anu | the React16-compat library with hooks | Frontend Framework library

May be content is dynamic so its hard to fetch from pandas as well as beautifulsoup what approach you can follow

1. Go to chrome developer mode and refresh your page and now go to the Network tab and click on xhr you will able to find links under Name tab

2. Click on links in which first link contains only first 20 data.

3. as you want all 416 data so go to web page click on show all result and xhr will have new link which is in code and it is type of json

4. Click on that link and copy the link address so now you can extract what so ever data you want from json data

Code:

You'll need to transform data as you need and store it another sheet :

The content function relies on document.documentElement.outerHTML, so you might get a formatted value. If the request returns a JSON you could grab the text from the response that goto returns:

• $group by sender and recipient, and calculate NetAmount
• $gt to get NetAmount greater than zero
• $sort by NetAmount in descending order
• $group by recipient only and and make array of documents
• $addFields to slice 3 documents from docs array
• $unwind deconstruct Docs array
• $project to show required fields

like this? I do not confirm your needs, but the internalstaff_2 column can refer to STRING_AGG() to replace the nested subquery in the following script.

You can "pivot" Hours into Billable and Non-Billable columns using pivot_table() and then convert to utilization:

The change in colors is the result of the specified heatmap (viridis instead of binary) as noted above in comments.

The output image has different coloration than the input image because OpenCV uses BGR rather than RGB for colors, so it's likely your red and blue channels are swapped. If you read an image with OpenCV and plot with Matplotlib or vice versa. There are two easy solutions:

1.) Both read and plot images with OpenCV. You can replace plt.imshow(im_orig) with:

Computing a single-precision square root via double-precision code is going to be inefficient, especially if the hardware provides no native double-precision operations.

The following assumes hardware that conforms to IEEE-754 (2008), except that subnormals are not supported and flushed to zero. Fused-multiply add (FMA) is supported. It further assumes an ISO-C99 compiler that maps float to IEEE-754 binary32, and that maps the hardware's single-precision FMA instruction to the standard math function fmaf().

From a hardware starting approximation for the reciprocal square root with a maximum relative error of 2^-6.75 one can get to a reciprocal square root accurate to 1 single-precision ulp with two Newton-Raphson iterations. Multiplying this with the original argument provides an accurate estimate of the square root. The square of this approximation is subtracted from the orginal argument to compute the approximation error for the square root. This error is then used to apply a correction to the square root approximation, resulting in a correctly-rounded square root.

However, this straightforward algorithm breaks down for arguments that are very small due to underflow or overflow in intermediate computation, in particular when the underlying arithmetic operates in flash-to-zero mode that flushes subnormals to zero. For such arguments we can construct a slowpath code that scales the input towards unity, and scales back the result accordingly once the square root has been computed. Code for handling special operands such as zeros, infinities, NaNs, and negative arguments other than zero is also added to this slowpath code.

The NaN generated by the slowpath code for invalid operations should be adjusted to match the system's existing operations. For example, for x86-based systems this would be a special QNaN called INDEFINITE, with a bit pattern of 0xffc00000, while for a GPU running CUDA it would be the canonical single-precision NaN with a bit pattern of 0x7fffffff.

For performance reasons it may be useful to inline the fastpath code while making the slowpath code a called outlined subroutine. Single-precision math functions with a single argument should always be tested exhaustively against a "golden" reference implementation, which takes just minutes on modern hardware.

Try this