<jupyter_start><jupyter_text>Second level headingThis cell contains simple[markdown](https://daringfireball.net/projects/markdown/syntax), a simple language for writing text that can be automatically converted to other formats, e.g. HTML, LaTeX or any of a number of others.**Bold**, *italics*, **_combined_**, ~~strikethrough~~, `inline code`.* bullet pointsor1. numbered3. lists**Equations:**inline $e^{i\pi} + 1 = 0$or on new line$$e^{i\pi} + 1 = 0$$Images:![Jupyter logo](https://jupyter.org/assets/main-logo.svg)Links:[One of many markdown cheat-sheets](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheetemphasis)<jupyter_code># a code cell can run statements of code.
# when you run this cell, the output is sent
# from the web page to a back-end process, run
# and the results are displayed to you
print("hello, world, from code cell")<jupyter_output>hello, world, from code cell<jupyter_text>Calculating pi using Monte Carlo methods  Relevant formulas- square area: $s = (2 r)^2$- circle area: $c = \pi r^2$- $c/s = (\pi r^2) / (4 r^2) = \pi / 4$- $\pi = 4 * c/s$  Image to visualize the concept![Darts](https://raw.githubusercontent.com/coderefinery/jupyter/main/example/darts.svg)<jupyter_code># importing modules that we will need

import random
import matplotlib.pyplot as plt
# initializing the number of "throws"

num_points = 1000
# here we "throw darts" and count the number of hits

points = []
hits = 0
for _ in range(num_points):
    x, y = random.random(), random.random()
    if x*x + y*y < 1.0:
        hits += 1
        points.append((x, y, "red"))
    else:
        points.append((x, y, "blue"))
# unzip points into 3 lists
x, y, colors = zip(*points)

# define figure dimensions
fig, ax = plt.subplots()
fig.set_size_inches(6.0, 6.0)

# plot results
ax.scatter(x, y, c=colors)
# compute and print the estimate

fraction = hits / num_points
4 * fraction<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1369078604893285, 'fraction_numerical': 0.014575741801145237, 'mean_word_length': 4.383753501400561, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 8, 'https://': 4, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '123633', 'license': 'MIT', 'n_tokens_mistral': 675, 'n_tokens_neox': 644, 'n_words': 210, 'path': 'exercise.ipynb', 'repo_name': 'irj-cyber/binder-demo', 'size': '1921'}