Data Exploration and Cleaning: Core Concepts, Steps, and Example Workflow

Data exploration aims to discover simple patterns or characteristics in a dataset, while data cleaning ensures reliable data by correcting or removing unreliable and noisy entries.

Core of data exploration includes:

1) Data quality analysis; 2) Data feature analysis, such as distribution, comparison, periodicity, correlation, and common statistical measures.

Data cleaning steps :

(1) Missing‑value handling – identified via descriptive statistics or zero‑value checks. Common approaches are deletion, imputation, or leaving unchanged. Imputation methods include mean, median, mode, fixed value, nearest‑neighbor, regression, Lagrange interpolation, Newton interpolation, and piecewise interpolation.

(2) Outlier handling – detected through scatter plots. Typical treatments are treating outliers as missing values, deletion, correction (e.g., using mean or median), or leaving them as is.

Cleaning example (illustrated with images):

Step 1: Data import.

Step 2: Missing‑value processing.

Step 3: Outlier processing.

Distribution analysis (histogram) :

Histogram illustration:

Histogram

Source: https://www.jianshu.com/p/97ed069bdfee