Find missing values

Understanding values · I16 · 8 MIN

Locate gaps before summarising.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt a requirement
TransferChoose and combine

Understand the idea

isna flags missing cells True and present cells False. Missing means unknown or absent; it is different from zero.

Illustration · not the exercise output

A small example

For [5, missing, 0, missing], isna() is [False, True, False, True]: 2 gaps out of 4, or 50%.

Follow the code

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

df.isna().sum()

What each part does

df.isna(): True at missing cells
.sum(): add down each column
None / NaN: missing-value representations

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
order	drink	size	price	tip	date
101	" latte "	LARGE	6.20	1	2026-06-01
102	TEA	small	3.10	0.5	2026-06-02
103	" mocha "	LARGE	oops	None	not a date
104	Latte	Small	None	0.8	2026-06-04
104	Latte	Small	None	0.8	2026-06-04
105	"tea "	SMALL	4.20	0.6	2026-06-05
106	ESPRESSO	small	2.50	0.2	2026-06-06
107	" mocha"	large	6.80	1.5	2026-06-07

Your task · Follow

Using df, return the number of missing values in every column.
Use: isna().

Hint

isna makes the missing mask; sum counts True values down each column.

Reveal solution

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

df.isna().sum()

Your task · Follow

Using df, return the number of missing values in every column.
Use: isna().

YOUR PYTHON

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

# Supplied setup — editable
import pandas as pd

data = {
    "order": [101, 102, 103, 104, 104, 105, 106, 107],
    "drink": [
        " latte ",
        "TEA",
        " mocha ",
        "Latte",
        "Latte",
        "tea ",
        "ESPRESSO",
        " mocha",
    ],
    "size": ["LARGE", "small", "LARGE", "Small", "Small", "SMALL", "small", "large"],
    "price": ["6.20", "3.10", "oops", None, None, "4.20", "2.50", "6.80"],
    "tip": [1, 0.5, None, 0.8, 0.8, 0.6, 0.2, 1.5],
    "date": [
        "2026-06-01",
        "2026-06-02",
        "not a date",
        "2026-06-04",
        "2026-06-04",
        "2026-06-05",
        "2026-06-06",
        "2026-06-07",
    ],
}

df = pd.DataFrame(data)

# Your work
df.____().____()

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.