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Supports both scalar and vectorized functions, with lazy evaluation until result is materialized.","intents":["I need to apply a custom function to each row or column of a DataFrame","I want to map values in a Series using a dictionary or function","I need to transform every element in a DataFrame using a custom function"],"best_for":["data analysts applying domain-specific transformations","researchers implementing custom feature engineering logic","teams with complex business rules that can't be expressed with built-in operations"],"limitations":["apply() with custom Python functions is 10-100x slower than built-in operations because it bypasses Cython optimization","apply() on large DataFrames with complex functions can be memory-intensive due to intermediate result storage","No automatic parallelization; raw=True parameter only works with NumPy-compatible functions"],"requires":["Python 3.9+","NumPy 1.21.0+","custom functions must be picklable for parallel execution"],"input_types":["DataFrame or Series","custom function (callable)","axis parameter (0 for columns, 1 for rows)","args and kwargs for function parameters"],"output_types":["transformed DataFrame or Series","scalar value (if function returns single value)","custom user-defined output"],"categories":["data-processing-analysis","custom-transformations"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pandas__cap_11","uri":"capability://data.processing.analysis.statistical.analysis.and.descriptive.statistics","name":"statistical analysis and descriptive statistics","description":"Provides built-in statistical methods (mean, median, std, var, quantile, describe, corr, cov) optimized in Cython for numerical columns. 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Uses efficient algorithms (e.g., Welford's algorithm for rolling variance) to avoid recomputing from scratch for each window. 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Uses hash-based grouping internally with optional sorting, supporting both built-in aggregations (optimized in Cython) and user-defined functions with lazy evaluation until result is materialized.","intents":["I need to calculate summary statistics (mean, sum, count) grouped by categorical columns","I want to apply custom transformations to each group independently","I need to perform multi-level aggregations with different functions per column"],"best_for":["data analysts performing exploratory data analysis with group-level summaries","business intelligence teams building aggregated reports","machine learning engineers creating group-based features"],"limitations":["GroupBy operations on large groups with many unique keys can be memory-intensive due to intermediate result storage","Custom user-defined functions in apply() are not Cython-optimized and run in Python, causing 10-100x slowdown vs built-in aggregations","Grouping by high-cardinality columns (millions of unique values) requires careful memory management"],"requires":["Python 3.9+","NumPy 1.21.0+","Cython (for compiled aggregations, included in binary distribution)"],"input_types":["DataFrame","Series","grouping keys (column names, arrays, or functions)","level parameter for MultiIndex"],"output_types":["aggregated DataFrame/Series","transformed DataFrame/Series (same shape as input)","filtered DataFrame/Series","custom user-defined output"],"categories":["data-processing-analysis","aggregation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pandas__cap_4","uri":"capability://data.processing.analysis.missing.data.handling.with.multiple.imputation.strategies","name":"missing data handling with multiple imputation strategies","description":"Provides multiple strategies for handling missing values (NaN, None, pd.NA) through fillna(), dropna(), and interpolate() methods. Supports forward-fill, backward-fill, linear interpolation, and custom fill values, with configurable behavior per column and axis. Internally tracks missing values using NumPy NaN for floats and nullable dtypes (Int64, string) for other types.","intents":["I need to remove rows or columns with missing data before analysis","I want to fill missing values with the previous observation or a constant","I need to interpolate missing time-series values using linear or polynomial methods"],"best_for":["data engineers cleaning raw datasets before pipeline ingestion","time-series analysts handling gaps in sensor or market data","researchers preparing datasets for statistical analysis"],"limitations":["Forward-fill and backward-fill can propagate stale values far into the future; requires manual limit parameter to prevent data leakage","Interpolation assumes monotonic x-axis; fails on irregular time-series without explicit x parameter","Nullable dtypes (Int64, string) add 1-2% memory overhead compared to NumPy dtypes due to mask array storage"],"requires":["Python 3.9+","NumPy 1.21.0+","scipy (optional, for advanced interpolation methods)"],"input_types":["DataFrame with NaN, None, or pd.NA values","Series with missing values","fill_value (scalar or dict)","method parameter (ffill, bfill, linear, etc.)"],"output_types":["DataFrame/Series with missing values removed","DataFrame/Series with missing values filled","DataFrame/Series with interpolated values"],"categories":["data-processing-analysis","data-cleaning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pandas__cap_5","uri":"capability://data.processing.analysis.merge.and.join.operations.with.multiple.join.types","name":"merge and join operations with multiple join types","description":"Implements SQL-like join operations (inner, outer, left, right) through merge() and join() methods using hash-based join algorithms for performance. Supports joining on index, columns, or combinations thereof, with optional suffixes for overlapping column names. Internally uses hash tables for O(n) join performance on large datasets, with fallback to sort-merge for sorted data.","intents":["I need to combine two DataFrames on a common key column","I want to perform a left join to keep all rows from the left DataFrame","I need to join on multiple columns or index levels simultaneously"],"best_for":["data engineers combining data from multiple sources","analysts enriching datasets with lookup tables","database developers migrating SQL workflows to pandas"],"limitations":["Hash-based joins require materializing the entire right DataFrame in memory; very large right tables can cause OOM errors","Joining on non-unique keys creates a Cartesian product; many-to-many joins can explode result size unexpectedly","Join performance degrades with high cardinality keys; no built-in query optimization like SQL databases"],"requires":["Python 3.9+","NumPy 1.21.0+","both DataFrames must have compatible dtypes on join keys"],"input_types":["two DataFrames","join keys (column names, index, or arrays)","join type (inner, outer, left, right)","suffixes for overlapping columns"],"output_types":["merged DataFrame","joined DataFrame","DataFrame with combined rows and columns"],"categories":["data-processing-analysis","data-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pandas__cap_6","uri":"capability://data.processing.analysis.reshape.and.pivot.operations.for.data.transformation","name":"reshape and pivot operations for data transformation","description":"Provides pivot(), melt(), stack(), and unstack() methods to reshape data between wide and long formats. Uses MultiIndex internally to track hierarchical structure during reshaping, with optimized algorithms for common patterns. Supports aggregation during pivot (when multiple values map to same cell) and handles missing combinations through fill_value parameter.","intents":["I need to convert data from long format (one row per observation) to wide format (one row per entity)","I want to unpivot wide data back to long format for analysis","I need to create a cross-tabulation showing counts or sums across two dimensions"],"best_for":["data analysts preparing data for statistical modeling","business analysts creating pivot tables for reporting","researchers converting between experimental data formats"],"limitations":["Pivot operations with many unique values in pivot columns can create very wide DataFrames with memory and performance issues","Melt with many id_vars creates long DataFrames that may be slower for subsequent operations","Stack/unstack on large MultiIndex DataFrames can be slow due to index reconstruction overhead"],"requires":["Python 3.9+","NumPy 1.21.0+","data must have unique combinations of index/column values for pivot (or aggregation function)"],"input_types":["DataFrame in long or wide format","index, columns, values parameters for pivot","id_vars, value_vars for melt","level parameter for stack/unstack"],"output_types":["reshaped DataFrame","pivoted DataFrame","melted DataFrame","stacked/unstacked DataFrame with MultiIndex"],"categories":["data-processing-analysis","data-transformation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pandas__cap_7","uri":"capability://data.processing.analysis.i.o.operations.for.reading.and.writing.multiple.file.formats","name":"i/o operations for reading and writing multiple file formats","description":"Implements read_csv(), read_excel(), read_sql(), read_json(), read_parquet(), and write methods for multiple file formats using format-specific parsers (C engine for CSV, openpyxl for Excel, pyarrow for Parquet). Supports chunked reading for large files, dtype inference, and lazy evaluation through iterator patterns, with automatic compression detection for gzip/bzip2/zip.","intents":["I need to load data from CSV, Excel, or Parquet files into a DataFrame","I want to read large files in chunks to avoid memory overflow","I need to save my processed DataFrame to multiple formats for different tools"],"best_for":["data engineers building ETL pipelines","analysts working with data from various sources","teams sharing data across different tools and platforms"],"limitations":["CSV parsing with C engine is fast but has limited control over edge cases; Python engine is slower but more flexible","Excel reading requires openpyxl or xlrd; large Excel files are slow due to XML parsing overhead","Parquet reading requires pyarrow; no built-in support for other columnar formats like ORC","SQL reading requires sqlalchemy; performance depends on database driver and network latency"],"requires":["Python 3.9+","NumPy 1.21.0+","format-specific libraries: openpyxl (Excel), sqlalchemy (SQL), pyarrow (Parquet), etc."],"input_types":["file path (string or Path object)","file-like object (StringIO, BytesIO)","URL (for remote files)","SQL connection string","compressed files (gzip, bzip2, zip)"],"output_types":["DataFrame","iterator of DataFrames (for chunked reading)","CSV file","Excel file","Parquet file","JSON file","SQL table"],"categories":["data-processing-analysis","io-operations"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pandas__cap_8","uri":"capability://data.processing.analysis.vectorized.string.operations.on.series","name":"vectorized string operations on series","description":"Provides .str accessor for vectorized string operations (split, replace, contains, extract, etc.) on Series using NumPy's string functions and regex patterns. Operations are applied element-wise without explicit loops, with optional regex support through re module. Returns Series or DataFrame depending on operation, enabling efficient text processing on large datasets.","intents":["I need to clean text data (trim whitespace, convert case, remove special characters)","I want to extract patterns from text using regular expressions","I need to split text columns into multiple columns based on delimiters"],"best_for":["data engineers cleaning messy text data","NLP practitioners preparing text for model input","analysts extracting structured data from unstructured text"],"limitations":["String operations are slower than NumPy numeric operations due to Python object overhead; complex regex patterns can be 10-100x slower than simple operations","Regex operations are not vectorized at the C level; each pattern match is evaluated in Python","Missing values (NaN) propagate through string operations; requires explicit handling with fillna()"],"requires":["Python 3.9+","NumPy 1.21.0+","re module (built-in, for regex support)"],"input_types":["Series with string dtype","Series with object dtype containing strings","regex patterns (string)","delimiter strings"],"output_types":["Series with transformed strings","DataFrame from split/extract operations","Series with boolean values (contains, match)","Series with numeric values (len, count)"],"categories":["data-processing-analysis","text-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pandas__cap_9","uri":"capability://data.processing.analysis.categorical.data.representation.with.memory.optimization","name":"categorical data representation with memory optimization","description":"Implements Categorical dtype using integer codes (0, 1, 2...) mapped to category labels, reducing memory usage for repeated string values. 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