Python：如何按类型分割多行文本并按日期分类结果至数组？

Let's tackle each of your questions step by step, using Python for concrete examples (it's ideal for this kind of string and data structure work):

1. How to split the text by type/date blocks? #

First, let's break down the input string into individual elements, then use date keywords (Monday-Sunday) to split the text into date-specific chunks:

text = '''Monday 1-t type1 2-d type2 Tuesday 1-t type1 2-d type2 3-rd type3 4-th type4 Wednesday 1-t type1 Thursday 1-t type1 2-d type2 3-rd type3 4-th type4 Friday 2-d type2 3-rd type3 4-th type4 Saturday 2-d type2 3-rd type3 Sunday 1-t type1 2-d type2'''

# Split the entire text into a list of individual tokens
tokens = text.split()

# Define date markers to split the text around
date_keywords = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']

# Group tokens into date-based blocks
date_blocks = []
current_block = []
for token in tokens:
    if token in date_keywords and current_block:
        date_blocks.append(current_block)
        current_block = [token]
    else:
        current_block.append(token)
date_blocks.append(current_block)  # Add the final block

# Each block now looks like: ['Monday', '1-t', 'type1', '2-d', 'type2']

If you want to extract just the typeX entries from each block, filter them like this:

for block in date_blocks:
    types = [item for item in block if item.startswith('type')]
    print(f"{block[0]}'s types: {types}")

2. How to categorize the text by date (e.g., monday = [types..])? #

There are a few practical ways to do this, depending on your needs:

Method 1: Use a Dictionary (Recommended) #

A dictionary maps each date directly to its list of types, making it easy to access and maintain:

date_type_map = {}
for block in date_blocks:
    date = block[0]
    types = [item for item in block if item.startswith('type')]
    date_type_map[date] = types

# Access types for any date
print(date_type_map['Monday'])  # Output: ['type1', 'type2']
print(date_type_map['Tuesday']) # Output: ['type1', 'type2', 'type3', 'type4']

Method 2: Create Individual Variables (Less Flexible) #

If you specifically need separate variables for each date, you can dynamically create them (note: this is not ideal for most projects):

for block in date_blocks:
    date_var_name = block[0].lower()  # Convert to lowercase for valid variable names
    types = [item for item in block if item.startswith('type')]
    globals()[date_var_name] = types

# Now you can use variables like monday, tuesday, etc.
print(monday)  # Output: ['type1', 'type2']
print(tuesday) # Output: ['type1', 'type2', 'type3', 'type4']

Method 3: Wrap in a Class (For Structured Projects) #

For larger projects, organizing the data in a class keeps things clean:

class WeeklyTypeTracker:
    def __init__(self, date_type_dict):
        for date, types in date_type_dict.items():
            setattr(self, date.lower(), types)

tracker = WeeklyTypeTracker(date_type_map)
print(tracker.monday)  # Output: ['type1', 'type2']

3. How to store the categorized data in an array? #

If by "array" you mean a Python list (the standard array-like structure), here are a few options:

Option 1: List of Tuples (Date + Types) #

Store each date-type pair as a tuple in a list:

categorized_array = list(date_type_map.items())
print(categorized_array)
# Output: [('Monday', ['type1', 'type2']), ('Tuesday', ['type1', 'type2', 'type3', 'type4']), ...]

Option 2: List of Dictionaries (More Structured) #

Use dictionaries to add clarity to each entry:

categorized_array = [{'date': date, 'types': types} for date, types in date_type_map.items()]
print(categorized_array)
# Output: [{'date': 'Monday', 'types': ['type1', 'type2']}, {'date': 'Tuesday', 'types': ['type1', 'type2', 'type3', 'type4']}, ...]

Option 3: Typed Array (For Strict Data Types) #

If you need a strictly typed array (rare for this use case), use Python's array module:

import array

# Flatten all types into a single typed array
all_types_flat = []
for types in date_type_map.values():
    all_types_flat.extend(types)

typed_array = array.array('u', all_types_flat)  # 'u' for Unicode strings
print(typed_array)
# Output: array('u', 'type1type2type1type2type3type4...')

内容的提问来源于stack exchange，提问作者Noctious