import numpy as np
import pandas as pd

marks = [
    [90, 87],
    [90, 95],
    [92, 95]
]

marks

[[90, 87], [90, 95], [92, 95]]

df = pd.DataFrame(marks, columns=['maths', 'science'])

df

from sklearn import svm, datasets
from sklearn import metrics
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_breast_cancer
import matplotlib.pyplot as plt

# Load Breast Cancer Dataset …

import matplotlib.pyplot as plt
import numpy as np

score = np.array([0.9, 0.8, 0.7, 0.6, 0.55, 0.54, 0.53, 0.52, 0.51, 0.505, 0.4, 0 …

from gensim.utils import simple_preprocess
from gensim import corpora
from pprint import pprint

contents = [
    "More than half of survey participants also reported clicking on a headline expecting to read a balanced news account, only to …

import re
from nltk.tokenize import word_tokenize
from collections import Counter

word_counter = Counter(word_tokenize("""The cat is in the box. The cat likes the box. The box is over the cat."""))

print(word_counter)

Counter({'The': 3, 'cat …

import pandas as pd;
import numpy as np;
from datetime import date
import matplotlib.pyplot as plt; plt.rcdefaults()



sizes = np.full((10), 7, dtype=int);    
print(sizes);

from functools import reduce
product = reduce((lambda x, y: x * y), [1, 2, 3, 4])

[7 7 7 7 7 7 7 …

# https://matplotlib.org/3.3.1/gallery/misc/table_demo.html

import numpy as np
import matplotlib.pyplot as plt


data = [[ 66386, 174296,  75131, 577908,  32015],
        [ 58230, 381139,  78045,  99308, 160454],
        [ 89135,  80552, 152558, 497981 …

from PIL import Image
from colormap import rgb2hex
from os import listdir
from os.path import isfile, join, isdir
import os.path as path

NON_BARCODE_PAR = 500

def is_barcode_image(filename):

    im = Image.open(filename, 'r')
    #width, height = im …

# !pip install colormap

from PIL import Image
from colormap import rgb2hex
from os import listdir
from os.path import isfile, join, isdir
import os.path as path

NON_BARCODE_PAR = 500

def is_barcode_image(filename):

    im = Image.open(filename, 'r')
    #width, height = im.size
    pixel_values = list(im.getdata())

    #print(pixel_values)

    color_set = set()

    for …

!pip show gliner

Name: gliner
Version: 0.2.13
Summary: Generalist model for NER (Extract any entity types from texts)
Home-page: 
Author: Urchade Zaratiana, Nadi Tomeh, Pierre Holat, Thierry Charnois
Author-email: 
License: Apache-2.0
Location: /home/rajaraman/miniconda3/envs/ml312/lib/python3.12/site-packages
Requires …