What types are these?

Getting oriented · I05 · 8 MIN

Distinguish numeric columns from text.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt the workflow
TransferCombine earlier skills

Understand the idea

A dtype describes how a column is stored. Check storage before calculating: text that looks numeric is still text.

Illustration · not the exercise output

A small example

"12" is text; 12 is an integer; 12.5 is a decimal. Checking a dtype does not prove that every value is sensible.

Follow the code

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

df.info()
df.dtypes

What each part does

df.dtypes: a type for each column
df.info(): print a compact table summary
non-null: a value is present; it is not a guarantee of correctness

Your inputs

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

Candy shop · 6 synthetic rows
candy	flavour	price	rating	shelf
Gummy Bear	fruity	1.2	4.1	A
Choco Pop	chocolate	2.1	4.6	B
Mint Bite	mint	1.5	3.8	A
Berry Loop	fruity	2.8	4.4	B
Cocoa Cube	chocolate	3.4	4.9	A
Lemon Drop	fruity	1.8	4	B

Your task · Follow

Run df.info() to read the overview, then return df.dtypes.

Hint

info prints a report and returns None; dtypes is the Series to leave on the final line.

Reveal solution

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

df.info()
df.dtypes

Your task · Follow

Run df.info() to read the overview, then return df.dtypes.

YOUR PYTHON

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

# Supplied setup — editable
import pandas as pd

data = {
    "candy": [
        "Gummy Bear",
        "Choco Pop",
        "Mint Bite",
        "Berry Loop",
        "Cocoa Cube",
        "Lemon Drop",
    ],
    "flavour": ["fruity", "chocolate", "mint", "fruity", "chocolate", "fruity"],
    "price": [1.2, 2.1, 1.5, 2.8, 3.4, 1.8],
    "rating": [4.1, 4.6, 3.8, 4.4, 4.9, 4],
    "shelf": ["A", "B", "A", "B", "A", "B"],
}

df = pd.DataFrame(data)

# Your work
df.____()
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.