Interpolating Non-Timeseries Data with Pandas DataFrame Resampling

Resampling and interpolating data can be a crucial step in data analysis, especially when dealing with non-timeseries data that needs to be aligned or smoothed. In this article, we will explore how to resample and interpolate columns of a pandas DataFrame that do not contain timeseries data.

Introduction

Pandas is an excellent library for data manipulation and analysis in Python. Its powerful features allow us to easily handle structured data with various data types, including numerical and categorical values. However, sometimes, the data may not be perfectly aligned or require additional processing before being used for further analysis.

In this article, we will focus on resampling and interpolating columns of a pandas DataFrame that do not contain timeseries data. Specifically, we will explore how to handle data with overlapping but non-alignment values between two DataFrames.

Understanding Resampling

Resampling is a process in which the frequency or granularity of data is adjusted. This can be useful when dealing with data that has varying frequencies or when you need to convert data from one frequency to another.

In pandas, resampling is achieved using the resample() function. However, unlike timeseries data, non-timeseries data may require additional processing, such as interpolation, before being aligned.

Understanding Interpolation

Interpolation is a process used to estimate values between known data points. It can be useful when dealing with data that has gaps or missing values.

In pandas, interpolation can be achieved using various methods, including linear interpolation (np.interp()), polynomial interpolation (scipy.interpolate.PolynomialInterpolar()), and more.

Resolving Overlapping Data

When dealing with two DataFrames that have overlapping but non-aligned values, resampling and interpolation can help to align these values. However, the data must be properly prepared before resampling and interpolation.

To achieve this, we will need to create a range of x-values that cover all possible values in both DataFrames. We will use NumPy’s arange() function to generate these x-values.

Preparing Data for Resampling

First, let us assume we have two DataFrames, set1 and set2, with overlapping but non-aligned values.

import pandas as pd
import numpy as np

# Creating sample dataframes
set1 = pd.DataFrame({
    'x': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7],
    'y': [1, 2, 3, 2, 3, 4, 5]
})

set2 = pd.DataFrame({
    'x': [0.12, 0.21, 0.31, 0.44, 0.52, 0.61, 0.76],
    'y': [0, 2, 5, 4, 3, 1, 1]
})

Next, let us find the minimum and maximum values in both DataFrames to determine the range of x-values we need.

# Finding min and max x-values for resampling
min_x = np.min([set1['x'], set2['x']])
max_x = np.max([set1['x'], set2['x']])

print(f"Minimum x-value: {min_x}")
print(f"Maximum x-value: {max_x}")

Resampling and Interpolating Data

Now that we have the minimum and maximum values, we can generate an array of x-values using np.arange().

# Generating x-values for resampling
x_interpolation_points = np.arange(min_x, max_x, 0.001)

Next, let us create a new DataFrame with these x-values to serve as the base for our resampling and interpolation.

# Creating a new dataframe with x-interpolation points
df_resample = pd.DataFrame({
    'x': x_interpolation_points,
    'y': np.nan  # Initialize y values as NaN
})

Now, let us use set1 to fill in the y-values of our resampling DataFrame.

# Resampling and filling in y-values using set1
df_resample['y'] = np.interp(df_resample['x'], set1['x'], set1['y'])

We can repeat this process for set2.

# Resampling and filling in y-values using set2
df_resample['y'] += np.interp(df_resample['x'], set2['x'], set2['y'])

Conclusion

Resampling and interpolating columns of a pandas DataFrame that do not contain timeseries data can be achieved by creating a new DataFrame with x-interpolation points, resampling these points using set1 and set2, and filling in the resulting y-values.

In this article, we covered how to create an array of x-values, generate a new DataFrame for resampling, fill in y-values using both DataFrames, and perform the necessary calculations.

This method allows us to handle data with overlapping but non-aligned values, providing valuable insights into our analysis.

Example Use Cases

Here are some examples where this method can be applied:

• Handling categorical data from multiple sources
• Merging datasets with varying data types (e.g., string and integer columns)
• Creating a baseline for comparison between datasets

Last modified on 2025-01-08