josephmachado

s_suppkey,s_name,s_address,s_nationkey,s_phone,s_acctbal,s_comment
1,Supplier#000000001,"sdrGnXCDRcfriBvY0KL,ipCanOTyK t NN1",17,27-918-335-1736,5755.94, instructions. slyly unusual
2,Supplier#000000002,TRMhVHz3XiFuhapxucPo1,5,15-679-861-2259,4032.68, the pending packages. furiously expres
3,Supplier#000000003,BZ0kXcHUcHjx62L7CjZSql7gbWQ6RPn5X,1,11-383-516-1199,4192.40,ong the fluffily idle packages: bold foxes mold silent package
4,Supplier#000000004,qGTQJXogS83a7MBnEweGHKevK,15,25-843-787-7479,4641.08,al braids affix through the regular
5,Supplier#000000005,lONEYAh9sFWAF75tO,11,21-151-690-3663,-283.84,accounts. instructions ha
6,Supplier#000000006,zaux5FTzToEg,14,24-696-997-4969,1365.79, sleep fluffily against the blithely special dugouts. furiously express th
7,Supplier#000000007, 0W7IPdkpWycUbQ9Adp6B,23,33-990-965-2201,6820.35,ke across the slyly ironic packages. carefully special pinto beans wake blithely. even de
posits los
8,Supplier#000000008,S8AWPqjYlanEQlcDO2WV 97uCVtxCk,17,27-498-742-3860,7627.85,i

josephmachado

{"name":"Data Engineering","icon":"database","settings":"{\"settings\":\"{\\r\\n    \\\"python.analysis.autoImportCompletions\\\": true,\\r\\n    \\\"python.analysis.fixAll\\\": [\\\"source.unusedImports\\\"],  \\r\\n    \\\"editor.defaultFormatter\\\": \\\"charliermarsh.ruff\\\",\\r\\n    \\\"[python]\\\": {\\r\\n        \\\"editor.formatOnType\\\": true,\\r\\n        \\\"editor.formatOnSave\\\": true,\\r\\n        \\\"editor.codeActionsOnSave\\\": {\\r\\n            \\\"source.fixAll\\\": \\\"explicit\\\",\\r\\n            \\\"source.organizeImports\\\": \\\"explicit\\\"\\r\\n        }\\r\\n    },\\r\\n    \\\"editor.inlineSuggest.enabled\\\": true,\\r\\n    \\\"editor.lineHeight\\\": 17,\\r\\n    \\\"breadcrumbs.enabled\\\": false,\\r\\n    \\\"notebook.output.scrolling\\\": true,\\r\\n    \\\"jupyter.themeMatplotlibPlots\\\": true,\\r\\n    \\\"jupyter.widgetScriptSources\\\": [\\r\\n        \\\"unpkg.com\\\",\\r\\n        \\\"jsdelivr.com\\\"\\r\\n    ],\\r\\n    \\\"files.exclude\\\": {\\r\\n        \\\

josephmachado

{"name":"Data Engineering","icon":"database","settings":"{\"settings\":\"{\\r\\n    \\\"python.analysis.autoImportCompletions\\\": true,\\r\\n    \\\"python.analysis.fixAll\\\": [\\\"source.unusedImports\\\"],  \\r\\n    \\\"editor.defaultFormatter\\\": \\\"charliermarsh.ruff\\\",\\r\\n    \\\"[python]\\\": {\\r\\n        \\\"editor.formatOnType\\\": true,\\r\\n        \\\"editor.formatOnSave\\\": true\\r\\n    },\\r\\n    \\\"editor.inlineSuggest.enabled\\\": true,\\r\\n    \\\"editor.lineHeight\\\": 17,\\r\\n    \\\"breadcrumbs.enabled\\\": false,\\r\\n    \\\"notebook.output.scrolling\\\": true,\\r\\n    \\\"jupyter.themeMatplotlibPlots\\\": true,\\r\\n    \\\"jupyter.widgetScriptSources\\\": [\\r\\n        \\\"unpkg.com\\\",\\r\\n        \\\"jsdelivr.com\\\"\\r\\n    ],\\r\\n    \\\"files.exclude\\\": {\\r\\n        \\\"**/__pycache__\\\": true,\\r\\n        \\\"**/.cache\\\": true,\\r\\n        \\\"**/.coverage\\\": true,\\r\\n        \\\"**/.coverage.*\\\": true,\\r\\n        \\\"**/.hypothesis\\\": tru

josephmachado

import json
from fasthtml.common import * 

import requests
import logging
import sys

app, rt = fast_app(hdrs=(Script(src="https://cdn.plot.ly/plotly-2.32.0.min.js"),))

# Extract function

josephmachado

with orders as (
select
        order_id,
        customer_id,
        order_status,
        order_purchase_timestamp::TIMESTAMP AS order_purchase_timestamp,
        order_approved_at::TIMESTAMP AS order_approved_at,
        order_delivered_carrier_date::TIMESTAMP AS order_delivered_carrier_date,
        order_delivered_customer_date::TIMESTAMP AS order_delivered_customer_date,
        order_estimated_delivery_date::TIMESTAMP AS order_estimated_delivery_date

josephmachado

-- Create the employees table
CREATE TABLE employees (
    employee_id INTEGER PRIMARY KEY,
    name TEXT,
    position TEXT
);

-- Insert initial data into employees table
INSERT INTO employees (employee_id, name, position) VALUES
(1, 'John Doe', 'Manager'),

josephmachado

import polars as pl
from ydata_profiling import ProfileReport

# Load the Iris dataset using Seaborn
import seaborn as sns
df = sns.load_dataset('iris')

# Convert to Polars DataFrame
df_polars = pl.DataFrame(df)

josephmachado

import requests
import logging
import sys

# Extract function
def get_exchange_data(url):
    try:
        r = requests.get(url)
    except requests.ConnectionError as ce:
        logging.error(f"There was an error with the request, {ce}")

josephmachado

from faker import Faker
import json
import multiprocessing

fake = Faker()

def generate_fake_entry(_):
    return {
        "exchangeId": fake.domain_word(),
        "name": fake.company(),

josephmachado

WITH DailyTotals AS (
    SELECT
        o_orderdate as orderdate,
        SUM(o_totalprice) AS totalprice
    FROM orders
    GROUP BY o_orderdate
),
DailyTotalsWithLag AS (
    SELECT
        orderdate,