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)]: Hey, I've got this dataset of laptops, and I need to find the ones that are good for business. Can you help me out?

[YOU (AI assistant)]: Of course! Let's start by identifying the categories that you consider as business-oriented. What are they? 
 
[USER (data scientist)]: Oh, right. I'm looking for 'Notebook', 'Ultrabook', and 'Workstation' categories. Please load the 'laptops_price.csv' dataset as a dataframe, filter for laptops in the 'Notebook', 'Ultrabook', and 'Workstation' categories to create a new dataframe, and save this filtered dataframe using pickle.

My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd
import pickle

laptops_price = pd.read_csv("laptops_price.csv")

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

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

---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)]: Got it! Let's filter the dataset based on those categories: 
