Unicode handling in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read a Unicode text file correctly.

NLP

with open('text.txt', encoding=[1]) as f:
    content = f.read()

Drag options to blanks, or click blank then click option'

Aascii

Butf-8

Clatin-1

Dutf-16

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'ascii' which cannot handle many Unicode characters.

UTF-8 is the most common encoding for Unicode text files and supports all Unicode characters.

2fill in blank

medium

Complete the code to normalize Unicode text to NFC form.

NLP

import unicodedata
normalized_text = unicodedata.normalize([1], text)

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A'NFD'

B'NFKC'

C'NFC'

D'NFKD'

Attempts:

3 left

💡 Hint

Common Mistakes

Using NFD which decomposes characters instead of composing them.

NFC (Normalization Form C) composes characters to their composed form, which is standard for text processing.

3fill in blank

hard

Fix the error in decoding bytes to a Unicode string.

NLP

byte_data = b'caf\xc3\xa9'
text = byte_data.[1]('utf-8')

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Adecode

Btransform

Cencode

Dconvert

Attempts:

3 left

💡 Hint

Common Mistakes

Using encode on bytes which causes an error.

Bytes must be decoded to get a Unicode string. The decode method converts bytes to str.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

NLP

word_lengths = {word: [1] for word in words if len(word) [2] 3}

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Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using word instead of len(word) for the dictionary value.

Using '<' instead of '>' in the condition.

We want the length of each word (len(word)) and only words longer than 3 characters (len(word) > 3).

5fill in blank

hard

Fill all three blanks to filter and transform a dictionary with Unicode keys and values.

NLP

filtered = {{ [1]: [2] for k, v in data.items() if v [3] 0 }}

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Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using k.lower() instead of k.upper().

Using '<' instead of '>' in the condition.

We transform keys to uppercase (k.upper()), keep values as is (v), and filter values greater than zero (v > 0).