How to Properly Resample Time-Series Data in Pandas with Inexact Timestamps

Understanding the Problem with Pandas Resampling

When working with time-series data in pandas, it’s common to need to resample the data at specific intervals or frequencies. This can be done using various methods and functions within the pandas library. However, there’s a common issue when dealing with timestamps that are not exactly on seconds.

In this article, we’ll explore how to properly resample time-series data in pandas, focusing specifically on handling inexact timestamps.

Choosing a Resampling Method

To begin with, you need to choose a method for resampling. The two most commonly used methods are mean() and median(). These methods calculate the mean or median value of each group (or bin) when the data is binned into groups based on their values.

For example, if you want to resample your data at 250ms intervals, you can use:

df_resamp = df.resample('250ms').mean().interpolate('cubic')

In this case, resample() bins the data into groups of 250ms, and then mean() calculates the mean value for each group. The interpolate() function is used to fill in missing values.

Interpolating Missing Values

When using resample() with a specified interval, pandas creates groups that correspond to those intervals. For every full interval, it calculates the mean (or median) of the data points within that interval. However, if there are missing values within an interval (i.e., no data point is available), it leaves them as NaN.

To fill in these missing values, we can use interpolate(). There are several types of interpolation methods you can choose from:

linear: Linear interpolation between the two adjacent points.
nearest: The value of the nearest point to the missing one.
zero: Fills with zero.
slinear: Smoother, but still linear interpolation.
quadratic: More gradual than slinear.

In this example, we chose 'cubic' which is equivalent to a quadratic interpolation. This gives us a smoother curve.

Example Use Case

Let’s take a closer look at an example use case:

import pandas as pd
import numpy as np

# Create a sample dataframe with timestamps and values
np.random.seed(0)
df = pd.DataFrame({
    'timestamp': pd.date_range('2022-01-01', periods=100, freq='1min'),
    'hr': [np.random.randint(60, 120) for _ in range(100)]
})

# Plot the original data
print("Original Data:")
print(df.head())

# Resample and interpolate missing values
df_resamp = df.resample('1min').mean().interpolate('cubic')
print("\nResampled and Interpolated Data:")
print(df_resamp.head())

This example creates a sample dataframe with timestamps and heart rate values, then plots the original data. It resamples the data at 1-minute intervals, calculates the mean value for each interval, and interpolates any missing values using cubic interpolation.

By following these steps, you can properly handle inexact timestamps when working with time-series data in pandas, ensuring your analysis is accurate and reliable.

Conclusion

Resampling time-series data in pandas can be a bit tricky, especially when dealing with inexact timestamps. However, by choosing the right resampling method and interpolating missing values, you can create high-quality, accurate analyses of your data.

Last modified on 2023-09-08