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
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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
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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
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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
...
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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)
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