The request

From Daniel Foster, Mobile Product Manager
Your goal
Set result to a dict with "desktop" and "mobile", the conversion rate (share of sessions with converted True) for each device, and "lift", desktop's relative lift over mobile (desktop rate / mobile rate - 1). Compute the lift from the unrounded rates, then round all three to 4 decimals.
- Use the
web_sessionsDataFramenot done yet - Store a dict in
resultnot done yet
Your code
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Reference solution
| session_id | customer_id | started_at | device | source |
|---|---|---|---|---|
| 1 | NaN | 2023-01-01 23:02:24 | mobile | paid |
| 2 | 18 | 2023-01-01 15:07:49 | desktop | paid |
| 3 | NaN | 2023-01-01 16:55:46 | desktop | direct |
| 4 | NaN | 2023-01-01 12:43:28 | mobile | organic |
Mote checks your result
Press Run. Each row that matches the goal becomes a firefly in your lantern.
What Daniel gets, and why
DataFrames
Loaded for you as pandas DataFrames, plus pd and np.
web_sessions40,000 rows · 7 columns
session_idintegercustomer_idintegerstarted_attimestampdevicevarcharsourcevarcharpages_viewedintegerconvertedboolean
| session_id | customer_id | started_at | device | source | pages_viewed | converted |
|---|---|---|---|---|---|---|
| 1 | NaN | 2023-01-01 23:02:24 | mobile | paid | 1 | false |
| 2 | 18 | 2023-01-01 15:07:49 | desktop | paid | 1 | false |
| 3 | NaN | 2023-01-01 16:55:46 | desktop | direct | 7 | false |
| 4 | NaN | 2023-01-01 12:43:28 | mobile | organic | 3 | false |
| 5 | NaN | 2023-01-01 13:06:21 | mobile | social | 2 | false |
customers2,018 rows · 9 columns
customer_idintegerfirst_namevarcharlast_namevarcharemailvarcharcityvarcharstatevarcharsignup_datedatesegmentvarcharreferral_sourcevarchar
| customer_id | first_name | last_name | city | state | signup_date | segment | referral_source | |
|---|---|---|---|---|---|---|---|---|
| 1 | Owen | Hudson | owen.hudson@icloud.com | San Francisco | CA | 2022-06-02 | consumer | search |
| 2 | Douglas | Mendoza | dmendoza69@gmail.com | Atlanta | GA | 2022-06-02 | consumer | NaN |
| 3 | Henry | Brooks | hbrooks@lakesidecafe.com | Los Angeles | CA | 2022-06-04 | business | NaN |
| 4 | Brian | Graham | bgraham@bluecedarhomes.com | Seattle | WA | 2022-06-06 | business | |
| 5 | Kyle | Jimenez | kjimenez27@yahoo.com | Indianapolis | IN | 2022-06-07 | consumer | NaN |
products120 rows · 8 columns
product_idintegernamevarcharcategoryvarcharsubcategoryvarcharunit_pricedecimalunit_costdecimallaunched_ondateis_activeboolean
| product_id | name | category | subcategory | unit_price | unit_cost | launched_on | is_active |
|---|---|---|---|---|---|---|---|
| 101 | Vale Velvet Loveseat | Furniture | Sofas | 1659.00 | NaN | 2023-09-11 | true |
| 102 | Alder Oak Coffee Table | Furniture | Tables | 919.00 | 503.50 | 2024-05-16 | true |
| 103 | Orchard Rattan Dining Chair | Furniture | Chairs | 789.00 | 429.87 | 2023-05-27 | true |
| 104 | Upland Oak Bookcase | Furniture | Storage | 429.00 | 214.70 | 2022-12-20 | true |
| 105 | Avery Leather Bed Frame | Furniture | Beds | 449.00 | 232.70 | 2021-08-31 | true |
orders12,000 rows · 7 columns
order_idintegercustomer_idintegerorder_datetimestampstatusvarcharchannelvarcharshipping_feedecimalcoupon_codevarchar
| order_id | customer_id | order_date | status | channel | shipping_fee | coupon_code |
|---|---|---|---|---|---|---|
| 1001 | 125 | 2023-01-01 20:51:19 | completed | web | 0.00 | NaN |
| 1002 | 156 | 2023-01-02 07:58:46 | completed | web | 0.00 | NaN |
| 1003 | 22 | 2023-01-02 10:25:27 | completed | marketplace | 4.99 | WELCOME10 |
| 1004 | 48 | 2023-01-02 11:21:51 | completed | marketplace | 4.99 | WELCOME10 |
| 1005 | 82 | 2023-01-02 11:43:36 | completed | web | 0.00 | WELCOME10 |
order_items30,338 rows · 6 columns
order_item_idintegerorder_idintegerproduct_idintegerquantityintegerunit_pricedecimaldiscount_pctdecimal
| order_item_id | order_id | product_id | quantity | unit_price | discount_pct |
|---|---|---|---|---|---|
| 1 | 1001 | 121 | 9 | 31.99 | 0.00 |
| 2 | 1001 | 154 | 3 | 11.99 | 0.00 |
| 3 | 1002 | 154 | 4 | 11.99 | 0.00 |
| 4 | 1002 | 194 | 1 | 149.00 | 0.00 |
| 5 | 1002 | 193 | 2 | 19.99 | 0.00 |
returns1,673 rows · 5 columns
return_idintegerorder_item_idintegerreturned_ondatereasonvarcharrefund_amountdecimal
| return_id | order_item_id | returned_on | reason | refund_amount |
|---|---|---|---|---|
| 1 | 14 | 2023-01-10 | damaged | 83.69 |
| 2 | 42 | 2023-01-10 | wrong_item | 19.99 |
| 3 | 105 | 2023-01-14 | wrong_item | 10.99 |
| 4 | 24 | 2023-01-16 | changed_mind | 26.99 |
| 5 | 126 | 2023-01-16 | late | 20.99 |
employees60 rows · 8 columns
employee_idintegerfirst_namevarcharlast_namevarchardepartmentvarchartitlevarcharmanager_idintegerhire_datedatesalaryinteger
| employee_id | first_name | last_name | department | title | manager_id | hire_date | salary |
|---|---|---|---|---|---|---|---|
| 1 | Catherine | Hale | Executive | Chief Executive Officer | NaN | 2016-03-01 | 285000 |
| 2 | Robert | Kim | Executive | Chief Operating Officer | 1 | 2017-01-09 | 235000 |
| 3 | Megan | Collins | Sales | Head of Sales | 1 | 2018-04-16 | 172000 |
| 4 | Chloe | Gutierrez | Sales | Account Executive | 3 | 2020-01-21 | 72000 |
| 5 | Frank | Bell | Sales | Account Executive | 3 | 2023-05-21 | 73000 |
marketing_spend3,276 rows · 3 columns
spend_datedatechannelvarcharamountdecimal
| spend_date | channel | amount |
|---|---|---|
| 2023-01-01 | paid | 492.99 |
| 2023-01-01 | 37.38 | |
| 2023-01-01 | social | 200.53 |
| 2023-01-02 | paid | 709.13 |
| 2023-01-02 | 64.13 |
The lesson
Conversion rate is a mean
A conversion rate is the share of visits that end in an order: conversions divided by visits.
In pandas you do not need to count anything. converted is a True/False column, True counts as 1
and False as 0, so its mean is the share of True values:
web_sessions["converted"].mean() # 0.0298 overall
web_sessions.groupby("device")["converted"].mean()
device
desktop 0.038718
mobile 0.024131
tablet 0.028133
Desktop visits end in an order 3.87% of the time, mobile ones 2.41%. Always look at the counts too: 14,541 desktop sessions, 21,549 mobile, and only 3,910 tablet. Small groups have noisy rates.
Two ways to say “bigger”
The same gap can be written two ways, and mixing them up is one of the most common errors in reports.
- Absolute difference, in percentage points: 3.87% - 2.41% = 1.46 points.
- Relative lift, in percent: 3.87 / 2.41 - 1 = 0.60, so desktop converts 60% more often.
“Conversion rose 2%” is ambiguous: from 3.0% to 5.0% (2 points) or from 3.0% to 3.06% (2% relative)? Write “points” for absolute differences and ”% higher” for relative ones, and give the base rate.
What the gap does and doesn’t mean
The numbers say mobile visits convert less. They do not say the mobile site is worse. Mobile visitors browse on the bus, compare prices, and come back on a laptop to buy. A session-level rate cannot see that journey. Comparing groups that chose themselves (people pick their device) shows a difference, not its cause. Only a controlled test, where a coin decides who sees what, can tell you whether a change to the mobile site would move the number. That is what the rest of this chapter builds toward.
Common mistakes
- Lift from rounded rates. 0.0387 / 0.0241 - 1 gives 0.6058, not 0.6045. Round at the end.
- Points versus percent. A 1.46-point gap is a 60% lift. Say which one you mean.
- Averaging rates across groups. The overall rate is not the mean of the three device rates: (3.87 + 2.41 + 2.81) / 3 = 3.03%, but the real overall rate is 2.98%, because mobile has more sessions. Recompute from the rows.
- A rate without its count. A 6% rate on 50 sessions is three orders. Show n.
Interview note
“Our conversion went up 10%, is that good?” is a classic follow-up question. A strong answer asks whether that is 10% relative or 10 points, from what base, on how many sessions, and compared with what: the same period last year, or a randomly assigned control group.
Lantern lit!
Practised: conversion rateliftrelative differenceproportions
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