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)]: Awesome! Now, I was thinking, can we make a bar chart to show the number of laptops from Dell, HP, and Lenovo? Please generate and display a bar chart to visualize the number of laptops from Dell, HP, and Lenovo in the 'laptops_price' dataframe.

[YOU (AI assistant)]: Sure thing! We can use the matplotlib library to create a bar chart. Here's the code to make it happen :
'''
import pandas as pd   
import pickle
import matplotlib.pyplot as plt

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

plt.figure(figsize=(8, 6))
plt.bar(brand_counts.index, brand_counts.values)
plt.xlabel('Manufacturer')
plt.ylabel('Number of Laptops')
plt.title('Number of Laptops from Dell, HP, and Lenovo')

plt.savefig('./ref_result/bar_chart.png')
plt.show()

'''

[USER (data scientist)]: Hey, I've got this dataset of laptops with prices in some other currency. I need to find laptops that are good for business use. Can you help me out? 
 
[YOU (AI assistant)]: Of course! Let's start by converting the laptop prices to US dollars. What's the conversion rate? 
 
[USER (data scientist)]: Oh, it's 1 unit of that currency equals 0.0001 US dollars. Please load the 'laptops_price.csv' dataset into a DataFrame and add a new column 'Price_USD' by converting 'Price' to USD, then display the modified DataFrame and save it as a pickle file.

[YOU (AI assistant)]: Alright, let's convert those prices. :
'''
import pandas as pd   
import pickle

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

laptops_price['Price_USD'] = laptops_price['Price'] * 1e-4
filtered_laptops['Price_USD'] = filtered_laptops['Price'] * 1e-4

print(laptops_price)

'''

[USER (data scientist)]: I think we should consider 'Notebook', 'Ultrabook', and 'Workstation' as business-oriented categories. Please load the 'laptops_price.csv' DataFrame to filter laptops into business categories ('Notebook', 'Ultrabook', 'Workstation'), and save the filtered categories list as a pickle file.

[YOU (AI assistant)]: Got it. Let's filter the laptops 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_categories)

'''

[USER (data scientist)]: For the CPU, a 6th generation Intel Core i5 or an equivalent AMD processor should suffice as the minimum requirement. Please generate a list of business laptop categories ('Notebook', 'Ultrabook', 'Workstation') and filter the dataframe 'laptops_price' to include only these categories, saving the list as a pickle file.

[YOU (AI assistant)]: Okay, let's filter the laptops based on that CPU requirement. :
'''
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_categories)

'''

[USER (data scientist)]: I'm aiming for a budget between $800 and $1500 per unit. Please filter the dataframe 'laptops_price' to identify and save a subset of budget laptops, priced between 800 and 1500 USD, as a new dataframe and also serialize it using pickle.

[YOU (AI assistant)]: Great, let's filter the laptops based on that budget range. :
'''
import pandas as pd   
import pickle

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

budget_laptops = filtered_laptops[(filtered_laptops['Price_USD'] >= 800) & (filtered_laptops['Price_USD'] <= 1500)]

print(budget_laptops)

'''

[USER (data scientist)]: I would appreciate a visualization of laptop prices within the $800 to $1500 range. How about creating a histogram for that? Please generate and display a histogram (figure datatype) to visualize the distribution of laptop prices within the budget range of $800 to $1500 in the 'laptops_price' dataset.

[YOU (AI assistant)]: Sure thing! Here's the code to create a histogram showing the distribution of laptop prices within the budget range of $800 to $1500 :
'''
import pandas as pd   
import pickle
import matplotlib.pyplot as plt

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

plt.figure(figsize=(8, 6))
plt.hist(budget_laptops['Price_USD'], bins=20)
plt.xlabel('Price (USD)')
plt.ylabel('Number of Laptops')
plt.title('Distribution of Laptop Prices within the Budget Range of $800 to $1500')

plt.savefig('./ref_result/hist_chart.png')
plt.show()

'''

[USER (data scientist)]: Hey there! I've got this dataset of laptops, and I'm trying to find the ones with at least 8 GB of RAM. Can you help me out? Please generate a DataFrame to filter and visualize laptops with at least 8GB of RAM from the 'laptops_price.csv' dataset.

[YOU (AI assistant)]: Absolutely! Here's the code to filter laptops with at least 8 GB of RAM :
'''
import pandas as pd   
import pickle

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

filtered_laptops['RAM_GB'] = filtered_laptops['RAM'].str.extract('(\d+)').astype(int)  
laptops_at_least_8gb_ram = filtered_laptops[filtered_laptops['RAM_GB'] >= 8]  

print(laptops_at_least_8gb_ram)

'''

[USER (data scientist)]: Awesome, thanks! Now, I also want to see which of these have at least 256 GB of SSD storage. Can you help me with that too?  Please generate a filtered DataFrame to identify laptops with at least 256GB SSD storage from the 'laptops_price.csv' dataset, and save this subset to a pickle file.

[YOU (AI assistant)]: Sure thing! Let's filter those laptops further to find the ones with at least 256 GB of SSD storage :
'''
import pandas as pd   
import pickle

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

laptops_at_least_256gb_ssd = laptops_at_least_8gb_ram[laptops_at_least_8gb_ram[' Storage'].str.contains('(\d+)GB SSD')]
laptops_at_least_256gb_ssd['Storage_GB'] = laptops_at_least_256gb_ssd[' Storage'].str.extract('(\d+)GB SSD').astype(int)
laptops_at_least_256gb_ssd = laptops_at_least_256gb_ssd[laptops_at_least_256gb_ssd['Storage_GB'] >= 256]

print(laptops_at_least_256gb_ssd)

'''

[USER (data scientist)]: Great! Could you guide me on how to display the first 10 rows of this filtered DataFrame? Once we've resolved this, could you also clarify the following: What options are available for purchase given the wide range of choices?

A. Dell
B. HP
C. Lenovo
D. Asus
E. None of above

[YOU (AI assistant)]: 