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lecture 7.ipynb
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| { | |
| "cells": [ | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "table = [[2, 3, 2, 5],\n [1, 2, 5, 4],\n [4, 3, 5, 4],\n [5, 2, 3, 1]]\n\n# Задача: отсортировать таблицу table\n# по второму столбцу, а при совпадении элементов во втором\n# столбце — по первому", | |
| "execution_count": 1, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "def sort_2_then_1(table):\n def my_sort_key(element):\n return (element[1], element[0])\n # return element[1::-1]\n return sorted(table, key=my_sort_key)\n ### YOUR SOLUTION HERE\n\nassert sort_2_then_1(table) == [\n [1, 2, 5, 4],\n [5, 2, 3, 1],\n [2, 3, 2, 5],\n [4, 3, 5, 4]]", | |
| "execution_count": 10, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "sorted(['hello', 'world', 'test'], key=len)", | |
| "execution_count": 2, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": "['test', 'hello', 'world']" | |
| }, | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "def sort_2_then_1(table): \n return sorted(table, \n key=lambda element: (element[1], element[0]))\nsort_2_then_1(table)", | |
| "execution_count": 12, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": "[[1, 2, 5, 4], [5, 2, 3, 1], [2, 3, 2, 5], [4, 3, 5, 4]]" | |
| }, | |
| "execution_count": 12, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "(lambda x, y=2: x + y)(3, 4)", | |
| "execution_count": 20, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": "7" | |
| }, | |
| "execution_count": 20, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "incrementors = []\nfor i in range(10):\n incrementors.append(lambda x: x + i)", | |
| "execution_count": 22, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "incrementors[5](3)", | |
| "execution_count": 24, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": "12" | |
| }, | |
| "execution_count": 24, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "i = 100", | |
| "execution_count": 25, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "incrementors[5](3)", | |
| "execution_count": 26, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": "103" | |
| }, | |
| "execution_count": 26, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "incrementors = []\nfor i in range(10):\n incrementors.append(lambda x, i=i: x + i)", | |
| "execution_count": 27, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "incrementors[3](1)", | |
| "execution_count": 28, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": "4" | |
| }, | |
| "execution_count": 28, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "def foo(x, *args):\n print(\"x =\", x)\n print(\"args =\", args)", | |
| "execution_count": 29, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "foo(1, 2, 3)", | |
| "execution_count": 30, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": "x = 1\nargs = (2, 3)\n" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "def bar(x, **kwargs):\n print(\"x =\", x)\n print(\"kwargs =\", kwargs)", | |
| "execution_count": 34, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "bar(x=5, y=7, z=8)", | |
| "execution_count": 35, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": "x = 5\nkwargs = {'y': 7, 'z': 8}\n" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "import matplotlib.pyplot as plt\n%matplotlib inline", | |
| "execution_count": 37, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "points = [(1, 2), (2, 7), (1, 5), (2, 4)]\n# plt.plot([1, 2, 1, 2], [2, 7, 5, 4])", | |
| "execution_count": 43, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "for x, y in points:\n print(x)", | |
| "execution_count": 44, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": "1\n2\n1\n2\n" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "[x ** 2 for x in range(10)]", | |
| "execution_count": 45, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]" | |
| }, | |
| "execution_count": 45, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "def myplot(points, **kwargs):\n plt.plot([x for x, y in points],\n [y for x, y in points], **kwargs)", | |
| "execution_count": 46, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "def foo(x, y):\n return 2 * x + y\nfoo(x=4, y=5)", | |
| "execution_count": 52, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": "13" | |
| }, | |
| "execution_count": 52, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "foo(**{'x': 4, 'y': 5})", | |
| "execution_count": 53, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": "13" | |
| }, | |
| "execution_count": 53, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "myplot([(x ** 3, x ** 2) for x in range(-5, 6)],\n color='teal', lw=4, marker='*')", | |
| "execution_count": 51, | |
| "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": true | |
| }, | |
| "cell_type": "code", | |
| "source": "x", | |
| "execution_count": 55, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": "2" | |
| }, | |
| "execution_count": 55, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "locals()['x']", | |
| "execution_count": 56, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": "2" | |
| }, | |
| "execution_count": 56, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "import os", | |
| "execution_count": 57, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "os.getcwd()", | |
| "execution_count": 58, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": "'/Users/user/prj/oldhse-2010-11/repo/2019-20/nes-datascience'" | |
| }, | |
| "execution_count": 58, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "!ls", | |
| "execution_count": 59, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": "Lecture 1.ipynb Untitled1.ipynb lecture 4.ipynb ps01-6.ipynb\r\nLecture 2.ipynb \u001b[1m\u001b[36massignments\u001b[m\u001b[m lecture 5.ipynb test.txt\r\nUntitled.ipynb lecture 03.ipynb \u001b[35mold\u001b[m\u001b[m\r\n" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "#!dir for windows", | |
| "execution_count": 62, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "f = open(\"test.txt\")\nfor line in f:\n print(repr(line))\nf.close()", | |
| "execution_count": 64, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": "'Раз два три четыре пять\\n'\n'Вышел зайчик погулять\\n'\n'One two three\\n'\n'\\n'\n'This is my last line.'\n" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "f = open(\"test.txt\")\nfor line in f:\n print(line.rstrip())\nf.close()\nprint(\"Okay\")", | |
| "execution_count": 69, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": "Раз два три четыре пять\nВышел зайчик погулять\nOne two three\n\nThis is my last line.\nOkay\n" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "f = open(\"test.txt\")\nfor line in f:\n print(line, end=\"\")\nf.close()\nprint(\"Okay\")", | |
| "execution_count": 68, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": "Раз два три четыре пять\nВышел зайчик погулять\nOne two three\n\nThis is my last line.Okay\n" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "f = open(\"test.txt\")\nfor line in f:\n print(line.rstrip())\nprint(\"Once again\")\nf.close()\nf = open(\"test.txt\")\nfor line in f:\n print(line.rstrip())\nf.close()", | |
| "execution_count": 73, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": "Раз два три четыре пять\nВышел зайчик погулять\nOne two three\n\nThis is my last line.\nOnce again\nРаз два три четыре пять\nВышел зайчик погулять\nOne two three\n\nThis is my last line.\n" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "a = [1, 2, 3]\nfor i in a:\n print(i)\nfor i in a:\n print(i)", | |
| "execution_count": 72, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": "1\n2\n3\n1\n2\n3\n" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "f = open(\"test.txt\")\nfor line in f:\n print(line.rstrip())\nprint(\"Once again\")\nf.seek(0)\nfor line in f:\n print(line.rstrip())\nf.close()", | |
| "execution_count": 77, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": "Раз два три четыре пять\nВышел зайчик погулять\nOne two three\n\nThis is my last line.\nOnce again\nРаз два три четыре пять\nВышел зайчик погулять\nOne two three\n\nThis is my last line.\n" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "f = open(\"test.txt\")\ntext = f.read()\nf.close()", | |
| "execution_count": 78, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "text", | |
| "execution_count": 79, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": "'Раз два три четыре пять\\nВышел зайчик погулять\\nOne two three\\n\\nThis is my last line.'" | |
| }, | |
| "execution_count": 79, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "f = open(\"test.txt\")\nlines = f.readlines()\nf.close()", | |
| "execution_count": 80, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "lines", | |
| "execution_count": 81, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": "['Раз два три четыре пять\\n',\n 'Вышел зайчик погулять\\n',\n 'One two three\\n',\n '\\n',\n 'This is my last line.']" | |
| }, | |
| "execution_count": 81, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "with open('test.txt') as f:\n for line in f:\n print(line.rstrip())\n print(f.closed)\nprint(f.closed)", | |
| "execution_count": 83, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": "Раз два три четыре пять\nВышел зайчик погулять\nOne two three\n\nThis is my last line.\nFalse\nTrue\n" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "f.read()", | |
| "execution_count": 84, | |
| "outputs": [ | |
| { | |
| "ename": "ValueError", | |
| "evalue": "I/O operation on closed file.", | |
| "output_type": "error", | |
| "traceback": [ | |
| "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | |
| "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", | |
| "\u001b[0;32m<ipython-input-84-571e9fb02258>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\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[0m", | |
| "\u001b[0;31mValueError\u001b[0m: I/O operation on closed file." | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "with open('test-new.txt', encoding='cp1251') as f:\n for line in f:\n print(line.rstrip())", | |
| "execution_count": 87, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": "Раз два три четыре пять\nВышел зайчик погулять\nOne two three\n\nThis is my last line.\n" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "", | |
| "execution_count": null, | |
| "outputs": [] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3", | |
| "language": "python" | |
| }, | |
| "language_info": { | |
| "name": "python", | |
| "version": "3.7.2", | |
| "mimetype": "text/x-python", | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "pygments_lexer": "ipython3", | |
| "nbconvert_exporter": "python", | |
| "file_extension": ".py" | |
| }, | |
| "gist": { | |
| "id": "", | |
| "data": { | |
| "description": "lecture 7.ipynb", | |
| "public": false | |
| } | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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