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.

[YOU (AI assistant)]: Got it! Let's filter the dataset based on those categories :
'''
import pandas as pd   
import pickle
import matplotlib.pyplot as plt

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

business_categories = ['Notebook', 'Ultrabook', 'Workstation']
business_laptops = laptops_price[laptops_price['Category'].isin(business_categories)]

print(business_laptops)

'''

[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 filter the DataFrame 'laptops_price' to include only laptops belonging to the business categories ('Notebook', 'Ultrabook', 'Workstation'), and save the resulting DataFrame as a pickle file.

[YOU (AI assistant)]: Got it! Let's filter the dataset based on those categories :
'''
import pandas as pd   
import pickle

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

business_categories = ['Notebook', 'Ultrabook', 'Workstation']
business_laptops = laptops_price[laptops_price['Category'].isin(business_categories)]

print(business_laptops)

'''

[USER (data scientist)]: Thanks! Now I wanna make sure they have at least a 6th gen Intel Core i5 or an equivalent AMD processor. Any ideas? Please generate a DataFrame and a pickle file to store a subset of 'laptops_price' dataset, specifically filtering for business laptops with certain CPU models.

[YOU (AI assistant)]: Sure thing! For AMD, I believe the A10-Series and A12-Series processors are pretty close to Intel's i5 6th gen and above. Let's filter the laptops based on these processor requirements :
'''
import pandas as pd   
import pickle

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

filtered_laptops = business_laptops[business_laptops['CPU'].str.contains('Intel Core i5 6|Intel Core i5 7|Intel Core i5 8|Intel Core i5 9|Intel Core i5 10|Intel Core i5 11|Intel Core i7 6|Intel Core i7 7|Intel Core i7 8|Intel Core i7 9|Intel Core i7 10|Intel Core i7 11|Intel Core i9 6|Intel Core i9 7|Intel Core i9 8|Intel Core i9 9|Intel Core i9 10|Intel Core i9 11|AMD A10|AMD A12')]

print(filtered_laptops)

'''

[USER (data scientist)]: Hey, I've got this dataset of laptops, and I only want to look at the ones made by the big brands like Dell, HP, and Lenovo. Can you help me filter it?  Please generate and filter a DataFrame to only include laptops from reputable brands ('Dell', 'HP', 'Lenovo') in the 'laptops_price' dataset, and save the filtered DataFrame as a pickle file.

[YOU (AI assistant)]: Absolutely! Let's first filter the dataset to include only laptops manufactured by Dell, HP, and Lenovo. Here's how you can do it :
'''
import pandas as pd   
import pickle

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

reputable_brands = ['Dell', 'HP', 'Lenovo']
filtered_laptops_reputable = filtered_laptops[filtered_laptops['Manufacturer'].isin(reputable_brands)]

print(filtered_laptops_reputable)

'''

[USER (data scientist)]: Cool, now how about counting how many laptops we've got from each of these brands? Please generate a series (dataframe column) to visualize the distribution of laptop brands in the 'laptops_price' dataset, and store this series in a pickle file for future reference.

[YOU (AI assistant)]: No problem! You can use the value_counts() function to count the number of laptops from each brand. Check this out :
'''
import pandas as pd   
import pickle

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

brand_counts = filtered_laptops_reputable['Manufacturer'].value_counts()

print(brand_counts)

'''

[USER (data scientist)]: Great! Could we create a bar chart to display the quantity of laptops produced by Dell, HP, and Lenovo? Once we have that information, could you then determine which two manufacturers have a similar market share percentage (less than 1% difference) for laptops equipped with TPM, hardware-based encryption, and biometric authentication capabilities?

A. Dell & Lenovo
B. HP & Lenovo
C. Apple & Dell
D. Microsoft & HP
E. None of above

[YOU (AI assistant)]: 