import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import shapiro
import pandas as pd

data = pd.read_csv("cleared.csv")

columns_for_cor = [
    "experience",
    "employment",
    "salary_min",
    "salary_max",
    "area_краснодар",
    "area_москва",
    "area_санкт-петербург"
]

data["area"] = data["area"].map(lambda area: str(area).lower())

data["area"] = data["area"].astype("string")

_, p_min = shapiro(data["salary_min"])
_, p_max = shapiro(data["salary_max"])

data.info()

sns.kdeplot(data[["salary_max","salary_min"]])
sns.boxplot(data[["salary_max","salary_min"]])

data = data.groupby("area").filter(lambda count: len(count) > 30)

data_dum = pd.get_dummies(data, columns=["area"])
print(data_dum.columns)
data_dum.info()
sns.heatmap(data_dum[columns_for_cor].corr())
plt.show()
print(data[["salary_min", "salary_max", "area"]].groupby("area").mean())

print(data.info())