homework_3_week[reu_ml_school]_sageeeeee.ipynb

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      "cell_type": "markdown",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/sage-artem/REU_course/blob/main/homework_3_week%5Breu_ml_school%5D_sageeeeee.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# <center>**ML School by REU Data Scinece Club**</center>\n",
        "\n",
        "---\n",
        "## <center> Неделя №3 </center>\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "rAMb7F9WSadi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "На этой неделе вам предстоит обработать данные о зарплатах. Данные очень похожи на настоящие, поэтому вам будет в каких-то моментах неприятно) В них очень много мусора и поэтому понадобиться потратить время на обработку. DS - специалист работает с чем-то подобным крайне часто.\n",
        "Если столкнетесь с проблемой, пишите в чат, где вам помогут авторы и остальные участники, решать проблемы в коллективе - легче. Удачи!"
      ],
      "metadata": {
        "id": "8PW-v-OxUNgu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Про данные можно прочитать [тут](https://oscarbaruffa.com/messy/)"
      ],
      "metadata": {
        "id": "QG9nGPooJR9o"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Импортируем библиотеки которые могут вам понадобиться, а затем загрузим данные"
      ],
      "metadata": {
        "id": "9I3bUPLfUcmf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from scipy import stats"
      ],
      "metadata": {
        "id": "WPxpZrKLUcCC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Теперь давайте скачаем нужные данные\n"
      ],
      "metadata": {
        "id": "Eumm_Ns9gfOR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/MeinRespect/REU-Ml-SCHOOL.git"
      ],
      "metadata": {
        "id": "ylpCJ3d064vb",
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "a723e9ab-3ba0-4f91-ebc0-dfbcb5b1a274"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fatal: destination path 'REU-Ml-SCHOOL' already exists and is not an empty directory.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/REU-Ml-SCHOOL/right_data.csv')\n",
        "df.sample(5)"
      ],
      "metadata": {
        "id": "kqaO36SJUOYP",
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          "height": 860
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        "outputId": "8935fba7-de50-462a-95be-f57fc91edfd8"
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      "execution_count": null,
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              "                Timestamp How old are you? What industry do you work in?  \\\n",
              "19788  4/29/2021 11:30:50            25-34             Computing or Tech   \n",
              "27833   6/15/2022 1:50:02            25-34                    Nonprofits   \n",
              "17297  4/28/2021 19:58:05            35-44                        Retail   \n",
              "574    4/27/2021 11:12:00            25-34               Media & Digital   \n",
              "13111  4/28/2021 12:06:47            35-44                    Nonprofits   \n",
              "\n",
              "                                  Job title  \\\n",
              "19788              Online marketing manager   \n",
              "27833                  Executive Assistant    \n",
              "17297                         Store Manager   \n",
              "574                                Reporter   \n",
              "13111  Director of Training & Communication   \n",
              "\n",
              "      If your job title needs additional context, please clarify here:  \\\n",
              "19788                                                NaN                 \n",
              "27833                                                NaN                 \n",
              "17297                                             Luxury                 \n",
              "574                                                  NaN                 \n",
              "13111                                                NaN                 \n",
              "\n",
              "      What is your annual salary? (You'll indicate the currency in a later question. If you are part-time or hourly, please enter an annualized equivalent -- what you would earn if you worked the job 40 hours a week, 52 weeks a year.)  \\\n",
              "19788                                             39,000                                                                                                                                                                                     \n",
              "27833                                              56000                                                                                                                                                                                     \n",
              "17297                                             93,000                                                                                                                                                                                     \n",
              "574                                               36,000                                                                                                                                                                                     \n",
              "13111                                             72,300                                                                                                                                                                                     \n",
              "\n",
              "       How much additional monetary compensation do you get, if any (for example, bonuses or overtime in an average year)? Please only include monetary compensation here, not the value of benefits.  \\\n",
              "19788                                                NaN                                                                                                                                                \n",
              "27833                                                NaN                                                                                                                                                \n",
              "17297                                             1500.0                                                                                                                                                \n",
              "574                                                  0.0                                                                                                                                                \n",
              "13111                                                NaN                                                                                                                                                \n",
              "\n",
              "      Please indicate the currency  \\\n",
              "19788                          EUR   \n",
              "27833                          USD   \n",
              "17297                          USD   \n",
              "574                            USD   \n",
              "13111                          USD   \n",
              "\n",
              "      If \"Other,\" please indicate the currency here:   \\\n",
              "19788                                             NaN   \n",
              "27833                                             NaN   \n",
              "17297                                             NaN   \n",
              "574                                               NaN   \n",
              "13111                                             NaN   \n",
              "\n",
              "      If your income needs additional context, please provide it here:  \\\n",
              "19788                                                NaN                 \n",
              "27833                                                NaN                 \n",
              "17297                                                NaN                 \n",
              "574                                                  NaN                 \n",
              "13111                                                NaN                 \n",
              "\n",
              "      What country do you work in?  \\\n",
              "19788                      Germany   \n",
              "27833                          USA   \n",
              "17297                          USA   \n",
              "574                  United States   \n",
              "13111                United States   \n",
              "\n",
              "      If you're in the U.S., what state do you work in?  \\\n",
              "19788                                               NaN   \n",
              "27833                                        New Mexico   \n",
              "17297                                          New York   \n",
              "574                                        Pennsylvania   \n",
              "13111                                      Pennsylvania   \n",
              "\n",
              "      What city do you work in?  \\\n",
              "19788         Baden-Württemberg   \n",
              "27833                  Santa Fe   \n",
              "17297             New York city   \n",
              "574               Philadelphia    \n",
              "13111              Philadelphia   \n",
              "\n",
              "      How many years of professional work experience do you have overall?  \\\n",
              "19788                                        2 - 4 years                    \n",
              "27833                                      11 - 20 years                    \n",
              "17297                                      21 - 30 years                    \n",
              "574                                         8 - 10 years                    \n",
              "13111                                      11 - 20 years                    \n",
              "\n",
              "      How many years of professional work experience do you have in your field?  \\\n",
              "19788                                        2 - 4 years                          \n",
              "27833                                          5-7 years                          \n",
              "17297                                      11 - 20 years                          \n",
              "574                                         8 - 10 years                          \n",
              "13111                                        2 - 4 years                          \n",
              "\n",
              "      What is your highest level of education completed? What is your gender?  \\\n",
              "19788                                    Master's degree                Woman   \n",
              "27833                                     College degree                Woman   \n",
              "17297                                     College degree                Woman   \n",
              "574                                      Master's degree                  NaN   \n",
              "13111                                    Master's degree                Woman   \n",
              "\n",
              "      What is your race? (Choose all that apply.)  \n",
              "19788                     Asian or Asian American  \n",
              "27833         Hispanic, Latino, or Spanish origin  \n",
              "17297                                       White  \n",
              "574                                         White  \n",
              "13111                                       White  "
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              "      <th>27833</th>\n",
              "      <td>6/15/2022 1:50:02</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Nonprofits</td>\n",
              "      <td>Executive Assistant</td>\n",
              "      <td>NaN</td>\n",
              "      <td>56000</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA</td>\n",
              "      <td>New Mexico</td>\n",
              "      <td>Santa Fe</td>\n",
              "      <td>11 - 20 years</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>College degree</td>\n",
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              "      <th>17297</th>\n",
              "      <td>4/28/2021 19:58:05</td>\n",
              "      <td>35-44</td>\n",
              "      <td>Retail</td>\n",
              "      <td>Store Manager</td>\n",
              "      <td>Luxury</td>\n",
              "      <td>93,000</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>USD</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA</td>\n",
              "      <td>New York</td>\n",
              "      <td>New York city</td>\n",
              "      <td>21 - 30 years</td>\n",
              "      <td>11 - 20 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
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              "      <th>574</th>\n",
              "      <td>4/27/2021 11:12:00</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Media &amp; Digital</td>\n",
              "      <td>Reporter</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36,000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>USD</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>4/28/2021 12:06:47</td>\n",
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              "      <td>Director of Training &amp; Communication</td>\n",
              "      <td>NaN</td>\n",
              "      <td>72,300</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United States</td>\n",
              "      <td>Pennsylvania</td>\n",
              "      <td>Philadelphia</td>\n",
              "      <td>11 - 20 years</td>\n",
              "      <td>2 - 4 years</td>\n",
              "      <td>Master's degree</td>\n",
              "      <td>Woman</td>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Как можно заметить названия колонок оставляют желать лучшего, давайте это исправим"
      ],
      "metadata": {
        "id": "xFdSOvzZKxn7"
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      "cell_type": "code",
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        "df.columns = ['Timestamp', 'Age','Industry', 'Job', 'Job context', 'Salary',\n",
        "              'Amount of monetary compensation', 'Currency',\n",
        "              'Currency special', 'Income context', 'Country',\n",
        "              'State in USA', 'City', 'Work experience all',\n",
        "              'Work experience in current field', 'Education', 'Gender', 'Race'\n",
        "              ]\n",
        "df.sample(5, random_state=42)"
      ],
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            "text/plain": [
              "                Timestamp    Age                      Industry  \\\n",
              "14401  4/28/2021 14:34:34  18-24                   Health care   \n",
              "26226  5/17/2021 17:26:02  45-54               Media & Digital   \n",
              "11624   4/28/2021 2:54:22  25-34       Science (Chemistry R&D)   \n",
              "22345  4/30/2021 19:16:58  45-54  Education (Higher Education)   \n",
              "2595   4/27/2021 11:47:56  25-34   Marketing, Advertising & PR   \n",
              "\n",
              "                                    Job  \\\n",
              "14401  Development Operations Associate   \n",
              "26226        Manager, Content Marketing   \n",
              "11624                       QMS Manager   \n",
              "22345      Associate Clinical Professor   \n",
              "2595           Manager, Medical Writing   \n",
              "\n",
              "                                             Job context   Salary  \\\n",
              "14401                                                NaN   62,000   \n",
              "26226                                                NaN   118450   \n",
              "11624                                           ISO13485  123,000   \n",
              "22345                                                NaN    85000   \n",
              "2595   Manage one medical writer and work on medical ...  105,000   \n",
              "\n",
              "       Amount of monetary compensation Currency Currency special  \\\n",
              "14401                              NaN      USD              NaN   \n",
              "26226                           5000.0      USD              NaN   \n",
              "11624                              NaN  AUD/NZD              NaN   \n",
              "22345                          15000.0      USD              NaN   \n",
              "2595                           11000.0      USD              NaN   \n",
              "\n",
              "                        Income context    Country  State in USA  \\\n",
              "14401                              NaN        USA      New York   \n",
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            "text/plain": [
              "       Amount of monetary compensation\n",
              "count                     2.069000e+04\n",
              "mean                      1.821073e+04\n",
              "std                       8.355831e+05\n",
              "min                       0.000000e+00\n",
              "25%                       0.000000e+00\n",
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              "      <td>2.069000e+04</td>\n",
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              "      <td>1.821073e+04</td>\n",
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              "      <td>8.355831e+05</td>\n",
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              "      <td>0.000000e+00</td>\n",
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              "                                                     [key], {});\n",
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      "cell_type": "code",
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        "df.info()"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 27945 entries, 0 to 27944\n",
            "Data columns (total 18 columns):\n",
            " #   Column                            Non-Null Count  Dtype  \n",
            "---  ------                            --------------  -----  \n",
            " 0   Timestamp                         27945 non-null  object \n",
            " 1   Age                               27945 non-null  object \n",
            " 2   Industry                          27873 non-null  object \n",
            " 3   Job                               27945 non-null  object \n",
            " 4   Job context                       7233 non-null   object \n",
            " 5   Salary                            27945 non-null  object \n",
            " 6   Amount of monetary compensation   20690 non-null  float64\n",
            " 7   Currency                          27945 non-null  object \n",
            " 8   Currency special                  197 non-null    object \n",
            " 9   Income context                    3034 non-null   object \n",
            " 10  Country                           27945 non-null  object \n",
            " 11  State in USA                      22963 non-null  object \n",
            " 12  City                              27870 non-null  object \n",
            " 13  Work experience all               27945 non-null  object \n",
            " 14  Work experience in current field  27945 non-null  object \n",
            " 15  Education                         27731 non-null  object \n",
            " 16  Gender                            26744 non-null  object \n",
            " 17  Race                              27776 non-null  object \n",
            "dtypes: float64(1), object(17)\n",
            "memory usage: 3.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Задание 1. Чистка данных (6 баллов)**"
      ],
      "metadata": {
        "id": "6hsO1vFaJx7U"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Этап 1.1\n",
        "Теперь вам нужно привести колонки Salary и Amount of monetary compensation к единой шкале измерения - доллару.\n",
        "Для этого вы можете использовать два способа:\n",
        "\n",
        "1.   Простой. Вы берете сегодняшнюю дату и приводите все основные валюты из колонки Currency к доллару, а те строчки, где значение колонки равно \"Other\", вы удаляете.\n",
        "2.   Сложный. Вы смотрите на значение Timestamp и актуальный для этой даты курс. Если в Currency встречается значение \"Other\", то вы пытаетесь восстановить валюту по колонке Currency Special. Тут вам может пригодится библиотека [forex](https://forex-python.readthedocs.io/en/latest/usage.html).\n",
        "\n",
        "За оба решения вы получите **одинаковое количество баллов**, но учитывайте, что в простом вы теряете много полезной информации."
      ],
      "metadata": {
        "id": "_nvbkk4BM9wM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Ваш код тут\n",
        "print(df['Salary'].isna().sum(), df['Amount of monetary compensation'].isna().sum())\n",
        "print()\n",
        "df['Currency special'][~df['Currency special'].isnull()].iloc[1:10] #решил посмотреть, что вообще в себе несет эта колонка Currency special"
      ],
      "metadata": {
        "id": "GzKcgUR7PEga",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d02d88f-0319-426e-8425-3c50da8fe9e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 7255\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "603                                        Peso Argentino\n",
              "752                                            $76,302.34\n",
              "766     My bonus is based on performance up to 10% of ...\n",
              "776     I work for an online state university, managin...\n",
              "1001                                                    0\n",
              "1311                                                  MYR\n",
              "1840                                                  CHF\n",
              "1915                                                  KWD\n",
              "1924                                                  NOK\n",
              "Name: Currency special, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 187
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Currency special'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EmvAhtOHUkdE",
        "outputId": "87c52f57-4025-466b-b6c4-d60b36bb17a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "USD                       11\n",
              "INR                       10\n",
              "NOK                       10\n",
              "SGD                        9\n",
              "MYR                        8\n",
              "                          ..\n",
              "55,000                     1\n",
              "ILS/NIS                    1\n",
              "Norwegian kroner (NOK)     1\n",
              "Stock                      1\n",
              "NGN                        1\n",
              "Name: Currency special, Length: 114, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 188
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Currency'].value_counts() # смотрим какие валюты присутствуют в датасете"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IyiaQG1NVfFc",
        "outputId": "be430b67-1369-4544-8841-cccece1ea003"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "USD        23287\n",
              "CAD         1665\n",
              "GBP         1585\n",
              "EUR          637\n",
              "AUD/NZD      500\n",
              "Other        156\n",
              "CHF           37\n",
              "SEK           37\n",
              "JPY           23\n",
              "ZAR           14\n",
              "HKD            4\n",
              "Name: Currency, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 189
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1 CAD -> **0.72977** USD;\n",
        "\n",
        "1 GBP -> **1.23** USD;\n",
        "\n",
        "1 EUR -> **1.07** USD;\n",
        "\n",
        "1 AUD/NZD  -> **0.6713** USD;\n",
        "\n",
        "1 CHF -> **1.08** USD;\n",
        "\n",
        "1 SEK -> **0.095024** USD;\n",
        "\n",
        "1 JPY -> **0.007599** USD;\n",
        "\n",
        "1 ZAR -> **0.053985** USD;\n",
        "\n",
        "1 HKD -> **0.1276** USD;"
      ],
      "metadata": {
        "id": "XMQO3eaaVlUZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Salary'] = df['Salary'].apply(lambda x: int(x.replace(',','')))"
      ],
      "metadata": {
        "id": "zudDZHm5YvR2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "currency_dict = {\n",
        "                'CAD': 0.72977,\n",
        "                'GBP': 1.23,\n",
        "                'EUR': 1.07,\n",
        "                'AUD/NZD': 0.6713,\n",
        "                'CHF': 1.08,\n",
        "                'SEK': 0.095024,\n",
        "                'JPY': 0.007599,\n",
        "                'ZAR': 0.053985,\n",
        "                'HKD': 0.1276,\n",
        "                'USD': 1\n",
        "                }\n",
        "pd.options.mode.chained_assignment = None\n",
        "for i in range(len(df)):\n",
        "    try:\n",
        "        if df['Currency'].loc[i] != 'Other':\n",
        "            df['Salary'].loc[i] *= currency_dict[df['Currency'].loc[i]]\n",
        "            df['Amount of monetary compensation'].loc[i] *= currency_dict[df['Currency'].loc[i]]\n",
        "        else:\n",
        "            df.drop(i, axis=0, inplace=True)\n",
        "    except:\n",
        "        print(i)\n"
      ],
      "metadata": {
        "id": "Eax0HYwTVcSz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(10)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 808
        },
        "id": "8XFCB1TzbsEi",
        "outputId": "0e58a94b-44a5-486c-d479-cfef1e1854df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Timestamp    Age                       Industry  \\\n",
              "0  4/27/2021 11:02:10  25-34   Education (Higher Education)   \n",
              "1  4/27/2021 11:02:22  25-34              Computing or Tech   \n",
              "2  4/27/2021 11:02:38  25-34  Accounting, Banking & Finance   \n",
              "3  4/27/2021 11:02:41  25-34                     Nonprofits   \n",
              "4  4/27/2021 11:02:42  25-34  Accounting, Banking & Finance   \n",
              "5  4/27/2021 11:02:46  25-34   Education (Higher Education)   \n",
              "6  4/27/2021 11:02:51  25-34                     Publishing   \n",
              "7  4/27/2021 11:03:00  25-34  Education (Primary/Secondary)   \n",
              "8  4/27/2021 11:03:01  45-54              Computing or Tech   \n",
              "9  4/27/2021 11:03:02  35-44  Accounting, Banking & Finance   \n",
              "\n",
              "                                        Job                   Job context  \\\n",
              "0        Research and Instruction Librarian                           NaN   \n",
              "1  Change & Internal Communications Manager                           NaN   \n",
              "2                      Marketing Specialist                           NaN   \n",
              "3                           Program Manager                           NaN   \n",
              "4                        Accounting Manager                           NaN   \n",
              "5            Scholarly Publishing Librarian                           NaN   \n",
              "6                      Publishing Assistant                           NaN   \n",
              "7                                 Librarian               High school, FT   \n",
              "8                           Systems Analyst  Data developer/ETL Developer   \n",
              "9                         Senior Accountant                           NaN   \n",
              "\n",
              "     Salary  Amount of monetary compensation Currency Currency special  \\\n",
              "0   55000.0                              0.0      USD              NaN   \n",
              "1   67158.0                           4920.0      GBP              NaN   \n",
              "2   34000.0                              NaN      USD              NaN   \n",
              "3   62000.0                           3000.0      USD              NaN   \n",
              "4   60000.0                           7000.0      USD              NaN   \n",
              "5   62000.0                              NaN      USD              NaN   \n",
              "6   33000.0                           2000.0      USD              NaN   \n",
              "7   50000.0                              NaN      USD              NaN   \n",
              "8  112000.0                          10000.0      USD              NaN   \n",
              "9   45000.0                              0.0      USD              NaN   \n",
              "\n",
              "                Income context         Country    State in USA         City  \\\n",
              "0                          NaN   United States   Massachusetts       Boston   \n",
              "1                          NaN  United Kingdom             NaN    Cambridge   \n",
              "2                          NaN              US       Tennessee  Chattanooga   \n",
              "3                          NaN             USA       Wisconsin    Milwaukee   \n",
              "4                          NaN              US  South Carolina   Greenville   \n",
              "5                          NaN             USA   New Hampshire      Hanover   \n",
              "6                          NaN             USA  South Carolina     Columbia   \n",
              "7                          NaN   United States         Arizona         Yuma   \n",
              "8                          NaN              US        Missouri    St. Louis   \n",
              "9  I work for a Charter School   United States         Florida   Palm Coast   \n",
              "\n",
              "  Work experience all Work experience in current field        Education  \\\n",
              "0           5-7 years                        5-7 years  Master's degree   \n",
              "1        8 - 10 years                        5-7 years   College degree   \n",
              "2         2 - 4 years                      2 - 4 years   College degree   \n",
              "3        8 - 10 years                        5-7 years   College degree   \n",
              "4        8 - 10 years                        5-7 years   College degree   \n",
              "5        8 - 10 years                      2 - 4 years  Master's degree   \n",
              "6         2 - 4 years                      2 - 4 years   College degree   \n",
              "7           5-7 years                        5-7 years  Master's degree   \n",
              "8       21 - 30 years                    21 - 30 years   College degree   \n",
              "9       21 - 30 years                    21 - 30 years   College degree   \n",
              "\n",
              "  Gender                                        Race  \n",
              "0  Woman                                       White  \n",
              "1    NaN                                       White  \n",
              "2  Woman                                       White  \n",
              "3  Woman                                       White  \n",
              "4  Woman                                       White  \n",
              "5    Man                                       White  \n",
              "6  Woman                                       White  \n",
              "7    Man                                       White  \n",
              "8  Woman                                       White  \n",
              "9  Woman  Hispanic, Latino, or Spanish origin, White  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9a2c7e08-a0b1-444b-8ecd-af074f462421\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>Age</th>\n",
              "      <th>Industry</th>\n",
              "      <th>Job</th>\n",
              "      <th>Job context</th>\n",
              "      <th>Salary</th>\n",
              "      <th>Amount of monetary compensation</th>\n",
              "      <th>Currency</th>\n",
              "      <th>Currency special</th>\n",
              "      <th>Income context</th>\n",
              "      <th>Country</th>\n",
              "      <th>State in USA</th>\n",
              "      <th>City</th>\n",
              "      <th>Work experience all</th>\n",
              "      <th>Work experience in current field</th>\n",
              "      <th>Education</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Race</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4/27/2021 11:02:10</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Education (Higher Education)</td>\n",
              "      <td>Research and Instruction Librarian</td>\n",
              "      <td>NaN</td>\n",
              "      <td>55000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>USD</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United States</td>\n",
              "      <td>Massachusetts</td>\n",
              "      <td>Boston</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>Master's degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4/27/2021 11:02:22</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Computing or Tech</td>\n",
              "      <td>Change &amp; Internal Communications Manager</td>\n",
              "      <td>NaN</td>\n",
              "      <td>67158.0</td>\n",
              "      <td>4920.0</td>\n",
              "      <td>GBP</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Cambridge</td>\n",
              "      <td>8 - 10 years</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>NaN</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4/27/2021 11:02:38</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Accounting, Banking &amp; Finance</td>\n",
              "      <td>Marketing Specialist</td>\n",
              "      <td>NaN</td>\n",
              "      <td>34000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USD</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>US</td>\n",
              "      <td>Tennessee</td>\n",
              "      <td>Chattanooga</td>\n",
              "      <td>2 - 4 years</td>\n",
              "      <td>2 - 4 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4/27/2021 11:02:41</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Nonprofits</td>\n",
              "      <td>Program Manager</td>\n",
              "      <td>NaN</td>\n",
              "      <td>62000.0</td>\n",
              "      <td>3000.0</td>\n",
              "      <td>USD</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA</td>\n",
              "      <td>Wisconsin</td>\n",
              "      <td>Milwaukee</td>\n",
              "      <td>8 - 10 years</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4/27/2021 11:02:42</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Accounting, Banking &amp; Finance</td>\n",
              "      <td>Accounting Manager</td>\n",
              "      <td>NaN</td>\n",
              "      <td>60000.0</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>USD</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>US</td>\n",
              "      <td>South Carolina</td>\n",
              "      <td>Greenville</td>\n",
              "      <td>8 - 10 years</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4/27/2021 11:02:46</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Education (Higher Education)</td>\n",
              "      <td>Scholarly Publishing Librarian</td>\n",
              "      <td>NaN</td>\n",
              "      <td>62000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USD</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA</td>\n",
              "      <td>New Hampshire</td>\n",
              "      <td>Hanover</td>\n",
              "      <td>8 - 10 years</td>\n",
              "      <td>2 - 4 years</td>\n",
              "      <td>Master's degree</td>\n",
              "      <td>Man</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>4/27/2021 11:02:51</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Publishing</td>\n",
              "      <td>Publishing Assistant</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33000.0</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>USD</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA</td>\n",
              "      <td>South Carolina</td>\n",
              "      <td>Columbia</td>\n",
              "      <td>2 - 4 years</td>\n",
              "      <td>2 - 4 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>4/27/2021 11:03:00</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Education (Primary/Secondary)</td>\n",
              "      <td>Librarian</td>\n",
              "      <td>High school, FT</td>\n",
              "      <td>50000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USD</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United States</td>\n",
              "      <td>Arizona</td>\n",
              "      <td>Yuma</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>Master's degree</td>\n",
              "      <td>Man</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>4/27/2021 11:03:01</td>\n",
              "      <td>45-54</td>\n",
              "      <td>Computing or Tech</td>\n",
              "      <td>Systems Analyst</td>\n",
              "      <td>Data developer/ETL Developer</td>\n",
              "      <td>112000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>USD</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>US</td>\n",
              "      <td>Missouri</td>\n",
              "      <td>St. Louis</td>\n",
              "      <td>21 - 30 years</td>\n",
              "      <td>21 - 30 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>4/27/2021 11:03:02</td>\n",
              "      <td>35-44</td>\n",
              "      <td>Accounting, Banking &amp; Finance</td>\n",
              "      <td>Senior Accountant</td>\n",
              "      <td>NaN</td>\n",
              "      <td>45000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>USD</td>\n",
              "      <td>NaN</td>\n",
              "      <td>I work for a Charter School</td>\n",
              "      <td>United States</td>\n",
              "      <td>Florida</td>\n",
              "      <td>Palm Coast</td>\n",
              "      <td>21 - 30 years</td>\n",
              "      <td>21 - 30 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>Hispanic, Latino, or Spanish origin, White</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9a2c7e08-a0b1-444b-8ecd-af074f462421')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \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",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
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              "      background-color: #3B4455;\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9a2c7e08-a0b1-444b-8ecd-af074f462421 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-9a2c7e08-a0b1-444b-8ecd-af074f462421');\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": 192
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.reset_index(inplace=True, drop=True) #из-за удаления строк надо было сбросить индексы"
      ],
      "metadata": {
        "id": "FLqkxQ8jb52z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Currency'].value_counts() #other Больше нет"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zo6K8qyRcPiU",
        "outputId": "b3cef807-73e8-4a0e-f88c-57e2b2b304c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "USD        23287\n",
              "CAD         1665\n",
              "GBP         1585\n",
              "EUR          637\n",
              "AUD/NZD      500\n",
              "CHF           37\n",
              "SEK           37\n",
              "JPY           23\n",
              "ZAR           14\n",
              "HKD            4\n",
              "Name: Currency, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 194
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "После стандартизации к доллару удалите эти две колонки: Currency и Currency Special\n"
      ],
      "metadata": {
        "id": "DdVkFrkNqWbP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Ваш код тут\n",
        "df.drop(['Currency', 'Currency special'], inplace=True, axis=1)"
      ],
      "metadata": {
        "id": "Tz801617tSOC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()\n",
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 857
        },
        "id": "0ugXDocLdE3d",
        "outputId": "0b3b62d6-6224-42a7-dba7-d7fca36bd4df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 27789 entries, 0 to 27788\n",
            "Data columns (total 16 columns):\n",
            " #   Column                            Non-Null Count  Dtype  \n",
            "---  ------                            --------------  -----  \n",
            " 0   Timestamp                         27789 non-null  object \n",
            " 1   Age                               27789 non-null  object \n",
            " 2   Industry                          27718 non-null  object \n",
            " 3   Job                               27789 non-null  object \n",
            " 4   Job context                       7188 non-null   object \n",
            " 5   Salary                            27789 non-null  float64\n",
            " 6   Amount of monetary compensation   20583 non-null  float64\n",
            " 7   Income context                    3011 non-null   object \n",
            " 8   Country                           27789 non-null  object \n",
            " 9   State in USA                      22950 non-null  object \n",
            " 10  City                              27714 non-null  object \n",
            " 11  Work experience all               27789 non-null  object \n",
            " 12  Work experience in current field  27789 non-null  object \n",
            " 13  Education                         27579 non-null  object \n",
            " 14  Gender                            26595 non-null  object \n",
            " 15  Race                              27623 non-null  object \n",
            "dtypes: float64(2), object(14)\n",
            "memory usage: 3.4+ MB\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Timestamp    Age                       Industry  \\\n",
              "0  4/27/2021 11:02:10  25-34   Education (Higher Education)   \n",
              "1  4/27/2021 11:02:22  25-34              Computing or Tech   \n",
              "2  4/27/2021 11:02:38  25-34  Accounting, Banking & Finance   \n",
              "3  4/27/2021 11:02:41  25-34                     Nonprofits   \n",
              "4  4/27/2021 11:02:42  25-34  Accounting, Banking & Finance   \n",
              "\n",
              "                                        Job Job context   Salary  \\\n",
              "0        Research and Instruction Librarian         NaN  55000.0   \n",
              "1  Change & Internal Communications Manager         NaN  67158.0   \n",
              "2                      Marketing Specialist         NaN  34000.0   \n",
              "3                           Program Manager         NaN  62000.0   \n",
              "4                        Accounting Manager         NaN  60000.0   \n",
              "\n",
              "   Amount of monetary compensation Income context         Country  \\\n",
              "0                              0.0            NaN   United States   \n",
              "1                           4920.0            NaN  United Kingdom   \n",
              "2                              NaN            NaN              US   \n",
              "3                           3000.0            NaN             USA   \n",
              "4                           7000.0            NaN              US   \n",
              "\n",
              "     State in USA         City Work experience all  \\\n",
              "0   Massachusetts       Boston           5-7 years   \n",
              "1             NaN    Cambridge        8 - 10 years   \n",
              "2       Tennessee  Chattanooga         2 - 4 years   \n",
              "3       Wisconsin    Milwaukee        8 - 10 years   \n",
              "4  South Carolina   Greenville        8 - 10 years   \n",
              "\n",
              "  Work experience in current field        Education Gender   Race  \n",
              "0                        5-7 years  Master's degree  Woman  White  \n",
              "1                        5-7 years   College degree    NaN  White  \n",
              "2                      2 - 4 years   College degree  Woman  White  \n",
              "3                        5-7 years   College degree  Woman  White  \n",
              "4                        5-7 years   College degree  Woman  White  "
            ],
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              "      <th>2</th>\n",
              "      <td>4/27/2021 11:02:38</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Accounting, Banking &amp; Finance</td>\n",
              "      <td>Marketing Specialist</td>\n",
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              "      <td>34000.0</td>\n",
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              "      <td>2 - 4 years</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4/27/2021 11:02:41</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Nonprofits</td>\n",
              "      <td>Program Manager</td>\n",
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              "      <td>62000.0</td>\n",
              "      <td>3000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA</td>\n",
              "      <td>Wisconsin</td>\n",
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              "    </tr>\n",
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              "      <th>4</th>\n",
              "      <td>4/27/2021 11:02:42</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Accounting, Banking &amp; Finance</td>\n",
              "      <td>Accounting Manager</td>\n",
              "      <td>NaN</td>\n",
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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-c53c0c63-2b72-4b23-8b9a-c1469c200567');\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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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 196
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Этап 1.2\n",
        "Приведите страны к единым названиям, например в данных не должно быть одновременно USA и United States. Различных значений много, но постарайтесь сохранить как можно больше информации. Строки с нерелевантными значениями удалите. В качестве помощи мы уже подготовили значения для замены некоторых стран. Обрабатывать все наблюдения необязательно, но постарайтесь восстановить как можно больше значений.                                   "
      ],
      "metadata": {
        "id": "wAzlq7RXtQTp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "usa_names = ['United States', 'US', 'USA',\n",
        "       'usa', 'U.S.', 'United States ', 'us',\n",
        "       'Usa', 'United States of America', 'United states', 'USA ',\n",
        "       'United states ','united states', 'United States of America ',\n",
        "       'U.S. ', 'U.S>', 'ISA', 'US ', 'United State', 'U.S.A',\n",
        "       'U.S.A.', 'America', 'united states of america', 'Us', 'The United States',\n",
        "       'United State of America', 'United Stated','u.s.', 'UNITED STATES', 'united States',\n",
        "       'USA-- Virgin Islands', 'U.S', 'Unites States ', 'Usa ', 'U.S.A. ',\n",
        "       'U. S. ', 'United Sates', 'United States of American ''Uniited States',\n",
        "       'Worldwide (based in US but short term trips aroudn the world)',\n",
        "       'United Sates of America', 'United States', 'america', 'Unted States', 'United Statesp',\n",
        "       'United Stattes', 'United Statea', 'Unites States', 'United Statees',\n",
        "       'UNited States', 'Uniyed states', 'Uniyes States', 'United States of Americas',\n",
        "       'U.A.', 'U. S.' 'US of A', 'United States of america ',\n",
        "       'U.SA', 'United Status', ' U.S.', 'Serbia', 'Canada and USA',\n",
        "       'Virginia', 'U.s.', 'U.s.a.', 'USS', 'Uniteed States', 'United Stares',\n",
        "       ' US', 'Unites states ', 'Us ', 'Unite States', 'The US',\n",
        "       'united states ', 'United States (I work from home and my clients are all over the US/Canada/PR',\n",
        "       'United states of America ', 'For the United States government, but posted overseas',\n",
        "       'From Romania, but for an US based company', 'UnitedStates', 'Uniited States',\n",
        "       'United States of america', 'United statew', ' United States', 'United Statues',\n",
        "       'Untied States', 'United States of American ', 'USA (company is based in a US territory, I work remote)',\n",
        "       'USAB', 'Unitied States', 'United Sttes', 'united stated', 'United States Of America',\n",
        "       'Uniter Statez', 'U. S ', 'United states of America',\n",
        "       'USA tomorrow ', 'United Stateds', 'Japan, US Gov position',\n",
        "       'n/a (remote from wherever I want)',  'US govt employee overseas, country withheld', 'usa ',\n",
        "       'San Francisco', 'Usat', '🇺🇸 ', 'Unitef Stated', 'UA',\n",
        "       'United Stares ', 'United STates', 'USaa', 'uSA',\n",
        "       'United States- Puerto Rico','California ', 'US of A',\n",
        "       'uS', 'USD', \"USA, but for foreign gov't\", 'U. S.', 'United y',\n",
        "       'United Statss', 'United states of america', 'UsA',\n",
        "       'I work for a UAE-based organization, though I am personally in the US.',\n",
        "       'United  States',  'United States is America',\n",
        "       'United States of American', 'U.S.A ', 'United Statws']\n",
        "\n",
        "uk_names = ['United Kingdom', 'United Kingdom ', 'Scotland ',\n",
        "           'United Kingdom', 'UK ', 'England/UK',\n",
        "           'UK', 'Great Britain ', 'Englang',\n",
        "           'Scotland', 'UK (England)',\n",
        "           'England', 'UK', 'Wales (United Kingdom)', 'England ',\n",
        "           'Ireland', 'United kingdom ', 'Uk', 'united kingdom',\n",
        "           'Great Britain', 'UK for U.S. company',\n",
        "           'England', 'U.K. (northern England)', 'U.K',\n",
        "           'Ireland', 'Wales (UK)', 'ireland'\n",
        "           'Northern Ireland',  'europe', 'United Kingdom (England)',\n",
        "           'United Kingdom.', 'Northern Ireland ', 'england',\n",
        "           'United kingdom', 'United Kingdomk', 'UK (Northern Ireland)',\n",
        "           'England, Gb', 'UK, remote', 'ENGLAND', 'England, UK.',\n",
        "           'England, United Kingdom', 'U.K.', 'Scotland, UK', 'Wales', 'United Kindom',\n",
        "           'Wales, UK', 'Unites kingdom',  'U.K. ',\n",
        "           'Wales, UK', 'England, UK',  'Uk ',  'uk',\n",
        "           'Northern Ireland, United Kingdom', 'uk',\n",
        "           'UK, but for globally fully remote company']\n",
        "\n",
        "canada_names = ['canada', 'Canada ', 'Canada, Ottawa, ontario', 'CANADA ',\n",
        "               'Canadw', 'CANADA', 'Can', 'Canda', 'Canad', 'Csnada', 'Canadá', 'Canada ', 'Canad', 'Csnada']\n",
        "\n",
        "ger_names = ['Germany ', 'germany', 'Company in Germany. I work from Pakistan.']"
      ],
      "metadata": {
        "id": "zA6xFlYwASbW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Ваш код тут\n",
        "countries = [usa_names, uk_names, canada_names, ger_names]\n",
        "\n",
        "countries_names = ['USA', 'UK', 'Canada', 'Germany']\n",
        "\n",
        "pd.options.mode.chained_assignment = None\n",
        "for i in range(len(df)):\n",
        "    flag = False\n",
        "    index_country = 0\n",
        "    for cntry in countries:\n",
        "        if df['Country'].loc[i] in cntry:\n",
        "            df['Country'].loc[i] = countries_names[index_country]\n",
        "            flag = True\n",
        "            break\n",
        "        index_country += 1\n",
        "    if not flag:\n",
        "        df.drop(labels=i, axis=0, inplace=True)"
      ],
      "metadata": {
        "id": "GPwlgVbxGs9i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.reset_index(inplace=True, drop=True) #из-за удаления строк надо было сбросить индексы"
      ],
      "metadata": {
        "id": "7pD9UDE6mZ0m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df['Country'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dQ0CEEiamDfO",
        "outputId": "89cb46e6-b007-417e-d1d1-8b9ccc9522a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "USA        23105\n",
              "UK          1688\n",
              "Canada       116\n",
              "Germany       23\n",
              "Name: Country, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 200
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.sample(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 912
        },
        "id": "6PTOJDehmTrk",
        "outputId": "473ddcda-f3a2-4933-f2fd-b3a03b564253"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Timestamp    Age                       Industry  \\\n",
              "4020   4/27/2021 12:32:54  55-64                          Sales   \n",
              "8148   4/27/2021 16:57:43  25-34   Education (Higher Education)   \n",
              "21269   5/3/2021 17:05:34  25-34  Education (Primary/Secondary)   \n",
              "1853   4/27/2021 11:36:45  35-44  Accounting, Banking & Finance   \n",
              "19878  4/30/2021 18:44:33  25-34                     Nonprofits   \n",
              "14351  4/28/2021 17:31:06  25-34                  Entertainment   \n",
              "15282  4/28/2021 19:27:01  25-34           Hospitality & Events   \n",
              "6064   4/27/2021 13:59:04  35-44                        Biotech   \n",
              "9027   4/27/2021 19:30:51  55-64              Computing or Tech   \n",
              "13388  4/28/2021 15:56:48  35-44       Property or Construction   \n",
              "\n",
              "                             Job  \\\n",
              "4020    Senior Layout Specialist   \n",
              "8148                    Lecturer   \n",
              "21269        High School Teacher   \n",
              "1853           Financial Analyst   \n",
              "19878    Director of Development   \n",
              "14351           Learning Artist    \n",
              "15282              Event Manager   \n",
              "6064          Scientific Manager   \n",
              "9027   Technical Project Manager   \n",
              "13388        Executive Assistant   \n",
              "\n",
              "                                             Job context     Salary  \\\n",
              "4020   page layout and design for print and digital t...   50003.00   \n",
              "8148                                                 NaN   75000.00   \n",
              "21269                                                NaN   46429.00   \n",
              "1853                                                 NaN   87500.00   \n",
              "19878                                                NaN   70000.00   \n",
              "14351                      Theatre education department    29914.83   \n",
              "15282                                                NaN   45000.00   \n",
              "6064                                                 NaN  128000.00   \n",
              "9027                                                 NaN   92000.00   \n",
              "13388                                                NaN   95250.00   \n",
              "\n",
              "       Amount of monetary compensation  \\\n",
              "4020                            2000.0   \n",
              "8148                               NaN   \n",
              "21269                           5000.0   \n",
              "1853                               0.0   \n",
              "19878                              NaN   \n",
              "14351                              NaN   \n",
              "15282                              NaN   \n",
              "6064                           16000.0   \n",
              "9027                               NaN   \n",
              "13388                           5000.0   \n",
              "\n",
              "                                          Income context Country  \\\n",
              "4020                                                 NaN     USA   \n",
              "8148                                                 NaN     USA   \n",
              "21269  The extra is a local millage that must be rene...     USA   \n",
              "1853                                                 NaN     USA   \n",
              "19878                                                NaN     USA   \n",
              "14351                                                NaN      UK   \n",
              "15282                                                NaN     USA   \n",
              "6064                                                 NaN     USA   \n",
              "9027                                                 NaN     USA   \n",
              "13388                                                NaN     USA   \n",
              "\n",
              "         State in USA           City Work experience all  \\\n",
              "4020   North Carolina    Chapel Hill       21 - 30 years   \n",
              "8148        Wisconsin        Madison         2 - 4 years   \n",
              "21269         Florida      Bradenton           5-7 years   \n",
              "1853            Texas         Irving       11 - 20 years   \n",
              "19878  North Carolina  Winston Salem        8 - 10 years   \n",
              "14351             NaN  Birmingham UK           5-7 years   \n",
              "15282  North Carolina         Durham           5-7 years   \n",
              "6064    Massachusetts      Cambridge       11 - 20 years   \n",
              "9027       California        Oakland       31 - 40 years   \n",
              "13388        New York  New York City       11 - 20 years   \n",
              "\n",
              "      Work experience in current field        Education Gender  \\\n",
              "4020                     21 - 30 years   College degree  Woman   \n",
              "8148                       2 - 4 years              PhD  Woman   \n",
              "21269                        5-7 years   College degree  Woman   \n",
              "1853                         5-7 years  Master's degree    Man   \n",
              "19878                        5-7 years   College degree  Woman   \n",
              "14351                        5-7 years   College degree  Woman   \n",
              "15282                      2 - 4 years   College degree  Woman   \n",
              "6064                     11 - 20 years  Master's degree  Woman   \n",
              "9027                     31 - 40 years   College degree  Woman   \n",
              "13388                    11 - 20 years  Master's degree  Woman   \n",
              "\n",
              "                                                    Race  \n",
              "4020                                               White  \n",
              "8148                                               White  \n",
              "21269                                              White  \n",
              "1853                 Hispanic, Latino, or Spanish origin  \n",
              "19878                                              White  \n",
              "14351                                              White  \n",
              "15282                                              White  \n",
              "6064                                               White  \n",
              "9027   Asian or Asian American, Hispanic, Latino, or ...  \n",
              "13388                                              White  "
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>Age</th>\n",
              "      <th>Industry</th>\n",
              "      <th>Job</th>\n",
              "      <th>Job context</th>\n",
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              "      <th>Amount of monetary compensation</th>\n",
              "      <th>Income context</th>\n",
              "      <th>Country</th>\n",
              "      <th>State in USA</th>\n",
              "      <th>City</th>\n",
              "      <th>Work experience all</th>\n",
              "      <th>Work experience in current field</th>\n",
              "      <th>Education</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Race</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4020</th>\n",
              "      <td>4/27/2021 12:32:54</td>\n",
              "      <td>55-64</td>\n",
              "      <td>Sales</td>\n",
              "      <td>Senior Layout Specialist</td>\n",
              "      <td>page layout and design for print and digital t...</td>\n",
              "      <td>50003.00</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA</td>\n",
              "      <td>North Carolina</td>\n",
              "      <td>Chapel Hill</td>\n",
              "      <td>21 - 30 years</td>\n",
              "      <td>21 - 30 years</td>\n",
              "      <td>College degree</td>\n",
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              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8148</th>\n",
              "      <td>4/27/2021 16:57:43</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Education (Higher Education)</td>\n",
              "      <td>Lecturer</td>\n",
              "      <td>NaN</td>\n",
              "      <td>75000.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA</td>\n",
              "      <td>Wisconsin</td>\n",
              "      <td>Madison</td>\n",
              "      <td>2 - 4 years</td>\n",
              "      <td>2 - 4 years</td>\n",
              "      <td>PhD</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21269</th>\n",
              "      <td>5/3/2021 17:05:34</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Education (Primary/Secondary)</td>\n",
              "      <td>High School Teacher</td>\n",
              "      <td>NaN</td>\n",
              "      <td>46429.00</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>The extra is a local millage that must be rene...</td>\n",
              "      <td>USA</td>\n",
              "      <td>Florida</td>\n",
              "      <td>Bradenton</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1853</th>\n",
              "      <td>4/27/2021 11:36:45</td>\n",
              "      <td>35-44</td>\n",
              "      <td>Accounting, Banking &amp; Finance</td>\n",
              "      <td>Financial Analyst</td>\n",
              "      <td>NaN</td>\n",
              "      <td>87500.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA</td>\n",
              "      <td>Texas</td>\n",
              "      <td>Irving</td>\n",
              "      <td>11 - 20 years</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>Master's degree</td>\n",
              "      <td>Man</td>\n",
              "      <td>Hispanic, Latino, or Spanish origin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19878</th>\n",
              "      <td>4/30/2021 18:44:33</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Nonprofits</td>\n",
              "      <td>Director of Development</td>\n",
              "      <td>NaN</td>\n",
              "      <td>70000.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA</td>\n",
              "      <td>North Carolina</td>\n",
              "      <td>Winston Salem</td>\n",
              "      <td>8 - 10 years</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14351</th>\n",
              "      <td>4/28/2021 17:31:06</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Entertainment</td>\n",
              "      <td>Learning Artist</td>\n",
              "      <td>Theatre education department</td>\n",
              "      <td>29914.83</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>UK</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Birmingham UK</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15282</th>\n",
              "      <td>4/28/2021 19:27:01</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Hospitality &amp; Events</td>\n",
              "      <td>Event Manager</td>\n",
              "      <td>NaN</td>\n",
              "      <td>45000.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA</td>\n",
              "      <td>North Carolina</td>\n",
              "      <td>Durham</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>2 - 4 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6064</th>\n",
              "      <td>4/27/2021 13:59:04</td>\n",
              "      <td>35-44</td>\n",
              "      <td>Biotech</td>\n",
              "      <td>Scientific Manager</td>\n",
              "      <td>NaN</td>\n",
              "      <td>128000.00</td>\n",
              "      <td>16000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA</td>\n",
              "      <td>Massachusetts</td>\n",
              "      <td>Cambridge</td>\n",
              "      <td>11 - 20 years</td>\n",
              "      <td>11 - 20 years</td>\n",
              "      <td>Master's degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9027</th>\n",
              "      <td>4/27/2021 19:30:51</td>\n",
              "      <td>55-64</td>\n",
              "      <td>Computing or Tech</td>\n",
              "      <td>Technical Project Manager</td>\n",
              "      <td>NaN</td>\n",
              "      <td>92000.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA</td>\n",
              "      <td>California</td>\n",
              "      <td>Oakland</td>\n",
              "      <td>31 - 40 years</td>\n",
              "      <td>31 - 40 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>Asian or Asian American, Hispanic, Latino, or ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13388</th>\n",
              "      <td>4/28/2021 15:56:48</td>\n",
              "      <td>35-44</td>\n",
              "      <td>Property or Construction</td>\n",
              "      <td>Executive Assistant</td>\n",
              "      <td>NaN</td>\n",
              "      <td>95250.00</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA</td>\n",
              "      <td>New York</td>\n",
              "      <td>New York City</td>\n",
              "      <td>11 - 20 years</td>\n",
              "      <td>11 - 20 years</td>\n",
              "      <td>Master's degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-281d27e3-05d1-4d18-a527-4fa79379ca90')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-281d27e3-05d1-4d18-a527-4fa79379ca90 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-281d27e3-05d1-4d18-a527-4fa79379ca90');\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": 201
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Задание 2. Замена пропущенных значений (5 баллов)**\n",
        "\n"
      ],
      "metadata": {
        "id": "x9OHplr3W9X1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Этап 2.1\n",
        "Найдите в данных пропущенные значения и выведите их количество для каждой колонки:"
      ],
      "metadata": {
        "id": "hjY6L9W7XFEc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Ваш код тут\n",
        "df.isna().sum()"
      ],
      "metadata": {
        "id": "oNFkYdtRXYBV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3681675e-8497-4b52-8e01-8cca72f5bc66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp                               0\n",
              "Age                                     0\n",
              "Industry                               63\n",
              "Job                                     0\n",
              "Job context                         18422\n",
              "Salary                                  0\n",
              "Amount of monetary compensation      6286\n",
              "Income context                      22272\n",
              "Country                                 0\n",
              "State in USA                         2002\n",
              "City                                   63\n",
              "Work experience all                     0\n",
              "Work experience in current field        0\n",
              "Education                             175\n",
              "Gender                               1065\n",
              "Race                                  149\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Замените пропущенные значения во всех колонках **кроме Education**. Там где можно сделайте это с помощью моды, а в колонках по типу State in USA замените на определенное значение, в данном случае Another Country, а в Amount of monetary compensation на 0"
      ],
      "metadata": {
        "id": "HMja6aSaX0AZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Ваш код тут\n",
        "df['State in USA'][df['State in USA'].isnull()] = 'Another Country'\n",
        "df['Amount of monetary compensation'][df['Amount of monetary compensation'].isnull()] = 0\n"
      ],
      "metadata": {
        "id": "cxogy3mCX9vm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['Industry'].mode())\n",
        "df['Industry'][df['Industry'].isnull()] = 'Computing or Tech'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0-SyU10hngJS",
        "outputId": "3a115fb3-8083-49e1-c4da-bd48e6e889dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    Computing or Tech\n",
            "Name: Industry, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['Job context'].mode(), df['Job context'].value_counts(), sep='\\n')\n",
        "df['Job context'][df['Job context'].isnull()] = 'No information' #посчитал заменять Fundraising слишком опрометчиво"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v3stQP-_owTr",
        "outputId": "9bf08eb9-eb00-4892-98e8-f19c93463fe2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    Fundraising\n",
            "Name: Job context, dtype: object\n",
            "Fundraising                                                             19\n",
            "In commercial real estate industry                                      10\n",
            "Public library                                                           7\n",
            "Attorney                                                                 7\n",
            "Librarian                                                                7\n",
            "                                                                        ..\n",
            "Copywriting, editing and digital content. I don't manage any people.     1\n",
            "Biomedical sciences                                                      1\n",
            "Database and statistic work                                              1\n",
            "Aide in special education                                                1\n",
            "Automotive mechanical engineering                                        1\n",
            "Name: Job context, Length: 6272, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['Income context'][~df['Income context'].isnull()][:10])\n",
        "df['Income context'][df['Income context'].isnull()] = 'No information'"
      ],
      "metadata": {
        "id": "o4OffL8GpehU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2fe560e7-5a31-45d8-b008-6c1f3b7787d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9                            I work for a Charter School\n",
            "30                                      Commission based\n",
            "42                         Around 20,000 a year in stock\n",
            "56                       Extra money comes from overtime\n",
            "58     Technically 37.5 hours, but ends up being pret...\n",
            "79     Bonuses are variable based on company's perfor...\n",
            "98                     Doesn't include tuition remission\n",
            "100    I am, like most professors, a \"9 month\" employ...\n",
            "124    Bonus average was used; it varied depending on...\n",
            "134                                     tax-free stipend\n",
            "Name: Income context, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['City'].mode(), df['City'].value_counts())\n",
        "df['City'][df['City'].isnull()] = 'Boston'"
      ],
      "metadata": {
        "id": "ER0m5Klqp5f9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c74752de-b48b-4819-c230-111947ab9f85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    Boston\n",
            "Name: City, dtype: object Boston             771\n",
            "Chicago            749\n",
            "New York           709\n",
            "Seattle            690\n",
            "London             554\n",
            "                  ... \n",
            "Virtual              1\n",
            "Wailuku              1\n",
            "RICHMOND             1\n",
            "St. Croix Falls      1\n",
            "Spotsylvania         1\n",
            "Name: City, Length: 4123, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['Gender'].mode(), df['Gender'].value_counts())\n",
        "df['Gender'][df['Gender'].isnull()] = 'Woman'"
      ],
      "metadata": {
        "id": "b6ZGJp6uqQbl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e8c2a0b-86a4-4f6f-8e84-f1c094d41c37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    Woman\n",
            "Name: Gender, dtype: object Woman    19165\n",
            "Man       4702\n",
            "Name: Gender, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['Race'].mode(), df['Race'].value_counts())\n",
        "df['Race'][df['Race'].isnull()] = 'White'"
      ],
      "metadata": {
        "id": "ip92XkuRqdXq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b153a18b-0717-4911-98cb-7104faeb9f8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    White\n",
            "Name: Race, dtype: object White                                                                                                                            20783\n",
            "Asian or Asian American                                                                                                           1139\n",
            "Black or African American                                                                                                          617\n",
            "Hispanic, Latino, or Spanish origin                                                                                                532\n",
            "Another option not listed here or prefer not to answer                                                                             503\n",
            "Hispanic, Latino, or Spanish origin, White                                                                                         362\n",
            "Asian or Asian American, White                                                                                                     312\n",
            "Black or African American, White                                                                                                   116\n",
            "Middle Eastern or Northern African, White                                                                                           79\n",
            "Middle Eastern or Northern African                                                                                                  54\n",
            "Native American or Alaska Native, White                                                                                             52\n",
            "White, Another option not listed here or prefer not to answer                                                                       41\n",
            "Native American or Alaska Native                                                                                                    38\n",
            "Black or African American, Hispanic, Latino, or Spanish origin                                                                      26\n",
            "Asian or Asian American, Hispanic, Latino, or Spanish origin                                                                        14\n",
            "Asian or Asian American, Hispanic, Latino, or Spanish origin, White                                                                 13\n",
            "Black or African American, Hispanic, Latino, or Spanish origin, White                                                               11\n",
            "Hispanic, Latino, or Spanish origin, Native American or Alaska Native                                                                9\n",
            "Hispanic, Latino, or Spanish origin, Native American or Alaska Native, White                                                         9\n",
            "Asian or Asian American, Black or African American                                                                                   8\n",
            "Asian or Asian American, Middle Eastern or Northern African                                                                          6\n",
            "Asian or Asian American, Another option not listed here or prefer not to answer                                                      6\n",
            "Hispanic, Latino, or Spanish origin, Another option not listed here or prefer not to answer                                          6\n",
            "Asian or Asian American, Black or African American, White                                                                            6\n",
            "Asian or Asian American, White, Another option not listed here or prefer not to answer                                               5\n",
            "Black or African American, Middle Eastern or Northern African, White                                                                 4\n",
            "Asian or Asian American, Native American or Alaska Native, White                                                                     4\n",
            "Hispanic, Latino, or Spanish origin, Middle Eastern or Northern African, White                                                       4\n",
            "Black or African American, Hispanic, Latino, or Spanish origin, Native American or Alaska Native, White                              3\n",
            "Black or African American, Native American or Alaska Native, White                                                                   3\n",
            "Black or African American, Another option not listed here or prefer not to answer                                                    2\n",
            "Black or African American, Middle Eastern or Northern African                                                                        2\n",
            "Asian or Asian American, Middle Eastern or Northern African, White                                                                   1\n",
            "Hispanic, Latino, or Spanish origin, Middle Eastern or Northern African                                                              1\n",
            "Asian or Asian American, Hispanic, Latino, or Spanish origin, Another option not listed here or prefer not to answer                 1\n",
            "Asian or Asian American, Native American or Alaska Native                                                                            1\n",
            "Hispanic, Latino, or Spanish origin, Native American or Alaska Native, Another option not listed here or prefer not to answer        1\n",
            "Middle Eastern or Northern African, White, Another option not listed here or prefer not to answer                                    1\n",
            "Black or African American, Middle Eastern or Northern African, Native American or Alaska Native, White                               1\n",
            "Middle Eastern or Northern African, Native American or Alaska Native, White                                                          1\n",
            "Asian or Asian American, Black or African American, Native American or Alaska Native, White                                          1\n",
            "Asian or Asian American, Black or African American, Hispanic, Latino, or Spanish origin                                              1\n",
            "Hispanic, Latino, or Spanish origin, White, Another option not listed here or prefer not to answer                                   1\n",
            "Asian or Asian American, Hispanic, Latino, or Spanish origin, Native American or Alaska Native, White                                1\n",
            "Native American or Alaska Native, Another option not listed here or prefer not to answer                                             1\n",
            "Asian or Asian American, Hispanic, Latino, or Spanish origin, White, Another option not listed here or prefer not to answer          1\n",
            "Name: Race, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6XdIGvCeqqJq",
        "outputId": "90eda46c-ec7b-488d-b53d-07cbd3bf8ba1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp                             0\n",
              "Age                                   0\n",
              "Industry                              0\n",
              "Job                                   0\n",
              "Job context                           0\n",
              "Salary                                0\n",
              "Amount of monetary compensation       0\n",
              "Income context                        0\n",
              "Country                               0\n",
              "State in USA                          0\n",
              "City                                  0\n",
              "Work experience all                   0\n",
              "Work experience in current field      0\n",
              "Education                           175\n",
              "Gender                                0\n",
              "Race                                  0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 210
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.sample(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 910
        },
        "id": "kdmHtpOwqv-o",
        "outputId": "d34e07bc-f379-466e-8f7d-2fcd84e0b349"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Timestamp    Age                       Industry  \\\n",
              "846    4/27/2021 11:17:07  25-34                            Law   \n",
              "10560   4/28/2021 6:08:36  35-44   Engineering or Manufacturing   \n",
              "3493   4/27/2021 12:17:11  55-64  Accounting, Banking & Finance   \n",
              "13989  4/28/2021 17:14:50  35-44    Marketing, Advertising & PR   \n",
              "22842   5/6/2021 23:27:38  35-44  Accounting, Banking & Finance   \n",
              "14696  4/28/2021 18:00:33  25-34              Computing or Tech   \n",
              "9384   4/27/2021 20:45:24  55-64  Accounting, Banking & Finance   \n",
              "12480  4/28/2021 13:55:21  25-34    Marketing, Advertising & PR   \n",
              "10976   4/28/2021 9:33:58  25-34       Property or Construction   \n",
              "15955  4/28/2021 21:41:04  25-34                      Insurance   \n",
              "\n",
              "                                         Job  \\\n",
              "846                                Paralegal   \n",
              "10560                      Senior consultant   \n",
              "3493   Vice President Credit Card Operations   \n",
              "13989         Multimedia Content Coordinator   \n",
              "22842     Director of Experience & Strategy    \n",
              "14696                      Account Executive   \n",
              "9384                     Staff Accountant II   \n",
              "12480                        Account Manager   \n",
              "10976              Assistant Project Manager   \n",
              "15955                        Claims adjuster   \n",
              "\n",
              "                                             Job context    Salary  \\\n",
              "846                                       No information   43500.0   \n",
              "10560          Railway safety and compliance consultancy   67650.0   \n",
              "3493                                      No information  132000.0   \n",
              "13989  Social media coordinator and marketing writer ...   36316.0   \n",
              "22842  Experience design and corporate strategy for a...  136000.0   \n",
              "14696                    Public Sector Account Executive   75000.0   \n",
              "9384                                      No information   66000.0   \n",
              "12480                                     No information   70000.0   \n",
              "10976                                     No information   67500.0   \n",
              "15955                                     No information   43500.0   \n",
              "\n",
              "       Amount of monetary compensation  \\\n",
              "846                              200.0   \n",
              "10560                           4735.5   \n",
              "3493                           10000.0   \n",
              "13989                           3684.0   \n",
              "22842                          40000.0   \n",
              "14696                          65000.0   \n",
              "9384                               0.0   \n",
              "12480                          10000.0   \n",
              "10976                          12000.0   \n",
              "15955                              0.0   \n",
              "\n",
              "                                          Income context Country  \\\n",
              "846                                       No information     USA   \n",
              "10560  Salary and pension - other benefits are not mo...      UK   \n",
              "3493                                      No information     USA   \n",
              "13989                                     No information     USA   \n",
              "22842                                     No information     USA   \n",
              "14696                                     No information     USA   \n",
              "9384                                      No information     USA   \n",
              "12480                                     No information     USA   \n",
              "10976                                     No information     USA   \n",
              "15955                                     No information     USA   \n",
              "\n",
              "          State in USA              City Work experience all  \\\n",
              "846           Missouri       Springfield        8 - 10 years   \n",
              "10560  Another Country             Derby       11 - 20 years   \n",
              "3493          Delaware        Wilmington       21 - 30 years   \n",
              "13989       Washington           Spokane        8 - 10 years   \n",
              "22842    Massachusetts           Boston        11 - 20 years   \n",
              "14696         Maryland  Remotely from MD           5-7 years   \n",
              "9384        Washington         Vancouver       31 - 40 years   \n",
              "12480         New York               NYC           5-7 years   \n",
              "10976          Georgia            Athens           5-7 years   \n",
              "15955         New York           Buffalo           5-7 years   \n",
              "\n",
              "      Work experience in current field        Education Gender  \\\n",
              "846                          5-7 years   College degree  Woman   \n",
              "10560                    11 - 20 years  Master's degree    Man   \n",
              "3493                     11 - 20 years  Master's degree  Woman   \n",
              "13989                        5-7 years   College degree  Woman   \n",
              "22842                    11 - 20 years   College degree  Woman   \n",
              "14696                      2 - 4 years   College degree  Woman   \n",
              "9384                     31 - 40 years   College degree  Woman   \n",
              "12480                      2 - 4 years  Master's degree  Woman   \n",
              "10976                        5-7 years   College degree  Woman   \n",
              "15955                   1 year or less   College degree  Woman   \n",
              "\n",
              "                                      Race  \n",
              "846                                  White  \n",
              "10560                                White  \n",
              "3493                                 White  \n",
              "13989                                White  \n",
              "22842                                White  \n",
              "14696  Hispanic, Latino, or Spanish origin  \n",
              "9384                                 White  \n",
              "12480              Asian or Asian American  \n",
              "10976                                White  \n",
              "15955                                White  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8ff8ffb1-e476-4b70-904d-ba72aa10eebc\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>Age</th>\n",
              "      <th>Industry</th>\n",
              "      <th>Job</th>\n",
              "      <th>Job context</th>\n",
              "      <th>Salary</th>\n",
              "      <th>Amount of monetary compensation</th>\n",
              "      <th>Income context</th>\n",
              "      <th>Country</th>\n",
              "      <th>State in USA</th>\n",
              "      <th>City</th>\n",
              "      <th>Work experience all</th>\n",
              "      <th>Work experience in current field</th>\n",
              "      <th>Education</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Race</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>846</th>\n",
              "      <td>4/27/2021 11:17:07</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Law</td>\n",
              "      <td>Paralegal</td>\n",
              "      <td>No information</td>\n",
              "      <td>43500.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>No information</td>\n",
              "      <td>USA</td>\n",
              "      <td>Missouri</td>\n",
              "      <td>Springfield</td>\n",
              "      <td>8 - 10 years</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10560</th>\n",
              "      <td>4/28/2021 6:08:36</td>\n",
              "      <td>35-44</td>\n",
              "      <td>Engineering or Manufacturing</td>\n",
              "      <td>Senior consultant</td>\n",
              "      <td>Railway safety and compliance consultancy</td>\n",
              "      <td>67650.0</td>\n",
              "      <td>4735.5</td>\n",
              "      <td>Salary and pension - other benefits are not mo...</td>\n",
              "      <td>UK</td>\n",
              "      <td>Another Country</td>\n",
              "      <td>Derby</td>\n",
              "      <td>11 - 20 years</td>\n",
              "      <td>11 - 20 years</td>\n",
              "      <td>Master's degree</td>\n",
              "      <td>Man</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3493</th>\n",
              "      <td>4/27/2021 12:17:11</td>\n",
              "      <td>55-64</td>\n",
              "      <td>Accounting, Banking &amp; Finance</td>\n",
              "      <td>Vice President Credit Card Operations</td>\n",
              "      <td>No information</td>\n",
              "      <td>132000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>No information</td>\n",
              "      <td>USA</td>\n",
              "      <td>Delaware</td>\n",
              "      <td>Wilmington</td>\n",
              "      <td>21 - 30 years</td>\n",
              "      <td>11 - 20 years</td>\n",
              "      <td>Master's degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13989</th>\n",
              "      <td>4/28/2021 17:14:50</td>\n",
              "      <td>35-44</td>\n",
              "      <td>Marketing, Advertising &amp; PR</td>\n",
              "      <td>Multimedia Content Coordinator</td>\n",
              "      <td>Social media coordinator and marketing writer ...</td>\n",
              "      <td>36316.0</td>\n",
              "      <td>3684.0</td>\n",
              "      <td>No information</td>\n",
              "      <td>USA</td>\n",
              "      <td>Washington</td>\n",
              "      <td>Spokane</td>\n",
              "      <td>8 - 10 years</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22842</th>\n",
              "      <td>5/6/2021 23:27:38</td>\n",
              "      <td>35-44</td>\n",
              "      <td>Accounting, Banking &amp; Finance</td>\n",
              "      <td>Director of Experience &amp; Strategy</td>\n",
              "      <td>Experience design and corporate strategy for a...</td>\n",
              "      <td>136000.0</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>No information</td>\n",
              "      <td>USA</td>\n",
              "      <td>Massachusetts</td>\n",
              "      <td>Boston</td>\n",
              "      <td>11 - 20 years</td>\n",
              "      <td>11 - 20 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14696</th>\n",
              "      <td>4/28/2021 18:00:33</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Computing or Tech</td>\n",
              "      <td>Account Executive</td>\n",
              "      <td>Public Sector Account Executive</td>\n",
              "      <td>75000.0</td>\n",
              "      <td>65000.0</td>\n",
              "      <td>No information</td>\n",
              "      <td>USA</td>\n",
              "      <td>Maryland</td>\n",
              "      <td>Remotely from MD</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>2 - 4 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>Hispanic, Latino, or Spanish origin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9384</th>\n",
              "      <td>4/27/2021 20:45:24</td>\n",
              "      <td>55-64</td>\n",
              "      <td>Accounting, Banking &amp; Finance</td>\n",
              "      <td>Staff Accountant II</td>\n",
              "      <td>No information</td>\n",
              "      <td>66000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No information</td>\n",
              "      <td>USA</td>\n",
              "      <td>Washington</td>\n",
              "      <td>Vancouver</td>\n",
              "      <td>31 - 40 years</td>\n",
              "      <td>31 - 40 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12480</th>\n",
              "      <td>4/28/2021 13:55:21</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Marketing, Advertising &amp; PR</td>\n",
              "      <td>Account Manager</td>\n",
              "      <td>No information</td>\n",
              "      <td>70000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>No information</td>\n",
              "      <td>USA</td>\n",
              "      <td>New York</td>\n",
              "      <td>NYC</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>2 - 4 years</td>\n",
              "      <td>Master's degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>Asian or Asian American</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10976</th>\n",
              "      <td>4/28/2021 9:33:58</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Property or Construction</td>\n",
              "      <td>Assistant Project Manager</td>\n",
              "      <td>No information</td>\n",
              "      <td>67500.0</td>\n",
              "      <td>12000.0</td>\n",
              "      <td>No information</td>\n",
              "      <td>USA</td>\n",
              "      <td>Georgia</td>\n",
              "      <td>Athens</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
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              "    <tr>\n",
              "      <th>15955</th>\n",
              "      <td>4/28/2021 21:41:04</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Insurance</td>\n",
              "      <td>Claims adjuster</td>\n",
              "      <td>No information</td>\n",
              "      <td>43500.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No information</td>\n",
              "      <td>USA</td>\n",
              "      <td>New York</td>\n",
              "      <td>Buffalo</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>1 year or less</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 211
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Этап 2.2\n",
        "Давайте теперь попоробуем заменить пропущенные значения при помощи Sklearn. В этой библиотеке есть много классных методов для замены пропусков и они позволяют это делать более точнее. В данном случае мы возьмем самый простой - [SimpleImputer](https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html#sklearn.impute.SimpleImputer)\n",
        "\n",
        "Что нужно сделать:\n",
        "\n",
        "1.   Импортируйте библиотеку\n",
        "2.   Замените пропуски в колонке Education с помощью SimpleImputer(пример есть в документации, также лучше использовать параметр strategy=most_frequent).\n",
        "3.   Преобразуйте результат из numpy.array в датафрейм и поменяйте название колонок\n",
        "4.   Замените старый датасет на новый\n"
      ],
      "metadata": {
        "id": "fNhKREBwJv5b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Ваш код тут\n",
        "from sklearn.impute import SimpleImputer\n",
        "imp = SimpleImputer(missing_values=np.nan, strategy='most_frequent')\n",
        "education = df['Education'].to_numpy()\n",
        "educ = imp.fit_transform(education.reshape(-1,1))\n",
        "pd.Series(educ.reshape(-1))\n",
        "df['Education'] = educ"
      ],
      "metadata": {
        "id": "XhdLV-4VLyXq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.sample(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 806
        },
        "id": "2Kf7iey3tEju",
        "outputId": "60423e8b-1285-4270-c02f-2b2d0f9db25c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Timestamp    Age                               Industry  \\\n",
              "23595    6/2/2021 16:55:54  35-44                            Health care   \n",
              "5       4/27/2021 11:02:46  25-34           Education (Higher Education)   \n",
              "22769    5/6/2021 17:26:52  35-44                            Health care   \n",
              "15872   4/28/2021 21:24:46  45-54                          Supply Chain!   \n",
              "13682   4/28/2021 17:01:33  25-34   Government and Public Administration   \n",
              "19761   4/30/2021 17:57:48  35-44  Repair facility for heavy duty trucks   \n",
              "20039   4/30/2021 20:08:35  55-64               Property or Construction   \n",
              "1053    4/27/2021 11:21:02  35-44                     Museum - Nonprofit   \n",
              "8667    4/27/2021 18:13:09  45-54                            Health care   \n",
              "24190  10/26/2021 12:22:58  25-34                            Health care   \n",
              "\n",
              "                                     Job     Job context    Salary  \\\n",
              "23595                    General Manager  No information  140000.0   \n",
              "5         Scholarly Publishing Librarian  No information   62000.0   \n",
              "22769                    Finance Manager  No information  112000.0   \n",
              "15872     Senior Supply Chain Specialist  No information  144656.0   \n",
              "13682                     Budget Analyst  No information   75078.0   \n",
              "19761          Assistant Service Manager  No information   65000.0   \n",
              "20039                   Staff Accountant  No information   60000.0   \n",
              "1053   Exhibitions Manager and Registrar  No information   65000.0   \n",
              "8667                     Certified Coder  No information   43000.0   \n",
              "24190      Emergency Medical Technician   No information   43680.0   \n",
              "\n",
              "       Amount of monetary compensation  Income context Country   State in USA  \\\n",
              "23595                          28000.0  No information     USA       Illinois   \n",
              "5                                  0.0  No information     USA  New Hampshire   \n",
              "22769                          10000.0  No information     USA        Florida   \n",
              "15872                          21700.0  No information     USA     California   \n",
              "13682                              0.0  No information     USA      Minnesota   \n",
              "19761                              0.0  No information     USA    Connecticut   \n",
              "20039                              0.0  No information     USA       Illinois   \n",
              "1053                             200.0  No information     USA       Missouri   \n",
              "8667                            2000.0  No information     USA       Missouri   \n",
              "24190                           7000.0  No information     USA     New Jersey   \n",
              "\n",
              "                 City Work experience all Work experience in current field  \\\n",
              "23595         Chicago        8 - 10 years                      2 - 4 years   \n",
              "5             Hanover        8 - 10 years                      2 - 4 years   \n",
              "22769   Jacksonville        11 - 20 years                    11 - 20 years   \n",
              "15872        Milpitas       21 - 30 years                    21 - 30 years   \n",
              "13682        St. Paul         2 - 4 years                      2 - 4 years   \n",
              "19761            Nope       11 - 20 years                    11 - 20 years   \n",
              "20039     Bensenville       31 - 40 years                        5-7 years   \n",
              "1053      Kansas City       11 - 20 years                    11 - 20 years   \n",
              "8667     Springfield        21 - 30 years                     8 - 10 years   \n",
              "24190  New Brunswick        11 - 20 years                    11 - 20 years   \n",
              "\n",
              "             Education Gender  \\\n",
              "23595  Master's degree  Woman   \n",
              "5      Master's degree    Man   \n",
              "22769   College degree  Woman   \n",
              "15872   College degree  Woman   \n",
              "13682  Master's degree  Woman   \n",
              "19761      High School  Woman   \n",
              "20039   College degree  Woman   \n",
              "1053   Master's degree  Woman   \n",
              "8667      Some college  Woman   \n",
              "24190     Some college    Man   \n",
              "\n",
              "                                                    Race  \n",
              "23595                                              White  \n",
              "5                                                  White  \n",
              "22769                                              White  \n",
              "15872                                              White  \n",
              "13682                                              White  \n",
              "19761                                              White  \n",
              "20039                                              White  \n",
              "1053                                               White  \n",
              "8667                                               White  \n",
              "24190  Black or African American, Hispanic, Latino, o...  "
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              "      <th>Timestamp</th>\n",
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              "      <th>23595</th>\n",
              "      <td>6/2/2021 16:55:54</td>\n",
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              "      <td>8 - 10 years</td>\n",
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              "      <td>4/27/2021 11:02:46</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Education (Higher Education)</td>\n",
              "      <td>Scholarly Publishing Librarian</td>\n",
              "      <td>No information</td>\n",
              "      <td>62000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No information</td>\n",
              "      <td>USA</td>\n",
              "      <td>New Hampshire</td>\n",
              "      <td>Hanover</td>\n",
              "      <td>8 - 10 years</td>\n",
              "      <td>2 - 4 years</td>\n",
              "      <td>Master's degree</td>\n",
              "      <td>Man</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22769</th>\n",
              "      <td>5/6/2021 17:26:52</td>\n",
              "      <td>35-44</td>\n",
              "      <td>Health care</td>\n",
              "      <td>Finance Manager</td>\n",
              "      <td>No information</td>\n",
              "      <td>112000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>No information</td>\n",
              "      <td>USA</td>\n",
              "      <td>Florida</td>\n",
              "      <td>Jacksonville</td>\n",
              "      <td>11 - 20 years</td>\n",
              "      <td>11 - 20 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
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              "      <th>15872</th>\n",
              "      <td>4/28/2021 21:24:46</td>\n",
              "      <td>45-54</td>\n",
              "      <td>Supply Chain!</td>\n",
              "      <td>Senior Supply Chain Specialist</td>\n",
              "      <td>No information</td>\n",
              "      <td>144656.0</td>\n",
              "      <td>21700.0</td>\n",
              "      <td>No information</td>\n",
              "      <td>USA</td>\n",
              "      <td>California</td>\n",
              "      <td>Milpitas</td>\n",
              "      <td>21 - 30 years</td>\n",
              "      <td>21 - 30 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13682</th>\n",
              "      <td>4/28/2021 17:01:33</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Government and Public Administration</td>\n",
              "      <td>Budget Analyst</td>\n",
              "      <td>No information</td>\n",
              "      <td>75078.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No information</td>\n",
              "      <td>USA</td>\n",
              "      <td>Minnesota</td>\n",
              "      <td>St. Paul</td>\n",
              "      <td>2 - 4 years</td>\n",
              "      <td>2 - 4 years</td>\n",
              "      <td>Master's degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19761</th>\n",
              "      <td>4/30/2021 17:57:48</td>\n",
              "      <td>35-44</td>\n",
              "      <td>Repair facility for heavy duty trucks</td>\n",
              "      <td>Assistant Service Manager</td>\n",
              "      <td>No information</td>\n",
              "      <td>65000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No information</td>\n",
              "      <td>USA</td>\n",
              "      <td>Connecticut</td>\n",
              "      <td>Nope</td>\n",
              "      <td>11 - 20 years</td>\n",
              "      <td>11 - 20 years</td>\n",
              "      <td>High School</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20039</th>\n",
              "      <td>4/30/2021 20:08:35</td>\n",
              "      <td>55-64</td>\n",
              "      <td>Property or Construction</td>\n",
              "      <td>Staff Accountant</td>\n",
              "      <td>No information</td>\n",
              "      <td>60000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No information</td>\n",
              "      <td>USA</td>\n",
              "      <td>Illinois</td>\n",
              "      <td>Bensenville</td>\n",
              "      <td>31 - 40 years</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1053</th>\n",
              "      <td>4/27/2021 11:21:02</td>\n",
              "      <td>35-44</td>\n",
              "      <td>Museum - Nonprofit</td>\n",
              "      <td>Exhibitions Manager and Registrar</td>\n",
              "      <td>No information</td>\n",
              "      <td>65000.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>No information</td>\n",
              "      <td>USA</td>\n",
              "      <td>Missouri</td>\n",
              "      <td>Kansas City</td>\n",
              "      <td>11 - 20 years</td>\n",
              "      <td>11 - 20 years</td>\n",
              "      <td>Master's degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8667</th>\n",
              "      <td>4/27/2021 18:13:09</td>\n",
              "      <td>45-54</td>\n",
              "      <td>Health care</td>\n",
              "      <td>Certified Coder</td>\n",
              "      <td>No information</td>\n",
              "      <td>43000.0</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>No information</td>\n",
              "      <td>USA</td>\n",
              "      <td>Missouri</td>\n",
              "      <td>Springfield</td>\n",
              "      <td>21 - 30 years</td>\n",
              "      <td>8 - 10 years</td>\n",
              "      <td>Some college</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24190</th>\n",
              "      <td>10/26/2021 12:22:58</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Health care</td>\n",
              "      <td>Emergency Medical Technician</td>\n",
              "      <td>No information</td>\n",
              "      <td>43680.0</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>No information</td>\n",
              "      <td>USA</td>\n",
              "      <td>New Jersey</td>\n",
              "      <td>New Brunswick</td>\n",
              "      <td>11 - 20 years</td>\n",
              "      <td>11 - 20 years</td>\n",
              "      <td>Some college</td>\n",
              "      <td>Man</td>\n",
              "      <td>Black or African American, Hispanic, Latino, o...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7162e4fc-c03d-4da2-89c7-113ec460195d')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-7162e4fc-c03d-4da2-89c7-113ec460195d 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-7162e4fc-c03d-4da2-89c7-113ec460195d');\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": 213
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EgVgKptMto-c",
        "outputId": "55bb0999-69e9-4a16-e8dd-fac9a6c68025"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp                           0\n",
              "Age                                 0\n",
              "Industry                            0\n",
              "Job                                 0\n",
              "Job context                         0\n",
              "Salary                              0\n",
              "Amount of monetary compensation     0\n",
              "Income context                      0\n",
              "Country                             0\n",
              "State in USA                        0\n",
              "City                                0\n",
              "Work experience all                 0\n",
              "Work experience in current field    0\n",
              "Education                           0\n",
              "Gender                              0\n",
              "Race                                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 214
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Задание 3. Построение графиков (5 баллов)**"
      ],
      "metadata": {
        "id": "cvVW7R5YVe8Y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Давайте начинать исследование наших данных с помошью графиков. Для начала оцените по графику violin plot как распределна зарплата и есть ли в ней выбросы. Если у вас не получается построить нормальный график, то вам нужно удалить аномально большие значения.\n",
        "\n",
        "---\n",
        "\n",
        "**Важно! Графики должны быть читаемыми**"
      ],
      "metadata": {
        "id": "vC6KLMheWPDl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.boxplot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "MzpUH_zkw3ux",
        "outputId": "cb42e9bd-d534-49d8-fa62-d166ba0ae5b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 215
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(df['Salary']) # очевидно выбросы есть"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "T6iAZmXxxYKX",
        "outputId": "ee93c6d6-d41e-40d1-a704-5f019bb81357"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 216
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "z = np.abs(stats.zscore(df['Salary']))\n",
        "threshold = 2\n",
        "\n",
        "#df['Salary'][z < threshold].\n",
        "df_new = df[z < threshold]\n",
        "sns.boxplot(df_new['Salary']), len(df_new) #посмотрим сколько строк осталось"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "EYKHTwZYxxni",
        "outputId": "fe0bc645-ed8a-4fca-b9a6-c9e1d38148c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<Axes: >, 24500)"
            ]
          },
          "metadata": {},
          "execution_count": 217
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Ваш код тут\n",
        "sns.violinplot(df['Salary'])"
      ],
      "metadata": {
        "id": "nEVHJ_aZWaeG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "outputId": "d1b0d167-15f0-411c-c0ad-e258e4fc9bae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 218
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.violinplot(df_new['Salary'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "lv_7Q3cU0ALp",
        "outputId": "2f04bcc4-1654-494f-8705-7edfe9e5317e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 219
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Теперь давайте ответим на несколько вопросов о наших данных с помощью графиков"
      ],
      "metadata": {
        "id": "zQvux6FXFTXt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1)** У каких десяти индустрий самые высокие средние зарплаты?"
      ],
      "metadata": {
        "id": "VvEA88ulFWhf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Ваш код тут\n",
        "slr = df_new.groupby('Industry')['Salary'].mean()\n",
        "slr = slr.sort_values(ascending=False)[:15]\n",
        "slr.plot(kind='barh')"
      ],
      "metadata": {
        "id": "upXD0oXOFaMI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "39a662d4-0c80-4fd0-98e4-b1a6353cbd05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Industry'>"
            ]
          },
          "metadata": {},
          "execution_count": 220
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(*slr.index[:10].to_list(), sep='\\n')\n",
        "#индустрии с самой высокой средней зарплатой в порядке убывания (Хотя конечно надо еще препроцессить индустрии, но лень...)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cJG-To-zcJv5",
        "outputId": "dd3dd5d3-4d69-4e2b-df0b-3d4664aeae34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pharmaceutical/biotechnology\n",
            "Biotech/pharmaceuticals\n",
            "Big Pharma\n",
            "Medical/Pharmaceutical \n",
            "Petroleum\n",
            "consulting\n",
            "Clean Energy (eg. energy efficiency, renewables, etc.)\n",
            "Biotech / life sciences \n",
            "trade association\n",
            "strategy consulting\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2)** Влияет ли опыт работы на среднюю зарплату?"
      ],
      "metadata": {
        "id": "iOtobVBoFcu7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Ваш код тут\n",
        "slr = df_new.groupby('Work experience all')['Salary'].mean()\n",
        "slr = slr.sort_values(ascending=False)\n",
        "slr.plot(kind='barh', title='Зависимость средней зарплаты от общего опыта работы', figsize=(12,10))\n",
        "None"
      ],
      "metadata": {
        "id": "x6D4mAIKFhtg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "outputId": "bea4332f-2ef6-4f23-e450-42e705dc89e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "slr = df_new.groupby('Work experience in current field')['Salary'].mean()\n",
        "slr = slr.sort_values(ascending=False)\n",
        "slr.plot(kind='barh', title='Зависимость средней зарплаты от опыта работы в области', figsize=(12,10))\n",
        "None"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "KkOPCcGUdehk",
        "outputId": "9e93ccc1-53d7-4469-eb7a-427ce7065697"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***В целом зависимость есть, как и от опыта работы в принципе, так и от опыта работы в конкретной области. Чем выше опыт -> тем выше з/п, однако зависимость немного нарушается, когда опыт переваливает за 30 лет, видимо сказывается возраст человека на его способностях***"
      ],
      "metadata": {
        "id": "fcTXzSMPd0_4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3)** Выделяется ли в зарплате и премиях зависимость от образования? (Тут нужно построить scatter plot, при этом выбрать значения Amount of monetary compensation неравные 0)"
      ],
      "metadata": {
        "id": "6slvgLy7FiSG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Ваш код тут\n",
        "df_new.plot.scatter('Education', 'Salary', color='g', figsize=(12, 12))\n",
        "None"
      ],
      "metadata": {
        "id": "lg4A-37rFmFW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 749
        },
        "outputId": "f5e60412-47ee-4ae7-c289-73a9df4ab889"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/pandas/plotting/_matplotlib/core.py:1114: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  scatter = ax.scatter(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_new.loc[df_new['Amount of monetary compensation'] != 0].plot.scatter('Education', 'Amount of monetary compensation', color='b', figsize=(12, 12))\n",
        "None"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "id": "TLZMeyF2mykJ",
        "outputId": "4547e336-9649-4659-cf8d-76bcb23c0563"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Как видим, образование не коррелирует с высокой зарплатой, однако низкие зарплаты чаще встречаются у людей с образованием Master's degree и Colleg degree**\n",
        "\n",
        "\n",
        "**Премии также не особо выделяются зависимостью с уроавнем образования (не считая единичных точек, которые возможно могут быть выбросами)**\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "IXo_N_eTmovY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Задание 4. Создание новых признаков (4 балла)**"
      ],
      "metadata": {
        "id": "IGhcxXRecQbV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Давайте теперь создадим **2 новых признака**. Мы предлагаем вам три идеи, но вы вправе придумать что то свое. Главное условие, признаки не должны повторять предыдущие и быть линейно зависимыми. например нельзя просто умножить колонку Salary на два и добавить ее к датафрейму.\n",
        "\n",
        "Идеи которые предлагаем мы:\n",
        "\n",
        "1.   Преобразовать колонку Race и сделать ее полезной для будующей модели. Это не совсем создание нового признака, скорее преобразование старого. Все расы в ней разделены союзом or или запятой. Вы можете создать аналог дамми переменных и разделить расы на колонки. если человек указал данную расу - ставить 1.\n",
        "2.   Создать новую колонку с населением города, в котором живет человек. Для этого вы можете взять данные по пути - /content/REU-Ml-SCHOOL/city_population_data.csv.\n",
        "3. Добавить колонку со cредней зарплатой человека, основанной на его возрасте и опыте работы.\n"
      ],
      "metadata": {
        "id": "MrZQKalGcb4k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df_new"
      ],
      "metadata": {
        "id": "a0_b5V6uqHxn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.unique(df['Race'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_sw2GOY0q6u-",
        "outputId": "930312c6-63e9-41df-e789-c3c5080bb0ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Another option not listed here or prefer not to answer',\n",
              "       'Asian or Asian American',\n",
              "       'Asian or Asian American, Another option not listed here or prefer not to answer',\n",
              "       'Asian or Asian American, Black or African American',\n",
              "       'Asian or Asian American, Black or African American, Hispanic, Latino, or Spanish origin',\n",
              "       'Asian or Asian American, Black or African American, Native American or Alaska Native, White',\n",
              "       'Asian or Asian American, Black or African American, White',\n",
              "       'Asian or Asian American, Hispanic, Latino, or Spanish origin',\n",
              "       'Asian or Asian American, Hispanic, Latino, or Spanish origin, Another option not listed here or prefer not to answer',\n",
              "       'Asian or Asian American, Hispanic, Latino, or Spanish origin, Native American or Alaska Native, White',\n",
              "       'Asian or Asian American, Hispanic, Latino, or Spanish origin, White',\n",
              "       'Asian or Asian American, Hispanic, Latino, or Spanish origin, White, Another option not listed here or prefer not to answer',\n",
              "       'Asian or Asian American, Middle Eastern or Northern African',\n",
              "       'Asian or Asian American, Middle Eastern or Northern African, White',\n",
              "       'Asian or Asian American, Native American or Alaska Native, White',\n",
              "       'Asian or Asian American, White',\n",
              "       'Asian or Asian American, White, Another option not listed here or prefer not to answer',\n",
              "       'Black or African American',\n",
              "       'Black or African American, Another option not listed here or prefer not to answer',\n",
              "       'Black or African American, Hispanic, Latino, or Spanish origin',\n",
              "       'Black or African American, Hispanic, Latino, or Spanish origin, Native American or Alaska Native, White',\n",
              "       'Black or African American, Hispanic, Latino, or Spanish origin, White',\n",
              "       'Black or African American, Middle Eastern or Northern African',\n",
              "       'Black or African American, Middle Eastern or Northern African, Native American or Alaska Native, White',\n",
              "       'Black or African American, Middle Eastern or Northern African, White',\n",
              "       'Black or African American, Native American or Alaska Native, White',\n",
              "       'Black or African American, White',\n",
              "       'Hispanic, Latino, or Spanish origin',\n",
              "       'Hispanic, Latino, or Spanish origin, Another option not listed here or prefer not to answer',\n",
              "       'Hispanic, Latino, or Spanish origin, Middle Eastern or Northern African',\n",
              "       'Hispanic, Latino, or Spanish origin, Middle Eastern or Northern African, White',\n",
              "       'Hispanic, Latino, or Spanish origin, Native American or Alaska Native',\n",
              "       'Hispanic, Latino, or Spanish origin, Native American or Alaska Native, Another option not listed here or prefer not to answer',\n",
              "       'Hispanic, Latino, or Spanish origin, Native American or Alaska Native, White',\n",
              "       'Hispanic, Latino, or Spanish origin, White',\n",
              "       'Hispanic, Latino, or Spanish origin, White, Another option not listed here or prefer not to answer',\n",
              "       'Middle Eastern or Northern African',\n",
              "       'Middle Eastern or Northern African, Native American or Alaska Native, White',\n",
              "       'Middle Eastern or Northern African, White',\n",
              "       'Middle Eastern or Northern African, White, Another option not listed here or prefer not to answer',\n",
              "       'Native American or Alaska Native',\n",
              "       'Native American or Alaska Native, Another option not listed here or prefer not to answer',\n",
              "       'Native American or Alaska Native, White', 'White',\n",
              "       'White, Another option not listed here or prefer not to answer'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 227
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Ваш код тут\n",
        "df['White'] = np.where(df['Race'].apply(lambda x: 'white' in x.lower()), 1, 0)\n",
        "df['Asian'] = np.where(df['Race'].apply(lambda x: 'asian' in x.lower()), 1, 0)\n",
        "df['Black'] = np.where(df['Race'].apply(lambda x: 'black' in x.lower()), 1, 0)\n",
        "df['American'] = np.where(df['Race'].apply(lambda x: 'american' in x.lower()), 1, 0)\n",
        "df['Spanish'] = np.where(df['Race'].apply(lambda x: 'spanish' in x.lower()), 1, 0)\n",
        "df.sample(10)"
      ],
      "metadata": {
        "id": "_zZLZWozcxsg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 924
        },
        "outputId": "72945a0c-fd0c-4776-9a0a-0c9c3bd5e235"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Timestamp    Age                              Industry  \\\n",
              "4528    4/27/2021 12:52:31  25-34  Government and Public Administration   \n",
              "17851   4/29/2021 12:34:39  25-34                Transport or Logistics   \n",
              "24631  12/28/2021 16:52:57  35-44                            Nonprofits   \n",
              "61      4/27/2021 11:04:36  25-34                     Computing or Tech   \n",
              "20699    5/1/2021 19:37:44  25-34                     Computing or Tech   \n",
              "1       4/27/2021 11:02:22  25-34                     Computing or Tech   \n",
              "19692   4/30/2021 17:34:13  35-44                            Nonprofits   \n",
              "20674    5/1/2021 17:44:15  18-24                       Media & Digital   \n",
              "22145    5/5/2021 19:38:39  25-34                            Nonprofits   \n",
              "24539   11/5/2021 14:03:09  25-34                     Computing or Tech   \n",
              "\n",
              "                                            Job  \\\n",
              "4528                    Government analyst/PA I   \n",
              "17851                                  Director   \n",
              "24631         Senior Data and Policy Specialist   \n",
              "61                         Technical specialist   \n",
              "20699                        Senior UX Designer   \n",
              "1      Change & Internal Communications Manager   \n",
              "19692                           Senior Director   \n",
              "20674                         Software Engineer   \n",
              "22145                     Editorial Coordinator   \n",
              "24539                             Data engineer   \n",
              "\n",
              "                                             Job context    Salary  \\\n",
              "4528   Coded as a military contractor, level PA I (Pe...   43500.0   \n",
              "17851                                     No information  175000.0   \n",
              "24631                                     No information   89000.0   \n",
              "61                                        No information   34000.0   \n",
              "20699                                     No information  140000.0   \n",
              "1                                         No information   67158.0   \n",
              "19692                                     No information   80000.0   \n",
              "20674                Inside a large legacy media company   97000.0   \n",
              "22145       for scientific publications w/i a university   65000.0   \n",
              "24539                                     No information  140000.0   \n",
              "\n",
              "       Amount of monetary compensation  \\\n",
              "4528                             200.0   \n",
              "17851                          30000.0   \n",
              "24631                              0.0   \n",
              "61                              2000.0   \n",
              "20699                              0.0   \n",
              "1                               4920.0   \n",
              "19692                              0.0   \n",
              "20674                           4000.0   \n",
              "22145                              0.0   \n",
              "24539                          10000.0   \n",
              "\n",
              "                                          Income context Country  \\\n",
              "4528   Only bonuses are provided as $50 gift cards fo...     USA   \n",
              "17851                                     No information     USA   \n",
              "24631                                     No information     USA   \n",
              "61                                        No information     USA   \n",
              "20699  I'm a city government employee and took a pay ...     USA   \n",
              "1                                         No information      UK   \n",
              "19692                                     No information     USA   \n",
              "20674                                     No information     USA   \n",
              "22145                                     No information     USA   \n",
              "24539                                     No information     USA   \n",
              "\n",
              "               State in USA  ... Work experience all  \\\n",
              "4528               Kentucky  ...         2 - 4 years   \n",
              "17851              New York  ...        8 - 10 years   \n",
              "24631         Massachusetts  ...       11 - 20 years   \n",
              "61                Minnesota  ...           5-7 years   \n",
              "20699            California  ...       11 - 20 years   \n",
              "1           Another Country  ...        8 - 10 years   \n",
              "19692              Virginia  ...       11 - 20 years   \n",
              "20674  District of Columbia  ...         2 - 4 years   \n",
              "22145            Washington  ...           5-7 years   \n",
              "24539          Pennsylvania  ...        8 - 10 years   \n",
              "\n",
              "      Work experience in current field        Education Gender   Race White  \\\n",
              "4528                       2 - 4 years   College degree  Woman  White     1   \n",
              "17851                     8 - 10 years  Master's degree    Man  White     1   \n",
              "24631                     8 - 10 years              PhD  Woman  White     1   \n",
              "61                           5-7 years   College degree  Woman  White     1   \n",
              "20699                     8 - 10 years     Some college    Man  White     1   \n",
              "1                            5-7 years   College degree  Woman  White     1   \n",
              "19692                    11 - 20 years   College degree  Woman  White     1   \n",
              "20674                      2 - 4 years   College degree    Man  White     1   \n",
              "22145                        5-7 years   College degree  Woman  White     1   \n",
              "24539                        5-7 years  Master's degree    Man  White     1   \n",
              "\n",
              "       Asian  Black  American  Spanish  \n",
              "4528       0      0         0        0  \n",
              "17851      0      0         0        0  \n",
              "24631      0      0         0        0  \n",
              "61         0      0         0        0  \n",
              "20699      0      0         0        0  \n",
              "1          0      0         0        0  \n",
              "19692      0      0         0        0  \n",
              "20674      0      0         0        0  \n",
              "22145      0      0         0        0  \n",
              "24539      0      0         0        0  \n",
              "\n",
              "[10 rows x 21 columns]"
            ],
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              "  <div id=\"df-2fc1d103-8aed-45c6-b0b8-ad4c51ed6ee1\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>Age</th>\n",
              "      <th>Industry</th>\n",
              "      <th>Job</th>\n",
              "      <th>Job context</th>\n",
              "      <th>Salary</th>\n",
              "      <th>Amount of monetary compensation</th>\n",
              "      <th>Income context</th>\n",
              "      <th>Country</th>\n",
              "      <th>State in USA</th>\n",
              "      <th>...</th>\n",
              "      <th>Work experience all</th>\n",
              "      <th>Work experience in current field</th>\n",
              "      <th>Education</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Race</th>\n",
              "      <th>White</th>\n",
              "      <th>Asian</th>\n",
              "      <th>Black</th>\n",
              "      <th>American</th>\n",
              "      <th>Spanish</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4528</th>\n",
              "      <td>4/27/2021 12:52:31</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Government and Public Administration</td>\n",
              "      <td>Government analyst/PA I</td>\n",
              "      <td>Coded as a military contractor, level PA I (Pe...</td>\n",
              "      <td>43500.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>Only bonuses are provided as $50 gift cards fo...</td>\n",
              "      <td>USA</td>\n",
              "      <td>Kentucky</td>\n",
              "      <td>...</td>\n",
              "      <td>2 - 4 years</td>\n",
              "      <td>2 - 4 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17851</th>\n",
              "      <td>4/29/2021 12:34:39</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Transport or Logistics</td>\n",
              "      <td>Director</td>\n",
              "      <td>No information</td>\n",
              "      <td>175000.0</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>No information</td>\n",
              "      <td>USA</td>\n",
              "      <td>New York</td>\n",
              "      <td>...</td>\n",
              "      <td>8 - 10 years</td>\n",
              "      <td>8 - 10 years</td>\n",
              "      <td>Master's degree</td>\n",
              "      <td>Man</td>\n",
              "      <td>White</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24631</th>\n",
              "      <td>12/28/2021 16:52:57</td>\n",
              "      <td>35-44</td>\n",
              "      <td>Nonprofits</td>\n",
              "      <td>Senior Data and Policy Specialist</td>\n",
              "      <td>No information</td>\n",
              "      <td>89000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No information</td>\n",
              "      <td>USA</td>\n",
              "      <td>Massachusetts</td>\n",
              "      <td>...</td>\n",
              "      <td>11 - 20 years</td>\n",
              "      <td>8 - 10 years</td>\n",
              "      <td>PhD</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>4/27/2021 11:04:36</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Computing or Tech</td>\n",
              "      <td>Technical specialist</td>\n",
              "      <td>No information</td>\n",
              "      <td>34000.0</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>No information</td>\n",
              "      <td>USA</td>\n",
              "      <td>Minnesota</td>\n",
              "      <td>...</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20699</th>\n",
              "      <td>5/1/2021 19:37:44</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Computing or Tech</td>\n",
              "      <td>Senior UX Designer</td>\n",
              "      <td>No information</td>\n",
              "      <td>140000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>I'm a city government employee and took a pay ...</td>\n",
              "      <td>USA</td>\n",
              "      <td>California</td>\n",
              "      <td>...</td>\n",
              "      <td>11 - 20 years</td>\n",
              "      <td>8 - 10 years</td>\n",
              "      <td>Some college</td>\n",
              "      <td>Man</td>\n",
              "      <td>White</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4/27/2021 11:02:22</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Computing or Tech</td>\n",
              "      <td>Change &amp; Internal Communications Manager</td>\n",
              "      <td>No information</td>\n",
              "      <td>67158.0</td>\n",
              "      <td>4920.0</td>\n",
              "      <td>No information</td>\n",
              "      <td>UK</td>\n",
              "      <td>Another Country</td>\n",
              "      <td>...</td>\n",
              "      <td>8 - 10 years</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19692</th>\n",
              "      <td>4/30/2021 17:34:13</td>\n",
              "      <td>35-44</td>\n",
              "      <td>Nonprofits</td>\n",
              "      <td>Senior Director</td>\n",
              "      <td>No information</td>\n",
              "      <td>80000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No information</td>\n",
              "      <td>USA</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>...</td>\n",
              "      <td>11 - 20 years</td>\n",
              "      <td>11 - 20 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20674</th>\n",
              "      <td>5/1/2021 17:44:15</td>\n",
              "      <td>18-24</td>\n",
              "      <td>Media &amp; Digital</td>\n",
              "      <td>Software Engineer</td>\n",
              "      <td>Inside a large legacy media company</td>\n",
              "      <td>97000.0</td>\n",
              "      <td>4000.0</td>\n",
              "      <td>No information</td>\n",
              "      <td>USA</td>\n",
              "      <td>District of Columbia</td>\n",
              "      <td>...</td>\n",
              "      <td>2 - 4 years</td>\n",
              "      <td>2 - 4 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Man</td>\n",
              "      <td>White</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22145</th>\n",
              "      <td>5/5/2021 19:38:39</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Nonprofits</td>\n",
              "      <td>Editorial Coordinator</td>\n",
              "      <td>for scientific publications w/i a university</td>\n",
              "      <td>65000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No information</td>\n",
              "      <td>USA</td>\n",
              "      <td>Washington</td>\n",
              "      <td>...</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Woman</td>\n",
              "      <td>White</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24539</th>\n",
              "      <td>11/5/2021 14:03:09</td>\n",
              "      <td>25-34</td>\n",
              "      <td>Computing or Tech</td>\n",
              "      <td>Data engineer</td>\n",
              "      <td>No information</td>\n",
              "      <td>140000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>No information</td>\n",
              "      <td>USA</td>\n",
              "      <td>Pennsylvania</td>\n",
              "      <td>...</td>\n",
              "      <td>8 - 10 years</td>\n",
              "      <td>5-7 years</td>\n",
              "      <td>Master's degree</td>\n",
              "      <td>Man</td>\n",
              "      <td>White</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2fc1d103-8aed-45c6-b0b8-ad4c51ed6ee1')\"\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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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 228
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ppl = pd.read_csv('/content/REU-Ml-SCHOOL/city_population_data.csv', usecols=['city', 'population'])\n",
        "ppl.sample(10, random_state=42)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "NrHBSzqqsYpz",
        "outputId": "5dc027c7-7c31-467f-bd3e-35c9892aeff1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            city  population\n",
              "420        Davis     76997.0\n",
              "613     Gallatin     42918.0\n",
              "1181      Nevada      8699.0\n",
              "925    Leadville      5554.0\n",
              "429      Decatur     87236.0\n",
              "1502   San Ramon     75995.0\n",
              "527        Espoo    269802.0\n",
              "838        Keene     22786.0\n",
              "1627  Statesboro     42102.0\n",
              "651     Goodyear     86840.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dfe47702-444b-4ea9-90ed-778b1455eb8a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>city</th>\n",
              "      <th>population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>420</th>\n",
              "      <td>Davis</td>\n",
              "      <td>76997.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>613</th>\n",
              "      <td>Gallatin</td>\n",
              "      <td>42918.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1181</th>\n",
              "      <td>Nevada</td>\n",
              "      <td>8699.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>925</th>\n",
              "      <td>Leadville</td>\n",
              "      <td>5554.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>429</th>\n",
              "      <td>Decatur</td>\n",
              "      <td>87236.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1502</th>\n",
              "      <td>San Ramon</td>\n",
              "      <td>75995.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>527</th>\n",
              "      <td>Espoo</td>\n",
              "      <td>269802.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>838</th>\n",
              "      <td>Keene</td>\n",
              "      <td>22786.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1627</th>\n",
              "      <td>Statesboro</td>\n",
              "      <td>42102.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>651</th>\n",
              "      <td>Goodyear</td>\n",
              "      <td>86840.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dfe47702-444b-4ea9-90ed-778b1455eb8a button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dfe47702-444b-4ea9-90ed-778b1455eb8a');\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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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 229
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['City'], ppl['city']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rRXWSSElt5s1",
        "outputId": "1de16624-4cc4-4b49-a33f-48f8121dfbd9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0               Boston\n",
              " 1            Cambridge\n",
              " 2          Chattanooga\n",
              " 3            Milwaukee\n",
              " 4           Greenville\n",
              "              ...      \n",
              " 24927    New York City\n",
              " 24928           Dallas\n",
              " 24929        Brunswick\n",
              " 24930     Spotsylvania\n",
              " 24931          Chicago\n",
              " Name: City, Length: 24500, dtype: object, 0         A Coruña\n",
              " 1           Aachen\n",
              " 2       Abbotsford\n",
              " 3         Aberdeen\n",
              " 4          Abidjan\n",
              "            ...    \n",
              " 1893        Zagreb\n",
              " 1894          Zion\n",
              " 1895           Zug\n",
              " 1896        Zwolle\n",
              " 1897        Zürich\n",
              " Name: city, Length: 1898, dtype: object)"
            ]
          },
          "metadata": {},
          "execution_count": 230
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(ppl['city'] == 'Moscow').sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jt3-Gy_AyVdN",
        "outputId": "69e8b285-55b5-4be0-8714-1d7225f6d677"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 231
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ppl['city'] = ppl['city'].apply(lambda x: x.lower())\n",
        "df['City'] = df['City'].apply(lambda x: x.lower())"
      ],
      "metadata": {
        "id": "RXEAhtPNyzfX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['City'] = df['City'].apply(lambda x: x[:x.find(',')] if ',' in x else x)"
      ],
      "metadata": {
        "id": "4sr_GSPczGWz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['population'] = df['City'].map(ppl.set_index('city')['population'])\n"
      ],
      "metadata": {
        "id": "-Sy5Jo6PtP4l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['City'][df['population'].isna()] #опять же много надо препроцессить("
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A6apad9qxf07",
        "outputId": "369e2db4-c584-48c4-b814-adee33cf2d3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13                 saint paul\n",
              "24              washington dc\n",
              "33          research triangle\n",
              "51                         dc\n",
              "59       district of columbia\n",
              "                 ...         \n",
              "24912                     nyc\n",
              "24917               honolulu \n",
              "24918               columbus \n",
              "24927           new york city\n",
              "24930            spotsylvania\n",
              "Name: City, Length: 5383, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 235
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['population'][df['population'].isna()] = 100000"
      ],
      "metadata": {
        "id": "jRBQ6wkcz0aB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "На этом все, если вы качественно подготовили данные, то у вас на руках датасет, который можно использоват например для обучения модели по предсказанию зарплаты или для научной работы"
      ],
      "metadata": {
        "id": "6tyPA7jMHfM0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "id": "tvTSAr3G0sx7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef2fa7ab-72c0-42e2-d85a-c4ce862b40bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 24500 entries, 0 to 24931\n",
            "Data columns (total 22 columns):\n",
            " #   Column                            Non-Null Count  Dtype  \n",
            "---  ------                            --------------  -----  \n",
            " 0   Timestamp                         24500 non-null  object \n",
            " 1   Age                               24500 non-null  object \n",
            " 2   Industry                          24500 non-null  object \n",
            " 3   Job                               24500 non-null  object \n",
            " 4   Job context                       24500 non-null  object \n",
            " 5   Salary                            24500 non-null  float64\n",
            " 6   Amount of monetary compensation   24500 non-null  float64\n",
            " 7   Income context                    24500 non-null  object \n",
            " 8   Country                           24500 non-null  object \n",
            " 9   State in USA                      24500 non-null  object \n",
            " 10  City                              24500 non-null  object \n",
            " 11  Work experience all               24500 non-null  object \n",
            " 12  Work experience in current field  24500 non-null  object \n",
            " 13  Education                         24500 non-null  object \n",
            " 14  Gender                            24500 non-null  object \n",
            " 15  Race                              24500 non-null  object \n",
            " 16  White                             24500 non-null  int64  \n",
            " 17  Asian                             24500 non-null  int64  \n",
            " 18  Black                             24500 non-null  int64  \n",
            " 19  American                          24500 non-null  int64  \n",
            " 20  Spanish                           24500 non-null  int64  \n",
            " 21  population                        24500 non-null  float64\n",
            "dtypes: float64(3), int64(5), object(14)\n",
            "memory usage: 4.8+ MB\n"
          ]
        }
      ]
    }
  ]
}