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I made a change to part of your code:

I had this same problem and I got an answer here.

It's is because the line you read is always followed by a \n character. You need to remove it. Hence, just replace that last part of your code with this. .strip() will do for you. str(current_location).strip("\n")

Whenever you read a line from a text file, it adds a \n character to tell that new line started from there. You need to remove that while printing or it will mess up with your current statement

First, you're right: setColor has been broken at least since Catalina. Apple hasn't fixed it probably because it's so slow and inefficient, and nobody ever used it.

Second, docs say NSBitmapImageRep(cgImage: CGImage) produces a read-only bitmap so your code wouldn't have worked even if setColor worked.

As Alexander says, making your own CIFilter is the best way to change a photo's pixels to different colors. Writing and implementing the OpenGL isn't easy, but it's the best.

If you were to add an extension to NSBitmapImageRep like this:

I figured it on my own!

1 - Make triplet sets of the main sequence

2 - Detect to which set the user_input aligns with

3 - Find the alternatives for those sets in the dictionary

4 - add the new sets to the list and remove the old ones

5 - join the sets to reconstruct the new main sequence

To remove duplicated entries of 2 interchangeable columns, first reorder then remove duplicated rows of your dataframe.

After a lot of research, I found out that it was caused by the Wi-Fi Inspector feature of Avast Antivirus!
The pattern of tcpdump log when the Wi-Fi Inspector button is clicked is almost same.

Finally I got it

Usually, what I like to do when collecting data through webscraping is to build either :

• A list of dictionaries (which contains metadata) (option 1)
• Lists of metadata in a single dictionary with corresponding column names (data, title, price, etc.) (option 2)

(What I call "metadata" is all the pieces of information that describes a single item : in your case, that would be : the item price, the extraction date, the reviews on a specific item, and so on.)

When scraping is done, I build the DataFrame only as a final step.

As a final note, I didn't want to mess with your original script too much, but I think you should consider two things :

• Build a function to wrap your scraping steps (or maybe even a class so you can add a single function that handles all the similar things you're doing : collecting metadata on a product)
• You should probably replace "Sin Marc", "Sin Reviews" with np.nan, it will make your data processing and analysis easier

In the modification I did in your script, I choose option 2. I'm not sure about that, but I would guess that option 2 is more efficient than option 1. However, I find it useful, sometimes, when you handle more complex data to build a dictionary first and then, happen the dictionary corresponding to a single item into a list of dictionaries (that would be option 1) : it can make it easier to track each item at a time.

I rearranged the top part of the code, but once you get the final dataframe you can write that to csv as you were. Also, note I changed a couple list comprehensions to check for errors that I was getting. Also, url_list needs commas.