Skip to main content

Confusion between commands.Bot and discord.Client | Which one should I use?

Whenever you look at YouTube tutorials or code from this website there is a real variation. Some developers use client = discord.Client(intents=intents) while the others use bot = commands.Bot(command_prefix="something", intents=intents). Now I know slightly about the difference but I get errors from different places from my code when I use either of them and its confusing. Especially since there has a few changes over the years in discord.py it is hard to find the real difference.

I tried sticking to discord.Client then I found that there are more features in commands.Bot. Then I found errors when using commands.Bot.

An example of this is:

When I try to use commands.Bot

client = commands.Bot(command_prefix=">",intents=intents)

async def load():
    for filename in os.listdir("./Cogs"):
      if filename.endswith(".py"):
        client.load_extension(f"Cogs.{filename[:-3]}")

The above doesnt giveany response from my Cogs and also says

RuntimeWarning: coroutine 'BotBase.load_extension' was never awaited  
  client.load_extension(f"Cogs.{filename[:-3]}")
RuntimeWarning: Enable tracemalloc to get the object allocation traceback`.

Then when I try to use discord.Client

client = discord.Client(command_prefix=">",intents=intents)
async def load():
    for filename in os.listdir("./Cogs"):
      if filename.endswith(".py"):
        client.load_extension(f"Cogs.{filename[:-3]}")

The above also gives me an error: Exception has occurred: AttributeError 'Client' object has no attribute 'load_extension'

Which one is better in the long run? What is the exact difference?



source https://stackoverflow.com/questions/74503328/confusion-between-commands-bot-and-discord-client-which-one-should-i-use

Comments

Popular posts from this blog

How to show number of registered users in Laravel based on usertype?

i'm trying to display data from the database in the admin dashboard i used this: <?php use Illuminate\Support\Facades\DB; $users = DB::table('users')->count(); echo $users; ?> and i have successfully get the correct data from the database but what if i want to display a specific data for example in this user table there is "usertype" that specify if the user is normal user or admin i want to user the same code above but to display a specific usertype i tried this: <?php use Illuminate\Support\Facades\DB; $users = DB::table('users')->count()->WHERE usertype =admin; echo $users; ?> but it didn't work, what am i doing wrong? source https://stackoverflow.com/questions/68199726/how-to-show-number-of-registered-users-in-laravel-based-on-usertype

How to split a rinex file if I need 24 hours data

Trying to divide rinex file using the command gfzrnx but getting this error. While doing that getting this error msg 'gfzrnx' is not recognized as an internal or external command Trying to split rinex file using the command gfzrnx. also install'gfzrnx'. my doubt is I need to run this program in 'gfzrnx' or in 'cmdprompt'. I am expecting a rinex file with 24 hrs or 1 day data.I Have 48 hrs data in RINEX format. Please help me to solve this issue. source https://stackoverflow.com/questions/75385367/how-to-split-a-rinex-file-if-i-need-24-hours-data

ValueError: X has 10 features, but LinearRegression is expecting 1 features as input

So, I am trying to predict the model but its throwing error like it has 10 features but it expacts only 1. So I am confused can anyone help me with it? more importantly its not working for me when my friend runs it. It works perfectly fine dose anyone know the reason about it? cv = KFold(n_splits = 10) all_loss = [] for i in range(9): # 1st for loop over polynomial orders poly_order = i X_train = make_polynomial(x, poly_order) loss_at_order = [] # initiate a set to collect loss for CV for train_index, test_index in cv.split(X_train): print('TRAIN:', train_index, 'TEST:', test_index) X_train_cv, X_test_cv = X_train[train_index], X_test[test_index] t_train_cv, t_test_cv = t[train_index], t[test_index] reg.fit(X_train_cv, t_train_cv) loss_at_order.append(np.mean((t_test_cv - reg.predict(X_test_cv))**2)) # collect loss at fold all_loss.append(np.mean(loss_at_order)) # collect loss at order plt.plot(np.log(al...