322 lines
10 KiB
Python
322 lines
10 KiB
Python
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"""
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Module for formatting output data into CSV files.
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"""
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from __future__ import annotations
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import csv as csvlib
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import os
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from typing import (
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TYPE_CHECKING,
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Any,
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Hashable,
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Iterator,
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Sequence,
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cast,
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)
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import numpy as np
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from pandas._libs import writers as libwriters
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from pandas._typing import (
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CompressionOptions,
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FilePath,
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FloatFormatType,
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IndexLabel,
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StorageOptions,
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WriteBuffer,
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)
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from pandas.util._decorators import cache_readonly
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from pandas.core.dtypes.generic import (
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ABCDatetimeIndex,
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ABCIndex,
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ABCMultiIndex,
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ABCPeriodIndex,
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)
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from pandas.core.dtypes.missing import notna
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from pandas.core.indexes.api import Index
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from pandas.io.common import get_handle
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if TYPE_CHECKING:
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from pandas.io.formats.format import DataFrameFormatter
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class CSVFormatter:
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cols: np.ndarray
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def __init__(
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self,
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formatter: DataFrameFormatter,
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path_or_buf: FilePath | WriteBuffer[str] | WriteBuffer[bytes] = "",
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sep: str = ",",
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cols: Sequence[Hashable] | None = None,
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index_label: IndexLabel | None = None,
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mode: str = "w",
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encoding: str | None = None,
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errors: str = "strict",
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compression: CompressionOptions = "infer",
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quoting: int | None = None,
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lineterminator: str | None = "\n",
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chunksize: int | None = None,
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quotechar: str | None = '"',
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date_format: str | None = None,
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doublequote: bool = True,
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escapechar: str | None = None,
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storage_options: StorageOptions = None,
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) -> None:
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self.fmt = formatter
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self.obj = self.fmt.frame
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self.filepath_or_buffer = path_or_buf
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self.encoding = encoding
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self.compression: CompressionOptions = compression
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self.mode = mode
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self.storage_options = storage_options
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self.sep = sep
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self.index_label = self._initialize_index_label(index_label)
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self.errors = errors
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self.quoting = quoting or csvlib.QUOTE_MINIMAL
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self.quotechar = self._initialize_quotechar(quotechar)
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self.doublequote = doublequote
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self.escapechar = escapechar
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self.lineterminator = lineterminator or os.linesep
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self.date_format = date_format
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self.cols = self._initialize_columns(cols)
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self.chunksize = self._initialize_chunksize(chunksize)
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@property
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def na_rep(self) -> str:
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return self.fmt.na_rep
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@property
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def float_format(self) -> FloatFormatType | None:
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return self.fmt.float_format
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@property
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def decimal(self) -> str:
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return self.fmt.decimal
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@property
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def header(self) -> bool | Sequence[str]:
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return self.fmt.header
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@property
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def index(self) -> bool:
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return self.fmt.index
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def _initialize_index_label(self, index_label: IndexLabel | None) -> IndexLabel:
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if index_label is not False:
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if index_label is None:
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return self._get_index_label_from_obj()
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elif not isinstance(index_label, (list, tuple, np.ndarray, ABCIndex)):
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# given a string for a DF with Index
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return [index_label]
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return index_label
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def _get_index_label_from_obj(self) -> Sequence[Hashable]:
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if isinstance(self.obj.index, ABCMultiIndex):
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return self._get_index_label_multiindex()
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else:
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return self._get_index_label_flat()
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def _get_index_label_multiindex(self) -> Sequence[Hashable]:
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return [name or "" for name in self.obj.index.names]
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def _get_index_label_flat(self) -> Sequence[Hashable]:
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index_label = self.obj.index.name
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return [""] if index_label is None else [index_label]
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def _initialize_quotechar(self, quotechar: str | None) -> str | None:
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if self.quoting != csvlib.QUOTE_NONE:
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# prevents crash in _csv
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return quotechar
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return None
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@property
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def has_mi_columns(self) -> bool:
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return bool(isinstance(self.obj.columns, ABCMultiIndex))
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def _initialize_columns(self, cols: Sequence[Hashable] | None) -> np.ndarray:
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# validate mi options
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if self.has_mi_columns:
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if cols is not None:
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msg = "cannot specify cols with a MultiIndex on the columns"
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raise TypeError(msg)
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if cols is not None:
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if isinstance(cols, ABCIndex):
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cols = cols._format_native_types(**self._number_format)
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else:
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cols = list(cols)
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self.obj = self.obj.loc[:, cols]
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# update columns to include possible multiplicity of dupes
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# and make sure cols is just a list of labels
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new_cols = self.obj.columns
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return new_cols._format_native_types(**self._number_format)
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def _initialize_chunksize(self, chunksize: int | None) -> int:
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if chunksize is None:
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return (100000 // (len(self.cols) or 1)) or 1
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return int(chunksize)
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@property
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def _number_format(self) -> dict[str, Any]:
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"""Dictionary used for storing number formatting settings."""
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return {
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"na_rep": self.na_rep,
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"float_format": self.float_format,
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"date_format": self.date_format,
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"quoting": self.quoting,
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"decimal": self.decimal,
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}
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@cache_readonly
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def data_index(self) -> Index:
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data_index = self.obj.index
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if (
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isinstance(data_index, (ABCDatetimeIndex, ABCPeriodIndex))
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and self.date_format is not None
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):
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data_index = Index(
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[x.strftime(self.date_format) if notna(x) else "" for x in data_index]
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)
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elif isinstance(data_index, ABCMultiIndex):
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data_index = data_index.remove_unused_levels()
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return data_index
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@property
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def nlevels(self) -> int:
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if self.index:
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return getattr(self.data_index, "nlevels", 1)
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else:
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return 0
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@property
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def _has_aliases(self) -> bool:
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return isinstance(self.header, (tuple, list, np.ndarray, ABCIndex))
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@property
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def _need_to_save_header(self) -> bool:
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return bool(self._has_aliases or self.header)
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@property
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def write_cols(self) -> Sequence[Hashable]:
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if self._has_aliases:
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assert not isinstance(self.header, bool)
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if len(self.header) != len(self.cols):
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raise ValueError(
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f"Writing {len(self.cols)} cols but got {len(self.header)} aliases"
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)
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else:
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return self.header
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else:
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# self.cols is an ndarray derived from Index._format_native_types,
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# so its entries are strings, i.e. hashable
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return cast(Sequence[Hashable], self.cols)
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@property
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def encoded_labels(self) -> list[Hashable]:
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encoded_labels: list[Hashable] = []
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if self.index and self.index_label:
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assert isinstance(self.index_label, Sequence)
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encoded_labels = list(self.index_label)
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if not self.has_mi_columns or self._has_aliases:
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encoded_labels += list(self.write_cols)
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return encoded_labels
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def save(self) -> None:
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"""
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Create the writer & save.
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"""
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# apply compression and byte/text conversion
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with get_handle(
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self.filepath_or_buffer,
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self.mode,
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encoding=self.encoding,
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errors=self.errors,
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compression=self.compression,
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storage_options=self.storage_options,
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) as handles:
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# Note: self.encoding is irrelevant here
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self.writer = csvlib.writer(
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handles.handle,
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lineterminator=self.lineterminator,
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delimiter=self.sep,
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quoting=self.quoting,
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doublequote=self.doublequote,
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escapechar=self.escapechar,
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quotechar=self.quotechar,
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)
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self._save()
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def _save(self) -> None:
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if self._need_to_save_header:
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self._save_header()
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self._save_body()
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def _save_header(self) -> None:
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if not self.has_mi_columns or self._has_aliases:
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self.writer.writerow(self.encoded_labels)
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else:
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for row in self._generate_multiindex_header_rows():
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self.writer.writerow(row)
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def _generate_multiindex_header_rows(self) -> Iterator[list[Hashable]]:
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columns = self.obj.columns
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for i in range(columns.nlevels):
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# we need at least 1 index column to write our col names
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col_line = []
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if self.index:
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# name is the first column
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col_line.append(columns.names[i])
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if isinstance(self.index_label, list) and len(self.index_label) > 1:
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col_line.extend([""] * (len(self.index_label) - 1))
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col_line.extend(columns._get_level_values(i))
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yield col_line
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# Write out the index line if it's not empty.
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# Otherwise, we will print out an extraneous
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# blank line between the mi and the data rows.
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if self.encoded_labels and set(self.encoded_labels) != {""}:
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yield self.encoded_labels + [""] * len(columns)
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def _save_body(self) -> None:
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nrows = len(self.data_index)
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chunks = (nrows // self.chunksize) + 1
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for i in range(chunks):
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start_i = i * self.chunksize
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end_i = min(start_i + self.chunksize, nrows)
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if start_i >= end_i:
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break
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self._save_chunk(start_i, end_i)
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def _save_chunk(self, start_i: int, end_i: int) -> None:
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# create the data for a chunk
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slicer = slice(start_i, end_i)
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df = self.obj.iloc[slicer]
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res = df._mgr.to_native_types(**self._number_format)
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data = [res.iget_values(i) for i in range(len(res.items))]
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ix = self.data_index[slicer]._format_native_types(**self._number_format)
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libwriters.write_csv_rows(
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data,
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ix,
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self.nlevels,
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self.cols,
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self.writer,
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)
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