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 datafastfoodcontains the following columns:['restaurant', 'item', 'calories', 'cal_fat', 'total_fat', 'sat_fat', 'trans_fat', 'cholesterol', 'sodium', 'total_carb', 'fiber', 'sugar', 'protein', 'vit_a', 'vit_c', 'calcium', 'salad'].
--- The description for each column this data is:
restaurant: Name of fast food restaurant (McDonalds, Chick Fil-A, Sonic...etc)
item: Menu item at the restaurant (Artisan Grilled Chicken Sandwich, Single Bacon Smokehouse Burger, and Big Mac...etc) 
calories: Energy content of the food item (measured in calories) 
cal_fat: Calories in the item come from fats (measured in calories)
total_fat: Total amount of fat in the item (measured in grams)
sat_fat: The amount of saturated fat in the item (measured in grams) 
trans_fat: Trans fat content of the item (measured in grams) 
cholesterol: Cholesterol content of the item (measured in milligrams)
sodium: Sodium content of the item (measured in milligrams)
total_carb: Total carbohydrates in the item (measured in grams)
fiber: Dietary fiber content of the item (measured in grams)
sugar: Total sugar content in the item (measured in grams)
protein: Total protein content in the item (measured in grams)
vit_A: Vitamin A content in the item (measured in a percentage of the recommended daily intake) 
vit_C: Vitamin C content in the item (measured in a percentage of the recommended daily intake) 
calcium: Calcium content in the item (measured in a percentage of the recommended daily intake) 
salad: Type of food item (main course, side dish, or dessert)
---
Considering contents from the dataset and result types from user, you only need to generate codes and notations. Please note DO NOT CHANGE FILE AND VARIABLE NAMES THAT I HAVE SET!
Conversation begins:
[USER (data scientist)]:Please filter the dataset to include only main course items such as sandwiches, wraps, and salads, and exclude side dishes and desserts. Then, provide the filtered dataset containing only main course items.

[YOU (AI assistant)]:To filter the dataset to include only main course items, I need to know the criteria to identify main course items such as sandwiches, wraps, and salads. Please provide a condition or a list of keywords that can be used to filter the main course items.

[USER (data scientist)]:Use the following keywords to filter the main course items: 'sandwich', 'wrap', 'salad', 'burger', 'burrito', 'taco'. Make sure the filtering is case-insensitive. Please load the 'fastfood.csv' dataset into a DataFrame, then filter it to include only rows where the 'item' column contains one of several keywords related to fast food items (making the search case-insensitive), and finally, save the filtered DataFrame as a pickle file.

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

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

# Assuming the dataset is loaded as a DataFrame named 'fastfood'   
keywords = ['sandwich', 'wrap', 'salad', 'burger', 'burrito', 'taco']   
   
# Make the filtering case-insensitive   
fastfood['item_lower'] = fastfood['item'].str.lower()   
   
# Filter the dataset using the keywords   
filtered_fastfood = fastfood[fastfood['item_lower'].str.contains('|'.join(keywords))]   
   
# Drop the 'item_lower' column as it's not needed anymore   
filtered_fastfood = filtered_fastfood.drop(columns=['item_lower'])   

print(filtered_fastfood)

'''

[USER (data scientist)]: Which statement is incorrect regarding the distinctions between min-max normalization and standardization?

A. Min-max normalization scales data to a fixed range, typically 0 to 1, while standardization scales data based on its mean and standard deviation.
B. Min-max normalization is particularly sensitive to outliers, as extreme values can significantly affect the scaling of other data points.
C. Standardization transforms data in a way that the resulting distribution can have negative values and does not bind data to a specific range.
D. Standardization is less sensitive to outliers and is often used when the data follows or is assumed to follow a Gaussian distribution.
E. None of the above

[YOU (AI assistant)]: 