from __future__ import annotations
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from typing import Literal
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import warnings
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from pandas.util._exceptions import find_stack_level
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from pandas.core.dtypes.cast import maybe_box_native
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from pandas.core.dtypes.common import (
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is_extension_array_dtype,
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is_object_dtype,
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)
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from pandas import DataFrame
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from pandas.core import common as com
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def to_dict(
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df: DataFrame,
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orient: Literal[
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"dict", "list", "series", "split", "tight", "records", "index"
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] = "dict",
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into: type[dict] = dict,
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index: bool = True,
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) -> dict | list[dict]:
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"""
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Convert the DataFrame to a dictionary.
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The type of the key-value pairs can be customized with the parameters
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(see below).
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Parameters
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----------
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orient : str {'dict', 'list', 'series', 'split', 'tight', 'records', 'index'}
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Determines the type of the values of the dictionary.
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- 'dict' (default) : dict like {column -> {index -> value}}
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- 'list' : dict like {column -> [values]}
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- 'series' : dict like {column -> Series(values)}
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- 'split' : dict like
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{'index' -> [index], 'columns' -> [columns], 'data' -> [values]}
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- 'tight' : dict like
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{'index' -> [index], 'columns' -> [columns], 'data' -> [values],
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'index_names' -> [index.names], 'column_names' -> [column.names]}
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- 'records' : list like
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[{column -> value}, ... , {column -> value}]
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- 'index' : dict like {index -> {column -> value}}
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.. versionadded:: 1.4.0
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'tight' as an allowed value for the ``orient`` argument
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into : class, default dict
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The collections.abc.Mapping subclass used for all Mappings
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in the return value. Can be the actual class or an empty
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instance of the mapping type you want. If you want a
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collections.defaultdict, you must pass it initialized.
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index : bool, default True
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Whether to include the index item (and index_names item if `orient`
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is 'tight') in the returned dictionary. Can only be ``False``
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when `orient` is 'split' or 'tight'.
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.. versionadded:: 2.0.0
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Returns
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-------
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dict, list or collections.abc.Mapping
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Return a collections.abc.Mapping object representing the DataFrame.
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The resulting transformation depends on the `orient` parameter.
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"""
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if not df.columns.is_unique:
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warnings.warn(
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"DataFrame columns are not unique, some columns will be omitted.",
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UserWarning,
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stacklevel=find_stack_level(),
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)
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# GH16122
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into_c = com.standardize_mapping(into)
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# error: Incompatible types in assignment (expression has type "str",
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# variable has type "Literal['dict', 'list', 'series', 'split', 'tight',
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# 'records', 'index']")
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orient = orient.lower() # type: ignore[assignment]
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if not index and orient not in ["split", "tight"]:
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raise ValueError(
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"'index=False' is only valid when 'orient' is 'split' or 'tight'"
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)
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if orient == "series":
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# GH46470 Return quickly if orient series to avoid creating dtype objects
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return into_c((k, v) for k, v in df.items())
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box_native_indices = [
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i
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for i, col_dtype in enumerate(df.dtypes.values)
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if is_object_dtype(col_dtype) or is_extension_array_dtype(col_dtype)
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]
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are_all_object_dtype_cols = len(box_native_indices) == len(df.dtypes)
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if orient == "dict":
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return into_c((k, v.to_dict(into)) for k, v in df.items())
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elif orient == "list":
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object_dtype_indices_as_set = set(box_native_indices)
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return into_c(
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(
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k,
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list(map(maybe_box_native, v.tolist()))
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if i in object_dtype_indices_as_set
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else v.tolist(),
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)
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for i, (k, v) in enumerate(df.items())
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)
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elif orient == "split":
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data = df._create_data_for_split_and_tight_to_dict(
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are_all_object_dtype_cols, box_native_indices
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)
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return into_c(
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((("index", df.index.tolist()),) if index else ())
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+ (
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("columns", df.columns.tolist()),
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("data", data),
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)
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)
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elif orient == "tight":
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data = df._create_data_for_split_and_tight_to_dict(
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are_all_object_dtype_cols, box_native_indices
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)
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return into_c(
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((("index", df.index.tolist()),) if index else ())
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+ (
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("columns", df.columns.tolist()),
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(
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"data",
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[
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list(map(maybe_box_native, t))
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for t in df.itertuples(index=False, name=None)
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],
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),
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)
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+ ((("index_names", list(df.index.names)),) if index else ())
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+ (("column_names", list(df.columns.names)),)
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)
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elif orient == "records":
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columns = df.columns.tolist()
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if are_all_object_dtype_cols:
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rows = (
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dict(zip(columns, row)) for row in df.itertuples(index=False, name=None)
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)
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return [
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into_c((k, maybe_box_native(v)) for k, v in row.items()) for row in rows
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]
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else:
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data = [
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into_c(zip(columns, t)) for t in df.itertuples(index=False, name=None)
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]
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if box_native_indices:
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object_dtype_indices_as_set = set(box_native_indices)
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object_dtype_cols = {
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col
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for i, col in enumerate(df.columns)
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if i in object_dtype_indices_as_set
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}
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for row in data:
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for col in object_dtype_cols:
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row[col] = maybe_box_native(row[col])
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return data
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elif orient == "index":
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if not df.index.is_unique:
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raise ValueError("DataFrame index must be unique for orient='index'.")
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columns = df.columns.tolist()
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if are_all_object_dtype_cols:
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return into_c(
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(t[0], dict(zip(df.columns, map(maybe_box_native, t[1:]))))
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for t in df.itertuples(name=None)
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)
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elif box_native_indices:
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object_dtype_indices_as_set = set(box_native_indices)
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is_object_dtype_by_index = [
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i in object_dtype_indices_as_set for i in range(len(df.columns))
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]
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return into_c(
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(
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t[0],
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{
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columns[i]: maybe_box_native(v)
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if is_object_dtype_by_index[i]
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else v
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for i, v in enumerate(t[1:])
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},
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)
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for t in df.itertuples(name=None)
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)
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else:
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return into_c(
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(t[0], dict(zip(df.columns, t[1:]))) for t in df.itertuples(name=None)
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)
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else:
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raise ValueError(f"orient '{orient}' not understood")
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