<jupyter_start><jupyter_text><jupyter_code># test change 2
# load an example dataset
from vega_datasets import data
cars = data.cars()

import altair as alt

interval = alt.selection_interval()

points = alt.Chart(cars).mark_point().encode(
  x='Horsepower',
  y='Miles_per_Gallon',
  color=alt.condition(interval, 'Origin', alt.value('lightgray'))
).properties(
  selection=interval
)

histogram = alt.Chart(cars).mark_bar().encode(
  x='count()',
  y='Origin',
  color='Origin'
).transform_filter(interval)

points & histogram<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14695340501792115, 'fraction_numerical': 0.0017921146953405018, 'mean_word_length': 6.453333333333333, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, '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': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '299413', 'license': 'MIT', 'n_tokens_mistral': 201, 'n_tokens_neox': 194, 'n_words': 37, 'path': 'colab_demo.ipynb', 'repo_name': 'cjflanagan/AccessibleML', 'size': '558'}