Introduction to Time Series Data and Equipment Status Labeling

Understanding the Problem Statement

In this article, we will explore a problem involving time series data analysis. We have a pandas DataFrame containing temperature readings from various equipment over time. The task is to label each row as either “good” or “bad” based on the temperature reading, where “good” indicates a temperature within a specific range (35-45) and “bad” otherwise.

Background: Time Series Data Analysis

Overview of pandas DataFrame

A pandas DataFrame is a data structure used for tabular data in Python. It provides an efficient way to store and manipulate structured data with various features such as sorting, filtering, grouping, and merging.

import pandas as pd

# Create a sample DataFrame
data = {'Date': ['2022-01-01', '2022-01-02', '2022-01-03'],
        'Temperature': [30, 45, 40],
        'Equipment ID': ['A', 'B', 'C']}
df = pd.DataFrame(data)

Labeling Equipment Status

Basic Approach: Single Row Check

The basic approach to labeling equipment status involves checking each row individually. We can achieve this by using a simple conditional statement in Python.

# Define the labels for good and bad temperatures
good_temp_range = (35, 45)

# Create a new column 'status' based on the temperature range
df['status'] = ['bad' if x < good_temp_range[0] or x > good_temp_range[1] else 'good' for x in df['Temperature']]

Advanced Approach: Multi-Day Check

Grouping Temperature Readings

However, this basic approach has a limitation. It only checks the temperature reading on that single day and may not consider previous or future readings.

To address this issue, we can use grouping to check multiple days’ worth of data. We will create a function called group_check_maker to group rows by equipment ID and then apply a series of checks within each group.

# Define the minimum time window for the status change
min_time_window = pd.Timedelta(days=2)

def group_check_maker(index, row):
    def group_check(group):
        failed_status = False

        # Check if any neighboring measurements are bad
        for index2, row2 in group.drop(index).iterrows():
            if (row['Date'] > row2['Date']) and (row2['Temperature'] < 35 or row2['Temperature'] > 45):
                failed_status = True

        # If the status is already good, we need to check the time window
        if not failed_status:
            # Find the most recent good measurement within the time window
            recent_good_measurement_index = group[(group['Temperature'] >= good_temp_range[0]) & (group['Temperature'] <= good_temp_range[1])].index.max()

            # Check if there's at least one day of good measurements before this
            if row.name == recent_good_measurement_index + pd.Timedelta(days=1):
                failed_status = True

        return 'Bad' if failed_status else 'Good'

    return group_check

Creating the Row Checker Function

Applying Group Check to Each Row

We will create another function called row_checker_maker that applies the group_check_maker function to each row in the DataFrame.

def row_checker_maker(df):
    def row_checker(row):
        group_check = group_check_maker(row.name, row)
        return df[df['Equipment ID'] == row['Equipment ID']].groupby('Equipment ID').apply(group_check).iloc[0]
    return row_checker

# Apply the row checker function to each row in the DataFrame
row_checker = row_checker_maker(df)

df['Neighboring Day Status'] = df.apply(row_checker, axis=1)

Conclusion

Time Series Data Analysis with Equipment Status Labeling

In this article, we explored a problem involving time series data analysis and equipment status labeling. We started by discussing basic approaches to labeling equipment status using single row checks. However, these methods had limitations when dealing with multiple days’ worth of data.

To address these limitations, we introduced an advanced approach using grouping and a multi-day check. The group_check_maker function groups rows by equipment ID and applies a series of checks within each group. This allows us to consider previous and future readings when making status changes.

The final solution involves applying the row_checker_maker function to each row in the DataFrame, which returns the labeled ‘Neighboring Day Status’.

Last modified on 2024-07-02