Series
A Series is pandas’ one-dimensional labeled data structure that associates values with an index.
Pandas Workout: 200 exercises to make you a stronger data analyst
A pandas Series is a one-dimensional labeled array that can hold values of a common or compatible data type.
- How it is used
- Analyzes one-dimensional labeled data and supplies the column-level model behind DataFrames.
- How deep it goes
- Works through construction, labeled and positional retrieval, masks, vectorized arithmetic, statistics, file loading, and frequency analysis.
- Examples
- Uses monthly test scores, random temperatures, normally distributed values, and New York City taxi passenger and distance data.
- What you can do after
- Apply it via pandas to create, index, filter, summarize, align, and categorize one-dimensional data.
Read how Pandas Workout: 200 exercises to make you a stronger data analyst explains Series →
Natural Language Processing in Action, Second Edition
A pandas Series is a one-dimensional labeled data structure provided by the pandas Python library.
- How it is used
- Represents token counts, normalized frequencies, and other word-level vector dimensions for natural-language processing.
- How deep it goes
- Explains construction, label lookup, arithmetic, normalization, and DataFrame integration, but defers advanced high-dimensional vector improvements.
- Examples
- Demonstrates Series with token counts from sentences and rows representing lines from an article about algorithmic bias.
- What you can do after
- Apply it via pandas to inspect labeled counts, select dimensions, and perform arithmetic on text vectors.
Read how Natural Language Processing in Action, Second Edition explains Series →
Build Python Web Apps with Streamlit
A pandas Series is a one-dimensional labeled array-like data structure that stores values associated with an index.
- How it is used
- Extracts column values and distinct filter options from dashboard data.
- How deep it goes
- Defines column selection and demonstrates unique-value extraction, without descending into Series internals.
- Examples
- Uses df['age_group'], .unique(), list conversion, and a reusable get_unique_values function.
- What you can do after
- Apply it via pandas to extract column values and build data-driven filter options.
Read how Build Python Web Apps with Streamlit explains Series →
Fast Python: High performance techniques for large datasets
- How it is used
- Supports column-wise data processing by enabling arithmetic and aggregation without explicit row iteration.
- How deep it goes
- Demonstrates Series in a vectorized calculation but stops short of explaining indexing internals or implementation details.
- Examples
- Uses tip_amount and total_amount Series to compute a mean tip fraction.
- What you can do after
- Apply it via pandas to replace row-wise calculations with vectorized column operations.
Read how Fast Python: High performance techniques for large datasets explains Series →
Related concepts
- value_countsproduced by · value_counts is a pandas operation that counts the occurrences of each distinct value or combination of values in data.
- data framepart of · A data frame is a two-dimensional tabular data structure with labeled rows and columns that can contain values of different types.
- broadcastingproduced by · Broadcasting is the distribution or implicit replication of data from one source across multiple recipients or compatible positions for communication or computation.
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