You are an AI assistant that aids users in performing data analysis using Python and Pandas to find information. 
 
There is the dataset: you have the data laptops_price contains the following columns: ['Manufacturer', 'Model Name', 'Category', 'Screen Size', 'Screen', 'CPU', 'RAM', 'Storage', 'GPU', 'Operating System', 'Operating System Version', 'Weight', 'Price']. 
--- The description for each column this data is:
Manufacturer: Company that makes the laptop (HP, Asus, Dell ...etc)
Model Name: Specific name or identifier given to a laptop by the manufacturer ("15-bs053od (i7-7500U/6GB/1TB/W10)" from HP, "Rog GL753VE-DS74" from Asus)
Category: Laptop's intended use or design style (Notebook: General-purpose laptops for everyday tasks and moderate usage, Gaming: High-performance laptops for gaming and graphics-intensive applications, 2 in 1 Convertible: Laptops that double as tablets with foldable or detachable screens, Netbook: Small, lightweight, and inexpensive laptops for basic tasks, Ultrabook: Thin, lightweight laptops with powerful performance and long battery life, and Workstation: Laptops designed for professional, heavy-duty computing tasks)
Screen Size: Size of the laptop's display (measured diagonally in inches)
Screen: Details about the screen resolution and technology
CPU: Central Processing Unit
RAM: Random Access Memory
Storage: Size and type of the laptop's internal storage ("1TB HDD": "1TB" is size, and "HDD" is type)
GPU: Graphics Processing Unit
Operating System: Software platform on which the laptop operates (Windows, No OS, Chrome OS, Mac OS, Linux, and Android)
Operating System Version: Specific version of the operating system (Windows 10, Windows 7 ...etc)
Weight: Weight of the laptop (measured in kilograms)
Price: Cost of the laptop
---
Considering contents from the dataset and result types from user, you only need to generate codes and notations. 
Conversation begins: 
[USER (data scientist)]: Tapilot, please filter the dataset to include only laptops that meet the following criteria: 
  
A. Weight less than or equal to 3 pounds 
B. Screen size between 13 and 15 inches 
C. Price between $800 and $1500 
  
Result type: Filtered DataFrame with columns ['Manufacturer', 'Model Name', 'Category', 'Screen Size', 'Screen', 'CPU', 'RAM', 'Storage', 'GPU', 'Operating System', 'Operating System Version', 'Weight', 'Price']. Please clean and filter the dataframe 'laptops_price' to include laptops weighing up to 3 pounds, with screen sizes between 13 and 15 inches, and prices between $800 and $1500, after converting weights to pounds and prices to a new scale using a conversion rate. The resulting dataframe 'filtered_laptops', excluding the 'Storage' column, is saved as a pickle file.

[YOU (AI assistant)]: Sure, I can help you with that. :
'''
import pandas as pd   
import pickle

# Load the dataset
laptops_price = pd.read_csv("laptops_price.csv")

price_conversion_rate = 1e-4
 
# Remove 'kg' from the weight values and convert to float
laptops_price['Weight'] = laptops_price['Weight'].str.replace('kg', '')
laptops_price['Weight'] = pd.to_numeric(laptops_price['Weight'], errors='coerce')
 
# Drop rows with NaN values in the 'Weight' column
laptops_price = laptops_price.dropna(subset=['Weight'])
 
# Convert weight to pounds
laptops_price['Weight_pounds'] = laptops_price['Weight'] * 2.20462
 
# Apply the price conversion rate
laptops_price['Price'] = laptops_price['Price'] * price_conversion_rate
 
# Filter the dataset
filtered_laptops = laptops_price[
    (laptops_price['Weight_pounds'] <= 3) &
    (laptops_price['Screen Size'].str.replace('"', '').astype(float).between(13, 15)) &
    (laptops_price['Price'].between(800, 1500))
]
 
# Select the required columns (without 'Storage')
filtered_laptops = filtered_laptops[['Manufacturer', 'Model Name', 'Category', 'Screen Size', 'Screen', 'CPU', 'RAM', 'GPU', 'Operating System', 'Operating System Version', 'Weight', 'Price']]

print(filtered_laptops)

'''

[USER (data scientist)]:Great, now that we have filtered the dataset, please analyze the filtered dataset to identify laptops with a powerful CPU and at least 8GB of RAM. We also need bar chart showing the distribution of laptops with a powerful CPU and at least 8GB of RAM. Please generate a bar chart and save both the chart and a dataframe as .pkl and .png files, respectively, to visualize the distribution of laptops by manufacturer that have a powerful CPU and at least 8GB of RAM in the 'laptops_price' dataset.

My template of code snippet is:

---BEGIN CODE TEMPLATE--- 

import pandas as pd
import pickle

# Load the dataset   
laptops_price = pd.read_csv("laptops_price.csv")   

# YOUR SOLUTION BEGIN:
<code1>
[GENERATE YOUR CODE]
</code1>
# YOUR SOLUTION END: 

print(laptop_counts)
pickle.dump(laptop_counts,open("./pred_result/laptop_counts.pkl","wb"))

plt.savefig('./pred_result/powerful_laptops_distribution.png')
plt.show()

---END CODE TEMPLATE---

Please note that you have to generate the WHOLE python code instead of code segments based on the code snippet using Pandas library 2.0.3 version and Matplotlib library 3.7.4 version. You must keep all comments in code snippet unchanged.

[YOU (AI assistant)]: Sure, I can help you with that.
