Apply
In pandas, apply is a method that applies a callable across Series values or along a DataFrame axis and returns the results.
Pandas Workout: 200 exercises to make you a stronger data analyst
apply is a pandas method that applies a function to values or along an axis of a Series or DataFrame.
- How it is used
- Applies custom cleanup, calculation, or display functions across pandas values when built-in operations do not fit.
- How deep it goes
- Explains element-wise Series application through custom functions and returned values, without detailing pandas execution internals.
- Examples
- Uses Vehicle Make normalization, vowel counts in words, and formatted mean compensation by employment.
- What you can do after
- Apply it via pandas to transform Series values with a custom Python function and aggregate or inspect the results.
Read how Pandas Workout: 200 exercises to make you a stronger data analyst explains apply →
Build Python Web Apps with Streamlit
The pandas apply method applies a function across dataframe rows, columns, groups, or Series values.
- How it is used
- Builds daily metric data for a filtered dashboard time series.
- How deep it goes
- Explains grouped execution, lambda aggregation, group-label exclusion, and index restoration, but not broader performance trade-offs.
- Examples
- Demonstrates summing daily Sales from state, gender, and product records for a Plotly time series.
- What you can do after
- Builds grouped summary data and supplies it to a reusable time series chart.
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Fast Python: High performance techniques for large datasets
- How it is used
- Uses apply as a pandas-based alternative to explicit row iteration for per-row computations.
- How deep it goes
- Explains row-wise callable execution and benchmarks it, then defers to vectorization for substantially better performance.
- Examples
- Calculates taxi tip-to-total fractions with a row-wise lambda and compares timings against itertuples and vectorized Series arithmetic.
- What you can do after
- Evaluate apply-based row computations against iteration and vectorization using measured runtime rather than assumed performance.
Read how Fast Python: High performance techniques for large datasets explains apply →
Related concepts
- data frameused by · A data frame is a two-dimensional tabular data structure with labeled rows and columns that can contain values of different types.
- vectorizationalternative to · Vectorization is representing data or computations in vector or array form so they can be processed collectively rather than as separate scalar values.
- comprehensionalternative to · A comprehension is a language construct that creates a collection by iterating over one or more sources, optionally filtering elements and transforming them.
- columnuses · A column is a vertical sequence or field in a tabular, grid, or matrix structure whose elements share a common position, attribute, or role.
- pandaspart of · pandas is an open-source Python library that provides labeled data structures and tools for manipulating and analyzing tabular and other structured data.
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