# Copyright (c) 2012, Lambda Foundry, Inc. # See LICENSE for the license from base64 import decode from collections import defaultdict from csv import ( QUOTE_MINIMAL, QUOTE_NONE, QUOTE_NONNUMERIC, ) from errno import ENOENT import inspect import sys import time import warnings from pandas.util._exceptions import find_stack_level cimport cython from cpython.bytes cimport ( PyBytes_AsString, PyBytes_FromString, ) from cpython.exc cimport ( PyErr_Fetch, PyErr_Occurred, ) from cpython.object cimport PyObject from cpython.ref cimport ( Py_INCREF, Py_XDECREF, ) from cpython.unicode cimport ( PyUnicode_AsUTF8String, PyUnicode_Decode, PyUnicode_DecodeUTF8, ) from cython cimport Py_ssize_t from libc.stdlib cimport free from libc.string cimport ( strcasecmp, strlen, strncpy, ) cdef extern from "Python.h": object PyUnicode_FromString(char *v) import numpy as np cimport numpy as cnp from numpy cimport ( float64_t, int64_t, ndarray, uint8_t, uint64_t, ) cnp.import_array() from pandas._libs cimport util from pandas._libs.util cimport ( INT64_MAX, INT64_MIN, UINT64_MAX, ) import pandas._libs.lib as lib from pandas._libs.khash cimport ( kh_destroy_float64, kh_destroy_str, kh_destroy_str_starts, kh_destroy_strbox, kh_exist_str, kh_float64_t, kh_get_float64, kh_get_str, kh_get_str_starts_item, kh_get_strbox, kh_init_float64, kh_init_str, kh_init_str_starts, kh_init_strbox, kh_put_float64, kh_put_str, kh_put_str_starts_item, kh_put_strbox, kh_resize_float64, kh_resize_str_starts, kh_str_starts_t, kh_str_t, kh_strbox_t, khiter_t, ) from pandas.errors import ( EmptyDataError, ParserError, ParserWarning, ) from pandas.core.dtypes.common import ( is_bool_dtype, is_datetime64_dtype, is_extension_array_dtype, is_float_dtype, is_integer_dtype, is_object_dtype, ) from pandas.core.dtypes.dtypes import CategoricalDtype from pandas.core.dtypes.inference import is_dict_like cdef: float64_t INF = np.inf float64_t NEGINF = -INF int64_t DEFAULT_CHUNKSIZE = 256 * 1024 cdef extern from "headers/portable.h": # I *think* this is here so that strcasecmp is defined on Windows # so we don't get # `parsers.obj : error LNK2001: unresolved external symbol strcasecmp` # in Appveyor. # In a sane world, the `from libc.string cimport` above would fail # loudly. pass cdef extern from "parser/tokenizer.h": ctypedef enum ParserState: START_RECORD START_FIELD ESCAPED_CHAR IN_FIELD IN_QUOTED_FIELD ESCAPE_IN_QUOTED_FIELD QUOTE_IN_QUOTED_FIELD EAT_CRNL EAT_CRNL_NOP EAT_WHITESPACE EAT_COMMENT EAT_LINE_COMMENT WHITESPACE_LINE SKIP_LINE FINISHED enum: ERROR_OVERFLOW ctypedef enum BadLineHandleMethod: ERROR, WARN, SKIP ctypedef void* (*io_callback)(void *src, size_t nbytes, size_t *bytes_read, int *status, const char *encoding_errors) ctypedef int (*io_cleanup)(void *src) ctypedef struct parser_t: void *source io_callback cb_io io_cleanup cb_cleanup int64_t chunksize # Number of bytes to prepare for each chunk char *data # pointer to data to be processed int64_t datalen # amount of data available int64_t datapos # where to write out tokenized data char *stream uint64_t stream_len uint64_t stream_cap # Store words in (potentially ragged) matrix for now, hmm char **words int64_t *word_starts # where we are in the stream uint64_t words_len uint64_t words_cap uint64_t max_words_cap # maximum word cap encountered char *pword_start # pointer to stream start of current field int64_t word_start # position start of current field int64_t *line_start # position in words for start of line int64_t *line_fields # Number of fields in each line uint64_t lines # Number of lines observed uint64_t file_lines # Number of lines observed (with bad/skipped) uint64_t lines_cap # Vector capacity # Tokenizing stuff ParserState state int doublequote # is " represented by ""? */ char delimiter # field separator */ int delim_whitespace # consume tabs / spaces instead char quotechar # quote character */ char escapechar # escape character */ char lineterminator int skipinitialspace # ignore spaces following delimiter? */ int quoting # style of quoting to write */ char commentchar int allow_embedded_newline int usecols Py_ssize_t expected_fields BadLineHandleMethod on_bad_lines # floating point options char decimal char sci # thousands separator (comma, period) char thousands int header # Boolean: 1: has header, 0: no header int64_t header_start # header row start uint64_t header_end # header row end void *skipset PyObject *skipfunc int64_t skip_first_N_rows int64_t skipfooter # pick one, depending on whether the converter requires GIL float64_t (*double_converter)(const char *, char **, char, char, char, int, int *, int *) nogil # error handling char *warn_msg char *error_msg int64_t skip_empty_lines ctypedef struct coliter_t: char **words int64_t *line_start int64_t col ctypedef struct uint_state: int seen_sint int seen_uint int seen_null void uint_state_init(uint_state *self) int uint64_conflict(uint_state *self) void coliter_setup(coliter_t *it, parser_t *parser, int64_t i, int64_t start) nogil void COLITER_NEXT(coliter_t, const char *) nogil parser_t* parser_new() int parser_init(parser_t *self) nogil void parser_free(parser_t *self) nogil void parser_del(parser_t *self) nogil int parser_add_skiprow(parser_t *self, int64_t row) int parser_set_skipfirstnrows(parser_t *self, int64_t nrows) void parser_set_default_options(parser_t *self) int parser_consume_rows(parser_t *self, size_t nrows) int parser_trim_buffers(parser_t *self) int tokenize_all_rows(parser_t *self, const char *encoding_errors) nogil int tokenize_nrows(parser_t *self, size_t nrows, const char *encoding_errors) nogil int64_t str_to_int64(char *p_item, int64_t int_min, int64_t int_max, int *error, char tsep) nogil uint64_t str_to_uint64(uint_state *state, char *p_item, int64_t int_max, uint64_t uint_max, int *error, char tsep) nogil float64_t xstrtod(const char *p, char **q, char decimal, char sci, char tsep, int skip_trailing, int *error, int *maybe_int) nogil float64_t precise_xstrtod(const char *p, char **q, char decimal, char sci, char tsep, int skip_trailing, int *error, int *maybe_int) nogil float64_t round_trip(const char *p, char **q, char decimal, char sci, char tsep, int skip_trailing, int *error, int *maybe_int) nogil int to_boolean(const char *item, uint8_t *val) nogil cdef extern from "parser/io.h": void *new_rd_source(object obj) except NULL int del_rd_source(void *src) void* buffer_rd_bytes(void *source, size_t nbytes, size_t *bytes_read, int *status, const char *encoding_errors) cdef class TextReader: """ # source: StringIO or file object ..versionchange:: 1.2.0 removed 'compression', 'memory_map', and 'encoding' argument. These arguments are outsourced to CParserWrapper. 'source' has to be a file handle. """ cdef: parser_t *parser object na_fvalues object true_values, false_values object handle object orig_header bint na_filter, keep_default_na, verbose, has_usecols, has_mi_columns bint mangle_dupe_cols, allow_leading_cols uint64_t parser_start # this is modified after __init__ list clocks const char *encoding_errors kh_str_starts_t *false_set kh_str_starts_t *true_set int64_t buffer_lines, skipfooter list dtype_cast_order # list[np.dtype] list names # can be None set noconvert # set[int] cdef public: int64_t leading_cols, table_width object delimiter # bytes or str object converters object na_values list header # list[list[non-negative integers]] object index_col object skiprows object dtype object usecols set unnamed_cols # set[str] def __cinit__(self, source, delimiter=b',', # bytes | str header=0, int64_t header_start=0, uint64_t header_end=0, index_col=None, names=None, tokenize_chunksize=DEFAULT_CHUNKSIZE, bint delim_whitespace=False, converters=None, bint skipinitialspace=False, escapechar=None, # bytes | str bint doublequote=True, quotechar=b'"', quoting=0, # int lineterminator=None, # bytes | str comment=None, decimal=b'.', # bytes | str thousands=None, # bytes | str dtype=None, usecols=None, on_bad_lines=ERROR, bint na_filter=True, na_values=None, na_fvalues=None, bint keep_default_na=True, true_values=None, false_values=None, bint allow_leading_cols=True, skiprows=None, skipfooter=0, # int64_t bint verbose=False, bint mangle_dupe_cols=True, float_precision=None, bint skip_blank_lines=True, encoding_errors=b"strict"): # set encoding for native Python and C library if isinstance(encoding_errors, str): encoding_errors = encoding_errors.encode("utf-8") elif encoding_errors is None: encoding_errors = b"strict" Py_INCREF(encoding_errors) self.encoding_errors = PyBytes_AsString(encoding_errors) self.parser = parser_new() self.parser.chunksize = tokenize_chunksize self.mangle_dupe_cols = mangle_dupe_cols # For timekeeping self.clocks = [] self.parser.usecols = (usecols is not None) self._setup_parser_source(source) parser_set_default_options(self.parser) parser_init(self.parser) if delim_whitespace: self.parser.delim_whitespace = delim_whitespace else: if len(delimiter) > 1: raise ValueError('only length-1 separators excluded right now') self.parser.delimiter = ord(delimiter) # ---------------------------------------- # parser options self.parser.doublequote = doublequote self.parser.skipinitialspace = skipinitialspace self.parser.skip_empty_lines = skip_blank_lines if lineterminator is not None: if len(lineterminator) != 1: raise ValueError('Only length-1 line terminators supported') self.parser.lineterminator = ord(lineterminator) if len(decimal) != 1: raise ValueError('Only length-1 decimal markers supported') self.parser.decimal = ord(decimal) if thousands is not None: if len(thousands) != 1: raise ValueError('Only length-1 thousands markers supported') self.parser.thousands = ord(thousands) if escapechar is not None: if len(escapechar) != 1: raise ValueError('Only length-1 escapes supported') self.parser.escapechar = ord(escapechar) self._set_quoting(quotechar, quoting) dtype_order = ['int64', 'float64', 'bool', 'object'] if quoting == QUOTE_NONNUMERIC: # consistent with csv module semantics, cast all to float dtype_order = dtype_order[1:] self.dtype_cast_order = [np.dtype(x) for x in dtype_order] if comment is not None: if len(comment) > 1: raise ValueError('Only length-1 comment characters supported') self.parser.commentchar = ord(comment) self.parser.on_bad_lines = on_bad_lines self.skiprows = skiprows if skiprows is not None: self._make_skiprow_set() self.skipfooter = skipfooter # suboptimal if usecols is not None: self.has_usecols = 1 # GH-20558, validate usecols at higher level and only pass clean # usecols into TextReader. self.usecols = usecols # TODO: XXX? if skipfooter > 0: self.parser.on_bad_lines = SKIP self.delimiter = delimiter self.na_values = na_values if na_fvalues is None: na_fvalues = set() self.na_fvalues = na_fvalues self.true_values = _maybe_encode(true_values) + _true_values self.false_values = _maybe_encode(false_values) + _false_values self.true_set = kset_from_list(self.true_values) self.false_set = kset_from_list(self.false_values) self.keep_default_na = keep_default_na self.converters = converters self.na_filter = na_filter self.verbose = verbose if float_precision == "round_trip": # see gh-15140 self.parser.double_converter = round_trip elif float_precision == "legacy": self.parser.double_converter = xstrtod elif float_precision == "high" or float_precision is None: self.parser.double_converter = precise_xstrtod else: raise ValueError(f'Unrecognized float_precision option: ' f'{float_precision}') # Caller is responsible for ensuring we have one of # - None # - DtypeObj # - dict[Any, DtypeObj] self.dtype = dtype # XXX self.noconvert = set() self.index_col = index_col # ---------------------------------------- # header stuff self.allow_leading_cols = allow_leading_cols self.leading_cols = 0 # updated in _get_header # TODO: no header vs. header is not the first row self.has_mi_columns = 0 self.orig_header = header if header is None: # sentinel value self.parser.header_start = -1 self.parser.header_end = -1 self.parser.header = -1 self.parser_start = 0 prelim_header = [] else: if isinstance(header, list): if len(header) > 1: # need to artificially skip the final line # which is still a header line header = list(header) header.append(header[-1] + 1) self.parser.header_end = header[-1] self.has_mi_columns = 1 else: self.parser.header_end = header[0] self.parser_start = header[-1] + 1 self.parser.header_start = header[0] self.parser.header = header[0] prelim_header = header else: self.parser.header_start = header self.parser.header_end = header self.parser_start = header + 1 self.parser.header = header prelim_header = [header] self.names = names header, table_width, unnamed_cols = self._get_header(prelim_header) # header, table_width, and unnamed_cols are set here, never changed self.header = header self.table_width = table_width self.unnamed_cols = unnamed_cols if not self.table_width: raise EmptyDataError("No columns to parse from file") # Compute buffer_lines as function of table width. heuristic = 2**20 // self.table_width self.buffer_lines = 1 while self.buffer_lines * 2 < heuristic: self.buffer_lines *= 2 def __init__(self, *args, **kwargs): pass def __dealloc__(self): _close(self) parser_del(self.parser) def close(self): _close(self) def _set_quoting(self, quote_char: str | bytes | None, quoting: int): if not isinstance(quoting, int): raise TypeError('"quoting" must be an integer') if not QUOTE_MINIMAL <= quoting <= QUOTE_NONE: raise TypeError('bad "quoting" value') if not isinstance(quote_char, (str, bytes)) and quote_char is not None: dtype = type(quote_char).__name__ raise TypeError(f'"quotechar" must be string, not {dtype}') if quote_char is None or quote_char == '': if quoting != QUOTE_NONE: raise TypeError("quotechar must be set if quoting enabled") self.parser.quoting = quoting self.parser.quotechar = -1 elif len(quote_char) > 1: # 0-len case handled earlier raise TypeError('"quotechar" must be a 1-character string') else: self.parser.quoting = quoting self.parser.quotechar = ord(quote_char) cdef _make_skiprow_set(self): if util.is_integer_object(self.skiprows): parser_set_skipfirstnrows(self.parser, self.skiprows) elif not callable(self.skiprows): for i in self.skiprows: parser_add_skiprow(self.parser, i) else: self.parser.skipfunc = self.skiprows cdef _setup_parser_source(self, source): cdef: void *ptr ptr = new_rd_source(source) self.parser.source = ptr self.parser.cb_io = &buffer_rd_bytes self.parser.cb_cleanup = &del_rd_source cdef _get_header(self, list prelim_header): # header is now a list of lists, so field_count should use header[0] # # modifies: # self.parser attributes # self.parser_start # self.leading_cols cdef: Py_ssize_t i, start, field_count, passed_count, unnamed_count, level char *word str name, old_name uint64_t hr, data_line = 0 list header = [] set unnamed_cols = set() if self.parser.header_start >= 0: # Header is in the file for level, hr in enumerate(prelim_header): this_header = [] if self.parser.lines < hr + 1: self._tokenize_rows(hr + 2) if self.parser.lines == 0: field_count = 0 start = self.parser.line_start[0] # e.g., if header=3 and file only has 2 lines elif (self.parser.lines < hr + 1 and not isinstance(self.orig_header, list)) or ( self.parser.lines < hr): msg = self.orig_header if isinstance(msg, list): joined = ','.join(str(m) for m in msg) msg = f"[{joined}], len of {len(msg)}," raise ParserError( f'Passed header={msg} but only ' f'{self.parser.lines} lines in file') else: field_count = self.parser.line_fields[hr] start = self.parser.line_start[hr] unnamed_count = 0 unnamed_col_indices = [] for i in range(field_count): word = self.parser.words[start + i] name = PyUnicode_DecodeUTF8(word, strlen(word), self.encoding_errors) if name == '': if self.has_mi_columns: name = f'Unnamed: {i}_level_{level}' else: name = f'Unnamed: {i}' unnamed_count += 1 unnamed_col_indices.append(i) this_header.append(name) if not self.has_mi_columns and self.mangle_dupe_cols: # Ensure that regular columns are used before unnamed ones # to keep given names and mangle unnamed columns col_loop_order = [i for i in range(len(this_header)) if i not in unnamed_col_indices ] + unnamed_col_indices counts = {} for i in col_loop_order: col = this_header[i] old_col = col cur_count = counts.get(col, 0) if cur_count > 0: while cur_count > 0: counts[old_col] = cur_count + 1 col = f'{old_col}.{cur_count}' if col in this_header: cur_count += 1 else: cur_count = counts.get(col, 0) if ( self.dtype is not None and is_dict_like(self.dtype) and self.dtype.get(old_col) is not None and self.dtype.get(col) is None ): self.dtype.update({col: self.dtype.get(old_col)}) this_header[i] = col counts[col] = cur_count + 1 if self.has_mi_columns: # If we have grabbed an extra line, but it's not in our # format, save in the buffer, and create an blank extra # line for the rest of the parsing code. if hr == prelim_header[-1]: lc = len(this_header) ic = (len(self.index_col) if self.index_col is not None else 0) # if wrong number of blanks or no index, not our format if (lc != unnamed_count and lc - ic > unnamed_count) or ic == 0: hr -= 1 self.parser_start -= 1 this_header = [None] * lc data_line = hr + 1 header.append(this_header) unnamed_cols.update({this_header[i] for i in unnamed_col_indices}) if self.names is not None: header = [self.names] elif self.names is not None: # Names passed if self.parser.lines < 1: self._tokenize_rows(1) header = [self.names] if self.parser.lines < 1: field_count = len(header[0]) else: field_count = self.parser.line_fields[data_line] # Enforce this unless usecols if not self.has_usecols: self.parser.expected_fields = max(field_count, len(self.names)) else: # No header passed nor to be found in the file if self.parser.lines < 1: self._tokenize_rows(1) return None, self.parser.line_fields[0], unnamed_cols # Corner case, not enough lines in the file if self.parser.lines < data_line + 1: field_count = len(header[0]) else: # not self.has_usecols: field_count = self.parser.line_fields[data_line] # #2981 if self.names is not None: field_count = max(field_count, len(self.names)) passed_count = len(header[0]) if (self.has_usecols and self.allow_leading_cols and not callable(self.usecols)): nuse = len(self.usecols) if nuse == passed_count: self.leading_cols = 0 elif self.names is None and nuse < passed_count: self.leading_cols = field_count - passed_count elif passed_count != field_count: raise ValueError('Number of passed names did not match number of ' 'header fields in the file') # oh boy, #2442, #2981 elif self.allow_leading_cols and passed_count < field_count: self.leading_cols = field_count - passed_count return header, field_count, unnamed_cols def read(self, rows: int | None = None) -> dict[int, "ArrayLike"]: """ rows=None --> read all rows """ # Don't care about memory usage columns = self._read_rows(rows, 1) return columns def read_low_memory(self, rows: int | None)-> list[dict[int, "ArrayLike"]]: """ rows=None --> read all rows """ # Conserve intermediate space # Caller is responsible for concatenating chunks, # see c_parser_wrapper._concatenate_chunks cdef: size_t rows_read = 0 list chunks = [] if rows is None: while True: try: chunk = self._read_rows(self.buffer_lines, 0) if len(chunk) == 0: break except StopIteration: break else: chunks.append(chunk) else: while rows_read < rows: try: crows = min(self.buffer_lines, rows - rows_read) chunk = self._read_rows(crows, 0) if len(chunk) == 0: break rows_read += len(list(chunk.values())[0]) except StopIteration: break else: chunks.append(chunk) parser_trim_buffers(self.parser) if len(chunks) == 0: raise StopIteration return chunks cdef _tokenize_rows(self, size_t nrows): cdef: int status with nogil: status = tokenize_nrows(self.parser, nrows, self.encoding_errors) if self.parser.warn_msg != NULL: print(PyUnicode_DecodeUTF8( self.parser.warn_msg, strlen(self.parser.warn_msg), self.encoding_errors), file=sys.stderr) free(self.parser.warn_msg) self.parser.warn_msg = NULL if status < 0: raise_parser_error('Error tokenizing data', self.parser) # -> dict[int, "ArrayLike"] cdef _read_rows(self, rows, bint trim): cdef: int64_t buffered_lines int64_t irows self._start_clock() if rows is not None: irows = rows buffered_lines = self.parser.lines - self.parser_start if buffered_lines < irows: self._tokenize_rows(irows - buffered_lines) if self.skipfooter > 0: raise ValueError('skipfooter can only be used to read ' 'the whole file') else: with nogil: status = tokenize_all_rows(self.parser, self.encoding_errors) if self.parser.warn_msg != NULL: print(PyUnicode_DecodeUTF8( self.parser.warn_msg, strlen(self.parser.warn_msg), self.encoding_errors), file=sys.stderr) free(self.parser.warn_msg) self.parser.warn_msg = NULL if status < 0: raise_parser_error('Error tokenizing data', self.parser) if self.parser_start >= self.parser.lines: raise StopIteration self._end_clock('Tokenization') self._start_clock() columns = self._convert_column_data(rows) self._end_clock('Type conversion') self._start_clock() if len(columns) > 0: rows_read = len(list(columns.values())[0]) # trim parser_consume_rows(self.parser, rows_read) if trim: parser_trim_buffers(self.parser) self.parser_start -= rows_read self._end_clock('Parser memory cleanup') return columns cdef _start_clock(self): self.clocks.append(time.time()) cdef _end_clock(self, str what): if self.verbose: elapsed = time.time() - self.clocks.pop(-1) print(f'{what} took: {elapsed * 1000:.2f} ms') def set_noconvert(self, i: int) -> None: self.noconvert.add(i) def remove_noconvert(self, i: int) -> None: self.noconvert.remove(i) def _convert_column_data(self, rows: int | None) -> dict[int, "ArrayLike"]: cdef: int64_t i int nused kh_str_starts_t *na_hashset = NULL int64_t start, end object name, na_flist, col_dtype = None bint na_filter = 0 int64_t num_cols dict result start = self.parser_start if rows is None: end = self.parser.lines else: end = min(start + rows, self.parser.lines) num_cols = -1 # Py_ssize_t cast prevents build warning for i in range(self.parser.lines): num_cols = (num_cols < self.parser.line_fields[i]) * \ self.parser.line_fields[i] + \ (num_cols >= self.parser.line_fields[i]) * num_cols usecols_not_callable_and_exists = not callable(self.usecols) and self.usecols names_larger_num_cols = (self.names and len(self.names) - self.leading_cols > num_cols) if self.table_width - self.leading_cols > num_cols: if (usecols_not_callable_and_exists and self.table_width - self.leading_cols < len(self.usecols) or names_larger_num_cols): raise ParserError(f"Too many columns specified: expected " f"{self.table_width - self.leading_cols} " f"and found {num_cols}") if (usecols_not_callable_and_exists and all(isinstance(u, int) for u in self.usecols)): missing_usecols = [col for col in self.usecols if col >= num_cols] if missing_usecols: warnings.warn( "Defining usecols with out of bounds indices is deprecated " "and will raise a ParserError in a future version.", FutureWarning, stacklevel=find_stack_level(), ) results = {} nused = 0 is_default_dict_dtype = isinstance(self.dtype, defaultdict) for i in range(self.table_width): if i < self.leading_cols: # Pass through leading columns always name = i elif (self.usecols and not callable(self.usecols) and nused == len(self.usecols)): # Once we've gathered all requested columns, stop. GH5766 break else: name = self._get_column_name(i, nused) usecols = set() if callable(self.usecols): if self.usecols(name): usecols = {i} else: usecols = self.usecols if self.has_usecols and not (i in usecols or name in usecols): continue nused += 1 conv = self._get_converter(i, name) col_dtype = None if self.dtype is not None: if isinstance(self.dtype, dict): if name in self.dtype: col_dtype = self.dtype[name] elif i in self.dtype: col_dtype = self.dtype[i] elif is_default_dict_dtype: col_dtype = self.dtype[name] else: if self.dtype.names: # structured array col_dtype = np.dtype(self.dtype.descr[i][1]) else: col_dtype = self.dtype if conv: if col_dtype is not None: warnings.warn((f"Both a converter and dtype were specified " f"for column {name} - only the converter will " f"be used."), ParserWarning, stacklevel=find_stack_level()) results[i] = _apply_converter(conv, self.parser, i, start, end) continue # Collect the list of NaN values associated with the column. # If we aren't supposed to do that, or none are collected, # we set `na_filter` to `0` (`1` otherwise). na_flist = set() if self.na_filter: na_list, na_flist = self._get_na_list(i, name) if na_list is None: na_filter = 0 else: na_filter = 1 na_hashset = kset_from_list(na_list) else: na_filter = 0 # Attempt to parse tokens and infer dtype of the column. # Should return as the desired dtype (inferred or specified). try: col_res, na_count = self._convert_tokens( i, start, end, name, na_filter, na_hashset, na_flist, col_dtype) finally: # gh-21353 # # Cleanup the NaN hash that we generated # to avoid memory leaks. if na_filter: self._free_na_set(na_hashset) # don't try to upcast EAs if na_count > 0 and not is_extension_array_dtype(col_dtype): col_res = _maybe_upcast(col_res) if col_res is None: raise ParserError(f'Unable to parse column {i}') results[i] = col_res self.parser_start += end - start return results # -> tuple["ArrayLike", int]: cdef inline _convert_tokens(self, Py_ssize_t i, int64_t start, int64_t end, object name, bint na_filter, kh_str_starts_t *na_hashset, object na_flist, object col_dtype): if col_dtype is not None: col_res, na_count = self._convert_with_dtype( col_dtype, i, start, end, na_filter, 1, na_hashset, na_flist) # Fallback on the parse (e.g. we requested int dtype, # but its actually a float). if col_res is not None: return col_res, na_count if i in self.noconvert: return self._string_convert(i, start, end, na_filter, na_hashset) else: col_res = None for dt in self.dtype_cast_order: try: col_res, na_count = self._convert_with_dtype( dt, i, start, end, na_filter, 0, na_hashset, na_flist) except ValueError: # This error is raised from trying to convert to uint64, # and we discover that we cannot convert to any numerical # dtype successfully. As a result, we leave the data # column AS IS with object dtype. col_res, na_count = self._convert_with_dtype( np.dtype('object'), i, start, end, 0, 0, na_hashset, na_flist) except OverflowError: col_res, na_count = self._convert_with_dtype( np.dtype('object'), i, start, end, na_filter, 0, na_hashset, na_flist) if col_res is not None: break # we had a fallback parse on the dtype, so now try to cast if col_res is not None and col_dtype is not None: # If col_res is bool, it might actually be a bool array mixed with NaNs # (see _try_bool_flex()). Usually this would be taken care of using # _maybe_upcast(), but if col_dtype is a floating type we should just # take care of that cast here. if col_res.dtype == np.bool_ and is_float_dtype(col_dtype): mask = col_res.view(np.uint8) == na_values[np.uint8] col_res = col_res.astype(col_dtype) np.putmask(col_res, mask, np.nan) return col_res, na_count # NaNs are already cast to True here, so can not use astype if col_res.dtype == np.bool_ and is_integer_dtype(col_dtype): if na_count > 0: raise ValueError( f"cannot safely convert passed user dtype of " f"{col_dtype} for {np.bool_} dtyped data in " f"column {i} due to NA values" ) # only allow safe casts, eg. with a nan you cannot safely cast to int try: col_res = col_res.astype(col_dtype, casting='safe') except TypeError: # float -> int conversions can fail the above # even with no nans col_res_orig = col_res col_res = col_res.astype(col_dtype) if (col_res != col_res_orig).any(): raise ValueError( f"cannot safely convert passed user dtype of " f"{col_dtype} for {col_res_orig.dtype.name} dtyped data in " f"column {i}") return col_res, na_count cdef _convert_with_dtype(self, object dtype, Py_ssize_t i, int64_t start, int64_t end, bint na_filter, bint user_dtype, kh_str_starts_t *na_hashset, object na_flist): if isinstance(dtype, CategoricalDtype): # TODO: I suspect that _categorical_convert could be # optimized when dtype is an instance of CategoricalDtype codes, cats, na_count = _categorical_convert( self.parser, i, start, end, na_filter, na_hashset) # Method accepts list of strings, not encoded ones. true_values = [x.decode() for x in self.true_values] array_type = dtype.construct_array_type() cat = array_type._from_inferred_categories( cats, codes, dtype, true_values=true_values) return cat, na_count elif is_extension_array_dtype(dtype): result, na_count = self._string_convert(i, start, end, na_filter, na_hashset) array_type = dtype.construct_array_type() try: # use _from_sequence_of_strings if the class defines it if is_bool_dtype(dtype): true_values = [x.decode() for x in self.true_values] false_values = [x.decode() for x in self.false_values] result = array_type._from_sequence_of_strings( result, dtype=dtype, true_values=true_values, false_values=false_values) else: result = array_type._from_sequence_of_strings(result, dtype=dtype) except NotImplementedError: raise NotImplementedError( f"Extension Array: {array_type} must implement " f"_from_sequence_of_strings in order " f"to be used in parser methods") return result, na_count elif is_integer_dtype(dtype): try: result, na_count = _try_int64(self.parser, i, start, end, na_filter, na_hashset) if user_dtype and na_count is not None: if na_count > 0: raise ValueError(f"Integer column has NA values in column {i}") except OverflowError: result = _try_uint64(self.parser, i, start, end, na_filter, na_hashset) na_count = 0 if result is not None and dtype != 'int64': result = result.astype(dtype) return result, na_count elif is_float_dtype(dtype): result, na_count = _try_double(self.parser, i, start, end, na_filter, na_hashset, na_flist) if result is not None and dtype != 'float64': result = result.astype(dtype) return result, na_count elif is_bool_dtype(dtype): result, na_count = _try_bool_flex(self.parser, i, start, end, na_filter, na_hashset, self.true_set, self.false_set) if user_dtype and na_count is not None: if na_count > 0: raise ValueError(f"Bool column has NA values in column {i}") return result, na_count elif dtype.kind == 'S': # TODO: na handling width = dtype.itemsize if width > 0: result = _to_fw_string(self.parser, i, start, end, width) return result, 0 # treat as a regular string parsing return self._string_convert(i, start, end, na_filter, na_hashset) elif dtype.kind == 'U': width = dtype.itemsize if width > 0: raise TypeError(f"the dtype {dtype} is not supported for parsing") # unicode variable width return self._string_convert(i, start, end, na_filter, na_hashset) elif is_object_dtype(dtype): return self._string_convert(i, start, end, na_filter, na_hashset) elif is_datetime64_dtype(dtype): raise TypeError(f"the dtype {dtype} is not supported " f"for parsing, pass this column " f"using parse_dates instead") else: raise TypeError(f"the dtype {dtype} is not supported for parsing") # -> tuple[ndarray[object], int] cdef _string_convert(self, Py_ssize_t i, int64_t start, int64_t end, bint na_filter, kh_str_starts_t *na_hashset): return _string_box_utf8(self.parser, i, start, end, na_filter, na_hashset, self.encoding_errors) def _get_converter(self, i: int, name): if self.converters is None: return None if name is not None and name in self.converters: return self.converters[name] # Converter for position, if any return self.converters.get(i) cdef _get_na_list(self, Py_ssize_t i, name): # Note: updates self.na_values, self.na_fvalues if self.na_values is None: return None, set() if isinstance(self.na_values, dict): key = None values = None if name is not None and name in self.na_values: key = name elif i in self.na_values: key = i else: # No na_values provided for this column. if self.keep_default_na: return _NA_VALUES, set() return list(), set() values = self.na_values[key] if values is not None and not isinstance(values, list): values = list(values) fvalues = self.na_fvalues[key] if fvalues is not None and not isinstance(fvalues, set): fvalues = set(fvalues) return _ensure_encoded(values), fvalues else: if not isinstance(self.na_values, list): self.na_values = list(self.na_values) if not isinstance(self.na_fvalues, set): self.na_fvalues = set(self.na_fvalues) return _ensure_encoded(self.na_values), self.na_fvalues cdef _free_na_set(self, kh_str_starts_t *table): kh_destroy_str_starts(table) cdef _get_column_name(self, Py_ssize_t i, Py_ssize_t nused): cdef int64_t j if self.has_usecols and self.names is not None: if (not callable(self.usecols) and len(self.names) == len(self.usecols)): return self.names[nused] else: return self.names[i - self.leading_cols] else: if self.header is not None: j = i - self.leading_cols # generate extra (bogus) headers if there are more columns than headers # These should be strings, not integers, because otherwise we might get # issues with callables as usecols GH#46997 if j >= len(self.header[0]): return str(j) elif self.has_mi_columns: return tuple(header_row[j] for header_row in self.header) else: return self.header[0][j] else: return None # Factor out code common to TextReader.__dealloc__ and TextReader.close # It cannot be a class method, since calling self.close() in __dealloc__ # which causes a class attribute lookup and violates best practices # https://cython.readthedocs.io/en/latest/src/userguide/special_methods.html#finalization-method-dealloc cdef _close(TextReader reader): # also preemptively free all allocated memory parser_free(reader.parser) if reader.true_set: kh_destroy_str_starts(reader.true_set) reader.true_set = NULL if reader.false_set: kh_destroy_str_starts(reader.false_set) reader.false_set = NULL cdef: object _true_values = [b'True', b'TRUE', b'true'] object _false_values = [b'False', b'FALSE', b'false'] def _ensure_encoded(list lst): cdef: list result = [] for x in lst: if isinstance(x, str): x = PyUnicode_AsUTF8String(x) elif not isinstance(x, bytes): x = str(x).encode('utf-8') result.append(x) return result # common NA values # no longer excluding inf representations # '1.#INF','-1.#INF', '1.#INF000000', STR_NA_VALUES = { "-1.#IND", "1.#QNAN", "1.#IND", "-1.#QNAN", "#N/A N/A", "#N/A", "N/A", "n/a", "NA", "", "#NA", "NULL", "null", "NaN", "-NaN", "nan", "-nan", "", } _NA_VALUES = _ensure_encoded(list(STR_NA_VALUES)) def _maybe_upcast(arr): """ """ if issubclass(arr.dtype.type, np.integer): na_value = na_values[arr.dtype] arr = arr.astype(float) np.putmask(arr, arr == na_value, np.nan) elif arr.dtype == np.bool_: mask = arr.view(np.uint8) == na_values[np.uint8] arr = arr.astype(object) np.putmask(arr, mask, np.nan) return arr # ---------------------------------------------------------------------- # Type conversions / inference support code # -> tuple[ndarray[object], int] cdef _string_box_utf8(parser_t *parser, int64_t col, int64_t line_start, int64_t line_end, bint na_filter, kh_str_starts_t *na_hashset, const char *encoding_errors): cdef: int error, na_count = 0 Py_ssize_t i, lines coliter_t it const char *word = NULL ndarray[object] result int ret = 0 kh_strbox_t *table object pyval object NA = na_values[np.object_] khiter_t k table = kh_init_strbox() lines = line_end - line_start result = np.empty(lines, dtype=np.object_) coliter_setup(&it, parser, col, line_start) for i in range(lines): COLITER_NEXT(it, word) if na_filter: if kh_get_str_starts_item(na_hashset, word): # in the hash table na_count += 1 result[i] = NA continue k = kh_get_strbox(table, word) # in the hash table if k != table.n_buckets: # this increments the refcount, but need to test pyval = table.vals[k] else: # box it. new ref? pyval = PyUnicode_Decode(word, strlen(word), "utf-8", encoding_errors) k = kh_put_strbox(table, word, &ret) table.vals[k] = pyval result[i] = pyval kh_destroy_strbox(table) return result, na_count @cython.boundscheck(False) cdef _categorical_convert(parser_t *parser, int64_t col, int64_t line_start, int64_t line_end, bint na_filter, kh_str_starts_t *na_hashset): "Convert column data into codes, categories" cdef: int na_count = 0 Py_ssize_t i, size, lines coliter_t it const char *word = NULL int64_t NA = -1 int64_t[::1] codes int64_t current_category = 0 char *errors = "strict" int ret = 0 kh_str_t *table khiter_t k lines = line_end - line_start codes = np.empty(lines, dtype=np.int64) # factorize parsed values, creating a hash table # bytes -> category code with nogil: table = kh_init_str() coliter_setup(&it, parser, col, line_start) for i in range(lines): COLITER_NEXT(it, word) if na_filter: if kh_get_str_starts_item(na_hashset, word): # is in NA values na_count += 1 codes[i] = NA continue k = kh_get_str(table, word) # not in the hash table if k == table.n_buckets: k = kh_put_str(table, word, &ret) table.vals[k] = current_category current_category += 1 codes[i] = table.vals[k] # parse and box categories to python strings result = np.empty(table.n_occupied, dtype=np.object_) for k in range(table.n_buckets): if kh_exist_str(table, k): result[table.vals[k]] = PyUnicode_FromString(table.keys[k]) kh_destroy_str(table) return np.asarray(codes), result, na_count # -> ndarray[f'|S{width}'] cdef _to_fw_string(parser_t *parser, int64_t col, int64_t line_start, int64_t line_end, int64_t width): cdef: char *data ndarray result result = np.empty(line_end - line_start, dtype=f'|S{width}') data = result.data with nogil: _to_fw_string_nogil(parser, col, line_start, line_end, width, data) return result cdef inline void _to_fw_string_nogil(parser_t *parser, int64_t col, int64_t line_start, int64_t line_end, size_t width, char *data) nogil: cdef: int64_t i coliter_t it const char *word = NULL coliter_setup(&it, parser, col, line_start) for i in range(line_end - line_start): COLITER_NEXT(it, word) strncpy(data, word, width) data += width cdef: char* cinf = b'inf' char* cposinf = b'+inf' char* cneginf = b'-inf' char* cinfty = b'Infinity' char* cposinfty = b'+Infinity' char* cneginfty = b'-Infinity' # -> tuple[ndarray[float64_t], int] | tuple[None, None] cdef _try_double(parser_t *parser, int64_t col, int64_t line_start, int64_t line_end, bint na_filter, kh_str_starts_t *na_hashset, object na_flist): cdef: int error, na_count = 0 Py_ssize_t lines float64_t *data float64_t NA = na_values[np.float64] kh_float64_t *na_fset ndarray[float64_t] result bint use_na_flist = len(na_flist) > 0 lines = line_end - line_start result = np.empty(lines, dtype=np.float64) data = result.data na_fset = kset_float64_from_list(na_flist) with nogil: error = _try_double_nogil(parser, parser.double_converter, col, line_start, line_end, na_filter, na_hashset, use_na_flist, na_fset, NA, data, &na_count) kh_destroy_float64(na_fset) if error != 0: return None, None return result, na_count cdef inline int _try_double_nogil(parser_t *parser, float64_t (*double_converter)( const char *, char **, char, char, char, int, int *, int *) nogil, int64_t col, int64_t line_start, int64_t line_end, bint na_filter, kh_str_starts_t *na_hashset, bint use_na_flist, const kh_float64_t *na_flist, float64_t NA, float64_t *data, int *na_count) nogil: cdef: int error = 0, Py_ssize_t i, lines = line_end - line_start coliter_t it const char *word = NULL char *p_end khiter_t k64 na_count[0] = 0 coliter_setup(&it, parser, col, line_start) if na_filter: for i in range(lines): COLITER_NEXT(it, word) if kh_get_str_starts_item(na_hashset, word): # in the hash table na_count[0] += 1 data[0] = NA else: data[0] = double_converter(word, &p_end, parser.decimal, parser.sci, parser.thousands, 1, &error, NULL) if error != 0 or p_end == word or p_end[0]: error = 0 if (strcasecmp(word, cinf) == 0 or strcasecmp(word, cposinf) == 0 or strcasecmp(word, cinfty) == 0 or strcasecmp(word, cposinfty) == 0): data[0] = INF elif (strcasecmp(word, cneginf) == 0 or strcasecmp(word, cneginfty) == 0): data[0] = NEGINF else: return 1 if use_na_flist: k64 = kh_get_float64(na_flist, data[0]) if k64 != na_flist.n_buckets: na_count[0] += 1 data[0] = NA data += 1 else: for i in range(lines): COLITER_NEXT(it, word) data[0] = double_converter(word, &p_end, parser.decimal, parser.sci, parser.thousands, 1, &error, NULL) if error != 0 or p_end == word or p_end[0]: error = 0 if (strcasecmp(word, cinf) == 0 or strcasecmp(word, cposinf) == 0 or strcasecmp(word, cinfty) == 0 or strcasecmp(word, cposinfty) == 0): data[0] = INF elif (strcasecmp(word, cneginf) == 0 or strcasecmp(word, cneginfty) == 0): data[0] = NEGINF else: return 1 data += 1 return 0 cdef _try_uint64(parser_t *parser, int64_t col, int64_t line_start, int64_t line_end, bint na_filter, kh_str_starts_t *na_hashset): cdef: int error Py_ssize_t lines coliter_t it uint64_t *data ndarray result uint_state state lines = line_end - line_start result = np.empty(lines, dtype=np.uint64) data = result.data uint_state_init(&state) coliter_setup(&it, parser, col, line_start) with nogil: error = _try_uint64_nogil(parser, col, line_start, line_end, na_filter, na_hashset, data, &state) if error != 0: if error == ERROR_OVERFLOW: # Can't get the word variable raise OverflowError('Overflow') return None if uint64_conflict(&state): raise ValueError('Cannot convert to numerical dtype') if state.seen_sint: raise OverflowError('Overflow') return result cdef inline int _try_uint64_nogil(parser_t *parser, int64_t col, int64_t line_start, int64_t line_end, bint na_filter, const kh_str_starts_t *na_hashset, uint64_t *data, uint_state *state) nogil: cdef: int error Py_ssize_t i, lines = line_end - line_start coliter_t it const char *word = NULL coliter_setup(&it, parser, col, line_start) if na_filter: for i in range(lines): COLITER_NEXT(it, word) if kh_get_str_starts_item(na_hashset, word): # in the hash table state.seen_null = 1 data[i] = 0 continue data[i] = str_to_uint64(state, word, INT64_MAX, UINT64_MAX, &error, parser.thousands) if error != 0: return error else: for i in range(lines): COLITER_NEXT(it, word) data[i] = str_to_uint64(state, word, INT64_MAX, UINT64_MAX, &error, parser.thousands) if error != 0: return error return 0 cdef _try_int64(parser_t *parser, int64_t col, int64_t line_start, int64_t line_end, bint na_filter, kh_str_starts_t *na_hashset): cdef: int error, na_count = 0 Py_ssize_t lines coliter_t it int64_t *data ndarray result int64_t NA = na_values[np.int64] lines = line_end - line_start result = np.empty(lines, dtype=np.int64) data = result.data coliter_setup(&it, parser, col, line_start) with nogil: error = _try_int64_nogil(parser, col, line_start, line_end, na_filter, na_hashset, NA, data, &na_count) if error != 0: if error == ERROR_OVERFLOW: # Can't get the word variable raise OverflowError('Overflow') return None, None return result, na_count cdef inline int _try_int64_nogil(parser_t *parser, int64_t col, int64_t line_start, int64_t line_end, bint na_filter, const kh_str_starts_t *na_hashset, int64_t NA, int64_t *data, int *na_count) nogil: cdef: int error Py_ssize_t i, lines = line_end - line_start coliter_t it const char *word = NULL na_count[0] = 0 coliter_setup(&it, parser, col, line_start) if na_filter: for i in range(lines): COLITER_NEXT(it, word) if kh_get_str_starts_item(na_hashset, word): # in the hash table na_count[0] += 1 data[i] = NA continue data[i] = str_to_int64(word, INT64_MIN, INT64_MAX, &error, parser.thousands) if error != 0: return error else: for i in range(lines): COLITER_NEXT(it, word) data[i] = str_to_int64(word, INT64_MIN, INT64_MAX, &error, parser.thousands) if error != 0: return error return 0 # -> tuple[ndarray[bool], int] cdef _try_bool_flex(parser_t *parser, int64_t col, int64_t line_start, int64_t line_end, bint na_filter, const kh_str_starts_t *na_hashset, const kh_str_starts_t *true_hashset, const kh_str_starts_t *false_hashset): cdef: int error, na_count = 0 Py_ssize_t lines uint8_t *data ndarray result uint8_t NA = na_values[np.bool_] lines = line_end - line_start result = np.empty(lines, dtype=np.uint8) data = result.data with nogil: error = _try_bool_flex_nogil(parser, col, line_start, line_end, na_filter, na_hashset, true_hashset, false_hashset, NA, data, &na_count) if error != 0: return None, None return result.view(np.bool_), na_count cdef inline int _try_bool_flex_nogil(parser_t *parser, int64_t col, int64_t line_start, int64_t line_end, bint na_filter, const kh_str_starts_t *na_hashset, const kh_str_starts_t *true_hashset, const kh_str_starts_t *false_hashset, uint8_t NA, uint8_t *data, int *na_count) nogil: cdef: int error = 0 Py_ssize_t i, lines = line_end - line_start coliter_t it const char *word = NULL na_count[0] = 0 coliter_setup(&it, parser, col, line_start) if na_filter: for i in range(lines): COLITER_NEXT(it, word) if kh_get_str_starts_item(na_hashset, word): # in the hash table na_count[0] += 1 data[0] = NA data += 1 continue if kh_get_str_starts_item(true_hashset, word): data[0] = 1 data += 1 continue if kh_get_str_starts_item(false_hashset, word): data[0] = 0 data += 1 continue error = to_boolean(word, data) if error != 0: return error data += 1 else: for i in range(lines): COLITER_NEXT(it, word) if kh_get_str_starts_item(true_hashset, word): data[0] = 1 data += 1 continue if kh_get_str_starts_item(false_hashset, word): data[0] = 0 data += 1 continue error = to_boolean(word, data) if error != 0: return error data += 1 return 0 cdef kh_str_starts_t* kset_from_list(list values) except NULL: # caller takes responsibility for freeing the hash table cdef: Py_ssize_t i kh_str_starts_t *table int ret = 0 object val table = kh_init_str_starts() for i in range(len(values)): val = values[i] # None creeps in sometimes, which isn't possible here if not isinstance(val, bytes): kh_destroy_str_starts(table) raise ValueError('Must be all encoded bytes') kh_put_str_starts_item(table, PyBytes_AsString(val), &ret) if table.table.n_buckets <= 128: # Resize the hash table to make it almost empty, this # reduces amount of hash collisions on lookup thus # "key not in table" case is faster. # Note that this trades table memory footprint for lookup speed. kh_resize_str_starts(table, table.table.n_buckets * 8) return table cdef kh_float64_t* kset_float64_from_list(values) except NULL: # caller takes responsibility for freeing the hash table cdef: khiter_t k kh_float64_t *table int ret = 0 float64_t val object value table = kh_init_float64() for value in values: val = float(value) k = kh_put_float64(table, val, &ret) if table.n_buckets <= 128: # See reasoning in kset_from_list kh_resize_float64(table, table.n_buckets * 8) return table cdef raise_parser_error(object base, parser_t *parser): cdef: object old_exc object exc_type PyObject *type PyObject *value PyObject *traceback if PyErr_Occurred(): PyErr_Fetch(&type, &value, &traceback) Py_XDECREF(traceback) if value != NULL: old_exc = value Py_XDECREF(value) # PyErr_Fetch only returned the error message in *value, # so the Exception class must be extracted from *type. if isinstance(old_exc, str): if type != NULL: exc_type = type else: exc_type = ParserError Py_XDECREF(type) raise exc_type(old_exc) else: Py_XDECREF(type) raise old_exc message = f'{base}. C error: ' if parser.error_msg != NULL: message += parser.error_msg.decode('utf-8') else: message += 'no error message set' raise ParserError(message) # ---------------------------------------------------------------------- # NA values def _compute_na_values(): int64info = np.iinfo(np.int64) int32info = np.iinfo(np.int32) int16info = np.iinfo(np.int16) int8info = np.iinfo(np.int8) uint64info = np.iinfo(np.uint64) uint32info = np.iinfo(np.uint32) uint16info = np.iinfo(np.uint16) uint8info = np.iinfo(np.uint8) na_values = { np.float64: np.nan, np.int64: int64info.min, np.int32: int32info.min, np.int16: int16info.min, np.int8: int8info.min, np.uint64: uint64info.max, np.uint32: uint32info.max, np.uint16: uint16info.max, np.uint8: uint8info.max, np.bool_: uint8info.max, np.object_: np.nan # oof } return na_values na_values = _compute_na_values() for k in list(na_values): na_values[np.dtype(k)] = na_values[k] # -> ArrayLike cdef _apply_converter(object f, parser_t *parser, int64_t col, int64_t line_start, int64_t line_end): cdef: Py_ssize_t i, lines coliter_t it const char *word = NULL ndarray[object] result object val lines = line_end - line_start result = np.empty(lines, dtype=np.object_) coliter_setup(&it, parser, col, line_start) for i in range(lines): COLITER_NEXT(it, word) val = PyUnicode_FromString(word) result[i] = f(val) return lib.maybe_convert_objects(result) cdef list _maybe_encode(list values): if values is None: return [] return [x.encode('utf-8') if isinstance(x, str) else x for x in values] def sanitize_objects(ndarray[object] values, set na_values) -> int: """ Convert specified values, including the given set na_values to np.nan. Parameters ---------- values : ndarray[object] na_values : set Returns ------- na_count : int """ cdef: Py_ssize_t i, n object val, onan Py_ssize_t na_count = 0 dict memo = {} n = len(values) onan = np.nan for i in range(n): val = values[i] if val in na_values: values[i] = onan na_count += 1 elif val in memo: values[i] = memo[val] else: memo[val] = val return na_count