cia | Citizen Intelligence Agency , monitoring key

There are some things that are important depending on your case:

• Do you want it to be highly reproducible or extendable?
• Should it be highly performant?
• Is readability more important than performance/extendability?

I've found that in the far majority of the cases, the performance doesn't matter that much. As long as you're not dealing with enormous amount of data to process or you're not working on low-performing infrastructure, it should run sufficiently fast. Again, this depends on your use case.

What I find way more annoying/time-consuming is over-complex functions that you won't know how they work afterwards, or having severely nested functionality. Those can take enormous amount of time to fix once your data-format changes or you need to alter some small parts in the code.

I would therefore agree that the ideal workflow would be to first download and store the raw data for reproducibility. Then you should write a processing function that makes them 'DataFrame' ready. Whenever your raw data then changes, you only have to rewrite this single function and assert the processed data comes out the same format it used to. Moreover, whenever you decide that you don't want to use pandas anymore (because you want to use regular numpy arrays for example), it is an easier fix to exclude pandas from your code than when it is completely knitted in your workflow since the very beginning.

This would be my motivation to do the processing before reading into a DataFrame.

Your code is almost right. __name__ is your friend.

According to the Official Python3 Documentation, there exist a couple of "Built-in relevant read-only attributes".

You will notice that if you make a small modification to main.py it will work fine (see below).

Selecting Balanço Patrimonial Ativo and then to extract the data from the DFs Consolidadas / Balanço Patrimonial Ativo - (Reais Mil) table from the website you need to induce WebDriverWait for the visibility_of_element_located() and using DataFrame from Pandas you can use the following Locator Strategy:

Code Block:

You are iterating the outer array twice instead of iterating each items statistic in the inner loop. Try to use

The issue seems to relate to the feature of the PETSCII specification called shifting.

Assuming the graphics mode is unshifted, PETSCII has only uppercase letters in its powerup state.

In the shifted mode, the lowercase characters a-z occupy the same character space (0x41..0x5a) as the upper case characters A-Z in the unshifted mode. In this mode the upper case characters are located at (0x61..0x7a), which holds some graphical glyphs in the unshifted mode.

The evidence supports this, since the lower case letters are visualised as uppercase and the uppercase letters are shown as block graphics characters.

To solve this:

On C64 the sets are alternated by flipping bit 2 of the byte 53272

Alternatively I think it's possible to output the right character directly to the screen memory without using the KERNAL function. I'm not sure about this, since it's been quite a long time when I programmed C64. It might have been that instead the toggling of the character set needed to be disabled so that random key presses didn't alter the screen...

if you want to save the favorite movie of a user permanently, then you have to save the JSON data in firestore database.

1. Create a function to store the favorite movie json data to firestore, i,e , store the json data to collection like;

May be you are getting an error undefined at line 43 at the time of updating because of this code

The script gives a solution that is within the bounds -0.005<0.005 for all variables. One potential reason that the bounds are not observed is that the solver failed to find a solution. Switching to disp=True displays the solver output to ensure that a successful solution is found.