try: response = requests.get(url) words = response.text.split('\n') words = [w.strip() for w in words if w.strip()] return words except: # Fallback sample data (partial) return [ "the", "be", "to", "of", "and", "a", "in", "that", "have", "I", "it", "for", "not", "on", "with", "he", "as", "you", "do", "at", # ... full list would be 3000 words ]
: Instead of learning rare words, you focus on the vocabulary that appears in nearly every conversation, newspaper, and workplace.
: Indicates the word is among the top 1,000, 2,000, or 3,000 most frequent words in Spoken English. longman 3000 words excel
Here is a sample of what the Longman 3000 list might look like in Excel:
to manage this list allows you to track progress, filter by frequency (e.g., S1-S3 for spoken, W1-W3 for written), and create custom study schedules. try: response = requests
Using the sorting features in , Leo organized the words. The list had a special column: S for Spoken and W for Written.
| Pitfall | Excel Solution | | :--- | :--- | | | Keep columns to fewer than 8. Don't add phonetic symbols until Band 3. | | No context | Force yourself to fill the "Example Sentence" column before marking "Learned." | | Losing motivation | Use the SPARKLINE function to create a mini graph of weekly new words learned. | | Forgetting older words | Set up a recurring filter: "Review Date is less than today" and "Status is Mastered" | Here is a sample of what the Longman
Excel provides a range of tools and functions that can be used to analyze and manipulate the Longman 3000 list. Here are a few examples: