ischurov/Lesson11.ipynb Secret

## Lesson11.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Наука о данных\n### Совместный бакалавриат ВШЭ-РЭШ, 2021-2022 учебный год\n_Илья Щуров_\n\n[Страница курса](http://math-info.hse.ru/s21/j)"
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "indie-classic",
      "cell_type": "code",
      "source": "class Vector:\n    def __init__(self, values):\n        self.values = values\n    def __add__(self, other):\n        return Vector([x + y for x, y in zip(self.values, other.values)])\n    def __repr__(self):\n        return f\"Vector({self.values!r})\"\n",
      "execution_count": 7,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "inclusive-kruger",
      "cell_type": "code",
      "source": "u = Vector([1, 2, 3])\nv = Vector([10, 20, 30])\nu + v",
      "execution_count": 8,
      "outputs": [
        {
          "data": {
            "text/plain": "Vector([11, 22, 33])"
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "caroline-valentine",
      "cell_type": "code",
      "source": "import numpy as np",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "through-surge",
      "cell_type": "code",
      "source": "u = np.array([1, 2, 3])\nv = np.array([10, 30, 40])\nu + v",
      "execution_count": 10,
      "outputs": [
        {
          "data": {
            "text/plain": "array([11, 32, 43])"
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "eight-lightweight",
      "cell_type": "code",
      "source": "for x in u:\n    print(x)",
      "execution_count": 11,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "1\n2\n3\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "endangered-grade",
      "cell_type": "code",
      "source": "u[1:3]",
      "execution_count": 12,
      "outputs": [
        {
          "data": {
            "text/plain": "array([2, 3])"
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "olive-burton",
      "cell_type": "code",
      "source": "u[0] = 100",
      "execution_count": 13,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "civilian-camping",
      "cell_type": "code",
      "source": "u",
      "execution_count": 14,
      "outputs": [
        {
          "data": {
            "text/plain": "array([100,   2,   3])"
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "western-preview",
      "cell_type": "code",
      "source": "u[0]",
      "execution_count": 15,
      "outputs": [
        {
          "data": {
            "text/plain": "100"
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "included-shade",
      "cell_type": "code",
      "source": "u",
      "execution_count": 16,
      "outputs": [
        {
          "data": {
            "text/plain": "array([100,   2,   3])"
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "democratic-university",
      "cell_type": "code",
      "source": "u[0] = \"Hello\"",
      "execution_count": 17,
      "outputs": [
        {
          "ename": "ValueError",
          "evalue": "invalid literal for int() with base 10: 'Hello'",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-c16eb0ddf1ec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mu\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Hello\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'Hello'"
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "generic-harvard",
      "cell_type": "code",
      "source": "u.dtype",
      "execution_count": 18,
      "outputs": [
        {
          "data": {
            "text/plain": "dtype('int64')"
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "clean-enhancement",
      "cell_type": "code",
      "source": "u[0] = 12.34",
      "execution_count": 19,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "fifty-announcement",
      "cell_type": "code",
      "source": "u",
      "execution_count": 20,
      "outputs": [
        {
          "data": {
            "text/plain": "array([12,  2,  3])"
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "future-champion",
      "cell_type": "code",
      "source": "u[0]",
      "execution_count": 21,
      "outputs": [
        {
          "data": {
            "text/plain": "12"
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "color-elevation",
      "cell_type": "code",
      "source": "u[0] = \"12\"",
      "execution_count": 22,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "protected-spread",
      "cell_type": "code",
      "source": "u",
      "execution_count": 23,
      "outputs": [
        {
          "data": {
            "text/plain": "array([12,  2,  3])"
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "french-wagon",
      "cell_type": "code",
      "source": "heterogenious_array = np.array([12, \"Hello\", 34])",
      "execution_count": 24,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "pursuant-paragraph",
      "cell_type": "code",
      "source": "heterogenious_array",
      "execution_count": 25,
      "outputs": [
        {
          "data": {
            "text/plain": "array(['12', 'Hello', '34'], dtype='<U21')"
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "genetic-knowing",
      "cell_type": "code",
      "source": "heterogenious_array[0] = \"Hello, World! This is a test. Раз два три четыре пять, вышел зайчик погулять\"",
      "execution_count": 26,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "conservative-ghana",
      "cell_type": "code",
      "source": "heterogenious_array",
      "execution_count": 27,
      "outputs": [
        {
          "data": {
            "text/plain": "array(['Hello, World! This is', 'Hello', '34'], dtype='<U21')"
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "prepared-imaging",
      "cell_type": "code",
      "source": "numbers = [4.3, 1.3, 3.1, 4.6, 5.2] * 1000000",
      "execution_count": 44,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "arranged-moral",
      "cell_type": "code",
      "source": "%%timeit\nsquares = [x ** 2 for x in numbers]",
      "execution_count": 45,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "485 ms ± 8.75 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "planned-norman",
      "cell_type": "code",
      "source": "numpy_numbers = np.array(numbers)",
      "execution_count": 46,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "average-bacon",
      "cell_type": "code",
      "source": "%%timeit\nnumpy_squares = numpy_numbers ** 2",
      "execution_count": 47,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "11.4 ms ± 272 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "twenty-swedish",
      "cell_type": "code",
      "source": "numpy_numbers = np.array(numbers, dtype=object)",
      "execution_count": 48,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "characteristic-kazakhstan",
      "cell_type": "code",
      "source": "%%timeit\nnumpy_squares = numpy_numbers ** 2",
      "execution_count": 49,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "374 ms ± 6.39 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "matched-franchise",
      "cell_type": "code",
      "source": "x = np.array([1, 2, 10,  3, 2, 10, 4])\ny = np.array([1.2, 3, 2, 1, 3, 2, 15])",
      "execution_count": 50,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "urban-associate",
      "cell_type": "code",
      "source": "x * y",
      "execution_count": 51,
      "outputs": [
        {
          "data": {
            "text/plain": "array([ 1.2,  6. , 20. ,  3. ,  6. , 20. , 60. ])"
          },
          "execution_count": 51,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "valued-allah",
      "cell_type": "code",
      "source": "x * 4",
      "execution_count": 52,
      "outputs": [
        {
          "data": {
            "text/plain": "array([ 4,  8, 40, 12,  8, 40, 16])"
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "upset-trance",
      "cell_type": "code",
      "source": "x ** 2",
      "execution_count": 53,
      "outputs": [
        {
          "data": {
            "text/plain": "array([  1,   4, 100,   9,   4, 100,  16])"
          },
          "execution_count": 53,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "conditional-fabric",
      "cell_type": "code",
      "source": "np.sin(x)",
      "execution_count": 54,
      "outputs": [
        {
          "data": {
            "text/plain": "array([ 0.84147098,  0.90929743, -0.54402111,  0.14112001,  0.90929743,\n       -0.54402111, -0.7568025 ])"
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "reported-revelation",
      "cell_type": "code",
      "source": "import math",
      "execution_count": 55,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "thorough-briefing",
      "cell_type": "code",
      "source": "math.sin(x)",
      "execution_count": 56,
      "outputs": [
        {
          "ename": "TypeError",
          "evalue": "only size-1 arrays can be converted to Python scalars",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-56-514904a8605d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: only size-1 arrays can be converted to Python scalars"
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "conventional-yeast",
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt\n%matplotlib inline",
      "execution_count": 58,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "suited-objective",
      "cell_type": "code",
      "source": "plt.plot([1, 2, 4, 2], [3, 2, 5, 1])",
      "execution_count": 59,
      "outputs": [
        {
          "data": {
            "text/plain": "[<matplotlib.lines.Line2D at 0x7ff67af33910>]"
          },
          "execution_count": 59,
          "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"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "automotive-allah",
      "cell_type": "code",
      "source": "plt.plot([1, 2, 4, 2], [3, 2, 5, 1], 'o')",
      "execution_count": 60,
      "outputs": [
        {
          "data": {
            "text/plain": "[<matplotlib.lines.Line2D at 0x7ff67b29ef40>]"
          },
          "execution_count": 60,
          "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"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "single-arrival",
      "cell_type": "code",
      "source": "plt.plot([1, 2, 4, 2], [3, 2, 5, 1], 'x')",
      "execution_count": 61,
      "outputs": [
        {
          "data": {
            "text/plain": "[<matplotlib.lines.Line2D at 0x7ff67b25fd60>]"
          },
          "execution_count": 61,
          "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"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "familiar-october",
      "cell_type": "code",
      "source": "plt.plot([1, 2, 4, 2], [3, 2, 5, 1], '-o')",
      "execution_count": 62,
      "outputs": [
        {
          "data": {
            "text/plain": "[<matplotlib.lines.Line2D at 0x7ff67a430f70>]"
          },
          "execution_count": 62,
          "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"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "august-cement",
      "cell_type": "code",
      "source": "x = np.linspace(-3, 3, 300)",
      "execution_count": 71,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "specialized-berlin",
      "cell_type": "code",
      "source": "with plt.xkcd():\n    plt.plot(x, np.sin(x ** 2), '-', label='sin x^2')\n    plt.legend()\n    plt.xlabel(\"time\")\n    ",
      "execution_count": 82,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "resistant-certificate",
      "cell_type": "code",
      "source": "with plt.xkcd():\n    plt.plot(np.sin(x ** 2), x, '-', label='sin x^2')\n    plt.legend()\n    plt.xlabel(\"time\")",
      "execution_count": 83,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "frequent-peoples",
      "cell_type": "code",
      "source": "my_list = [0, 10, 20, 30, 40]\narr = np.array([0, 10, 20, 30, 40])",
      "execution_count": 85,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "critical-compound",
      "cell_type": "code",
      "source": "part_list = my_list[2:5]\npart_list",
      "execution_count": 86,
      "outputs": [
        {
          "data": {
            "text/plain": "[20, 30, 40]"
          },
          "execution_count": 86,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "complimentary-airport",
      "cell_type": "code",
      "source": "part_list[0] = 100",
      "execution_count": 87,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "departmental-deployment",
      "cell_type": "code",
      "source": "my_list",
      "execution_count": 88,
      "outputs": [
        {
          "data": {
            "text/plain": "[0, 10, 20, 30, 40]"
          },
          "execution_count": 88,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "brazilian-volume",
      "cell_type": "code",
      "source": "part_arr = arr[2:5]",
      "execution_count": 89,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "exceptional-nutrition",
      "cell_type": "code",
      "source": "part_arr",
      "execution_count": 90,
      "outputs": [
        {
          "data": {
            "text/plain": "array([20, 30, 40])"
          },
          "execution_count": 90,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "nonprofit-repeat",
      "cell_type": "code",
      "source": "part_arr[0] = 100",
      "execution_count": 91,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "stuffed-guidance",
      "cell_type": "code",
      "source": "part_arr",
      "execution_count": 92,
      "outputs": [
        {
          "data": {
            "text/plain": "array([100,  30,  40])"
          },
          "execution_count": 92,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "lasting-canberra",
      "cell_type": "code",
      "source": "arr",
      "execution_count": 93,
      "outputs": [
        {
          "data": {
            "text/plain": "array([  0,  10, 100,  30,  40])"
          },
          "execution_count": 93,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "pressed-advocacy",
      "cell_type": "code",
      "source": "part_arr.base",
      "execution_count": 96,
      "outputs": [
        {
          "data": {
            "text/plain": "array([  0,  10, 100,  30,  40])"
          },
          "execution_count": 96,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "retained-cambridge",
      "cell_type": "code",
      "source": "arr",
      "execution_count": 97,
      "outputs": [
        {
          "data": {
            "text/plain": "array([  0,  10, 100,  30,  40])"
          },
          "execution_count": 97,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "vital-playlist",
      "cell_type": "code",
      "source": "arr[np.array([2, 1, 1, 3])]",
      "execution_count": 101,
      "outputs": [
        {
          "data": {
            "text/plain": "array([100,  10,  10,  30])"
          },
          "execution_count": 101,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "serious-volunteer",
      "cell_type": "code",
      "source": "arr",
      "execution_count": 102,
      "outputs": [
        {
          "data": {
            "text/plain": "array([  0,  10, 100,  30,  40])"
          },
          "execution_count": 102,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "inappropriate-makeup",
      "cell_type": "code",
      "source": "arr[arr > 20]",
      "execution_count": 103,
      "outputs": [
        {
          "data": {
            "text/plain": "array([100,  30,  40])"
          },
          "execution_count": 103,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "other-darwin",
      "cell_type": "code",
      "source": "arr > 20",
      "execution_count": 104,
      "outputs": [
        {
          "data": {
            "text/plain": "array([False, False,  True,  True,  True])"
          },
          "execution_count": 104,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "laughing-circle",
      "cell_type": "code",
      "source": "arr",
      "execution_count": 107,
      "outputs": [
        {
          "data": {
            "text/plain": "array([  0,  10, 100,  30,  40])"
          },
          "execution_count": 107,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "seven-wheat",
      "cell_type": "code",
      "source": "arr[np.array([True, False, False, True, True])]",
      "execution_count": 106,
      "outputs": [
        {
          "data": {
            "text/plain": "array([ 0, 30, 40])"
          },
          "execution_count": 106,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "decent-dublin",
      "cell_type": "code",
      "source": "x = np.array([1, 2, 10])\ny = np.array([1, 2, 10])\nif x == y:\n    print(\"Arrays are equal\")\nelse:\n    print(\"Not equal\")",
      "execution_count": 118,
      "outputs": [
        {
          "ename": "ValueError",
          "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-118-11fd1d1bb62e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Arrays are equal\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "terminal-shooting",
      "cell_type": "code",
      "source": "x = np.array([1, 2, 10])\ny = np.array([1, 2, 10])\nif (x == y).all():\n    print(\"Arrays are equal\")\nelse:\n    print(\"Not equal\")",
      "execution_count": 111,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Arrays are equal\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "completed-command",
      "cell_type": "code",
      "source": "[1, 2, 10] == [1, 2, 10]",
      "execution_count": 109,
      "outputs": [
        {
          "data": {
            "text/plain": "True"
          },
          "execution_count": 109,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "serious-kazakhstan",
      "cell_type": "code",
      "source": "x == y",
      "execution_count": 110,
      "outputs": [
        {
          "data": {
            "text/plain": "array([ True,  True,  True])"
          },
          "execution_count": 110,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "deadly-phenomenon",
      "cell_type": "code",
      "source": "(x == y).all()",
      "execution_count": 112,
      "outputs": [
        {
          "data": {
            "text/plain": "True"
          },
          "execution_count": 112,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "joint-nigeria",
      "cell_type": "code",
      "source": "(x == y).any()",
      "execution_count": 113,
      "outputs": [
        {
          "data": {
            "text/plain": "True"
          },
          "execution_count": 113,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "conscious-affairs",
      "cell_type": "code",
      "source": "value = 11\ncompound = any(value % d == 0 for d in range(2, value))\ncompound",
      "execution_count": 117,
      "outputs": [
        {
          "data": {
            "text/plain": "False"
          },
          "execution_count": 117,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "caroline-argentina",
      "cell_type": "code",
      "source": "np.isclose(np.sin(np.pi), 0)",
      "execution_count": 121,
      "outputs": [
        {
          "data": {
            "text/plain": "True"
          },
          "execution_count": 121,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "guided-australian",
      "cell_type": "code",
      "source": "values = np.random.normal(size=1000)\nplt.hist(values, bins=100);",
      "execution_count": 142,
      "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"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "adjustable-advertiser",
      "cell_type": "code",
      "source": "values1 = np.random.normal(size=1000)\nvalues2 = np.random.normal(size=1000, loc=1, scale=10)\n#plt.hist(values1, bins=100)\nplt.hist(values2, color='Teal')",
      "execution_count": 152,
      "outputs": [
        {
          "data": {
            "text/plain": "(array([ 18.,  50., 114., 180., 206., 205., 117.,  80.,  23.,   7.]),\n array([-24.20808958, -18.6368702 , -13.06565082,  -7.49443144,\n         -1.92321206,   3.64800732,   9.2192267 ,  14.79044608,\n         20.36166546,  25.93288484,  31.50410421]),\n <BarContainer object of 10 artists>)"
          },
          "execution_count": 152,
          "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"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "radical-silly",
      "cell_type": "code",
      "source": "np.random.uniform(-2, 4, 100)",
      "execution_count": 148,
      "outputs": [
        {
          "data": {
            "text/plain": "array([ 2.66864001,  3.47810612,  2.25056051,  2.3342512 , -1.54351246,\n        1.89880301,  1.61455848,  2.31012656,  0.44392441,  1.53593466,\n       -1.94538903,  0.1439163 , -0.1433157 ,  2.58871048,  3.95938313,\n        0.43351315, -1.05021911,  2.63766886,  2.11875797,  0.34592219,\n        3.26263105,  1.92755096,  0.65437172, -1.78793963, -0.74143527,\n        1.19433011,  1.39433097,  3.82716185,  3.08320377, -0.9671178 ,\n        1.95468676,  1.68810981, -1.89268308,  2.03678119,  0.11306544,\n       -0.42265922, -0.55467852,  0.76229428,  2.96734466,  2.48042633,\n        0.03143637, -0.17326485,  0.13730616, -0.0238638 ,  0.19917877,\n       -0.34721017,  1.80801786, -0.2762259 ,  3.59911859,  0.31075282,\n       -1.11555527,  0.33415482,  3.72498591,  3.21013111, -0.98044113,\n        2.75290657, -0.90346265,  0.70556925,  0.40447406,  0.94125162,\n        2.77693473, -1.62998354,  0.81211675,  0.49436754,  0.31612544,\n        2.83390227,  0.23132306,  1.03085102,  1.82125946, -0.16213709,\n        1.2584932 ,  1.31922541, -1.9259999 ,  1.77171996,  2.9474032 ,\n       -0.82036008,  0.90272979, -1.20958501,  0.4161612 , -0.9711195 ,\n        1.93928119,  1.87077312, -0.53986656,  3.89570011,  1.93450949,\n       -0.12083531,  1.95466906, -0.87202968,  0.71347059,  1.19030547,\n        0.45941716,  1.613182  ,  1.81784656, -0.8888993 ,  0.66956034,\n        0.31188203, -1.70899769,  0.22243441, -1.75061687,  2.15764511])"
          },
          "execution_count": 148,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "external-processing",
      "cell_type": "code",
      "source": "M = np.array([[2, 3, 4],\n              [1, 3, 2]])",
      "execution_count": 158,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "bearing-specification",
      "cell_type": "code",
      "source": "M[0]",
      "execution_count": 160,
      "outputs": [
        {
          "data": {
            "text/plain": "array([2, 3, 4])"
          },
          "execution_count": 160,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "dense-christmas",
      "cell_type": "code",
      "source": "M[1]",
      "execution_count": 161,
      "outputs": [
        {
          "data": {
            "text/plain": "array([1, 3, 2])"
          },
          "execution_count": 161,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "criminal-croatia",
      "cell_type": "code",
      "source": "M[:, 0]",
      "execution_count": 162,
      "outputs": [
        {
          "data": {
            "text/plain": "array([2, 1])"
          },
          "execution_count": 162,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "combined-trigger",
      "cell_type": "code",
      "source": "M[1, 2]",
      "execution_count": 163,
      "outputs": [
        {
          "data": {
            "text/plain": "2"
          },
          "execution_count": 163,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "expired-diagnosis",
      "cell_type": "code",
      "source": "M",
      "execution_count": 164,
      "outputs": [
        {
          "data": {
            "text/plain": "array([[2, 3, 4],\n       [1, 3, 2]])"
          },
          "execution_count": 164,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "informational-museum",
      "cell_type": "code",
      "source": "M[:, 1:3]",
      "execution_count": 167,
      "outputs": [
        {
          "data": {
            "text/plain": "array([[3, 4],\n       [3, 2]])"
          },
          "execution_count": 167,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "exempt-paris",
      "cell_type": "code",
      "source": "M.shape",
      "execution_count": 168,
      "outputs": [
        {
          "data": {
            "text/plain": "(2, 3)"
          },
          "execution_count": 168,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "moderate-minister",
      "cell_type": "code",
      "source": "M",
      "execution_count": 169,
      "outputs": [
        {
          "data": {
            "text/plain": "array([[2, 3, 4],\n       [1, 3, 2]])"
          },
          "execution_count": 169,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "close-groove",
      "cell_type": "code",
      "source": "M @ np.array([2, 3, 1])",
      "execution_count": 171,
      "outputs": [
        {
          "data": {
            "text/plain": "array([17, 13])"
          },
          "execution_count": 171,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "collect-school",
      "cell_type": "code",
      "source": "np.array([1, 2, 3]) @ np.array([2, 10, 3])",
      "execution_count": 172,
      "outputs": [
        {
          "data": {
            "text/plain": "31"
          },
          "execution_count": 172,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "endangered-planner",
      "cell_type": "code",
      "source": "np.array([1, 2, 3]).T",
      "execution_count": 173,
      "outputs": [
        {
          "data": {
            "text/plain": "array([1, 2, 3])"
          },
          "execution_count": 173,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "invisible-nerve",
      "cell_type": "code",
      "source": "np.array([[1, 2, 3]])",
      "execution_count": 174,
      "outputs": [
        {
          "data": {
            "text/plain": "array([[1, 2, 3]])"
          },
          "execution_count": 174,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "modern-compact",
      "cell_type": "code",
      "source": "np.array([[1, 2, 3]]).T",
      "execution_count": 175,
      "outputs": [
        {
          "data": {
            "text/plain": "array([[1],\n       [2],\n       [3]])"
          },
          "execution_count": 175,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "composed-dictionary",
      "cell_type": "code",
      "source": "np.array([1, 2, 3]).reshape(3, 1)",
      "execution_count": 176,
      "outputs": [
        {
          "data": {
            "text/plain": "array([[1],\n       [2],\n       [3]])"
          },
          "execution_count": 176,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "global-blackjack",
      "cell_type": "code",
      "source": "np.array([1, 2, 3]).reshape(1, 3)",
      "execution_count": 177,
      "outputs": [
        {
          "data": {
            "text/plain": "array([[1, 2, 3]])"
          },
          "execution_count": 177,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "adaptive-variable",
      "cell_type": "code",
      "source": "np.array([1, 2, 3, 4, 5, 6]).reshape(2, 3)",
      "execution_count": 178,
      "outputs": [
        {
          "data": {
            "text/plain": "array([[1, 2, 3],\n       [4, 5, 6]])"
          },
          "execution_count": 178,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "normal-studio",
      "cell_type": "code",
      "source": "np.array([1, 2, 3, 4, 5, 6]).reshape(3, 2)",
      "execution_count": 179,
      "outputs": [
        {
          "data": {
            "text/plain": "array([[1, 2],\n       [3, 4],\n       [5, 6]])"
          },
          "execution_count": 179,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "located-cornell",
      "cell_type": "code",
      "source": "np.array([1, 2, 3, 4, 5, 6]).reshape(3, -1)",
      "execution_count": 180,
      "outputs": [
        {
          "data": {
            "text/plain": "array([[1, 2],\n       [3, 4],\n       [5, 6]])"
          },
          "execution_count": 180,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "interim-programming",
      "cell_type": "code",
      "source": "np.array([1, 2, 3, 4, 5, 6, 78]).reshape(3, -1)",
      "execution_count": 181,
      "outputs": [
        {
          "ename": "ValueError",
          "evalue": "cannot reshape array of size 7 into shape (3,newaxis)",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-181-cfbfb019a3fc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m78\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 7 into shape (3,newaxis)"
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "isolated-emission",
      "cell_type": "code",
      "source": "np.array([[1, 2], [3, 4]]) @ np.array([1, 2])",
      "execution_count": 182,
      "outputs": [
        {
          "data": {
            "text/plain": "array([ 5, 11])"
          },
          "execution_count": 182,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "decimal-criminal",
      "cell_type": "code",
      "source": "A = np.array([[1, 2], [3, 4]])\nB = np.array([[10, 20], [30, 10]])",
      "execution_count": 183,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "occupational-granny",
      "cell_type": "code",
      "source": "A * B",
      "execution_count": 184,
      "outputs": [
        {
          "data": {
            "text/plain": "array([[10, 40],\n       [90, 40]])"
          },
          "execution_count": 184,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "rapid-principal",
      "cell_type": "code",
      "source": "A @ B",
      "execution_count": 185,
      "outputs": [
        {
          "data": {
            "text/plain": "array([[ 70,  40],\n       [150, 100]])"
          },
          "execution_count": 185,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "radio-fundamentals",
      "cell_type": "code",
      "source": "np.linalg.inv(A)",
      "execution_count": 187,
      "outputs": [
        {
          "data": {
            "text/plain": "array([[-2. ,  1. ],\n       [ 1.5, -0.5]])"
          },
          "execution_count": 187,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "experimental-delight",
      "cell_type": "code",
      "source": "np.linalg.det(A)",
      "execution_count": 188,
      "outputs": [
        {
          "data": {
            "text/plain": "-2.0000000000000004"
          },
          "execution_count": 188,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "fatty-nitrogen",
      "cell_type": "code",
      "source": "np.linalg.eig(A)",
      "execution_count": 189,
      "outputs": [
        {
          "data": {
            "text/plain": "(array([-0.37228132,  5.37228132]),\n array([[-0.82456484, -0.41597356],\n        [ 0.56576746, -0.90937671]]))"
          },
          "execution_count": 189,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "mysterious-greek",
      "cell_type": "code",
      "source": "from sympy import symbols, sin, exp, expand, factor, solve, cos, latex",
      "execution_count": 201,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "presidential-friendship",
      "cell_type": "code",
      "source": "x, y = symbols('x y')",
      "execution_count": 202,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "sharing-wheat",
      "cell_type": "code",
      "source": "solve(x ** 2 - 5 * x + 6, x)",
      "execution_count": 203,
      "outputs": [
        {
          "data": {
            "text/plain": "[2, 3]"
          },
          "execution_count": 203,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "political-bullet",
      "cell_type": "code",
      "source": "print(latex(sin(cos(exp(sin(cos(x ** 2 + y))))).diff(x)))",
      "execution_count": 205,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "2 x e^{\\sin{\\left(\\cos{\\left(x^{2} + y \\right)} \\right)}} \\sin{\\left(x^{2} + y \\right)} \\sin{\\left(e^{\\sin{\\left(\\cos{\\left(x^{2} + y \\right)} \\right)}} \\right)} \\cos{\\left(\\cos{\\left(x^{2} + y \\right)} \\right)} \\cos{\\left(\\cos{\\left(e^{\\sin{\\left(\\cos{\\left(x^{2} + y \\right)} \\right)}} \\right)} \\right)}\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "casual-candy",
      "cell_type": "code",
      "source": "sin(cos(exp(sin(cos(x ** 2 + y))))).diff(y)",
      "execution_count": 207,
      "outputs": [
        {
          "data": {
            "text/latex": "$\\displaystyle e^{\\sin{\\left(\\cos{\\left(x^{2} + y \\right)} \\right)}} \\sin{\\left(x^{2} + y \\right)} \\sin{\\left(e^{\\sin{\\left(\\cos{\\left(x^{2} + y \\right)} \\right)}} \\right)} \\cos{\\left(\\cos{\\left(x^{2} + y \\right)} \\right)} \\cos{\\left(\\cos{\\left(e^{\\sin{\\left(\\cos{\\left(x^{2} + y \\right)} \\right)}} \\right)} \\right)}$",
            "text/plain": "exp(sin(cos(x**2 + y)))*sin(x**2 + y)*sin(exp(sin(cos(x**2 + y))))*cos(cos(x**2 + y))*cos(cos(exp(sin(cos(x**2 + y)))))"
          },
          "execution_count": 207,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "caring-packing",
      "cell_type": "code",
      "source": "solve(x ** 3 + 5 * x - 1, x)[2].evalf()",
      "execution_count": 217,
      "outputs": [
        {
          "data": {
            "text/latex": "$\\displaystyle 0.198437214538623$",
            "text/plain": "0.198437214538623"
          },
          "execution_count": 217,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "fixed-turning",
      "cell_type": "code",
      "source": "solve(x ** 4 + 5 * x - 1, x)[0]",
      "execution_count": 213,
      "outputs": [
        {
          "data": {
            "text/latex": "$\\displaystyle - \\frac{\\sqrt{- \\frac{2}{3 \\sqrt[3]{\\frac{25}{16} + \\frac{\\sqrt{51393}}{144}}} + 2 \\sqrt[3]{\\frac{25}{16} + \\frac{\\sqrt{51393}}{144}}}}{2} + \\frac{\\sqrt{- 2 \\sqrt[3]{\\frac{25}{16} + \\frac{\\sqrt{51393}}{144}} + \\frac{2}{3 \\sqrt[3]{\\frac{25}{16} + \\frac{\\sqrt{51393}}{144}}} + \\frac{10}{\\sqrt{- \\frac{2}{3 \\sqrt[3]{\\frac{25}{16} + \\frac{\\sqrt{51393}}{144}}} + 2 \\sqrt[3]{\\frac{25}{16} + \\frac{\\sqrt{51393}}{144}}}}}}{2}$",
            "text/plain": "-sqrt(-2/(3*(25/16 + sqrt(51393)/144)**(1/3)) + 2*(25/16 + sqrt(51393)/144)**(1/3))/2 + sqrt(-2*(25/16 + sqrt(51393)/144)**(1/3) + 2/(3*(25/16 + sqrt(51393)/144)**(1/3)) + 10/sqrt(-2/(3*(25/16 + sqrt(51393)/144)**(1/3)) + 2*(25/16 + sqrt(51393)/144)**(1/3)))/2"
          },
          "execution_count": 213,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "cognitive-agent",
      "cell_type": "code",
      "source": "from sympy import pi",
      "execution_count": 215,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "virtual-genome",
      "cell_type": "code",
      "source": "type(pi.evalf(n=30000))",
      "execution_count": 224,
      "outputs": [
        {
          "data": {
            "text/plain": "sympy.core.numbers.Float"
          },
          "execution_count": 224,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "supposed-routine",
      "cell_type": "code",
      "source": "print(pi.evalf(n=30000))",
      "execution_count": 222,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": 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48\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "appreciated-reporter",
      "cell_type": "code",
      "source": "str.copy",
      "execution_count": 239,
      "outputs": [
        {
          "ename": "AttributeError",
          "evalue": "type object 'str' has no attribute 'copy'",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-239-48aa67c82f43>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: type object 'str' has no attribute 'copy'"
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "fresh-sapphire",
      "cell_type": "code",
      "source": "sin(pi) == 0",
      "execution_count": 226,
      "outputs": [
        {
          "data": {
            "text/plain": "True"
          },
          "execution_count": 226,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "equipped-peter",
      "cell_type": "code",
      "source": "import random",
      "execution_count": 228,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "immune-pizza",
      "cell_type": "code",
      "source": "la1 = random.randrange(-4, 5)\nla2 = random.randrange(-3, 8)\nx = symbols('x')\nexpand((x - la1) * (x - la2))",
      "execution_count": 236,
      "outputs": [
        {
          "data": {
            "text/latex": "$\\displaystyle x^{2} - 10 x + 21$",
            "text/plain": "x**2 - 10*x + 21"
          },
          "execution_count": 236,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "continued-solution",
      "cell_type": "code",
      "source": "sin(np.pi)",
      "execution_count": 237,
      "outputs": [
        {
          "data": {
            "text/latex": "$\\displaystyle 1.22464679914735 \\cdot 10^{-16}$",
            "text/plain": "1.22464679914735e-16"
          },
          "execution_count": 237,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "confused-musical",
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "py39_system",
      "display_name": "Python 3.9 (system)",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.9.0",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "Lesson11.ipynb",
        "public": false
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}