71 lines
1.8 KiB
Python
71 lines
1.8 KiB
Python
import pandas as pd
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import ast
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import numpy as np
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import seaborn as sns
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import matplotlib.pyplot as plt
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data = pd.read_csv("raw_dataset.csv")
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columns = [
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"experience",
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"employment",
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"salary_min",
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"salary_max",
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]
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columns_for_cor = [
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"experience",
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"employment",
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"salary_min",
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"salary_max",
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"area_Краснодар",
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"area_Москва",
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"area_Санкт-Петербург"
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]
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schedule = {
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"Нет опыта": 0,
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"От 1 года до 3 лет": 1,
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"От 3 до 6 лет": 2,
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"Более 6 лет": 3
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}
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employements = {
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"Полная занятость": 0,
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"Частичная занятость": 1,
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"Проектная работа": 2,
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"Волонтерство": 3,
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"Стажировка": 4
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}
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data["area"] = data["area"].map(lambda city: ast.literal_eval(city)["name"])
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data["experience"] = data["experience"].map(lambda experience: ast.literal_eval(experience)["name"])
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data["employment"] = data["employment"].map(lambda employment: ast.literal_eval(employment)["name"])
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data["salary_min"] = data["salary"].map(
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lambda salary_min: ast.literal_eval(salary_min)["from"]
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if ast.literal_eval(salary_min)["from"] is not None
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else ast.literal_eval(salary_min)["to"]
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)
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data["salary_max"] = data["salary"].map(
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lambda salary_max: ast.literal_eval(salary_max)["to"]
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if ast.literal_eval(salary_max)["to"] is not None
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else ast.literal_eval(salary_max)["from"]
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)
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data["experience"] = data["experience"].map(lambda experience: schedule[experience])
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data["employment"] = data["employment"].map(lambda employment: employements[employment])
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data = data.groupby("area").filter(lambda values: len(values) > 30)
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data = pd.get_dummies(data, columns=["area"])
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data_cor = data[columns_for_cor].corr()
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sns.heatmap(data_cor)
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plt.show()
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print(data_cor)
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