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.