I’m not a specialist but the read/write master database and read-only slaves pattern is a “common” pattern, especially for big applications doing mostly read accesses or data warehouses: it allows to scale (you add more read-only slaves if required) it allows to tune the databases differently (for either efficient reads or efficient writes) What would … Read more
You get it via SELECT LAST_INSERT_ID(); or via having your framework/MySQL library (in whatever language) call mysql_insert_id(). That won’t work. There you have to query the IDs after inserting.
I would go with option 1. It is perfectly fine to have two foreign key columns referencing the same primary key column in a different table since each foreign key value will reference a different record in the related table. I’m sure option 2 would work, but you would essentially have a 1-to-1 relationship between … Read more
Just specify vmin=0, vmax=1. By default, imshow normalizes the data to its min and max. You can control this with either the vmin and vmax arguments or with the norm argument (if you want a non-linear scaling). As a quick example: import matplotlib.pyplot as plt data = [[0, 0.25], [0.5, 0.75]] fig, ax = plt.subplots() … Read more
Broadcasting is really good for this: row_sums = a.sum(axis=1) new_matrix = a / row_sums[:, numpy.newaxis] row_sums[:, numpy.newaxis] reshapes row_sums from being (3,) to being (3, 1). When you do a / b, a and b are broadcast against each other. You can learn more about broadcasting here or even better here.
I actually started putting an answer together, but I ran into complications, because you (quite understandably) want a simple example. The problem is manifold. First I don’t have a good idea of your level of actual expertise re Relational Databases and 5NF; I don’t have a starting point to take up and then discuss the … Read more
To overcome the broadcasting issue, you can use the div method: df.div(df.sum(axis=1), axis=0) See pandas User Guide: Matching / broadcasting behavior
My answer to this is the same as in an answer to your earlier question. For a probability density function, the integral over the entire space is 1. Dividing by the sum will not give you the correct density. To get the right density, you must divide by the area. To illustrate my point, try … Read more
I’ve recently done a similar task, although I was matching new data to existing names in a database, rather than looking for duplicates within one set. Name matching is actually a well-studied task, with a number of factors beyond what you’d consider for matching generic strings. First, I’d recommend taking a look at a paper, … Read more
Just specify vmin=0, vmax=1. By default, imshow normalizes the data to its min and max. You can control this with either the vmin and vmax arguments or with the norm argument (if you want a non-linear scaling). As a quick example: import matplotlib.pyplot as plt data = [[0, 0.25], [0.5, 0.75]] fig, ax = plt.subplots() … Read more