Visualize personal email stats over time

To visualize your personal email statistics over time, you can create a time-series chart that displays metrics like:

• Number of emails sent and received per day/week/month

• Top senders and recipients

• Email response times

• Categories or labels (e.g., work, personal, promotions)

• Average word count or reading time

Here’s a simple workflow:

1. Extract Email Data

Use one of the following tools or methods:

• Gmail: Use Google Takeout or the Gmail API to export data

• Outlook: Export to CSV or use Microsoft Graph API

2. Structure the Data

Key fields to extract:

• Date/time of email

• Sender/recipient

• Subject/label

• Thread ID (for grouping)

• Word count or body length

3. Visualize Using Tools

You can use:

• Google Sheets / Excel: Create line or bar charts

• Python (matplotlib/seaborn/plotly): For more advanced graphs

• Power BI / Tableau: Interactive dashboards

Example Python visualization snippet (using matplotlib):

python
CopyEdit
import pandas as pd
import matplotlib.pyplot as plt

# Load your structured CSV data
df = pd.read_csv('emails.csv')
df['date'] = pd.to_datetime(df['date'])

# Group by month and count emails
monthly_counts = df.resample('M', on='date').size()

# Plot
plt.figure(figsize=(12,6))
monthly_counts.plot()
plt.title('Emails Over Time')
plt.xlabel('Date')
plt.ylabel('Number of Emails')
plt.grid(True)
plt.show()

Would you like help setting this up with your actual email data?