Why text data requires special handling in Data Analysis Python - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert a text column to lowercase.

Data Analysis Python

df['text'] = df['text'].[1]()

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

Alower

Bupper

Cstrip

Dsplit

Attempts:

3 left

💡 Hint

Common Mistakes

Using upper() changes text to uppercase, which is not standard for normalization.

Using strip() removes spaces but does not change case.

Using split() breaks text into words, not case conversion.

Using lower() converts all text to lowercase, which helps standardize text data for analysis.

2fill in blank

medium

Complete the code to remove punctuation from a text string using Python's string module.

Data Analysis Python

import string
text = 'Hello, world!'
clean_text = ''.join(char for char in text if char not in [1])

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

Astring.ascii_letters

Bstring.digits

Cstring.punctuation

Dstring.whitespace

Attempts:

3 left

💡 Hint

Common Mistakes

Using string.whitespace removes spaces, which are needed to separate words.

Using string.ascii_letters removes letters, which we want to keep.

Using string.digits removes numbers, which may or may not be desired.

string.punctuation contains all punctuation characters. Filtering them out cleans the text.

3fill in blank

hard

Fix the error in the code to split text into words correctly.

Data Analysis Python

text = 'Data science is fun'
words = text.[1](' ')

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

Asplit

Bjoin

Creplace

Dstrip

Attempts:

3 left

💡 Hint

Common Mistakes

Using join() combines a list into a string, not splitting.

Using replace() changes characters but does not split.

Using strip() removes spaces at ends but does not split.

The split(' ') method breaks the string into a list of words separated by spaces.

4fill in blank

hard

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

Data Analysis Python

words = ['data', 'is', 'fun', 'science']
lengths = {word: [1] for word in words if len(word) [2] 3}

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

Alen(word)

Bword

C>

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using word instead of len(word) gives the word itself, not its length.

Using '<=' includes short words, not filtering correctly.

We use len(word) to get word length and filter words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase words with length greater than 2.

Data Analysis Python

words = ['cat', 'is', 'big', 'dog']
result = [1] for word in words if len(word) [2] 2]
result = {w.[3](): w for w in result}

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

A[word.upper()

B>

Cupper

Dword.upper()

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word.upper()' in the first blank is incorrect syntax for list comprehension start.

Using '<' instead of '>' filters wrong words.

Using 'upper()' with parentheses in dictionary key causes error; method name only is needed.

First, we create a list of words with length greater than 2 using a list comprehension starting with [word.upper(). Then we filter with >. Finally, we create a dictionary with keys as uppercase words using upper method.

Practice

1. Why does text data need special handling in data analysis?

easy

A. Because text is unstructured and must be cleaned and converted to numbers

B. Because text data is always numeric and needs no changes

C. Because text data is smaller than numeric data

D. Because text data cannot be stored in computers

Solution

1. Step 1: Understand text data nature

   Text data is unstructured, meaning it is not in a simple numeric form that computers can analyze directly.

2. Step 2: Recognize need for cleaning and conversion

   To analyze text, we must clean it (remove noise) and convert it into numbers using techniques like vectorization.

3. Final Answer:

   Because text is unstructured and must be cleaned and converted to numbers -> Option A

4. Quick Check:

   Text needs cleaning and conversion = C [OK]

Hint: Text is not numbers; convert it before analysis [OK]

Common Mistakes:

• Thinking text is already numeric
• Ignoring cleaning steps
• Assuming text is smaller data

2. Which Python tool is commonly used to convert text data into numbers for analysis?

easy

A. Matplotlib

B. Pandas DataFrame

C. CountVectorizer

D. Seaborn

Solution

1. Step 1: Identify text vectorization tools

   CountVectorizer is a tool from scikit-learn that converts text into numeric vectors by counting word occurrences.

2. Step 2: Differentiate from visualization and data structures

   Matplotlib and Seaborn are for plotting, Pandas DataFrame is for data storage, not text conversion.

3. Final Answer:

   CountVectorizer -> Option C

4. Quick Check:

   Text to numbers tool = CountVectorizer [OK]

Hint: CountVectorizer counts words to make numbers [OK]

Common Mistakes:

• Confusing visualization tools with text conversion
• Using DataFrame as conversion tool
• Choosing unrelated libraries

3. What will be the output of this code snippet?

from sklearn.feature_extraction.text import CountVectorizer
texts = ['apple apple orange', 'banana apple']
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(texts)
print(X.toarray())
print(vectorizer.get_feature_names_out())

medium

A. [[2 0 1]
[1 1 0]] and ['apple' 'banana' 'orange']

B. [[1 1 1]
[1 1 1]] and ['apple' 'banana' 'orange']

C. [[2 1 0]
[0 1 1]] and ['apple' 'banana' 'orange']

D. Error because CountVectorizer needs numeric input

Solution

1. Step 1: Understand CountVectorizer output

   CountVectorizer counts word occurrences per document. 'apple' appears twice in first text, 'orange' once, 'banana' only in second text.

2. Step 2: Match counts and feature names

   Output array rows correspond to texts, columns to words in alphabetical order: 'apple', 'banana', 'orange'. So first row: 2 apples, 0 bananas, 1 orange; second row: 1 apple, 1 banana, 0 orange.

3. Final Answer:

   [[2 0 1] [1 1 0]] and ['apple' 'banana' 'orange'] -> Option A

4. Quick Check:

   Word counts match array and features = A [OK]

Hint: CountVectorizer counts words alphabetically [OK]

Common Mistakes:

• Mixing word order in output
• Expecting all ones instead of counts
• Thinking CountVectorizer needs numeric input

4. Identify the error in this code that tries to convert text to numbers:

from sklearn.feature_extraction.text import CountVectorizer
texts = ['cat dog', 'dog cat']
vectorizer = CountVectorizer()
X = vectorizer.transform(texts)
print(X.toarray())

medium

A. texts list should be a string, not list

B. CountVectorizer cannot handle multiple texts

C. toarray() is not a method of X

D. transform() used before fit() causes error

Solution

1. Step 1: Check CountVectorizer usage

   CountVectorizer requires fit() or fit_transform() before transform() to learn vocabulary from texts.

2. Step 2: Identify missing fit() call

   Here, transform() is called directly without fitting, causing an error because vocabulary is unknown.

3. Final Answer:

   transform() used before fit() causes error -> Option D

4. Quick Check:

   Must fit before transform = D [OK]

Hint: Always fit before transform with CountVectorizer [OK]

Common Mistakes:

• Calling transform without fit
• Thinking toarray() is invalid
• Assuming texts must be a single string

5. You have a list of customer reviews with mixed uppercase and lowercase words and some punctuation. Which steps are best to prepare this text data for analysis?

hard

A. Use CountVectorizer directly without changes

B. Convert all text to lowercase, remove punctuation, then use CountVectorizer

C. Remove all spaces and convert text to uppercase

D. Replace all words with numbers manually before analysis

Solution

1. Step 1: Normalize text case and clean punctuation

   Converting text to lowercase and removing punctuation ensures consistent word matching and reduces noise.

2. Step 2: Apply CountVectorizer after cleaning

   CountVectorizer works best on cleaned, normalized text to create accurate numeric features.

3. Final Answer:

   Convert all text to lowercase, remove punctuation, then use CountVectorizer -> Option B

4. Quick Check:

   Clean and normalize text before vectorizing = A [OK]

Hint: Clean text (lowercase, no punctuation) before vectorizing [OK]

Common Mistakes:

• Skipping cleaning steps
• Using raw text directly
• Replacing words manually without tools