conserve | Using dd and lvm snapshots | Continuous Backup library

Background story: I am processing some messy data sheets made in Excel. Reading this with pandas (preferable) does sometimes conserve the Excel data type of the cells. As all entries I would like to process have a value in a certain column that can be converted to an integer, I would like to execute a code block when this column can be converted (i.e. does not throw an error). I know there are some ways to work around this problem, but I would like to know what you would do if you would like to execute code when no error is returned from a statement without the try/except statement.

The try/except block is what I came up with so far:

Whenever I upload a file to my Cloud Storage bucket, I expect it to conserve the actual date that file was created. However, whenever I access the file through the storage client API on Python, and check the generation metadata field of the blob, it displays the date when that file was uploaded to the storage.

These are the steps to reproduce:

I was do manual analysis to this code. Would wait that the value of variable D change to 1 after first call to F1 function, but conserve his value on 2. Also i noticed that comment or not comment the *Y--; has no effect.

The output is:

While answering this question I tried to use the patterns functionality together with data.table::melt:

I'll keep it simple, I have a List with UUID identified items. This list is selectable, when EditMode() is entered user can tap items, and these get added to the selection variable:

And the selection variable is:

I had a problem with UDP Datagrams in that I could not receive UDP packets from a server but I could send them. I looked through many examples but could not figure out what was wrong with my code. I finally found hints to what was going wrong from different sites.

I have thus updated the question here in case it might help someone in the future. The code below is working over a WiFi network on a LG phone and was built on Android Studio 4.2 (29/4/2021); SDK Platform 30; Kotlin 1.5.0

At the end of the code section below I have written some comments as to what was causing my code not to work.

This is my MainActivity code

• The issue seems to be with displaying the HTML with styling rendered by pandas in Google Chrome and Microsoft Edge.

• JupyterLab in Firefox correctly displays all of the styled rows and correctly renders an output HTML file.

• The updated questions are

  1. Why doesn't the HTML rendered by pandas completely display all the styling in Chrome or Edge?
  2. Is there a more efficient way to apply the styling done by pandas so the HTML also works in Chrome and Edge?

• Versions:

  • pandas v1.2.4
  • Chrome v90.0.4430.93 (Official Build) (64-bit)
  • Edge v90.0.818.56 (Official build) (64-bit)

• Questions - they are interrelated:
  1. Why aren't all of the rows displaying the background styling in Jupyter or writing to HTML?
     • All rows should have green styling, but the last 5 do not display the styling.
  2. How can all of the rows be made to display the background styling?
• Given a large dataframe, in this case 474 rows x 35 columns, the applied styling stops displaying.
• If the number of rows or columns increases beyond this size, then more rows aren't displayed.
• We can see from the styling map, that the rows are correctly mapped with a background color, but it isn't displayed.
• If the number of rows or columns is reduced, then all of the rows display the correct styling.
• Tested in jupyterlab v3.0.11
• Tested in PyCharm 2021.1 (Professional Edition) Build #PY-211.6693.115, built on April 6, 2021 saving the redendered styler to a file has the same result, so this isn't just an issue with jupyter.
• Tested in the console
• This issue is reproducible on two different systems, that I have tried.
• If the shape is reduced to 471 rows × 35 columns or 474 rows × 34 columns, then all rows correctly display the highlighting.
• Associated pandas bug report: 40913

In first steps in algorithmic design and analysis I am following the book Algorithm Design by Kleinberg and Tardos I came across the Question/Case you can find down the page the solution is indeed f(n) = sqrt(n). My concerns are :

1- Why I still find log(n) more acceptable .I still could not grasp the plus value from sqrt(n) even when it is said that we will use more jars / trials .

2- from where did we get the sqrt(n) ?. Using k jars (trials) I could think of n/k incrementation but then lim n→∞ f(n) /n toward infinity is 1/k which is not 0. I got the feeling that the '2' in n^1/2 is tightly related to k = 2 , if yes how.

Thank you.

Case: Assume that a factory is doing some stress-testing on various models of glass jars to determine the height from which they can be dropped and still not break. The setup for this experiment, on a particular type of jar, is as follows. You have a ladder with n rungs, and you want to find the highest rung from which you can drop a copy of the jar and not have it break. We call this the highest safe rung. It might be natural to try binary search: drop a jar from the middle rung, see if it breaks, and then recursively try from rung n/4 or 3n/4 depending on the outcome. But this has the drawback that you could break a lot of jars in finding the answer. If your primary goal were to conserve jars, on the other hand, you could try the following strategy. Start by dropping a jar from the first rung, then the second rung, and so forth, climbing one higher each time until the jar breaks. In this way, you only need a single jar—at the moment it breaks, you have the correct answer—but you may have to drop it n times. So here is the trade-off: it seems you can perform fewer drops if you’re willing to break more jars. To understand better how this trade- off works at a quantitative level, let’s consider how to run this experiment given a fixed “budget” of k ≥ 1 jars. In other words, you have to determine the correct answer—the highest safe rung—and can use at most k jars in doing so. Now, please solve these two questions:

1. Suppose you are given a budget of k = 2 jars. Describe a strategy for finding the highest safe rung that requires you to drop a jar at most f(n) times, for some function f(n) that grows slower than linearly. (In other words, it should be the case that lim n→∞ f(n)/n = 0.)

I'm working on a project with DVC (Data Version Control), when I push files in my remote storage, the name of the files are changed. How I can conserve the names?