Search⌘ K
AI Features
Home
Courses
Grokking the Data Science Interview

Pandas Fundamentals and Data Cleaning

Explore essential pandas techniques for data cleaning and validation. Learn to normalize string fields, convert data types, deduplicate based on keys, and filter rows properly. Understand the importance of defining a data cleaning contract, checking data types, and explaining each cleaning operation to ensure accuracy before analysis.

We'll cover the following...

A users table can look ready for analysis while its identifiers and rows disagree about who exists. Consider five records with user_id values such as “ 101”, “101 ”, “102”, None, and “103”. Suppose two rows describe the same signup, country appears as “US”, “us ”, and None, and an optional bio field is absent for several users. A user-level count built from this table can overcount duplicate users, split one user across identifiers, or discard valid users for an unrelated missing field.

The users table requires a short sequence of data-cleaning and validation steps. Create the users DataFrame and inspect its data types and missing values. Normalize identifier and category strings, then convert user_id to a numeric type. Apply an explicit deduplication rule, retain records with valid user identifiers, and verify the final row count and key uniqueness.

After deduplication, the cleaned DataFrame should contain one row per unique user, as defined by the user_id key. Apply cleaning and filtering rules only to columns required by the defined data contract.

Before touching pandas, we need a cleaning contract, a short statement of what the table must mean when cleaning finishes. For this users table, a defensible contract could be:

  • user_id is the required key and must be a valid integer.

  • signup_date distinguishes separate signup records if the source can contain re-registrations.

  • A duplicate means repeated (user_id, signup_date) rather than two rows that merely share a country.

  • country is optional but must use a consistent representation when present.

  • bio is optional and must not determine whether a user record survives.

This contract makes each later operation testable. When removing duplicates changes the table from five rows to four, we can identify exactly which key pair collapsed: the two spellings of 101. When dropping records with missing user_id removes one more row, we can explain why that row cannot support user-level metrics. We should also inspect the result in a stable ...