Skip to main content

Sonoma Python 3.8 was manually installed yet system shows 3.9.6

I have a mac running on Sonoma OS. I know it comes with python automatically, but I need a version 3.8 or less for some projects Im working on for school. However, I have installed 3.8 using homebrew which was installed, but the version of python didnt change. I've tried to update it using pyenv and I haven't been able to update the version. The crazy thing is that even though the version is stated as 3.9.6, if I look for it, I can't find it.

Here is what I did so far:

$ brew update
$ brew install python@3.8

$ python3 --version
Python 3.9.6

$ which python3
/usr/bin/python3

$ which python3.8
/usr/local/bin/python3

$ which python3.9
python3.9 not found

$ brew install pyenv

echo 'export PYENV_ROOT="$HOME/.pyenv"' >> ~/.bash_profile
echo 'export PATH="$PYENV_ROOT/bin:$PATH"' >> ~/.bash_profile
echo 'eval "$(pyenv init -)"' >> ~/.bash_profile
source ~/.bash_profile
Restart your shell so the path changes take effect
exec "$SHELL"
---> this came from an answer to another thread about downgrading python

$ pyenv install 3.8

$ pyenv global 3.8

$ python3 --version
Python 3.9.6

$ which python3
/usr/bin/python3

$ which python3.8
/usr/local/bin/python3

$ which python3.9
python3.9 not found


source https://stackoverflow.com/questions/77848213/sonoma-python-3-8-was-manually-installed-yet-system-shows-3-9-6

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

Why is my reports service not connecting?

I am trying to pull some data from a Postgres database using Node.js and node-postures but I can't figure out why my service isn't connecting. my routes/index.js file: const express = require('express'); const router = express.Router(); const ordersCountController = require('../controllers/ordersCountController'); const ordersController = require('../controllers/ordersController'); const weeklyReportsController = require('../controllers/weeklyReportsController'); router.get('/orders_count', ordersCountController); router.get('/orders', ordersController); router.get('/weekly_reports', weeklyReportsController); module.exports = router; My controllers/weeklyReportsController.js file: const weeklyReportsService = require('../services/weeklyReportsService'); const weeklyReportsController = async (req, res) => { try { const data = await weeklyReportsService; res.json({data}) console...

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...