>>> ["foo", "bar", "baz"].index("bar")
1
Reference: Data Structures > More on Lists
Caveats follow
Note that while this is perhaps the cleanest way to answer the question as asked, index
is a rather weak component of the list
API, and I can't remember the last time I used it in anger. It's been pointed out to me in the comments that because this answer is heavily referenced, it should be made more complete. Some caveats about list.index
follow. It is probably worth initially taking a look at the documentation for it:
list.index(x[, start[, end]])
Return zero-based index in the list of the first item whose value is equal to x. Raises a ValueError
if there is no such item.
The optional arguments start and end are interpreted as in the slice notation and are used to limit the search to a particular subsequence of the list. The returned index is computed relative to the beginning of the full sequence rather than the start argument.
Linear time-complexity in list length
An index
call checks every element of the list in order, until it finds a match. If your list is long, and you don't know roughly where in the list it occurs, this search could become a bottleneck. In that case, you should consider a different data structure. Note that if you know roughly where to find the match, you can give index
a hint. For instance, in this snippet, l.index(999_999, 999_990, 1_000_000)
is roughly five orders of magnitude faster than straight l.index(999_999)
, because the former only has to search 10 entries, while the latter searches a million:
>>> import timeit
>>> timeit.timeit('l.index(999_999)', setup='l = list(range(0, 1_000_000))', number=1000)
9.356267921015387
>>> timeit.timeit('l.index(999_999, 999_990, 1_000_000)', setup='l = list(range(0, 1_000_000))', number=1000)
0.0004404920036904514
Only returns the index of the first match to its argument
A call to index
searches through the list in order until it finds a match, and stops there. If you expect to need indices of more matches, you should use a list comprehension, or generator expression.
>>> [1, 1].index(1)
0
>>> [i for i, e in enumerate([1, 2, 1]) if e == 1]
[0, 2]
>>> g = (i for i, e in enumerate([1, 2, 1]) if e == 1)
>>> next(g)
0
>>> next(g)
2
Most places where I once would have used index
, I now use a list comprehension or generator expression because they're more generalizable. So if you're considering reaching for index
, take a look at these excellent Python features.
Throws if element not present in list
A call to index
results in a ValueError
if the item's not present.
>>> [1, 1].index(2)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: 2 is not in list
If the item might not be present in the list, you should either
- Check for it first with
item in my_list
(clean, readable approach), or
- Wrap the
index
call in a try/except
block which catches ValueError
(probably faster, at least when the list to search is long, and the item is usually present.)
You can use GROUP_CONCAT
:
SELECT person_id,
GROUP_CONCAT(hobbies SEPARATOR ', ')
FROM peoples_hobbies
GROUP BY person_id;
As Ludwig stated in his comment, you can add the DISTINCT
operator to avoid duplicates:
SELECT person_id,
GROUP_CONCAT(DISTINCT hobbies SEPARATOR ', ')
FROM peoples_hobbies
GROUP BY person_id;
As Jan stated in their comment, you can also sort the values before imploding it using ORDER BY
:
SELECT person_id,
GROUP_CONCAT(hobbies ORDER BY hobbies ASC SEPARATOR ', ')
FROM peoples_hobbies
GROUP BY person_id;
As Dag stated in his comment, there is a 1024 byte limit on the result. To solve this, run this query before your query:
SET group_concat_max_len = 2048;
Of course, you can change 2048
according to your needs. To calculate and assign the value:
SET group_concat_max_len = CAST(
(SELECT SUM(LENGTH(hobbies)) + COUNT(*) * LENGTH(', ')
FROM peoples_hobbies
GROUP BY person_id) AS UNSIGNED);
Best Answer
Indexes take up space in memory (RAM); Too many or too large of indexes and the DB is going to have to be swapping them to and from the disk. They also increase insert and delete time (each index must be updated for every piece of data inserted/deleted/updated).
You don't have infinite memory. Making it so all indexes fit in RAM = good.
You don't have infinite time. Indexing only the columns you need indexed minimizes the insert/delete/update performance hit.