import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df = pd.read_csv(r'C:\Users\HP\Desktop\amazon_products.csv')
df

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10 entries, 0 to 9
Data columns (total 4 columns):
 #   Column   Non-Null Count  Dtype  
---  ------   --------------  -----  
 0   Product  10 non-null     object 
 1   Price    10 non-null     int64  
 2   Rating   10 non-null     float64
 3   Reviews  10 non-null     int64  
dtypes: float64(1), int64(2), object(1)
memory usage: 452.0+ bytes

df.describe()

deals = df[df["Price"] <= 1000].sort_values("Price")
deals

top_reviews = df.sort_values("Reviews", ascending=False).head(5)
top_reviews

plt.figure(figsize=(10,6))
sns.scatterplot(data=df, x="Rating", y="Price", size="Reviews", hue="Product", legend=False)
plt.title("Product Price vs Rating")
plt.xlabel("Rating")
plt.ylabel("Price")
plt.grid(True)
plt.show()

sns.scatterplot(data=df, x="Rating", y="Price", size="Reviews", hue="Product", legend=False)
plt.title("Product Price vs Rating")
plt.xlabel("Rating")
plt.ylabel("Price")
plt.grid(True)
plt.show()

plt.figure(figsize=(8,5))
sns.histplot(df["Rating"], bins=5, kde=True, color='skyblue')
plt.title("Distribution of Product Ratings")
plt.xlabel("Rating")
plt.ylabel("Number of Products")
plt.grid(True)
plt.show()

df["Price_per_rating"] = df["Price"] / df["Rating"]
df.sort_values("Price_per_rating")