Pandas Data Merging: Basic Operations of merge and concat, Suitable for Beginners

This article introduces two data merging tools in pandas: `merge` and `concat`, suitable for beginners to quickly master. **concat**: No associated keys, direct concatenation, either row-wise (axis=0) or column-wise (axis=1). Row concatenation (axis=0) is suitable for tables with the same structure (e.g., multi-month data), and it is important to use `ignore_index=True` to reset the index and avoid duplicates. Column concatenation (axis=1) requires the number of rows to be consistent, used for merging by row identifiers (e.g., student information + grade table). **merge**: Merging based on common keys (e.g., name, ID), similar to SQL JOIN, supporting four methods: `inner` (default, retains common keys), `left` (retains left table), `right` (retains right table), and `outer` (retains all). When key names differ, use `left_on`/`right_on` to specify. The default merging method is `inner`. **Key Difference**: concat concatenates without keys, while merge matches by keys. Beginners should note: for column-wise concat, the number of rows must be consistent; merge uses the `how` parameter to control the merge scope, and avoid index duplication and key name mismatch issues.