diff --git a/tasks_pandas_1-8.ipynb b/tasks_pandas_1-8.ipynb
new file mode 100644
index 0000000..8b2ac81
--- /dev/null
+++ b/tasks_pandas_1-8.ipynb
@@ -0,0 +1,3610 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 184,
+ "id": "7da1116b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1e452cec",
+ "metadata": {},
+ "source": [
+ "# 1. Загружаем данные из файла works"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 185,
+ "id": "ada21dce",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " salary \n",
+ " educationType \n",
+ " jobTitle \n",
+ " qualification \n",
+ " gender \n",
+ " dateModify \n",
+ " skills \n",
+ " otherInfo \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 60000 \n",
+ " Высшее \n",
+ " Специалист пресс-службы \n",
+ " Магистр \n",
+ " Мужской \n",
+ " 2021-04-01 \n",
+ " <p>Аналитическое мышление, <span cla... \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 85000 \n",
+ " Высшее \n",
+ " менеджер проектов \n",
+ " NaN \n",
+ " Мужской \n",
+ " 2021-04-01 \n",
+ " NaN \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 15000 \n",
+ " Среднее профессиональное \n",
+ " .... \n",
+ " NaN \n",
+ " Женский \n",
+ " 2021-06-01 \n",
+ " NaN \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 30000 \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " Женский \n",
+ " 2021-04-03 \n",
+ " NaN \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 45000 \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " Мужской \n",
+ " 2021-06-28 \n",
+ " NaN \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 32678 \n",
+ " 15000 \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " Женский \n",
+ " 2021-06-23 \n",
+ " NaN \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 32679 \n",
+ " 13000 \n",
+ " Среднее \n",
+ " уборщица \n",
+ " NaN \n",
+ " Женский \n",
+ " 2021-07-29 \n",
+ " NaN \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 32680 \n",
+ " 16000 \n",
+ " Среднее профессиональное \n",
+ " кочегар машинист котельной \n",
+ " NaN \n",
+ " Мужской \n",
+ " 2021-11-11 \n",
+ " NaN \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 32681 \n",
+ " 35000 \n",
+ " Высшее \n",
+ " NaN \n",
+ " NaN \n",
+ " Мужской \n",
+ " 2020-04-21 \n",
+ " NaN \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 32682 \n",
+ " 30000 \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " Мужской \n",
+ " 2021-06-20 \n",
+ " NaN \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
32683 rows × 8 columns
\n",
+ "
Аналитическое мышление, \n",
+ "RangeIndex: 32683 entries, 0 to 32682\n",
+ "Data columns (total 8 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 salary 32683 non-null int64 \n",
+ " 1 educationType 24750 non-null object\n",
+ " 2 jobTitle 20259 non-null object\n",
+ " 3 qualification 12176 non-null object\n",
+ " 4 gender 31296 non-null object\n",
+ " 5 dateModify 32682 non-null object\n",
+ " 6 skills 8972 non-null object\n",
+ " 7 otherInfo 2316 non-null object\n",
+ "dtypes: int64(1), object(7)\n",
+ "memory usage: 2.0+ MB\n"
+ ]
+ }
+ ],
+ "source": [
+ "data.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 191,
+ "id": "9b63dae4",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "8972"
+ ]
+ },
+ "execution_count": 191,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data['skills'].notnull().sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 192,
+ "id": "815e62ec",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "8972"
+ ]
+ },
+ "execution_count": 192,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data['skills'].notnull().values.sum()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "79e1a2b9",
+ "metadata": {},
+ "source": [
+ "# 4. Получить все заполненные скиллы"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6ba377d3",
+ "metadata": {},
+ "source": [
+ "Получим все заполненные скилы с помощью фильтрации по столбцу"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 193,
+ "id": "52a9828b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 Аналитическое мышление, Коммуникабельность
\n",
+ "13 Ответственность в работе
\n",
+ "21 Усидчивость, умение удерживать в памяти нуж...\n",
+ " ... \n",
+ "32665
Отвественность
Исполнительность
Высокая работоспособность, нацеленность на ...\n",
+ "32672 исполнительный
\n",
+ "32674 Нацелен на результат. Считаю себя командным...\n",
+ "32675
трудоспособен
\n",
+ "Name: skills, Length: 8972, dtype: object"
+ ]
+ },
+ "execution_count": 193,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data[data['skills'].notna()]['skills']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f21915af",
+ "metadata": {},
+ "source": [
+ "# 5. Вывести зарплату только у тех, у которых в скиллах есть Python (Питон)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ee181301",
+ "metadata": {},
+ "source": [
+ "Получим все строки в которых присутствутет слово python или питон. Для этого при получении троки переведем все \n",
+ "символы в нижний регистр"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 194,
+ "id": "39d8095e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = data.skills.dropna().str.lower().str.contains('python|питон')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 195,
+ "id": "a30b9f50",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "230 35000\n",
+ "334 20000\n",
+ "2394 35000\n",
+ "8096 15000\n",
+ "9014 25000\n",
+ "9667 90000\n",
+ "20930 30000\n",
+ "22530 50000\n",
+ "28286 23000\n",
+ "30430 23000\n",
+ "Name: salary, dtype: int64"
+ ]
+ },
+ "execution_count": 195,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data[data.skills.notna()][df]['salary']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "454adfab",
+ "metadata": {},
+ "source": [
+ "# 6. Построить перцентили по заработной плате у мужчин и женщин"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f88b7a85",
+ "metadata": {},
+ "source": [
+ "Для вычисления перцентилей получим одномерный массив распределения от 0,1 до 1. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 196,
+ "id": "b180df5b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])"
+ ]
+ },
+ "execution_count": 196,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "percent_linspace = np.linspace(.1, 1, 10)\n",
+ "percent_linspace"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 197,
+ "id": "e510cefd",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " salary \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0.1 \n",
+ " 15000.0 \n",
+ " \n",
+ " \n",
+ " 0.2 \n",
+ " 20000.0 \n",
+ " \n",
+ " \n",
+ " 0.3 \n",
+ " 25000.0 \n",
+ " \n",
+ " \n",
+ " 0.4 \n",
+ " 30000.0 \n",
+ " \n",
+ " \n",
+ " 0.5 \n",
+ " 30000.0 \n",
+ " \n",
+ " \n",
+ " 0.6 \n",
+ " 35000.0 \n",
+ " \n",
+ " \n",
+ " 0.7 \n",
+ " 40000.0 \n",
+ " \n",
+ " \n",
+ " 0.8 \n",
+ " 50000.0 \n",
+ " \n",
+ " \n",
+ " 0.9 \n",
+ " 60000.0 \n",
+ " \n",
+ " \n",
+ " 1.0 \n",
+ " 1000000.0 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10, 5)) \n",
+ "plt.title('Заработная плата мужчин в перцентилях') \n",
+ "plt.xlabel('Перцентили') \n",
+ "plt.ylabel('Зарплата мужчин') \n",
+ "plt.grid() \n",
+ "\n",
+ "plt.plot(male_salary) \n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 199,
+ "id": "7511ffff",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " salary \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0.1 \n",
+ " 15000.0 \n",
+ " \n",
+ " \n",
+ " 0.2 \n",
+ " 18000.0 \n",
+ " \n",
+ " \n",
+ " 0.3 \n",
+ " 20000.0 \n",
+ " \n",
+ " \n",
+ " 0.4 \n",
+ " 22000.0 \n",
+ " \n",
+ " \n",
+ " 0.5 \n",
+ " 25000.0 \n",
+ " \n",
+ " \n",
+ " 0.6 \n",
+ " 30000.0 \n",
+ " \n",
+ " \n",
+ " 0.7 \n",
+ " 30000.0 \n",
+ " \n",
+ " \n",
+ " 0.8 \n",
+ " 35000.0 \n",
+ " \n",
+ " \n",
+ " 0.9 \n",
+ " 47000.0 \n",
+ " \n",
+ " \n",
+ " 1.0 \n",
+ " 900000.0 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10, 5)) \n",
+ "plt.title('Заработная плата женщин в перцентилях') \n",
+ "plt.xlabel('Перцентили') \n",
+ "plt.ylabel('Зарплата женщин') \n",
+ "plt.grid() \n",
+ "\n",
+ "plt.plot(female_salary) \n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "01aef10d",
+ "metadata": {},
+ "source": [
+ "# 7. Построить графики распределения по заработной плате мужчин и женщин в зависимости от высшего образования"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 201,
+ "id": "5d73aec6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " educationType \n",
+ " salary \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " Высшее \n",
+ " 38536.860696 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " Незаконченное высшее \n",
+ " 36036.267699 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " Среднее \n",
+ " 28511.660356 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " Среднее профессиональное \n",
+ " 29848.650757 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " educationType \n",
+ " salary \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " Высшее \n",
+ " 33826.008009 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " Незаконченное высшее \n",
+ " 29171.382643 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " Среднее \n",
+ " 24958.723963 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " Среднее профессиональное \n",
+ " 25834.460409 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "education = male_salary['educationType'].values\n",
+ "male_salaries = male_salary['salary'].values\n",
+ "female_salaries = female_salary['salary'].values\n",
+ "\n",
+ "index = np.arange(len(education))\n",
+ "\n",
+ "bw = 0.4\n",
+ "plt.bar(index-bw/2, male_salaries, bw, color='blue', label='Средняя зарплата мужчин')\n",
+ "plt.bar(index+bw/2, female_salaries, bw, color='red', label='Средняя зарплата женщин')\n",
+ "plt.xticks(index, education, rotation=90)\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3c8325c2",
+ "metadata": {},
+ "source": [
+ "### Построение графика распределения заработной платы для мужчин\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "db7b7ce2",
+ "metadata": {},
+ "source": [
+ "Для начала отсортируем датафрейм. Оставим строки в которых у мужчин есть высшее образование"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 204,
+ "id": "a3838d05",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " salary \n",
+ " educationType \n",
+ " jobTitle \n",
+ " qualification \n",
+ " gender \n",
+ " dateModify \n",
+ " skills \n",
+ " otherInfo \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 60000 \n",
+ " Высшее \n",
+ " Специалист пресс-службы \n",
+ " Магистр \n",
+ " Мужской \n",
+ " 2021-04-01 \n",
+ " <p>Аналитическое мышление, <span cla... \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 85000 \n",
+ " Высшее \n",
+ " менеджер проектов \n",
+ " NaN \n",
+ " Мужской \n",
+ " 2021-04-01 \n",
+ " NaN \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 10 \n",
+ " 100000 \n",
+ " Высшее \n",
+ " Начальник участка \n",
+ " NaN \n",
+ " Мужской \n",
+ " 2021-04-01 \n",
+ " <p>Коммуникабельность </p> \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 16 \n",
+ " 100000 \n",
+ " Высшее \n",
+ " Технический директор \n",
+ " Экономист \n",
+ " Мужской \n",
+ " 2021-04-01 \n",
+ " NaN \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 28 \n",
+ " 50000 \n",
+ " Высшее \n",
+ " Монтажник электрических подьемников \n",
+ " NaN \n",
+ " Мужской \n",
+ " 2021-04-01 \n",
+ " <p>Знание программного обеспечения от ведущих ... \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 32654 \n",
+ " 50000 \n",
+ " Высшее \n",
+ " экономист 8 разряда в подразделение \"Сектор по... \n",
+ " инженер-экономист \n",
+ " Мужской \n",
+ " 2021-01-26 \n",
+ " <p>инициативность;</p><p>внимательность;</p><p... \n",
+ " <p>Знание-Бухгалтерский учет, управленческий у... \n",
+ " \n",
+ " \n",
+ " 32659 \n",
+ " 45000 \n",
+ " Высшее \n",
+ " NaN \n",
+ " Инженер по организации и управлением на морско... \n",
+ " Мужской \n",
+ " 2020-06-26 \n",
+ " NaN \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 32663 \n",
+ " 40000 \n",
+ " Высшее \n",
+ " Слесарь по ремонту подвижного состава 2-3 разряда \n",
+ " Техник \n",
+ " Мужской \n",
+ " 2021-11-17 \n",
+ " <p>Командообразование, лидерство, опыт работы ... \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 32668 \n",
+ " 40000 \n",
+ " Высшее \n",
+ " заместитель начальника юридического отдела \n",
+ " юрист \n",
+ " Мужской \n",
+ " 2021-03-24 \n",
+ " <p>Высокая работоспособность, нацеленность на ... \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 32681 \n",
+ " 35000 \n",
+ " Высшее \n",
+ " NaN \n",
+ " NaN \n",
+ " Мужской \n",
+ " 2020-04-21 \n",
+ " NaN \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
3095 rows × 8 columns
\n",
+ "
Аналитическое мышление, Коммуникабельность
\n",
+ "16 NaN \n",
+ "28 Знание программного обеспечения от ведущих ... \n",
+ "... ... \n",
+ "32654
инициативность;
внимательность;
Командообразование, лидерство, опыт работы ... \n",
+ "32668 Высокая работоспособность, нацеленность на ... \n",
+ "32681 NaN \n",
+ "\n",
+ " otherInfo \n",
+ "0 NaN \n",
+ "1 NaN \n",
+ "10 NaN \n",
+ "16 NaN \n",
+ "28 NaN \n",
+ "... ... \n",
+ "32654
Знание-Бухгалтерский учет, управленческий у... \n",
+ "32659 NaN \n",
+ "32663 NaN \n",
+ "32668 NaN \n",
+ "32681 NaN \n",
+ "\n",
+ "[3095 rows x 8 columns]"
+ ]
+ },
+ "execution_count": 204,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "male_height = data.query('gender == \"Мужской\" and educationType == \"Высшее\"')\n",
+ "male_height"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ca279cab",
+ "metadata": {},
+ "source": [
+ "### Распределение зарплат для мужчин с высшим образованием\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 205,
+ "id": "032f6af5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10, 5)) \n",
+ "plt.title('Распределение по заработной плате у мужчин с высшим образованием') \n",
+ "plt.xlabel('Зарплата мужчин') \n",
+ "plt.ylabel('Количество мужчин') \n",
+ "plt.grid() \n",
+ "\n",
+ "plt.hist(male_height['salary'], \n",
+ " bins=25) \n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 206,
+ "id": "1e10bc06",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
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+ " \n",
+ " \n",
+ " salary \n",
+ " educationType \n",
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+ " gender \n",
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+ " 2021-05-20 \n",
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+ " Бакалавр \n",
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+ " 2019-05-16 \n",
+ " <p>Многозадачность, Контроль исполнения решени... \n",
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+ " \n",
+ " \n",
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+ " 25000 \n",
+ " Незаконченное высшее \n",
+ " Продавец-кавист/бармен \n",
+ " NaN \n",
+ " Мужской \n",
+ " 2021-10-09 \n",
+ " <p>Стрессоустойчивость, коммуникабельность,&nb... \n",
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+ " \n",
+ " \n",
+ " 32622 \n",
+ " 15000 \n",
+ " Незаконченное высшее \n",
+ " Экспедитор-грузчик \n",
+ " NaN \n",
+ " Мужской \n",
+ " 2020-06-16 \n",
+ " <p>Работа с компьютером</p> \n",
+ " NaN \n",
+ " \n",
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+ " 32645 \n",
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+ " Незаконченное высшее \n",
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+ " NaN \n",
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+ " NaN \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ "
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+ "
348 rows × 8 columns
\n",
+ "
Внимательность, ответственность, обучаемост... NaN \n",
+ "193 NaN NaN \n",
+ "202