!python --version

Python 3.10.5

#!pip install ipywidgets
#!jupyter labextension install @jupyter-widgets/jupyterlab-manager

!jupyter labextension list

JupyterLab v4.2.6
/home/rajaraman/miniconda3/envs/ml3105/share/jupyter/labextensions
        jupyterlab_pygments v0.3.0 enabled OK (python, jupyterlab_pygments)
        jupyterlab-plotly v5.24.1 enabled …

# https://rajasgs.gitbook.io/pynotes/gcp#data-modeling
# https://github.com/tactlabs/student-hiring-prediction-mle/tree/main/dataset

import pyutil as pyu
pyu.get_local_pyinfo()

'conda env: ml311; pyv: 3.11.10 (main, Oct  3 2024, 07:29:13) [GCC 11.2.0]'

# !pip install duckdb

!pip show duckdb | grep "Version:"

Version: 1.1 …

# https://rajasgs.gitbook.io/pynotes/gcp#data-modeling
# https://github.com/tactlabs/student-hiring-prediction-mle/tree/main/dataset

import pyutil as pyu
pyu.get_local_pyinfo()

'conda env: ml311; pyv: 3.11.10 (main, Oct  3 2024, 07:29:13) [GCC 11.2.0]'

# !pip install duckdb

!pip show duckdb | grep "Version:"

Version: 1.1 …

# https://rajasgs.gitbook.io/pynotes/gcp#data-modeling
# https://github.com/tactlabs/student-hiring-prediction-mle/tree/main/dataset

import pyutil as pyu
pyu.get_local_pyinfo()

'conda env: ml311; pyv: 3.11.10 (main, Oct  3 2024, 07:29:13) [GCC 11.2.0]'

# !pip install duckdb

!pip show duckdb | grep "Version:"

Version: 1.1 …

import pyutil as pyu
pyu.get_local_pyinfo()

'conda env: ml311; pyv: 3.11.10 (main, Oct  3 2024, 07:29:13) [GCC 11.2.0]'

import duckdb
import pandas as pd

# Path to your CSV file
csv_file_path = '../dataset/student_data.csv'

# Create a DuckDB connection
con = duckdb.connect(database=':memory:', read_only=False …

from bokeh.plotting import figure, output_file, show
from bokeh.io import output_notebook
from bokeh.models import ColumnDataSource

# Set Bokeh output to notebook
output_notebook()

import tensorflow as tf

import os

# Just disables the warning, doesn't enable AVX/FMA
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

tf.enable_eager_execution()

a = tf.constant([1.0, 2.0])

a

<tf.Tensor: id=3, shape …

animals = ['cat', 'dog', 'monkey']

for idx, animal in enumerate(animals):
    print ('#%d: %s' % (idx , animal))

#0: cat
#1: dog
#2: monkey

data = ['Toronto', 'Montreal', 'Waterloo', 'Chennai', 'Bangalore', 123, ['One', 'Two', 'Three']]

for x in enumerate(data):
    print(x)

(0, 'Toronto')
(1, 'Montreal')
(2, 'Waterloo')
(3, 'Chennai')
(4, 'Bangalore')
(5, 123)
(6, ['One', 'Two', 'Three'])

for i, x in …

import matplotlib
import numpy
import perfplot
from scipy.spatial import distance

def linalg_norm(data):
    a, b = data
    return numpy.linalg.norm(a-b, axis=1)

def sqrt_sum(data):
    a, b = data
    return numpy.sqrt(numpy.sum((a-b …