diff --git a/.idea/.gitignore b/.idea/.gitignore
new file mode 100644
index 0000000..26d3352
--- /dev/null
+++ b/.idea/.gitignore
@@ -0,0 +1,3 @@
+# Default ignored files
+/shelf/
+/workspace.xml
diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
new file mode 100644
index 0000000..105ce2d
--- /dev/null
+++ b/.idea/inspectionProfiles/profiles_settings.xml
@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
new file mode 100644
index 0000000..d56657a
--- /dev/null
+++ b/.idea/misc.xml
@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9" project-jdk-type="Python SDK" />
+</project>
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
new file mode 100644
index 0000000..2cdf9da
--- /dev/null
+++ b/.idea/modules.xml
@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/pandas_task.iml" filepath="$PROJECT_DIR$/.idea/pandas_task.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file
diff --git a/.idea/pandas_task.iml b/.idea/pandas_task.iml
new file mode 100644
index 0000000..d0876a7
--- /dev/null
+++ b/.idea/pandas_task.iml
@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 0000000..94a25f7
--- /dev/null
+++ b/.idea/vcs.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>
\ No newline at end of file
diff --git a/ANSWER.md b/ANSWER.md
new file mode 100644
index 0000000..ab14341
--- /dev/null
+++ b/ANSWER.md
@@ -0,0 +1,19 @@
+<h1>Задание №8</h1>
+
+1. Из 1068 людей не совпадают профессия и должность у 793.
+
+2. Топ образований людей, которые работают менеджерами:
+
+бакалавр 11 
+менеджер 10 
+специалист 6 
+экономист 6 
+экономист-менеджер 4
+
+3. Топ должностей людей, которые по диплому являются инженерами:
+
+заместитель директора 3 
+главный инженер 3 
+ведущий инженер-конструктор 2 
+инженер лесопользования 2 
+директор 2
\ No newline at end of file
diff --git a/task_1_2.py b/task_1_2.py
new file mode 100644
index 0000000..be173ac
--- /dev/null
+++ b/task_1_2.py
@@ -0,0 +1,7 @@
+import pandas as pd
+
+data = pd.read_csv('works.csv')
+
+print('Количество всех записей: ', data.shape[0])
+print('Количество мужчин: ', data[data['gender'] == 'Мужской'].shape[0])
+print('Количество женщин: ', (data['gender'] == 'Женский').values.sum())
\ No newline at end of file
diff --git a/task_3_4.py b/task_3_4.py
new file mode 100644
index 0000000..7a1d09b
--- /dev/null
+++ b/task_3_4.py
@@ -0,0 +1,11 @@
+import pandas as pd
+
+data = pd.read_csv('works.csv')
+
+print('Количество skills не NaN ', data.skills.notna().values.sum())
+print(data[data.skills.notna()]['skills'])
+
+salary = 20000
+group = 'Женский'
+
+print(data.query('salary == @salary and gender == @group'))
\ No newline at end of file
diff --git a/task_5.py b/task_5.py
new file mode 100644
index 0000000..42d0406
--- /dev/null
+++ b/task_5.py
@@ -0,0 +1,7 @@
+import pandas as pd
+
+data = pd.read_csv('works.csv')
+df = data.skills.dropna().str.lower().str.contains('python|питон')
+
+print('Зарплата людей, у которых в skills есть Python\n', data[data.skills.notna()][df]['salary'])
+
diff --git a/task_6.ipynb b/task_6.ipynb
new file mode 100644
index 0000000..7201666
--- /dev/null
+++ b/task_6.ipynb
@@ -0,0 +1,102 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(\"./works.csv\")\n",
+    "percentiles = np.linspace(.1, 1, 10)\n",
+    "\n",
+    "salary_men = data.query('gender == \"Мужской\"').quantile(percentiles)\n",
+    "salary_women = data.query('gender == \"Женский\"').quantile(percentiles)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig1, ax = plt.subplots()\n",
+    "ax.plot(percentiles, salary_men)\n",
+    "plt.xlabel(\"Перцентили\")\n",
+    "plt.ylabel(\"Зарплата мужчин\")\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "fig2, ax = plt.subplots()\n",
+    "ax.plot(percentiles, salary_men)\n",
+    "plt.xlabel(\"Перцентили\")\n",
+    "plt.ylabel(\"Зарплата женщин\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/task_7.ipynb b/task_7.ipynb
new file mode 100644
index 0000000..176add6
--- /dev/null
+++ b/task_7.ipynb
@@ -0,0 +1,238 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(\"./works.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000026159E172B0>]],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Распределение по зарплате с высшем образованием\n",
+    "data.query(\"gender == 'Мужской' and educationType == 'Высшее'\").hist(bins=100, alpha=0.5)\n",
+    "data.query(\"gender == 'Женский' and educationType == 'Высшее'\").hist(bins=100, alpha=0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002615A11C130>]],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Распределение по зарплате с незаконченным высшем образованием\n",
+    "data.query(\"gender == 'Мужской' and educationType == 'Незаконченное высшее'\").hist(bins=100, alpha=0.5)\n",
+    "data.query(\"gender == 'Женский' and educationType == 'Незаконченное высшее'\").hist(bins=100, alpha=0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002615A253130>]],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Распределение по зарплате со средним образованием\n",
+    "data.query(\"gender == 'Мужской' and educationType == 'Среднее'\").hist(bins=100, alpha=0.5)\n",
+    "data.query(\"gender == 'Женский' and educationType == 'Среднее'\").hist(bins=100, alpha=0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002615B396AC0>]],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Распределение по зарплате со средним профессиональным образованием\n",
+    "data.query(\"gender == 'Мужской' and educationType == 'Среднее профессиональное'\").hist(bins=100, alpha=0.5)\n",
+    "data.query(\"gender == 'Женский' and educationType == 'Среднее профессиональное'\").hist(bins=100, alpha=0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/task_8.py b/task_8.py
new file mode 100644
index 0000000..e6122c7
--- /dev/null
+++ b/task_8.py
@@ -0,0 +1,36 @@
+import pandas as pd
+
+
+def no_match_counter(firs_param, second_param, elements):
+    count = 0
+
+    for (f1, f2) in zip(elements[firs_param], elements[second_param]):
+        if not (contains(f1, f2) or contains(f2, f1)):
+            count += 1
+
+    return count
+
+
+def contains(first, second):
+
+    for word in first.lower().replace('-', ' ').split():
+        if word in second.lower():
+            return True
+
+    return False
+
+
+def get_job_list(size, df, searched_f, returned_f, search_s):
+    return df[df[searched_f].str.lower().str.contains(search_s[:-2])][returned_f].str.lower().value_counts().head(size)
+
+
+data = pd.read_csv("works.csv").dropna()
+count_not_matches_job = no_match_counter("jobTitle", "qualification", data)
+
+print(f"Из {data.shape[0]} людей не совпадают профессия и должность у {count_not_matches_job}")
+
+print("\nЛюди с таким образованием становятся менеджерами: ")
+print(get_job_list(5, data, "jobTitle", "qualification", "менеджер"))
+
+print("\nКем работают люди имеющие диплом инженера: ")
+print(get_job_list(5, data, "qualification", "jobTitle", "инженер"))
\ No newline at end of file