n",
    "friends_counts = Counter(num_friends)\n",
    "xs = range(103)\n",
    "ys = [friends_counts[x] for x in xs]\n",
    "plt.bar(xs, ys)\n",
    "plt.axis([0, 101, 0, 25])\n",
    "plt.title(\"# of friends\")\n",
    "plt.xlabel(\"friends\")\n",
    "plt.ylabel(\"people\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "efe6463e",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (3684533320.py, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Input \u001b[1;32mIn [4]\u001b[1;36m\u001b[0m\n\u001b[1;33m    largest_valfrom collections import Counter\u001b[0m\n\u001b[1;37m                    ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "\n",
    "num_points = len(num_friends)\n",
    "largest_valfrom collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "num_friends = [100.0,49,41,40,25,21,21,19,19,18,18,16,15,15,15,15,14,14,13,13,13,13,12,12,11,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,8,8,8,8,8,8,8,8,8,8,8,8,8,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]\n",
    "\n",
    "friends_counts = Counter(num_friends)\n",
    "xs = range(103)\n",
    "ys = [friends_counts[x] for x in xs]\n",
    "plt.bar(xs, ys)\n",
    "plt.axis([0, 101, 0, 25])\n",
    "plt.title(\"# of friends\")\n",
    "plt.xlabel(\"friends\")\n",
    "plt.ylabel(\"people\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b526d63d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "num_friends = [100.0,49,41,40,25,21,21,19,19,18,18,16,15,15,15,15,14,14,13,13,13,13,12,12,11,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,8,8,8,8,8,8,8,8,8,8,8,8,8,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]\n",
    "\n",
    "friends_counts = Counter(num_friends)\n",
    "xs = range(103)\n",
    "ys = [friends_counts[x] for x in xs]\n",
    "plt.bar(xs, ys)\n",
    "plt.axis([0, 101, 0, 25])\n",
    "plt.title(\"# of friends\")\n",
    "plt.xlabel(\"friends\")\n",
    "plt.ylabel(\"people\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "160f7956",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'people')"
      ]
     },
     "execution_count": 9,
     "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": [
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "from typing import List\n",
    "\n",
    "num_friends = [100.0,49,41,40,25,21,21,19,19,18,18,16,15,15,15,15,14,14,13,13,13,13,12,12,11,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,8,8,8,8,8,8,8,8,8,8,8,8,8,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]\n",
    "\n",
    "friends_counts = Counter(num_friends)\n",
    "xs = range(103)\n",
    "ys = [friends_counts[x] for x in xs]\n",
    "plt.bar(xs, ys)\n",
    "plt.axis([0, 101, 0, 25])\n",
    "plt.title(\"# of friends\")\n",
    "plt.xlabel(\"friends\")\n",
    "plt.ylabel(\"people\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b5ba8c45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.333333333333333"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def mean(x: List[float]) -> float:\n",
    "    return sum(x)/len(x)\n",
    "\n",
    "\n",
    "mean(num_friends)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "49f14496",
   "metadata": {},
   "outputs": [],
   "source": [
    "def _median_odd(xs: List[float]) -> float:\n",
    "    return sorted(xs)[len(xs)//2]\n",
    "\n",
    "def _median_even(xs: List[float]) ->float:\n",
    "    sorted_xs =sorted(xs)\n",
    "    hi_midpoint = len(xs)//2\n",
    "    return (sorted_xs[hi_midpoint - 1] + sorted_xs[hi_midpoint])/2\n",
    "\n",
    "def median(v: List[float]) -> float:\n",
    "    return _median_even(v) if len(v) % 2 == 0 else _median_odd(v)\n",
    "\n",
    "\n",
    "assert median([1, 10, 2, 9, 5])\n",
    "assert median([1, 9, 2, 10]) == (2 + 9) / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f714c47e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.0\n"
     ]
    }
   ],
   "source": [
    "print(median(num_friends))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3c9af8f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def quantile(xs: List[float], p: float) -> float:\n",
    "    p_index = int(p * len(xs)) #Преобразует% в индекс списка\n",
    "    return sorted(xs) [p_index]\n",
    "    \n",
    "assert quantile(num_friends, 0.10) == 1\n",
    "assert quantile(num_friends, 0.25) == 3 # Нижний квантиль\n",
    "assert quantile(num_friends, 0.75) == 9 # Верхний квантиль\n",
    "assert quantile(num_friends, 0.90) == 13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "61d33c84",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mode(x: List[float]) -> List[float]:\n",
    "    \"\"\"Возвращает список, т. к. может быть более одной моды\"\"\"\n",
    "    counts = Counter(x)\n",
    "    max_count = max(counts.values())\n",
    "    return [x_i for x_i, count in counts.items()\n",
    "        if count == max_count]\n",
    "\n",
    "assert set(mode(num_friends)) == {1, 6}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7aeb6639",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ключевое слово \"range\" (размах) в Python уже имеет\n",
    "# свой смысл, поэтому берем другое\n",
    "def data_range(xs: List[float]) -> float:\n",
    "    return max(xs) - min(xs)\n",
    "\n",
    "\n",
    "assert data_range(num_friends) == 99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f808036b",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'scratch.linear_algebra'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m        Traceback (most recent call last)",
      "Input \u001b[1;32mIn [28]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscratch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlinear_algebra\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m sum_of_squares\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mde_mean\u001b[39m(xs: List[\u001b[38;5;28mfloat\u001b[39m]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m List[\u001b[38;5;28mfloat\u001b[39m]:\n\u001b[0;32m      4\u001b[0m     \u001b[38;5;124;03m\"\"\"Транслировать xs путем вычитания его среднего\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;124;03m    (результат имеет нулевое среднее)\"\"\"\u001b[39;00m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'scratch.linear_algebra'"
     ]
    }
   ],
   "source": [
    "from scratch.linear_algebra import sum_of_squares\n",
    "\n",
    "def de_mean(xs: List[float]) -> List[float]:\n",
    "    \"\"\"Транслировать xs путем вычитания его среднего\n",
    "    (результат имеет нулевое среднее)\"\"\"\n",
    "    x_bar = mean(xs)\n",
    "    return [х - x_bar for х in xs]\n",
    "\n",
    "def variance(xs: List[float]) -> float:\n",
    "    \"\" \"Почти среднекв·адратическое отклонение от среднего\"\"\"\n",
    "    assert len(xs) >= 2, \"дисперсия требует наличия не менее двух элементов\"\n",
    "    n = len(xs)\n",
    "    deviations = de_mean(xs)\n",
    "    return surn_of_squares(deviations) / (n - 1)\n",
    "\n",
    "\n",
    "assert 81.54 < variance(nurn_friends) < 81.55"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8b968f45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting scratch\n",
      "  Downloading scratch-1.0.0.tar.gz (4.3 kB)\n",
      "  Preparing metadata (setup.py): started\n",
      "  Preparing metadata (setup.py): finished with status 'done'\n",
      "Using legacy 'setup.py install' for scratch, since package 'wheel' is not installed.\n",
      "Installing collected packages: scratch\n",
      "  Running setup.py install for scratch: started\n",
      "  Running setup.py install for scratch: finished with status 'done'\n",
      "Successfully installed scratch-1.0.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bfc35840",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'scratch.linear_algebra'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m        Traceback (most recent call last)",
      "Input \u001b[1;32mIn [1]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscratch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlinear_algebra\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m sum_of_squares\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mde_mean\u001b[39m(xs: List[\u001b[38;5;28mfloat\u001b[39m]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m List[\u001b[38;5;28mfloat\u001b[39m]:\n\u001b[0;32m      4\u001b[0m     \u001b[38;5;124;03m\"\"\"Транслировать xs путем вычитания его среднего\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;124;03m    (результат имеет нулевое среднее)\"\"\"\u001b[39;00m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'scratch.linear_algebra'"
     ]
    }
   ],
   "source": [
    "from scratch.linear_algebra import sum_of_squares\n",
    "\n",
    "def de_mean(xs: List[float]) -> List[float]:\n",
    "    \"\"\"Транслировать xs путем вычитания его среднего\n",
    "    (результат имеет нулевое среднее)\"\"\"\n",
    "    x_bar = mean(xs)\n",
    "    return [х - x_bar for х in xs]\n",
    "\n",
    "def variance(xs: List[float]) -> float:\n",
    "    \"\" \"Почти среднекв·адратическое отклонение от среднего\"\"\"\n",
    "    assert len(xs) >= 2, \"дисперсия требует наличия не менее двух элементов\"\n",
    "    n = len(xs)\n",
    "    deviations = de_mean(xs)\n",
    "    return surn_of_squares(deviations) / (n - 1)\n",
    "\n",
    "\n",
    "assert 81.54 < variance(nurn_friends) < 81.55"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ec67af31",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'scratch.linear_algebra'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m        Traceback (most recent call last)",
      "Input \u001b[1;32mIn [1]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscratch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlinear_algebra\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m sum_of_squares\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mde_mean\u001b[39m(xs: List[\u001b[38;5;28mfloat\u001b[39m]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m List[\u001b[38;5;28mfloat\u001b[39m]:\n\u001b[0;32m      4\u001b[0m     \u001b[38;5;124;03m\"\"\"Транслировать xs путем вычитания его среднего\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;124;03m    (результат имеет нулевое среднее)\"\"\"\u001b[39;00m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'scratch.linear_algebra'"
     ]
    }
   ],
   "source": [
    "from scratch.linear_algebra import sum_of_squares\n",
    "\n",
    "def de_mean(xs: List[float]) -> List[float]:\n",
    "    \"\"\"Транслировать xs путем вычитания его среднего\n",
    "    (результат имеет нулевое среднее)\"\"\"\n",
    "    x_bar = mean(xs)\n",
    "    return [х - x_bar for х in xs]\n",
    "\n",
    "def variance(xs: List[float]) -> float:\n",
    "    \"\" \"Почти среднекв·адратическое отклонение от среднего\"\"\"\n",
    "    assert len(xs) >= 2, \"дисперсия требует наличия не менее двух элементов\"\n",
    "    n = len(xs)\n",
    "    deviations = de_mean(xs)\n",
    "    return surn_of_squares(deviations) / (n - 1)\n",
    "\n",
    "\n",
    "assert 81.54 < variance(nurn_friends) < 81.55"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7b04d4f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scratch in c:\\users\\eldorado\\pycharmprojects\\ds_from_the_scratch\\venv\\lib\\site-packages (1.0.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "23887074",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'scratch.liniar_algebra'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m        Traceback (most recent call last)",
      "Input \u001b[1;32mIn [2]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mscratch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mliniar_algebra\u001b[39;00m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'scratch.liniar_algebra'"
     ]
    }
   ],
   "source": [
    "import scratch.liniar_algebra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "75d7c978",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: Could not find a version that satisfies the requirement scratch==3.0 (from versions: 0.0.1a0, 1.0.0)\n",
      "ERROR: No matching distribution found for scratch==3.0\n"
     ]
    }
   ],
   "source": [
    "pip install scratch==3.0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "af302b09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hi\n"
     ]
    }
   ],
   "source": [
    "print(\"hi\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "29b90c3b",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'linear_algebra'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Input \u001b[1;32mIn [2]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlinear_algebra\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m sum_of_squares\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mde_mean\u001b[39m(xs: List[\u001b[38;5;28mfloat\u001b[39m]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m List[\u001b[38;5;28mfloat\u001b[39m]:\n\u001b[0;32m      4\u001b[0m     \u001b[38;5;124;03m\"\"\"Транслировать xs путем вычитания его среднего\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;124;03m    (результат имеет нулевое среднее)\"\"\"\u001b[39;00m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'linear_algebra'"
     ]
    }
   ],
   "source": [
    "from linear_algebra import sum_of_squares\n",
    "\n",
    "def de_mean(xs: List[float]) -> List[float]:\n",
    "    \"\"\"Транслировать xs путем вычитания его среднего\n",
    "    (результат имеет нулевое среднее)\"\"\"\n",
    "    x_bar = mean(xs)\n",
    "    return [х - x_bar for х in xs]\n",
    "\n",
    "def variance(xs: List[float]) -> float:\n",
    "    \"\" \"Почти среднекв·адратическое отклонение от среднего\"\"\"\n",
    "    assert len(xs) >= 2, \"дисперсия требует наличия не менее двух элементов\"\n",
    "    n = len(xs)\n",
    "    deviations = de_mean(xs)\n",
    "    return surn_of_squares(deviations) / (n - 1)\n",
    "\n",
    "\n",
    "assert 81.54 < variance(nurn_friends) < 81.55"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7236d3a7",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'DS_from_the_scratch'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Input \u001b[1;32mIn [3]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mDS_from_the_scratch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlinear_algebra\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m sum_of_squares\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mde_mean\u001b[39m(xs: List[\u001b[38;5;28mfloat\u001b[39m]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m List[\u001b[38;5;28mfloat\u001b[39m]:\n\u001b[0;32m      4\u001b[0m     \u001b[38;5;124;03m\"\"\"Транслировать xs путем вычитания его среднего\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;124;03m    (результат имеет нулевое среднее)\"\"\"\u001b[39;00m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'DS_from_the_scratch'"
     ]
    }
   ],
   "source": [
    "from DS_from_the_scratch.linear_algebra import sum_of_squares\n",
    "\n",
    "def de_mean(xs: List[float]) -> List[float]:\n",
    "    \"\"\"Транслировать xs путем вычитания его среднего\n",
    "    (результат имеет нулевое среднее)\"\"\"\n",
    "    x_bar = mean(xs)\n",
    "    return [х - x_bar for х in xs]\n",
    "\n",
    "def variance(xs: List[float]) -> float:\n",
    "    \"\" \"Почти среднекв·адратическое отклонение от среднего\"\"\"\n",
    "    assert len(xs) >= 2, \"дисперсия требует наличия не менее двух элементов\"\n",
    "    n = len(xs)\n",
    "    deviations = de_mean(xs)\n",
    "    return surn_of_squares(deviations) / (n - 1)\n",
    "\n",
    "\n",
    "assert 81.54 < variance(nurn_friends) < 81.55"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8054ec68",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'List' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [4]\u001b[0m, in \u001b[0;36m<cell line: 3>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlinear_algera\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m sum_of_squares\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mde_mean\u001b[39m(xs: \u001b[43mList\u001b[49m[\u001b[38;5;28mfloat\u001b[39m]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m List[\u001b[38;5;28mfloat\u001b[39m]:\n\u001b[0;32m      4\u001b[0m     \u001b[38;5;124;03m\"\"\"Транслировать xs путем вычитания его среднего\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;124;03m    (результат имеет нулевое среднее)\"\"\"\u001b[39;00m\n\u001b[0;32m      6\u001b[0m     x_bar \u001b[38;5;241m=\u001b[39m mean(xs)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'List' is not defined"
     ]
    }
   ],
   "source": [
    "from linear_algera import sum_of_squares\n",
    "\n",
    "def de_mean(xs: List[float]) -> List[float]:\n",
    "    \"\"\"Транслировать xs путем вычитания его среднего\n",
    "    (результат имеет нулевое среднее)\"\"\"\n",
    "    x_bar = mean(xs)\n",
    "    return [х - x_bar for х in xs]\n",
    "\n",
    "def variance(xs: List[float]) -> float:\n",
    "    \"\" \"Почти среднекв·адратическое отклонение от среднего\"\"\"\n",
    "    assert len(xs) >= 2, \"дисперсия требует наличия не менее двух элементов\"\n",
    "    n = len(xs)\n",
    "    deviations = de_mean(xs)\n",
    "    return surn_of_squares(deviations) / (n - 1)\n",
    "\n",
    "\n",
    "assert 81.54 < variance(nurn_friends) < 81.55"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2a902ec2",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'num_friends' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [6]\u001b[0m, in \u001b[0;36m<cell line: 18>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     14\u001b[0m     deviations \u001b[38;5;241m=\u001b[39m de_mean(xs)\n\u001b[0;32m     15\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m sum_of_squares(deviations) \u001b[38;5;241m/\u001b[39m (n \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m---> 18\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;241m81.54\u001b[39m \u001b[38;5;241m<\u001b[39m variance(\u001b[43mnum_friends\u001b[49m) \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m81.55\u001b[39m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'num_friends' is not defined"
     ]
    }
   ],
   "source": [
    "from linear_algera import sum_of_squares\n",
    "from typing import List\n",
    "\n",
    "def de_mean(xs: List[float]) -> List[float]:\n",
    "    \"\"\"Транслировать xs путем вычитания его среднего\n",
    "    (результат имеет нулевое среднее)\"\"\"\n",
    "    x_bar = mean(xs)\n",
    "    return [х - x_bar for х in xs]\n",
    "\n",
    "def variance(xs: List[float]) -> float:\n",
    "    \"\" \"Почти среднекв·адратическое отклонение от среднего\"\"\"\n",
    "    assert len(xs) >= 2, \"дисперсия требует наличия не менее двух элементов\"\n",
    "    n = len(xs)\n",
    "    deviations = de_mean(xs)\n",
    "    return sum_of_squares(deviations) / (n - 1)\n",
    "\n",
    "\n",
    "assert 81.54 < variance(num_friends) < 81.55"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6843c51",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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