normal distrib + min analyze

analyze.py

@@ -0,0 +1,39 @@
+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())

clearing_data.py

@@ -1,14 +1,14 @@
 import pandas as pd
 import ast
 import numpy as np
-import seaborn as sns
+
-import matplotlib.pyplot as plt
 data = pd.read_csv("raw_dataset.csv")
 columns = [
     "experience",
     "employment",
     "salary_min",
     "salary_max",
+    "area"
 ]
 
 columns_for_cor = [
@@ -56,15 +56,5 @@ data["salary_max"] = data["salary"].map(
 data["experience"] = data["experience"].map(lambda experience: schedule[experience])
 data["employment"] = data["employment"].map(lambda employment: employements[employment])
 
-data = data.groupby("area").filter(lambda values: len(values) > 30)
+data[columns].to_csv("cleared.csv")
 
-data = pd.get_dummies(data, columns=["area"])
-
-
-
-
-data_cor = data[columns_for_cor].corr()
-
-sns.heatmap(data_cor)
-plt.show()
-print(data_cor)