from PIL import Image

# Open the image
im = Image.open("/Users/rajacsp/datasets/barcode_images/orange.jpg")

im.save("/Users/rajacsp/datasets/barcode_images/orange_3.jpg", format="JPEG", quality=70)

from PIL import Image
import cv2

# Open the image
img = cv2.imread('/Users/rajacsp/datasets/barcode_images/orange.jpg')


# save image with lower compression—bigger file size but faster decoding
#cv2.imwrite('/Users/str-kwml0011/datasets/random_images …

from scipy.constants import pi

pi

3.141592653589793

from scipy.constants import golden

golden

1.618033988749895

from scipy.constants import G

G

6.67408e-11

from scipy.constants import speed_of_light as sol

sol

299792458.0

import requests
import json

response = requests.get("https://randomuser.me/api/")

response_json = response.json()

print(response_json)

{'results': [{'gender': 'female', 'name': {'title': 'Ms', 'first': 'Mia', 'last': 'Walker'}, 'location': {'street': {'number': 8562, 'name': 'Port Hills Road'}, 'city': 'Hamilton', 'state': 'Waikato', 'country': 'New Zealand', 'postcode': 45938, 'coordinates': {'latitude': '54.8821', 'longitude': '15.6902 …

from gensim.summarization import summarize, keywords
from pprint import pprint
from smart_open import smart_open

text = " ".join((line for line in smart_open('sample.txt', encoding='utf-8')))

text

'More than half of survey participants also reported clicking on …

import copy

a = [1, 2, 3]

b = copy.copy(a)

b

[1, 2, 3]

b.append(4)

a

[1, 2, 3]

b

[1, 2, 3, 4]

# deep copy

c = copy.deepcopy(a)

c

[1, 2, 3 …

import asyncio 
import time

async def test():
    print('inside test')

async def foo():
    await test()  # No exception raised.
    print('foo')

foo()

<coroutine object foo at 0x7f54f8e83940>

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("pandas"))

pandas==2.2.3

import pandas as pd
import numpy as np

def histogram_intersection(a, b):
    v …

# https://www.machinelearningplus.com/plots/top-50-matplotlib-visualizations-the-master-plots-python/

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

import seaborn as sns
import warnings; warnings.filterwarnings(action='once')

# Import Data
df = pd.read_csv("https …

import re
from collections import Counter

words = re.findall(r'\w+', open('../../../assets/news.txt').read().lower())
Counter(words).most_common(10)

[('a', 37),
 ('and', 36),
 ('to', 30),
 ('the', 27),
 ('that', 24),
 ('of', 18),
 ('employees', 17 …