From 2b8f8919ed96ffaf0aab96dd909c33a7d5304b6f Mon Sep 17 00:00:00 2001
From: pi4 <mp61034@st.amu.edu.pl>
Date: Mon, 17 Apr 2023 22:46:22 +0200
Subject: [PATCH] 2nd commit

---
 skrypt.py | 69 +++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 69 insertions(+)
 create mode 100644 skrypt.py

diff --git a/skrypt.py b/skrypt.py
new file mode 100644
index 0000000..5bd04e6
--- /dev/null
+++ b/skrypt.py
@@ -0,0 +1,69 @@
+import os
+import zipfile
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+
+
+os.system("kaggle datasets download -d imoore/7k-indian-famous-women")
+with zipfile.ZipFile("7k-indian-famous-women.zip", "r") as zip_ref:
+    zip_ref.extractall("7k-indian-famous-women")
+
+
+csv_file = None
+for root, dirs, files in os.walk("7k-indian-famous-women"):
+    for file in files:
+        if file.endswith(".csv"):
+            csv_file = os.path.join(root, file)
+            break
+if csv_file is None:
+    raise FileNotFoundError("CSV file not found in the extracted dataset")
+
+
+data = pd.read_csv(csv_file)
+
+
+train_data, temp_data = train_test_split(data, test_size=0.4, random_state=42)
+dev_data, test_data = train_test_split(temp_data, test_size=0.5, random_state=42)
+
+
+def print_stats(df, name):
+    print(f"\nStatystyki dla {name}:")
+    print(f"Wielkość zbioru: {len(df)}")
+    for col in df.columns:
+        if df[col].dtype != "object":
+            print(f"\nParametr: {col}")
+            print(f"Minimum: {df[col].min()}")
+            print(f"Maksimum: {df[col].max()}")
+            print(f"Średnia: {df[col].mean()}")
+            print(f"Odchylenie standardowe: {df[col].std()}")
+            print(f"Mediana: {df[col].median()}")
+
+
+print_stats(data, "Cały zbiór")
+print_stats(train_data, "Zbiór treningowy")
+print_stats(dev_data, "Zbiór walidacyjny")
+print_stats(test_data, "Zbiór testowy")
+
+
+def normalize_data(df, columns):
+    scaler = MinMaxScaler()
+    for col in columns:
+        if df[col].dtype != "object":
+            df[col] = scaler.fit_transform(df[[col]])
+
+normalize_data(train_data, train_data.columns)
+normalize_data(dev_data, dev_data.columns)
+normalize_data(test_data, test_data.columns)
+
+
+def clean_data(df):
+    df.dropna(inplace=True)
+    df.drop_duplicates(inplace=True)
+  
+
+clean_data(data)
+clean_data(train_data)
+clean_data(dev_data)
+clean_data(test_data)