to_numeric() for safe conversion in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of pandas to_numeric() function?

It converts values in a column or series to numeric types safely, handling errors and invalid parsing.

beginner

What does the errors='coerce' parameter do in to_numeric()?

It replaces invalid parsing values with NaN instead of raising an error.

intermediate

How does to_numeric() help when working with messy data?

It safely converts columns with mixed types or strings to numbers, marking bad data as NaN for easy cleaning.

intermediate

What happens if you use to_numeric() without specifying errors and there is invalid data?

It raises a ValueError stopping the program, because it cannot convert invalid strings to numbers.

beginner

Show a simple example of using to_numeric() with errors='coerce'.

Example:<br><code>import pandas as pd<br>data = ['10', '20', 'abc', '30']<br>pd.to_numeric(data, errors='coerce')<br># Output: [10.0, 20.0, NaN, 30.0]</code>

What does pd.to_numeric() do?

A Converts data to strings

B Converts data to numeric types safely

C Deletes non-numeric data

D Sorts numeric data

to_numeric() converts data to numbers, handling errors safely.

Which errors option replaces invalid values with NaN?

A 'raise'

B 'ignore'

C 'drop'

D 'coerce'

errors='coerce' replaces invalid parsing with NaN.

What happens if errors='raise' and invalid data exists?

A A ValueError is raised

B Invalid data is converted to zero

C Invalid data is ignored

D Invalid data is converted to strings

errors='raise' causes a ValueError on invalid data.

Why use to_numeric() instead of astype(float)?

A to_numeric() converts to strings

B astype(float) is faster

C to_numeric() handles errors safely

D astype(float) ignores errors

to_numeric() can handle errors without crashing, unlike astype().

What type of output does to_numeric() return?

A Series or array of numeric types

B DataFrame

C List

D Dictionary

to_numeric() returns a Series or array with numeric types.

Explain how to_numeric() helps when converting messy data columns to numbers.

Think about what happens when data has strings that can't be numbers.

Describe the difference between errors='raise' and errors='coerce' in to_numeric().

Consider what happens when invalid data is found.

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