Calculating Percentage Change per User per Month

When working with time-series data, it’s common to need to calculate percentage changes or differences over time. In this article, we’ll explore how to achieve this for a specific use case involving user ID and month.

Background on Time Series Analysis

Time series analysis is the study of data points collected over continuous time intervals. This type of data is often characterized by fluctuations in value over time. Common applications include stock prices, weather patterns, or website traffic. When working with time-series data, it’s essential to understand how to calculate changes or differences over time.

Overview of Pandas and DataFrames

Pandas is a popular Python library for data manipulation and analysis. A DataFrame is a two-dimensional table of data with rows and columns, similar to an Excel spreadsheet. In this article, we’ll use Pandas and its associated functions to perform the desired calculation.

The Challenge: Percentage Change per User_id Month by Month

The problem presented in the Stack Overflow question is as follows:

• A DataFrame has a structure that includes user ID and date.
• We need to calculate the percentage change in volume (in USD) for each user, month by month.

Let’s examine how this could be achieved using Pandas.

Solution Approach

Our approach involves several steps:

1. Data Preparation: Ensure the data is clean and organized.
2. Grouping and Aggregation: Use grouping to aggregate data across users while maintaining the monthly structure.
3. Calculating Percentage Change: Apply a suitable function to calculate the percentage change.

Step 1: Data Preparation

Before we can perform any calculations, our DataFrame must be prepared correctly.

## Step 1: Data Preparation
To prepare our data for analysis, ensure that there are no missing values and consider converting date columns into datetime format if necessary.

Let’s create a sample DataFrame to demonstrate the steps:

## Sample DataFrame Creation
# Create a sample dataframe
df = pd.DataFrame({
    'user_id': [1, 1, 1, 2, 2],
    'date': ['2020-01', '2020-02', '2020-03', '2020-04', '2020-05'],
    'volume_usd': [10000, 15000, 12000, 5000, 7000]
})

# Display the dataframe
print(df)

This will output:

Step 2: Grouping and Aggregation

To perform the percentage change calculation, we’ll group our data by user ID while maintaining the monthly structure.

## Grouping and Aggregation
# Group the dataframe by 'user_id'
grouped_df = df.groupby('user_id')

# Apply a function to each group to calculate the volume changes
percentage_change = grouped_df.agg({'volume_usd': ['mean', 'min', 'max']})

# Display the result
print(percentage_change)

However, this approach does not provide us with the monthly changes as requested. We need to re-strategize.

Step 3: Calculating Percentage Change

To achieve the desired percentage change by month per user ID, we can modify our approach using the pct_change function after converting the date column into a datetime format.

## Calculating Percentage Change
# Convert 'date' to datetime type if necessary
df['date'] = pd.to_datetime(df['date'])

# Calculate percentage change within each group (user_id)
percentage_change_df = df.set_index(['user_id', 'date'])['volume_usd'].pct_change().reset_index()

# Display the result
print(percentage_change_df)

This will output:

By utilizing the pct_change function, we have successfully calculated the percentage change in volume by month per user ID.

Last modified on 2024-12-19