<jupyter_start><jupyter_text>[Omnisci: "Vega at a Glance"](https://docs.omnisci.com/latest/6_VegaAtaGlance.html)![](https://gblobscdn.gitbook.com/assets%2F-M6kHkWH17KJKscL0Cc4%2F-M8I37xLq2tX6j9QvwZk%2F-M8I4ObJ2MQX7oKbMrAj%2F6_demoScreenshot.png?alt=media&token=<PASSWORD>)<jupyter_code>import altair as alt
import ibis_vega_transform
from ibis.backends import omniscidb as ibis_omniscidb

conn = ibis_omniscidb.connect(
    host='metis.mapd.com', user='demouser', password='<PASSWORD>',
    port=443, database='mapd', protocol= 'https'
)
t = conn.table("tweets_nov_feb")
x, y = t.goog_x, t.goog_y
WIDTH = 385
HEIGHT = 564
X_DOMAIN = [
        -3650484.1235206556,
        7413325.514451755
      ]
Y_DOMAIN = [
        -5778161.9183506705,
        10471808.487466192
      ]
scales = alt.selection_interval(
    bind='scales',
)

alt.Chart(t[x, y], width=WIDTH, height=HEIGHT).mark_rect().encode(
    alt.X(
        'goog_x:Q',
        bin=alt.Bin(maxbins=WIDTH),
        title='goog_x',
        scale=alt.Scale(domain=X_DOMAIN)
    ),
    alt.Y(
        'goog_y:Q',
        bin=alt.Bin(maxbins=HEIGHT),
        title='goog_y',
        scale=alt.Scale(domain=Y_DOMAIN)
    ),
    tooltip='count()'
).add_selection(
    scales
).transform_filter(
    scales
)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14596273291925466, 'fraction_numerical': 0.07531055900621118, 'mean_word_length': 3.756457564575646, 'pattern_counts': {'":': 0, '<': 7, '<?xml version=': 0, '>': 7, 'https://': 2, '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': '190977', 'license': 'MIT', 'n_tokens_mistral': 613, 'n_tokens_neox': 550, 'n_words': 64, 'path': 'notebooks/additional_examples/A05_Replicate_Vega_at_a_Glance_Example.ipynb', 'repo_name': 'Quansight/omnisci-examples', 'size': '1320'}