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SELECT count(*) FROM t1 INNER JOIN t2 ON t1.a = t2.b;

for t1_row in t1.rows:
	for t2_row in t2.rows:
		if (t1_row.a = t2_row.a):
			output_match()

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let hash_table = build_hash_table(t2.rows.a); // build hash table over column a in t2
for t1_row in t1.rows:
	for t2_row in hash_table(t1_row.a): // lookup the corresponding entry for t1’s row
		if (t1_row.a = t2_row.a):
			output_match()

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let hash_table = build_hash_table(t2.rows.a); // build hash table over column a in t2
for t1_row in t1.rows:
	output_match(hash_table(t1_row.a)):

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SELECT
  tweets.location,
  tweets.follower_count as size,
  zipcodes.number as color
FROM
  tweets
  LEFT JOIN zipcodes on ST_Contains(zipcodes.polygon, tweets.location);

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let hash_table = build_hash_table(zipcodes.rows.polygon); // build hash table over polygon column
for t1_row in t1.rows:
	for t2_row in hash_table(t1_row.point):
		if ST_Contains(t2_row.polygon, t1_row.point):
			output_match()

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with mlflow.start_run(run_name="Random_Forest_Undercount"):
    
    from sklearn.ensemble import RandomForestRegressor
    
    # model parameters
    params = {'n_estimators': 1000, 'random_state': 41}

    # log model params
    for key in params:
        mlflow.log_param(key, params[key])

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# get predictions
y_pred = RFtree.predict(test_features)

# compute accuracy metric, then log
R2_score = r2_score(test_labels, y_pred)
print("R2_Score: ", R2_score)
mlflow.log_metric("r2", R2_score)

# plot and save plot graphic linked to model run
xgb.plot_importance(gbtree)

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select  
	sum(number_injured) as number_injured, 
	avg(party_count) as avg_party_count,
	b.osm_id 
from la_collisions a, 
	la_street_buffer b 
where 
	st_contains(b.omnisci_geo,st_setsrid(st_point(a.point_x, a.point_y), 4326))
group by 
	b.osm_id

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select 
	max(probability) as probability,
	b.FID 
from
	usgs_8ft_flame_length_probability a,
	california_building_footprints b
where
	ST_Contains(b.defensible_space, a.omnisci_geo)
group by 
	b.FID

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pip install pyomnisci