📊 How to Calculate YOY, QOQ, MOM, YTD, QTD and MTD in Python Using Pandas

In the world of data analytics, especially financial and business reporting, metrics like YOY (Year Over Year), QOQ (Quarter Over Quarter), MOM (Month Over Month), YTD (Year to Date), Quarter to Date (QTD) and MTD (Month to Date) are commonly used to evaluate performance over time.

In this post, we'll break down what each term means and show you how to calculate them using Python and Pandas.

🧠 What Do These Terms Mean?

🧪 Sample Dataset

Let’s create a simple dataset of monthly revenue:

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import seaborn as sns

date_range = pd.date_range(start='2022-01-01', end='2025-05-01', freq='MS')

df = pd.DataFrame({

'date': date_range,

'revenue': np.random.randint(1000, 5000, len(date_range))

})

📈 Calculating YOY (Year Over Year)

To calculate YOY, we compare each month's revenue to the same month in the previous year.

This gives the percent change from the same month last year.

df['YOY'] = df['revenue'].pct_change(periods=12) * 100

df['YOY']=df['YOY'].apply(lambda x:np.round(x,2))

# Time features

df['year'] = df['date'].dt.year

df['month'] = df['date'].dt.month

df['quarter'] = df['date'].dt.to_period('Q')

📈 Revenue Over Time

plt.figure()

sns.lineplot(data=df, x='date', y='revenue', marker='o')

plt.title('Monthly Revenue Over Time')

plt.ylabel('Revenue')

plt.show()

📆 Calculating MOM (Month Over Month)

This gives the percent change from the previous month.

📊 YOY Percentage Change

plt.figure(figsize=(12, 5))

sns.barplot(data=df, x='date', y='YOY', color='skyblue')

plt.title('Year Over Year (YOY) % Change')

plt.xticks(rotation=45)

plt.axhline(0, color='gray', linestyle='--')

plt.ylabel('% Change')

plt.tight_layout()

plt.show()

# MOM: Month Over Month % Change

df['MOM'] = df['revenue'].pct_change(periods=1) * 100

df['MOM']=df['MOM'].apply(lambda x:np.round(x,2))

📉 MOM Percentage Change

plt.figure(figsize=(12, 5))

sns.barplot(data=df, x='date', y='MOM', color='orange')

plt.title('Month Over Month (MOM) % Change')

plt.xticks(rotation=45)

plt.axhline(0, color='gray', linestyle='--')

plt.ylabel('% Change')

plt.tight_layout()

plt.show()

📈 Calculate QOQ (Quarter over Quarter % Change)

This gives the percent change from the previous Quarter.

quarterly_df = df.groupby('quarter').agg({'revenue': 'sum'}).reset_index()

quarterly_df['QOQ'] = quarterly_df['revenue'].pct_change() * 100

quarterly_df['QOQ']=quarterly_df['QOQ'].apply(lambda x:np.round(x,2))

📉 QOQ Plot (Quarterly % Change)

plt.figure(figsize=(10, 5))

sns.barplot(data=quarterly_df, x='quarter', y='QOQ', color='purple')

plt.title('Quarter over Quarter (QOQ) % Change')

plt.axhline(0, color='gray', linestyle='--')

plt.xticks(rotation=45)

plt.ylabel('% Change')

plt.tight_layout()

plt.show()

🧪 Sample Dataset

Let’s create a simple dataset of daily revenue:

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import seaborn as sns

# Generate a sample date range

date_range = pd.date_range(start='2022-01-01', end='2025-05-01', freq='D')

# Create a DataFrame

df = pd.DataFrame({

'date': date_range,

'revenue': np.random.randint(1000, 5000, len(date_range))

})

df['year'] = df['date'].dt.year

df['month'] = df['date'].dt.month

df['quarter'] = df['date'].dt.to_period('Q')

📅 Calculating YTD (Year to Date)

Aggregates values from the beginning of the year up to a specified date. This will reset every January and give a running total of revenue within each year.

df['YTD'] = df.groupby(df['date'].dt.year)['revenue'].cumsum()

df['YTD']=df['YTD'].apply(lambda x:np.round(x,2))

print(df)

📊 Calculate QTD (Quarter to Date)

Cumulative value from the start of the quarter to the current date. This works just like YTD, but resets every quarter.

df['QTD'] = df.groupby(df['quarter'])['revenue'].cumsum()

print(df)

📅 Calculating MTD (Month to Date)

Aggregates values from the beginning of the month up to a specified date.

df['MTD'] = df.groupby([df['date'].dt.year, df['date'].dt.month])['revenue'].cumsum()

ptint(df)

Visualize Total Revenue

1. Year-wise total revenue

2. Quarter-wise total revenue

3. Month-wise total revenue (aggregated across all years)

These give a clean, high-level summary of how your revenue behaves over different time periods.

📌 1. Year-wise Revenue (Total per Year)

yearly_rev = df.groupby('year')['revenue'].sum().reset_index()

plt.figure(figsize=(8, 5))

sns.barplot(data=yearly_rev, x='year', y='revenue', palette='Blues_d')

plt.title('Total Revenue per Year')

plt.ylabel('Revenue')

plt.xlabel('Year')

plt.tight_layout()

plt.show()

📌 2. Quarter-wise Revenue (All Quarters Across Years)

# Convert 'quarter' to string for better x-axis labels

df['quarter_str'] = df['quarter'].astype(str)

quarterly_rev = df.groupby('quarter_str')['revenue'].sum().reset_index()

plt.figure(figsize=(10, 5))

sns.barplot(data=quarterly_rev, x='quarter_str', y='revenue', palette='Purples')

plt.title('Total Revenue per Quarter')

plt.ylabel('Revenue')

plt.xlabel('Quarter')

plt.xticks(rotation=45)

plt.tight_layout()

plt.show()

📌 3. Month-wise Revenue (Across All Years Combined)

# Aggregate by month number across all years

month_labels = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',

'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']

monthly_rev = df.groupby('month')['revenue'].sum().reset_index()

monthly_rev['month_name'] = monthly_rev['month'].apply(lambda x: month_labels[x - 1])

plt.figure(figsize=(10, 5))

sns.barplot(data=monthly_rev, x='month_name', y='revenue', palette='Oranges')

plt.title('Total Revenue per Month (All Years Combined)')

plt.ylabel('Revenue')

plt.xlabel('Month')

plt.tight_layout()

plt.show()

✅ Key Takeaways

📌 1. Understand Essential Time-Based Metrics

• YOY (Year over Year) shows long-term annual growth by comparing a month’s value to the same month last year.

• MOM (Month over Month) tracks short-term performance by comparing the current month to the previous one.

• YTD (Year to Date) aggregates cumulative performance from January to the current date.

• MTD (Month to Date) captures partial month performance up to the current day.

• QOQ (Quarter over Quarter) compares the current quarter’s performance to the previous one — useful for quarterly reporting.

• QTD (Quarter to Date) aggregates progress made during the current quarter so far.

🐍 2. Leverage Python and Pandas for Quick Calculation

• Metrics like YOY, MOM, QOQ can be computed using pct_change().

• Cumulative metrics like YTD, MTD, and QTD are easily handled with groupby().cumsum().

• Pandas makes it easy to slice and summarize time-based data by year, quarter, or month.

📊 3. Visualization is Crucial

• Line plots are excellent for showing trends over time (e.g. YTD, QTD).

• Bar charts are ideal for comparing aggregated values by period (e.g. total revenue per year, quarter, or month).

• Annotated charts make the story clearer to stakeholders and non-technical audiences.

📅 4. Seasonal and Trend Analysis Made Simple

• With month-wise and quarter-wise aggregations, you can identify seasonality (e.g. recurring spikes in Q4 or dips in summer).

• Year-wise and QOQ plots help track growth, decline, or plateaus across time.

🧠 5. Reusability and Scalability

• Once the logic is in place, this framework can be reused for any KPI — not just revenue (e.g. users, sales, expenses).

• It scales to daily, weekly, or financial calendars with slight tweaks.

Kishore Babu Valluri

Senior Data Scientist | Freelance Consultant | AI/ML & GenAI Expert

With deep expertise in machine learning, artificial intelligence, and Generative AI, I work as a Senior Data Scientist, freelance consultant, and AI agent developer. I help businesses unlock value through intelligent automation, predictive modeling, and cutting-edge AI solutions.