A plain-language pandas glossary
Short definitions to help you read the lessons and official documentation.
| Term | Meaning |
|---|---|
| Aggregation | Combining several values into a summary, such as one total per group. |
| Alignment | Matching values by labels when pandas combines or calculates with labeled objects. |
| Axis | A direction in a table: rows or columns. Read each method's documentation to see what its axis parameter controls. |
| Boolean mask | A sequence of True/False values used to select rows or values. |
| Category | A value drawn from a defined set of labels, optionally with a meaningful order. |
| Column | A named series of values describing the same field across rows. |
| Copy-on-Write | A pandas behavior that separates changes to derived objects from their parent. Use one explicit assignment to the object you intend to change. |
| DataFrame | A two-dimensional table with labeled rows and columns. |
| Data type (dtype) | The representation and supported operations for values in a column or array. |
| Dictionary | A Python mapping from keys to values. A dictionary of equal-length lists can describe table columns. |
| Duplicate | A repeated row or repeated combination of selected fields. Define what counts as a duplicate before removing one. |
| Group | Rows that share selected key values and are analyzed together. |
| Index | The labels used to identify rows or Series entries; these labels do not have to be consecutive integers. |
| Join key | The field or fields used to match records between tables. |
| JSON | A text format for structured values such as objects, lists, numbers, strings, and null. |
| Missing value | A recorded absence of a value. It does not automatically mean zero or an empty string. |
| Nullable type | A dtype that can represent missing data alongside its ordinary values, such as pandas Int64. |
| Observation | The entity or event represented by a row. State this explicitly before analysis. |
| Resampling | Grouping time-indexed observations into time bins to calculate summaries or change frequency. |
| Schema | The expected fields, data types, and structural rules of a dataset. |
| Series | A one-dimensional sequence of values with an index. |
| Shape | The number of rows and columns in a table. |
| Transformation | A calculation that changes values or returns group calculations aligned with original rows. |
| Validation | Checking that data and outputs meet explicit expectations. |
| Vectorized operation | An operation expressed over an entire Series or array, rather than a Python loop over individual rows. |
Continue with the task reference or official pandas user guide.