⏱️ Introduction: Time Needs Precision
Tracking time is easy.
Analysing time correctly is not.
This notebook builds on earlier timesheet analysis by introducing proper datetime handling. Instead of relying on pre-calculated durations or loose assumptions, it treats time as a first-class data type — improving accuracy and unlocking deeper insights.
This is where time analysis gets serious.
🎯 Purpose: Accurate Time-Based Analysis
The goal of this notebook is to help learners understand how to:
- Work with datetime values correctly
- Convert raw timestamps into usable datetime objects
- Calculate durations reliably
- Analyse time data across days, weeks, and projects
- Avoid common pitfalls with manual time calculations
This is essential for any real productivity or analytics system.
🧠 How It Works: From Timestamps to Insight
At a high level, the workflow follows these steps:
- Load timesheet data from CSV / Excel
- Parse start and end times as datetime objects
- Calculate durations programmatically
- Aggregate time spent by project, task, or date
- Prepare accurate summaries for reporting
This approach mirrors how professional time-tracking systems work under the hood.
🧩 The Technical Part: Datetime in Practice
A simplified example of the core technique looks like this:
import pandas as pd
df["start_time"] = pd.to_datetime(df["start_time"])
df["end_time"] = pd.to_datetime(df["end_time"])
df["duration_hours"] = (
df["end_time"] - df["start_time"]
).dt.total_seconds() / 3600
🔍 What This Demonstrates
- 📅 Datetime parsing with Pandas
- ⏱️ Reliable duration calculation
- 🧠 Avoiding rounding and manual errors
- 📊 Preparing data for time-based aggregation
Once datetimes are handled correctly, analysis becomes both simpler and more trustworthy.
💡 Key Takeaways: Why Datetime Matters
This notebook reinforces several important lessons:
- ⏱️ Time should be computed, not assumed
- 📅 Datetime types unlock powerful analysis
- 🧠 Accuracy compounds over long datasets
- 🛠 Proper parsing prevents subtle bugs
For any analytics involving schedules, logs, or activity tracking, this is non-negotiable.
🏁 Conclusion: Treat Time as Data
The Timesheet Analysis (Datetime) notebook represents a clear step up in analytical maturity:
When time is handled properly, insight follows naturally.
With this foundation, it becomes much easier to build:
- Weekly and monthly summaries
- Productivity trends
- Burn-rate calculations
- Dashboards and automated reports
This notebook pairs perfectly with your broader Project Pulse and productivity analytics work.
🔗 Link to Notebook
Notebook link: Coming Soon