to_numeric() for safe conversion in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert the 'age' column to numeric values safely.

Pandas

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

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

A"coerce"

B"raise"

C"safe"

D"ignore"

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'ignore' does not convert invalid values.

Using 'raise' causes errors on invalid data.

Using errors='coerce' converts invalid parsing to NaN safely.

2fill in blank

medium

Complete the code to convert a list of strings to numeric, forcing errors to NaN.

Pandas

numbers = pd.to_numeric(['10', '20', 'abc', '30'], errors=[1])

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

A"raise"

B"ignore"

C"coerce"

D"fail"

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'ignore' leaves invalid strings as is.

Using 'raise' throws an error.

errors='coerce' converts non-numeric strings to NaN.

3fill in blank

hard

Fix the error in the code to convert 'score' column to numeric, replacing errors with NaN.

Pandas

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

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

A"coerce"

B"ignore"

C"raise"

D"fail"

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'raise' causes exceptions on invalid data.

Using 'ignore' does not convert invalid values.

To safely convert and replace errors with NaN, use errors='coerce'.

4fill in blank

hard

Fill both blanks to create a numeric conversion that replaces errors with NaN and downcasts to float.

Pandas

df['value'] = pd.to_numeric(df['value'], errors=[1], downcast=[2])

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

A"coerce"

B"ignore"

C"float"

D"integer"

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'ignore' does not convert errors.

Using 'integer' downcast may fail if NaNs exist.

Use errors='coerce' to replace errors with NaN and downcast='float' to reduce memory.

5fill in blank

hard

Fill all three blanks to convert a column safely, downcast to integer, and handle errors by coercion.

Pandas

df['count'] = pd.to_numeric(df['count'], errors=[1], downcast=[2]).fillna([3]).astype(int)

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

A"coerce"

B"ignore"

C"integer"

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Not filling NaNs before converting to int causes errors.

Using 'ignore' leaves invalid values unconverted.

Use errors='coerce' to convert errors to NaN, downcast to integer, fill NaNs with 0, then convert to int type.