diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..723ef36
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+.idea
\ No newline at end of file
diff --git a/.idea/.gitignore b/.idea/.gitignore
new file mode 100644
index 0000000..13566b8
--- /dev/null
+++ b/.idea/.gitignore
@@ -0,0 +1,8 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml
diff --git a/homework.py b/homework.py
new file mode 100644
index 0000000..3480ec4
--- /dev/null
+++ b/homework.py
@@ -0,0 +1,38 @@
+import pandas as pd
+
+
+def non_matches(firs_param, second_param, data):
+    count = 0
+    for (f1, f2) in zip(data[firs_param], data[second_param]):
+        if not is_contains(f1, f2) and not is_contains(f2, f1):
+            count += 1
+    return count
+
+
+def is_contains(first_field, second_field):
+    for word in first_field.lower().replace('-', ' ').split():
+        if word in second_field.lower():
+            return True
+    return False
+
+
+def get_top(size, data, search_field, return_field, word_to_search):
+    return data[data[search_field].str.lower().str.contains(word_to_search[:-2])][return_field]\
+        .str\
+        .lower()\
+        .value_counts()\
+        .head(size)
+
+
+works = pd.read_csv("works.csv").dropna()
+not_matches_count = non_matches("jobTitle", "qualification", works)
+managers = get_top(5, works, "jobTitle", "qualification", "менеджер")
+engineers = get_top(5, works, "qualification", "jobTitle", "инженер")
+output_string = f"Всего записей {works.shape[0]} из них не совпадают {not_matches_count}\n\n" \
+                f"Топ - 5 образовний менеджеров\n" \
+                f"{managers}\n\n" \
+                f"Топ - 5 должностей инженеров\n" \
+                f"{engineers}"
+
+with open('homework_output.txt', 'w', encoding='utf-8') as file:
+    file.write(output_string)
diff --git a/homework_output.txt b/homework_output.txt
new file mode 100644
index 0000000..8afc8ea
--- /dev/null
+++ b/homework_output.txt
@@ -0,0 +1,17 @@
+Всего записей 1068 из них не совпадают 793
+
+Топ - 5 образовний менеджеров
+бакалавр              11
+менеджер              10
+специалист             6
+экономист              6
+экономист-менеджер     4
+Name: qualification, dtype: int64
+
+Топ - 5 должностей инженеров
+заместитель директора          3
+главный инженер                3
+ведущий инженер-конструктор    2
+инженер лесопользования        2
+директор                       2
+Name: jobTitle, dtype: int64
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diff --git a/task.py b/task.py
new file mode 100644
index 0000000..5998b3f
--- /dev/null
+++ b/task.py
@@ -0,0 +1,50 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as mp
+
+works = pd.read_csv("works.csv")
+
+# 1
+print("Общее количество записей:", works.shape[0])
+
+# 2
+print("Количество мужчин:", works[works["gender"] == "Мужской"].shape[0])
+print("Количество женщин:", (works["gender"] == "Женский").sum())
+
+# 3
+print("Количество не NaN значений", works["skills"].count())
+
+# 4
+print("Все заполненные скиллы\n", works['skills'].dropna())
+
+# 5
+skills_bool = works['skills'].str.lower().str.contains('python | питон') & works['skills'].notnull()
+print("Зарплата тех, у кого в скиллах есть Python (Питон)\n", works[skills_bool]['salary'])
+
+# 6
+salary_p = np.linspace(0.1, 1, 10)
+w = works[works.gender == "Женский"]['salary'].quantile(salary_p)
+m = works[works.gender == "Мужской"]['salary'].quantile(salary_p)
+
+mp.plot(m, salary_p, color='blue')
+mp.plot(w, salary_p, color='r')
+mp.xlabel('salary')
+mp.ylabel('quantile')
+mp.show()
+
+# 7
+men_salary = works.query("gender == 'Мужской'").groupby("educationType").agg("mean").reset_index()
+women_salary = works.query("gender == 'Женский'").groupby("educationType").agg("mean").reset_index()
+
+educationTypes = men_salary["educationType"].values
+men_salaries = men_salary["salary"].values
+women_salary = women_salary["salary"].values
+
+index = np.arange(len(educationTypes))
+
+bw = 0.4
+mp.bar(index-bw/2, men_salaries, bw, color="b", label="Средняя зарплата мужчин")
+mp.bar(index+bw/2, women_salary, bw, color="r", label="Средняя зарплата женщин")
+mp.xticks(index, educationTypes, rotation=45)
+mp.legend()
+mp.show()
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