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Building a Study Habit Tracker with Easy Date Formatter, Easy Stats, and Easy Data Visualization

Combine Easy Date Formatter, Easy Stats, and Easy Data Visualization to track daily study sessions, analyze study consistency, and visualize your progress across the week.

What we are building

Whether you are preparing for exams or building a daily coding habit, seeing your study patterns helps you stay consistent. In this project, we track daily study sessions, calculate your weekly average and variability with easy_stats, format report timestamps with easy_date_formatter, and render a clean progress line chart with easy_data_visualization.

Create study_tracker.py:

from py_simple import (
    data_range,
    get_pretty_date,
    mean,
    median,
    plot_line_chart,
)

# 1. Study time recorded across the week (in minutes)
study_sessions = [
    {"day": "Mon", "minutes": 45},
    {"day": "Tue", "minutes": 60},
    {"day": "Wed", "minutes": 30},
    {"day": "Thu", "minutes": 90},
    {"day": "Fri", "minutes": 50},
    {"day": "Sat", "minutes": 120},
    {"day": "Sun", "minutes": 75},
]

days = [entry["day"] for entry in study_sessions]
minutes = [entry["minutes"] for entry in study_sessions]

# 2. Analyze the week's study stats
avg_minutes = mean(minutes)
median_minutes = median(minutes)
spread = data_range(minutes)
total_hours = sum(minutes) / 60

# 3. Print the weekly summary with a formatted date
report_date = get_pretty_date()
print(f"=== Study Habit Report ({report_date}) ===")
print(f"Total time studied: {total_hours:.1f} hours ({sum(minutes)} mins)")
print(f"Daily average:      {avg_minutes:.1f} mins/day")
print(f"Median session:     {median_minutes:.1f} mins")
print(f"Session spread:     {spread:.1f} mins (difference between shortest and longest)")

# 4. Plot the study trend over the week
plot_line_chart(
    labels=days,
    values=minutes,
    title="Weekly Study Trend",
    x_label="Day of Week",
    y_label="Minutes Studied",
)

Run the script to see your formatted habit summary in the terminal (note that the exact date in the header will depend on the day you run the script) and a clean line chart showing your daily progress:

=== Study Habit Report (Saturday, October 24, 2026) ===
Total time studied: 7.8 hours (470 mins)
Daily average:      67.1 mins/day
Median session:     60.0 mins
Session spread:     90.0 mins (difference between shortest and longest)

What happened?

  1. get_pretty_date() generates a human-friendly date string including the weekday (such as "Saturday, October 24, 2026") for your report without needing complex strftime directives (the exact date and weekday will reflect when the script runs).
  2. mean(minutes) computes your average daily study time, while median(minutes) gives the middle session length, showing your true typical study effort even when a weekend study marathon spikes the average.
  3. data_range(minutes) calculates the difference between your highest (120 mins) and lowest (30 mins) days to show habit variance.
  4. plot_line_chart(days, minutes, ...) draws a clean line chart with marked data points and labels the axes without raw Matplotlib boilerplate.

Why use these helpers?

Building this analysis from scratch in Python usually requires: - Importing datetime and looking up formatting codes (%B %d, %Y) just to print the current date cleanly. - Importing the statistics module or writing manual loops for sums, means, medians, and ranges. - Writing several lines of matplotlib.pyplot code to initialize subplots, draw plots, configure titles, remove unnecessary frame borders, and call plt.show().

By combining easy_date_formatter, easy_stats, and easy_data_visualization, you get a complete personal analytics tool in under 30 readable lines of code.