Flagging Changes in Time Series Data with Python

Introduction

When working with time series data, it’s often useful to identify patterns or trends over time. In this article, we’ll explore how to flag changes in column values for an increase or decrease over a specified period using Python.

Time series analysis is a powerful tool for understanding data that varies over time. With large datasets containing multiple years’ worth of information, identifying patterns can be crucial in making informed decisions or predicting future trends. One common task in time series analysis is flagging changes in column values to indicate increases or decreases over specific periods.

Understanding Time Series Data

Before we dive into the code, it’s essential to understand how time series data is structured and represented. A typical row in a time series dataset consists of monthly or yearly data points, with each point representing a specific date or time interval.

Let’s assume our timeseries data is set up so that each row represents monthly data. For example:

   | Date       | Value
-----------------|---------
 1 | 2020-01-01 | 100
 2 | 2020-02-01 | 120
 3 | 2020-03-01 | 110

In this example, each row corresponds to a specific date and contains the corresponding value.

Identifying Changes with Pandas

Pandas is an excellent library for data manipulation and analysis in Python. To identify changes in column values, we can use various pandas functions, such as diff() or pct_change(). However, these methods are primarily designed to calculate differences between consecutive values rather than flagging specific changes.

To achieve our goal, we’ll need to create a new column that indicates whether the value has increased or decreased over a specified period. We can do this by applying a custom function to each row in the dataframe.

Applying a Custom Function

One way to apply a custom function is using the apply() method, which allows us to execute a specific code block for each row in the dataframe. In our case, we want to create a new column called ‘flag’ that indicates whether the value has increased or decreased over the last 6 months (or 12 months).

We can use a lambda function (an anonymous function) to achieve this:

df['delta'] = df['Value'].diff()
df['flag'] = df['delta'].apply(lambda x: 'increase' if x > 0 else 'decrease')

In this code:

1. df['delta'] calculates the difference between consecutive values.
2. df['flag'] applies the lambda function to each value in delta.
   • If x is greater than 0, it returns 'increase'.
   • Otherwise, it returns 'decrease'.

However, this approach has limitations, as it only works for rows where there are consecutive values. For example, if we have a row with a single value (i.e., no previous value), the diff() method will return NaN.

Handling Missing Values

To overcome this limitation, we can use alternative methods to identify changes in column values. One approach is to use the pandas.Grouper and rolling() functions to calculate moving averages or sums over a specified period.

Let’s say we want to flag customers who had an increase or decrease in salary over the last 6 months:

import pandas as pd

# Create sample data
df = pd.DataFrame({'Salary': [1000, 1200, 1100, 1300, 1500]})

# Calculate moving averages (6-month window)
df['moving_avg'] = df['Salary'].rolling(window=6).mean()

# Flag increases/decreases
df['flag'] = np.where(df['Salary'] > df['moving_avg'], 'increase', 'decrease')

In this code:

1. df['moving_avg'] calculates the 6-month moving average of the salary values.
2. df['flag'] uses NumPy’s where() function to create a new column that indicates whether the current value is greater than the moving average.

While this approach provides more flexibility, it requires a specific window size and may not work well for all scenarios.

Using Rolling Delta

Another alternative is to use the rolling.delta method, which calculates the difference between the current value and the previous value within a specified period:

df['rolling_delta'] = df['Salary'].rolling(window=6).delta()

This approach provides more flexibility than using diff() but may be slower for larger datasets.

Using Pandas’ Built-in Functions

Unfortunately, pandas does not have a built-in function that directly flags increases/decreases over a specified period. However, we can use other libraries like NumPy or SciPy to achieve this.

For example, we can use NumPy’s where() function with vectorized operations:

import numpy as np

# Create sample data
df = pd.DataFrame({'Salary': [1000, 1200, 1100, 1300, 1500]})

# Calculate differences over a specified period (6 months)
df['diff'] = np.where(df['Salary'].values[:-1] < df['Salary'].values[1:], 'decrease', 'increase')

print(df)

In this code:

1. np.where() checks whether each value is less than the next value.
2. If true, it assigns 'decrease' to the corresponding row in diff. Otherwise, it assigns 'increase'.

While this approach works well for small datasets, it may not be efficient for larger datasets.

Conclusion

Flagging changes in column values over a specified period is an essential task in time series analysis. While pandas provides various methods and functions to achieve this, we’ve seen that some approaches have limitations or require additional steps.

In this article, we explored using custom functions, pandas.Grouper, and rolling averages to identify increases/decreases over specific periods. We also discussed the use of NumPy’s vectorized operations as an alternative approach.

By understanding these methods and techniques, you can create efficient and accurate solutions for flagging changes in your time series data with Python.

Last modified on 2025-02-07