Aditya1001001

class Vector():
    def __new__(cls, x, y):
        print("__new__ was invoked")
        instance = object.__new__(cls)
        return instance

    def __init__(self, x, y):
        print("__init__ was invoked")
        self.x = x
        self.y = y

Aditya1001001

import seaborn as sns
sns.set(rc={'figure.figsize':(12,8)})


df = sns.load_dataset('iris')
  
sns.regplot(x = "sepal_length",
            y = "petal_length",
            data = df,
           color="r")

Aditya1001001

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import numpy as np

fig = plt.figure()
ax = fig.gca(projection='3d')   # Create the axes

# Data
X = np.linspace(-8, 8, 100)

Aditya1001001

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
% matplotlib inline
from sklearn.preprocessing import PolynomialFeatures

n_samples = 100
X = np.linspace(0, 10, 100)
y = X ** 3 + np.random.randn(n_samples) * 100 + 100

Aditya1001001

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
% matplotlib inline
from sklearn.linear_model import LinearRegression

n_samples = 100
X = np.linspace(0, 10, 100)
y = X ** 3 + np.random.randn(n_samples) * 100 + 100

Aditya1001001

import os
count = 0
for i in os.listdir():
    os.rename(i,str(count)+ '.'+ i.split('.')[-1])
    count+=1

Aditya1001001

Aditya1001001

def dividend_info(article):
  headline = nlp(article['title'])
  if 'date' in [token.text.lower() for token in headline]:
    date = get_date(headline)
    if date:
      org = get_org(headline)
      ticker = get_ticker(headline)
      amount = get_amount_summary(nlp(article['summary']))
      pay_date = get_pay_date(nlp(article['summary']))
      print("HEADLINE: " + article['title'])

Aditya1001001

def tagged_document(list_of_list_of_words):
   for i, list_of_words in enumerate(list_of_list_of_words):
      yield gensim.models.doc2vec.TaggedDocument(list_of_words, [i])
 
training_data = list(tagged_document(data))
model = gensim.models.doc2vec.Doc2Vec(vector_size=40, min_count=2, epochs=30)
 
model.build_vocab(training_data)
model.train(training_data, total_examples=model.corpus_count, epochs=model.epochs)

Aditya1001001

from newscatcher import describe_url

websites = ['nytimes.com', 'cronachediordinariorazzismo.org', 'libertaegiustizia.it']

for website in websites:
   print(describe_url(website))