<jupyter_start><jupyter_text><jupyter_code>#@title Try running this section by clicking the play button on the left. 

#@markdown This calculates 10*10 and displays the result...
print("10 times 10 = ",10*10)<jupyter_output><empty_output><jupyter_text>Please run the entire section below by clicking the "Play" button on the left. This may take a bit more time, because the code is importing and installing a few useful modules. If you see the message `Completed successfully!` displayed in the end, you are good to go!<jupyter_code>#@title Click the play button at the left
import os
import sys
assert sys.version_info >= (3, 5)   # Python ≥3.5 is required
import urllib

import numpy as np   # numpy for numerical linear algebra
import pandas as pd  # pandas for managing dataframes

#import tensorflow as tf
#from tensorflow.keras import layers

import sklearn
assert sklearn.__version__ >= "0.20"  # Scikit-Learn version ≥0.20 required
import sklearn.metrics as metrics

# adjust reporting in pandas: max 10 rows and two-digit precision
#pd.options.display.max_rows = 10
pd.options.display.float_format = "{:,.2f}".format

# import matplotlib and pyplot: critical packages for plotting 
import matplotlib as mpl
import matplotlib.pyplot as plt
# Make sure Matplotlib runs inline, for nice figures
%matplotlib inline  
mpl.rc('axes', labelsize=14)
mpl.rc('xtick', labelsize=12)
mpl.rc('ytick', labelsize=12)

# install the latest version of seaborn for nicer graphics
!pip install --prefix {sys.prefix} seaborn==0.11.0  &> /dev/null
import seaborn as sns

# install facets overview
!pip install facets-overview &> /dev/null
# import facets overview + some other relevant packages
from IPython.core.display import display, HTML
import base64
from facets_overview.feature_statistics_generator import FeatureStatisticsGenerator

# install graphviz for visualizing decision trees
!pip install graphviz  &> /dev/null
import graphviz

# Ignore useless some warnings
import warnings
warnings.simplefilter(action="ignore")

print("Completed successfully!")<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.07880826525708794, 'fraction_numerical': 0.01874098990869774, 'mean_word_length': 5.328267477203648, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 14, 'https://': 0, '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': '428673', 'license': 'MIT', 'n_tokens_mistral': 648, 'n_tokens_neox': 598, 'n_words': 242, 'path': 'ML_Pipeline/S03_04_Test.ipynb', 'repo_name': 'dan-a-iancu/airm', 'size': '2081'}