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()

# https://docs.bokeh.org/en/latest/docs/first_steps/first_steps_1.html

# https://docs.bokeh.org/en/latest/_images/notebook_inline.png

# !pip install jupyter_bokeh

!pip show | grep "Version:"

WARNING: ERROR: Please provide a package name or names.


# !pip install bokeh

x = [1, 2, 3, 4 …

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()

# !pip install pandas_datareader

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 yfinance as yf
import pandas as pd
import matplotlib …

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

data = json.load(open('book-review.json'))

twt = data['reviews'][1 …

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

contents = [
    "The Star obtained a copy of the email outlining the latest in a series of Progressive Conservative provincial budget cuts …

import pandas as pd
import numpy as np

abc = np.array([
    [9, 13, 10],
    [7, 12, 9],
    [19, 11, 8]
])

abc

array([[ 9, 13, 10],
       [ 7, 12,  9],
       [19, 11,  8]])

df2 = pd.DataFrame(abc, columns …

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://github.com/selva86/datasets/raw/master/mpg_ggplot2 …

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5, 6, 7, 8]

# Amount I spent on the breakfast 
y = [9.12, 10.25, 11.25, 7.80, 8.90, 9.80, 10.12, 10 …

import matplotlib.pyplot as plt
import collections
import numpy as np

# Generate random data
data = [tuple(pair)
        for pair in np.random.uniform(5, size=(20, 2))
        for c in range(np.random.randint(1, 50))]  # Replaced random_integers

# Count occurrences of each point
count = collections.Counter(data)

# Print the count …