regex_and_examples_python.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNYFLEtWLLP2lt1fb3ONXKU",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Sinrez/PythonProjects/blob/main/regex_and_examples_python.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "99R3EnM9Arl5",
        "outputId": "7c74b3e3-9a2f-49c3-8e73-0a2fac8f7d29"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'C:\\\\file.txt'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "common_string = 'C:\\file.txt' # Обычная строка\n",
        "raw_string = r'C:\\file.txt' # Сырая строка\n",
        "raw_string"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Используем 2 префикса одновременно:\n",
        "raw_f_string = rf'C:\\file.txt'\n",
        "f_raw_string = fr'C:\\file.txt'\n",
        "print(raw_f_string)\n",
        "print(f_raw_string )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UXsGG6CmA6Mt",
        "outputId": "29dd02cf-7808-45ca-80b1-59fdf74c9ce1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "C:\\file.txt\n",
            "C:\\file.txt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(rf\"\\\\n\")\n",
        "\n",
        "print(\"\\\\\\\\n\")\n",
        "\n",
        "print(r\"\\\\n\")\n",
        "\n",
        "print(fr\"\\\\n\")\n",
        "\n",
        "print(\"\\\\n\")\n",
        "\n",
        "print(f\"\\\\n\")\n",
        "\n",
        "print(\"\\\\\\n\")\n",
        "\n",
        "print(\"r\\\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rYUuixD_BNzN",
        "outputId": "d5da2585-a3ec-483f-934c-9232f11cbb9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\\\\n\n",
            "\\\\n\n",
            "\\\\n\n",
            "\\\\n\n",
            "\\n\n",
            "\\n\n",
            "\\\n",
            "\n",
            "r\\n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from string import printable\n",
        "\n",
        "print(printable)"
      ],
      "metadata": {
        "id": "0qPY4QyMOJGG",
        "outputId": "fa12d848-4840-48eb-9c8c-7476882c032b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~ \t\n",
            "\r\u000b\f\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len([] + [[[[]]]]))"
      ],
      "metadata": {
        "id": "ACLK5tz6OMl_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b1d84a06-01cc-41f6-f889-cb1f33fd9e1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len([] + [[[]], []] + [[[[]]]] + [[[], [], []], [[], [[]]]]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sjz78z8w4Ovc",
        "outputId": "4d2f6296-8da8-4207-8531-4aa2b391df51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('\\\\\\\\')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jurXtrDd8SwB",
        "outputId": "9300574f-59af-4982-fa49-6836738a7b41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\\\\\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s = \"abracadabra\"\n",
        "print(s[3::-1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MKpvslVc6V4Q",
        "outputId": "ad5d4e95-dcdc-4b2b-8a10-e218c4603e4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "arba\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s = input()\n",
        "for i in range(len(s)):\n",
        "    if i % 3 == 0:\n",
        "        print(s[i], end='')\n",
        "print(s[::-1])\n",
        "print(s[5:-5])\n",
        "print(s[4::2])\n"
      ],
      "metadata": {
        "id": "rUc5Hw08eZsl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# put your python code here\n",
        "password = input()\n",
        "\n",
        "# Проверка на длину и содержание заглавных и строчных букв\n",
        "if len(password) >= 8 and any(c.isupper() for c in password) and any(c.islower() for c in password):\n",
        "    print(\"YES\")\n",
        "else:\n",
        "    print(\"NO\")\n"
      ],
      "metadata": {
        "id": "GbKsOVhteakC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "s = \"hello WORLD \"\n",
        "print(s[len(s) // 2:] + s[:len(s) // 2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b6cGQHW0gM2b",
        "outputId": "6c869a3f-a596-455c-e4cc-fdd6735b65ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WORLD hello \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# n, k = int(input()),int(input())\n",
        "n,k = 3,100\n",
        "# lst_str = input().lower().split()\n",
        "lst_str = 'Придумайте какую-нибудь кРаКаЗяБрУ для кУрСА по алгебре. Не забудьте использовать интерполяционый многочлен в решении.'.lower().replace('.','').split()\n",
        "print(lst_str)\n",
        "# penalty = []\n",
        "penalty = ['многочлен','краказябр','курс']\n",
        "count = 0\n",
        "# for _ in range(n):\n",
        "#     penalty.append(input().lower())\n",
        "for p in penalty:\n",
        "    for l in lst_str:\n",
        "      if l.startswith(p):\n",
        "        count += k\n",
        "print(count)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4JfzfWCZFngK",
        "outputId": "01361e29-f9ee-41a5-cb54-5e1264e9f91b"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['придумайте', 'какую-нибудь', 'краказябру', 'для', 'курса', 'по', 'алгебре', 'не', 'забудьте', 'использовать', 'интерполяционый', 'многочлен', 'в', 'решении']\n",
            "300\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n = int(input())\n",
        "k = int(input())\n",
        "s = input().lower()\n",
        "amount = 0\n",
        "for i in range(n):\n",
        "    word = input()\n",
        "    amount += s.count(word)\n",
        "print(amount * k)"
      ],
      "metadata": {
        "id": "vzXCVPruHzDV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(*[i if i not in 'аоуэыиеёяю' else 'у' for i in input()], sep='')"
      ],
      "metadata": {
        "id": "xgrnOi6Pf2MG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "l = [[[1, 2, 3]], [[4, 5, 6], [39, 40, 41]], [[7, 8, 9], [10, 11, 12]]]\n",
        "\n",
        "print(l[1][1][0])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BcYKFKGAf2tq",
        "outputId": "085efea0-2e75-4cf6-ec54-af234322ab93"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "39\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "l = [[0, \"12\", 3], [4, \"56\"], \"789\"]\n",
        "print(l[1][1][1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "11VTMYlL1GiQ",
        "outputId": "2b09f065-9ee0-4041-9e66-a859c01c4c4d"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Шахматная раскраска\n",
        "\n",
        "Напиши программу, которая выведет таблицу размером n * n, которая будет заполнена числами 0 и 1 в шахматном порядке (в левом верхнем углу таблице стоит 0)\n",
        "\n",
        "В единственной строчке вводится число n"
      ],
      "metadata": {
        "id": "20U2_GVw8Q2K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n = int(input())\n",
        "l = []\n",
        "for i in range(n):\n",
        "    row = [] # создаем вложенный список, который будет элементом l\n",
        "    for j in range(n):\n",
        "        if i % 2 == 0:\n",
        "            if j % 2 == 0:\n",
        "                row.append(0)\n",
        "            else:\n",
        "                row.append(1)\n",
        "        elif i % 2 != 0:\n",
        "            if j % 2 == 0:\n",
        "                row.append(1)\n",
        "            else:\n",
        "                row.append(0)\n",
        "    l.append(row) \n",
        "for r in l:\n",
        "    print(*r)"
      ],
      "metadata": {
        "id": "eFlbDhEl8RpG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len((' ' * 100).split()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nl5BlYYvJV0L",
        "outputId": "68a1f723-56f7-493e-8a9d-039e02cc6a21"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# create two series\n",
        "s1 = pd.Series([1, 2])\n",
        "s2 = pd.Series([3, 4])\n",
        "\n",
        "# append s2 to s1\n",
        "s3 = s1.append(s2)\n",
        "\n",
        "print(s3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_3Vq5bocNasZ",
        "outputId": "de025650-9d10-4bd7-e467-d1dabd8550eb"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    1\n",
            "1    2\n",
            "0    3\n",
            "1    4\n",
            "dtype: int64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-1-8b14cec91858>:8: FutureWarning: The series.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  s3 = s1.append(s2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Создаем DataFrame с пропусками\n",
        "df = pd.DataFrame([[np.nan, 2, np.nan, 0],\n",
        "                   [3, 4, np.nan, 1],\n",
        "                   [np.nan, np.nan, np.nan, np.nan],\n",
        "                   [np.nan, 3, np.nan, 4]],\n",
        "                  columns=list('ABCD'))\n",
        "\n",
        "# Выводим исходный DataFrame\n",
        "print(df)\n",
        "\n",
        "\n",
        "\n",
        "# Заполняем все пропуски нулями\n",
        "df1 = df.fillna(0)\n",
        "\n",
        "print(df1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dyiXhBmOLo0Q",
        "outputId": "a43c9d6f-a51e-413c-c15d-e51c6ad1a398"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     A    B   C    D\n",
            "0  NaN  2.0 NaN  0.0\n",
            "1  3.0  4.0 NaN  1.0\n",
            "2  NaN  NaN NaN  NaN\n",
            "3  NaN  3.0 NaN  4.0\n",
            "     A    B    C    D\n",
            "0  0.0  2.0  0.0  0.0\n",
            "1  3.0  4.0  0.0  1.0\n",
            "2  0.0  0.0  0.0  0.0\n",
            "3  0.0  3.0  0.0  4.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# create a series\n",
        "s = pd.Series([1, 2, 3])\n",
        "\n",
        "# apply a function to each element in the series\n",
        "s = s.apply(lambda x: x * 2)\n",
        "\n",
        "print(s)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6AlYI0drPfma",
        "outputId": "2f647d30-92c4-4d69-c4d3-ca0d2199f714"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    2\n",
            "1    4\n",
            "2    6\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "s = pd.Series([1, 2, 3])\n",
        "\n",
        "def add_one(x):\n",
        "    return x + 1\n",
        "\n",
        "s.apply(add_one)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "onYLYXpAPh1e",
        "outputId": "778e491f-ee89-4c2a-c4e4-db254f5a3c79"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2\n",
              "1    3\n",
              "2    4\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# создадим серию с дублирующимися значениями\n",
        "s = pd.Series([1, 2, 2])\n",
        "\n",
        "# удалим дубликаты\n",
        "s = s.drop_duplicates()\n",
        "\n",
        "print(s)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OOatEzMyPybq",
        "outputId": "bbfd043e-6dc3-45ee-c74c-5a5816260731"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    1\n",
            "1    2\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# создать серию и словарь для сопоставления с новыми значениями\n",
        "s = pd.Series(['cat', 'dog'])\n",
        "mapping = {'cat': 'feline', 'dog': 'canine'}\n",
        "\n",
        "# сопоставить значения в серии с новыми значениями с помощью .map()\n",
        "s = s.map(mapping)\n",
        "\n",
        "print(s)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gSxwWm63mHEK",
        "outputId": "f2dfaa36-8677-4b7f-cc8b-214e07ec8a30"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    feline\n",
            "1    canine\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "def solution():\n",
        "    n = int(input())\n",
        "    list_ = [int(input()) for _ in range(n)]\n",
        "    s1 = pd.Series(list_)\n",
        "    result = s1.apply(lambda x: x ** 2)\n",
        "    print(result)\n",
        "solution()"
      ],
      "metadata": {
        "id": "Ioi-tmjYvscd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "def solution():\n",
        "    n = int(input())\n",
        "    list_ = [int(input()) for _ in range(n)]\n",
        "    s1 = pd.Series(list_)\n",
        "    result = s1.apply(lambda x: x / s1.mean())\n",
        "    print(result)\n",
        "solution()"
      ],
      "metadata": {
        "id": "umRTFs9Ivs7G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],\n",
        "                    'B': ['B0', 'B1', 'B2', 'B3'],\n",
        "                    'C': ['C0', 'C1', 'C2', 'C3'],\n",
        "                    'D': ['D0', 'D1', 'D2', 'D3']})\n",
        "\n",
        "df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],\n",
        "                    'B': ['B4', 'B5', 'B6', 'B7'],\n",
        "                    'C': ['C4', 'C5', 'C6', 'C7'],\n",
        "                    'D': ['D4', 'D5', 'D6', 'D7']})\n",
        "\n",
        "df3 = pd.DataFrame({'A': ['A8', 'A9', 'A10', 'A11'],\n",
        "                    'B': ['B8', 'B9', 'B10', 'B11'],\n",
        "                    'C': ['C8', 'C9', 'C10', 'C11'],\n",
        "                    'D': ['D8', 'D9', 'D10', 'D11']})\n",
        "\n",
        "frames = [df1, df2, df3]\n",
        "\n",
        "result = pd.concat(frames)\n",
        "\n",
        "print(result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MTnC7NMmv99M",
        "outputId": "8af7c255-96f1-4239-e2f1-0ae1872d834a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     A    B    C    D\n",
            "0   A0   B0   C0   D0\n",
            "1   A1   B1   C1   D1\n",
            "2   A2   B2   C2   D2\n",
            "3   A3   B3   C3   D3\n",
            "0   A4   B4   C4   D4\n",
            "1   A5   B5   C5   D5\n",
            "2   A6   B6   C6   D6\n",
            "3   A7   B7   C7   D7\n",
            "0   A8   B8   C8   D8\n",
            "1   A9   B9   C9   D9\n",
            "2  A10  B10  C10  D10\n",
            "3  A11  B11  C11  D11\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "result = pd.concat([df1, df2], axis=0)\n",
        "result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "LpJ1hh94wYeX",
        "outputId": "aad65882-9580-49b5-b09b-3a7602405ef6"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    A   B   C   D\n",
              "0  A0  B0  C0  D0\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "result = pd.concat([df1, df2], axis=1)\n",
        "result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "uyFficGawff8",
        "outputId": "976ae382-08e1-47c3-d7bf-1176bea291be"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    A   B   C   D   A   B   C   D\n",
              "0  A0  B0  C0  D0  A4  B4  C4  D4\n",
              "1  A1  B1  C1  D1  A5  B5  C5  D5\n",
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              "\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7c8352df-6db4-4c97-a74f-962a42bb2ac4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7c8352df-6db4-4c97-a74f-962a42bb2ac4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = pd.DataFrame({'key': ['a', 'b', 'c', 'd'], 'value1': [1, 2, 3, 4]})\n",
        "df2 = pd.DataFrame({'key': ['a', 'b', 'e', 'f'], 'value2': [5, 6, 7, 8]})\n",
        "df1.merge(df2, on='key') # объединение по ключу с помощью метода merge"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "0eeQvl-KwvZ_",
        "outputId": "54b32119-4a56-4394-fee9-d304c63af34c"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  key  value1  value2\n",
              "0   a       1       5\n",
              "1   b       2       6"
            ],
            "text/html": [
              "\n",
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              "      <td>6</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-69de80d2-498e-4550-bf2e-aeb786708e84')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      \n",
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              "    }\n",
              "\n",
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              "      background-color: #E8F0FE;\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-69de80d2-498e-4550-bf2e-aeb786708e84 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-69de80d2-498e-4550-bf2e-aeb786708e84');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# преобразование строк в словари\n",
        "dict1 = eval(\"{'text': ['i', 'love']}\")\n",
        "dict2 = eval(\"{'text': ['python']}\")\n",
        "\n",
        "# преобразование словаря в DataFrame\n",
        "import pandas as pd\n",
        "df1 = pd.DataFrame(dict1)\n",
        "df2 = pd.DataFrame(dict2)\n",
        "\n",
        "# вывод результата\n",
        "print(pd.concat([df1, df2], axis=0))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ROZcrfpPw8oO",
        "outputId": "06d81ad1-d8a9-4489-dc8f-c54bdae5a50e"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     text\n",
            "0       i\n",
            "1    love\n",
            "0  python\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.DataFrame({'name': ['John Smith','Jane Doe','Joe Schmo'], \n",
        "                   'address': ['123 Main St.','456 Maple Ave.','789 Broadway'], \n",
        "                   'age':[34,28,51]})\n",
        "\n",
        "df[['first','last']] = df['name'].str.split(expand=True)\n",
        "\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0PfX-tLUD91C",
        "outputId": "4c41760e-7037-44b0-df5b-86647c8bb37f"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         name         address  age first   last\n",
            "0  John Smith    123 Main St.   34  John  Smith\n",
            "1    Jane Doe  456 Maple Ave.   28  Jane    Doe\n",
            "2   Joe Schmo    789 Broadway   51   Joe  Schmo\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Создаем DataFrame\n",
        "df = pd.DataFrame({'name': ['Alice', 'Bob', 'Charlie', 'David'],\n",
        "                   'age': [25, 32, 18, 47],\n",
        "                   'gender': ['F', 'M', 'M', 'M']})\n",
        "\n",
        "# Фильтруем DataFrame по столбцу age\n",
        "df_filtered = df.query('age > 30')\n",
        "\n",
        "# Выводим отфильтрованный DataFrame\n",
        "print(df_filtered)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VQuAMp_yNzyA",
        "outputId": "befe7e79-b0d5-43b1-f738-a4b310d73eaf"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    name  age gender\n",
            "1    Bob   32      M\n",
            "3  David   47      M\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "data = {'name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],\n",
        "        'city': ['New York', 'Los Angeles', 'New York', 'Chicago', 'Los Angeles'],\n",
        "        'salary': [5000, 4000, 6000, 4500, 3500]}\n",
        "df = pd.DataFrame(data)\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-HweF4Bq7s2B",
        "outputId": "c4c3f38e-b878-4c51-c757-76f01ae5e991"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      name         city  salary\n",
            "0    Alice     New York    5000\n",
            "1      Bob  Los Angeles    4000\n",
            "2  Charlie     New York    6000\n",
            "3    David      Chicago    4500\n",
            "4      Eve  Los Angeles    3500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('city')['salary'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-5R6TkTn7xjw",
        "outputId": "cf031aca-8d96-409a-b2aa-81163389862d"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "city\n",
              "Chicago        4500.0\n",
              "Los Angeles    3750.0\n",
              "New York       5500.0\n",
              "Name: salary, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Создание DataFrame\n",
        "data = {'name': ['John', 'Mike', 'Sarah', 'Kate'],\n",
        "        'age': [25, 30, 28, 21],\n",
        "        'city': ['New York', 'Los Angeles', 'Chicago', 'Houston']}\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Исходный DataFrame\n",
        "print('Исходный DataFrame:')\n",
        "print(df)\n",
        "\n",
        "# Исключение столбцов 'name' и 'age'\n",
        "df.drop(['name', 'age'], axis=1, inplace=True)\n",
        "\n",
        "# Измененный DataFrame\n",
        "print('Измененный DataFrame:')\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VHYUGbjU7yyN",
        "outputId": "dc475355-0aad-4586-fdb4-8c224916fab6"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Исходный DataFrame:\n",
            "    name  age         city\n",
            "0   John   25     New York\n",
            "1   Mike   30  Los Angeles\n",
            "2  Sarah   28      Chicago\n",
            "3   Kate   21      Houston\n",
            "Измененный DataFrame:\n",
            "          city\n",
            "0     New York\n",
            "1  Los Angeles\n",
            "2      Chicago\n",
            "3      Houston\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "data = {\n",
        "    \"name\": [\"Alice\", \"Bob\", \"Charlie\", \"David\", \"Eve\"],\n",
        "    \"age\": [25, 30, 35, 40, 45],\n",
        "    \"gender\": [\"F\", \"M\", \"M\", \"M\", \"F\"],\n",
        "    \"salary\": [4000, 5000, 6000, 7000, 8000],\n",
        "    \"city\": [\"New York\", \"Los Angeles\", \"Chicago\", \"Houston\", \"Phoenix\"]\n",
        "}\n",
        "\n",
        "df = pd.DataFrame(data)\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PL4dOfh_g40H",
        "outputId": "f80bf775-e113-4a65-d2a9-5b764e549b51"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      name  age gender  salary         city\n",
            "0    Alice   25      F    4000     New York\n",
            "1      Bob   30      M    5000  Los Angeles\n",
            "2  Charlie   35      M    6000      Chicago\n",
            "3    David   40      M    7000      Houston\n",
            "4      Eve   45      F    8000      Phoenix\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Выбрать столбец name\n",
        "df[\"name\"]\n",
        "\n",
        "# Выбрать несколько столбцов name и age\n",
        "df[[\"name\", \"age\"]]\n",
        "\n",
        "# Выбрать строки с индексами от 0 до 2\n",
        "df[0:3]\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "N-lcknNBg_Eg",
        "outputId": "1ac70568-fdf2-4e4b-edb1-014caae81b26"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      name  age gender  salary         city\n",
              "0    Alice   25      F    4000     New York\n",
              "1      Bob   30      M    5000  Los Angeles\n",
              "2  Charlie   35      M    6000      Chicago"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-496fe265-3aaa-4f04-8493-96432cef61d2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>age</th>\n",
              "      <th>gender</th>\n",
              "      <th>salary</th>\n",
              "      <th>city</th>\n",
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              "      <td>4000</td>\n",
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              "      <td>5000</td>\n",
              "      <td>Los Angeles</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Charlie</td>\n",
              "      <td>35</td>\n",
              "      <td>M</td>\n",
              "      <td>6000</td>\n",
              "      <td>Chicago</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-496fe265-3aaa-4f04-8493-96432cef61d2')\"\n",
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              "      </button>\n",
              "      \n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-496fe265-3aaa-4f04-8493-96432cef61d2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Выбрать строку с позицией 2 (то же самое, что и индексом)\n",
        "df.iloc[2]\n",
        "\n",
        "# Выбрать столбец с позицией 0 (то же самое, что и названием name)\n",
        "df.iloc[:, 0]\n",
        "\n",
        "# Выбрать несколько столбцов с позициями от 0 до 1 (то же самое, что и названиями name и age)\n",
        "df.iloc[:, 0:2]\n",
        "\n",
        "# Выбрать строки с позициями от 1 до 3 и столбцы с позициями от 2 до 3 (то же самое, что и индексами от 1 до 3 и названиями gender и salary)\n",
        "df.iloc[1:4, 2:4]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "Jp1p8nk1PJ0a",
        "outputId": "86506f3f-cfe0-4571-db54-ef5807362080"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  gender  salary\n",
              "1      M    5000\n",
              "2      M    6000\n",
              "3      M    7000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fdd68cf3-6c77-449a-8371-d6a0dda068f7\">\n",
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              "      <th>3</th>\n",
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    {
      "cell_type": "code",
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        "# Фильтровать строки, где age больше 30\n",
        "df[df[\"age\"] > 30]\n",
        "\n",
        "# Фильтровать строки, где gender равен F\n",
        "df[df[\"gender\"] == \"F\"]\n",
        "\n",
        "# Фильтровать строки, где salary меньше или равен 6000 и city не равен New York\n",
        "df[(df[\"salary\"] <= 6000) & (df[\"city\"] != \"New York\")]\n",
        "\n",
        "\n"
      ],
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          "height": 112
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        "outputId": "3ab298de-e25f-432b-9918-05b4708ab4a9"
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      "execution_count": 7,
      "outputs": [
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          "data": {
            "text/plain": [
              "      name  age gender  salary         city\n",
              "1      Bob   30      M    5000  Los Angeles\n",
              "2  Charlie   35      M    6000      Chicago"
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    {
      "cell_type": "code",
      "source": [
        "# Группировать данные по gender и city и вычислить количество name для каждой комбинации gender и city\n",
        "df.groupby(by=[\"gender\", \"city\"], as_index=False).count()[[\"gender\", \"city\", \"name\"]]"
      ],
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        "id": "bFvVnTLPPdIx",
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      },
      "execution_count": 8,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "  gender         city  name\n",
              "0      F     New York     1\n",
              "1      F      Phoenix     1\n",
              "2      M      Chicago     1\n",
              "3      M      Houston     1\n",
              "4      M  Los Angeles     1"
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              "                                                     [key], {});\n",
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          },
          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd # импорт библиотеки Pandas \n",
        "df = pd.read_csv('sales.csv') # чтение данных из файла data.csv в фрейм данных df \n",
        "df.head() # вывод фрейма данных "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "NM29QKUBTrOx",
        "outputId": "95b3da3f-b338-4410-8a66-31d4e28f185b"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   book_id                              title       author       genre  \\\n",
              "0     1001                        Война и мир   Л. Толстой    Классика   \n",
              "1     1002  Гарри Поттер и философский камень  Дж. Роулинг     Фэнтези   \n",
              "2     1003                                NaN  М. Булгаков      Сатира   \n",
              "3     1004                               1984   Дж. Оруэлл  Антиутопия   \n",
              "4     1005                 Мастер и Маргарита  М. Булгаков         NaN   \n",
              "\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1003</td>\n",
              "      <td>NaN</td>\n",
              "      <td>М. Булгаков</td>\n",
              "      <td>Сатира</td>\n",
              "      <td>15.0</td>\n",
              "      <td>7500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1004</td>\n",
              "      <td>1984</td>\n",
              "      <td>Дж. Оруэлл</td>\n",
              "      <td>Антиутопия</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1005</td>\n",
              "      <td>Мастер и Маргарита</td>\n",
              "      <td>М. Булгаков</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5000.0</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dd74a4e5-b45f-4d5f-b3c6-3ee012c1b76c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dd74a4e5-b45f-4d5f-b3c6-3ee012c1b76c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop_duplicates(subset=['book_id'],inplace=True)\n",
        "df.fillna(0)\n",
        "df = df[(df[\"quantity\"] > 0 & (df[\"price\"] > 0))].fillna(0)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "HDp_R8dJTxNT",
        "outputId": "45636b71-84b8-4ca8-acd4-e7e61b4c4633"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   book_id                              title       author     genre  \\\n",
              "0     1001                        Война и мир   Л. Толстой  Классика   \n",
              "1     1002  Гарри Поттер и философский камень  Дж. Роулинг   Фэнтези   \n",
              "2     1003                                  0  М. Булгаков    Сатира   \n",
              "4     1005                 Мастер и Маргарита  М. Булгаков         0   \n",
              "6     1007                 Убить пересмешника            0     Роман   \n",
              "7     1008                             Хоббит   Дж. Толкин         0   \n",
              "\n",
              "   quantity    price  \n",
              "0      12.0  12000.0  \n",
              "1      20.0  10000.0  \n",
              "2      15.0   7500.0  \n",
              "4      10.0   5000.0  \n",
              "6       8.0   4000.0  \n",
              "7      18.0      0.0  "
            ],
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              "      <th>4</th>\n",
              "      <td>1005</td>\n",
              "      <td>Мастер и Маргарита</td>\n",
              "      <td>М. Булгаков</td>\n",
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              "      <th>6</th>\n",
              "      <td>1007</td>\n",
              "      <td>Убить пересмешника</td>\n",
              "      <td>0</td>\n",
              "      <td>Роман</td>\n",
              "      <td>8.0</td>\n",
              "      <td>4000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1008</td>\n",
              "      <td>Хоббит</td>\n",
              "      <td>Дж. Толкин</td>\n",
              "      <td>0</td>\n",
              "      <td>18.0</td>\n",
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              "  </svg>\n",
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              "      background-color: #E8F0FE;\n",
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              "\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a414d1d3-0899-44ab-89e0-9dcc8ac9a6a4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a414d1d3-0899-44ab-89e0-9dcc8ac9a6a4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    }
  ]
}