Data Manipulation and Reshaping

import pandas as pd
from pandas import concat, merge, pivot_table, melt

def concat(objs, axis=0, join='outer', ignore_index=False, keys=None, levels=None, names=None, verify_integrity=False, sort=False, copy=True):
    """
    Concatenate pandas objects along a particular axis.
    
    Parameters:
    - objs: sequence or mapping of Series or DataFrame objects
    - axis: int, axis to concatenate along (0='index', 1='columns')
    - join: str, how to handle indexes on other axis ('inner'/'outer')
    - ignore_index: bool, do not use index values along concatenation axis
    - keys: sequence, construct hierarchical index using passed keys
    - levels: list of sequences, specific levels to use for constructing MultiIndex
    - names: list, names for levels in hierarchical index
    - verify_integrity: bool, check whether new concatenated axis contains duplicates
    - sort: bool, sort non-concatenation axis if not already aligned
    
    Returns:
    object, type of objects being concatenated
    """

def merge(left, right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False, validate=None):
    """
    Merge DataFrame or named Series objects with a database-style join.
    
    Parameters:
    - left: DataFrame or named Series
    - right: DataFrame or named Series 
    - how: str, type of merge ('left', 'right', 'outer', 'inner', 'cross')
    - on: label or list, column names to join on
    - left_on: label or list, left DataFrame column names to join on
    - right_on: label or list, right DataFrame column names to join on
    - left_index: bool, use left DataFrame index as join key
    - right_index: bool, use right DataFrame index as join key
    - sort: bool, sort join keys lexicographically
    - suffixes: list-like, suffix to apply to overlapping column names
    - indicator: bool or str, add column to output indicating source of each row
    - validate: str, validate uniqueness of merge keys
    
    Returns:
    DataFrame
    """

def merge_asof(left, right, on=None, left_on=None, right_on=None, left_index=False, right_index=False, by=None, left_by=None, right_by=None, suffixes=('_x', '_y'), tolerance=None, allow_exact_matches=True, direction='backward'):
    """
    Perform a merge by key distance.
    
    Parameters:
    - left: DataFrame or named Series
    - right: DataFrame or named Series
    - on: label, column name to merge on (must be sorted)
    - by: column name or list of column names, match on these columns before searching
    - tolerance: int or Timedelta, select closest key within this distance
    - allow_exact_matches: bool, allow matching with exact same key
    - direction: str, search direction ('backward', 'forward', 'nearest')
    
    Returns:
    DataFrame
    """

def merge_ordered(left, right, on=None, left_on=None, right_on=None, left_by=None, right_by=None, fill_method=None, suffixes=('_x', '_y'), how='outer'):
    """
    Perform merge with optional filling/interpolation.
    
    Parameters:
    - left: DataFrame or named Series
    - right: DataFrame or named Series
    - fill_method: str, interpolation method ('ffill')
    - how: str, type of merge ('left', 'right', 'outer', 'inner')
    
    Returns:
    DataFrame
    """

def pivot(data, index=None, columns=None, values=None):
    """
    Return reshaped DataFrame organized by given index/column values.
    
    Parameters:
    - data: DataFrame
    - index: column to use to make new frame's index
    - columns: column to use to make new frame's columns
    - values: column(s) to use for populating new frame's values
    
    Returns:
    DataFrame
    """

def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All', observed=False, sort=True):
    """
    Create a spreadsheet-style pivot table as a DataFrame.
    
    Parameters:
    - data: DataFrame
    - values: column to aggregate
    - index: column, Grouper, array, list of columns to use as index
    - columns: column, Grouper, array, list of columns to use as columns
    - aggfunc: function, function to use for aggregation ('mean', 'sum', 'count', etc.)
    - fill_value: scalar, value to replace missing values
    - margins: bool, add row/column margins (subtotals)
    - dropna: bool, do not include columns with all NaN values
    - margins_name: str, name of row/column containing totals
    - observed: bool, for categorical columns, consider only observed categories
    
    Returns:
    DataFrame
    """

def melt(data, id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None, ignore_index=True):
    """
    Unpivot a DataFrame from wide to long format.
    
    Parameters:
    - data: DataFrame
    - id_vars: column(s) to use as identifier variables
    - value_vars: column(s) to unpivot, defaults to all columns not in id_vars
    - var_name: str, name to use for variable column
    - value_name: str, name to use for value column
    - col_level: int or str, level in columns to melt
    - ignore_index: bool, ignore index in result
    
    Returns:
    DataFrame
    """

def wide_to_long(df, stubnames, i, j, sep='', suffix='\\d+'):
    """
    Pivot a wide table to long (stacked) format.
    
    Parameters:
    - df: DataFrame
    - stubnames: str or list, stub name(s)
    - i: column(s) to use as id variable(s)
    - j: str, suffix of wide variables
    - sep: str, separator between stub names and suffix
    - suffix: str, regular expression for suffix
    
    Returns:
    DataFrame
    """

def lreshape(data, groups, dropna=True):
    """
    Reshape wide-format data to long.
    
    Parameters:
    - data: DataFrame
    - groups: dict, mapping of column names to group labels
    - dropna: bool, drop rows containing missing values
    
    Returns:
    DataFrame
    """

def get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False, drop_first=False, dtype=None):
    """
    Convert categorical variable(s) into dummy/indicator variables.
    
    Parameters:
    - data: array-like, Series, or DataFrame
    - prefix: str, list of str, or dict of str, string to append DataFrame column names
    - prefix_sep: str, separator/delimiter to use when adding prefix
    - dummy_na: bool, add column to indicate NaNs
    - columns: list-like, column names in DataFrame to encode
    - sparse: bool, return SparseArray (True) or NumPy array (False)
    - drop_first: bool, remove first level of categorical variable
    - dtype: dtype, data type for new columns
    
    Returns:
    DataFrame
    """

def from_dummies(data, sep=None, default_category=None):
    """
    Create a categorical DataFrame from a DataFrame of dummy variables.
    
    Parameters:
    - data: DataFrame, data of which to get dummy indicators
    - sep: str, separator used in column names of dummy DataFrame
    - default_category: None, str, or dict of str, name of column containing default category
    
    Returns:
    DataFrame
    """

def crosstab(index, columns, values=None, rownames=None, colnames=None, aggfunc=None, margins=False, margins_name='All', dropna=True, normalize=False):
    """
    Compute a simple cross-tabulation of two (or more) factors.
    
    Parameters:
    - index: array-like, values to group by in rows
    - columns: array-like, values to group by in columns
    - values: array-like, array of values to aggregate according to factors
    - rownames: sequence, names for row index
    - colnames: sequence, names for column index
    - aggfunc: function, aggregation function to use
    - margins: bool, add row/column margins (subtotals)
    - dropna: bool, do not include columns with all NaN values
    - normalize: bool, normalize by dividing all values by sum
    
    Returns:
    DataFrame
    """

def cut(x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False, duplicates='raise', ordered=True):
    """
    Bin values into discrete intervals.
    
    Parameters:
    - x: array-like, input array to be binned
    - bins: int, sequence of scalars, or IntervalIndex, criteria to bin by
    - right: bool, indicates whether bins include rightmost edge
    - labels: array or bool, specifies labels for returned bins
    - retbins: bool, return bins or not
    - precision: int, precision at which to store and display bins labels
    - include_lowest: bool, whether first interval should be left-inclusive
    - duplicates: str, behavior when bin edges are not unique ('raise' or 'drop')
    - ordered: bool, whether returned Categorical will be ordered
    
    Returns:
    Categorical, Series, or array of intervals
    """

def qcut(x, q, labels=None, retbins=False, precision=3, duplicates='raise'):
    """
    Quantile-based discretization function.
    
    Parameters:
    - x: array-like, input array to be binned
    - q: int or list-like of float, number of quantiles or quantile boundaries
    - labels: array or bool, used as labels for resulting bins
    - retbins: bool, return (bins, labels) or not
    - precision: int, precision at which to store and display bins labels
    - duplicates: str, behavior when bin edges are not unique ('raise' or 'drop')
    
    Returns:
    Categorical, Series, or array of intervals
    """

# DataFrame transformation methods (already covered in core-data-structures.md)
# These are methods of DataFrame/Series classes:

# DataFrame.pivot(index=None, columns=None, values=None)
# DataFrame.transpose() / DataFrame.T
# DataFrame.stack(level=-1, dropna=True)
# DataFrame.unstack(level=-1, fill_value=None)  
# DataFrame.explode(column, ignore_index=False)
# Series.explode(ignore_index=False)

# Additional utility functions
def eval(expr, parser='pandas', engine=None, local_dict=None, global_dict=None, resolvers=(), level=0, target=None, inplace=False):
    """
    Evaluate a Python expression as a string using pandas objects.
    
    Parameters:
    - expr: str, expression to evaluate
    - parser: str, parser to use ('pandas' or 'python')
    - engine: str, engine to use ('python', 'numexpr')
    - local_dict: dict, local variable scope
    - global_dict: dict, global variable scope
    - level: int, number of prior stack frames to traverse
    - target: object, assign result to this variable
    - inplace: bool, perform operation in-place
    
    Returns:
    ndarray, numeric scalar, DataFrame, Series
    """

# MultiIndex creation and manipulation
def MultiIndex.from_arrays(arrays, sortorder=None, names=None):
    """Create MultiIndex from arrays."""

def MultiIndex.from_tuples(tuples, sortorder=None, names=None):
    """Create MultiIndex from list of tuples."""

def MultiIndex.from_product(iterables, sortorder=None, names=None):
    """Create MultiIndex from cartesian product of iterables."""

def MultiIndex.from_frame(df, sortorder=None, names=None):
    """Create MultiIndex from DataFrame."""

# These are methods of DataFrames with MultiIndex:
# df.stack() - pivot columns to rows (wide to long)  
# df.unstack() - pivot rows to columns (long to wide)
# df.swaplevel() - swap levels in MultiIndex
# df.reorder_levels() - rearrange index levels

# Additional merge validation options
class MergeValidation:
    ONE_TO_ONE = '1:1'
    ONE_TO_MANY = '1:m' 
    MANY_TO_ONE = 'm:1'
    MANY_TO_MANY = 'm:m'

# Join methods (these are DataFrame methods)
# df.join(other, on=None, how='left', lsuffix='', rsuffix='', sort=False)
# df.combine(other, func, fill_value=None, overwrite=True)
# df.combine_first(other) - combine with other, using non-null values from calling DataFrame
# df.update(other, join='left', overwrite=True, filter_func=None, errors='ignore')

# Merge and join types
MergeHow = Literal['left', 'right', 'outer', 'inner', 'cross']
JoinHow = Literal['left', 'right', 'outer', 'inner']

# Pivot aggregation functions
AggFunc = Union[str, Callable, List[Union[str, Callable]], Dict[str, Union[str, Callable]]]

# Binning edge behavior
BinEdge = Literal['left', 'right']

# Categorical ordering
CategoricalOrdered = bool

# Cross-tabulation normalization
NormalizeOptions = Union[bool, Literal['all', 'index', 'columns']]