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)]: Sweet! Can you show me how to display, like, the first 10 rows of this filtered DataFrame? Please generate and display the first 10 rows of the dataframe filtered for laptops with at least 256GB SSD from the 'laptops_price.csv' dataset, and save this dataframe as a pickle file.

[YOU (AI assistant)]: Of course! Here's the code to display the first 10 rows of the filtered DataFrame :
'''
import pandas as pd   
import pickle

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

print(laptops_at_least_256gb_ssd.head(10))

'''

[USER (data scientist)]: Hey, I've got this dataset of laptops, and I need to find the ones that are good for data scientists and people working with graphics. Can you help me filter them? Please generate and display a DataFrame to identify laptops with dedicated GPUs ('Nvidia GeForce' or 'AMD Radeon') from the 'laptops_price.csv' dataset, and store this filtered DataFrame as a pickle file.

[YOU (AI assistant)]: Absolutely! Let's start by filtering the laptops with dedicated GPUs, like Nvidia GeForce and AMD Radeon. Here's the code for that :
'''
import pandas as pd   
import pickle

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

dedicated_gpu_keywords = ['Nvidia GeForce', 'AMD Radeon']
laptops_dedicated_gpu = filtered_laptops[filtered_laptops['GPU'].str.contains('|'.join(dedicated_gpu_keywords))]

print(laptops_dedicated_gpu)

'''

[USER (data scientist)]: Cool, that's a good start! Now, I only want the ones that come with Windows 10 pre-installed. Can you help me with that? Please load the 'laptops_price.csv' DataFrame, filter it to include only laptops with a dedicated GPU and running Windows 10, and then display and save this subset as a DataFrame and a pickle file, respectively.

[YOU (AI assistant)]: Sure thing! Let's filter those laptops to only include the ones with Windows 10 pre-installed :
'''
import pandas as pd   
import pickle

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

laptops_windows_10 = laptops_dedicated_gpu[filtered_laptops['Operating System'] == 'Windows']
laptops_windows_10 = laptops_windows_10[laptops_windows_10['Operating System Version'] == '10']

print(laptops_windows_10)

'''

[USER (data scientist)]: Great, thank you! Could you also assist me in identifying which of these options have a minimum of 256 GB SSD storage? Once we've determined that, could you then tell me who is the top manufacturer?

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

[YOU (AI assistant)]: 