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Data Foundations · A little practice goes a long way.

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Types, dates & missingness · W12 · 8 MIN

Convert numeric text

Turn numeric-looking strings into usable numbers.

Exercises within this concept

  1. FollowFollow the techniqueCurrent exercise
  2. ChangeAdapt a requirement
  3. TransferChoose and combine

Understand the idea

to_numeric parses numeric text. Choose what happens when a string cannot be interpreted as a number.

Concept sketch: numeric text → numbers; invalid → NaNtext"2.5""bad""4"number2.5NaN4numeric text → numbers; invalid → NaN
Illustration · not the exercise output

A small example

["8.5", "bad", missing] → errors="coerce" → [8.5, missing, missing]. Only "bad" caused a new gap.

Follow the code

Apply the idea to the supplied table. Read from top to bottom; the final line displays the result.

df["price"] = pd.to_numeric(df["price"], errors="coerce")
df

What each part does

df["price"] =
Replace the text column with the parsed numeric values.
pd.to_numeric(df["price"], errors="coerce")
Try to turn each price string into a number.
errors="coerce"
Make invalid text missing (NaN) instead of stopping with an error.

Your inputs

The editable setup on the right creates df. Run executes the setup and your work from top to bottom.

Messy café orders · 8 synthetic rows
orderdrinksizepricetipdate
101" latte "LARGE6.2012026-06-01
102TEAsmall3.100.52026-06-02
103" mocha "LARGEoopsNonenot a date
104LatteSmallNone0.82026-06-04
104LatteSmallNone0.82026-06-04
105"tea "SMALL4.200.62026-06-05
106ESPRESSOsmall2.500.22026-06-06
107" mocha"large6.801.52026-06-07

Your task · Follow

  1. Convert price to numeric in df, making invalid text missing.
  2. Preserve every row.
  3. Keep the changes in df and display it.
  4. Use: to_numeric().
Hint

errors="coerce" creates missing values for invalid strings; it does not repair the original text.

Reveal solution

One way to do it. Keep any supplied setup in the editor and use this in the Your work section.

df["price"] = pd.to_numeric(df["price"], errors="coerce")
df
Your task · Follow
  1. Convert price to numeric in df, making invalid text missing.
  2. Preserve every row.
  3. Keep the changes in df and display it.
  4. Use: to_numeric().

Tab: indent · Shift+Tab: outdent · Esc, then Tab: leave editor

Edit Python. Control or Command plus Enter runs it. Tab indents by four spaces. Shift plus Tab outdents. Press Escape, then Tab or Shift plus Tab to leave the editor.

Each run executes all editor code in a fresh Python session. Display a value by leaving it on the final line.

Python starts when you open a lesson.

Output

Run your code to see what Python returns.