vault backup: 2025-07-21 18:17:57
Affected files: .obsidian/workspace.json 0 Journal/0 Daily/2025-07-21.md Temporary/Pandas.md
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.obsidian/workspace.json
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.obsidian/workspace.json
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"state": {
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"state": {
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"type": "markdown",
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"type": "markdown",
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"state": {
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"state": {
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"file": "0 Journal/0 Daily/2025-07-18.md",
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"file": "0 Journal/0 Daily/2025-07-21.md",
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"icon": "lucide-file",
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"icon": "lucide-file",
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"title": "2025-07-18"
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"title": "2025-07-21"
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"icon": "lucide-file",
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"icon": "lucide-file",
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"title": "AI Agents"
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"title": "AI Agents"
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}
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{
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"id": "6154fa36d4764b0f",
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"type": "leaf",
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"state": {
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"type": "markdown",
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"state": {
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"file": "Temporary/Pandas.md",
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"mode": "source",
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"source": false
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},
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"icon": "lucide-file",
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"title": "Pandas"
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}
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}
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"Temporary/n8n.md",
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"0 Journal/0 Daily/2025-07-18.md",
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"0 Journal/0 Daily/2025-07-18.md",
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"Temporary/n8n.md",
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"Temporary/Pandas.md",
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"0 Journal/0 Daily/2025-07-21.md",
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"0 Journal/0 Daily/2025-07-21.md",
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"2 Personal/Lists/Business Ideas.md",
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"Temporary/AI Agents.md",
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"Temporary/AI Agents.md",
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"Attachments/Pasted image 20250721142129.png",
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"Attachments/Pasted image 20250721142129.png",
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"Excalidraw/Drawing 2025-06-30 16.39.01.excalidraw.md",
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"Excalidraw/Drawing 2025-06-30 16.39.01.excalidraw.md",
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"Attachments/Pasted image 20250630155216.png",
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"Attachments/madgwick_internal_report.pdf",
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"Attachments/madgwick_internal_report.pdf",
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"Temporary/2025-06-29.md",
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"99 Work/0 OneSec/OneSecNotes/40 - User Manuals",
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"99 Work/0 OneSec/OneSecNotes/40 - User Manuals",
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---
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---
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## OneSec Pilot Planning
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## OneSec Pilot Planning
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### Status Quo
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- [ ] How can I build an agent to help with the pilot planning?
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### Goal: Where do we want to be for the Pilot Project?
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-
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19
Temporary/Pandas.md
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19
Temporary/Pandas.md
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---
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title: Pandas
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created_date: 2025-07-21
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updated_date: 2025-07-21
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aliases:
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tags:
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---
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# Pandas
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## Stack, Unstack
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Unstack converts a multi index into a column index
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Stack converts a multi column into a multi index
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## Melting and Pivoting
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They are very specific --> you can do stack and unstack to achieve the same result
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## get_dummies()
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You can get categorical data (sex: male / female) into 2 columns: sex_Male (0 / 1), sex_Female (0/ 1)
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