import nltk

content = "The Democrats — including more than 50 freshmen — are mindful that impeachment poses political risks that could endanger the seats of moderates and their majority, as well as strengthen Mr. Trump’s hand. "

content

'The …

from textblob.classifiers import NaiveBayesClassifier
from textblob import TextBlob

train = [
     ('I love this sandwich.', 'pos'),
     ('this is an amazing place!', 'pos'),
     ('I feel very good about these beers.', 'pos'),
     ('this is my best work.', 'pos'),
     ("what an …

from IPython.display import Markdown, display

# Path to your PNG file
image_path = 'deer.jpg'

# Display the image inline
display(Markdown(f"![Image Description]({image_path})"))

import pandas as pd

times = pd.date_range('2000-01-01', periods=4)

print(times)

DatetimeIndex(['2000-01-01', '2000-01-02', '2000-01-03', '2000-01-04'], dtype='datetime64[ns]', freq='D')

for time in times:
    print(time)

2000-01-01 00:00:00
2000-01-02 …

import timeit
import numpy as np

def add(x, y):
    return x + y

repeat_number = 100000
e = timeit.repeat(
    stmt='''add(a, b)''', 
    setup='''a=2; b=3;from __main__ import add''', 
    repeat=3,
    number=repeat_number)

print('Method: {}, Avg …

import timeit

def some_function():
    return map(lambda x: x^2, range(10))

time1 = timeit.timeit(some_function)
print(time1)

0.39235781200113706

print(time1)

0.39235781200113706

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()

!python --version

Python 3.10.5

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

# Load the dataset
tips = sns.load_dataset("tips")

# Create a PairGrid with regression and histogram plots
g = sns.PairGrid(tips, diag_sharey=False, hue="sex", palette="Set2")
g.map_upper(sns.scatterplot, alpha=0 …

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier, export_graphviz
import pydotplus
from IPython.display import Image

# Load the Titanic dataset
url = "https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv"
titanic = pd.read_csv(url)

# Preprocess the dataset
# Select features and target variable
features = ['Pclass …

!python --version

Python 3.10.5

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from deepchecks.tabular import Dataset
from deepchecks.tabular.suites import full_suite

# Load the Titanic dataset
url = "https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv …