SQL: Complex patterns

SQL cell
comment
app-builder
auto-run
Atriedes DB
chevron-down
Browse schema
GENERATE
close
select order_date,
sum(number_of_items) as total_items
from prod.dim_orders
group by 1
order by 1 desc
output-arrow
dataframe

SQL: Error debugging

SQL cell
comment
app-builder
auto-run
Atriedes DB
chevron-down
Browse schema
GENERATE
close
select cast(date_trun('month', orders.ordered_at) as date)as month,
category,
is_spicy,
sum(order_details.price) AS order_total
count(distinct orders.order_id) as count
from prod.dim_orders orders
left join prod.order_details order_details on order_details.order_id = orders.order_id
output-arrow
dataframe

SQL: Finicky syntax

SQL cell
comment
app-builder
auto-run
Atriedes DB
chevron-down
Browse schema
GENERATE
close
select ordered_at,
category,
is_spicy
from prod.order_details
order by ordered_at desc
output-arrow
dataframe

SQL: Query from scratch

SQL cell
comment
app-builder
auto-run
Atriedes DB
chevron-down
Browse schema
GENERATE
close
Type SQL or use some magic...
output-arrow
dataframe

Python: Pandas Made Accessible

Python cell
comment
app-builder
auto-run
GENERATE
close
data

Python: Magic Error Fixing

Python cell
comment
app-builder
auto-run
GENERATE
close
Type Python or use some magic...

Python: Deep knowledge

Python cell
comment
app-builder
auto-run
GENERATE
close
Type Python or use some magic...

Debug

SQL cell
comment
app-builder
auto-run
Atriedes DB
chevron-down
Browse schema
GENERATE
close
select cast(date_trun('month', orders.ordered_at) as date) as month,
category,
is_spicy,
sum(order_details.price) AS order_total,
count(distinct orders.order_id) as count
from prod.dim_orders orders
left join prod.order_details order_details on order_details.order_id = orders.order_id
output-arrow
dataframe

Explain

Python cell
comment
app-builder
auto-run
GENERATE
close
model = ensembleModels.RandomForestClassifier(
random_state = 222
)
 
upsample = SMOTE(random_state = 111)
scaler = StandardScaler()
 
features_names = [col for col in data.columns if col != 'Churn']
features = data[feature_names]
scaled_features = scaler.fit_transform(features)
target = data['Churn'].to_numpy()
r_features, r_target = upsampler.fit_resample(scaled_features, target)