Visualize Your Mood Diary with Python and Matplotlib

Last night, I stared at the ceiling for half an hour again. Where did this terrible mood come from? Am I really unpredictable, or is there a pattern to it? If I could record my mood every day, maybe I could discover something. Wait, am I not a Python expert? Why not use code to solve this problem?

Set Up Your Mood Database

We need a place to store our daily mood data. Don’t be scared by the word “database”; it’s just a text file! We’ll use CSV format to store it because it’s simple to read and can be opened directly with Excel, making it a must-have for home and travel.

import csv
from datetime import datetime

def record_mood(date, mood, notes):
    with open('mood_diary.csv', 'a', newline='') as file:
        writer = csv.writer(file)
        writer.writerow([date, mood, notes])

# Record today's mood
today = datetime.now().strftime('%Y-%m-%d')
record_mood(today, 7, "Writing Python code makes me happy!")

Look, this is our mood recorder. <span>mood</span> is a number from 1 to 10, representing the mood index. A score of 10 means “I feel like I could fly!”, while a score of 1 means “Can this world get any worse?” In the <span>notes</span> field, you can write short reflections, like “Today’s coffee was especially fragrant” or “I got dissed by my boss again”.

Tip: Keeping a record is a technical task. Don’t expect yourself to remember to fill it out every night; setting a phone reminder is the way to go.

Data Visualization: Let the Numbers Speak

Alright, let’s assume you’ve been keeping track of your mood for a month (give yourself a pat on the back!). Now it’s time to bring those numbers to life. We will use the amazing plotting tool Matplotlib to visualize your emotional ups and downs.

import matplotlib.pyplot as plt
import csv
from datetime import datetime

dates, moods = [], []
with open('mood_diary.csv', 'r') as file:
    reader = csv.reader(file)
    for row in reader:
        dates.append(datetime.strptime(row[0], '%Y-%m-%d'))
        moods.append(int(row[1]))

plt.figure(figsize=(12, 6))
plt.plot(dates, moods, marker='o')
plt.title('My Emotional Roller Coaster')
plt.xlabel('Date')
plt.ylabel('Mood Index')
plt.ylim(0, 10)
plt.grid(True)
plt.savefig('mood_chart.png')
plt.show()

This piece of code is like giving your emotions a CT scan. It will generate a beautiful line chart, with the x-axis representing dates and the y-axis representing your mood index. Do you see those peaks and valleys? Those are your emotional highs and lows.

Discover Your Emotional Patterns

Now, let’s take it up a notch. We want to find out which day of the week you are most likely to feel happy; you might discover some interesting patterns.

from collections import defaultdict

weekday_moods = defaultdict(list)
for date, mood in zip(dates, moods):
    weekday = date.strftime('%A')
    weekday_moods[weekday].append(mood)

avg_moods = {day: sum(moods)/len(moods) for day, moods in weekday_moods.items()}
best_day = max(avg_moods, key=avg_moods.get)
worst_day = min(avg_moods, key=avg_moods.get)

print(f"Your happiest day is usually {best_day}, with an average mood index of {avg_moods[best_day]:.2f}")
print(f"Your gloomiest day is often {worst_day}, with an average mood index of {avg_moods[worst_day]:.2f}")

This code acts like your personal emotional detective. It will tell you which day is most likely to make you feel elated and which day is most likely to make you want to throw your alarm clock out the window. You might find that you feel great every Friday, while Monday always brings a frown. Hey, at least now you know when to schedule important meetings, right?

Tip: Don’t let data lead you by the nose. Even if statistics say Wednesday is your worst day, don’t treat it as a jinxed day. Data is meant to help us, not restrict us.

This little tool can be used for much more than just mood tracking. You can use it to track weight loss progress, study efficiency, or even plant growth. Python is that magical; it can turn dull data into vibrant stories.

Next time you’re staring blankly at Excel, remember that Python and Matplotlib are always ready to turn those cold numbers into vivid images. Maybe your next “Aha!” moment is hidden in those charts!