For simple equality checks (=), a B-Tree index on a varchar or text column is simple and the best choice. It certainly helps performance a lot. And a UNIQUE constraint (like you mentioned) is already implemented with such an index, so you would not create another one. Of course, a B-Tree index on a simple … Read more
You can pass a boolean mask to your df based on notnull() of ‘Survive’ column and select the cols of interest: In [2]: # make some data df = pd.DataFrame(np.random.randn(5,7), columns= [‘Survive’, ‘Age’,’Fare’, ‘Group_Size’,’deck’, ‘Pclass’, ‘Title’ ]) df[‘Survive’].iloc[2] = np.NaN df Out[2]: Survive Age Fare Group_Size deck Pclass Title 0 1.174206 -0.056846 0.454437 0.496695 1.401509 … Read more
Historically, databases have used 1-based indexing for bound parameters. This probably reflects the origins of relational databases in set theory and mathematics, which index elements starting with one, and use zero to represent a null or empty set. In shell scripts and regular expressions, the zero index usually means something “special”. For example, in the … Read more
1) Drop the existing clustered index first (IX_TableX_FieldB): DROP INDEX TableX.IX_TableX_FieldB 2) Create a (temporary) UNIQUE constraint on the unique fields referenced in the primary key ALTER TABLE TableX ADD CONSTRAINT UQ_TableX UNIQUE(FieldA) 3) Drop the PRIMARY KEY ALTER TABLE TableX DROP CONSTRAINT PK_TableX 4) Recreate the PRIMARY KEY as CLUSTERED ALTER TABLE TableX ADD … Read more
The answer from @Taymon using list.index was great. FWIW, here’s a functional approach using the itertools module. It works with any iterable input, not just lists: >>> from itertools import compress, count, imap, islice >>> from functools import partial >>> from operator import eq >>> def nth_item(n, item, iterable): indicies = compress(count(), imap(partial(eq, item), iterable)) … Read more
you can use pct_change() or/and diff() methods Demo: In [138]: df.Close.pct_change() * 100 Out[138]: 0 NaN 1 0.469484 2 0.467290 3 -0.930233 4 0.469484 5 0.467290 6 0.000000 7 -3.255814 8 -3.365385 9 -0.497512 Name: Close, dtype: float64 In [139]: df.Close.diff() Out[139]: 0 NaN 1 0.125 2 0.125 3 -0.250 4 0.125 5 0.125 6 … Read more
Suppose a = [‘a’, ‘b’, ‘c’, 3, 4, ‘d’, 6, 7, 8] and the list of indexes is stored in b= [0, 1, 2, 4, 6, 7, 8] then a simple one-line solution will be c = [a[i] for i in b]
Sept 2021 edit: I’ve been using MySQL 8.0 for a couple years now, so here’s some updated info. The MySQL manual now has a very informative page regarding conversion between utf8mb3 (currently also known as utf8) and utf8mb4. utf8mb3 is deprecated and will be removed eventually; and when it is removed, its current alias, utf8, … Read more
Usually you get this error when you use a VARCHAR(Max) try using: [Column(TypeName = “VARCHAR”)] [StringLength(n)] [Index] public string CreatedBy { set; get; } where n is between 1 and 450.
SELECT ‘ CREATE ‘ + CASE WHEN I.is_unique = 1 THEN ‘ UNIQUE ‘ ELSE ” END + I.type_desc COLLATE DATABASE_DEFAULT + ‘ INDEX ‘ + I.name + ‘ ON ‘ + SCHEMA_NAME(T.schema_id) + ‘.’ + T.name + ‘ ( ‘ + KeyColumns + ‘ ) ‘ + ISNULL(‘ INCLUDE (‘ + IncludedColumns + ‘ … Read more