pandas / Basic: labels, types and accepted input

pandas β€” Basic: labels, types and accepted input

Define analytical grain, select rows deliberately and preserve source meaning.

6 lessons Β· No account required

What you will learn

  • Explain Series/DataFrame axes, label alignment and missing values.
  • Review CSV contracts, direct assignments and deterministic ranking.

  1. Series, DataFrames and labeled axes

    Define what each row represents and distinguish labeled pandas objects from ordinary positional containers.

    Start learning

  2. Selection, loc, iloc and alignment

    Choose labels or positions deliberately and predict how labeled operands align before arithmetic or assignment.

    Start learning

  3. Dtypes, nullable values and missingness

    Choose a representation that preserves meaning and keep missing observations distinct from zero, empty text and invalid input.

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  4. CSV import contracts and validation

    Preserve identifiers and missing-value meaning at ingestion, then validate structure and coverage before analysis.

    Start learning

  5. Vectorized columns and safe assignment

    Express column operations with explicit units, masks and mutation targets while preserving label relationships.

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  6. Sorting, filtering and deterministic outputs

    Define a reproducible selected population and tie policy before taking top rows or exporting results.

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