Data inspection is the first step you do after loading a dataset in Python. The purpose is to understand what the data looks like, what it contains, and what problems it may have before you start cleaning or analysing it. Good inspection helps you avoid wrong assumptions and prevents errors later in your analysis.
During inspection, you check the size of the dataset, meaning how many rows and columns it has. You also preview the first few rows to understand the structure and to see whether columns are correctly loaded. Then you review column names to confirm they match what you expect and to identify columns that may need renaming for clarity.
A major part of inspection is checking data types. Many issues happen when numbers are stored as text, dates are not recognised as dates, or categorical columns have inconsistent formatting. You also look for missing values and understand how common they are. Missing values are not always bad, but you need to know where they occur and how they affect your analysis. Duplicate rows are another issue to check, especially in datasets created from repeated exports or multiple sources.
Inspection also includes understanding the range and distribution of values. For numeric columns, you check minimum, maximum, average, and unusual outliers. For categorical columns, you check how many unique values exist and whether there are spelling or case differences that create duplicate categories.
In short, data inspection is like a quick health check of your dataset. It helps you plan the right cleaning steps, choose the correct analysis approach, and ensure that your final insights are based on trustworthy data.
