to_numeric() for safe conversion in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of to_numeric() with errors='coerce'

What is the output of the following code snippet?

Pandas

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

A

0    10.0
1    20.0
2     NaN
3    30.0
dtype: float64

B

0     10
1     20
2    abc
3     30
dtype: object

C

0    10
1    20
2    NaN
3    30
dtype: float64

D ValueError: Unable to parse string 'abc' at position 2

💡 Hint

errors='coerce' replaces invalid parsing with NaN.

The to_numeric() function converts the series to numbers. With errors='coerce', invalid values like 'abc' become NaN. The result is a float series because of NaN.

❓ data_output

intermediate

Count of non-numeric values after conversion

After converting this Series with errors='coerce', how many values become NaN?

Pandas

import pandas as pd
s = pd.Series(['5', '7', 'nine', '11', 'twelve'])
converted = pd.to_numeric(s, errors='coerce')
count_nan = converted.isna().sum()
print(count_nan)

A 1

B 0

C 3

D 2

💡 Hint

Check which strings cannot be converted to numbers.

'nine' and 'twelve' are not numeric and become NaN, so count is 2.

🔧 Debug

advanced

Identify the error in to_numeric() usage

What error does this code raise?

Pandas

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

A No error, output is a numeric series with NaN

B KeyError: 'three'

C ValueError: Unable to parse string 'three' at position 2

D TypeError: errors parameter must be 'ignore', 'coerce', or 'raise'

💡 Hint

errors='raise' stops on first invalid parsing.

With errors='raise', to_numeric() throws a ValueError when it finds a non-numeric string.

🚀 Application

advanced

Safe conversion of mixed data with fallback

You have a DataFrame column with mixed types. Which option safely converts the column to numeric, replacing errors with zero?

Pandas

import pandas as pd
df = pd.DataFrame({'values': ['100', 'abc', '200', None, '300']})

A df['values'] = pd.to_numeric(df['values'], errors='coerce').fillna(0)

B df['values'] = pd.to_numeric(df['values'], errors='ignore').fillna(0)

C df['values'] = pd.to_numeric(df['values'], errors='raise').fillna(0)

D df['values'] = df['values'].astype(float).fillna(0)

💡 Hint

Use errors='coerce' to convert invalid to NaN, then fillna to replace.

Option A converts invalid entries to NaN, then replaces NaN with 0. Option A leaves invalid as original strings, so fillna does nothing. Option A raises error on invalid. Option A fails because astype(float) raises error on 'abc'.

🧠 Conceptual

expert

Understanding dtype changes with to_numeric()

After applying pd.to_numeric() with errors='coerce' on a Series containing integers and invalid strings, what is the resulting dtype and why?

A int64, because original numbers are integers

B float64, because NaN requires a floating-point dtype

C object, because mixed types remain

D string, because invalid strings are kept

💡 Hint

NaN is a special floating-point value.

When invalid strings become NaN, the Series must use a floating-point dtype to hold NaN values, so dtype becomes float64.

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