Common dtype errors and fixes in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert the 'age' column to integer type.

Pandas

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

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

Aconvert

Bto_numeric

Cparse

Dastype

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'to_numeric' without parentheses

Trying to use 'convert' which is not a pandas method

Using 'parse' which is unrelated

The astype() method converts the column to the specified dtype, here integer.

2fill in blank

medium

Complete the code to safely convert the 'price' column to numeric, forcing errors to NaN.

Pandas

df['price'] = pd.to_numeric(df['price'], errors=[1])

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

A'raise'

B'coerce'

C'ignore'

D'convert'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'raise' which throws an error on invalid data

Using 'ignore' which leaves invalid data unchanged

Using 'convert' which is not a valid option

The errors='coerce' argument converts invalid parsing to NaN instead of raising an error.

3fill in blank

hard

Fix the error in this code that tries to convert a column to datetime.

Pandas

df['date'] = pd.to_datetime(df['date'], format=[1])

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

A'%m-%d-%Y'

B'%d-%m-%Y'

C'%Y-%m-%d'

D'%Y/%m/%d'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a format string that does not match the data

Forgetting to include separators in the format string

Using slashes instead of dashes when data uses dashes

The format string must match the date format in the data. '%Y-%m-%d' is the standard ISO format.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

Pandas

{word: [1] for word in words if [2]

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

Alen(word)

Blen(word) > 3

Cword.startswith('a')

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of length

Using a wrong condition like startswith

Not filtering words by length

The dictionary maps each word to its length using len(word). The condition filters words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their lengths only if length is greater than 4.

Pandas

{ [1]: [2] for word in words if [3] }

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

Aword.upper()

Blen(word)

Clen(word) > 4

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase instead of uppercase for keys

Not filtering by length correctly

Using word itself as value instead of length

The key is the uppercase word using word.upper(). The value is the length len(word). The condition filters words longer than 4 characters.