# Created: 20250103

import pyutil as pyu
pyu.get_local_pyinfo()

'conda env: ml312-2024; pyv: 3.12.7 | packaged by Anaconda, Inc. | (main, Oct  4 2024, 13:27:36) [GCC 11.2.0]'

print(pyu.ps2("requests"))

requests==2.32.3

import pandas as pd

# Download the list of S&P 500 …

# x and y given as array_like objects
import plotly.express as px

fig = px.scatter(x=[0, 1, 2, 3, 4], y=[0, 1, 4, 9, 16])
fig.show()

# https://seaborn.pydata.org/generated/seaborn.lineplot.html
# https://rajacsp.github.io/mlnotes/python/data-wrangling/food_points/

import seaborn as sns

flights = sns.load_dataset("flights")
flights.head()

# https://seaborn.pydata.org/installing.html
# https://stackabuse.com/seaborn-line-plot-tutorial-and-examples/

!pip show seaborn | grep "Version:"

Version: 0.13.2

# !pip install seaborn

import matplotlib.pyplot as plt
import seaborn as sns

sns.set_theme(style="darkgrid")

x = [
    0,
    220,
    380,
    500,
    540,
    540,
    590
]

sns.lineplot(x)
plt.show()

import pandas …

import re

data = [
    "name['toronto'] = 'One'",
    "state['ontario'] = 'Two'",
    "country['canada']['ca'] = 'Three'",
    "extra['maple'] = 'Four'"
]

REGEX = re.compile(r"\['(?P<word>.*?)'\]")

for line in data:
    found = REGEX.search(line)
    if found:
        print(found.group('word'))

toronto …

from nltk.book import text1

*** Introductory Examples for the NLTK Book ***
Loading text1, ..., text9 and sent1, ..., sent9
Type the name of the text or sentence to view it.
Type: 'texts()' or 'sents()' to list the materials.
text1: Moby …

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]'

print(pyu.ps2("haystack-ai ollama-haystack python-dotenv"))

haystack-ai==2.8.0
ollama-haystack is not installed in the current environment.
python-dotenv==0.21.0

def format_time(seconds):
    hours …

import os
import sys

from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, ForeignKey, Integer, String

Base = declarative_base()

class City(Base):
    __tablename__ = 'city'
    # Here we define …

import numpy as np
import pandas as pd

df = pd.read_csv('abc.csv')

df

import json
from pprint import pprint
from nltk.sentiment.vader import SentimentIntensityAnalyzer
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import urllib.request, json 

data = None
with urllib.request.urlopen …