Reusing a Query (and Views)
While using TidierDB, you may need to generate part of a query and reuse it multiple times. There are two ways to do this
from_query(query)
or its aliast(query)
@create_view(name)
Setup¤
using TidierDB
con = connect(duckdb());
mtcars_path = "https://gist.githubusercontent.com/seankross/a412dfbd88b3db70b74b/raw/5f23f993cd87c283ce766e7ac6b329ee7cc2e1d1/mtcars.csv";
mtcars = dt(con, mtcars_path);
Start a query to analyze fuel efficiency by number of cylinders. However, to further build on this query later, end the chain without using @show_query
or @collect
query = @chain mtcars begin
@group_by cyl
@summarize begin
across(mpg, (mean, minimum, maximum))
num_cars = n()
end
@mutate begin
efficiency = case_when(
mpg_mean >= 25, "High",
mpg_mean >= 15, "Moderate",
"Low" )
end
end;
from_query()
or t(query)
¤
from_query
, or t()
a convienece wrapper, will allow you to reuse the query to calculate the average horsepower for each efficiency category
@chain query begin
@left_join(mtcars, cyl)
@group_by(efficiency)
@summarize(avg_hp = mean(hp))
@collect
end
Row | efficiency | avg_hp |
---|---|---|
String | Float64 | |
1 | Moderate | 180.238 |
2 | High | 82.6364 |
@create_view¤
Queries can also be reused as views.
query2 = @chain mtcars @filter(mpg>20) @mutate(mpg = mpg *4);
@chain mtcars begin
@group_by cyl
@summarize begin
across(mpg, (mean, minimum, maximum))
num_cars = n()
end
@mutate begin
efficiency = case_when(
mpg_mean >= 25, "High",
mpg_mean >= 15, "Moderate",
"Low" )
end
@create_view(viewer)
end;
@chain dt(con, "viewer") begin
@left_join(query2, cyl == cyl)
@summarize(avg_mean = mean(mpg), _by = efficiency)
@mutate(mean = avg_mean / 4 )
@collect
end
Row | efficiency | avg_mean | mean |
---|---|---|---|
String | Float64 | Float64 | |
1 | Moderate | 84.5333 | 21.1333 |
2 | High | 106.655 | 26.6636 |
Preview or save an intermediate table¤
While querying a dataset, you may wish to see an intermediate table, or even save it. You can use @aside
and from_query(_)
, illustrated below, to do just that. While we opted to print the results in this simple example below, we could have saved them by using name = @chain...
import ClickHouse;
conn = conn = connect(clickhouse(); host="localhost", port=19000, database="default", user="default", password="")
path = "https://huggingface.co/datasets/maharshipandya/spotify-tracks-dataset/resolve/refs%2Fconvert%2Fparquet/default/train/0000.parquet"
@chain dt(conn, path) begin
@count(artists)
@aside println(@chain from_query(_) @head(5) @collect)
@arrange(desc(count))
@collect
end
5×2 DataFrame
Row │ artists count
│ String? UInt64
─────┼─────────────────
1 │ missing 1
2 │ Wizo 3
3 │ MAGIC! 3
4 │ Macaco 1
5 │ SOYOU 1
31438×2 DataFrame
Row │ artists count
│ String? UInt64
───────┼─────────────────────────
1 │ The Beatles 279
2 │ George Jones 271
3 │ Stevie Wonder 236
4 │ Linkin Park 224
5 │ Ella Fitzgerald 222
6 │ Prateek Kuhad 217
7 │ Feid 202
⋮ │ ⋮ ⋮
31432 │ Leonard 1
31433 │ marcos g 1
31434 │ BLVKSHP 1
31435 │ Memtrix 1
31436 │ SOYOU 1
31437 │ Macaco 1
31438 │ missing 1
31424 rows omitted
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