Back to questions
You have a table of 1000 customer records from a small-business based in Australia.
Find all customers where the 2nd and 3rd letter in their name is "e".
Example: "Reece Smith"
| Column Name | Type |
|---|---|
| customer_id | integer |
| customer_name | string |
| gender | string |
| age | integer |
| zip_code | integer |
| city | string |
| state | string |
| customer_id | customer_name | gender | age | zip_code | city | state |
|---|---|---|---|---|---|---|
| 1 | Ignace Whillock | Male | 30 | 5464 | Johnstonhaven | Northern Territory |
| 2 | Gray Eskrick | Female | 69 | 8223 | New Zacharyfort | South Australia |
| 3 | Ellswerth Laurent | Male | 59 | 5661 | Aliburgh | Australian Capital Territory |
| ... | .... | ... | .... | ... | ... | ... |
| customer_id | customer_name | gender | age | zip_code | city | state |
|---|---|---|---|---|---|---|
| 549 | Bee Andrieux | Female | 50 | 1919 | Gleasonburgh | Northern Territory |
| 620 | Reena Hudless | Female | 35 | 902 | Jettstad | Australian Capital Territory |
| 898 | Keelia Baverstock | Female | 41 | 3377 | Angelinaport | New South Wales |
Because the 2nd and 3rd letter have to be 'e', we don't care what the first letter is at all, hence the single character wildcard .
We also don't care how many letters come after in the name, or what's in the last name, so we'll use the multi-character wildcard symbol () to handle that.
Putting this together, we get: