Data Interpolation and Imputation

This tutorial demonstrates data-wrangler’s capabilities for handling missing data through interpolation and imputation techniques. These are essential for cleaning real-world datasets.

Overview

Data-wrangler provides several approaches for handling missing data:

• Interpolation: Fill missing values using mathematical interpolation methods

• Model-based imputation: Use machine learning models to predict missing values

• Statistical imputation: Fill with statistical measures (mean, median, mode)

• Custom imputation: Define your own missing data handling strategies

Let’s explore these techniques with practical examples.

import datawrangler as dw
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datawrangler.decorate import funnel, interpolate