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3

With a bit of parsing via BeautifulSoup, we can get a pandas.Dataframe using pandas.read_html() like: Code: def get_tables(source): elems = iter(BeautifulSoup(source, 'lxml').find_all(['table', 'h1'])) df = pd.DataFrame( pd.read_html(str(next(elems)), header=0)[0].iloc[0].rename(h1.text) for h1 in elems) df.index.names = ['...


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Yes, they can connect natively. You can manage data and then put it all in different services like it is showed next. As you can see you can use SQL database, blob storate and also PowerBI. Here you can find a tutorial on how to do streaming analytics with Azure and PowerBI.


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You could give the plotlypackage a try. You basically code up the plot you want first with ggplot2 and then call ggplotly() at the end, which will render an interactive version of it. You get zooming in on mouse selection for example and labels of points on hover. It has a pretty extensive documentation too. I can try and help you out with a specific example ...


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Based on your explanation, $z$ is not a parameter so your function can be simplified like this: $$f(x,y,a) = x*y*z(x)*a\text{, where } x,y \in \mathbb{Q}\cap[0,1000], a\in\{2,3\} $$ Note that $z(x)$ is a piecewise function. Also $a$ can have only two values so $f$ can be divided into $f_2$ and $f_3$, each of these having only two arguments so it simplifies ...


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You need to clean up the data. Load it into the Power Query editor. Split the column with the variables by the delimiter ; now you have a lot of individual columns, some have values, some are empty select all columns except the newly split ones and use Unpivot > Unpivot Other Columns on the transform ribbon. Now you have one row for each combination of ...


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Posting this as an answer. Turns out the relations were set to Cross filter direction: Single on all relations (and the one from Month table.Month to A.Month was not set to active). Setting Cross filter direction: Both fixed the problem.


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Count distinct is a measure (aggregate function). When the measure is used in the total row, it is not summing the distinct count values about but is calculating the function in the context of all the table data. Without seeing the data I would make the assumption that there are 85 distinct values between those 4 months. March, for example, happens to ...


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