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.

Practice

1.

What does the pandas.to_numeric() function do?

easy

A. It converts values to numeric types safely, handling errors.

B. It sorts numeric columns in a DataFrame.

C. It creates a new DataFrame from numeric data.

D. It converts numeric data to strings.

Solution

1. Step 1: Understand the purpose of to_numeric()

   This function is designed to convert data to numeric types like int or float.

2. Step 2: Recognize safe conversion feature

   It can handle errors safely by converting invalid parsing to NaN if specified.

3. Final Answer:

   It converts values to numeric types safely, handling errors. -> Option A

4. Quick Check:

   to_numeric() = safe numeric conversion [OK]

Hint: to_numeric() changes data to numbers safely [OK]

Common Mistakes:

• Thinking it sorts data
• Confusing it with DataFrame creation
• Assuming it converts numbers to strings

2.

Which of the following is the correct syntax to convert a pandas Series s to numeric, turning errors into NaN?

?

easy

A. pd.to_numeric(s, errors='raise')

B. pd.to_numeric(s, errors='ignore')

C. pd.to_numeric(s, errors='coerce')

D. pd.to_numeric(s, errors='drop')

Solution

1. Step 1: Recall error handling options in to_numeric()

   The parameter errors can be 'raise', 'ignore', or 'coerce'.

2. Step 2: Identify which option converts bad data to NaN

   'coerce' converts invalid parsing to NaN safely.

3. Final Answer:

   pd.to_numeric(s, errors='coerce') -> Option C

4. Quick Check:

   errors='coerce' = convert errors to NaN [OK]

Hint: Use errors='coerce' to get NaN for bad data [OK]

Common Mistakes:

• Using errors='drop' which is invalid
• Confusing errors='ignore' with coercion
• Forgetting to specify errors parameter

3.

What will be the output of this code?

import pandas as pd
s = pd.Series(['10', '20', 'abc', '30'])
s_num = pd.to_numeric(s, errors='coerce')
s_num.tolist()

medium

A. Raises ValueError

B. [10, 20, 'abc', 30]

C. [10, 20, 0, 30]

D. [10.0, 20.0, nan, 30.0]

Solution

1. Step 1: Convert series with invalid string using errors='coerce'

   The string 'abc' cannot convert to number, so it becomes NaN.

2. Step 2: Check the output list format

   Valid numbers become floats, invalid become NaN, so output is [10.0, 20.0, nan, 30.0].

3. Final Answer:

   [10.0, 20.0, nan, 30.0] -> Option D

4. Quick Check:

   Invalid strings become NaN with errors='coerce' [OK]

Hint: Invalid strings become NaN with errors='coerce' [OK]

Common Mistakes:

• Expecting original string 'abc' to remain
• Expecting integer output instead of float
• Thinking it raises an error

4.

Identify the error in this code snippet:

import pandas as pd
s = pd.Series(['1', '2', 'three'])
s_num = pd.to_numeric(s, errors='drop')
print(s_num)

medium

A. The parameter 'errors' does not accept 'drop'.

B. The Series contains non-numeric strings.

C. Missing import statement for pandas.

D. The print statement is incorrect.

Solution

1. Step 1: Check valid values for 'errors' in to_numeric()

   Valid values are 'raise', 'coerce', and 'ignore'. 'drop' is invalid.

2. Step 2: Understand the error caused by invalid parameter

   Using 'drop' causes a TypeError or ValueError because it's not supported.

3. Final Answer:

   The parameter 'errors' does not accept 'drop'. -> Option A

4. Quick Check:

   errors='drop' is invalid in to_numeric() [OK]

Hint: Check allowed 'errors' values: 'raise', 'coerce', 'ignore' only [OK]

Common Mistakes:

• Assuming 'drop' removes bad data automatically
• Ignoring error messages
• Confusing Series content with syntax error

5.

You have a DataFrame df with a column 'price' containing strings like '100', '200', and some invalid entries like 'unknown'. You want to convert 'price' to numeric, replacing invalid entries with 0. Which code snippet achieves this?

hard

A. df['price'] = pd.to_numeric(df['price'], errors='raise').fillna(0)

B. df['price'] = pd.to_numeric(df['price'], errors='coerce').fillna(0)

C. df['price'] = pd.to_numeric(df['price'], errors='ignore').fillna(0)

D. df['price'] = pd.to_numeric(df['price'], errors='coerce').dropna()

Solution

1. Step 1: Convert 'price' column with errors='coerce'

   This converts invalid strings to NaN safely.

2. Step 2: Replace NaN with 0 using fillna(0)

   This replaces all invalid entries with 0 as required.

3. Final Answer:

   df['price'] = pd.to_numeric(df['price'], errors='coerce').fillna(0) -> Option B

4. Quick Check:

   Use errors='coerce' + fillna(0) to replace bad data [OK]

Hint: Use fillna(0) after errors='coerce' to replace invalids [OK]

Common Mistakes:

• Using errors='ignore' which keeps invalid strings
• Using dropna() which removes rows instead of replacing
• Using errors='raise' which causes exceptions