Python读取DNA序列文件遇空格映射失败，如何解决？

Got it, let's sort out this issue you're facing! The problem is exactly what you suspected—those spaces (and possibly newlines or other whitespace characters) in your mytype.txt file aren't in your mappings dictionary, so when your loop hits them, it throws a KeyError and crashes.

Here's a revised version of your function that handles whitespace (and any other non-DNA characters) properly, plus some best practices for file handling:

def DNA_no(): 
    mappings = {"A":"1","T":"2","C":"3","G":"4"} 
    result = [] 
    # Use a `with` statement to auto-close the file (safer than manual open/close)
    with open("mytype.txt","r") as DNAseq:
        DNA_seq = DNAseq.read()
        # Filter out any characters that aren't A/T/C/G (including spaces, newlines, etc.)
        cleaned_dna = ''.join([char for char in DNA_seq if char in mappings])
        
        print("Original sequence (raw):", DNA_seq)
        print("Cleaned DNA sequence (no whitespace):", cleaned_dna)
        print("Length of cleaned sequence:", len(cleaned_dna))
        
        for base in cleaned_dna: 
            result.append(mappings[base])  # Append is more efficient than += for lists
    
    # Return a single string of numbers instead of a list (adjust if you need the list)
    return ''.join(result)

Key improvements explained: #

• Automatic file handling: The with statement ensures the file is closed immediately after reading, even if an error occurs—no more forgetting to call DNAseq.close()!
• Robust sequence cleaning: The list comprehension filters out any character that isn't in your mappings dict. This handles not just spaces, but also newlines, tabs, or accidental typos in the file.
• Efficient list building: Using append() instead of result += mappings[current] is faster, especially for longer DNA sequences.
• Clearer output: Added a print for the cleaned sequence so you can verify what's being processed.

If you specifically want to only remove spaces (and keep other potential characters, though that's not typical for DNA data), you could replace the cleaning line with:

cleaned_dna = DNA_seq.replace(" ", "").replace("\n", "").replace("\t", "")

But filtering for valid DNA bases is the more reliable approach.

内容的提问来源于stack exchange，提问作者Bulbul Chuck Brahma