I asked the same thing a while ago here There isn’t a built in Index property, but you can set the AlternationCount of your ItemsControl to something higher than your item count, and bind to the AlternationIndex <TextBlock Text=”{Binding Path=(ItemsControl.AlternationIndex), RelativeSource={RelativeSource Mode=TemplatedParent}, FallbackValue=FAIL, StringFormat={}Index is {0}}” /> It should be noted that this solution may … Read more
If you create a composite primary key, on (x, y, z), PostgreSQL implements this with the help of one UNIQUE multi-column btree index on (x, y, z). In addition, all three columns are NOT NULL (implicitly), which is the main difference between a PRIMARY KEY and a UNIQUE INDEX. Besides obvious restrictions on your data, … Read more
You probably want something like this: >>> a = np.array([True, True, True, False, False]) >>> b = np.array([[1,2,3,4,5], [1,2,3,4,5]]) >>> b[:,a] array([[1, 2, 3], [1, 2, 3]]) Note that for this kind of indexing to work, it needs to be an ndarray, like you were using, not a list, or it’ll interpret the False and … Read more
To get access to index, you need to convert your list to a map using the asMap operator. Example final fruitList = [‘apple’, ‘orange’, ‘mango’]; final fruitMap = fruitList.asMap(); // {0: ‘apple’, 1: ‘orange’, 2: ‘mango’} // To access ‘orange’ use the index 1. final myFruit = fruitMap[1] // ‘orange’ // To convert back to … Read more
Index is an object, and default index starts from 0: >>> result.index Int64Index([0, 1, 2], dtype=int64) You can shift this index by 1 with >>> result.index += 1 >>> result.index Int64Index([1, 2, 3], dtype=int64)
Use multi-axis indexing, e.g. df.loc[‘a’, ‘1’] When you use df[‘1’][‘a’], you are first accessing the series object s = df[‘1’], and then accessing the series element s[‘a’], resulting in two __getitem__ calls, both of which are heavily overloaded (handle a lot of scenarios, like slicing, boolean mask indexing, and so on). It’s much more efficient … Read more
This approach uses .groupby() and .ngroup() (new in Pandas 0.20.2) to create the id column: df[‘id’] = df.groupby([‘LastName’,’FirstName’]).ngroup() >>> df First Second id 0 Tom Jones 0 1 Tom Jones 0 2 David Smith 1 3 Alex Thompson 2 4 Alex Thompson 2 I checked timings and, for the small dataset in this example, Alexander’s … Read more
Advanced indexing doesn’t take a list of lists of slices. Instead, you can use numpy.r_. This function doesn’t take negative indices, but you can get round this by using np.ndarray.shape: A = B[:, np.r_[1:6, 7:B.shape[1]-1]] Remember to add 1 to the second part, since a: b does not include b, in the same way slice(a, … Read more
There are situations where the redundancy doesn’t hold. For example, say ColumnC was a huuge field, but you’d sometimes have to retrieve it quickly. Your index 1 would not require a key lookup for: select ColumnC from YourTable where ColumnnA = 12 On the other hand index 2 is much smaller, so it can be … Read more