[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-90a024f8fc9fd261-automate-weekly-pdf-reports-with-python-etl-pipeli-summary":3,"summaries-facets-categories":317,"summary-related-90a024f8fc9fd261-automate-weekly-pdf-reports-with-python-etl-pipeli-summary":3902},{"id":4,"title":5,"ai":6,"body":13,"categories":278,"created_at":280,"date_modified":280,"description":31,"extension":281,"faq":280,"featured":282,"kicker_label":280,"meta":283,"navigation":72,"path":300,"published_at":301,"question":280,"scraped_at":302,"seo":303,"sitemap":304,"source_id":305,"source_name":306,"source_type":307,"source_url":308,"stem":309,"tags":310,"thumbnail_url":280,"tldr":314,"tweet":280,"unknown_tags":315,"__hash__":316},"summaries\u002Fsummaries\u002F90a024f8fc9fd261-automate-weekly-pdf-reports-with-python-etl-pipeli-summary.md","Automate Weekly PDF Reports with Python ETL Pipeline",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",8933,2254,17256,0.00289095,{"type":14,"value":15,"toc":273},"minimark",[16,21,25,92,107,123,133,136,140,143,188,191,194,197,201,204,207,263,266,269],[17,18,20],"h2",{"id":19},"merge-raw-datasets-into-actionable-business-data","Merge Raw Datasets into Actionable Business Data",[22,23,24],"p",{},"Start by loading six Olist e-commerce CSVs (orders, customers, items, payments, products, reviews) with pandas.read_csv, then merge on keys like customer_id, order_id, product_id:",[26,27,32],"pre",{"className":28,"code":29,"language":30,"meta":31,"style":31},"language-python shiki shiki-themes github-light github-dark","def load_data():\n    return {\n        \"orders\": pd.read_csv(\"data\u002Folist_orders_dataset.csv\"),\n        # ... other datasets\n    }\n\ndf = data[\"orders\"].merge(data[\"customers\"], on=\"customer_id\", how=\"left\") \\\n    .merge(data[\"items\"], on=\"order_id\", how=\"left\") \\\n    # ... other merges\n","python","",[33,34,35,43,49,55,61,67,74,80,86],"code",{"__ignoreMap":31},[36,37,40],"span",{"class":38,"line":39},"line",1,[36,41,42],{},"def load_data():\n",[36,44,46],{"class":38,"line":45},2,[36,47,48],{},"    return {\n",[36,50,52],{"class":38,"line":51},3,[36,53,54],{},"        \"orders\": pd.read_csv(\"data\u002Folist_orders_dataset.csv\"),\n",[36,56,58],{"class":38,"line":57},4,[36,59,60],{},"        # ... other datasets\n",[36,62,64],{"class":38,"line":63},5,[36,65,66],{},"    }\n",[36,68,70],{"class":38,"line":69},6,[36,71,73],{"emptyLinePlaceholder":72},true,"\n",[36,75,77],{"class":38,"line":76},7,[36,78,79],{},"df = data[\"orders\"].merge(data[\"customers\"], on=\"customer_id\", how=\"left\") \\\n",[36,81,83],{"class":38,"line":82},8,[36,84,85],{},"    .merge(data[\"items\"], on=\"order_id\", how=\"left\") \\\n",[36,87,89],{"class":38,"line":88},9,[36,90,91],{},"    # ... other merges\n",[22,93,94,95,98,99,102,103,106],{},"Convert timestamps to datetime for time-based calcs: df",[36,96,97],{},"\"order_purchase_timestamp\""," = pd.to_datetime(...). Compute delivery delays as (delivered - estimated).dt.days > 0 for is_delayed. Derive revenue = price + freight_value, profit = price - freight_value. Aggregate metrics like revenue_current = df",[36,100,101],{},"\"revenue\"",".sum(), orders_current = df",[36,104,105],{},"\"order_id\"",".nunique(), AOV = revenue \u002F orders.",[22,108,109,110,113,114,116,117,113,120,122],{},"Group by month for trends: monthly = df.groupby(\"month\").agg({\"revenue\": \"sum\", \"order_id\": \"nunique\"}); monthly",[36,111,112],{},"\"growth\""," = monthly",[36,115,101],{},".pct_change() * 100; monthly",[36,118,119],{},"\"moving_avg\"",[36,121,101],{},".rolling(3).mean().",[22,124,125,126,132],{},"Simulate weekly reporting with cutoff: df_sim = df",[36,127,128,129,131],{},"df",[36,130,97],{}," \u003C= cutoff_date",", advancing cutoff_date = start_date + pd.Timedelta(days=7 * run_count) via state.txt to mimic live cycles without reprocessing all history.",[22,134,135],{},"This standardization ensures consistent metric definitions across runs, turning scattered CSVs into a unified view of who bought what, payment amounts, delivery times, and satisfaction.",[17,137,139],{"id":138},"add-rule-based-insights-and-build-pdf-reports","Add Rule-Based Insights and Build PDF Reports",[22,141,142],{},"Metrics alone fail without context—use simple if-conditions to interpret:",[26,144,146],{"className":28,"code":145,"language":30,"meta":31,"style":31},"def generate_insights(metrics):\n    insights = []\n    if metrics[\"profit_current\"] \u003C metrics[\"revenue_current\"]:\n        insights.append(\"Revenue growing but profit margin thin, high logistics costs.\")\n    growth_volatility = metrics[\"monthly\"][\"growth\"].std()\n    if growth_volatility > 50:\n        insights.append(\"Revenue growth highly volatile, unstable performance.\")\n    # ...\n",[33,147,148,153,158,163,168,173,178,183],{"__ignoreMap":31},[36,149,150],{"class":38,"line":39},[36,151,152],{},"def generate_insights(metrics):\n",[36,154,155],{"class":38,"line":45},[36,156,157],{},"    insights = []\n",[36,159,160],{"class":38,"line":51},[36,161,162],{},"    if metrics[\"profit_current\"] \u003C metrics[\"revenue_current\"]:\n",[36,164,165],{"class":38,"line":57},[36,166,167],{},"        insights.append(\"Revenue growing but profit margin thin, high logistics costs.\")\n",[36,169,170],{"class":38,"line":63},[36,171,172],{},"    growth_volatility = metrics[\"monthly\"][\"growth\"].std()\n",[36,174,175],{"class":38,"line":69},[36,176,177],{},"    if growth_volatility > 50:\n",[36,179,180],{"class":38,"line":76},[36,181,182],{},"        insights.append(\"Revenue growth highly volatile, unstable performance.\")\n",[36,184,185],{"class":38,"line":82},[36,186,187],{},"    # ...\n",[22,189,190],{},"Generate PDF with ReportLab: create executive summary (e.g., 2018 revenue \u003C 2017, orders down, AOV stable, 9.36% delay rate, 3.91 avg review score), KPI trends (Jan 2018 revenue\u002Fprofit >600% over 2017 but slowing; AOV 2-14% lower, driven by transaction volume), top products (relogios_presentes\u002Fbeleza_saude ~510K revenue each), delivery (SE state 33% delays, casa_conforto_2 60%; overall -10.76 avg delay days = early deliveries), payments (credit card 75%, boleto 19.1%), reviews (5-stars dominant, avg 3.91).",[22,192,193],{},"Key patterns: thin margins from costs; volatile growth; new-customer reliance; delays hurt scores; SP top region; credit users spend more.",[22,195,196],{},"Code charts with matplotlib (plt.savefig(\"revenue_chart.png\")), insert via Image(width=450,height=220), tables via Table(table_data). Central pipeline: data → transform → metrics → insights → generate_report().",[17,198,200],{"id":199},"schedule-email-delivery-with-github-actions","Schedule Email Delivery with GitHub Actions",[22,202,203],{},"Automate email: use smtplib.SMTP_SSL('smtp.gmail.com',465), login via os.getenv(\"EMAIL_SENDER\u002FPASSWORD\"), attach PDF, dynamic subject. Secure creds in GitHub Secrets (EMAIL_SENDER, EMAIL_PASSWORD, EMAIL_RECEIVER).",[22,205,206],{},"Deploy via .github\u002Fworkflows\u002Fauto-report.yml:",[26,208,212],{"className":209,"code":210,"language":211,"meta":31,"style":31},"language-yaml shiki shiki-themes github-light github-dark","on:\n  schedule:\n    - cron: '0 1 * * 1'  # Mondays 1AM UTC\njobs:\n  # setup env, pip install, run main.py\n","yaml",[33,213,214,224,232,251,258],{"__ignoreMap":31},[36,215,216,220],{"class":38,"line":39},[36,217,219],{"class":218},"sj4cs","on",[36,221,223],{"class":222},"sVt8B",":\n",[36,225,226,230],{"class":38,"line":45},[36,227,229],{"class":228},"s9eBZ","  schedule",[36,231,223],{"class":222},[36,233,234,237,240,243,247],{"class":38,"line":51},[36,235,236],{"class":222},"    - ",[36,238,239],{"class":228},"cron",[36,241,242],{"class":222},": ",[36,244,246],{"class":245},"sZZnC","'0 1 * * 1'",[36,248,250],{"class":249},"sJ8bj","  # Mondays 1AM UTC\n",[36,252,253,256],{"class":38,"line":57},[36,254,255],{"class":228},"jobs",[36,257,223],{"class":222},[36,259,260],{"class":38,"line":63},[36,261,262],{"class":249},"  # setup env, pip install, run main.py\n",[22,264,265],{},"Triggers workflow: installs deps, executes pipeline (advances run_count), generates\u002Fsends report. No local runs—wake to delivered emails. Full loop: cron → ETL → PDF → email → state update for next cutoff.",[22,267,268],{},"Trade-offs: Relies on GitHub free tier (2k min\u002Fmonth); Gmail app passwords needed; rule-insights basic (extend with ML if needed). Scales to live data sources by swapping CSVs for APIs\u002FDBs.",[270,271,272],"style",{},"html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html pre.shiki code .sj4cs, html code.shiki .sj4cs{--shiki-default:#005CC5;--shiki-dark:#79B8FF}html pre.shiki code .sVt8B, html code.shiki .sVt8B{--shiki-default:#24292E;--shiki-dark:#E1E4E8}html pre.shiki code .s9eBZ, html code.shiki .s9eBZ{--shiki-default:#22863A;--shiki-dark:#85E89D}html pre.shiki code .sZZnC, html code.shiki .sZZnC{--shiki-default:#032F62;--shiki-dark:#9ECBFF}html pre.shiki code .sJ8bj, html code.shiki .sJ8bj{--shiki-default:#6A737D;--shiki-dark:#6A737D}",{"title":31,"searchDepth":45,"depth":45,"links":274},[275,276,277],{"id":19,"depth":45,"text":20},{"id":138,"depth":45,"text":139},{"id":199,"depth":45,"text":200},[279],"Data Science & Visualization",null,"md",false,{"content_references":284,"triage":297},[285,291],{"type":286,"title":287,"author":288,"url":289,"context":290},"dataset","Brazilian Ecommerce Public Dataset by Olist","Olist","https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Folistbr\u002Fbrazilian-ecommerce","mentioned",{"type":292,"title":293,"author":294,"url":295,"context":296},"other","Weekly-Business-Report-Automation","jihanKamilah","https:\u002F\u002Fgithub.com\u002FjihanKamilah\u002FWeekly-Business-Report-Automation\u002F","recommended",{"relevance":63,"novelty":51,"quality":57,"actionability":63,"composite":298,"reasoning":299},4.35,"Category: AI Automation. The article provides a detailed guide on automating weekly reports using a Python ETL pipeline, which directly addresses the audience's need for practical automation solutions. It includes specific code examples and actionable steps, making it highly relevant and immediately applicable for those building AI-powered products.","\u002Fsummaries\u002F90a024f8fc9fd261-automate-weekly-pdf-reports-with-python-etl-pipeli-summary","2026-04-21 13:31:02","2026-04-21 15:26:14",{"title":5,"description":31},{"loc":300},"90a024f8fc9fd261","Learning Data","article","https:\u002F\u002Fmedium.com\u002Flearning-data\u002Fi-was-tired-of-weekly-reports-so-i-automated-the-entire-thing-f63f88de59ce?source=rss----eec44e936bf1---4","summaries\u002F90a024f8fc9fd261-automate-weekly-pdf-reports-with-python-etl-pipeli-summary",[30,311,312,313],"automation","data-science","data-visualization","Load\u002Fmerge e-commerce datasets, compute revenue\u002Fprofit\u002FAOV\u002Fgrowth metrics, generate PDF with matplotlib\u002FReportLab charts and rule-based insights, email via smtplib, schedule weekly via GitHub Actions 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Convert blobs to bytes via ",[33,3929,3930],{},"to_bytes()"," which decodes base64 strings or lists. Decompress if gzip header (",[33,3933,3934],{},"b'\\x1f\\x8b'","), then auto-detect format in ",[33,3937,3938],{},"parse_task()",": prioritize ",[33,3941,3942],{},"tarfile.open()"," for archives (extract files as str\u002Fbytes), fall back to ",[33,3945,3946],{},"ZipFile",", then ",[33,3949,3950],{},"json.loads()"," (or JSONL line-by-line), plain text decode, or binary. This yields dicts with ",[33,3953,3954],{},"format",", ",[33,3957,3958],{},"files"," (for archives), ",[33,3961,3962],{},"content",", plus ",[33,3965,3966],{},"raw_size","\u002F",[33,3969,3970],{},"compressed_size",". Example: first sample decompresses from compressed bytes to raw, revealing tar with JSON metadata and .py code files.",[22,3973,3974,3975,3978],{},"Use ",[33,3976,3977],{},"show_task()"," to preview: breakdown by extension (e.g., .json, .py), truncate JSON to 1500 chars, code to 600. Trade-off: Streaming processes samples in real-time but requires robust error handling for malformed blobs (e.g., UnicodeDecodeError keeps as bytes).",[17,3980,3982],{"id":3981},"uncover-dataset-structure-via-counters-and-plots","Uncover Dataset Structure via Counters and Plots",[22,3984,3985,3986,3989],{},"Extract source from ",[33,3987,3988],{},"path"," prefix (split on last '-'): top 15 sources dominate test split (e.g., count thousands each). Track compressed sizes: log-scale histogram shows median p50 KB, p95 ~higher KB—most tasks compact, outliers bulkier. Inspect 200 samples: common filenames (e.g., task.json, README.md top counts), JSON keys (e.g., instruction, tests frequent). Full listings reveal 5-10 files per tar\u002Fzip typically.",[22,3991,3992,3993,3996,3997,3955,4000,4003],{},"Aggregate in ",[33,3994,3995],{},"TaskTroveExplorer.summary(limit=1000)",": group by source for n tasks, mean compressed\u002Fraw KB (log y-scale bar chart top 12), mean files. Enables quick profiling—e.g., some sources average 10+ KB raw, others leaner. Polars DataFrame slice of 500 tasks captures ",[33,3998,3999],{},"source",[33,4001,4002],{},"is_verified",", sizes, instruction preview for downstream modeling.",[17,4005,4007],{"id":4006},"detect-verifiers-and-export-rl-ready-tasks","Detect Verifiers and Export RL-Ready Tasks",[22,4009,4010,4011,4014],{},"Flag evaluation-ready tasks with ",[33,4012,4013],{},"has_verifier()",": scan filenames for 'verifier'\u002F'judge'\u002F'grader', JSON keys like 'verifier_config'\u002F'rubric'\u002F'test_patch', or content strings. Multi-signal boosts recall—e.g., verified tasks have dedicated verifier.py or JSON. Per-source rates vary (bar chart: green high % usable for RL); hunt first verified sample to inspect (e.g., grader JSON with tests).",[22,4016,4017,4020,4021,4024,4025,4028,4029,4032],{},[33,4018,4019],{},"TaskTroveExplorer"," class unifies: ",[33,4022,4023],{},"iter()"," filters sources, ",[33,4026,4027],{},"sample(n=5)"," parses + adds metadata, ",[33,4030,4031],{},"export()"," writes dirs with files\u002FJSON. Saves Parquet slice (500 rows, ~KB): boosts workflows by filtering verified tasks (sum across sources). Full pipeline scales to validation split; lists HF repo subdirs for all sources (~dozens).",{"title":31,"searchDepth":45,"depth":45,"links":4034},[4035,4036,4037],{"id":3915,"depth":45,"text":3916},{"id":3981,"depth":45,"text":3982},{"id":4006,"depth":45,"text":4007},[279],{"content_references":4040,"triage":4047},[4041,4044],{"type":286,"title":4042,"url":4043,"context":290},"TaskTrove","https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002Fopen-thoughts\u002FTaskTrove",{"type":292,"title":4045,"url":4046,"context":296},"Full Codes with Notebook","https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Agents-Projects-Tutorials\u002Fblob\u002Fmain\u002FLLM%20Projects\u002Ftasktrove_exploration_pipeline_marktechpost.py",{"relevance":63,"novelty":57,"quality":57,"actionability":57,"composite":298,"reasoning":4048},"Category: Data Science & Visualization. The article provides a detailed guide on streaming and parsing a specific dataset, which is highly relevant for developers looking to integrate AI features using real-world data. It includes practical code examples and techniques for handling large datasets, making it actionable for the target audience.","\u002Fsummaries\u002F0cdee908eb39d657-stream-parse-tasktrove-dataset-for-ai-task-insight-summary","2026-05-03 21:26:42","2026-05-04 16:13:43",{"title":3905,"description":31},{"loc":4049},"0cdee908eb39d657","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F05\u002F03\u002Fa-coding-implementation-to-explore-and-analyze-the-tasktrove-dataset-with-streaming-parsing-visualization-and-verifier-detection\u002F","summaries\u002F0cdee908eb39d657-stream-parse-tasktrove-dataset-for-ai-task-insight-summary",[30,312,313],"Stream multi-GB TaskTrove dataset without full download; parse gzip-compressed tar\u002Fzip\u002FJSON binaries to analyze sources, sizes (median  p50 KB compressed), filenames, and detect verifiers for RL-ready tasks via multi-signal heuristics.",[],"vJBe85PNXCRjjCrLU1WGvZnO0Dhqgjb6ThGkJ-rMnRQ",{"id":4063,"title":4064,"ai":4065,"body":4070,"categories":4306,"created_at":280,"date_modified":280,"description":31,"extension":281,"faq":280,"featured":282,"kicker_label":280,"meta":4307,"navigation":72,"path":4316,"published_at":4317,"question":280,"scraped_at":4318,"seo":4319,"sitemap":4320,"source_id":4321,"source_name":4322,"source_type":307,"source_url":4323,"stem":4324,"tags":4325,"thumbnail_url":280,"tldr":4326,"tweet":280,"unknown_tags":4327,"__hash__":4328},"summaries\u002Fsummaries\u002F9083ba0dfd966742-build-queryable-options-iv-db-from-live-api-polls-summary.md","Build Queryable Options IV DB from Live API Polls",{"provider":7,"model":8,"input_tokens":4066,"output_tokens":4067,"processing_time_ms":4068,"cost_usd":4069},9219,1883,33987,0.00227845,{"type":14,"value":4071,"toc":4301},[4072,4076,4135,4159,4163,4201,4225,4247,4251,4273,4291],[17,4073,4075],{"id":4074},"dual-table-schema-enables-time-series-audits-and-instant-current-views","Dual-Table Schema Enables Time-Series Audits and Instant Current Views",[22,4077,4078,4079,4082,4083,4086,4087,4090,4091,4094,4095,4098,4099,4102,4103,4106,4107,4110,4111,4114,4115,4118,4119,4122,4123,4126,4127,4130,4131,4134],{},"Store live options analytics in two SQLite tables for balanced access patterns. ",[33,4080,4081],{},"implied_quote_history"," is append-only, preserving every snapshot with ",[33,4084,4085],{},"id"," autoincrement primary key, ",[33,4088,4089],{},"asof_ts"," (UTC ISO timestamp per poll), and ",[33,4092,4093],{},"option_key"," (stable identifier: ",[33,4096,4097],{},"symbol|expiry|strike|cp|at|ts",") as join key. Indexes on ",[33,4100,4101],{},"(symbol, expiry, asof_ts)"," and ",[33,4104,4105],{},"(option_key, asof_ts)"," speed expiry-time or option-timeline queries. Columns capture surface IV (",[33,4108,4109],{},"s_vol","), ATM vol (",[33,4112,4113],{},"atm_vol","), Greeks (delta, gamma, theta, vega), underlying price (",[33,4116,4117],{},"u_prc","), years to expiry (",[33,4120,4121],{},"years","), rate, bid\u002Fask\u002FIVs, ",[33,4124,4125],{},"calc_source"," (filter to \"Loop\" for consistent snapshots), ",[33,4128,4129],{},"quote_ok"," flag (1 if bid\u002Fask non-zero), and ",[33,4132,4133],{},"src_ts",".",[22,4136,4137,4140,4141,4143,4144,4147,4148,4151,4152,4102,4155,4158],{},[33,4138,4139],{},"implied_quote_latest"," uses ",[33,4142,4093],{}," primary key for upserts: each poll overwrites with newest values, setting ",[33,4145,4146],{},"last_asof_ts"," to current snapshot time. Same columns and index on ",[33,4149,4150],{},"(symbol, expiry)",". PRAGMA ",[33,4153,4154],{},"journal_mode=WAL",[33,4156,4157],{},"synchronous=NORMAL"," ensure reliable writes. This split avoids full-history scans for \"current surface\" while retaining audit trail—history grows unbounded (e.g., 1454 rows\u002Fsnapshot × 9 polls = 12,806 total), latest stays flat at ~1454 rows.",[17,4160,4162],{"id":4161},"normalize-and-poll-api-for-reliable-snapshots","Normalize and Poll API for Reliable Snapshots",[22,4164,4165,4166,4169,4170,4173,4174,3955,4177,3955,4180,3955,4183,4186,4187,4190,4191,3955,4194,4197,4198,4134],{},"Fetch via REST ",[33,4167,4168],{},"getmsgs"," on ",[33,4171,4172],{},"https:\u002F\u002Fmlink-live.nms.saturn.spiderrockconnect.com\u002Frest\u002Fjson"," with ",[33,4175,4176],{},"apiKey",[33,4178,4179],{},"msgType=LiveImpliedQuote",[33,4181,4182],{},"where=okey.tk:eq:TSLA",[33,4184,4185],{},"limit=2000",". Response: list of messages ending in ",[33,4188,4189],{},"QueryResult","; filter to ",[33,4192,4193],{},"mTyp=LiveImpliedQuote",[33,4195,4196],{},"calcSource=Loop",", non-zero ",[33,4199,4200],{},"sVol",[22,4202,4203,4204,4207,4208,4210,4211,4214,4215,4217,4218,4220,4221,4224],{},"Flatten nested ",[33,4205,4206],{},"pkey.okey"," into ",[33,4209,4093],{}," via ",[33,4212,4213],{},"|",". Build DataFrame rows with all fields; sort by ",[33,4216,4133],{},", dedupe latest per ",[33,4219,4093],{},". ",[33,4222,4223],{},"quote_ok = int(not (o_bid == 0 and o_ask == 0))"," flags quoted options without dropping analytics-only rows.",[22,4226,4227,4228,4231,4232,4235,4236,4238,4239,4242,4243,4246],{},"Loop polls every ",[33,4229,4230],{},"poll_interval_s=10"," for ",[33,4233,4234],{},"poll_duration_s=120",": timestamp ",[33,4237,4089],{},", fetch\u002Fnormalize\u002Fwrite. Batch ",[33,4240,4241],{},"executemany"," inserts history; upsert latest with ",[33,4244,4245],{},"on conflict(option_key) do update set"," all fields. Handles varying row counts (e.g., 1454 → snapshot_rows fluctuates due to limit). Production tip: pin expiries\u002Fstrikes or interpolate to fixed moneyness for stability.",[17,4248,4250],{"id":4249},"reconstruct-smiles-skew-and-metrics-from-history-queries","Reconstruct Smiles, Skew, and Metrics from History Queries",[22,4252,4253,4254,4257,4258,4261,4262,4265,4266,4268,4269,4272],{},"Query history for analysis: count rows per expiry (",[33,4255,4256],{},"group by expiry order by n desc limit 10",") to pick representative like ",[33,4259,4260],{},"2026-11-20"," (highest coverage). Pull ",[33,4263,4264],{},"asof_ts, strike, cp, s_vol, u_prc"," for expiry\u002Fsymbol; filter calls; plot ",[33,4267,4109],{}," vs strike for timestamps (first\u002Fmid\u002Flast of ",[33,4270,4271],{},"ts_list",").",[22,4274,4275,4276,4279,4280,4283,4284,4287,4288,4134],{},"Zoom near spot: ",[33,4277,4278],{},"s0 = u_prc.median()",", strikes in ",[33,4281,4282],{},"[s0*0.6, s0*1.4]"," reveals ATM shifts invisible in full range. Enables questions like \"TSLA surface at 10:32?\" or \"when skew steepened?\"—replay via ",[33,4285,4286],{},"where symbol=? and expiry=?"," or ",[33,4289,4290],{},"option_key, asof_ts",[22,4292,4293,4294,4296,4297,4300],{},"Track evolution: query timelines per option\u002Fexpiry to compute ATM IV (min ",[33,4295,4109],{}," near spot), skew proxies (wing vs ATM deltas). Stored ",[33,4298,4299],{},"u_prc, years, rate"," support smile rebuilds or Greeks audits without re-API calls. Trade-off: API fees for data; limit caps chains; no interpolation here keeps ingestion simple but may vary strikes across polls.",{"title":31,"searchDepth":45,"depth":45,"links":4302},[4303,4304,4305],{"id":4074,"depth":45,"text":4075},{"id":4161,"depth":45,"text":4162},{"id":4249,"depth":45,"text":4250},[279],{"content_references":4308,"triage":4313},[4309],{"type":4310,"title":4311,"url":4312,"context":290},"tool","SpiderRock MLink LiveImpliedQuote","https:\u002F\u002Fdocs.spiderrockconnect.com\u002Fdocs\u002Fnext\u002FMessageSchemas\u002FSchema\u002FTopics\u002Fanalytics\u002FLiveImpliedQuote\u002F",{"relevance":57,"novelty":51,"quality":57,"actionability":57,"composite":4314,"reasoning":4315},3.8,"Category: AI Automation. The article provides a practical guide on building a queryable database from live API data, addressing the audience's need for actionable content in automation. It details a specific implementation using SQLite and Python, which can be directly applied by developers looking to integrate live data into their products.","\u002Fsummaries\u002F9083ba0dfd966742-build-queryable-options-iv-db-from-live-api-polls-summary","2026-05-03 16:03:23","2026-05-03 17:01:13",{"title":4064,"description":31},{"loc":4316},"9083ba0dfd966742","Data Driven Investor","https:\u002F\u002Fmedium.datadriveninvestor.com\u002Ffrom-live-options-analytics-to-a-queryable-database-in-python-95fd1bd4ea92?source=rss----32881626c9c9---4","summaries\u002F9083ba0dfd966742-build-queryable-options-iv-db-from-live-api-polls-summary",[30,312,311],"Capture SpiderRock LiveImpliedQuote snapshots for TSLA every 10s into SQLite: append full history for audits (12k+ rows in 2min), upsert latest view per option_key. Query to reconstruct vol smiles and track ATM IV\u002Fskew changes over time.",[],"mCfcLLxXWQrSwDkhpH5mS0KYJ0Zntifa3yboHqyovqg",{"id":4330,"title":4331,"ai":4332,"body":4337,"categories":4369,"created_at":280,"date_modified":280,"description":31,"extension":281,"faq":280,"featured":282,"kicker_label":280,"meta":4370,"navigation":72,"path":4371,"published_at":4372,"question":280,"scraped_at":280,"seo":4373,"sitemap":4374,"source_id":4375,"source_name":4376,"source_type":307,"source_url":4377,"stem":4378,"tags":4379,"thumbnail_url":280,"tldr":4380,"tweet":280,"unknown_tags":4381,"__hash__":4382},"summaries\u002Fsummaries\u002Fevent-driven-data-pipelines-watchdog-pandas-summary.md","Event-Driven Data Pipelines: Watchdog + Pandas",{"provider":7,"model":8,"input_tokens":4333,"output_tokens":4334,"processing_time_ms":4335,"cost_usd":4336},3672,1993,14921,0.00170825,{"type":14,"value":4338,"toc":4364},[4339,4343,4350,4354,4357,4361],[17,4340,4342],{"id":4341},"pollings-hidden-costs-and-event-driven-fix","Polling's Hidden Costs and Event-Driven Fix",[22,4344,4345,4346,4349],{},"Manual scripts force explicit runs for new files in a folder, while polling via CRON or ",[33,4347,4348],{},"while True"," loops checks repeatedly—wasting CPU cycles on empty folders and delaying processing until the next interval. Event-driven listening with Watchdog solves this by reacting only to actual filesystem events like file creation, enabling near-instant data ingestion without idle overhead.",[17,4351,4353],{"id":4352},"building-the-reactive-pipeline","Building the Reactive Pipeline",[22,4355,4356],{},"Monitor a target directory for incoming files using Watchdog's observer pattern, then pipe events directly to Pandas for cleaning and processing. The article outlines a step-by-step implementation: set up the event handler, define processing logic in Pandas (e.g., load CSV, transform data), and run the observer daemonized for always-on operation.",[17,4358,4360],{"id":4359},"production-trade-offs","Production Trade-offs",[22,4362,4363],{},"For reliability, handle edge cases like duplicate events or partial writes by adding file locks or size checks before processing. Run as a service (e.g., systemd) rather than inline to ensure persistence across restarts, balancing reactivity with stability in live data flows.",{"title":31,"searchDepth":45,"depth":45,"links":4365},[4366,4367,4368],{"id":4341,"depth":45,"text":4342},{"id":4352,"depth":45,"text":4353},{"id":4359,"depth":45,"text":4360},[377],{},"\u002Fsummaries\u002Fevent-driven-data-pipelines-watchdog-pandas-summary","2026-04-08 21:21:18",{"title":4331,"description":31},{"loc":4371},"06b360c4dd4cb0c9","Python in Plain English","https:\u002F\u002Funknown","summaries\u002Fevent-driven-data-pipelines-watchdog-pandas-summary",[30,311,312],"Replace manual scripts and polling loops with Watchdog to trigger instant Pandas processing on file arrivals, cutting resource waste and delays.",[],"zebps7hAlDCnfeGpkEs2GwoXW7t5u4ph6Akc4DENnxg",{"id":4384,"title":4385,"ai":4386,"body":4391,"categories":4440,"created_at":280,"date_modified":280,"description":31,"extension":281,"faq":280,"featured":282,"kicker_label":280,"meta":4441,"navigation":72,"path":4455,"published_at":4456,"question":280,"scraped_at":4457,"seo":4458,"sitemap":4459,"source_id":4460,"source_name":4055,"source_type":307,"source_url":4461,"stem":4462,"tags":4463,"thumbnail_url":280,"tldr":4465,"tweet":280,"unknown_tags":4466,"__hash__":4467},"summaries\u002Fsummaries\u002F56100a2f235e4ed4-production-ml-pipelines-with-zenml-custom-material-summary.md","Production ML Pipelines with ZenML: Custom Materializers & HPO",{"provider":7,"model":8,"input_tokens":4387,"output_tokens":4388,"processing_time_ms":4389,"cost_usd":4390},9247,2138,40785,0.0028959,{"type":14,"value":4392,"toc":4434},[4393,4397,4400,4404,4411,4415,4427,4431],[17,4394,4396],{"id":4395},"custom-materializers-enable-metadata-rich-data-handling","Custom Materializers Enable Metadata-Rich Data Handling",[22,4398,4399],{},"Define DatasetBundle to encapsulate X, y, feature_names, and stats from sklearn's load_breast_cancer (569 samples, 30 features). Pair it with DatasetBundleMaterializer inheriting BaseMaterializer: save() stores X.npy, y.npy, and meta.json with feature_names\u002Fstats; load() reconstructs from files; extract_metadata() computes n_samples, n_features, class_distribution (e.g., {0: 357, 1: 212}). This auto-logs queryable metadata to artifacts, ensuring domain objects serialize seamlessly without pickling issues, while supporting ZenML's reproducibility.",[17,4401,4403],{"id":4402},"modular-steps-log-hyperparameters-and-metrics-at-every-stage","Modular Steps Log Hyperparameters and Metrics at Every Stage",[22,4405,4406,4407,4410],{},"Use @step(enable_cache=True) for load_data() returning Annotated",[36,4408,4409],{},"DatasetBundle, \"raw_dataset\"",". split_and_scale() performs stratified train_test_split (default test_size=0.2), StandardScaler fit\u002Ftransform, logs train_size\u002Ftest_size via log_metadata(). train_candidate() supports model_type=\"random_forest\"|\"gradient_boosting\"|\"logistic\" with n_estimators=100, max_depth=5 defaults, fits on X_train\u002Fy_train, logs model_type\u002Fhyperparameters. evaluate_candidate() computes accuracy, f1, roc_auc on X_test\u002Fy_test (using predict_proba if available), logs all metrics with label. These steps cache outputs, track lineage, and expose metadata for debugging\u002Fproduction monitoring.",[17,4412,4414],{"id":4413},"fan-out-hpo-and-fan-in-selection-promote-best-model","Fan-Out HPO and Fan-In Selection Promote Best Model",[22,4416,4417,4418,4422,4423,4426],{},"SEARCH_SPACE defines 4 configs: {\"model_type\": \"random_forest\", \"n_estimators\": 50\u002F200, \"max_depth\": 3\u002F7}, {\"gradient_boosting\": 100\u002F3}, {\"logistic\":1\u002F1}. @pipeline(model=PRODUCTION_MODEL) training_pipeline() fans out: load_data → split_and_scale → loop over train_candidate(id=f\"train_",[4419,4420,4421],"em",{"i":31},"\") and evaluate_candidate(id=f\"eval","\", label=f\"{type}(n={n},d={d})\"). Fan-in via select_best(): picks max ROC AUC index, logs winning_metrics\u002Fchosen_candidate to model metadata, returns production_model to versioned breast_cancer_classifier (tags=",[36,4424,4425],{},"\"tutorial\",\"advanced\"","). Generates 8 step runs (4 train+4 eval), automates promotion via Model control plane.",[17,4428,4430],{"id":4429},"client-api-ensures-inspection-caching-and-zero-recompute-reruns","Client API Ensures Inspection, Caching, and Zero-Recompute Reruns",[22,4432,4433],{},"Post-run, Client().get_pipeline_run() shows status, step counts (e.g., 9 steps), aggregated metadata. get_model_version(\"latest\") reveals version.number, linked artifacts, run_metadata (e.g., chosen_candidate). Reload prod_model = get_artifact_version(\"production_model\").load(), verify accuracy_score on stored X_test\u002Fy_test. raw_dataset metadata includes n_samples=569, n_features=30, class_distribution. Rerun hits cache (enable_cache=True), skips recompute. list_pipeline_runs(), list_model_versions(), list_artifact_versions() enable querying; full notebook at GitHub confirms 100% reproducibility without redundant work.",{"title":31,"searchDepth":45,"depth":45,"links":4435},[4436,4437,4438,4439],{"id":4395,"depth":45,"text":4396},{"id":4402,"depth":45,"text":4403},{"id":4413,"depth":45,"text":4414},{"id":4429,"depth":45,"text":4430},[279],{"content_references":4442,"triage":4452},[4443,4446,4449],{"type":4310,"title":4444,"url":4445,"context":290},"ZenML","https:\u002F\u002Fgithub.com\u002Fzenml-io\u002Fzenml",{"type":292,"title":4447,"url":4448,"context":296},"zenml_advanced_end_to_end_pipeline_Marktechpost.ipynb","https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Agents-Projects-Tutorials\u002Fblob\u002Fmain\u002FML%20Project%20Codes\u002Fzenml_advanced_end_to_end_pipeline_Marktechpost.ipynb",{"type":286,"title":4450,"author":4451,"context":290},"breast_cancer","sklearn.datasets",{"relevance":63,"novelty":57,"quality":57,"actionability":63,"composite":4453,"reasoning":4454},4.55,"Category: AI Automation. The article provides a detailed guide on building production-grade ML pipelines using ZenML, addressing practical aspects like custom materializers and hyperparameter optimization, which are crucial for the target audience. It includes specific steps and code examples that the audience can directly implement in their projects.","\u002Fsummaries\u002F56100a2f235e4ed4-production-ml-pipelines-with-zenml-custom-material-summary","2026-05-04 22:11:37","2026-05-05 16:09:56",{"title":4385,"description":31},{"loc":4455},"56100a2f235e4ed4","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F05\u002F04\u002Fhow-to-build-an-end-to-end-production-grade-machine-learning-pipeline-with-zenml-including-custom-materializers-metadata-tracking-and-hyperparameter-optimization\u002F","summaries\u002F56100a2f235e4ed4-production-ml-pipelines-with-zenml-custom-material-summary",[4464,30,312,311],"machine-learning","ZenML enables end-to-end ML pipelines with custom DatasetBundle materializers for metadata-rich serialization, fan-out over 4 hyperparameter configs for RandomForest\u002FGradientBoosting\u002FLogisticRegression, fan-in best-model selection by ROC AUC, full artifact tracking, and cache-driven reproducibility on breast cancer dataset.",[],"_jyeZef15FOC-726KyxSOjynaY54SFmoVQfVvb811WU"]