Pandas_DataFrame.ipynb

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        "<a href=\"https://colab.research.google.com/github/Sinrez/PythonProjects/blob/main/Pandas_DataFrame.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
        "id": "MyhGL-A2VagI"
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      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s1 = pd.Series(range(5))\n",
        "s1.name = 'col1'\n",
        "s1"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "95251253-6705-4b43-b202-66747d0cfd2b"
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      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "0    0\n",
              "1    1\n",
              "2    2\n",
              "3    3\n",
              "4    4\n",
              "Name: col1, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s1 = pd.Series(['Ivan', 'Andrey', 'Igor'])\n",
        "s1.name = 'users_name'"
      ],
      "metadata": {
        "id": "fDvgCkYQd5Ey"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(s1)\n",
        "df"
      ],
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          "height": 143
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  users_name\n",
              "0       Ivan\n",
              "1     Andrey\n",
              "2       Igor"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>users_name</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Ivan</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Andrey</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Igor</td>\n",
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              "\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",
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          },
          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "s = df['users_name']\n",
        "print(s, type(s), s.dtype)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lEGscqsFekap",
        "outputId": "cb12bc28-4dea-43a7-b928-cd67f011c3e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0      Ivan\n",
            "1    Andrey\n",
            "2      Igor\n",
            "Name: users_name, dtype: object <class 'pandas.core.series.Series'> object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Повторяющиеся индексы"
      ],
      "metadata": {
        "id": "N_P9O3fqgOat"
      }
    },
    {
      "cell_type": "code",
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        "id": "z1rGQk0sgx8u"
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      "outputs": []
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      "cell_type": "code",
      "source": [
        "s = pd.Series([1,2,3], index = ['a', 'b', 'a'])\n",
        "s['a']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "6c0a43cb-dcc1-456c-d049-7cbe0ef23d2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    1\n",
              "a    3\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Заполнение DataFrame"
      ],
      "metadata": {
        "id": "bQXhyq-AhcMp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = {'city': ['Moscow', 'Moscow', 'Moscow', 'Kazan', 'Kazan', 'Kazan'],\n",
        "        'year': [2022,2021,2020,2022,2021,2020],\n",
        "        'visits': [500,2000,1500,100,230,200]}\n",
        "data"
      ],
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      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'city': ['Moscow', 'Moscow', 'Moscow', 'Kazan', 'Kazan', 'Kazan'],\n",
              " 'year': [2022, 2021, 2020, 2022, 2021, 2020],\n",
              " 'visits': [500, 2000, 1500, 100, 230, 200]}"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(data)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "_PvSBVRwhe1L",
        "outputId": "2d6e0136-303f-4774-e34e-960b36a78d4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     city  year  visits\n",
              "0  Moscow  2022     500\n",
              "1  Moscow  2021    2000\n",
              "2  Moscow  2020    1500\n",
              "3   Kazan  2022     100\n",
              "4   Kazan  2021     230\n",
              "5   Kazan  2020     200"
            ],
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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        "df.head(2)"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     city  year  visits\n",
              "0  Moscow  2022     500\n",
              "1  Moscow  2021    2000"
            ],
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              "      <th>city</th>\n",
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              "      <th>1</th>\n",
              "      <td>Moscow</td>\n",
              "      <td>2021</td>\n",
              "      <td>2000</td>\n",
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    },
    {
      "cell_type": "code",
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        "df2 = pd.DataFrame(data, columns=['visits','city','year'])\n",
        "df2"
      ],
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      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "   visits    city  year\n",
              "0     500  Moscow  2022\n",
              "1    2000  Moscow  2021\n",
              "2    1500  Moscow  2020\n",
              "3     100   Kazan  2022\n",
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    {
      "cell_type": "code",
      "source": [
        "df2 = pd.DataFrame(data, columns=['visits','city','years'])\n",
        "df2"
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          "height": 238
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        {
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          "data": {
            "text/plain": [
              "   visits    city years\n",
              "0     500  Moscow   NaN\n",
              "1    2000  Moscow   NaN\n",
              "2    1500  Moscow   NaN\n",
              "3     100   Kazan   NaN\n",
              "4     230   Kazan   NaN\n",
              "5     200   Kazan   NaN"
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              "                                                     [key], {});\n",
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              "\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "df3 = pd.DataFrame(data, columns=['visits','city','year'], index= ['a','b','c','d','e','f'])\n",
        "df3\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "jnCoHb5di5mC",
        "outputId": "7b090512-46fb-478d-ec67-7c20ef6a9499"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   visits    city  year\n",
              "a     500  Moscow  2022\n",
              "b    2000  Moscow  2021\n",
              "c    1500  Moscow  2020\n",
              "d     100   Kazan  2022\n",
              "e     230   Kazan  2021\n",
              "f     200   Kazan  2020"
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              "          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": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df3['city']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZEwOnJ-dj5sL",
        "outputId": "f746efde-f2e2-4241-a527-44628238af4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    Moscow\n",
              "b    Moscow\n",
              "c    Moscow\n",
              "d     Kazan\n",
              "e     Kazan\n",
              "f     Kazan\n",
              "Name: city, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df3.city"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3zfeQ7ZHj_F5",
        "outputId": "9c5498c6-12c8-4a71-b4f2-348199530e3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    Moscow\n",
              "b    Moscow\n",
              "c    Moscow\n",
              "d     Kazan\n",
              "e     Kazan\n",
              "f     Kazan\n",
              "Name: city, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Доступ к строкые (индексу)"
      ],
      "metadata": {
        "id": "Y2oKwCAXk_7O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df3.loc['a'], type(df3.loc['a']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TFOZLbiIlESv",
        "outputId": "51410618-b914-4666-d0b2-deca9e6e61fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "visits       500\n",
            "city      Moscow\n",
            "year        2022\n",
            "Name: a, dtype: object <class 'pandas.core.series.Series'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Работа с колонками"
      ],
      "metadata": {
        "id": "X32kzngvlh5p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2['years'] = 0\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "jcWZDoaOlljq",
        "outputId": "3fe60c4e-64a8-4b8d-fb4f-414f38af63e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   visits    city  years\n",
              "0     500  Moscow      0\n",
              "1    2000  Moscow      0\n",
              "2    1500  Moscow      0\n",
              "3     100   Kazan      0\n",
              "4     230   Kazan      0\n",
              "5     200   Kazan      0"
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        }
      ]
    },
    {
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      "source": [
        "df2['new_column'] = range(100,106)\n",
        "df2"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "Htp3zURfl6zU",
        "outputId": "9a5523f9-ce07-494c-a536-449c3fb48c31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   visits    city  years  new_column\n",
              "0     500  Moscow      0         100\n",
              "1    2000  Moscow      0         101\n",
              "2    1500  Moscow      0         102\n",
              "3     100   Kazan      0         103\n",
              "4     230   Kazan      0         104\n",
              "5     200   Kazan      0         105"
            ],
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8cc7231a-bbee-47f4-b142-56e616569ee0');\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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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2['years'] = [1,2,3,4,5,6]\n",
        "df2"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
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        "id": "d5q_bNlnmphd",
        "outputId": "024bd089-4ac5-4591-c520-d65caba053d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   visits    city  years  new_column\n",
              "0     500  Moscow      1         100\n",
              "1    2000  Moscow      2         101\n",
              "2    1500  Moscow      3         102\n",
              "3     100   Kazan      4         103\n",
              "4     230   Kazan      5         104\n",
              "5     200   Kazan      6         105"
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              "                                                     [key], {});\n",
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              "\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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    },
    {
      "cell_type": "code",
      "source": [
        "df2['new_column'] = pd.Series([2022,2022], index = [0,3])\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "1vgiPjN_m22-",
        "outputId": "9266d629-d075-4b8a-d142-894b04743776"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   visits    city  years  new_column\n",
              "0     500  Moscow      1      2022.0\n",
              "1    2000  Moscow      2         NaN\n",
              "2    1500  Moscow      3         NaN\n",
              "3     100   Kazan      4      2022.0\n",
              "4     230   Kazan      5         NaN\n",
              "5     200   Kazan      6         NaN"
            ],
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              "\n",
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              "            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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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "data = {'user' : ['Ivan', 'Petr', 'Nikolay', 'Andrey'],\n",
        "            'time_per_page' :  [10, 30 , 15, 5],\n",
        "             'pages' : [7, 9, 12, 3]}\n",
        "df = pd.DataFrame(data, index=['user1','user2','user3','user4'])\n",
        "s1 = pd.Series([200,150], index=['user3','user4'])\n",
        "\n",
        "def solution(df, s1):\n",
        "    df['balance'] = pd.Series(s1, index=['user3','user4'])\n",
        "    return df\n",
        "df1 = solution(df,s1)\n",
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "GlbG3aA1okp-",
        "outputId": "1a56d33c-53d6-436f-fef8-2c8d3fcd2e59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          user  time_per_page  pages  balance\n",
              "user1     Ivan             10      7      NaN\n",
              "user2     Petr             30      9      NaN\n",
              "user3  Nikolay             15     12    200.0\n",
              "user4   Andrey              5      3    150.0"
            ],
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c0230ed1-be25-44ae-8983-f2ac1e69ec9e');\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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1[df1['balance'].isnull()].index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GbX26sWE1YZv",
        "outputId": "fb84f753-538f-4933-f6a3-0affb6e2da6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['user1', 'user2'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1['total_time'] = df['time_per_page'] * df['pages']\n",
        "# df1['is_correct'] = df1[\n",
        "df1['is_correct'] = df1['total_time'] < 100\n",
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "QHpBgOSj2gOC",
        "outputId": "3d7fd98b-c775-42c1-b5fe-87e9dd51d0b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          user  time_per_page  pages  balance  total_time  is_correct\n",
              "user1     Ivan             10      7      NaN          70        True\n",
              "user2     Petr             30      9      NaN         270       False\n",
              "user3  Nikolay             15     12    200.0         180       False\n",
              "user4   Andrey              5      3    150.0          15        True"
            ],
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              "\n",
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              "  "
            ]
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    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "def solution(_df):\n",
        "    return 'f' in _df.index"
      ],
      "metadata": {
        "id": "ha-H1l9n817O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "data = [['Ivan', 25, 4, 50, 1],\n",
        "\n",
        "        ['Petr', 40, 9, 250, 8],\n",
        "\n",
        "        ['Nikolay', 19, 12, 25, 1],\n",
        "\n",
        "        ['Sergey', 33, 6, 115, 6],\n",
        "\n",
        "        ['Andrey', 38, 2, 152, 4],\n",
        "\n",
        "        ['Ilya', 20, 18, 15, 2],\n",
        "\n",
        "        ['Igor', 19, 2, 10, 1]]\n",
        "\n",
        "_df = pd.DataFrame(data, columns=['user_name', 'user_age', 'user_clicks', 'user_balance', 'user_history'], index=list('abcdefg'))\n",
        "new_col = [d.replace(r'user_', '') for d in _df.columns]\n",
        "_df.columns = new_col\n",
        "_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "mrBLiGui82yl",
        "outputId": "40ee1e99-bffb-4c4e-e88c-7172c1947138"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      name  age  clicks  balance  history\n",
              "a     Ivan   25       4       50        1\n",
              "b     Petr   40       9      250        8\n",
              "c  Nikolay   19      12       25        1\n",
              "d   Sergey   33       6      115        6\n",
              "e   Andrey   38       2      152        4\n",
              "f     Ilya   20      18       15        2\n",
              "g     Igor   19       2       10        1"
            ],
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              "\n",
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              "      <td>Igor</td>\n",
              "      <td>19</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
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              "          const element = document.querySelector('#df-53993e84-f514-4283-a9d4-15bab5323060');\n",
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              "            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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              "  "
            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "def solution(_df):\n",
        "    # ваш код здесь\n",
        "    return _df[['name', 'balance']][-3:]"
      ],
      "metadata": {
        "id": "EvWGRvQSFRL8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "data = {'col1': [10,20,30,40], 'col2': [2,11,23,4], 'col3': [8,1,4,5]}\n",
        "\n",
        "df1 = pd.DataFrame(data, index = ['a','b','c','d'])\n",
        "\n",
        "df2 = df1.T\n",
        "\n",
        "df1.add(df2, fill_value = 0)"
      ],
      "metadata": {
        "id": "HbSXMwE9FSLi",
        "outputId": "d6bb1dea-567b-4cae-a6a7-7ddf8216e6f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         a     b     c  col1  col2  col3     d\n",
              "a      NaN   NaN   NaN  10.0   2.0   8.0   NaN\n",
              "b      NaN   NaN   NaN  20.0  11.0   1.0   NaN\n",
              "c      NaN   NaN   NaN  30.0  23.0   4.0   NaN\n",
              "col1  10.0  20.0  30.0   NaN   NaN   NaN  40.0\n",
              "col2   2.0  11.0  23.0   NaN   NaN   NaN   4.0\n",
              "col3   8.0   1.0   4.0   NaN   NaN   NaN   5.0\n",
              "d      NaN   NaN   NaN  40.0   4.0   5.0   NaN"
            ],
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>col1</th>\n",
              "      <td>10.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>col2</th>\n",
              "      <td>2.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>col3</th>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8f4185c2-ea36-4a10-9ef8-d1cf4e034a50')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8f4185c2-ea36-4a10-9ef8-d1cf4e034a50 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-8f4185c2-ea36-4a10-9ef8-d1cf4e034a50');\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": 1
        }
      ]
    }
  ]
}