Count
A count is a numerical tally or operation determining how many items, occurrences, records, or values satisfy specified criteria.
Pandas Workout: 200 exercises to make you a stronger data analyst
count is a pandas operation that returns the number of non-missing values in an object or along an axis.
- How it is used
- Counts populated observations after filtering, across columns, and within groups for practical data-analysis questions.
- How deep it goes
- Defines Series and DataFrame return shapes, then works through filtering, sorting, grouping, and missing-value limitations without implementation internals.
- Examples
- Uses New York taxi trips, developer-survey language responses, university records, and a small NaN-filled DataFrame.
- What you can do after
- Apply it via pandas to count non-missing selected values, compare subsets, and rank group totals.
Read how Pandas Workout: 200 exercises to make you a stronger data analyst explains count →
Graph Algorithms for Data Science
The Cypher count function returns the number of rows or the number of non-null values supplied to it.
- How it is used
- Summarizes matched graph patterns, property completeness, network interactions, and filtered groups with Cypher totals.
- How deep it goes
- Explains row and non-null semantics, distinct counting, cardinality effects, and staged aggregation without detailing function internals.
- Examples
- Demonstrates User registration-date completeness, mentioned-user totals, sequential tweet counts, hashtag pairs, and weakly connected component sizes.
- What you can do after
- Apply it via Cypher to distinguish rows from non-null values, deduplicate totals, and control intermediate cardinality.
Statistics Slam Dunk
The count() function from dplyr counts observations by specified variables and returns the resulting counts in a tabular object.
- How it is used
- Deploys count() for frequency tables, subgroup comparisons, and integrity checks in NBA data wrangling.
- How deep it goes
- Explains calls on variables and logical conditions, then extends results with sorting, percentages, and cumulative totals.
- Examples
- Counts first-quarter result combinations, quarter-specific team outcomes, and equal opposing-condition frequencies in NBA datasets.
- What you can do after
- Apply it via dplyr to produce frequency tables and extend them into sorted, percentage-based summaries.
Apache Kafka in Action
count is a Kafka Streams aggregation operation that maintains the number of records for each key or grouping and produces aggregate results.
- How it is used
- Counts events per key in continuous streams, supporting current-state analysis, trend detection, billing, and usage analysis.
- How deep it goes
- Explains grouping, incrementing, initial state, state stores, partition limits, and crash recovery, but stops short of implementation internals.
- Examples
- Uses product price-change events, groupByKey().count(), per-key increments, and a state-store changelog diagram.
- What you can do after
- Apply it via Kafka Streams to maintain recoverable counts for each record key.
ScyllaDB in Action
A COUNT function is a database aggregate function that returns the number of rows matching a query.
- How it is used
- Answers application questions such as how many articles have been published without manually counting returned rows.
- How deep it goes
- Defines aggregate counting, demonstrates partition-level queries, and warns against expensive full-table counts.
- Examples
- Uses article summaries in a date bucket and an authors table to demonstrate returned counts.
- What you can do after
- Apply it via CQL to count rows in a specified ScyllaDB partition.
Related concepts
- data aggregationkind of · Data aggregation is the combination of multiple values, records, observations, or results into a consolidated or summarized output.
- stateuses · State is information or a configuration describing the condition of a system, entity, or computation at a particular time and potentially influencing subsequent behavior or decisions.
- distinct keywordused by · A distinct operation eliminates duplicate values, elements, or rows from a result, retaining one occurrence of each unique item.
- nanmeasures · NaN is a special floating-point value representing an undefined or unrepresentable numerical result that may also be used as a marker for missing numeric data.
- missing valueuses · A missing value is a representation or condition indicating that an expected data value or object reference is absent, unknown, unavailable, or inapplicable.
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