3990 lines
137 KiB
Python
3990 lines
137 KiB
Python
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"""
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Module contains tools for processing files into DataFrames or other objects
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"""
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from collections import abc, defaultdict
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import csv
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import datetime
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from io import StringIO
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import itertools
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import re
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import sys
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from textwrap import fill
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from typing import (
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Any,
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Dict,
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Iterable,
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Iterator,
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List,
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Optional,
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Sequence,
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Set,
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Type,
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cast,
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)
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import warnings
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import numpy as np
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import pandas._libs.lib as lib
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import pandas._libs.ops as libops
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import pandas._libs.parsers as parsers
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from pandas._libs.parsers import STR_NA_VALUES
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from pandas._libs.tslibs import parsing
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from pandas._typing import FilePathOrBuffer, StorageOptions, Union
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from pandas.errors import (
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AbstractMethodError,
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EmptyDataError,
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ParserError,
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ParserWarning,
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)
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from pandas.util._decorators import Appender
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from pandas.core.dtypes.cast import astype_nansafe
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from pandas.core.dtypes.common import (
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ensure_object,
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ensure_str,
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is_bool_dtype,
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is_categorical_dtype,
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is_dict_like,
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is_dtype_equal,
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is_extension_array_dtype,
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is_file_like,
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is_float,
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is_integer,
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is_integer_dtype,
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is_list_like,
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is_object_dtype,
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is_scalar,
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is_string_dtype,
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pandas_dtype,
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)
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from pandas.core.dtypes.dtypes import CategoricalDtype
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from pandas.core.dtypes.missing import isna
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from pandas.core import algorithms, generic
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from pandas.core.arrays import Categorical
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from pandas.core.frame import DataFrame
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from pandas.core.indexes.api import (
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Index,
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MultiIndex,
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RangeIndex,
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ensure_index_from_sequences,
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)
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from pandas.core.series import Series
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from pandas.core.tools import datetimes as tools
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from pandas.io.common import IOHandles, get_handle, validate_header_arg
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from pandas.io.date_converters import generic_parser
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# BOM character (byte order mark)
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# This exists at the beginning of a file to indicate endianness
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# of a file (stream). Unfortunately, this marker screws up parsing,
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# so we need to remove it if we see it.
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_BOM = "\ufeff"
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_doc_read_csv_and_table = (
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r"""
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{summary}
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Also supports optionally iterating or breaking of the file
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into chunks.
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Additional help can be found in the online docs for
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`IO Tools <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_.
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Parameters
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----------
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filepath_or_buffer : str, path object or file-like object
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Any valid string path is acceptable. The string could be a URL. Valid
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URL schemes include http, ftp, s3, gs, and file. For file URLs, a host is
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expected. A local file could be: file://localhost/path/to/table.csv.
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If you want to pass in a path object, pandas accepts any ``os.PathLike``.
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By file-like object, we refer to objects with a ``read()`` method, such as
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a file handle (e.g. via builtin ``open`` function) or ``StringIO``.
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sep : str, default {_default_sep}
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Delimiter to use. If sep is None, the C engine cannot automatically detect
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the separator, but the Python parsing engine can, meaning the latter will
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be used and automatically detect the separator by Python's builtin sniffer
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tool, ``csv.Sniffer``. In addition, separators longer than 1 character and
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different from ``'\s+'`` will be interpreted as regular expressions and
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will also force the use of the Python parsing engine. Note that regex
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delimiters are prone to ignoring quoted data. Regex example: ``'\r\t'``.
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delimiter : str, default ``None``
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Alias for sep.
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header : int, list of int, default 'infer'
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Row number(s) to use as the column names, and the start of the
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data. Default behavior is to infer the column names: if no names
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are passed the behavior is identical to ``header=0`` and column
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names are inferred from the first line of the file, if column
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names are passed explicitly then the behavior is identical to
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``header=None``. Explicitly pass ``header=0`` to be able to
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replace existing names. The header can be a list of integers that
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specify row locations for a multi-index on the columns
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e.g. [0,1,3]. Intervening rows that are not specified will be
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skipped (e.g. 2 in this example is skipped). Note that this
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parameter ignores commented lines and empty lines if
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``skip_blank_lines=True``, so ``header=0`` denotes the first line of
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data rather than the first line of the file.
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names : array-like, optional
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List of column names to use. If the file contains a header row,
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then you should explicitly pass ``header=0`` to override the column names.
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Duplicates in this list are not allowed.
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index_col : int, str, sequence of int / str, or False, default ``None``
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Column(s) to use as the row labels of the ``DataFrame``, either given as
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string name or column index. If a sequence of int / str is given, a
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MultiIndex is used.
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Note: ``index_col=False`` can be used to force pandas to *not* use the first
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column as the index, e.g. when you have a malformed file with delimiters at
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the end of each line.
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usecols : list-like or callable, optional
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Return a subset of the columns. If list-like, all elements must either
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be positional (i.e. integer indices into the document columns) or strings
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that correspond to column names provided either by the user in `names` or
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inferred from the document header row(s). For example, a valid list-like
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`usecols` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``.
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Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``.
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To instantiate a DataFrame from ``data`` with element order preserved use
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``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns
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in ``['foo', 'bar']`` order or
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``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]``
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for ``['bar', 'foo']`` order.
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If callable, the callable function will be evaluated against the column
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names, returning names where the callable function evaluates to True. An
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example of a valid callable argument would be ``lambda x: x.upper() in
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['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster
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parsing time and lower memory usage.
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squeeze : bool, default False
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If the parsed data only contains one column then return a Series.
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prefix : str, optional
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Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...
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mangle_dupe_cols : bool, default True
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Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than
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'X'...'X'. Passing in False will cause data to be overwritten if there
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are duplicate names in the columns.
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dtype : Type name or dict of column -> type, optional
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Data type for data or columns. E.g. {{'a': np.float64, 'b': np.int32,
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'c': 'Int64'}}
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Use `str` or `object` together with suitable `na_values` settings
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to preserve and not interpret dtype.
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If converters are specified, they will be applied INSTEAD
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of dtype conversion.
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engine : {{'c', 'python'}}, optional
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Parser engine to use. The C engine is faster while the python engine is
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currently more feature-complete.
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converters : dict, optional
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Dict of functions for converting values in certain columns. Keys can either
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be integers or column labels.
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true_values : list, optional
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Values to consider as True.
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false_values : list, optional
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Values to consider as False.
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skipinitialspace : bool, default False
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Skip spaces after delimiter.
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skiprows : list-like, int or callable, optional
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Line numbers to skip (0-indexed) or number of lines to skip (int)
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at the start of the file.
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If callable, the callable function will be evaluated against the row
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indices, returning True if the row should be skipped and False otherwise.
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An example of a valid callable argument would be ``lambda x: x in [0, 2]``.
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skipfooter : int, default 0
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Number of lines at bottom of file to skip (Unsupported with engine='c').
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nrows : int, optional
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Number of rows of file to read. Useful for reading pieces of large files.
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na_values : scalar, str, list-like, or dict, optional
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Additional strings to recognize as NA/NaN. If dict passed, specific
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per-column NA values. By default the following values are interpreted as
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NaN: '"""
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+ fill("', '".join(sorted(STR_NA_VALUES)), 70, subsequent_indent=" ")
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+ """'.
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keep_default_na : bool, default True
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Whether or not to include the default NaN values when parsing the data.
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Depending on whether `na_values` is passed in, the behavior is as follows:
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* If `keep_default_na` is True, and `na_values` are specified, `na_values`
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is appended to the default NaN values used for parsing.
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* If `keep_default_na` is True, and `na_values` are not specified, only
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the default NaN values are used for parsing.
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* If `keep_default_na` is False, and `na_values` are specified, only
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the NaN values specified `na_values` are used for parsing.
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* If `keep_default_na` is False, and `na_values` are not specified, no
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strings will be parsed as NaN.
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Note that if `na_filter` is passed in as False, the `keep_default_na` and
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`na_values` parameters will be ignored.
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na_filter : bool, default True
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Detect missing value markers (empty strings and the value of na_values). In
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data without any NAs, passing na_filter=False can improve the performance
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of reading a large file.
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verbose : bool, default False
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Indicate number of NA values placed in non-numeric columns.
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skip_blank_lines : bool, default True
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If True, skip over blank lines rather than interpreting as NaN values.
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parse_dates : bool or list of int or names or list of lists or dict, \
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default False
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The behavior is as follows:
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* boolean. If True -> try parsing the index.
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* list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3
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each as a separate date column.
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* list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as
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a single date column.
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* dict, e.g. {{'foo' : [1, 3]}} -> parse columns 1, 3 as date and call
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result 'foo'
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If a column or index cannot be represented as an array of datetimes,
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say because of an unparsable value or a mixture of timezones, the column
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or index will be returned unaltered as an object data type. For
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non-standard datetime parsing, use ``pd.to_datetime`` after
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``pd.read_csv``. To parse an index or column with a mixture of timezones,
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specify ``date_parser`` to be a partially-applied
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:func:`pandas.to_datetime` with ``utc=True``. See
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:ref:`io.csv.mixed_timezones` for more.
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Note: A fast-path exists for iso8601-formatted dates.
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infer_datetime_format : bool, default False
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If True and `parse_dates` is enabled, pandas will attempt to infer the
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format of the datetime strings in the columns, and if it can be inferred,
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switch to a faster method of parsing them. In some cases this can increase
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the parsing speed by 5-10x.
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keep_date_col : bool, default False
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If True and `parse_dates` specifies combining multiple columns then
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keep the original columns.
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date_parser : function, optional
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Function to use for converting a sequence of string columns to an array of
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datetime instances. The default uses ``dateutil.parser.parser`` to do the
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conversion. Pandas will try to call `date_parser` in three different ways,
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advancing to the next if an exception occurs: 1) Pass one or more arrays
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(as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the
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string values from the columns defined by `parse_dates` into a single array
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and pass that; and 3) call `date_parser` once for each row using one or
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more strings (corresponding to the columns defined by `parse_dates`) as
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arguments.
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dayfirst : bool, default False
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DD/MM format dates, international and European format.
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cache_dates : bool, default True
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If True, use a cache of unique, converted dates to apply the datetime
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conversion. May produce significant speed-up when parsing duplicate
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date strings, especially ones with timezone offsets.
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.. versionadded:: 0.25.0
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iterator : bool, default False
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Return TextFileReader object for iteration or getting chunks with
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``get_chunk()``.
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.. versionchanged:: 1.2
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``TextFileReader`` is a context manager.
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chunksize : int, optional
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Return TextFileReader object for iteration.
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See the `IO Tools docs
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<https://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_
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for more information on ``iterator`` and ``chunksize``.
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.. versionchanged:: 1.2
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``TextFileReader`` is a context manager.
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compression : {{'infer', 'gzip', 'bz2', 'zip', 'xz', None}}, default 'infer'
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For on-the-fly decompression of on-disk data. If 'infer' and
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`filepath_or_buffer` is path-like, then detect compression from the
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following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no
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decompression). If using 'zip', the ZIP file must contain only one data
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file to be read in. Set to None for no decompression.
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thousands : str, optional
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Thousands separator.
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decimal : str, default '.'
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Character to recognize as decimal point (e.g. use ',' for European data).
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lineterminator : str (length 1), optional
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Character to break file into lines. Only valid with C parser.
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quotechar : str (length 1), optional
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The character used to denote the start and end of a quoted item. Quoted
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items can include the delimiter and it will be ignored.
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quoting : int or csv.QUOTE_* instance, default 0
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Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of
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QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).
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doublequote : bool, default ``True``
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When quotechar is specified and quoting is not ``QUOTE_NONE``, indicate
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whether or not to interpret two consecutive quotechar elements INSIDE a
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field as a single ``quotechar`` element.
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escapechar : str (length 1), optional
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One-character string used to escape other characters.
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comment : str, optional
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Indicates remainder of line should not be parsed. If found at the beginning
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of a line, the line will be ignored altogether. This parameter must be a
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single character. Like empty lines (as long as ``skip_blank_lines=True``),
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fully commented lines are ignored by the parameter `header` but not by
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`skiprows`. For example, if ``comment='#'``, parsing
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``#empty\\na,b,c\\n1,2,3`` with ``header=0`` will result in 'a,b,c' being
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treated as the header.
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encoding : str, optional
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Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python
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standard encodings
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<https://docs.python.org/3/library/codecs.html#standard-encodings>`_ .
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.. versionchanged:: 1.2
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When ``encoding`` is ``None``, ``errors="replace"`` is passed to
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|
|
``open()``. Otherwise, ``errors="strict"`` is passed to ``open()``.
|
||
|
|
This behavior was previously only the case for ``engine="python"``.
|
||
|
|
dialect : str or csv.Dialect, optional
|
||
|
|
If provided, this parameter will override values (default or not) for the
|
||
|
|
following parameters: `delimiter`, `doublequote`, `escapechar`,
|
||
|
|
`skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to
|
||
|
|
override values, a ParserWarning will be issued. See csv.Dialect
|
||
|
|
documentation for more details.
|
||
|
|
error_bad_lines : bool, default True
|
||
|
|
Lines with too many fields (e.g. a csv line with too many commas) will by
|
||
|
|
default cause an exception to be raised, and no DataFrame will be returned.
|
||
|
|
If False, then these "bad lines" will dropped from the DataFrame that is
|
||
|
|
returned.
|
||
|
|
warn_bad_lines : bool, default True
|
||
|
|
If error_bad_lines is False, and warn_bad_lines is True, a warning for each
|
||
|
|
"bad line" will be output.
|
||
|
|
delim_whitespace : bool, default False
|
||
|
|
Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be
|
||
|
|
used as the sep. Equivalent to setting ``sep='\\s+'``. If this option
|
||
|
|
is set to True, nothing should be passed in for the ``delimiter``
|
||
|
|
parameter.
|
||
|
|
low_memory : bool, default True
|
||
|
|
Internally process the file in chunks, resulting in lower memory use
|
||
|
|
while parsing, but possibly mixed type inference. To ensure no mixed
|
||
|
|
types either set False, or specify the type with the `dtype` parameter.
|
||
|
|
Note that the entire file is read into a single DataFrame regardless,
|
||
|
|
use the `chunksize` or `iterator` parameter to return the data in chunks.
|
||
|
|
(Only valid with C parser).
|
||
|
|
memory_map : bool, default False
|
||
|
|
If a filepath is provided for `filepath_or_buffer`, map the file object
|
||
|
|
directly onto memory and access the data directly from there. Using this
|
||
|
|
option can improve performance because there is no longer any I/O overhead.
|
||
|
|
float_precision : str, optional
|
||
|
|
Specifies which converter the C engine should use for floating-point
|
||
|
|
values. The options are ``None`` or 'high' for the ordinary converter,
|
||
|
|
'legacy' for the original lower precision pandas converter, and
|
||
|
|
'round_trip' for the round-trip converter.
|
||
|
|
|
||
|
|
.. versionchanged:: 1.2
|
||
|
|
|
||
|
|
{storage_options}
|
||
|
|
|
||
|
|
.. versionadded:: 1.2
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
DataFrame or TextParser
|
||
|
|
A comma-separated values (csv) file is returned as two-dimensional
|
||
|
|
data structure with labeled axes.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.
|
||
|
|
read_csv : Read a comma-separated values (csv) file into DataFrame.
|
||
|
|
read_fwf : Read a table of fixed-width formatted lines into DataFrame.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> pd.{func_name}('data.csv') # doctest: +SKIP
|
||
|
|
"""
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def validate_integer(name, val, min_val=0):
|
||
|
|
"""
|
||
|
|
Checks whether the 'name' parameter for parsing is either
|
||
|
|
an integer OR float that can SAFELY be cast to an integer
|
||
|
|
without losing accuracy. Raises a ValueError if that is
|
||
|
|
not the case.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
name : string
|
||
|
|
Parameter name (used for error reporting)
|
||
|
|
val : int or float
|
||
|
|
The value to check
|
||
|
|
min_val : int
|
||
|
|
Minimum allowed value (val < min_val will result in a ValueError)
|
||
|
|
"""
|
||
|
|
msg = f"'{name:s}' must be an integer >={min_val:d}"
|
||
|
|
|
||
|
|
if val is not None:
|
||
|
|
if is_float(val):
|
||
|
|
if int(val) != val:
|
||
|
|
raise ValueError(msg)
|
||
|
|
val = int(val)
|
||
|
|
elif not (is_integer(val) and val >= min_val):
|
||
|
|
raise ValueError(msg)
|
||
|
|
|
||
|
|
return val
|
||
|
|
|
||
|
|
|
||
|
|
def _validate_names(names):
|
||
|
|
"""
|
||
|
|
Raise ValueError if the `names` parameter contains duplicates or has an
|
||
|
|
invalid data type.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
names : array-like or None
|
||
|
|
An array containing a list of the names used for the output DataFrame.
|
||
|
|
|
||
|
|
Raises
|
||
|
|
------
|
||
|
|
ValueError
|
||
|
|
If names are not unique or are not ordered (e.g. set).
|
||
|
|
"""
|
||
|
|
if names is not None:
|
||
|
|
if len(names) != len(set(names)):
|
||
|
|
raise ValueError("Duplicate names are not allowed.")
|
||
|
|
if not (
|
||
|
|
is_list_like(names, allow_sets=False) or isinstance(names, abc.KeysView)
|
||
|
|
):
|
||
|
|
raise ValueError("Names should be an ordered collection.")
|
||
|
|
|
||
|
|
|
||
|
|
def _read(filepath_or_buffer: FilePathOrBuffer, kwds):
|
||
|
|
"""Generic reader of line files."""
|
||
|
|
if kwds.get("date_parser", None) is not None:
|
||
|
|
if isinstance(kwds["parse_dates"], bool):
|
||
|
|
kwds["parse_dates"] = True
|
||
|
|
|
||
|
|
# Extract some of the arguments (pass chunksize on).
|
||
|
|
iterator = kwds.get("iterator", False)
|
||
|
|
chunksize = validate_integer("chunksize", kwds.get("chunksize", None), 1)
|
||
|
|
nrows = kwds.get("nrows", None)
|
||
|
|
|
||
|
|
# Check for duplicates in names.
|
||
|
|
_validate_names(kwds.get("names", None))
|
||
|
|
|
||
|
|
# Create the parser.
|
||
|
|
parser = TextFileReader(filepath_or_buffer, **kwds)
|
||
|
|
|
||
|
|
if chunksize or iterator:
|
||
|
|
return parser
|
||
|
|
|
||
|
|
with parser:
|
||
|
|
return parser.read(nrows)
|
||
|
|
|
||
|
|
|
||
|
|
_parser_defaults = {
|
||
|
|
"delimiter": None,
|
||
|
|
"escapechar": None,
|
||
|
|
"quotechar": '"',
|
||
|
|
"quoting": csv.QUOTE_MINIMAL,
|
||
|
|
"doublequote": True,
|
||
|
|
"skipinitialspace": False,
|
||
|
|
"lineterminator": None,
|
||
|
|
"header": "infer",
|
||
|
|
"index_col": None,
|
||
|
|
"names": None,
|
||
|
|
"prefix": None,
|
||
|
|
"skiprows": None,
|
||
|
|
"skipfooter": 0,
|
||
|
|
"nrows": None,
|
||
|
|
"na_values": None,
|
||
|
|
"keep_default_na": True,
|
||
|
|
"true_values": None,
|
||
|
|
"false_values": None,
|
||
|
|
"converters": None,
|
||
|
|
"dtype": None,
|
||
|
|
"cache_dates": True,
|
||
|
|
"thousands": None,
|
||
|
|
"comment": None,
|
||
|
|
"decimal": ".",
|
||
|
|
# 'engine': 'c',
|
||
|
|
"parse_dates": False,
|
||
|
|
"keep_date_col": False,
|
||
|
|
"dayfirst": False,
|
||
|
|
"date_parser": None,
|
||
|
|
"usecols": None,
|
||
|
|
# 'iterator': False,
|
||
|
|
"chunksize": None,
|
||
|
|
"verbose": False,
|
||
|
|
"encoding": None,
|
||
|
|
"squeeze": False,
|
||
|
|
"compression": None,
|
||
|
|
"mangle_dupe_cols": True,
|
||
|
|
"infer_datetime_format": False,
|
||
|
|
"skip_blank_lines": True,
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
_c_parser_defaults = {
|
||
|
|
"delim_whitespace": False,
|
||
|
|
"na_filter": True,
|
||
|
|
"low_memory": True,
|
||
|
|
"memory_map": False,
|
||
|
|
"error_bad_lines": True,
|
||
|
|
"warn_bad_lines": True,
|
||
|
|
"float_precision": None,
|
||
|
|
}
|
||
|
|
|
||
|
|
_fwf_defaults = {"colspecs": "infer", "infer_nrows": 100, "widths": None}
|
||
|
|
|
||
|
|
_c_unsupported = {"skipfooter"}
|
||
|
|
_python_unsupported = {"low_memory", "float_precision"}
|
||
|
|
|
||
|
|
_deprecated_defaults: Dict[str, Any] = {}
|
||
|
|
_deprecated_args: Set[str] = set()
|
||
|
|
|
||
|
|
|
||
|
|
@Appender(
|
||
|
|
_doc_read_csv_and_table.format(
|
||
|
|
func_name="read_csv",
|
||
|
|
summary="Read a comma-separated values (csv) file into DataFrame.",
|
||
|
|
_default_sep="','",
|
||
|
|
storage_options=generic._shared_docs["storage_options"],
|
||
|
|
)
|
||
|
|
)
|
||
|
|
def read_csv(
|
||
|
|
filepath_or_buffer: FilePathOrBuffer,
|
||
|
|
sep=lib.no_default,
|
||
|
|
delimiter=None,
|
||
|
|
# Column and Index Locations and Names
|
||
|
|
header="infer",
|
||
|
|
names=None,
|
||
|
|
index_col=None,
|
||
|
|
usecols=None,
|
||
|
|
squeeze=False,
|
||
|
|
prefix=None,
|
||
|
|
mangle_dupe_cols=True,
|
||
|
|
# General Parsing Configuration
|
||
|
|
dtype=None,
|
||
|
|
engine=None,
|
||
|
|
converters=None,
|
||
|
|
true_values=None,
|
||
|
|
false_values=None,
|
||
|
|
skipinitialspace=False,
|
||
|
|
skiprows=None,
|
||
|
|
skipfooter=0,
|
||
|
|
nrows=None,
|
||
|
|
# NA and Missing Data Handling
|
||
|
|
na_values=None,
|
||
|
|
keep_default_na=True,
|
||
|
|
na_filter=True,
|
||
|
|
verbose=False,
|
||
|
|
skip_blank_lines=True,
|
||
|
|
# Datetime Handling
|
||
|
|
parse_dates=False,
|
||
|
|
infer_datetime_format=False,
|
||
|
|
keep_date_col=False,
|
||
|
|
date_parser=None,
|
||
|
|
dayfirst=False,
|
||
|
|
cache_dates=True,
|
||
|
|
# Iteration
|
||
|
|
iterator=False,
|
||
|
|
chunksize=None,
|
||
|
|
# Quoting, Compression, and File Format
|
||
|
|
compression="infer",
|
||
|
|
thousands=None,
|
||
|
|
decimal: str = ".",
|
||
|
|
lineterminator=None,
|
||
|
|
quotechar='"',
|
||
|
|
quoting=csv.QUOTE_MINIMAL,
|
||
|
|
doublequote=True,
|
||
|
|
escapechar=None,
|
||
|
|
comment=None,
|
||
|
|
encoding=None,
|
||
|
|
dialect=None,
|
||
|
|
# Error Handling
|
||
|
|
error_bad_lines=True,
|
||
|
|
warn_bad_lines=True,
|
||
|
|
# Internal
|
||
|
|
delim_whitespace=False,
|
||
|
|
low_memory=_c_parser_defaults["low_memory"],
|
||
|
|
memory_map=False,
|
||
|
|
float_precision=None,
|
||
|
|
storage_options: StorageOptions = None,
|
||
|
|
):
|
||
|
|
kwds = locals()
|
||
|
|
del kwds["filepath_or_buffer"]
|
||
|
|
del kwds["sep"]
|
||
|
|
|
||
|
|
kwds_defaults = _refine_defaults_read(
|
||
|
|
dialect, delimiter, delim_whitespace, engine, sep, defaults={"delimiter": ","}
|
||
|
|
)
|
||
|
|
kwds.update(kwds_defaults)
|
||
|
|
|
||
|
|
return _read(filepath_or_buffer, kwds)
|
||
|
|
|
||
|
|
|
||
|
|
@Appender(
|
||
|
|
_doc_read_csv_and_table.format(
|
||
|
|
func_name="read_table",
|
||
|
|
summary="Read general delimited file into DataFrame.",
|
||
|
|
_default_sep=r"'\\t' (tab-stop)",
|
||
|
|
storage_options=generic._shared_docs["storage_options"],
|
||
|
|
)
|
||
|
|
)
|
||
|
|
def read_table(
|
||
|
|
filepath_or_buffer: FilePathOrBuffer,
|
||
|
|
sep=lib.no_default,
|
||
|
|
delimiter=None,
|
||
|
|
# Column and Index Locations and Names
|
||
|
|
header="infer",
|
||
|
|
names=None,
|
||
|
|
index_col=None,
|
||
|
|
usecols=None,
|
||
|
|
squeeze=False,
|
||
|
|
prefix=None,
|
||
|
|
mangle_dupe_cols=True,
|
||
|
|
# General Parsing Configuration
|
||
|
|
dtype=None,
|
||
|
|
engine=None,
|
||
|
|
converters=None,
|
||
|
|
true_values=None,
|
||
|
|
false_values=None,
|
||
|
|
skipinitialspace=False,
|
||
|
|
skiprows=None,
|
||
|
|
skipfooter=0,
|
||
|
|
nrows=None,
|
||
|
|
# NA and Missing Data Handling
|
||
|
|
na_values=None,
|
||
|
|
keep_default_na=True,
|
||
|
|
na_filter=True,
|
||
|
|
verbose=False,
|
||
|
|
skip_blank_lines=True,
|
||
|
|
# Datetime Handling
|
||
|
|
parse_dates=False,
|
||
|
|
infer_datetime_format=False,
|
||
|
|
keep_date_col=False,
|
||
|
|
date_parser=None,
|
||
|
|
dayfirst=False,
|
||
|
|
cache_dates=True,
|
||
|
|
# Iteration
|
||
|
|
iterator=False,
|
||
|
|
chunksize=None,
|
||
|
|
# Quoting, Compression, and File Format
|
||
|
|
compression="infer",
|
||
|
|
thousands=None,
|
||
|
|
decimal: str = ".",
|
||
|
|
lineterminator=None,
|
||
|
|
quotechar='"',
|
||
|
|
quoting=csv.QUOTE_MINIMAL,
|
||
|
|
doublequote=True,
|
||
|
|
escapechar=None,
|
||
|
|
comment=None,
|
||
|
|
encoding=None,
|
||
|
|
dialect=None,
|
||
|
|
# Error Handling
|
||
|
|
error_bad_lines=True,
|
||
|
|
warn_bad_lines=True,
|
||
|
|
# Internal
|
||
|
|
delim_whitespace=False,
|
||
|
|
low_memory=_c_parser_defaults["low_memory"],
|
||
|
|
memory_map=False,
|
||
|
|
float_precision=None,
|
||
|
|
):
|
||
|
|
kwds = locals()
|
||
|
|
del kwds["filepath_or_buffer"]
|
||
|
|
del kwds["sep"]
|
||
|
|
|
||
|
|
kwds_defaults = _refine_defaults_read(
|
||
|
|
dialect, delimiter, delim_whitespace, engine, sep, defaults={"delimiter": "\t"}
|
||
|
|
)
|
||
|
|
kwds.update(kwds_defaults)
|
||
|
|
|
||
|
|
return _read(filepath_or_buffer, kwds)
|
||
|
|
|
||
|
|
|
||
|
|
def read_fwf(
|
||
|
|
filepath_or_buffer: FilePathOrBuffer,
|
||
|
|
colspecs="infer",
|
||
|
|
widths=None,
|
||
|
|
infer_nrows=100,
|
||
|
|
**kwds,
|
||
|
|
):
|
||
|
|
r"""
|
||
|
|
Read a table of fixed-width formatted lines into DataFrame.
|
||
|
|
|
||
|
|
Also supports optionally iterating or breaking of the file
|
||
|
|
into chunks.
|
||
|
|
|
||
|
|
Additional help can be found in the `online docs for IO Tools
|
||
|
|
<https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
filepath_or_buffer : str, path object or file-like object
|
||
|
|
Any valid string path is acceptable. The string could be a URL. Valid
|
||
|
|
URL schemes include http, ftp, s3, and file. For file URLs, a host is
|
||
|
|
expected. A local file could be:
|
||
|
|
``file://localhost/path/to/table.csv``.
|
||
|
|
|
||
|
|
If you want to pass in a path object, pandas accepts any
|
||
|
|
``os.PathLike``.
|
||
|
|
|
||
|
|
By file-like object, we refer to objects with a ``read()`` method,
|
||
|
|
such as a file handle (e.g. via builtin ``open`` function)
|
||
|
|
or ``StringIO``.
|
||
|
|
colspecs : list of tuple (int, int) or 'infer'. optional
|
||
|
|
A list of tuples giving the extents of the fixed-width
|
||
|
|
fields of each line as half-open intervals (i.e., [from, to[ ).
|
||
|
|
String value 'infer' can be used to instruct the parser to try
|
||
|
|
detecting the column specifications from the first 100 rows of
|
||
|
|
the data which are not being skipped via skiprows (default='infer').
|
||
|
|
widths : list of int, optional
|
||
|
|
A list of field widths which can be used instead of 'colspecs' if
|
||
|
|
the intervals are contiguous.
|
||
|
|
infer_nrows : int, default 100
|
||
|
|
The number of rows to consider when letting the parser determine the
|
||
|
|
`colspecs`.
|
||
|
|
|
||
|
|
.. versionadded:: 0.24.0
|
||
|
|
**kwds : optional
|
||
|
|
Optional keyword arguments can be passed to ``TextFileReader``.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
DataFrame or TextParser
|
||
|
|
A comma-separated values (csv) file is returned as two-dimensional
|
||
|
|
data structure with labeled axes.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.
|
||
|
|
read_csv : Read a comma-separated values (csv) file into DataFrame.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> pd.read_fwf('data.csv') # doctest: +SKIP
|
||
|
|
"""
|
||
|
|
# Check input arguments.
|
||
|
|
if colspecs is None and widths is None:
|
||
|
|
raise ValueError("Must specify either colspecs or widths")
|
||
|
|
elif colspecs not in (None, "infer") and widths is not None:
|
||
|
|
raise ValueError("You must specify only one of 'widths' and 'colspecs'")
|
||
|
|
|
||
|
|
# Compute 'colspecs' from 'widths', if specified.
|
||
|
|
if widths is not None:
|
||
|
|
colspecs, col = [], 0
|
||
|
|
for w in widths:
|
||
|
|
colspecs.append((col, col + w))
|
||
|
|
col += w
|
||
|
|
|
||
|
|
kwds["colspecs"] = colspecs
|
||
|
|
kwds["infer_nrows"] = infer_nrows
|
||
|
|
kwds["engine"] = "python-fwf"
|
||
|
|
return _read(filepath_or_buffer, kwds)
|
||
|
|
|
||
|
|
|
||
|
|
class TextFileReader(abc.Iterator):
|
||
|
|
"""
|
||
|
|
|
||
|
|
Passed dialect overrides any of the related parser options
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, f, engine=None, **kwds):
|
||
|
|
|
||
|
|
self.f = f
|
||
|
|
|
||
|
|
if engine is not None:
|
||
|
|
engine_specified = True
|
||
|
|
else:
|
||
|
|
engine = "python"
|
||
|
|
engine_specified = False
|
||
|
|
self.engine = engine
|
||
|
|
self._engine_specified = kwds.get("engine_specified", engine_specified)
|
||
|
|
|
||
|
|
_validate_skipfooter(kwds)
|
||
|
|
|
||
|
|
dialect = _extract_dialect(kwds)
|
||
|
|
if dialect is not None:
|
||
|
|
kwds = _merge_with_dialect_properties(dialect, kwds)
|
||
|
|
|
||
|
|
if kwds.get("header", "infer") == "infer":
|
||
|
|
kwds["header"] = 0 if kwds.get("names") is None else None
|
||
|
|
|
||
|
|
self.orig_options = kwds
|
||
|
|
|
||
|
|
# miscellanea
|
||
|
|
self._currow = 0
|
||
|
|
|
||
|
|
options = self._get_options_with_defaults(engine)
|
||
|
|
options["storage_options"] = kwds.get("storage_options", None)
|
||
|
|
|
||
|
|
self.chunksize = options.pop("chunksize", None)
|
||
|
|
self.nrows = options.pop("nrows", None)
|
||
|
|
self.squeeze = options.pop("squeeze", False)
|
||
|
|
|
||
|
|
self._check_file_or_buffer(f, engine)
|
||
|
|
self.options, self.engine = self._clean_options(options, engine)
|
||
|
|
|
||
|
|
if "has_index_names" in kwds:
|
||
|
|
self.options["has_index_names"] = kwds["has_index_names"]
|
||
|
|
|
||
|
|
self._engine = self._make_engine(self.engine)
|
||
|
|
|
||
|
|
def close(self):
|
||
|
|
self._engine.close()
|
||
|
|
|
||
|
|
def _get_options_with_defaults(self, engine):
|
||
|
|
kwds = self.orig_options
|
||
|
|
|
||
|
|
options = {}
|
||
|
|
|
||
|
|
for argname, default in _parser_defaults.items():
|
||
|
|
value = kwds.get(argname, default)
|
||
|
|
|
||
|
|
# see gh-12935
|
||
|
|
if argname == "mangle_dupe_cols" and not value:
|
||
|
|
raise ValueError("Setting mangle_dupe_cols=False is not supported yet")
|
||
|
|
else:
|
||
|
|
options[argname] = value
|
||
|
|
|
||
|
|
for argname, default in _c_parser_defaults.items():
|
||
|
|
if argname in kwds:
|
||
|
|
value = kwds[argname]
|
||
|
|
|
||
|
|
if engine != "c" and value != default:
|
||
|
|
if "python" in engine and argname not in _python_unsupported:
|
||
|
|
pass
|
||
|
|
elif value == _deprecated_defaults.get(argname, default):
|
||
|
|
pass
|
||
|
|
else:
|
||
|
|
raise ValueError(
|
||
|
|
f"The {repr(argname)} option is not supported with the "
|
||
|
|
f"{repr(engine)} engine"
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
value = _deprecated_defaults.get(argname, default)
|
||
|
|
options[argname] = value
|
||
|
|
|
||
|
|
if engine == "python-fwf":
|
||
|
|
# pandas\io\parsers.py:907: error: Incompatible types in assignment
|
||
|
|
# (expression has type "object", variable has type "Union[int, str,
|
||
|
|
# None]") [assignment]
|
||
|
|
for argname, default in _fwf_defaults.items(): # type: ignore[assignment]
|
||
|
|
options[argname] = kwds.get(argname, default)
|
||
|
|
|
||
|
|
return options
|
||
|
|
|
||
|
|
def _check_file_or_buffer(self, f, engine):
|
||
|
|
# see gh-16530
|
||
|
|
if is_file_like(f) and engine != "c" and not hasattr(f, "__next__"):
|
||
|
|
# The C engine doesn't need the file-like to have the "__next__"
|
||
|
|
# attribute. However, the Python engine explicitly calls
|
||
|
|
# "__next__(...)" when iterating through such an object, meaning it
|
||
|
|
# needs to have that attribute
|
||
|
|
raise ValueError(
|
||
|
|
"The 'python' engine cannot iterate through this file buffer."
|
||
|
|
)
|
||
|
|
|
||
|
|
def _clean_options(self, options, engine):
|
||
|
|
result = options.copy()
|
||
|
|
|
||
|
|
fallback_reason = None
|
||
|
|
|
||
|
|
# C engine not supported yet
|
||
|
|
if engine == "c":
|
||
|
|
if options["skipfooter"] > 0:
|
||
|
|
fallback_reason = "the 'c' engine does not support skipfooter"
|
||
|
|
engine = "python"
|
||
|
|
|
||
|
|
sep = options["delimiter"]
|
||
|
|
delim_whitespace = options["delim_whitespace"]
|
||
|
|
|
||
|
|
if sep is None and not delim_whitespace:
|
||
|
|
if engine == "c":
|
||
|
|
fallback_reason = (
|
||
|
|
"the 'c' engine does not support "
|
||
|
|
"sep=None with delim_whitespace=False"
|
||
|
|
)
|
||
|
|
engine = "python"
|
||
|
|
elif sep is not None and len(sep) > 1:
|
||
|
|
if engine == "c" and sep == r"\s+":
|
||
|
|
result["delim_whitespace"] = True
|
||
|
|
del result["delimiter"]
|
||
|
|
elif engine not in ("python", "python-fwf"):
|
||
|
|
# wait until regex engine integrated
|
||
|
|
fallback_reason = (
|
||
|
|
"the 'c' engine does not support "
|
||
|
|
"regex separators (separators > 1 char and "
|
||
|
|
r"different from '\s+' are interpreted as regex)"
|
||
|
|
)
|
||
|
|
engine = "python"
|
||
|
|
elif delim_whitespace:
|
||
|
|
if "python" in engine:
|
||
|
|
result["delimiter"] = r"\s+"
|
||
|
|
elif sep is not None:
|
||
|
|
encodeable = True
|
||
|
|
encoding = sys.getfilesystemencoding() or "utf-8"
|
||
|
|
try:
|
||
|
|
if len(sep.encode(encoding)) > 1:
|
||
|
|
encodeable = False
|
||
|
|
except UnicodeDecodeError:
|
||
|
|
encodeable = False
|
||
|
|
if not encodeable and engine not in ("python", "python-fwf"):
|
||
|
|
fallback_reason = (
|
||
|
|
f"the separator encoded in {encoding} "
|
||
|
|
"is > 1 char long, and the 'c' engine "
|
||
|
|
"does not support such separators"
|
||
|
|
)
|
||
|
|
engine = "python"
|
||
|
|
|
||
|
|
quotechar = options["quotechar"]
|
||
|
|
if quotechar is not None and isinstance(quotechar, (str, bytes)):
|
||
|
|
if (
|
||
|
|
len(quotechar) == 1
|
||
|
|
and ord(quotechar) > 127
|
||
|
|
and engine not in ("python", "python-fwf")
|
||
|
|
):
|
||
|
|
fallback_reason = (
|
||
|
|
"ord(quotechar) > 127, meaning the "
|
||
|
|
"quotechar is larger than one byte, "
|
||
|
|
"and the 'c' engine does not support such quotechars"
|
||
|
|
)
|
||
|
|
engine = "python"
|
||
|
|
|
||
|
|
if fallback_reason and self._engine_specified:
|
||
|
|
raise ValueError(fallback_reason)
|
||
|
|
|
||
|
|
if engine == "c":
|
||
|
|
for arg in _c_unsupported:
|
||
|
|
del result[arg]
|
||
|
|
|
||
|
|
if "python" in engine:
|
||
|
|
for arg in _python_unsupported:
|
||
|
|
if fallback_reason and result[arg] != _c_parser_defaults[arg]:
|
||
|
|
raise ValueError(
|
||
|
|
"Falling back to the 'python' engine because "
|
||
|
|
f"{fallback_reason}, but this causes {repr(arg)} to be "
|
||
|
|
"ignored as it is not supported by the 'python' engine."
|
||
|
|
)
|
||
|
|
del result[arg]
|
||
|
|
|
||
|
|
if fallback_reason:
|
||
|
|
warnings.warn(
|
||
|
|
(
|
||
|
|
"Falling back to the 'python' engine because "
|
||
|
|
f"{fallback_reason}; you can avoid this warning by specifying "
|
||
|
|
"engine='python'."
|
||
|
|
),
|
||
|
|
ParserWarning,
|
||
|
|
stacklevel=5,
|
||
|
|
)
|
||
|
|
|
||
|
|
index_col = options["index_col"]
|
||
|
|
names = options["names"]
|
||
|
|
converters = options["converters"]
|
||
|
|
na_values = options["na_values"]
|
||
|
|
skiprows = options["skiprows"]
|
||
|
|
|
||
|
|
validate_header_arg(options["header"])
|
||
|
|
|
||
|
|
for arg in _deprecated_args:
|
||
|
|
parser_default = _c_parser_defaults[arg]
|
||
|
|
depr_default = _deprecated_defaults[arg]
|
||
|
|
if result.get(arg, depr_default) != depr_default:
|
||
|
|
msg = (
|
||
|
|
f"The {arg} argument has been deprecated and will be "
|
||
|
|
"removed in a future version.\n\n"
|
||
|
|
)
|
||
|
|
warnings.warn(msg, FutureWarning, stacklevel=2)
|
||
|
|
else:
|
||
|
|
result[arg] = parser_default
|
||
|
|
|
||
|
|
if index_col is True:
|
||
|
|
raise ValueError("The value of index_col couldn't be 'True'")
|
||
|
|
if _is_index_col(index_col):
|
||
|
|
if not isinstance(index_col, (list, tuple, np.ndarray)):
|
||
|
|
index_col = [index_col]
|
||
|
|
result["index_col"] = index_col
|
||
|
|
|
||
|
|
names = list(names) if names is not None else names
|
||
|
|
|
||
|
|
# type conversion-related
|
||
|
|
if converters is not None:
|
||
|
|
if not isinstance(converters, dict):
|
||
|
|
raise TypeError(
|
||
|
|
"Type converters must be a dict or subclass, "
|
||
|
|
f"input was a {type(converters).__name__}"
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
converters = {}
|
||
|
|
|
||
|
|
# Converting values to NA
|
||
|
|
keep_default_na = options["keep_default_na"]
|
||
|
|
na_values, na_fvalues = _clean_na_values(na_values, keep_default_na)
|
||
|
|
|
||
|
|
# handle skiprows; this is internally handled by the
|
||
|
|
# c-engine, so only need for python parsers
|
||
|
|
if engine != "c":
|
||
|
|
if is_integer(skiprows):
|
||
|
|
skiprows = list(range(skiprows))
|
||
|
|
if skiprows is None:
|
||
|
|
skiprows = set()
|
||
|
|
elif not callable(skiprows):
|
||
|
|
skiprows = set(skiprows)
|
||
|
|
|
||
|
|
# put stuff back
|
||
|
|
result["names"] = names
|
||
|
|
result["converters"] = converters
|
||
|
|
result["na_values"] = na_values
|
||
|
|
result["na_fvalues"] = na_fvalues
|
||
|
|
result["skiprows"] = skiprows
|
||
|
|
|
||
|
|
return result, engine
|
||
|
|
|
||
|
|
def __next__(self):
|
||
|
|
try:
|
||
|
|
return self.get_chunk()
|
||
|
|
except StopIteration:
|
||
|
|
self.close()
|
||
|
|
raise
|
||
|
|
|
||
|
|
def _make_engine(self, engine="c"):
|
||
|
|
mapping: Dict[str, Type[ParserBase]] = {
|
||
|
|
"c": CParserWrapper,
|
||
|
|
"python": PythonParser,
|
||
|
|
"python-fwf": FixedWidthFieldParser,
|
||
|
|
}
|
||
|
|
if engine not in mapping:
|
||
|
|
raise ValueError(
|
||
|
|
f"Unknown engine: {engine} (valid options are {mapping.keys()})"
|
||
|
|
)
|
||
|
|
# error: Too many arguments for "ParserBase"
|
||
|
|
return mapping[engine](self.f, **self.options) # type: ignore[call-arg]
|
||
|
|
|
||
|
|
def _failover_to_python(self):
|
||
|
|
raise AbstractMethodError(self)
|
||
|
|
|
||
|
|
def read(self, nrows=None):
|
||
|
|
nrows = validate_integer("nrows", nrows)
|
||
|
|
index, columns, col_dict = self._engine.read(nrows)
|
||
|
|
|
||
|
|
if index is None:
|
||
|
|
if col_dict:
|
||
|
|
# Any column is actually fine:
|
||
|
|
new_rows = len(next(iter(col_dict.values())))
|
||
|
|
index = RangeIndex(self._currow, self._currow + new_rows)
|
||
|
|
else:
|
||
|
|
new_rows = 0
|
||
|
|
else:
|
||
|
|
new_rows = len(index)
|
||
|
|
|
||
|
|
df = DataFrame(col_dict, columns=columns, index=index)
|
||
|
|
|
||
|
|
self._currow += new_rows
|
||
|
|
|
||
|
|
if self.squeeze and len(df.columns) == 1:
|
||
|
|
return df[df.columns[0]].copy()
|
||
|
|
return df
|
||
|
|
|
||
|
|
def get_chunk(self, size=None):
|
||
|
|
if size is None:
|
||
|
|
size = self.chunksize
|
||
|
|
if self.nrows is not None:
|
||
|
|
if self._currow >= self.nrows:
|
||
|
|
raise StopIteration
|
||
|
|
size = min(size, self.nrows - self._currow)
|
||
|
|
return self.read(nrows=size)
|
||
|
|
|
||
|
|
def __enter__(self):
|
||
|
|
return self
|
||
|
|
|
||
|
|
def __exit__(self, exc_type, exc_value, traceback):
|
||
|
|
self.close()
|
||
|
|
|
||
|
|
|
||
|
|
def _is_index_col(col):
|
||
|
|
return col is not None and col is not False
|
||
|
|
|
||
|
|
|
||
|
|
def _is_potential_multi_index(
|
||
|
|
columns, index_col: Optional[Union[bool, Sequence[int]]] = None
|
||
|
|
):
|
||
|
|
"""
|
||
|
|
Check whether or not the `columns` parameter
|
||
|
|
could be converted into a MultiIndex.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
columns : array-like
|
||
|
|
Object which may or may not be convertible into a MultiIndex
|
||
|
|
index_col : None, bool or list, optional
|
||
|
|
Column or columns to use as the (possibly hierarchical) index
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
boolean : Whether or not columns could become a MultiIndex
|
||
|
|
"""
|
||
|
|
if index_col is None or isinstance(index_col, bool):
|
||
|
|
index_col = []
|
||
|
|
|
||
|
|
return (
|
||
|
|
len(columns)
|
||
|
|
and not isinstance(columns, MultiIndex)
|
||
|
|
and all(isinstance(c, tuple) for c in columns if c not in list(index_col))
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def _evaluate_usecols(usecols, names):
|
||
|
|
"""
|
||
|
|
Check whether or not the 'usecols' parameter
|
||
|
|
is a callable. If so, enumerates the 'names'
|
||
|
|
parameter and returns a set of indices for
|
||
|
|
each entry in 'names' that evaluates to True.
|
||
|
|
If not a callable, returns 'usecols'.
|
||
|
|
"""
|
||
|
|
if callable(usecols):
|
||
|
|
return {i for i, name in enumerate(names) if usecols(name)}
|
||
|
|
return usecols
|
||
|
|
|
||
|
|
|
||
|
|
def _validate_usecols_names(usecols, names):
|
||
|
|
"""
|
||
|
|
Validates that all usecols are present in a given
|
||
|
|
list of names. If not, raise a ValueError that
|
||
|
|
shows what usecols are missing.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
usecols : iterable of usecols
|
||
|
|
The columns to validate are present in names.
|
||
|
|
names : iterable of names
|
||
|
|
The column names to check against.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
usecols : iterable of usecols
|
||
|
|
The `usecols` parameter if the validation succeeds.
|
||
|
|
|
||
|
|
Raises
|
||
|
|
------
|
||
|
|
ValueError : Columns were missing. Error message will list them.
|
||
|
|
"""
|
||
|
|
missing = [c for c in usecols if c not in names]
|
||
|
|
if len(missing) > 0:
|
||
|
|
raise ValueError(
|
||
|
|
f"Usecols do not match columns, columns expected but not found: {missing}"
|
||
|
|
)
|
||
|
|
|
||
|
|
return usecols
|
||
|
|
|
||
|
|
|
||
|
|
def _validate_skipfooter_arg(skipfooter):
|
||
|
|
"""
|
||
|
|
Validate the 'skipfooter' parameter.
|
||
|
|
|
||
|
|
Checks whether 'skipfooter' is a non-negative integer.
|
||
|
|
Raises a ValueError if that is not the case.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
skipfooter : non-negative integer
|
||
|
|
The number of rows to skip at the end of the file.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
validated_skipfooter : non-negative integer
|
||
|
|
The original input if the validation succeeds.
|
||
|
|
|
||
|
|
Raises
|
||
|
|
------
|
||
|
|
ValueError : 'skipfooter' was not a non-negative integer.
|
||
|
|
"""
|
||
|
|
if not is_integer(skipfooter):
|
||
|
|
raise ValueError("skipfooter must be an integer")
|
||
|
|
|
||
|
|
if skipfooter < 0:
|
||
|
|
raise ValueError("skipfooter cannot be negative")
|
||
|
|
|
||
|
|
return skipfooter
|
||
|
|
|
||
|
|
|
||
|
|
def _validate_usecols_arg(usecols):
|
||
|
|
"""
|
||
|
|
Validate the 'usecols' parameter.
|
||
|
|
|
||
|
|
Checks whether or not the 'usecols' parameter contains all integers
|
||
|
|
(column selection by index), strings (column by name) or is a callable.
|
||
|
|
Raises a ValueError if that is not the case.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
usecols : list-like, callable, or None
|
||
|
|
List of columns to use when parsing or a callable that can be used
|
||
|
|
to filter a list of table columns.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
usecols_tuple : tuple
|
||
|
|
A tuple of (verified_usecols, usecols_dtype).
|
||
|
|
|
||
|
|
'verified_usecols' is either a set if an array-like is passed in or
|
||
|
|
'usecols' if a callable or None is passed in.
|
||
|
|
|
||
|
|
'usecols_dtype` is the inferred dtype of 'usecols' if an array-like
|
||
|
|
is passed in or None if a callable or None is passed in.
|
||
|
|
"""
|
||
|
|
msg = (
|
||
|
|
"'usecols' must either be list-like of all strings, all unicode, "
|
||
|
|
"all integers or a callable."
|
||
|
|
)
|
||
|
|
if usecols is not None:
|
||
|
|
if callable(usecols):
|
||
|
|
return usecols, None
|
||
|
|
|
||
|
|
if not is_list_like(usecols):
|
||
|
|
# see gh-20529
|
||
|
|
#
|
||
|
|
# Ensure it is iterable container but not string.
|
||
|
|
raise ValueError(msg)
|
||
|
|
|
||
|
|
usecols_dtype = lib.infer_dtype(usecols, skipna=False)
|
||
|
|
|
||
|
|
if usecols_dtype not in ("empty", "integer", "string"):
|
||
|
|
raise ValueError(msg)
|
||
|
|
|
||
|
|
usecols = set(usecols)
|
||
|
|
|
||
|
|
return usecols, usecols_dtype
|
||
|
|
return usecols, None
|
||
|
|
|
||
|
|
|
||
|
|
def _validate_parse_dates_arg(parse_dates):
|
||
|
|
"""
|
||
|
|
Check whether or not the 'parse_dates' parameter
|
||
|
|
is a non-boolean scalar. Raises a ValueError if
|
||
|
|
that is the case.
|
||
|
|
"""
|
||
|
|
msg = (
|
||
|
|
"Only booleans, lists, and dictionaries are accepted "
|
||
|
|
"for the 'parse_dates' parameter"
|
||
|
|
)
|
||
|
|
|
||
|
|
if parse_dates is not None:
|
||
|
|
if is_scalar(parse_dates):
|
||
|
|
if not lib.is_bool(parse_dates):
|
||
|
|
raise TypeError(msg)
|
||
|
|
|
||
|
|
elif not isinstance(parse_dates, (list, dict)):
|
||
|
|
raise TypeError(msg)
|
||
|
|
|
||
|
|
return parse_dates
|
||
|
|
|
||
|
|
|
||
|
|
class ParserBase:
|
||
|
|
def __init__(self, kwds):
|
||
|
|
|
||
|
|
self.names = kwds.get("names")
|
||
|
|
self.orig_names: Optional[List] = None
|
||
|
|
self.prefix = kwds.pop("prefix", None)
|
||
|
|
|
||
|
|
self.index_col = kwds.get("index_col", None)
|
||
|
|
self.unnamed_cols: Set = set()
|
||
|
|
self.index_names: Optional[List] = None
|
||
|
|
self.col_names = None
|
||
|
|
|
||
|
|
self.parse_dates = _validate_parse_dates_arg(kwds.pop("parse_dates", False))
|
||
|
|
self.date_parser = kwds.pop("date_parser", None)
|
||
|
|
self.dayfirst = kwds.pop("dayfirst", False)
|
||
|
|
self.keep_date_col = kwds.pop("keep_date_col", False)
|
||
|
|
|
||
|
|
self.na_values = kwds.get("na_values")
|
||
|
|
self.na_fvalues = kwds.get("na_fvalues")
|
||
|
|
self.na_filter = kwds.get("na_filter", False)
|
||
|
|
self.keep_default_na = kwds.get("keep_default_na", True)
|
||
|
|
|
||
|
|
self.true_values = kwds.get("true_values")
|
||
|
|
self.false_values = kwds.get("false_values")
|
||
|
|
self.mangle_dupe_cols = kwds.get("mangle_dupe_cols", True)
|
||
|
|
self.infer_datetime_format = kwds.pop("infer_datetime_format", False)
|
||
|
|
self.cache_dates = kwds.pop("cache_dates", True)
|
||
|
|
|
||
|
|
self._date_conv = _make_date_converter(
|
||
|
|
date_parser=self.date_parser,
|
||
|
|
dayfirst=self.dayfirst,
|
||
|
|
infer_datetime_format=self.infer_datetime_format,
|
||
|
|
cache_dates=self.cache_dates,
|
||
|
|
)
|
||
|
|
|
||
|
|
# validate header options for mi
|
||
|
|
self.header = kwds.get("header")
|
||
|
|
if isinstance(self.header, (list, tuple, np.ndarray)):
|
||
|
|
if not all(map(is_integer, self.header)):
|
||
|
|
raise ValueError("header must be integer or list of integers")
|
||
|
|
if any(i < 0 for i in self.header):
|
||
|
|
raise ValueError(
|
||
|
|
"cannot specify multi-index header with negative integers"
|
||
|
|
)
|
||
|
|
if kwds.get("usecols"):
|
||
|
|
raise ValueError(
|
||
|
|
"cannot specify usecols when specifying a multi-index header"
|
||
|
|
)
|
||
|
|
if kwds.get("names"):
|
||
|
|
raise ValueError(
|
||
|
|
"cannot specify names when specifying a multi-index header"
|
||
|
|
)
|
||
|
|
|
||
|
|
# validate index_col that only contains integers
|
||
|
|
if self.index_col is not None:
|
||
|
|
is_sequence = isinstance(self.index_col, (list, tuple, np.ndarray))
|
||
|
|
if not (
|
||
|
|
is_sequence
|
||
|
|
and all(map(is_integer, self.index_col))
|
||
|
|
or is_integer(self.index_col)
|
||
|
|
):
|
||
|
|
raise ValueError(
|
||
|
|
"index_col must only contain row numbers "
|
||
|
|
"when specifying a multi-index header"
|
||
|
|
)
|
||
|
|
elif self.header is not None:
|
||
|
|
# GH 27394
|
||
|
|
if self.prefix is not None:
|
||
|
|
raise ValueError(
|
||
|
|
"Argument prefix must be None if argument header is not None"
|
||
|
|
)
|
||
|
|
# GH 16338
|
||
|
|
elif not is_integer(self.header):
|
||
|
|
raise ValueError("header must be integer or list of integers")
|
||
|
|
# GH 27779
|
||
|
|
elif self.header < 0:
|
||
|
|
raise ValueError(
|
||
|
|
"Passing negative integer to header is invalid. "
|
||
|
|
"For no header, use header=None instead"
|
||
|
|
)
|
||
|
|
|
||
|
|
self._name_processed = False
|
||
|
|
|
||
|
|
self._first_chunk = True
|
||
|
|
|
||
|
|
self.handles: Optional[IOHandles] = None
|
||
|
|
|
||
|
|
def _open_handles(self, src: FilePathOrBuffer, kwds: Dict[str, Any]) -> None:
|
||
|
|
"""
|
||
|
|
Let the readers open IOHanldes after they are done with their potential raises.
|
||
|
|
"""
|
||
|
|
self.handles = get_handle(
|
||
|
|
src,
|
||
|
|
"r",
|
||
|
|
encoding=kwds.get("encoding", None),
|
||
|
|
compression=kwds.get("compression", None),
|
||
|
|
memory_map=kwds.get("memory_map", False),
|
||
|
|
storage_options=kwds.get("storage_options", None),
|
||
|
|
)
|
||
|
|
|
||
|
|
def _validate_parse_dates_presence(self, columns: List[str]) -> None:
|
||
|
|
"""
|
||
|
|
Check if parse_dates are in columns.
|
||
|
|
|
||
|
|
If user has provided names for parse_dates, check if those columns
|
||
|
|
are available.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
columns : list
|
||
|
|
List of names of the dataframe.
|
||
|
|
|
||
|
|
Raises
|
||
|
|
------
|
||
|
|
ValueError
|
||
|
|
If column to parse_date is not in dataframe.
|
||
|
|
|
||
|
|
"""
|
||
|
|
cols_needed: Iterable
|
||
|
|
if is_dict_like(self.parse_dates):
|
||
|
|
cols_needed = itertools.chain(*self.parse_dates.values())
|
||
|
|
elif is_list_like(self.parse_dates):
|
||
|
|
# a column in parse_dates could be represented
|
||
|
|
# ColReference = Union[int, str]
|
||
|
|
# DateGroups = List[ColReference]
|
||
|
|
# ParseDates = Union[DateGroups, List[DateGroups],
|
||
|
|
# Dict[ColReference, DateGroups]]
|
||
|
|
cols_needed = itertools.chain.from_iterable(
|
||
|
|
col if is_list_like(col) else [col] for col in self.parse_dates
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
cols_needed = []
|
||
|
|
|
||
|
|
# get only columns that are references using names (str), not by index
|
||
|
|
missing_cols = ", ".join(
|
||
|
|
sorted(
|
||
|
|
{
|
||
|
|
col
|
||
|
|
for col in cols_needed
|
||
|
|
if isinstance(col, str) and col not in columns
|
||
|
|
}
|
||
|
|
)
|
||
|
|
)
|
||
|
|
if missing_cols:
|
||
|
|
raise ValueError(
|
||
|
|
f"Missing column provided to 'parse_dates': '{missing_cols}'"
|
||
|
|
)
|
||
|
|
|
||
|
|
def close(self):
|
||
|
|
if self.handles is not None:
|
||
|
|
self.handles.close()
|
||
|
|
|
||
|
|
@property
|
||
|
|
def _has_complex_date_col(self):
|
||
|
|
return isinstance(self.parse_dates, dict) or (
|
||
|
|
isinstance(self.parse_dates, list)
|
||
|
|
and len(self.parse_dates) > 0
|
||
|
|
and isinstance(self.parse_dates[0], list)
|
||
|
|
)
|
||
|
|
|
||
|
|
def _should_parse_dates(self, i):
|
||
|
|
if isinstance(self.parse_dates, bool):
|
||
|
|
return self.parse_dates
|
||
|
|
else:
|
||
|
|
if self.index_names is not None:
|
||
|
|
name = self.index_names[i]
|
||
|
|
else:
|
||
|
|
name = None
|
||
|
|
j = self.index_col[i]
|
||
|
|
|
||
|
|
if is_scalar(self.parse_dates):
|
||
|
|
return (j == self.parse_dates) or (
|
||
|
|
name is not None and name == self.parse_dates
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
return (j in self.parse_dates) or (
|
||
|
|
name is not None and name in self.parse_dates
|
||
|
|
)
|
||
|
|
|
||
|
|
def _extract_multi_indexer_columns(
|
||
|
|
self, header, index_names, col_names, passed_names=False
|
||
|
|
):
|
||
|
|
"""
|
||
|
|
extract and return the names, index_names, col_names
|
||
|
|
header is a list-of-lists returned from the parsers
|
||
|
|
"""
|
||
|
|
if len(header) < 2:
|
||
|
|
return header[0], index_names, col_names, passed_names
|
||
|
|
|
||
|
|
# the names are the tuples of the header that are not the index cols
|
||
|
|
# 0 is the name of the index, assuming index_col is a list of column
|
||
|
|
# numbers
|
||
|
|
ic = self.index_col
|
||
|
|
if ic is None:
|
||
|
|
ic = []
|
||
|
|
|
||
|
|
if not isinstance(ic, (list, tuple, np.ndarray)):
|
||
|
|
ic = [ic]
|
||
|
|
sic = set(ic)
|
||
|
|
|
||
|
|
# clean the index_names
|
||
|
|
index_names = header.pop(-1)
|
||
|
|
index_names, names, index_col = _clean_index_names(
|
||
|
|
index_names, self.index_col, self.unnamed_cols
|
||
|
|
)
|
||
|
|
|
||
|
|
# extract the columns
|
||
|
|
field_count = len(header[0])
|
||
|
|
|
||
|
|
def extract(r):
|
||
|
|
return tuple(r[i] for i in range(field_count) if i not in sic)
|
||
|
|
|
||
|
|
columns = list(zip(*(extract(r) for r in header)))
|
||
|
|
names = ic + columns
|
||
|
|
|
||
|
|
# If we find unnamed columns all in a single
|
||
|
|
# level, then our header was too long.
|
||
|
|
for n in range(len(columns[0])):
|
||
|
|
if all(ensure_str(col[n]) in self.unnamed_cols for col in columns):
|
||
|
|
header = ",".join(str(x) for x in self.header)
|
||
|
|
raise ParserError(
|
||
|
|
f"Passed header=[{header}] are too many rows "
|
||
|
|
"for this multi_index of columns"
|
||
|
|
)
|
||
|
|
|
||
|
|
# Clean the column names (if we have an index_col).
|
||
|
|
if len(ic):
|
||
|
|
col_names = [
|
||
|
|
r[0] if ((r[0] is not None) and r[0] not in self.unnamed_cols) else None
|
||
|
|
for r in header
|
||
|
|
]
|
||
|
|
else:
|
||
|
|
col_names = [None] * len(header)
|
||
|
|
|
||
|
|
passed_names = True
|
||
|
|
|
||
|
|
return names, index_names, col_names, passed_names
|
||
|
|
|
||
|
|
def _maybe_dedup_names(self, names):
|
||
|
|
# see gh-7160 and gh-9424: this helps to provide
|
||
|
|
# immediate alleviation of the duplicate names
|
||
|
|
# issue and appears to be satisfactory to users,
|
||
|
|
# but ultimately, not needing to butcher the names
|
||
|
|
# would be nice!
|
||
|
|
if self.mangle_dupe_cols:
|
||
|
|
names = list(names) # so we can index
|
||
|
|
# pandas\io\parsers.py:1559: error: Need type annotation for
|
||
|
|
# 'counts' [var-annotated]
|
||
|
|
counts = defaultdict(int) # type: ignore[var-annotated]
|
||
|
|
is_potential_mi = _is_potential_multi_index(names, self.index_col)
|
||
|
|
|
||
|
|
for i, col in enumerate(names):
|
||
|
|
cur_count = counts[col]
|
||
|
|
|
||
|
|
while cur_count > 0:
|
||
|
|
counts[col] = cur_count + 1
|
||
|
|
|
||
|
|
if is_potential_mi:
|
||
|
|
col = col[:-1] + (f"{col[-1]}.{cur_count}",)
|
||
|
|
else:
|
||
|
|
col = f"{col}.{cur_count}"
|
||
|
|
cur_count = counts[col]
|
||
|
|
|
||
|
|
names[i] = col
|
||
|
|
counts[col] = cur_count + 1
|
||
|
|
|
||
|
|
return names
|
||
|
|
|
||
|
|
def _maybe_make_multi_index_columns(self, columns, col_names=None):
|
||
|
|
# possibly create a column mi here
|
||
|
|
if _is_potential_multi_index(columns):
|
||
|
|
columns = MultiIndex.from_tuples(columns, names=col_names)
|
||
|
|
return columns
|
||
|
|
|
||
|
|
def _make_index(self, data, alldata, columns, indexnamerow=False):
|
||
|
|
if not _is_index_col(self.index_col) or not self.index_col:
|
||
|
|
index = None
|
||
|
|
|
||
|
|
elif not self._has_complex_date_col:
|
||
|
|
index = self._get_simple_index(alldata, columns)
|
||
|
|
index = self._agg_index(index)
|
||
|
|
elif self._has_complex_date_col:
|
||
|
|
if not self._name_processed:
|
||
|
|
(self.index_names, _, self.index_col) = _clean_index_names(
|
||
|
|
list(columns), self.index_col, self.unnamed_cols
|
||
|
|
)
|
||
|
|
self._name_processed = True
|
||
|
|
index = self._get_complex_date_index(data, columns)
|
||
|
|
index = self._agg_index(index, try_parse_dates=False)
|
||
|
|
|
||
|
|
# add names for the index
|
||
|
|
if indexnamerow:
|
||
|
|
coffset = len(indexnamerow) - len(columns)
|
||
|
|
# pandas\io\parsers.py:1604: error: Item "None" of "Optional[Any]"
|
||
|
|
# has no attribute "set_names" [union-attr]
|
||
|
|
index = index.set_names(indexnamerow[:coffset]) # type: ignore[union-attr]
|
||
|
|
|
||
|
|
# maybe create a mi on the columns
|
||
|
|
columns = self._maybe_make_multi_index_columns(columns, self.col_names)
|
||
|
|
|
||
|
|
return index, columns
|
||
|
|
|
||
|
|
_implicit_index = False
|
||
|
|
|
||
|
|
def _get_simple_index(self, data, columns):
|
||
|
|
def ix(col):
|
||
|
|
if not isinstance(col, str):
|
||
|
|
return col
|
||
|
|
raise ValueError(f"Index {col} invalid")
|
||
|
|
|
||
|
|
to_remove = []
|
||
|
|
index = []
|
||
|
|
for idx in self.index_col:
|
||
|
|
i = ix(idx)
|
||
|
|
to_remove.append(i)
|
||
|
|
index.append(data[i])
|
||
|
|
|
||
|
|
# remove index items from content and columns, don't pop in
|
||
|
|
# loop
|
||
|
|
for i in sorted(to_remove, reverse=True):
|
||
|
|
data.pop(i)
|
||
|
|
if not self._implicit_index:
|
||
|
|
columns.pop(i)
|
||
|
|
|
||
|
|
return index
|
||
|
|
|
||
|
|
def _get_complex_date_index(self, data, col_names):
|
||
|
|
def _get_name(icol):
|
||
|
|
if isinstance(icol, str):
|
||
|
|
return icol
|
||
|
|
|
||
|
|
if col_names is None:
|
||
|
|
raise ValueError(f"Must supply column order to use {icol!s} as index")
|
||
|
|
|
||
|
|
for i, c in enumerate(col_names):
|
||
|
|
if i == icol:
|
||
|
|
return c
|
||
|
|
|
||
|
|
to_remove = []
|
||
|
|
index = []
|
||
|
|
for idx in self.index_col:
|
||
|
|
name = _get_name(idx)
|
||
|
|
to_remove.append(name)
|
||
|
|
index.append(data[name])
|
||
|
|
|
||
|
|
# remove index items from content and columns, don't pop in
|
||
|
|
# loop
|
||
|
|
for c in sorted(to_remove, reverse=True):
|
||
|
|
data.pop(c)
|
||
|
|
col_names.remove(c)
|
||
|
|
|
||
|
|
return index
|
||
|
|
|
||
|
|
def _agg_index(self, index, try_parse_dates=True) -> Index:
|
||
|
|
arrays = []
|
||
|
|
|
||
|
|
for i, arr in enumerate(index):
|
||
|
|
|
||
|
|
if try_parse_dates and self._should_parse_dates(i):
|
||
|
|
arr = self._date_conv(arr)
|
||
|
|
|
||
|
|
if self.na_filter:
|
||
|
|
col_na_values = self.na_values
|
||
|
|
col_na_fvalues = self.na_fvalues
|
||
|
|
else:
|
||
|
|
col_na_values = set()
|
||
|
|
col_na_fvalues = set()
|
||
|
|
|
||
|
|
if isinstance(self.na_values, dict):
|
||
|
|
# pandas\io\parsers.py:1678: error: Value of type
|
||
|
|
# "Optional[Any]" is not indexable [index]
|
||
|
|
col_name = self.index_names[i] # type: ignore[index]
|
||
|
|
if col_name is not None:
|
||
|
|
col_na_values, col_na_fvalues = _get_na_values(
|
||
|
|
col_name, self.na_values, self.na_fvalues, self.keep_default_na
|
||
|
|
)
|
||
|
|
|
||
|
|
arr, _ = self._infer_types(arr, col_na_values | col_na_fvalues)
|
||
|
|
arrays.append(arr)
|
||
|
|
|
||
|
|
names = self.index_names
|
||
|
|
index = ensure_index_from_sequences(arrays, names)
|
||
|
|
|
||
|
|
return index
|
||
|
|
|
||
|
|
def _convert_to_ndarrays(
|
||
|
|
self, dct, na_values, na_fvalues, verbose=False, converters=None, dtypes=None
|
||
|
|
):
|
||
|
|
result = {}
|
||
|
|
for c, values in dct.items():
|
||
|
|
conv_f = None if converters is None else converters.get(c, None)
|
||
|
|
if isinstance(dtypes, dict):
|
||
|
|
cast_type = dtypes.get(c, None)
|
||
|
|
else:
|
||
|
|
# single dtype or None
|
||
|
|
cast_type = dtypes
|
||
|
|
|
||
|
|
if self.na_filter:
|
||
|
|
col_na_values, col_na_fvalues = _get_na_values(
|
||
|
|
c, na_values, na_fvalues, self.keep_default_na
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
col_na_values, col_na_fvalues = set(), set()
|
||
|
|
|
||
|
|
if conv_f is not None:
|
||
|
|
# conv_f applied to data before inference
|
||
|
|
if cast_type is not None:
|
||
|
|
warnings.warn(
|
||
|
|
(
|
||
|
|
"Both a converter and dtype were specified "
|
||
|
|
f"for column {c} - only the converter will be used"
|
||
|
|
),
|
||
|
|
ParserWarning,
|
||
|
|
stacklevel=7,
|
||
|
|
)
|
||
|
|
|
||
|
|
try:
|
||
|
|
values = lib.map_infer(values, conv_f)
|
||
|
|
except ValueError:
|
||
|
|
mask = algorithms.isin(values, list(na_values)).view(np.uint8)
|
||
|
|
values = lib.map_infer_mask(values, conv_f, mask)
|
||
|
|
|
||
|
|
cvals, na_count = self._infer_types(
|
||
|
|
values, set(col_na_values) | col_na_fvalues, try_num_bool=False
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
is_ea = is_extension_array_dtype(cast_type)
|
||
|
|
is_str_or_ea_dtype = is_ea or is_string_dtype(cast_type)
|
||
|
|
# skip inference if specified dtype is object
|
||
|
|
# or casting to an EA
|
||
|
|
try_num_bool = not (cast_type and is_str_or_ea_dtype)
|
||
|
|
|
||
|
|
# general type inference and conversion
|
||
|
|
cvals, na_count = self._infer_types(
|
||
|
|
values, set(col_na_values) | col_na_fvalues, try_num_bool
|
||
|
|
)
|
||
|
|
|
||
|
|
# type specified in dtype param or cast_type is an EA
|
||
|
|
if cast_type and (
|
||
|
|
not is_dtype_equal(cvals, cast_type)
|
||
|
|
or is_extension_array_dtype(cast_type)
|
||
|
|
):
|
||
|
|
if not is_ea and na_count > 0:
|
||
|
|
try:
|
||
|
|
if is_bool_dtype(cast_type):
|
||
|
|
raise ValueError(
|
||
|
|
f"Bool column has NA values in column {c}"
|
||
|
|
)
|
||
|
|
except (AttributeError, TypeError):
|
||
|
|
# invalid input to is_bool_dtype
|
||
|
|
pass
|
||
|
|
cvals = self._cast_types(cvals, cast_type, c)
|
||
|
|
|
||
|
|
result[c] = cvals
|
||
|
|
if verbose and na_count:
|
||
|
|
print(f"Filled {na_count} NA values in column {c!s}")
|
||
|
|
return result
|
||
|
|
|
||
|
|
def _infer_types(self, values, na_values, try_num_bool=True):
|
||
|
|
"""
|
||
|
|
Infer types of values, possibly casting
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
values : ndarray
|
||
|
|
na_values : set
|
||
|
|
try_num_bool : bool, default try
|
||
|
|
try to cast values to numeric (first preference) or boolean
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
converted : ndarray
|
||
|
|
na_count : int
|
||
|
|
"""
|
||
|
|
na_count = 0
|
||
|
|
if issubclass(values.dtype.type, (np.number, np.bool_)):
|
||
|
|
mask = algorithms.isin(values, list(na_values))
|
||
|
|
na_count = mask.sum()
|
||
|
|
if na_count > 0:
|
||
|
|
if is_integer_dtype(values):
|
||
|
|
values = values.astype(np.float64)
|
||
|
|
np.putmask(values, mask, np.nan)
|
||
|
|
return values, na_count
|
||
|
|
|
||
|
|
if try_num_bool and is_object_dtype(values.dtype):
|
||
|
|
# exclude e.g DatetimeIndex here
|
||
|
|
try:
|
||
|
|
result = lib.maybe_convert_numeric(values, na_values, False)
|
||
|
|
except (ValueError, TypeError):
|
||
|
|
# e.g. encountering datetime string gets ValueError
|
||
|
|
# TypeError can be raised in floatify
|
||
|
|
result = values
|
||
|
|
na_count = parsers.sanitize_objects(result, na_values, False)
|
||
|
|
else:
|
||
|
|
na_count = isna(result).sum()
|
||
|
|
else:
|
||
|
|
result = values
|
||
|
|
if values.dtype == np.object_:
|
||
|
|
na_count = parsers.sanitize_objects(values, na_values, False)
|
||
|
|
|
||
|
|
if result.dtype == np.object_ and try_num_bool:
|
||
|
|
result = libops.maybe_convert_bool(
|
||
|
|
np.asarray(values),
|
||
|
|
true_values=self.true_values,
|
||
|
|
false_values=self.false_values,
|
||
|
|
)
|
||
|
|
|
||
|
|
return result, na_count
|
||
|
|
|
||
|
|
def _cast_types(self, values, cast_type, column):
|
||
|
|
"""
|
||
|
|
Cast values to specified type
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
values : ndarray
|
||
|
|
cast_type : string or np.dtype
|
||
|
|
dtype to cast values to
|
||
|
|
column : string
|
||
|
|
column name - used only for error reporting
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
converted : ndarray
|
||
|
|
"""
|
||
|
|
if is_categorical_dtype(cast_type):
|
||
|
|
known_cats = (
|
||
|
|
isinstance(cast_type, CategoricalDtype)
|
||
|
|
and cast_type.categories is not None
|
||
|
|
)
|
||
|
|
|
||
|
|
if not is_object_dtype(values) and not known_cats:
|
||
|
|
# TODO: this is for consistency with
|
||
|
|
# c-parser which parses all categories
|
||
|
|
# as strings
|
||
|
|
values = astype_nansafe(values, str)
|
||
|
|
|
||
|
|
cats = Index(values).unique().dropna()
|
||
|
|
values = Categorical._from_inferred_categories(
|
||
|
|
cats, cats.get_indexer(values), cast_type, true_values=self.true_values
|
||
|
|
)
|
||
|
|
|
||
|
|
# use the EA's implementation of casting
|
||
|
|
elif is_extension_array_dtype(cast_type):
|
||
|
|
# ensure cast_type is an actual dtype and not a string
|
||
|
|
cast_type = pandas_dtype(cast_type)
|
||
|
|
array_type = cast_type.construct_array_type()
|
||
|
|
try:
|
||
|
|
return array_type._from_sequence_of_strings(values, dtype=cast_type)
|
||
|
|
except NotImplementedError as err:
|
||
|
|
raise NotImplementedError(
|
||
|
|
f"Extension Array: {array_type} must implement "
|
||
|
|
"_from_sequence_of_strings in order to be used in parser methods"
|
||
|
|
) from err
|
||
|
|
|
||
|
|
else:
|
||
|
|
try:
|
||
|
|
values = astype_nansafe(values, cast_type, copy=True, skipna=True)
|
||
|
|
except ValueError as err:
|
||
|
|
raise ValueError(
|
||
|
|
f"Unable to convert column {column} to type {cast_type}"
|
||
|
|
) from err
|
||
|
|
return values
|
||
|
|
|
||
|
|
def _do_date_conversions(self, names, data):
|
||
|
|
# returns data, columns
|
||
|
|
|
||
|
|
if self.parse_dates is not None:
|
||
|
|
data, names = _process_date_conversion(
|
||
|
|
data,
|
||
|
|
self._date_conv,
|
||
|
|
self.parse_dates,
|
||
|
|
self.index_col,
|
||
|
|
self.index_names,
|
||
|
|
names,
|
||
|
|
keep_date_col=self.keep_date_col,
|
||
|
|
)
|
||
|
|
|
||
|
|
return names, data
|
||
|
|
|
||
|
|
|
||
|
|
class CParserWrapper(ParserBase):
|
||
|
|
def __init__(self, src: FilePathOrBuffer, **kwds):
|
||
|
|
self.kwds = kwds
|
||
|
|
kwds = kwds.copy()
|
||
|
|
|
||
|
|
ParserBase.__init__(self, kwds)
|
||
|
|
|
||
|
|
# #2442
|
||
|
|
kwds["allow_leading_cols"] = self.index_col is not False
|
||
|
|
|
||
|
|
# GH20529, validate usecol arg before TextReader
|
||
|
|
self.usecols, self.usecols_dtype = _validate_usecols_arg(kwds["usecols"])
|
||
|
|
kwds["usecols"] = self.usecols
|
||
|
|
|
||
|
|
# open handles
|
||
|
|
self._open_handles(src, kwds)
|
||
|
|
assert self.handles is not None
|
||
|
|
for key in ("storage_options", "encoding", "memory_map", "compression"):
|
||
|
|
kwds.pop(key, None)
|
||
|
|
if self.handles.is_mmap and hasattr(self.handles.handle, "mmap"):
|
||
|
|
# pandas\io\parsers.py:1861: error: Item "IO[Any]" of
|
||
|
|
# "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase,
|
||
|
|
# TextIOWrapper, mmap]" has no attribute "mmap" [union-attr]
|
||
|
|
|
||
|
|
# pandas\io\parsers.py:1861: error: Item "RawIOBase" of
|
||
|
|
# "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase,
|
||
|
|
# TextIOWrapper, mmap]" has no attribute "mmap" [union-attr]
|
||
|
|
|
||
|
|
# pandas\io\parsers.py:1861: error: Item "BufferedIOBase" of
|
||
|
|
# "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase,
|
||
|
|
# TextIOWrapper, mmap]" has no attribute "mmap" [union-attr]
|
||
|
|
|
||
|
|
# pandas\io\parsers.py:1861: error: Item "TextIOBase" of
|
||
|
|
# "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase,
|
||
|
|
# TextIOWrapper, mmap]" has no attribute "mmap" [union-attr]
|
||
|
|
|
||
|
|
# pandas\io\parsers.py:1861: error: Item "TextIOWrapper" of
|
||
|
|
# "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase,
|
||
|
|
# TextIOWrapper, mmap]" has no attribute "mmap" [union-attr]
|
||
|
|
|
||
|
|
# pandas\io\parsers.py:1861: error: Item "mmap" of "Union[IO[Any],
|
||
|
|
# RawIOBase, BufferedIOBase, TextIOBase, TextIOWrapper, mmap]" has
|
||
|
|
# no attribute "mmap" [union-attr]
|
||
|
|
self.handles.handle = self.handles.handle.mmap # type: ignore[union-attr]
|
||
|
|
|
||
|
|
try:
|
||
|
|
self._reader = parsers.TextReader(self.handles.handle, **kwds)
|
||
|
|
except Exception:
|
||
|
|
self.handles.close()
|
||
|
|
raise
|
||
|
|
self.unnamed_cols = self._reader.unnamed_cols
|
||
|
|
|
||
|
|
passed_names = self.names is None
|
||
|
|
|
||
|
|
if self._reader.header is None:
|
||
|
|
self.names = None
|
||
|
|
else:
|
||
|
|
if len(self._reader.header) > 1:
|
||
|
|
# we have a multi index in the columns
|
||
|
|
(
|
||
|
|
self.names,
|
||
|
|
self.index_names,
|
||
|
|
self.col_names,
|
||
|
|
passed_names,
|
||
|
|
) = self._extract_multi_indexer_columns(
|
||
|
|
self._reader.header, self.index_names, self.col_names, passed_names
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
self.names = list(self._reader.header[0])
|
||
|
|
|
||
|
|
if self.names is None:
|
||
|
|
if self.prefix:
|
||
|
|
self.names = [
|
||
|
|
f"{self.prefix}{i}" for i in range(self._reader.table_width)
|
||
|
|
]
|
||
|
|
else:
|
||
|
|
self.names = list(range(self._reader.table_width))
|
||
|
|
|
||
|
|
# gh-9755
|
||
|
|
#
|
||
|
|
# need to set orig_names here first
|
||
|
|
# so that proper indexing can be done
|
||
|
|
# with _set_noconvert_columns
|
||
|
|
#
|
||
|
|
# once names has been filtered, we will
|
||
|
|
# then set orig_names again to names
|
||
|
|
self.orig_names = self.names[:]
|
||
|
|
|
||
|
|
if self.usecols:
|
||
|
|
usecols = _evaluate_usecols(self.usecols, self.orig_names)
|
||
|
|
|
||
|
|
# GH 14671
|
||
|
|
# assert for mypy, orig_names is List or None, None would error in issubset
|
||
|
|
assert self.orig_names is not None
|
||
|
|
if self.usecols_dtype == "string" and not set(usecols).issubset(
|
||
|
|
self.orig_names
|
||
|
|
):
|
||
|
|
_validate_usecols_names(usecols, self.orig_names)
|
||
|
|
|
||
|
|
if len(self.names) > len(usecols):
|
||
|
|
self.names = [
|
||
|
|
n
|
||
|
|
for i, n in enumerate(self.names)
|
||
|
|
if (i in usecols or n in usecols)
|
||
|
|
]
|
||
|
|
|
||
|
|
if len(self.names) < len(usecols):
|
||
|
|
_validate_usecols_names(usecols, self.names)
|
||
|
|
|
||
|
|
self._validate_parse_dates_presence(self.names)
|
||
|
|
self._set_noconvert_columns()
|
||
|
|
|
||
|
|
self.orig_names = self.names
|
||
|
|
|
||
|
|
if not self._has_complex_date_col:
|
||
|
|
if self._reader.leading_cols == 0 and _is_index_col(self.index_col):
|
||
|
|
|
||
|
|
self._name_processed = True
|
||
|
|
(index_names, self.names, self.index_col) = _clean_index_names(
|
||
|
|
self.names, self.index_col, self.unnamed_cols
|
||
|
|
)
|
||
|
|
|
||
|
|
if self.index_names is None:
|
||
|
|
self.index_names = index_names
|
||
|
|
|
||
|
|
if self._reader.header is None and not passed_names:
|
||
|
|
# pandas\io\parsers.py:1997: error: Argument 1 to "len" has
|
||
|
|
# incompatible type "Optional[Any]"; expected "Sized"
|
||
|
|
# [arg-type]
|
||
|
|
self.index_names = [None] * len(
|
||
|
|
self.index_names # type: ignore[arg-type]
|
||
|
|
)
|
||
|
|
|
||
|
|
self._implicit_index = self._reader.leading_cols > 0
|
||
|
|
|
||
|
|
def close(self) -> None:
|
||
|
|
super().close()
|
||
|
|
|
||
|
|
# close additional handles opened by C parser
|
||
|
|
try:
|
||
|
|
self._reader.close()
|
||
|
|
except ValueError:
|
||
|
|
pass
|
||
|
|
|
||
|
|
def _set_noconvert_columns(self):
|
||
|
|
"""
|
||
|
|
Set the columns that should not undergo dtype conversions.
|
||
|
|
|
||
|
|
Currently, any column that is involved with date parsing will not
|
||
|
|
undergo such conversions.
|
||
|
|
"""
|
||
|
|
names = self.orig_names
|
||
|
|
if self.usecols_dtype == "integer":
|
||
|
|
# A set of integers will be converted to a list in
|
||
|
|
# the correct order every single time.
|
||
|
|
usecols = list(self.usecols)
|
||
|
|
usecols.sort()
|
||
|
|
elif callable(self.usecols) or self.usecols_dtype not in ("empty", None):
|
||
|
|
# The names attribute should have the correct columns
|
||
|
|
# in the proper order for indexing with parse_dates.
|
||
|
|
usecols = self.names[:]
|
||
|
|
else:
|
||
|
|
# Usecols is empty.
|
||
|
|
|
||
|
|
# pandas\io\parsers.py:2030: error: Incompatible types in
|
||
|
|
# assignment (expression has type "None", variable has type
|
||
|
|
# "List[Any]") [assignment]
|
||
|
|
usecols = None # type: ignore[assignment]
|
||
|
|
|
||
|
|
def _set(x):
|
||
|
|
if usecols is not None and is_integer(x):
|
||
|
|
x = usecols[x]
|
||
|
|
|
||
|
|
if not is_integer(x):
|
||
|
|
# assert for mypy, names is List or None, None would error when calling
|
||
|
|
# .index()
|
||
|
|
assert names is not None
|
||
|
|
x = names.index(x)
|
||
|
|
|
||
|
|
self._reader.set_noconvert(x)
|
||
|
|
|
||
|
|
if isinstance(self.parse_dates, list):
|
||
|
|
for val in self.parse_dates:
|
||
|
|
if isinstance(val, list):
|
||
|
|
for k in val:
|
||
|
|
_set(k)
|
||
|
|
else:
|
||
|
|
_set(val)
|
||
|
|
|
||
|
|
elif isinstance(self.parse_dates, dict):
|
||
|
|
for val in self.parse_dates.values():
|
||
|
|
if isinstance(val, list):
|
||
|
|
for k in val:
|
||
|
|
_set(k)
|
||
|
|
else:
|
||
|
|
_set(val)
|
||
|
|
|
||
|
|
elif self.parse_dates:
|
||
|
|
if isinstance(self.index_col, list):
|
||
|
|
for k in self.index_col:
|
||
|
|
_set(k)
|
||
|
|
elif self.index_col is not None:
|
||
|
|
_set(self.index_col)
|
||
|
|
|
||
|
|
def set_error_bad_lines(self, status):
|
||
|
|
self._reader.set_error_bad_lines(int(status))
|
||
|
|
|
||
|
|
def read(self, nrows=None):
|
||
|
|
try:
|
||
|
|
data = self._reader.read(nrows)
|
||
|
|
except StopIteration:
|
||
|
|
if self._first_chunk:
|
||
|
|
self._first_chunk = False
|
||
|
|
names = self._maybe_dedup_names(self.orig_names)
|
||
|
|
index, columns, col_dict = _get_empty_meta(
|
||
|
|
names,
|
||
|
|
self.index_col,
|
||
|
|
self.index_names,
|
||
|
|
dtype=self.kwds.get("dtype"),
|
||
|
|
)
|
||
|
|
columns = self._maybe_make_multi_index_columns(columns, self.col_names)
|
||
|
|
|
||
|
|
if self.usecols is not None:
|
||
|
|
columns = self._filter_usecols(columns)
|
||
|
|
|
||
|
|
col_dict = {k: v for k, v in col_dict.items() if k in columns}
|
||
|
|
|
||
|
|
return index, columns, col_dict
|
||
|
|
|
||
|
|
else:
|
||
|
|
self.close()
|
||
|
|
raise
|
||
|
|
|
||
|
|
# Done with first read, next time raise StopIteration
|
||
|
|
self._first_chunk = False
|
||
|
|
|
||
|
|
names = self.names
|
||
|
|
|
||
|
|
if self._reader.leading_cols:
|
||
|
|
if self._has_complex_date_col:
|
||
|
|
raise NotImplementedError("file structure not yet supported")
|
||
|
|
|
||
|
|
# implicit index, no index names
|
||
|
|
arrays = []
|
||
|
|
|
||
|
|
for i in range(self._reader.leading_cols):
|
||
|
|
if self.index_col is None:
|
||
|
|
values = data.pop(i)
|
||
|
|
else:
|
||
|
|
values = data.pop(self.index_col[i])
|
||
|
|
|
||
|
|
values = self._maybe_parse_dates(values, i, try_parse_dates=True)
|
||
|
|
arrays.append(values)
|
||
|
|
|
||
|
|
index = ensure_index_from_sequences(arrays)
|
||
|
|
|
||
|
|
if self.usecols is not None:
|
||
|
|
names = self._filter_usecols(names)
|
||
|
|
|
||
|
|
names = self._maybe_dedup_names(names)
|
||
|
|
|
||
|
|
# rename dict keys
|
||
|
|
data = sorted(data.items())
|
||
|
|
data = {k: v for k, (i, v) in zip(names, data)}
|
||
|
|
|
||
|
|
names, data = self._do_date_conversions(names, data)
|
||
|
|
|
||
|
|
else:
|
||
|
|
# rename dict keys
|
||
|
|
data = sorted(data.items())
|
||
|
|
|
||
|
|
# ugh, mutation
|
||
|
|
|
||
|
|
# assert for mypy, orig_names is List or None, None would error in list(...)
|
||
|
|
assert self.orig_names is not None
|
||
|
|
names = list(self.orig_names)
|
||
|
|
names = self._maybe_dedup_names(names)
|
||
|
|
|
||
|
|
if self.usecols is not None:
|
||
|
|
names = self._filter_usecols(names)
|
||
|
|
|
||
|
|
# columns as list
|
||
|
|
alldata = [x[1] for x in data]
|
||
|
|
|
||
|
|
data = {k: v for k, (i, v) in zip(names, data)}
|
||
|
|
|
||
|
|
names, data = self._do_date_conversions(names, data)
|
||
|
|
index, names = self._make_index(data, alldata, names)
|
||
|
|
|
||
|
|
# maybe create a mi on the columns
|
||
|
|
names = self._maybe_make_multi_index_columns(names, self.col_names)
|
||
|
|
|
||
|
|
return index, names, data
|
||
|
|
|
||
|
|
def _filter_usecols(self, names):
|
||
|
|
# hackish
|
||
|
|
usecols = _evaluate_usecols(self.usecols, names)
|
||
|
|
if usecols is not None and len(names) != len(usecols):
|
||
|
|
names = [
|
||
|
|
name for i, name in enumerate(names) if i in usecols or name in usecols
|
||
|
|
]
|
||
|
|
return names
|
||
|
|
|
||
|
|
def _get_index_names(self):
|
||
|
|
names = list(self._reader.header[0])
|
||
|
|
idx_names = None
|
||
|
|
|
||
|
|
if self._reader.leading_cols == 0 and self.index_col is not None:
|
||
|
|
(idx_names, names, self.index_col) = _clean_index_names(
|
||
|
|
names, self.index_col, self.unnamed_cols
|
||
|
|
)
|
||
|
|
|
||
|
|
return names, idx_names
|
||
|
|
|
||
|
|
def _maybe_parse_dates(self, values, index, try_parse_dates=True):
|
||
|
|
if try_parse_dates and self._should_parse_dates(index):
|
||
|
|
values = self._date_conv(values)
|
||
|
|
return values
|
||
|
|
|
||
|
|
|
||
|
|
def TextParser(*args, **kwds):
|
||
|
|
"""
|
||
|
|
Converts lists of lists/tuples into DataFrames with proper type inference
|
||
|
|
and optional (e.g. string to datetime) conversion. Also enables iterating
|
||
|
|
lazily over chunks of large files
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
data : file-like object or list
|
||
|
|
delimiter : separator character to use
|
||
|
|
dialect : str or csv.Dialect instance, optional
|
||
|
|
Ignored if delimiter is longer than 1 character
|
||
|
|
names : sequence, default
|
||
|
|
header : int, default 0
|
||
|
|
Row to use to parse column labels. Defaults to the first row. Prior
|
||
|
|
rows will be discarded
|
||
|
|
index_col : int or list, optional
|
||
|
|
Column or columns to use as the (possibly hierarchical) index
|
||
|
|
has_index_names: bool, default False
|
||
|
|
True if the cols defined in index_col have an index name and are
|
||
|
|
not in the header.
|
||
|
|
na_values : scalar, str, list-like, or dict, optional
|
||
|
|
Additional strings to recognize as NA/NaN.
|
||
|
|
keep_default_na : bool, default True
|
||
|
|
thousands : str, optional
|
||
|
|
Thousands separator
|
||
|
|
comment : str, optional
|
||
|
|
Comment out remainder of line
|
||
|
|
parse_dates : bool, default False
|
||
|
|
keep_date_col : bool, default False
|
||
|
|
date_parser : function, optional
|
||
|
|
skiprows : list of integers
|
||
|
|
Row numbers to skip
|
||
|
|
skipfooter : int
|
||
|
|
Number of line at bottom of file to skip
|
||
|
|
converters : dict, optional
|
||
|
|
Dict of functions for converting values in certain columns. Keys can
|
||
|
|
either be integers or column labels, values are functions that take one
|
||
|
|
input argument, the cell (not column) content, and return the
|
||
|
|
transformed content.
|
||
|
|
encoding : str, optional
|
||
|
|
Encoding to use for UTF when reading/writing (ex. 'utf-8')
|
||
|
|
squeeze : bool, default False
|
||
|
|
returns Series if only one column.
|
||
|
|
infer_datetime_format: bool, default False
|
||
|
|
If True and `parse_dates` is True for a column, try to infer the
|
||
|
|
datetime format based on the first datetime string. If the format
|
||
|
|
can be inferred, there often will be a large parsing speed-up.
|
||
|
|
float_precision : str, optional
|
||
|
|
Specifies which converter the C engine should use for floating-point
|
||
|
|
values. The options are `None` or `high` for the ordinary converter,
|
||
|
|
`legacy` for the original lower precision pandas converter, and
|
||
|
|
`round_trip` for the round-trip converter.
|
||
|
|
|
||
|
|
.. versionchanged:: 1.2
|
||
|
|
"""
|
||
|
|
kwds["engine"] = "python"
|
||
|
|
return TextFileReader(*args, **kwds)
|
||
|
|
|
||
|
|
|
||
|
|
def count_empty_vals(vals) -> int:
|
||
|
|
return sum(1 for v in vals if v == "" or v is None)
|
||
|
|
|
||
|
|
|
||
|
|
class PythonParser(ParserBase):
|
||
|
|
def __init__(self, f: Union[FilePathOrBuffer, List], **kwds):
|
||
|
|
"""
|
||
|
|
Workhorse function for processing nested list into DataFrame
|
||
|
|
"""
|
||
|
|
ParserBase.__init__(self, kwds)
|
||
|
|
|
||
|
|
self.data: Optional[Iterator[str]] = None
|
||
|
|
self.buf: List = []
|
||
|
|
self.pos = 0
|
||
|
|
self.line_pos = 0
|
||
|
|
|
||
|
|
self.skiprows = kwds["skiprows"]
|
||
|
|
|
||
|
|
if callable(self.skiprows):
|
||
|
|
self.skipfunc = self.skiprows
|
||
|
|
else:
|
||
|
|
self.skipfunc = lambda x: x in self.skiprows
|
||
|
|
|
||
|
|
self.skipfooter = _validate_skipfooter_arg(kwds["skipfooter"])
|
||
|
|
self.delimiter = kwds["delimiter"]
|
||
|
|
|
||
|
|
self.quotechar = kwds["quotechar"]
|
||
|
|
if isinstance(self.quotechar, str):
|
||
|
|
self.quotechar = str(self.quotechar)
|
||
|
|
|
||
|
|
self.escapechar = kwds["escapechar"]
|
||
|
|
self.doublequote = kwds["doublequote"]
|
||
|
|
self.skipinitialspace = kwds["skipinitialspace"]
|
||
|
|
self.lineterminator = kwds["lineterminator"]
|
||
|
|
self.quoting = kwds["quoting"]
|
||
|
|
self.usecols, _ = _validate_usecols_arg(kwds["usecols"])
|
||
|
|
self.skip_blank_lines = kwds["skip_blank_lines"]
|
||
|
|
|
||
|
|
self.warn_bad_lines = kwds["warn_bad_lines"]
|
||
|
|
self.error_bad_lines = kwds["error_bad_lines"]
|
||
|
|
|
||
|
|
self.names_passed = kwds["names"] or None
|
||
|
|
|
||
|
|
self.has_index_names = False
|
||
|
|
if "has_index_names" in kwds:
|
||
|
|
self.has_index_names = kwds["has_index_names"]
|
||
|
|
|
||
|
|
self.verbose = kwds["verbose"]
|
||
|
|
self.converters = kwds["converters"]
|
||
|
|
|
||
|
|
self.dtype = kwds["dtype"]
|
||
|
|
self.thousands = kwds["thousands"]
|
||
|
|
self.decimal = kwds["decimal"]
|
||
|
|
|
||
|
|
self.comment = kwds["comment"]
|
||
|
|
|
||
|
|
# Set self.data to something that can read lines.
|
||
|
|
if isinstance(f, list):
|
||
|
|
# read_excel: f is a list
|
||
|
|
self.data = cast(Iterator[str], f)
|
||
|
|
else:
|
||
|
|
self._open_handles(f, kwds)
|
||
|
|
assert self.handles is not None
|
||
|
|
assert hasattr(self.handles.handle, "readline")
|
||
|
|
try:
|
||
|
|
self._make_reader(self.handles.handle)
|
||
|
|
except (csv.Error, UnicodeDecodeError):
|
||
|
|
self.close()
|
||
|
|
raise
|
||
|
|
|
||
|
|
# Get columns in two steps: infer from data, then
|
||
|
|
# infer column indices from self.usecols if it is specified.
|
||
|
|
self._col_indices = None
|
||
|
|
try:
|
||
|
|
(
|
||
|
|
self.columns,
|
||
|
|
self.num_original_columns,
|
||
|
|
self.unnamed_cols,
|
||
|
|
) = self._infer_columns()
|
||
|
|
except (TypeError, ValueError):
|
||
|
|
self.close()
|
||
|
|
raise
|
||
|
|
|
||
|
|
# Now self.columns has the set of columns that we will process.
|
||
|
|
# The original set is stored in self.original_columns.
|
||
|
|
if len(self.columns) > 1:
|
||
|
|
# we are processing a multi index column
|
||
|
|
(
|
||
|
|
self.columns,
|
||
|
|
self.index_names,
|
||
|
|
self.col_names,
|
||
|
|
_,
|
||
|
|
) = self._extract_multi_indexer_columns(
|
||
|
|
self.columns, self.index_names, self.col_names
|
||
|
|
)
|
||
|
|
# Update list of original names to include all indices.
|
||
|
|
self.num_original_columns = len(self.columns)
|
||
|
|
else:
|
||
|
|
self.columns = self.columns[0]
|
||
|
|
|
||
|
|
# get popped off for index
|
||
|
|
self.orig_names = list(self.columns)
|
||
|
|
|
||
|
|
# needs to be cleaned/refactored
|
||
|
|
# multiple date column thing turning into a real spaghetti factory
|
||
|
|
|
||
|
|
if not self._has_complex_date_col:
|
||
|
|
(index_names, self.orig_names, self.columns) = self._get_index_name(
|
||
|
|
self.columns
|
||
|
|
)
|
||
|
|
self._name_processed = True
|
||
|
|
if self.index_names is None:
|
||
|
|
self.index_names = index_names
|
||
|
|
|
||
|
|
self._validate_parse_dates_presence(self.columns)
|
||
|
|
if self.parse_dates:
|
||
|
|
self._no_thousands_columns = self._set_no_thousands_columns()
|
||
|
|
else:
|
||
|
|
self._no_thousands_columns = None
|
||
|
|
|
||
|
|
if len(self.decimal) != 1:
|
||
|
|
raise ValueError("Only length-1 decimal markers supported")
|
||
|
|
|
||
|
|
if self.thousands is None:
|
||
|
|
self.nonnum = re.compile(fr"[^-^0-9^{self.decimal}]+")
|
||
|
|
else:
|
||
|
|
self.nonnum = re.compile(fr"[^-^0-9^{self.thousands}^{self.decimal}]+")
|
||
|
|
|
||
|
|
def _set_no_thousands_columns(self):
|
||
|
|
# Create a set of column ids that are not to be stripped of thousands
|
||
|
|
# operators.
|
||
|
|
noconvert_columns = set()
|
||
|
|
|
||
|
|
def _set(x):
|
||
|
|
if is_integer(x):
|
||
|
|
noconvert_columns.add(x)
|
||
|
|
else:
|
||
|
|
noconvert_columns.add(self.columns.index(x))
|
||
|
|
|
||
|
|
if isinstance(self.parse_dates, list):
|
||
|
|
for val in self.parse_dates:
|
||
|
|
if isinstance(val, list):
|
||
|
|
for k in val:
|
||
|
|
_set(k)
|
||
|
|
else:
|
||
|
|
_set(val)
|
||
|
|
|
||
|
|
elif isinstance(self.parse_dates, dict):
|
||
|
|
for val in self.parse_dates.values():
|
||
|
|
if isinstance(val, list):
|
||
|
|
for k in val:
|
||
|
|
_set(k)
|
||
|
|
else:
|
||
|
|
_set(val)
|
||
|
|
|
||
|
|
elif self.parse_dates:
|
||
|
|
if isinstance(self.index_col, list):
|
||
|
|
for k in self.index_col:
|
||
|
|
_set(k)
|
||
|
|
elif self.index_col is not None:
|
||
|
|
_set(self.index_col)
|
||
|
|
|
||
|
|
return noconvert_columns
|
||
|
|
|
||
|
|
def _make_reader(self, f):
|
||
|
|
sep = self.delimiter
|
||
|
|
|
||
|
|
if sep is None or len(sep) == 1:
|
||
|
|
if self.lineterminator:
|
||
|
|
raise ValueError(
|
||
|
|
"Custom line terminators not supported in python parser (yet)"
|
||
|
|
)
|
||
|
|
|
||
|
|
class MyDialect(csv.Dialect):
|
||
|
|
delimiter = self.delimiter
|
||
|
|
quotechar = self.quotechar
|
||
|
|
escapechar = self.escapechar
|
||
|
|
doublequote = self.doublequote
|
||
|
|
skipinitialspace = self.skipinitialspace
|
||
|
|
quoting = self.quoting
|
||
|
|
lineterminator = "\n"
|
||
|
|
|
||
|
|
dia = MyDialect
|
||
|
|
|
||
|
|
if sep is not None:
|
||
|
|
dia.delimiter = sep
|
||
|
|
else:
|
||
|
|
# attempt to sniff the delimiter from the first valid line,
|
||
|
|
# i.e. no comment line and not in skiprows
|
||
|
|
line = f.readline()
|
||
|
|
lines = self._check_comments([[line]])[0]
|
||
|
|
while self.skipfunc(self.pos) or not lines:
|
||
|
|
self.pos += 1
|
||
|
|
line = f.readline()
|
||
|
|
lines = self._check_comments([[line]])[0]
|
||
|
|
|
||
|
|
# since `line` was a string, lines will be a list containing
|
||
|
|
# only a single string
|
||
|
|
line = lines[0]
|
||
|
|
|
||
|
|
self.pos += 1
|
||
|
|
self.line_pos += 1
|
||
|
|
sniffed = csv.Sniffer().sniff(line)
|
||
|
|
dia.delimiter = sniffed.delimiter
|
||
|
|
|
||
|
|
# Note: encoding is irrelevant here
|
||
|
|
line_rdr = csv.reader(StringIO(line), dialect=dia)
|
||
|
|
self.buf.extend(list(line_rdr))
|
||
|
|
|
||
|
|
# Note: encoding is irrelevant here
|
||
|
|
reader = csv.reader(f, dialect=dia, strict=True)
|
||
|
|
|
||
|
|
else:
|
||
|
|
|
||
|
|
def _read():
|
||
|
|
line = f.readline()
|
||
|
|
pat = re.compile(sep)
|
||
|
|
|
||
|
|
yield pat.split(line.strip())
|
||
|
|
|
||
|
|
for line in f:
|
||
|
|
yield pat.split(line.strip())
|
||
|
|
|
||
|
|
reader = _read()
|
||
|
|
|
||
|
|
# pandas\io\parsers.py:2427: error: Incompatible types in assignment
|
||
|
|
# (expression has type "_reader", variable has type "Union[IO[Any],
|
||
|
|
# RawIOBase, BufferedIOBase, TextIOBase, TextIOWrapper, mmap, None]")
|
||
|
|
# [assignment]
|
||
|
|
self.data = reader # type: ignore[assignment]
|
||
|
|
|
||
|
|
def read(self, rows=None):
|
||
|
|
try:
|
||
|
|
content = self._get_lines(rows)
|
||
|
|
except StopIteration:
|
||
|
|
if self._first_chunk:
|
||
|
|
content = []
|
||
|
|
else:
|
||
|
|
self.close()
|
||
|
|
raise
|
||
|
|
|
||
|
|
# done with first read, next time raise StopIteration
|
||
|
|
self._first_chunk = False
|
||
|
|
|
||
|
|
# pandas\io\parsers.py:2480: error: Argument 1 to "list" has
|
||
|
|
# incompatible type "Optional[Any]"; expected "Iterable[Any]"
|
||
|
|
# [arg-type]
|
||
|
|
columns = list(self.orig_names) # type: ignore[arg-type]
|
||
|
|
if not len(content): # pragma: no cover
|
||
|
|
# DataFrame with the right metadata, even though it's length 0
|
||
|
|
names = self._maybe_dedup_names(self.orig_names)
|
||
|
|
index, columns, col_dict = _get_empty_meta(
|
||
|
|
names, self.index_col, self.index_names, self.dtype
|
||
|
|
)
|
||
|
|
columns = self._maybe_make_multi_index_columns(columns, self.col_names)
|
||
|
|
return index, columns, col_dict
|
||
|
|
|
||
|
|
# handle new style for names in index
|
||
|
|
count_empty_content_vals = count_empty_vals(content[0])
|
||
|
|
indexnamerow = None
|
||
|
|
if self.has_index_names and count_empty_content_vals == len(columns):
|
||
|
|
indexnamerow = content[0]
|
||
|
|
content = content[1:]
|
||
|
|
|
||
|
|
alldata = self._rows_to_cols(content)
|
||
|
|
data = self._exclude_implicit_index(alldata)
|
||
|
|
|
||
|
|
columns = self._maybe_dedup_names(self.columns)
|
||
|
|
columns, data = self._do_date_conversions(columns, data)
|
||
|
|
|
||
|
|
data = self._convert_data(data)
|
||
|
|
index, columns = self._make_index(data, alldata, columns, indexnamerow)
|
||
|
|
|
||
|
|
return index, columns, data
|
||
|
|
|
||
|
|
def _exclude_implicit_index(self, alldata):
|
||
|
|
names = self._maybe_dedup_names(self.orig_names)
|
||
|
|
|
||
|
|
if self._implicit_index:
|
||
|
|
excl_indices = self.index_col
|
||
|
|
|
||
|
|
data = {}
|
||
|
|
offset = 0
|
||
|
|
for i, col in enumerate(names):
|
||
|
|
while i + offset in excl_indices:
|
||
|
|
offset += 1
|
||
|
|
data[col] = alldata[i + offset]
|
||
|
|
else:
|
||
|
|
data = {k: v for k, v in zip(names, alldata)}
|
||
|
|
|
||
|
|
return data
|
||
|
|
|
||
|
|
# legacy
|
||
|
|
def get_chunk(self, size=None):
|
||
|
|
if size is None:
|
||
|
|
# pandas\io\parsers.py:2528: error: "PythonParser" has no attribute
|
||
|
|
# "chunksize" [attr-defined]
|
||
|
|
size = self.chunksize # type: ignore[attr-defined]
|
||
|
|
return self.read(rows=size)
|
||
|
|
|
||
|
|
def _convert_data(self, data):
|
||
|
|
# apply converters
|
||
|
|
def _clean_mapping(mapping):
|
||
|
|
"""converts col numbers to names"""
|
||
|
|
clean = {}
|
||
|
|
for col, v in mapping.items():
|
||
|
|
# pandas\io\parsers.py:2537: error: Unsupported right operand
|
||
|
|
# type for in ("Optional[Any]") [operator]
|
||
|
|
if (
|
||
|
|
isinstance(col, int)
|
||
|
|
and col not in self.orig_names # type: ignore[operator]
|
||
|
|
):
|
||
|
|
# pandas\io\parsers.py:2538: error: Value of type
|
||
|
|
# "Optional[Any]" is not indexable [index]
|
||
|
|
col = self.orig_names[col] # type: ignore[index]
|
||
|
|
clean[col] = v
|
||
|
|
return clean
|
||
|
|
|
||
|
|
clean_conv = _clean_mapping(self.converters)
|
||
|
|
if not isinstance(self.dtype, dict):
|
||
|
|
# handles single dtype applied to all columns
|
||
|
|
clean_dtypes = self.dtype
|
||
|
|
else:
|
||
|
|
clean_dtypes = _clean_mapping(self.dtype)
|
||
|
|
|
||
|
|
# Apply NA values.
|
||
|
|
clean_na_values = {}
|
||
|
|
clean_na_fvalues = {}
|
||
|
|
|
||
|
|
if isinstance(self.na_values, dict):
|
||
|
|
for col in self.na_values:
|
||
|
|
na_value = self.na_values[col]
|
||
|
|
na_fvalue = self.na_fvalues[col]
|
||
|
|
|
||
|
|
# pandas\io\parsers.py:2558: error: Unsupported right operand
|
||
|
|
# type for in ("Optional[Any]") [operator]
|
||
|
|
if (
|
||
|
|
isinstance(col, int)
|
||
|
|
and col not in self.orig_names # type: ignore[operator]
|
||
|
|
):
|
||
|
|
# pandas\io\parsers.py:2559: error: Value of type
|
||
|
|
# "Optional[Any]" is not indexable [index]
|
||
|
|
col = self.orig_names[col] # type: ignore[index]
|
||
|
|
|
||
|
|
clean_na_values[col] = na_value
|
||
|
|
clean_na_fvalues[col] = na_fvalue
|
||
|
|
else:
|
||
|
|
clean_na_values = self.na_values
|
||
|
|
clean_na_fvalues = self.na_fvalues
|
||
|
|
|
||
|
|
return self._convert_to_ndarrays(
|
||
|
|
data,
|
||
|
|
clean_na_values,
|
||
|
|
clean_na_fvalues,
|
||
|
|
self.verbose,
|
||
|
|
clean_conv,
|
||
|
|
clean_dtypes,
|
||
|
|
)
|
||
|
|
|
||
|
|
def _infer_columns(self):
|
||
|
|
names = self.names
|
||
|
|
num_original_columns = 0
|
||
|
|
clear_buffer = True
|
||
|
|
# pandas\io\parsers.py:2580: error: Need type annotation for
|
||
|
|
# 'unnamed_cols' (hint: "unnamed_cols: Set[<type>] = ...")
|
||
|
|
# [var-annotated]
|
||
|
|
unnamed_cols = set() # type: ignore[var-annotated]
|
||
|
|
|
||
|
|
if self.header is not None:
|
||
|
|
header = self.header
|
||
|
|
|
||
|
|
if isinstance(header, (list, tuple, np.ndarray)):
|
||
|
|
have_mi_columns = len(header) > 1
|
||
|
|
# we have a mi columns, so read an extra line
|
||
|
|
if have_mi_columns:
|
||
|
|
header = list(header) + [header[-1] + 1]
|
||
|
|
else:
|
||
|
|
have_mi_columns = False
|
||
|
|
header = [header]
|
||
|
|
|
||
|
|
# pandas\io\parsers.py:2594: error: Need type annotation for
|
||
|
|
# 'columns' (hint: "columns: List[<type>] = ...") [var-annotated]
|
||
|
|
columns = [] # type: ignore[var-annotated]
|
||
|
|
for level, hr in enumerate(header):
|
||
|
|
try:
|
||
|
|
line = self._buffered_line()
|
||
|
|
|
||
|
|
while self.line_pos <= hr:
|
||
|
|
line = self._next_line()
|
||
|
|
|
||
|
|
except StopIteration as err:
|
||
|
|
if self.line_pos < hr:
|
||
|
|
raise ValueError(
|
||
|
|
f"Passed header={hr} but only {self.line_pos + 1} lines in "
|
||
|
|
"file"
|
||
|
|
) from err
|
||
|
|
|
||
|
|
# We have an empty file, so check
|
||
|
|
# if columns are provided. That will
|
||
|
|
# serve as the 'line' for parsing
|
||
|
|
if have_mi_columns and hr > 0:
|
||
|
|
if clear_buffer:
|
||
|
|
self._clear_buffer()
|
||
|
|
columns.append([None] * len(columns[-1]))
|
||
|
|
return columns, num_original_columns, unnamed_cols
|
||
|
|
|
||
|
|
if not self.names:
|
||
|
|
raise EmptyDataError("No columns to parse from file") from err
|
||
|
|
|
||
|
|
line = self.names[:]
|
||
|
|
|
||
|
|
this_columns = []
|
||
|
|
this_unnamed_cols = []
|
||
|
|
|
||
|
|
for i, c in enumerate(line):
|
||
|
|
if c == "":
|
||
|
|
if have_mi_columns:
|
||
|
|
col_name = f"Unnamed: {i}_level_{level}"
|
||
|
|
else:
|
||
|
|
col_name = f"Unnamed: {i}"
|
||
|
|
|
||
|
|
this_unnamed_cols.append(i)
|
||
|
|
this_columns.append(col_name)
|
||
|
|
else:
|
||
|
|
this_columns.append(c)
|
||
|
|
|
||
|
|
if not have_mi_columns and self.mangle_dupe_cols:
|
||
|
|
# pandas\io\parsers.py:2639: error: Need type annotation
|
||
|
|
# for 'counts' [var-annotated]
|
||
|
|
counts = defaultdict(int) # type: ignore[var-annotated]
|
||
|
|
|
||
|
|
for i, col in enumerate(this_columns):
|
||
|
|
cur_count = counts[col]
|
||
|
|
|
||
|
|
while cur_count > 0:
|
||
|
|
counts[col] = cur_count + 1
|
||
|
|
col = f"{col}.{cur_count}"
|
||
|
|
cur_count = counts[col]
|
||
|
|
|
||
|
|
this_columns[i] = col
|
||
|
|
counts[col] = cur_count + 1
|
||
|
|
elif have_mi_columns:
|
||
|
|
|
||
|
|
# if we have grabbed an extra line, but its not in our
|
||
|
|
# format so save in the buffer, and create an blank extra
|
||
|
|
# line for the rest of the parsing code
|
||
|
|
if hr == header[-1]:
|
||
|
|
lc = len(this_columns)
|
||
|
|
ic = len(self.index_col) if self.index_col is not None else 0
|
||
|
|
unnamed_count = len(this_unnamed_cols)
|
||
|
|
|
||
|
|
if lc != unnamed_count and lc - ic > unnamed_count:
|
||
|
|
clear_buffer = False
|
||
|
|
# pandas\io\parsers.py:2663: error: List item 0 has
|
||
|
|
# incompatible type "None"; expected "str"
|
||
|
|
# [list-item]
|
||
|
|
this_columns = [None] * lc # type: ignore[list-item]
|
||
|
|
self.buf = [self.buf[-1]]
|
||
|
|
|
||
|
|
# pandas\io\parsers.py:2666: error: Argument 1 to "append" of
|
||
|
|
# "list" has incompatible type "List[str]"; expected
|
||
|
|
# "List[None]" [arg-type]
|
||
|
|
columns.append(this_columns) # type: ignore[arg-type]
|
||
|
|
unnamed_cols.update({this_columns[i] for i in this_unnamed_cols})
|
||
|
|
|
||
|
|
if len(columns) == 1:
|
||
|
|
num_original_columns = len(this_columns)
|
||
|
|
|
||
|
|
if clear_buffer:
|
||
|
|
self._clear_buffer()
|
||
|
|
|
||
|
|
if names is not None:
|
||
|
|
if (self.usecols is not None and len(names) != len(self.usecols)) or (
|
||
|
|
self.usecols is None and len(names) != len(columns[0])
|
||
|
|
):
|
||
|
|
raise ValueError(
|
||
|
|
"Number of passed names did not match "
|
||
|
|
"number of header fields in the file"
|
||
|
|
)
|
||
|
|
if len(columns) > 1:
|
||
|
|
raise TypeError("Cannot pass names with multi-index columns")
|
||
|
|
|
||
|
|
if self.usecols is not None:
|
||
|
|
# Set _use_cols. We don't store columns because they are
|
||
|
|
# overwritten.
|
||
|
|
self._handle_usecols(columns, names)
|
||
|
|
else:
|
||
|
|
self._col_indices = None
|
||
|
|
num_original_columns = len(names)
|
||
|
|
columns = [names]
|
||
|
|
else:
|
||
|
|
columns = self._handle_usecols(columns, columns[0])
|
||
|
|
else:
|
||
|
|
try:
|
||
|
|
line = self._buffered_line()
|
||
|
|
|
||
|
|
except StopIteration as err:
|
||
|
|
if not names:
|
||
|
|
raise EmptyDataError("No columns to parse from file") from err
|
||
|
|
|
||
|
|
line = names[:]
|
||
|
|
|
||
|
|
ncols = len(line)
|
||
|
|
num_original_columns = ncols
|
||
|
|
|
||
|
|
if not names:
|
||
|
|
if self.prefix:
|
||
|
|
# pandas\io\parsers.py:2711: error: List comprehension has
|
||
|
|
# incompatible type List[str]; expected List[None] [misc]
|
||
|
|
columns = [
|
||
|
|
[
|
||
|
|
f"{self.prefix}{i}" # type: ignore[misc]
|
||
|
|
for i in range(ncols)
|
||
|
|
]
|
||
|
|
]
|
||
|
|
else:
|
||
|
|
# pandas\io\parsers.py:2713: error: Argument 1 to "list"
|
||
|
|
# has incompatible type "range"; expected "Iterable[None]"
|
||
|
|
# [arg-type]
|
||
|
|
columns = [list(range(ncols))] # type: ignore[arg-type]
|
||
|
|
columns = self._handle_usecols(columns, columns[0])
|
||
|
|
else:
|
||
|
|
if self.usecols is None or len(names) >= num_original_columns:
|
||
|
|
columns = self._handle_usecols([names], names)
|
||
|
|
num_original_columns = len(names)
|
||
|
|
else:
|
||
|
|
if not callable(self.usecols) and len(names) != len(self.usecols):
|
||
|
|
raise ValueError(
|
||
|
|
"Number of passed names did not match number of "
|
||
|
|
"header fields in the file"
|
||
|
|
)
|
||
|
|
# Ignore output but set used columns.
|
||
|
|
self._handle_usecols([names], names)
|
||
|
|
columns = [names]
|
||
|
|
num_original_columns = ncols
|
||
|
|
|
||
|
|
return columns, num_original_columns, unnamed_cols
|
||
|
|
|
||
|
|
def _handle_usecols(self, columns, usecols_key):
|
||
|
|
"""
|
||
|
|
Sets self._col_indices
|
||
|
|
|
||
|
|
usecols_key is used if there are string usecols.
|
||
|
|
"""
|
||
|
|
if self.usecols is not None:
|
||
|
|
if callable(self.usecols):
|
||
|
|
col_indices = _evaluate_usecols(self.usecols, usecols_key)
|
||
|
|
elif any(isinstance(u, str) for u in self.usecols):
|
||
|
|
if len(columns) > 1:
|
||
|
|
raise ValueError(
|
||
|
|
"If using multiple headers, usecols must be integers."
|
||
|
|
)
|
||
|
|
col_indices = []
|
||
|
|
|
||
|
|
for col in self.usecols:
|
||
|
|
if isinstance(col, str):
|
||
|
|
try:
|
||
|
|
col_indices.append(usecols_key.index(col))
|
||
|
|
except ValueError:
|
||
|
|
_validate_usecols_names(self.usecols, usecols_key)
|
||
|
|
else:
|
||
|
|
col_indices.append(col)
|
||
|
|
else:
|
||
|
|
col_indices = self.usecols
|
||
|
|
|
||
|
|
columns = [
|
||
|
|
[n for i, n in enumerate(column) if i in col_indices]
|
||
|
|
for column in columns
|
||
|
|
]
|
||
|
|
self._col_indices = col_indices
|
||
|
|
return columns
|
||
|
|
|
||
|
|
def _buffered_line(self):
|
||
|
|
"""
|
||
|
|
Return a line from buffer, filling buffer if required.
|
||
|
|
"""
|
||
|
|
if len(self.buf) > 0:
|
||
|
|
return self.buf[0]
|
||
|
|
else:
|
||
|
|
return self._next_line()
|
||
|
|
|
||
|
|
def _check_for_bom(self, first_row):
|
||
|
|
"""
|
||
|
|
Checks whether the file begins with the BOM character.
|
||
|
|
If it does, remove it. In addition, if there is quoting
|
||
|
|
in the field subsequent to the BOM, remove it as well
|
||
|
|
because it technically takes place at the beginning of
|
||
|
|
the name, not the middle of it.
|
||
|
|
"""
|
||
|
|
# first_row will be a list, so we need to check
|
||
|
|
# that that list is not empty before proceeding.
|
||
|
|
if not first_row:
|
||
|
|
return first_row
|
||
|
|
|
||
|
|
# The first element of this row is the one that could have the
|
||
|
|
# BOM that we want to remove. Check that the first element is a
|
||
|
|
# string before proceeding.
|
||
|
|
if not isinstance(first_row[0], str):
|
||
|
|
return first_row
|
||
|
|
|
||
|
|
# Check that the string is not empty, as that would
|
||
|
|
# obviously not have a BOM at the start of it.
|
||
|
|
if not first_row[0]:
|
||
|
|
return first_row
|
||
|
|
|
||
|
|
# Since the string is non-empty, check that it does
|
||
|
|
# in fact begin with a BOM.
|
||
|
|
first_elt = first_row[0][0]
|
||
|
|
if first_elt != _BOM:
|
||
|
|
return first_row
|
||
|
|
|
||
|
|
first_row_bom = first_row[0]
|
||
|
|
|
||
|
|
if len(first_row_bom) > 1 and first_row_bom[1] == self.quotechar:
|
||
|
|
start = 2
|
||
|
|
quote = first_row_bom[1]
|
||
|
|
end = first_row_bom[2:].index(quote) + 2
|
||
|
|
|
||
|
|
# Extract the data between the quotation marks
|
||
|
|
new_row = first_row_bom[start:end]
|
||
|
|
|
||
|
|
# Extract any remaining data after the second
|
||
|
|
# quotation mark.
|
||
|
|
if len(first_row_bom) > end + 1:
|
||
|
|
new_row += first_row_bom[end + 1 :]
|
||
|
|
|
||
|
|
else:
|
||
|
|
|
||
|
|
# No quotation so just remove BOM from first element
|
||
|
|
new_row = first_row_bom[1:]
|
||
|
|
return [new_row] + first_row[1:]
|
||
|
|
|
||
|
|
def _is_line_empty(self, line):
|
||
|
|
"""
|
||
|
|
Check if a line is empty or not.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
line : str, array-like
|
||
|
|
The line of data to check.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
boolean : Whether or not the line is empty.
|
||
|
|
"""
|
||
|
|
return not line or all(not x for x in line)
|
||
|
|
|
||
|
|
def _next_line(self):
|
||
|
|
if isinstance(self.data, list):
|
||
|
|
while self.skipfunc(self.pos):
|
||
|
|
self.pos += 1
|
||
|
|
|
||
|
|
while True:
|
||
|
|
try:
|
||
|
|
line = self._check_comments([self.data[self.pos]])[0]
|
||
|
|
self.pos += 1
|
||
|
|
# either uncommented or blank to begin with
|
||
|
|
if not self.skip_blank_lines and (
|
||
|
|
self._is_line_empty(self.data[self.pos - 1]) or line
|
||
|
|
):
|
||
|
|
break
|
||
|
|
elif self.skip_blank_lines:
|
||
|
|
ret = self._remove_empty_lines([line])
|
||
|
|
if ret:
|
||
|
|
line = ret[0]
|
||
|
|
break
|
||
|
|
except IndexError:
|
||
|
|
raise StopIteration
|
||
|
|
else:
|
||
|
|
while self.skipfunc(self.pos):
|
||
|
|
self.pos += 1
|
||
|
|
# assert for mypy, data is Iterator[str] or None, would error in next
|
||
|
|
assert self.data is not None
|
||
|
|
next(self.data)
|
||
|
|
|
||
|
|
while True:
|
||
|
|
orig_line = self._next_iter_line(row_num=self.pos + 1)
|
||
|
|
self.pos += 1
|
||
|
|
|
||
|
|
if orig_line is not None:
|
||
|
|
line = self._check_comments([orig_line])[0]
|
||
|
|
|
||
|
|
if self.skip_blank_lines:
|
||
|
|
ret = self._remove_empty_lines([line])
|
||
|
|
|
||
|
|
if ret:
|
||
|
|
line = ret[0]
|
||
|
|
break
|
||
|
|
elif self._is_line_empty(orig_line) or line:
|
||
|
|
break
|
||
|
|
|
||
|
|
# This was the first line of the file,
|
||
|
|
# which could contain the BOM at the
|
||
|
|
# beginning of it.
|
||
|
|
if self.pos == 1:
|
||
|
|
line = self._check_for_bom(line)
|
||
|
|
|
||
|
|
self.line_pos += 1
|
||
|
|
self.buf.append(line)
|
||
|
|
return line
|
||
|
|
|
||
|
|
def _alert_malformed(self, msg, row_num):
|
||
|
|
"""
|
||
|
|
Alert a user about a malformed row.
|
||
|
|
|
||
|
|
If `self.error_bad_lines` is True, the alert will be `ParserError`.
|
||
|
|
If `self.warn_bad_lines` is True, the alert will be printed out.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
msg : The error message to display.
|
||
|
|
row_num : The row number where the parsing error occurred.
|
||
|
|
Because this row number is displayed, we 1-index,
|
||
|
|
even though we 0-index internally.
|
||
|
|
"""
|
||
|
|
if self.error_bad_lines:
|
||
|
|
raise ParserError(msg)
|
||
|
|
elif self.warn_bad_lines:
|
||
|
|
base = f"Skipping line {row_num}: "
|
||
|
|
sys.stderr.write(base + msg + "\n")
|
||
|
|
|
||
|
|
def _next_iter_line(self, row_num):
|
||
|
|
"""
|
||
|
|
Wrapper around iterating through `self.data` (CSV source).
|
||
|
|
|
||
|
|
When a CSV error is raised, we check for specific
|
||
|
|
error messages that allow us to customize the
|
||
|
|
error message displayed to the user.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
row_num : The row number of the line being parsed.
|
||
|
|
"""
|
||
|
|
try:
|
||
|
|
# assert for mypy, data is Iterator[str] or None, would error in next
|
||
|
|
assert self.data is not None
|
||
|
|
return next(self.data)
|
||
|
|
except csv.Error as e:
|
||
|
|
if self.warn_bad_lines or self.error_bad_lines:
|
||
|
|
msg = str(e)
|
||
|
|
|
||
|
|
if "NULL byte" in msg or "line contains NUL" in msg:
|
||
|
|
msg = (
|
||
|
|
"NULL byte detected. This byte "
|
||
|
|
"cannot be processed in Python's "
|
||
|
|
"native csv library at the moment, "
|
||
|
|
"so please pass in engine='c' instead"
|
||
|
|
)
|
||
|
|
|
||
|
|
if self.skipfooter > 0:
|
||
|
|
reason = (
|
||
|
|
"Error could possibly be due to "
|
||
|
|
"parsing errors in the skipped footer rows "
|
||
|
|
"(the skipfooter keyword is only applied "
|
||
|
|
"after Python's csv library has parsed "
|
||
|
|
"all rows)."
|
||
|
|
)
|
||
|
|
msg += ". " + reason
|
||
|
|
|
||
|
|
self._alert_malformed(msg, row_num)
|
||
|
|
return None
|
||
|
|
|
||
|
|
def _check_comments(self, lines):
|
||
|
|
if self.comment is None:
|
||
|
|
return lines
|
||
|
|
ret = []
|
||
|
|
for line in lines:
|
||
|
|
rl = []
|
||
|
|
for x in line:
|
||
|
|
if not isinstance(x, str) or self.comment not in x:
|
||
|
|
rl.append(x)
|
||
|
|
else:
|
||
|
|
x = x[: x.find(self.comment)]
|
||
|
|
if len(x) > 0:
|
||
|
|
rl.append(x)
|
||
|
|
break
|
||
|
|
ret.append(rl)
|
||
|
|
return ret
|
||
|
|
|
||
|
|
def _remove_empty_lines(self, lines):
|
||
|
|
"""
|
||
|
|
Iterate through the lines and remove any that are
|
||
|
|
either empty or contain only one whitespace value
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
lines : array-like
|
||
|
|
The array of lines that we are to filter.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
filtered_lines : array-like
|
||
|
|
The same array of lines with the "empty" ones removed.
|
||
|
|
"""
|
||
|
|
ret = []
|
||
|
|
for line in lines:
|
||
|
|
# Remove empty lines and lines with only one whitespace value
|
||
|
|
if (
|
||
|
|
len(line) > 1
|
||
|
|
or len(line) == 1
|
||
|
|
and (not isinstance(line[0], str) or line[0].strip())
|
||
|
|
):
|
||
|
|
ret.append(line)
|
||
|
|
return ret
|
||
|
|
|
||
|
|
def _check_thousands(self, lines):
|
||
|
|
if self.thousands is None:
|
||
|
|
return lines
|
||
|
|
|
||
|
|
return self._search_replace_num_columns(
|
||
|
|
lines=lines, search=self.thousands, replace=""
|
||
|
|
)
|
||
|
|
|
||
|
|
def _search_replace_num_columns(self, lines, search, replace):
|
||
|
|
ret = []
|
||
|
|
for line in lines:
|
||
|
|
rl = []
|
||
|
|
for i, x in enumerate(line):
|
||
|
|
if (
|
||
|
|
not isinstance(x, str)
|
||
|
|
or search not in x
|
||
|
|
or (self._no_thousands_columns and i in self._no_thousands_columns)
|
||
|
|
or self.nonnum.search(x.strip())
|
||
|
|
):
|
||
|
|
rl.append(x)
|
||
|
|
else:
|
||
|
|
rl.append(x.replace(search, replace))
|
||
|
|
ret.append(rl)
|
||
|
|
return ret
|
||
|
|
|
||
|
|
def _check_decimal(self, lines):
|
||
|
|
if self.decimal == _parser_defaults["decimal"]:
|
||
|
|
return lines
|
||
|
|
|
||
|
|
return self._search_replace_num_columns(
|
||
|
|
lines=lines, search=self.decimal, replace="."
|
||
|
|
)
|
||
|
|
|
||
|
|
def _clear_buffer(self):
|
||
|
|
self.buf = []
|
||
|
|
|
||
|
|
_implicit_index = False
|
||
|
|
|
||
|
|
def _get_index_name(self, columns):
|
||
|
|
"""
|
||
|
|
Try several cases to get lines:
|
||
|
|
|
||
|
|
0) There are headers on row 0 and row 1 and their
|
||
|
|
total summed lengths equals the length of the next line.
|
||
|
|
Treat row 0 as columns and row 1 as indices
|
||
|
|
1) Look for implicit index: there are more columns
|
||
|
|
on row 1 than row 0. If this is true, assume that row
|
||
|
|
1 lists index columns and row 0 lists normal columns.
|
||
|
|
2) Get index from the columns if it was listed.
|
||
|
|
"""
|
||
|
|
orig_names = list(columns)
|
||
|
|
columns = list(columns)
|
||
|
|
|
||
|
|
try:
|
||
|
|
line = self._next_line()
|
||
|
|
except StopIteration:
|
||
|
|
line = None
|
||
|
|
|
||
|
|
try:
|
||
|
|
next_line = self._next_line()
|
||
|
|
except StopIteration:
|
||
|
|
next_line = None
|
||
|
|
|
||
|
|
# implicitly index_col=0 b/c 1 fewer column names
|
||
|
|
implicit_first_cols = 0
|
||
|
|
if line is not None:
|
||
|
|
# leave it 0, #2442
|
||
|
|
# Case 1
|
||
|
|
if self.index_col is not False:
|
||
|
|
implicit_first_cols = len(line) - self.num_original_columns
|
||
|
|
|
||
|
|
# Case 0
|
||
|
|
if next_line is not None:
|
||
|
|
if len(next_line) == len(line) + self.num_original_columns:
|
||
|
|
# column and index names on diff rows
|
||
|
|
self.index_col = list(range(len(line)))
|
||
|
|
self.buf = self.buf[1:]
|
||
|
|
|
||
|
|
for c in reversed(line):
|
||
|
|
columns.insert(0, c)
|
||
|
|
|
||
|
|
# Update list of original names to include all indices.
|
||
|
|
orig_names = list(columns)
|
||
|
|
self.num_original_columns = len(columns)
|
||
|
|
return line, orig_names, columns
|
||
|
|
|
||
|
|
if implicit_first_cols > 0:
|
||
|
|
# Case 1
|
||
|
|
self._implicit_index = True
|
||
|
|
if self.index_col is None:
|
||
|
|
self.index_col = list(range(implicit_first_cols))
|
||
|
|
|
||
|
|
index_name = None
|
||
|
|
|
||
|
|
else:
|
||
|
|
# Case 2
|
||
|
|
(index_name, columns_, self.index_col) = _clean_index_names(
|
||
|
|
columns, self.index_col, self.unnamed_cols
|
||
|
|
)
|
||
|
|
|
||
|
|
return index_name, orig_names, columns
|
||
|
|
|
||
|
|
def _rows_to_cols(self, content):
|
||
|
|
col_len = self.num_original_columns
|
||
|
|
|
||
|
|
if self._implicit_index:
|
||
|
|
col_len += len(self.index_col)
|
||
|
|
|
||
|
|
max_len = max(len(row) for row in content)
|
||
|
|
|
||
|
|
# Check that there are no rows with too many
|
||
|
|
# elements in their row (rows with too few
|
||
|
|
# elements are padded with NaN).
|
||
|
|
if max_len > col_len and self.index_col is not False and self.usecols is None:
|
||
|
|
|
||
|
|
footers = self.skipfooter if self.skipfooter else 0
|
||
|
|
bad_lines = []
|
||
|
|
|
||
|
|
iter_content = enumerate(content)
|
||
|
|
content_len = len(content)
|
||
|
|
content = []
|
||
|
|
|
||
|
|
for (i, l) in iter_content:
|
||
|
|
actual_len = len(l)
|
||
|
|
|
||
|
|
if actual_len > col_len:
|
||
|
|
if self.error_bad_lines or self.warn_bad_lines:
|
||
|
|
row_num = self.pos - (content_len - i + footers)
|
||
|
|
bad_lines.append((row_num, actual_len))
|
||
|
|
|
||
|
|
if self.error_bad_lines:
|
||
|
|
break
|
||
|
|
else:
|
||
|
|
content.append(l)
|
||
|
|
|
||
|
|
for row_num, actual_len in bad_lines:
|
||
|
|
msg = (
|
||
|
|
f"Expected {col_len} fields in line {row_num + 1}, saw "
|
||
|
|
f"{actual_len}"
|
||
|
|
)
|
||
|
|
if (
|
||
|
|
self.delimiter
|
||
|
|
and len(self.delimiter) > 1
|
||
|
|
and self.quoting != csv.QUOTE_NONE
|
||
|
|
):
|
||
|
|
# see gh-13374
|
||
|
|
reason = (
|
||
|
|
"Error could possibly be due to quotes being "
|
||
|
|
"ignored when a multi-char delimiter is used."
|
||
|
|
)
|
||
|
|
msg += ". " + reason
|
||
|
|
|
||
|
|
self._alert_malformed(msg, row_num + 1)
|
||
|
|
|
||
|
|
# see gh-13320
|
||
|
|
zipped_content = list(lib.to_object_array(content, min_width=col_len).T)
|
||
|
|
|
||
|
|
if self.usecols:
|
||
|
|
if self._implicit_index:
|
||
|
|
zipped_content = [
|
||
|
|
a
|
||
|
|
for i, a in enumerate(zipped_content)
|
||
|
|
if (
|
||
|
|
i < len(self.index_col)
|
||
|
|
# pandas\io\parsers.py:3159: error: Unsupported right
|
||
|
|
# operand type for in ("Optional[Any]") [operator]
|
||
|
|
or i - len(self.index_col) # type: ignore[operator]
|
||
|
|
in self._col_indices
|
||
|
|
)
|
||
|
|
]
|
||
|
|
else:
|
||
|
|
zipped_content = [
|
||
|
|
# pandas\io\parsers.py:3164: error: Unsupported right
|
||
|
|
# operand type for in ("Optional[Any]") [operator]
|
||
|
|
a
|
||
|
|
for i, a in enumerate(zipped_content)
|
||
|
|
if i in self._col_indices # type: ignore[operator]
|
||
|
|
]
|
||
|
|
return zipped_content
|
||
|
|
|
||
|
|
def _get_lines(self, rows=None):
|
||
|
|
lines = self.buf
|
||
|
|
new_rows = None
|
||
|
|
|
||
|
|
# already fetched some number
|
||
|
|
if rows is not None:
|
||
|
|
# we already have the lines in the buffer
|
||
|
|
if len(self.buf) >= rows:
|
||
|
|
new_rows, self.buf = self.buf[:rows], self.buf[rows:]
|
||
|
|
|
||
|
|
# need some lines
|
||
|
|
else:
|
||
|
|
rows -= len(self.buf)
|
||
|
|
|
||
|
|
if new_rows is None:
|
||
|
|
if isinstance(self.data, list):
|
||
|
|
if self.pos > len(self.data):
|
||
|
|
raise StopIteration
|
||
|
|
if rows is None:
|
||
|
|
new_rows = self.data[self.pos :]
|
||
|
|
new_pos = len(self.data)
|
||
|
|
else:
|
||
|
|
new_rows = self.data[self.pos : self.pos + rows]
|
||
|
|
new_pos = self.pos + rows
|
||
|
|
|
||
|
|
# Check for stop rows. n.b.: self.skiprows is a set.
|
||
|
|
if self.skiprows:
|
||
|
|
new_rows = [
|
||
|
|
row
|
||
|
|
for i, row in enumerate(new_rows)
|
||
|
|
if not self.skipfunc(i + self.pos)
|
||
|
|
]
|
||
|
|
|
||
|
|
lines.extend(new_rows)
|
||
|
|
self.pos = new_pos
|
||
|
|
|
||
|
|
else:
|
||
|
|
new_rows = []
|
||
|
|
try:
|
||
|
|
if rows is not None:
|
||
|
|
for _ in range(rows):
|
||
|
|
# assert for mypy, data is Iterator[str] or None, would
|
||
|
|
# error in next
|
||
|
|
assert self.data is not None
|
||
|
|
new_rows.append(next(self.data))
|
||
|
|
lines.extend(new_rows)
|
||
|
|
else:
|
||
|
|
rows = 0
|
||
|
|
|
||
|
|
while True:
|
||
|
|
new_row = self._next_iter_line(row_num=self.pos + rows + 1)
|
||
|
|
rows += 1
|
||
|
|
|
||
|
|
if new_row is not None:
|
||
|
|
new_rows.append(new_row)
|
||
|
|
|
||
|
|
except StopIteration:
|
||
|
|
if self.skiprows:
|
||
|
|
new_rows = [
|
||
|
|
row
|
||
|
|
for i, row in enumerate(new_rows)
|
||
|
|
if not self.skipfunc(i + self.pos)
|
||
|
|
]
|
||
|
|
lines.extend(new_rows)
|
||
|
|
if len(lines) == 0:
|
||
|
|
raise
|
||
|
|
self.pos += len(new_rows)
|
||
|
|
|
||
|
|
self.buf = []
|
||
|
|
else:
|
||
|
|
lines = new_rows
|
||
|
|
|
||
|
|
if self.skipfooter:
|
||
|
|
lines = lines[: -self.skipfooter]
|
||
|
|
|
||
|
|
lines = self._check_comments(lines)
|
||
|
|
if self.skip_blank_lines:
|
||
|
|
lines = self._remove_empty_lines(lines)
|
||
|
|
lines = self._check_thousands(lines)
|
||
|
|
return self._check_decimal(lines)
|
||
|
|
|
||
|
|
|
||
|
|
def _make_date_converter(
|
||
|
|
date_parser=None, dayfirst=False, infer_datetime_format=False, cache_dates=True
|
||
|
|
):
|
||
|
|
def converter(*date_cols):
|
||
|
|
if date_parser is None:
|
||
|
|
strs = parsing.concat_date_cols(date_cols)
|
||
|
|
|
||
|
|
try:
|
||
|
|
return tools.to_datetime(
|
||
|
|
ensure_object(strs),
|
||
|
|
utc=None,
|
||
|
|
dayfirst=dayfirst,
|
||
|
|
errors="ignore",
|
||
|
|
infer_datetime_format=infer_datetime_format,
|
||
|
|
cache=cache_dates,
|
||
|
|
).to_numpy()
|
||
|
|
|
||
|
|
except ValueError:
|
||
|
|
return tools.to_datetime(
|
||
|
|
parsing.try_parse_dates(strs, dayfirst=dayfirst), cache=cache_dates
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
try:
|
||
|
|
result = tools.to_datetime(
|
||
|
|
date_parser(*date_cols), errors="ignore", cache=cache_dates
|
||
|
|
)
|
||
|
|
if isinstance(result, datetime.datetime):
|
||
|
|
raise Exception("scalar parser")
|
||
|
|
return result
|
||
|
|
except Exception:
|
||
|
|
try:
|
||
|
|
return tools.to_datetime(
|
||
|
|
parsing.try_parse_dates(
|
||
|
|
parsing.concat_date_cols(date_cols),
|
||
|
|
parser=date_parser,
|
||
|
|
dayfirst=dayfirst,
|
||
|
|
),
|
||
|
|
errors="ignore",
|
||
|
|
)
|
||
|
|
except Exception:
|
||
|
|
return generic_parser(date_parser, *date_cols)
|
||
|
|
|
||
|
|
return converter
|
||
|
|
|
||
|
|
|
||
|
|
def _process_date_conversion(
|
||
|
|
data_dict,
|
||
|
|
converter,
|
||
|
|
parse_spec,
|
||
|
|
index_col,
|
||
|
|
index_names,
|
||
|
|
columns,
|
||
|
|
keep_date_col=False,
|
||
|
|
):
|
||
|
|
def _isindex(colspec):
|
||
|
|
return (isinstance(index_col, list) and colspec in index_col) or (
|
||
|
|
isinstance(index_names, list) and colspec in index_names
|
||
|
|
)
|
||
|
|
|
||
|
|
new_cols = []
|
||
|
|
new_data = {}
|
||
|
|
|
||
|
|
orig_names = columns
|
||
|
|
columns = list(columns)
|
||
|
|
|
||
|
|
date_cols = set()
|
||
|
|
|
||
|
|
if parse_spec is None or isinstance(parse_spec, bool):
|
||
|
|
return data_dict, columns
|
||
|
|
|
||
|
|
if isinstance(parse_spec, list):
|
||
|
|
# list of column lists
|
||
|
|
for colspec in parse_spec:
|
||
|
|
if is_scalar(colspec):
|
||
|
|
if isinstance(colspec, int) and colspec not in data_dict:
|
||
|
|
colspec = orig_names[colspec]
|
||
|
|
if _isindex(colspec):
|
||
|
|
continue
|
||
|
|
data_dict[colspec] = converter(data_dict[colspec])
|
||
|
|
else:
|
||
|
|
new_name, col, old_names = _try_convert_dates(
|
||
|
|
converter, colspec, data_dict, orig_names
|
||
|
|
)
|
||
|
|
if new_name in data_dict:
|
||
|
|
raise ValueError(f"New date column already in dict {new_name}")
|
||
|
|
new_data[new_name] = col
|
||
|
|
new_cols.append(new_name)
|
||
|
|
date_cols.update(old_names)
|
||
|
|
|
||
|
|
elif isinstance(parse_spec, dict):
|
||
|
|
# dict of new name to column list
|
||
|
|
for new_name, colspec in parse_spec.items():
|
||
|
|
if new_name in data_dict:
|
||
|
|
raise ValueError(f"Date column {new_name} already in dict")
|
||
|
|
|
||
|
|
_, col, old_names = _try_convert_dates(
|
||
|
|
converter, colspec, data_dict, orig_names
|
||
|
|
)
|
||
|
|
|
||
|
|
new_data[new_name] = col
|
||
|
|
new_cols.append(new_name)
|
||
|
|
date_cols.update(old_names)
|
||
|
|
|
||
|
|
data_dict.update(new_data)
|
||
|
|
new_cols.extend(columns)
|
||
|
|
|
||
|
|
if not keep_date_col:
|
||
|
|
for c in list(date_cols):
|
||
|
|
data_dict.pop(c)
|
||
|
|
new_cols.remove(c)
|
||
|
|
|
||
|
|
return data_dict, new_cols
|
||
|
|
|
||
|
|
|
||
|
|
def _try_convert_dates(parser, colspec, data_dict, columns):
|
||
|
|
colset = set(columns)
|
||
|
|
colnames = []
|
||
|
|
|
||
|
|
for c in colspec:
|
||
|
|
if c in colset:
|
||
|
|
colnames.append(c)
|
||
|
|
elif isinstance(c, int) and c not in columns:
|
||
|
|
colnames.append(columns[c])
|
||
|
|
else:
|
||
|
|
colnames.append(c)
|
||
|
|
|
||
|
|
new_name = "_".join(str(x) for x in colnames)
|
||
|
|
to_parse = [data_dict[c] for c in colnames if c in data_dict]
|
||
|
|
|
||
|
|
new_col = parser(*to_parse)
|
||
|
|
return new_name, new_col, colnames
|
||
|
|
|
||
|
|
|
||
|
|
def _clean_na_values(na_values, keep_default_na=True):
|
||
|
|
|
||
|
|
if na_values is None:
|
||
|
|
if keep_default_na:
|
||
|
|
na_values = STR_NA_VALUES
|
||
|
|
else:
|
||
|
|
na_values = set()
|
||
|
|
# pandas\io\parsers.py:3387: error: Need type annotation for
|
||
|
|
# 'na_fvalues' (hint: "na_fvalues: Set[<type>] = ...") [var-annotated]
|
||
|
|
na_fvalues = set() # type: ignore[var-annotated]
|
||
|
|
elif isinstance(na_values, dict):
|
||
|
|
old_na_values = na_values.copy()
|
||
|
|
na_values = {} # Prevent aliasing.
|
||
|
|
|
||
|
|
# Convert the values in the na_values dictionary
|
||
|
|
# into array-likes for further use. This is also
|
||
|
|
# where we append the default NaN values, provided
|
||
|
|
# that `keep_default_na=True`.
|
||
|
|
for k, v in old_na_values.items():
|
||
|
|
if not is_list_like(v):
|
||
|
|
v = [v]
|
||
|
|
|
||
|
|
if keep_default_na:
|
||
|
|
v = set(v) | STR_NA_VALUES
|
||
|
|
|
||
|
|
na_values[k] = v
|
||
|
|
# pandas\io\parsers.py:3404: error: Incompatible types in assignment
|
||
|
|
# (expression has type "Dict[Any, Any]", variable has type "Set[Any]")
|
||
|
|
# [assignment]
|
||
|
|
na_fvalues = { # type: ignore[assignment]
|
||
|
|
k: _floatify_na_values(v) for k, v in na_values.items()
|
||
|
|
}
|
||
|
|
else:
|
||
|
|
if not is_list_like(na_values):
|
||
|
|
na_values = [na_values]
|
||
|
|
na_values = _stringify_na_values(na_values)
|
||
|
|
if keep_default_na:
|
||
|
|
na_values = na_values | STR_NA_VALUES
|
||
|
|
|
||
|
|
na_fvalues = _floatify_na_values(na_values)
|
||
|
|
|
||
|
|
return na_values, na_fvalues
|
||
|
|
|
||
|
|
|
||
|
|
def _clean_index_names(columns, index_col, unnamed_cols):
|
||
|
|
if not _is_index_col(index_col):
|
||
|
|
return None, columns, index_col
|
||
|
|
|
||
|
|
columns = list(columns)
|
||
|
|
|
||
|
|
cp_cols = list(columns)
|
||
|
|
index_names = []
|
||
|
|
|
||
|
|
# don't mutate
|
||
|
|
index_col = list(index_col)
|
||
|
|
|
||
|
|
for i, c in enumerate(index_col):
|
||
|
|
if isinstance(c, str):
|
||
|
|
index_names.append(c)
|
||
|
|
for j, name in enumerate(cp_cols):
|
||
|
|
if name == c:
|
||
|
|
index_col[i] = j
|
||
|
|
columns.remove(name)
|
||
|
|
break
|
||
|
|
else:
|
||
|
|
name = cp_cols[c]
|
||
|
|
columns.remove(name)
|
||
|
|
index_names.append(name)
|
||
|
|
|
||
|
|
# Only clean index names that were placeholders.
|
||
|
|
for i, name in enumerate(index_names):
|
||
|
|
if isinstance(name, str) and name in unnamed_cols:
|
||
|
|
# pandas\io\parsers.py:3445: error: No overload variant of
|
||
|
|
# "__setitem__" of "list" matches argument types "int", "None"
|
||
|
|
# [call-overload]
|
||
|
|
index_names[i] = None # type: ignore[call-overload]
|
||
|
|
|
||
|
|
return index_names, columns, index_col
|
||
|
|
|
||
|
|
|
||
|
|
def _get_empty_meta(columns, index_col, index_names, dtype=None):
|
||
|
|
columns = list(columns)
|
||
|
|
|
||
|
|
# Convert `dtype` to a defaultdict of some kind.
|
||
|
|
# This will enable us to write `dtype[col_name]`
|
||
|
|
# without worrying about KeyError issues later on.
|
||
|
|
if not isinstance(dtype, dict):
|
||
|
|
# if dtype == None, default will be object.
|
||
|
|
default_dtype = dtype or object
|
||
|
|
dtype = defaultdict(lambda: default_dtype)
|
||
|
|
else:
|
||
|
|
# Save a copy of the dictionary.
|
||
|
|
_dtype = dtype.copy()
|
||
|
|
dtype = defaultdict(lambda: object)
|
||
|
|
|
||
|
|
# Convert column indexes to column names.
|
||
|
|
for k, v in _dtype.items():
|
||
|
|
col = columns[k] if is_integer(k) else k
|
||
|
|
dtype[col] = v
|
||
|
|
|
||
|
|
# Even though we have no data, the "index" of the empty DataFrame
|
||
|
|
# could for example still be an empty MultiIndex. Thus, we need to
|
||
|
|
# check whether we have any index columns specified, via either:
|
||
|
|
#
|
||
|
|
# 1) index_col (column indices)
|
||
|
|
# 2) index_names (column names)
|
||
|
|
#
|
||
|
|
# Both must be non-null to ensure a successful construction. Otherwise,
|
||
|
|
# we have to create a generic empty Index.
|
||
|
|
if (index_col is None or index_col is False) or index_names is None:
|
||
|
|
index = Index([])
|
||
|
|
else:
|
||
|
|
data = [Series([], dtype=dtype[name]) for name in index_names]
|
||
|
|
index = ensure_index_from_sequences(data, names=index_names)
|
||
|
|
index_col.sort()
|
||
|
|
|
||
|
|
for i, n in enumerate(index_col):
|
||
|
|
columns.pop(n - i)
|
||
|
|
|
||
|
|
col_dict = {col_name: Series([], dtype=dtype[col_name]) for col_name in columns}
|
||
|
|
|
||
|
|
return index, columns, col_dict
|
||
|
|
|
||
|
|
|
||
|
|
def _floatify_na_values(na_values):
|
||
|
|
# create float versions of the na_values
|
||
|
|
result = set()
|
||
|
|
for v in na_values:
|
||
|
|
try:
|
||
|
|
v = float(v)
|
||
|
|
if not np.isnan(v):
|
||
|
|
result.add(v)
|
||
|
|
except (TypeError, ValueError, OverflowError):
|
||
|
|
pass
|
||
|
|
return result
|
||
|
|
|
||
|
|
|
||
|
|
def _stringify_na_values(na_values):
|
||
|
|
""" return a stringified and numeric for these values """
|
||
|
|
result = []
|
||
|
|
for x in na_values:
|
||
|
|
result.append(str(x))
|
||
|
|
result.append(x)
|
||
|
|
try:
|
||
|
|
v = float(x)
|
||
|
|
|
||
|
|
# we are like 999 here
|
||
|
|
if v == int(v):
|
||
|
|
v = int(v)
|
||
|
|
result.append(f"{v}.0")
|
||
|
|
result.append(str(v))
|
||
|
|
|
||
|
|
# pandas\io\parsers.py:3522: error: Argument 1 to "append" of
|
||
|
|
# "list" has incompatible type "float"; expected "str" [arg-type]
|
||
|
|
result.append(v) # type: ignore[arg-type]
|
||
|
|
except (TypeError, ValueError, OverflowError):
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
# pandas\io\parsers.py:3526: error: Argument 1 to "append" of
|
||
|
|
# "list" has incompatible type "int"; expected "str" [arg-type]
|
||
|
|
result.append(int(x)) # type: ignore[arg-type]
|
||
|
|
except (TypeError, ValueError, OverflowError):
|
||
|
|
pass
|
||
|
|
return set(result)
|
||
|
|
|
||
|
|
|
||
|
|
def _get_na_values(col, na_values, na_fvalues, keep_default_na):
|
||
|
|
"""
|
||
|
|
Get the NaN values for a given column.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
col : str
|
||
|
|
The name of the column.
|
||
|
|
na_values : array-like, dict
|
||
|
|
The object listing the NaN values as strings.
|
||
|
|
na_fvalues : array-like, dict
|
||
|
|
The object listing the NaN values as floats.
|
||
|
|
keep_default_na : bool
|
||
|
|
If `na_values` is a dict, and the column is not mapped in the
|
||
|
|
dictionary, whether to return the default NaN values or the empty set.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
nan_tuple : A length-two tuple composed of
|
||
|
|
|
||
|
|
1) na_values : the string NaN values for that column.
|
||
|
|
2) na_fvalues : the float NaN values for that column.
|
||
|
|
"""
|
||
|
|
if isinstance(na_values, dict):
|
||
|
|
if col in na_values:
|
||
|
|
return na_values[col], na_fvalues[col]
|
||
|
|
else:
|
||
|
|
if keep_default_na:
|
||
|
|
return STR_NA_VALUES, set()
|
||
|
|
|
||
|
|
return set(), set()
|
||
|
|
else:
|
||
|
|
return na_values, na_fvalues
|
||
|
|
|
||
|
|
|
||
|
|
def _get_col_names(colspec, columns):
|
||
|
|
colset = set(columns)
|
||
|
|
colnames = []
|
||
|
|
for c in colspec:
|
||
|
|
if c in colset:
|
||
|
|
colnames.append(c)
|
||
|
|
elif isinstance(c, int):
|
||
|
|
colnames.append(columns[c])
|
||
|
|
return colnames
|
||
|
|
|
||
|
|
|
||
|
|
class FixedWidthReader(abc.Iterator):
|
||
|
|
"""
|
||
|
|
A reader of fixed-width lines.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, f, colspecs, delimiter, comment, skiprows=None, infer_nrows=100):
|
||
|
|
self.f = f
|
||
|
|
self.buffer = None
|
||
|
|
self.delimiter = "\r\n" + delimiter if delimiter else "\n\r\t "
|
||
|
|
self.comment = comment
|
||
|
|
if colspecs == "infer":
|
||
|
|
self.colspecs = self.detect_colspecs(
|
||
|
|
infer_nrows=infer_nrows, skiprows=skiprows
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
self.colspecs = colspecs
|
||
|
|
|
||
|
|
if not isinstance(self.colspecs, (tuple, list)):
|
||
|
|
raise TypeError(
|
||
|
|
"column specifications must be a list or tuple, "
|
||
|
|
f"input was a {type(colspecs).__name__}"
|
||
|
|
)
|
||
|
|
|
||
|
|
for colspec in self.colspecs:
|
||
|
|
if not (
|
||
|
|
isinstance(colspec, (tuple, list))
|
||
|
|
and len(colspec) == 2
|
||
|
|
and isinstance(colspec[0], (int, np.integer, type(None)))
|
||
|
|
and isinstance(colspec[1], (int, np.integer, type(None)))
|
||
|
|
):
|
||
|
|
raise TypeError(
|
||
|
|
"Each column specification must be "
|
||
|
|
"2 element tuple or list of integers"
|
||
|
|
)
|
||
|
|
|
||
|
|
def get_rows(self, infer_nrows, skiprows=None):
|
||
|
|
"""
|
||
|
|
Read rows from self.f, skipping as specified.
|
||
|
|
|
||
|
|
We distinguish buffer_rows (the first <= infer_nrows
|
||
|
|
lines) from the rows returned to detect_colspecs
|
||
|
|
because it's simpler to leave the other locations
|
||
|
|
with skiprows logic alone than to modify them to
|
||
|
|
deal with the fact we skipped some rows here as
|
||
|
|
well.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
infer_nrows : int
|
||
|
|
Number of rows to read from self.f, not counting
|
||
|
|
rows that are skipped.
|
||
|
|
skiprows: set, optional
|
||
|
|
Indices of rows to skip.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
detect_rows : list of str
|
||
|
|
A list containing the rows to read.
|
||
|
|
|
||
|
|
"""
|
||
|
|
if skiprows is None:
|
||
|
|
skiprows = set()
|
||
|
|
buffer_rows = []
|
||
|
|
detect_rows = []
|
||
|
|
for i, row in enumerate(self.f):
|
||
|
|
if i not in skiprows:
|
||
|
|
detect_rows.append(row)
|
||
|
|
buffer_rows.append(row)
|
||
|
|
if len(detect_rows) >= infer_nrows:
|
||
|
|
break
|
||
|
|
self.buffer = iter(buffer_rows)
|
||
|
|
return detect_rows
|
||
|
|
|
||
|
|
def detect_colspecs(self, infer_nrows=100, skiprows=None):
|
||
|
|
# Regex escape the delimiters
|
||
|
|
delimiters = "".join(fr"\{x}" for x in self.delimiter)
|
||
|
|
pattern = re.compile(f"([^{delimiters}]+)")
|
||
|
|
rows = self.get_rows(infer_nrows, skiprows)
|
||
|
|
if not rows:
|
||
|
|
raise EmptyDataError("No rows from which to infer column width")
|
||
|
|
max_len = max(map(len, rows))
|
||
|
|
mask = np.zeros(max_len + 1, dtype=int)
|
||
|
|
if self.comment is not None:
|
||
|
|
rows = [row.partition(self.comment)[0] for row in rows]
|
||
|
|
for row in rows:
|
||
|
|
for m in pattern.finditer(row):
|
||
|
|
mask[m.start() : m.end()] = 1
|
||
|
|
shifted = np.roll(mask, 1)
|
||
|
|
shifted[0] = 0
|
||
|
|
edges = np.where((mask ^ shifted) == 1)[0]
|
||
|
|
edge_pairs = list(zip(edges[::2], edges[1::2]))
|
||
|
|
return edge_pairs
|
||
|
|
|
||
|
|
def __next__(self):
|
||
|
|
if self.buffer is not None:
|
||
|
|
try:
|
||
|
|
line = next(self.buffer)
|
||
|
|
except StopIteration:
|
||
|
|
self.buffer = None
|
||
|
|
line = next(self.f)
|
||
|
|
else:
|
||
|
|
line = next(self.f)
|
||
|
|
# Note: 'colspecs' is a sequence of half-open intervals.
|
||
|
|
return [line[fromm:to].strip(self.delimiter) for (fromm, to) in self.colspecs]
|
||
|
|
|
||
|
|
|
||
|
|
class FixedWidthFieldParser(PythonParser):
|
||
|
|
"""
|
||
|
|
Specialization that Converts fixed-width fields into DataFrames.
|
||
|
|
See PythonParser for details.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, f, **kwds):
|
||
|
|
# Support iterators, convert to a list.
|
||
|
|
self.colspecs = kwds.pop("colspecs")
|
||
|
|
self.infer_nrows = kwds.pop("infer_nrows")
|
||
|
|
PythonParser.__init__(self, f, **kwds)
|
||
|
|
|
||
|
|
def _make_reader(self, f):
|
||
|
|
self.data = FixedWidthReader(
|
||
|
|
f,
|
||
|
|
self.colspecs,
|
||
|
|
self.delimiter,
|
||
|
|
self.comment,
|
||
|
|
self.skiprows,
|
||
|
|
self.infer_nrows,
|
||
|
|
)
|
||
|
|
|
||
|
|
def _remove_empty_lines(self, lines) -> List:
|
||
|
|
"""
|
||
|
|
Returns the list of lines without the empty ones. With fixed-width
|
||
|
|
fields, empty lines become arrays of empty strings.
|
||
|
|
|
||
|
|
See PythonParser._remove_empty_lines.
|
||
|
|
"""
|
||
|
|
return [
|
||
|
|
line
|
||
|
|
for line in lines
|
||
|
|
if any(not isinstance(e, str) or e.strip() for e in line)
|
||
|
|
]
|
||
|
|
|
||
|
|
|
||
|
|
def _refine_defaults_read(
|
||
|
|
dialect: Union[str, csv.Dialect],
|
||
|
|
delimiter: Union[str, object],
|
||
|
|
delim_whitespace: bool,
|
||
|
|
engine: str,
|
||
|
|
sep: Union[str, object],
|
||
|
|
defaults: Dict[str, Any],
|
||
|
|
):
|
||
|
|
"""Validate/refine default values of input parameters of read_csv, read_table.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
dialect : str or csv.Dialect
|
||
|
|
If provided, this parameter will override values (default or not) for the
|
||
|
|
following parameters: `delimiter`, `doublequote`, `escapechar`,
|
||
|
|
`skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to
|
||
|
|
override values, a ParserWarning will be issued. See csv.Dialect
|
||
|
|
documentation for more details.
|
||
|
|
delimiter : str or object
|
||
|
|
Alias for sep.
|
||
|
|
delim_whitespace : bool
|
||
|
|
Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be
|
||
|
|
used as the sep. Equivalent to setting ``sep='\\s+'``. If this option
|
||
|
|
is set to True, nothing should be passed in for the ``delimiter``
|
||
|
|
parameter.
|
||
|
|
engine : {{'c', 'python'}}
|
||
|
|
Parser engine to use. The C engine is faster while the python engine is
|
||
|
|
currently more feature-complete.
|
||
|
|
sep : str or object
|
||
|
|
A delimiter provided by the user (str) or a sentinel value, i.e.
|
||
|
|
pandas._libs.lib.no_default.
|
||
|
|
defaults: dict
|
||
|
|
Default values of input parameters.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
kwds : dict
|
||
|
|
Input parameters with correct values.
|
||
|
|
|
||
|
|
Raises
|
||
|
|
------
|
||
|
|
ValueError : If a delimiter was specified with ``sep`` (or ``delimiter``) and
|
||
|
|
``delim_whitespace=True``.
|
||
|
|
"""
|
||
|
|
# fix types for sep, delimiter to Union(str, Any)
|
||
|
|
delim_default = defaults["delimiter"]
|
||
|
|
kwds: Dict[str, Any] = {}
|
||
|
|
# gh-23761
|
||
|
|
#
|
||
|
|
# When a dialect is passed, it overrides any of the overlapping
|
||
|
|
# parameters passed in directly. We don't want to warn if the
|
||
|
|
# default parameters were passed in (since it probably means
|
||
|
|
# that the user didn't pass them in explicitly in the first place).
|
||
|
|
#
|
||
|
|
# "delimiter" is the annoying corner case because we alias it to
|
||
|
|
# "sep" before doing comparison to the dialect values later on.
|
||
|
|
# Thus, we need a flag to indicate that we need to "override"
|
||
|
|
# the comparison to dialect values by checking if default values
|
||
|
|
# for BOTH "delimiter" and "sep" were provided.
|
||
|
|
if dialect is not None:
|
||
|
|
kwds["sep_override"] = delimiter is None and (
|
||
|
|
sep is lib.no_default or sep == delim_default
|
||
|
|
)
|
||
|
|
|
||
|
|
# Alias sep -> delimiter.
|
||
|
|
if delimiter is None:
|
||
|
|
delimiter = sep
|
||
|
|
|
||
|
|
if delim_whitespace and (delimiter is not lib.no_default):
|
||
|
|
raise ValueError(
|
||
|
|
"Specified a delimiter with both sep and "
|
||
|
|
"delim_whitespace=True; you can only specify one."
|
||
|
|
)
|
||
|
|
|
||
|
|
if delimiter is lib.no_default:
|
||
|
|
# assign default separator value
|
||
|
|
kwds["delimiter"] = delim_default
|
||
|
|
else:
|
||
|
|
kwds["delimiter"] = delimiter
|
||
|
|
|
||
|
|
if engine is not None:
|
||
|
|
kwds["engine_specified"] = True
|
||
|
|
else:
|
||
|
|
kwds["engine"] = "c"
|
||
|
|
kwds["engine_specified"] = False
|
||
|
|
|
||
|
|
return kwds
|
||
|
|
|
||
|
|
|
||
|
|
def _extract_dialect(kwds: Dict[str, Any]) -> Optional[csv.Dialect]:
|
||
|
|
"""
|
||
|
|
Extract concrete csv dialect instance.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
csv.Dialect or None
|
||
|
|
"""
|
||
|
|
if kwds.get("dialect") is None:
|
||
|
|
return None
|
||
|
|
|
||
|
|
dialect = kwds["dialect"]
|
||
|
|
if dialect in csv.list_dialects():
|
||
|
|
dialect = csv.get_dialect(dialect)
|
||
|
|
|
||
|
|
_validate_dialect(dialect)
|
||
|
|
|
||
|
|
return dialect
|
||
|
|
|
||
|
|
|
||
|
|
MANDATORY_DIALECT_ATTRS = (
|
||
|
|
"delimiter",
|
||
|
|
"doublequote",
|
||
|
|
"escapechar",
|
||
|
|
"skipinitialspace",
|
||
|
|
"quotechar",
|
||
|
|
"quoting",
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def _validate_dialect(dialect: csv.Dialect) -> None:
|
||
|
|
"""
|
||
|
|
Validate csv dialect instance.
|
||
|
|
|
||
|
|
Raises
|
||
|
|
------
|
||
|
|
ValueError
|
||
|
|
If incorrect dialect is provided.
|
||
|
|
"""
|
||
|
|
for param in MANDATORY_DIALECT_ATTRS:
|
||
|
|
if not hasattr(dialect, param):
|
||
|
|
raise ValueError(f"Invalid dialect {dialect} provided")
|
||
|
|
|
||
|
|
|
||
|
|
def _merge_with_dialect_properties(
|
||
|
|
dialect: csv.Dialect,
|
||
|
|
defaults: Dict[str, Any],
|
||
|
|
) -> Dict[str, Any]:
|
||
|
|
"""
|
||
|
|
Merge default kwargs in TextFileReader with dialect parameters.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
dialect : csv.Dialect
|
||
|
|
Concrete csv dialect. See csv.Dialect documentation for more details.
|
||
|
|
defaults : dict
|
||
|
|
Keyword arguments passed to TextFileReader.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
kwds : dict
|
||
|
|
Updated keyword arguments, merged with dialect parameters.
|
||
|
|
"""
|
||
|
|
kwds = defaults.copy()
|
||
|
|
|
||
|
|
for param in MANDATORY_DIALECT_ATTRS:
|
||
|
|
dialect_val = getattr(dialect, param)
|
||
|
|
|
||
|
|
parser_default = _parser_defaults[param]
|
||
|
|
provided = kwds.get(param, parser_default)
|
||
|
|
|
||
|
|
# Messages for conflicting values between the dialect
|
||
|
|
# instance and the actual parameters provided.
|
||
|
|
conflict_msgs = []
|
||
|
|
|
||
|
|
# Don't warn if the default parameter was passed in,
|
||
|
|
# even if it conflicts with the dialect (gh-23761).
|
||
|
|
if provided != parser_default and provided != dialect_val:
|
||
|
|
msg = (
|
||
|
|
f"Conflicting values for '{param}': '{provided}' was "
|
||
|
|
f"provided, but the dialect specifies '{dialect_val}'. "
|
||
|
|
"Using the dialect-specified value."
|
||
|
|
)
|
||
|
|
|
||
|
|
# Annoying corner case for not warning about
|
||
|
|
# conflicts between dialect and delimiter parameter.
|
||
|
|
# Refer to the outer "_read_" function for more info.
|
||
|
|
if not (param == "delimiter" and kwds.pop("sep_override", False)):
|
||
|
|
conflict_msgs.append(msg)
|
||
|
|
|
||
|
|
if conflict_msgs:
|
||
|
|
warnings.warn("\n\n".join(conflict_msgs), ParserWarning, stacklevel=2)
|
||
|
|
kwds[param] = dialect_val
|
||
|
|
return kwds
|
||
|
|
|
||
|
|
|
||
|
|
def _validate_skipfooter(kwds: Dict[str, Any]) -> None:
|
||
|
|
"""
|
||
|
|
Check whether skipfooter is compatible with other kwargs in TextFileReader.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
kwds : dict
|
||
|
|
Keyword arguments passed to TextFileReader.
|
||
|
|
|
||
|
|
Raises
|
||
|
|
------
|
||
|
|
ValueError
|
||
|
|
If skipfooter is not compatible with other parameters.
|
||
|
|
"""
|
||
|
|
if kwds.get("skipfooter"):
|
||
|
|
if kwds.get("iterator") or kwds.get("chunksize"):
|
||
|
|
raise ValueError("'skipfooter' not supported for iteration")
|
||
|
|
if kwds.get("nrows"):
|
||
|
|
raise ValueError("'skipfooter' not supported with 'nrows'")
|