""" Module contains tools for processing Stata files into DataFrames The StataReader below was originally written by Joe Presbrey as part of PyDTA. It has been extended and improved by Skipper Seabold from the Statsmodels project who also developed the StataWriter and was finally added to pandas in a once again improved version. You can find more information on http://presbrey.mit.edu/PyDTA and https://www.statsmodels.org/devel/ """ from __future__ import annotations from collections import abc from datetime import ( datetime, timedelta, ) from io import BytesIO import os import struct import sys from typing import ( IO, TYPE_CHECKING, AnyStr, Callable, Final, cast, ) import warnings import numpy as np from pandas._libs import lib from pandas._libs.lib import infer_dtype from pandas._libs.writers import max_len_string_array from pandas.errors import ( CategoricalConversionWarning, InvalidColumnName, PossiblePrecisionLoss, ValueLabelTypeMismatch, ) from pandas.util._decorators import ( Appender, doc, ) from pandas.util._exceptions import find_stack_level from pandas.core.dtypes.base import ExtensionDtype from pandas.core.dtypes.common import ( ensure_object, is_numeric_dtype, is_string_dtype, ) from pandas.core.dtypes.dtypes import CategoricalDtype from pandas import ( Categorical, DatetimeIndex, NaT, Timestamp, isna, to_datetime, to_timedelta, ) from pandas.core.frame import DataFrame from pandas.core.indexes.base import Index from pandas.core.indexes.range import RangeIndex from pandas.core.series import Series from pandas.core.shared_docs import _shared_docs from pandas.io.common import get_handle if TYPE_CHECKING: from collections.abc import ( Hashable, Sequence, ) from types import TracebackType from typing import Literal from pandas._typing import ( CompressionOptions, FilePath, ReadBuffer, Self, StorageOptions, WriteBuffer, ) _version_error = ( "Version of given Stata file is {version}. pandas supports importing " "versions 105, 108, 111 (Stata 7SE), 113 (Stata 8/9), " "114 (Stata 10/11), 115 (Stata 12), 117 (Stata 13), 118 (Stata 14/15/16)," "and 119 (Stata 15/16, over 32,767 variables)." ) _statafile_processing_params1 = """\ convert_dates : bool, default True Convert date variables to DataFrame time values. convert_categoricals : bool, default True Read value labels and convert columns to Categorical/Factor variables.""" _statafile_processing_params2 = """\ index_col : str, optional Column to set as index. convert_missing : bool, default False Flag indicating whether to convert missing values to their Stata representations. If False, missing values are replaced with nan. If True, columns containing missing values are returned with object data types and missing values are represented by StataMissingValue objects. preserve_dtypes : bool, default True Preserve Stata datatypes. If False, numeric data are upcast to pandas default types for foreign data (float64 or int64). columns : list or None Columns to retain. Columns will be returned in the given order. None returns all columns. order_categoricals : bool, default True Flag indicating whether converted categorical data are ordered.""" _chunksize_params = """\ chunksize : int, default None Return StataReader object for iterations, returns chunks with given number of lines.""" _iterator_params = """\ iterator : bool, default False Return StataReader object.""" _reader_notes = """\ Notes ----- Categorical variables read through an iterator may not have the same categories and dtype. This occurs when a variable stored in a DTA file is associated to an incomplete set of value labels that only label a strict subset of the values.""" _read_stata_doc = f""" Read Stata file into DataFrame. 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.dta``. 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``. {_statafile_processing_params1} {_statafile_processing_params2} {_chunksize_params} {_iterator_params} {_shared_docs["decompression_options"] % "filepath_or_buffer"} {_shared_docs["storage_options"]} Returns ------- DataFrame or pandas.api.typing.StataReader See Also -------- io.stata.StataReader : Low-level reader for Stata data files. DataFrame.to_stata: Export Stata data files. {_reader_notes} Examples -------- Creating a dummy stata for this example >>> df = pd.DataFrame({{'animal': ['falcon', 'parrot', 'falcon', 'parrot'], ... 'speed': [350, 18, 361, 15]}}) # doctest: +SKIP >>> df.to_stata('animals.dta') # doctest: +SKIP Read a Stata dta file: >>> df = pd.read_stata('animals.dta') # doctest: +SKIP Read a Stata dta file in 10,000 line chunks: >>> values = np.random.randint(0, 10, size=(20_000, 1), dtype="uint8") # doctest: +SKIP >>> df = pd.DataFrame(values, columns=["i"]) # doctest: +SKIP >>> df.to_stata('filename.dta') # doctest: +SKIP >>> with pd.read_stata('filename.dta', chunksize=10000) as itr: # doctest: +SKIP >>> for chunk in itr: ... # Operate on a single chunk, e.g., chunk.mean() ... pass # doctest: +SKIP """ _read_method_doc = f"""\ Reads observations from Stata file, converting them into a dataframe Parameters ---------- nrows : int Number of lines to read from data file, if None read whole file. {_statafile_processing_params1} {_statafile_processing_params2} Returns ------- DataFrame """ _stata_reader_doc = f"""\ Class for reading Stata dta files. Parameters ---------- path_or_buf : path (string), buffer or path object string, path object (pathlib.Path or py._path.local.LocalPath) or object implementing a binary read() functions. {_statafile_processing_params1} {_statafile_processing_params2} {_chunksize_params} {_shared_docs["decompression_options"]} {_shared_docs["storage_options"]} {_reader_notes} """ _date_formats = ["%tc", "%tC", "%td", "%d", "%tw", "%tm", "%tq", "%th", "%ty"] stata_epoch: Final = datetime(1960, 1, 1) def _stata_elapsed_date_to_datetime_vec(dates: Series, fmt: str) -> Series: """ Convert from SIF to datetime. https://www.stata.com/help.cgi?datetime Parameters ---------- dates : Series The Stata Internal Format date to convert to datetime according to fmt fmt : str The format to convert to. Can be, tc, td, tw, tm, tq, th, ty Returns Returns ------- converted : Series The converted dates Examples -------- >>> dates = pd.Series([52]) >>> _stata_elapsed_date_to_datetime_vec(dates , "%tw") 0 1961-01-01 dtype: datetime64[ns] Notes ----- datetime/c - tc milliseconds since 01jan1960 00:00:00.000, assuming 86,400 s/day datetime/C - tC - NOT IMPLEMENTED milliseconds since 01jan1960 00:00:00.000, adjusted for leap seconds date - td days since 01jan1960 (01jan1960 = 0) weekly date - tw weeks since 1960w1 This assumes 52 weeks in a year, then adds 7 * remainder of the weeks. The datetime value is the start of the week in terms of days in the year, not ISO calendar weeks. monthly date - tm months since 1960m1 quarterly date - tq quarters since 1960q1 half-yearly date - th half-years since 1960h1 yearly date - ty years since 0000 """ MIN_YEAR, MAX_YEAR = Timestamp.min.year, Timestamp.max.year MAX_DAY_DELTA = (Timestamp.max - datetime(1960, 1, 1)).days MIN_DAY_DELTA = (Timestamp.min - datetime(1960, 1, 1)).days MIN_MS_DELTA = MIN_DAY_DELTA * 24 * 3600 * 1000 MAX_MS_DELTA = MAX_DAY_DELTA * 24 * 3600 * 1000 def convert_year_month_safe(year, month) -> Series: """ Convert year and month to datetimes, using pandas vectorized versions when the date range falls within the range supported by pandas. Otherwise it falls back to a slower but more robust method using datetime. """ if year.max() < MAX_YEAR and year.min() > MIN_YEAR: return to_datetime(100 * year + month, format="%Y%m") else: index = getattr(year, "index", None) return Series([datetime(y, m, 1) for y, m in zip(year, month)], index=index) def convert_year_days_safe(year, days) -> Series: """ Converts year (e.g. 1999) and days since the start of the year to a datetime or datetime64 Series """ if year.max() < (MAX_YEAR - 1) and year.min() > MIN_YEAR: return to_datetime(year, format="%Y") + to_timedelta(days, unit="d") else: index = getattr(year, "index", None) value = [ datetime(y, 1, 1) + timedelta(days=int(d)) for y, d in zip(year, days) ] return Series(value, index=index) def convert_delta_safe(base, deltas, unit) -> Series: """ Convert base dates and deltas to datetimes, using pandas vectorized versions if the deltas satisfy restrictions required to be expressed as dates in pandas. """ index = getattr(deltas, "index", None) if unit == "d": if deltas.max() > MAX_DAY_DELTA or deltas.min() < MIN_DAY_DELTA: values = [base + timedelta(days=int(d)) for d in deltas] return Series(values, index=index) elif unit == "ms": if deltas.max() > MAX_MS_DELTA or deltas.min() < MIN_MS_DELTA: values = [ base + timedelta(microseconds=(int(d) * 1000)) for d in deltas ] return Series(values, index=index) else: raise ValueError("format not understood") base = to_datetime(base) deltas = to_timedelta(deltas, unit=unit) return base + deltas # TODO(non-nano): If/when pandas supports more than datetime64[ns], this # should be improved to use correct range, e.g. datetime[Y] for yearly bad_locs = np.isnan(dates) has_bad_values = False if bad_locs.any(): has_bad_values = True dates._values[bad_locs] = 1.0 # Replace with NaT dates = dates.astype(np.int64) if fmt.startswith(("%tc", "tc")): # Delta ms relative to base base = stata_epoch ms = dates conv_dates = convert_delta_safe(base, ms, "ms") elif fmt.startswith(("%tC", "tC")): warnings.warn( "Encountered %tC format. Leaving in Stata Internal Format.", stacklevel=find_stack_level(), ) conv_dates = Series(dates, dtype=object) if has_bad_values: conv_dates[bad_locs] = NaT return conv_dates # Delta days relative to base elif fmt.startswith(("%td", "td", "%d", "d")): base = stata_epoch days = dates conv_dates = convert_delta_safe(base, days, "d") # does not count leap days - 7 days is a week. # 52nd week may have more than 7 days elif fmt.startswith(("%tw", "tw")): year = stata_epoch.year + dates // 52 days = (dates % 52) * 7 conv_dates = convert_year_days_safe(year, days) elif fmt.startswith(("%tm", "tm")): # Delta months relative to base year = stata_epoch.year + dates // 12 month = (dates % 12) + 1 conv_dates = convert_year_month_safe(year, month) elif fmt.startswith(("%tq", "tq")): # Delta quarters relative to base year = stata_epoch.year + dates // 4 quarter_month = (dates % 4) * 3 + 1 conv_dates = convert_year_month_safe(year, quarter_month) elif fmt.startswith(("%th", "th")): # Delta half-years relative to base year = stata_epoch.year + dates // 2 month = (dates % 2) * 6 + 1 conv_dates = convert_year_month_safe(year, month) elif fmt.startswith(("%ty", "ty")): # Years -- not delta year = dates first_month = np.ones_like(dates) conv_dates = convert_year_month_safe(year, first_month) else: raise ValueError(f"Date fmt {fmt} not understood") if has_bad_values: # Restore NaT for bad values conv_dates[bad_locs] = NaT return conv_dates def _datetime_to_stata_elapsed_vec(dates: Series, fmt: str) -> Series: """ Convert from datetime to SIF. https://www.stata.com/help.cgi?datetime Parameters ---------- dates : Series Series or array containing datetime or datetime64[ns] to convert to the Stata Internal Format given by fmt fmt : str The format to convert to. Can be, tc, td, tw, tm, tq, th, ty """ index = dates.index NS_PER_DAY = 24 * 3600 * 1000 * 1000 * 1000 US_PER_DAY = NS_PER_DAY / 1000 def parse_dates_safe( dates: Series, delta: bool = False, year: bool = False, days: bool = False ): d = {} if lib.is_np_dtype(dates.dtype, "M"): if delta: time_delta = dates - Timestamp(stata_epoch).as_unit("ns") d["delta"] = time_delta._values.view(np.int64) // 1000 # microseconds if days or year: date_index = DatetimeIndex(dates) d["year"] = date_index._data.year d["month"] = date_index._data.month if days: days_in_ns = dates._values.view(np.int64) - to_datetime( d["year"], format="%Y" )._values.view(np.int64) d["days"] = days_in_ns // NS_PER_DAY elif infer_dtype(dates, skipna=False) == "datetime": if delta: delta = dates._values - stata_epoch def f(x: timedelta) -> float: return US_PER_DAY * x.days + 1000000 * x.seconds + x.microseconds v = np.vectorize(f) d["delta"] = v(delta) if year: year_month = dates.apply(lambda x: 100 * x.year + x.month) d["year"] = year_month._values // 100 d["month"] = year_month._values - d["year"] * 100 if days: def g(x: datetime) -> int: return (x - datetime(x.year, 1, 1)).days v = np.vectorize(g) d["days"] = v(dates) else: raise ValueError( "Columns containing dates must contain either " "datetime64, datetime or null values." ) return DataFrame(d, index=index) bad_loc = isna(dates) index = dates.index if bad_loc.any(): if lib.is_np_dtype(dates.dtype, "M"): dates._values[bad_loc] = to_datetime(stata_epoch) else: dates._values[bad_loc] = stata_epoch if fmt in ["%tc", "tc"]: d = parse_dates_safe(dates, delta=True) conv_dates = d.delta / 1000 elif fmt in ["%tC", "tC"]: warnings.warn( "Stata Internal Format tC not supported.", stacklevel=find_stack_level(), ) conv_dates = dates elif fmt in ["%td", "td"]: d = parse_dates_safe(dates, delta=True) conv_dates = d.delta // US_PER_DAY elif fmt in ["%tw", "tw"]: d = parse_dates_safe(dates, year=True, days=True) conv_dates = 52 * (d.year - stata_epoch.year) + d.days // 7 elif fmt in ["%tm", "tm"]: d = parse_dates_safe(dates, year=True) conv_dates = 12 * (d.year - stata_epoch.year) + d.month - 1 elif fmt in ["%tq", "tq"]: d = parse_dates_safe(dates, year=True) conv_dates = 4 * (d.year - stata_epoch.year) + (d.month - 1) // 3 elif fmt in ["%th", "th"]: d = parse_dates_safe(dates, year=True) conv_dates = 2 * (d.year - stata_epoch.year) + (d.month > 6).astype(int) elif fmt in ["%ty", "ty"]: d = parse_dates_safe(dates, year=True) conv_dates = d.year else: raise ValueError(f"Format {fmt} is not a known Stata date format") conv_dates = Series(conv_dates, dtype=np.float64, copy=False) missing_value = struct.unpack(" DataFrame: """ Checks the dtypes of the columns of a pandas DataFrame for compatibility with the data types and ranges supported by Stata, and converts if necessary. Parameters ---------- data : DataFrame The DataFrame to check and convert Notes ----- Numeric columns in Stata must be one of int8, int16, int32, float32 or float64, with some additional value restrictions. int8 and int16 columns are checked for violations of the value restrictions and upcast if needed. int64 data is not usable in Stata, and so it is downcast to int32 whenever the value are in the int32 range, and sidecast to float64 when larger than this range. If the int64 values are outside of the range of those perfectly representable as float64 values, a warning is raised. bool columns are cast to int8. uint columns are converted to int of the same size if there is no loss in precision, otherwise are upcast to a larger type. uint64 is currently not supported since it is concerted to object in a DataFrame. """ ws = "" # original, if small, if large conversion_data: tuple[ tuple[type, type, type], tuple[type, type, type], tuple[type, type, type], tuple[type, type, type], tuple[type, type, type], ] = ( (np.bool_, np.int8, np.int8), (np.uint8, np.int8, np.int16), (np.uint16, np.int16, np.int32), (np.uint32, np.int32, np.int64), (np.uint64, np.int64, np.float64), ) float32_max = struct.unpack("= 2**53: ws = precision_loss_doc.format("uint64", "float64") data[col] = data[col].astype(dtype) # Check values and upcast if necessary if dtype == np.int8 and not empty_df: if data[col].max() > 100 or data[col].min() < -127: data[col] = data[col].astype(np.int16) elif dtype == np.int16 and not empty_df: if data[col].max() > 32740 or data[col].min() < -32767: data[col] = data[col].astype(np.int32) elif dtype == np.int64: if empty_df or ( data[col].max() <= 2147483620 and data[col].min() >= -2147483647 ): data[col] = data[col].astype(np.int32) else: data[col] = data[col].astype(np.float64) if data[col].max() >= 2**53 or data[col].min() <= -(2**53): ws = precision_loss_doc.format("int64", "float64") elif dtype in (np.float32, np.float64): if np.isinf(data[col]).any(): raise ValueError( f"Column {col} contains infinity or -infinity" "which is outside the range supported by Stata." ) value = data[col].max() if dtype == np.float32 and value > float32_max: data[col] = data[col].astype(np.float64) elif dtype == np.float64: if value > float64_max: raise ValueError( f"Column {col} has a maximum value ({value}) outside the range " f"supported by Stata ({float64_max})" ) if is_nullable_int: if orig_missing.any(): # Replace missing by Stata sentinel value sentinel = StataMissingValue.BASE_MISSING_VALUES[data[col].dtype.name] data.loc[orig_missing, col] = sentinel if ws: warnings.warn( ws, PossiblePrecisionLoss, stacklevel=find_stack_level(), ) return data class StataValueLabel: """ Parse a categorical column and prepare formatted output Parameters ---------- catarray : Series Categorical Series to encode encoding : {"latin-1", "utf-8"} Encoding to use for value labels. """ def __init__( self, catarray: Series, encoding: Literal["latin-1", "utf-8"] = "latin-1" ) -> None: if encoding not in ("latin-1", "utf-8"): raise ValueError("Only latin-1 and utf-8 are supported.") self.labname = catarray.name self._encoding = encoding categories = catarray.cat.categories self.value_labels = enumerate(categories) self._prepare_value_labels() def _prepare_value_labels(self) -> None: """Encode value labels.""" self.text_len = 0 self.txt: list[bytes] = [] self.n = 0 # Offsets (length of categories), converted to int32 self.off = np.array([], dtype=np.int32) # Values, converted to int32 self.val = np.array([], dtype=np.int32) self.len = 0 # Compute lengths and setup lists of offsets and labels offsets: list[int] = [] values: list[float] = [] for vl in self.value_labels: category: str | bytes = vl[1] if not isinstance(category, str): category = str(category) warnings.warn( value_label_mismatch_doc.format(self.labname), ValueLabelTypeMismatch, stacklevel=find_stack_level(), ) category = category.encode(self._encoding) offsets.append(self.text_len) self.text_len += len(category) + 1 # +1 for the padding values.append(vl[0]) self.txt.append(category) self.n += 1 if self.text_len > 32000: raise ValueError( "Stata value labels for a single variable must " "have a combined length less than 32,000 characters." ) # Ensure int32 self.off = np.array(offsets, dtype=np.int32) self.val = np.array(values, dtype=np.int32) # Total length self.len = 4 + 4 + 4 * self.n + 4 * self.n + self.text_len def generate_value_label(self, byteorder: str) -> bytes: """ Generate the binary representation of the value labels. Parameters ---------- byteorder : str Byte order of the output Returns ------- value_label : bytes Bytes containing the formatted value label """ encoding = self._encoding bio = BytesIO() null_byte = b"\x00" # len bio.write(struct.pack(byteorder + "i", self.len)) # labname labname = str(self.labname)[:32].encode(encoding) lab_len = 32 if encoding not in ("utf-8", "utf8") else 128 labname = _pad_bytes(labname, lab_len + 1) bio.write(labname) # padding - 3 bytes for i in range(3): bio.write(struct.pack("c", null_byte)) # value_label_table # n - int32 bio.write(struct.pack(byteorder + "i", self.n)) # textlen - int32 bio.write(struct.pack(byteorder + "i", self.text_len)) # off - int32 array (n elements) for offset in self.off: bio.write(struct.pack(byteorder + "i", offset)) # val - int32 array (n elements) for value in self.val: bio.write(struct.pack(byteorder + "i", value)) # txt - Text labels, null terminated for text in self.txt: bio.write(text + null_byte) return bio.getvalue() class StataNonCatValueLabel(StataValueLabel): """ Prepare formatted version of value labels Parameters ---------- labname : str Value label name value_labels: Dictionary Mapping of values to labels encoding : {"latin-1", "utf-8"} Encoding to use for value labels. """ def __init__( self, labname: str, value_labels: dict[float, str], encoding: Literal["latin-1", "utf-8"] = "latin-1", ) -> None: if encoding not in ("latin-1", "utf-8"): raise ValueError("Only latin-1 and utf-8 are supported.") self.labname = labname self._encoding = encoding self.value_labels = sorted( # type: ignore[assignment] value_labels.items(), key=lambda x: x[0] ) self._prepare_value_labels() class StataMissingValue: """ An observation's missing value. Parameters ---------- value : {int, float} The Stata missing value code Notes ----- More information: Integer missing values make the code '.', '.a', ..., '.z' to the ranges 101 ... 127 (for int8), 32741 ... 32767 (for int16) and 2147483621 ... 2147483647 (for int32). Missing values for floating point data types are more complex but the pattern is simple to discern from the following table. np.float32 missing values (float in Stata) 0000007f . 0008007f .a 0010007f .b ... 00c0007f .x 00c8007f .y 00d0007f .z np.float64 missing values (double in Stata) 000000000000e07f . 000000000001e07f .a 000000000002e07f .b ... 000000000018e07f .x 000000000019e07f .y 00000000001ae07f .z """ # Construct a dictionary of missing values MISSING_VALUES: dict[float, str] = {} bases: Final = (101, 32741, 2147483621) for b in bases: # Conversion to long to avoid hash issues on 32 bit platforms #8968 MISSING_VALUES[b] = "." for i in range(1, 27): MISSING_VALUES[i + b] = "." + chr(96 + i) float32_base: bytes = b"\x00\x00\x00\x7f" increment_32: int = struct.unpack(" 0: MISSING_VALUES[key] += chr(96 + i) int_value = struct.unpack(" 0: MISSING_VALUES[key] += chr(96 + i) int_value = struct.unpack("q", struct.pack(" None: self._value = value # Conversion to int to avoid hash issues on 32 bit platforms #8968 value = int(value) if value < 2147483648 else float(value) self._str = self.MISSING_VALUES[value] @property def string(self) -> str: """ The Stata representation of the missing value: '.', '.a'..'.z' Returns ------- str The representation of the missing value. """ return self._str @property def value(self) -> float: """ The binary representation of the missing value. Returns ------- {int, float} The binary representation of the missing value. """ return self._value def __str__(self) -> str: return self.string def __repr__(self) -> str: return f"{type(self)}({self})" def __eq__(self, other: object) -> bool: return ( isinstance(other, type(self)) and self.string == other.string and self.value == other.value ) @classmethod def get_base_missing_value(cls, dtype: np.dtype) -> float: if dtype.type is np.int8: value = cls.BASE_MISSING_VALUES["int8"] elif dtype.type is np.int16: value = cls.BASE_MISSING_VALUES["int16"] elif dtype.type is np.int32: value = cls.BASE_MISSING_VALUES["int32"] elif dtype.type is np.float32: value = cls.BASE_MISSING_VALUES["float32"] elif dtype.type is np.float64: value = cls.BASE_MISSING_VALUES["float64"] else: raise ValueError("Unsupported dtype") return value class StataParser: def __init__(self) -> None: # type code. # -------------------- # str1 1 = 0x01 # str2 2 = 0x02 # ... # str244 244 = 0xf4 # byte 251 = 0xfb (sic) # int 252 = 0xfc # long 253 = 0xfd # float 254 = 0xfe # double 255 = 0xff # -------------------- # NOTE: the byte type seems to be reserved for categorical variables # with a label, but the underlying variable is -127 to 100 # we're going to drop the label and cast to int self.DTYPE_MAP = dict( [(i, np.dtype(f"S{i}")) for i in range(1, 245)] + [ (251, np.dtype(np.int8)), (252, np.dtype(np.int16)), (253, np.dtype(np.int32)), (254, np.dtype(np.float32)), (255, np.dtype(np.float64)), ] ) self.DTYPE_MAP_XML: dict[int, np.dtype] = { 32768: np.dtype(np.uint8), # Keys to GSO 65526: np.dtype(np.float64), 65527: np.dtype(np.float32), 65528: np.dtype(np.int32), 65529: np.dtype(np.int16), 65530: np.dtype(np.int8), } self.TYPE_MAP = list(tuple(range(251)) + tuple("bhlfd")) self.TYPE_MAP_XML = { # Not really a Q, unclear how to handle byteswap 32768: "Q", 65526: "d", 65527: "f", 65528: "l", 65529: "h", 65530: "b", } # NOTE: technically, some of these are wrong. there are more numbers # that can be represented. it's the 27 ABOVE and BELOW the max listed # numeric data type in [U] 12.2.2 of the 11.2 manual float32_min = b"\xff\xff\xff\xfe" float32_max = b"\xff\xff\xff\x7e" float64_min = b"\xff\xff\xff\xff\xff\xff\xef\xff" float64_max = b"\xff\xff\xff\xff\xff\xff\xdf\x7f" self.VALID_RANGE = { "b": (-127, 100), "h": (-32767, 32740), "l": (-2147483647, 2147483620), "f": ( np.float32(struct.unpack(" None: super().__init__() # Arguments to the reader (can be temporarily overridden in # calls to read). self._convert_dates = convert_dates self._convert_categoricals = convert_categoricals self._index_col = index_col self._convert_missing = convert_missing self._preserve_dtypes = preserve_dtypes self._columns = columns self._order_categoricals = order_categoricals self._original_path_or_buf = path_or_buf self._compression = compression self._storage_options = storage_options self._encoding = "" self._chunksize = chunksize self._using_iterator = False self._entered = False if self._chunksize is None: self._chunksize = 1 elif not isinstance(chunksize, int) or chunksize <= 0: raise ValueError("chunksize must be a positive integer when set.") # State variables for the file self._close_file: Callable[[], None] | None = None self._missing_values = False self._can_read_value_labels = False self._column_selector_set = False self._value_labels_read = False self._data_read = False self._dtype: np.dtype | None = None self._lines_read = 0 self._native_byteorder = _set_endianness(sys.byteorder) def _ensure_open(self) -> None: """ Ensure the file has been opened and its header data read. """ if not hasattr(self, "_path_or_buf"): self._open_file() def _open_file(self) -> None: """ Open the file (with compression options, etc.), and read header information. """ if not self._entered: warnings.warn( "StataReader is being used without using a context manager. " "Using StataReader as a context manager is the only supported method.", ResourceWarning, stacklevel=find_stack_level(), ) handles = get_handle( self._original_path_or_buf, "rb", storage_options=self._storage_options, is_text=False, compression=self._compression, ) if hasattr(handles.handle, "seekable") and handles.handle.seekable(): # If the handle is directly seekable, use it without an extra copy. self._path_or_buf = handles.handle self._close_file = handles.close else: # Copy to memory, and ensure no encoding. with handles: self._path_or_buf = BytesIO(handles.handle.read()) self._close_file = self._path_or_buf.close self._read_header() self._setup_dtype() def __enter__(self) -> Self: """enter context manager""" self._entered = True return self def __exit__( self, exc_type: type[BaseException] | None, exc_value: BaseException | None, traceback: TracebackType | None, ) -> None: if self._close_file: self._close_file() def close(self) -> None: """Close the handle if its open. .. deprecated: 2.0.0 The close method is not part of the public API. The only supported way to use StataReader is to use it as a context manager. """ warnings.warn( "The StataReader.close() method is not part of the public API and " "will be removed in a future version without notice. " "Using StataReader as a context manager is the only supported method.", FutureWarning, stacklevel=find_stack_level(), ) if self._close_file: self._close_file() def _set_encoding(self) -> None: """ Set string encoding which depends on file version """ if self._format_version < 118: self._encoding = "latin-1" else: self._encoding = "utf-8" def _read_int8(self) -> int: return struct.unpack("b", self._path_or_buf.read(1))[0] def _read_uint8(self) -> int: return struct.unpack("B", self._path_or_buf.read(1))[0] def _read_uint16(self) -> int: return struct.unpack(f"{self._byteorder}H", self._path_or_buf.read(2))[0] def _read_uint32(self) -> int: return struct.unpack(f"{self._byteorder}I", self._path_or_buf.read(4))[0] def _read_uint64(self) -> int: return struct.unpack(f"{self._byteorder}Q", self._path_or_buf.read(8))[0] def _read_int16(self) -> int: return struct.unpack(f"{self._byteorder}h", self._path_or_buf.read(2))[0] def _read_int32(self) -> int: return struct.unpack(f"{self._byteorder}i", self._path_or_buf.read(4))[0] def _read_int64(self) -> int: return struct.unpack(f"{self._byteorder}q", self._path_or_buf.read(8))[0] def _read_char8(self) -> bytes: return struct.unpack("c", self._path_or_buf.read(1))[0] def _read_int16_count(self, count: int) -> tuple[int, ...]: return struct.unpack( f"{self._byteorder}{'h' * count}", self._path_or_buf.read(2 * count), ) def _read_header(self) -> None: first_char = self._read_char8() if first_char == b"<": self._read_new_header() else: self._read_old_header(first_char) def _read_new_header(self) -> None: # The first part of the header is common to 117 - 119. self._path_or_buf.read(27) # stata_dta>
self._format_version = int(self._path_or_buf.read(3)) if self._format_version not in [117, 118, 119]: raise ValueError(_version_error.format(version=self._format_version)) self._set_encoding() self._path_or_buf.read(21) # self._byteorder = ">" if self._path_or_buf.read(3) == b"MSF" else "<" self._path_or_buf.read(15) # self._nvar = ( self._read_uint16() if self._format_version <= 118 else self._read_uint32() ) self._path_or_buf.read(7) # self._nobs = self._get_nobs() self._path_or_buf.read(11) # self._time_stamp = self._get_time_stamp() self._path_or_buf.read(26) #
self._path_or_buf.read(8) # 0x0000000000000000 self._path_or_buf.read(8) # position of self._seek_vartypes = self._read_int64() + 16 self._seek_varnames = self._read_int64() + 10 self._seek_sortlist = self._read_int64() + 10 self._seek_formats = self._read_int64() + 9 self._seek_value_label_names = self._read_int64() + 19 # Requires version-specific treatment self._seek_variable_labels = self._get_seek_variable_labels() self._path_or_buf.read(8) # self._data_location = self._read_int64() + 6 self._seek_strls = self._read_int64() + 7 self._seek_value_labels = self._read_int64() + 14 self._typlist, self._dtyplist = self._get_dtypes(self._seek_vartypes) self._path_or_buf.seek(self._seek_varnames) self._varlist = self._get_varlist() self._path_or_buf.seek(self._seek_sortlist) self._srtlist = self._read_int16_count(self._nvar + 1)[:-1] self._path_or_buf.seek(self._seek_formats) self._fmtlist = self._get_fmtlist() self._path_or_buf.seek(self._seek_value_label_names) self._lbllist = self._get_lbllist() self._path_or_buf.seek(self._seek_variable_labels) self._variable_labels = self._get_variable_labels() # Get data type information, works for versions 117-119. def _get_dtypes( self, seek_vartypes: int ) -> tuple[list[int | str], list[str | np.dtype]]: self._path_or_buf.seek(seek_vartypes) typlist = [] dtyplist = [] for _ in range(self._nvar): typ = self._read_uint16() if typ <= 2045: typlist.append(typ) dtyplist.append(str(typ)) else: try: typlist.append(self.TYPE_MAP_XML[typ]) # type: ignore[arg-type] dtyplist.append(self.DTYPE_MAP_XML[typ]) # type: ignore[arg-type] except KeyError as err: raise ValueError(f"cannot convert stata types [{typ}]") from err return typlist, dtyplist # type: ignore[return-value] def _get_varlist(self) -> list[str]: # 33 in order formats, 129 in formats 118 and 119 b = 33 if self._format_version < 118 else 129 return [self._decode(self._path_or_buf.read(b)) for _ in range(self._nvar)] # Returns the format list def _get_fmtlist(self) -> list[str]: if self._format_version >= 118: b = 57 elif self._format_version > 113: b = 49 elif self._format_version > 104: b = 12 else: b = 7 return [self._decode(self._path_or_buf.read(b)) for _ in range(self._nvar)] # Returns the label list def _get_lbllist(self) -> list[str]: if self._format_version >= 118: b = 129 elif self._format_version > 108: b = 33 else: b = 9 return [self._decode(self._path_or_buf.read(b)) for _ in range(self._nvar)] def _get_variable_labels(self) -> list[str]: if self._format_version >= 118: vlblist = [ self._decode(self._path_or_buf.read(321)) for _ in range(self._nvar) ] elif self._format_version > 105: vlblist = [ self._decode(self._path_or_buf.read(81)) for _ in range(self._nvar) ] else: vlblist = [ self._decode(self._path_or_buf.read(32)) for _ in range(self._nvar) ] return vlblist def _get_nobs(self) -> int: if self._format_version >= 118: return self._read_uint64() else: return self._read_uint32() def _get_data_label(self) -> str: if self._format_version >= 118: strlen = self._read_uint16() return self._decode(self._path_or_buf.read(strlen)) elif self._format_version == 117: strlen = self._read_int8() return self._decode(self._path_or_buf.read(strlen)) elif self._format_version > 105: return self._decode(self._path_or_buf.read(81)) else: return self._decode(self._path_or_buf.read(32)) def _get_time_stamp(self) -> str: if self._format_version >= 118: strlen = self._read_int8() return self._path_or_buf.read(strlen).decode("utf-8") elif self._format_version == 117: strlen = self._read_int8() return self._decode(self._path_or_buf.read(strlen)) elif self._format_version > 104: return self._decode(self._path_or_buf.read(18)) else: raise ValueError() def _get_seek_variable_labels(self) -> int: if self._format_version == 117: self._path_or_buf.read(8) # , throw away # Stata 117 data files do not follow the described format. This is # a work around that uses the previous label, 33 bytes for each # variable, 20 for the closing tag and 17 for the opening tag return self._seek_value_label_names + (33 * self._nvar) + 20 + 17 elif self._format_version >= 118: return self._read_int64() + 17 else: raise ValueError() def _read_old_header(self, first_char: bytes) -> None: self._format_version = int(first_char[0]) if self._format_version not in [104, 105, 108, 111, 113, 114, 115]: raise ValueError(_version_error.format(version=self._format_version)) self._set_encoding() self._byteorder = ">" if self._read_int8() == 0x1 else "<" self._filetype = self._read_int8() self._path_or_buf.read(1) # unused self._nvar = self._read_uint16() self._nobs = self._get_nobs() self._data_label = self._get_data_label() self._time_stamp = self._get_time_stamp() # descriptors if self._format_version > 108: typlist = [int(c) for c in self._path_or_buf.read(self._nvar)] else: buf = self._path_or_buf.read(self._nvar) typlistb = np.frombuffer(buf, dtype=np.uint8) typlist = [] for tp in typlistb: if tp in self.OLD_TYPE_MAPPING: typlist.append(self.OLD_TYPE_MAPPING[tp]) else: typlist.append(tp - 127) # bytes try: self._typlist = [self.TYPE_MAP[typ] for typ in typlist] except ValueError as err: invalid_types = ",".join([str(x) for x in typlist]) raise ValueError(f"cannot convert stata types [{invalid_types}]") from err try: self._dtyplist = [self.DTYPE_MAP[typ] for typ in typlist] except ValueError as err: invalid_dtypes = ",".join([str(x) for x in typlist]) raise ValueError(f"cannot convert stata dtypes [{invalid_dtypes}]") from err if self._format_version > 108: self._varlist = [ self._decode(self._path_or_buf.read(33)) for _ in range(self._nvar) ] else: self._varlist = [ self._decode(self._path_or_buf.read(9)) for _ in range(self._nvar) ] self._srtlist = self._read_int16_count(self._nvar + 1)[:-1] self._fmtlist = self._get_fmtlist() self._lbllist = self._get_lbllist() self._variable_labels = self._get_variable_labels() # ignore expansion fields (Format 105 and later) # When reading, read five bytes; the last four bytes now tell you # the size of the next read, which you discard. You then continue # like this until you read 5 bytes of zeros. if self._format_version > 104: while True: data_type = self._read_int8() if self._format_version > 108: data_len = self._read_int32() else: data_len = self._read_int16() if data_type == 0: break self._path_or_buf.read(data_len) # necessary data to continue parsing self._data_location = self._path_or_buf.tell() def _setup_dtype(self) -> np.dtype: """Map between numpy and state dtypes""" if self._dtype is not None: return self._dtype dtypes = [] # Convert struct data types to numpy data type for i, typ in enumerate(self._typlist): if typ in self.NUMPY_TYPE_MAP: typ = cast(str, typ) # only strs in NUMPY_TYPE_MAP dtypes.append((f"s{i}", f"{self._byteorder}{self.NUMPY_TYPE_MAP[typ]}")) else: dtypes.append((f"s{i}", f"S{typ}")) self._dtype = np.dtype(dtypes) return self._dtype def _decode(self, s: bytes) -> str: # have bytes not strings, so must decode s = s.partition(b"\0")[0] try: return s.decode(self._encoding) except UnicodeDecodeError: # GH 25960, fallback to handle incorrect format produced when 117 # files are converted to 118 files in Stata encoding = self._encoding msg = f""" One or more strings in the dta file could not be decoded using {encoding}, and so the fallback encoding of latin-1 is being used. This can happen when a file has been incorrectly encoded by Stata or some other software. You should verify the string values returned are correct.""" warnings.warn( msg, UnicodeWarning, stacklevel=find_stack_level(), ) return s.decode("latin-1") def _read_value_labels(self) -> None: self._ensure_open() if self._value_labels_read: # Don't read twice return if self._format_version <= 108: # Value labels are not supported in version 108 and earlier. self._value_labels_read = True self._value_label_dict: dict[str, dict[float, str]] = {} return if self._format_version >= 117: self._path_or_buf.seek(self._seek_value_labels) else: assert self._dtype is not None offset = self._nobs * self._dtype.itemsize self._path_or_buf.seek(self._data_location + offset) self._value_labels_read = True self._value_label_dict = {} while True: if self._format_version >= 117: if self._path_or_buf.read(5) == b" break # end of value label table slength = self._path_or_buf.read(4) if not slength: break # end of value label table (format < 117) if self._format_version <= 117: labname = self._decode(self._path_or_buf.read(33)) else: labname = self._decode(self._path_or_buf.read(129)) self._path_or_buf.read(3) # padding n = self._read_uint32() txtlen = self._read_uint32() off = np.frombuffer( self._path_or_buf.read(4 * n), dtype=f"{self._byteorder}i4", count=n ) val = np.frombuffer( self._path_or_buf.read(4 * n), dtype=f"{self._byteorder}i4", count=n ) ii = np.argsort(off) off = off[ii] val = val[ii] txt = self._path_or_buf.read(txtlen) self._value_label_dict[labname] = {} for i in range(n): end = off[i + 1] if i < n - 1 else txtlen self._value_label_dict[labname][val[i]] = self._decode( txt[off[i] : end] ) if self._format_version >= 117: self._path_or_buf.read(6) # self._value_labels_read = True def _read_strls(self) -> None: self._path_or_buf.seek(self._seek_strls) # Wrap v_o in a string to allow uint64 values as keys on 32bit OS self.GSO = {"0": ""} while True: if self._path_or_buf.read(3) != b"GSO": break if self._format_version == 117: v_o = self._read_uint64() else: buf = self._path_or_buf.read(12) # Only tested on little endian file on little endian machine. v_size = 2 if self._format_version == 118 else 3 if self._byteorder == "<": buf = buf[0:v_size] + buf[4 : (12 - v_size)] else: # This path may not be correct, impossible to test buf = buf[0:v_size] + buf[(4 + v_size) :] v_o = struct.unpack("Q", buf)[0] typ = self._read_uint8() length = self._read_uint32() va = self._path_or_buf.read(length) if typ == 130: decoded_va = va[0:-1].decode(self._encoding) else: # Stata says typ 129 can be binary, so use str decoded_va = str(va) # Wrap v_o in a string to allow uint64 values as keys on 32bit OS self.GSO[str(v_o)] = decoded_va def __next__(self) -> DataFrame: self._using_iterator = True return self.read(nrows=self._chunksize) def get_chunk(self, size: int | None = None) -> DataFrame: """ Reads lines from Stata file and returns as dataframe Parameters ---------- size : int, defaults to None Number of lines to read. If None, reads whole file. Returns ------- DataFrame """ if size is None: size = self._chunksize return self.read(nrows=size) @Appender(_read_method_doc) def read( self, nrows: int | None = None, convert_dates: bool | None = None, convert_categoricals: bool | None = None, index_col: str | None = None, convert_missing: bool | None = None, preserve_dtypes: bool | None = None, columns: Sequence[str] | None = None, order_categoricals: bool | None = None, ) -> DataFrame: self._ensure_open() # Handle options if convert_dates is None: convert_dates = self._convert_dates if convert_categoricals is None: convert_categoricals = self._convert_categoricals if convert_missing is None: convert_missing = self._convert_missing if preserve_dtypes is None: preserve_dtypes = self._preserve_dtypes if columns is None: columns = self._columns if order_categoricals is None: order_categoricals = self._order_categoricals if index_col is None: index_col = self._index_col if nrows is None: nrows = self._nobs # Handle empty file or chunk. If reading incrementally raise # StopIteration. If reading the whole thing return an empty # data frame. if (self._nobs == 0) and nrows == 0: self._can_read_value_labels = True self._data_read = True data = DataFrame(columns=self._varlist) # Apply dtypes correctly for i, col in enumerate(data.columns): dt = self._dtyplist[i] if isinstance(dt, np.dtype): if dt.char != "S": data[col] = data[col].astype(dt) if columns is not None: data = self._do_select_columns(data, columns) return data if (self._format_version >= 117) and (not self._value_labels_read): self._can_read_value_labels = True self._read_strls() # Read data assert self._dtype is not None dtype = self._dtype max_read_len = (self._nobs - self._lines_read) * dtype.itemsize read_len = nrows * dtype.itemsize read_len = min(read_len, max_read_len) if read_len <= 0: # Iterator has finished, should never be here unless # we are reading the file incrementally if convert_categoricals: self._read_value_labels() raise StopIteration offset = self._lines_read * dtype.itemsize self._path_or_buf.seek(self._data_location + offset) read_lines = min(nrows, self._nobs - self._lines_read) raw_data = np.frombuffer( self._path_or_buf.read(read_len), dtype=dtype, count=read_lines ) self._lines_read += read_lines if self._lines_read == self._nobs: self._can_read_value_labels = True self._data_read = True # if necessary, swap the byte order to native here if self._byteorder != self._native_byteorder: raw_data = raw_data.byteswap().view(raw_data.dtype.newbyteorder()) if convert_categoricals: self._read_value_labels() if len(raw_data) == 0: data = DataFrame(columns=self._varlist) else: data = DataFrame.from_records(raw_data) data.columns = Index(self._varlist) # If index is not specified, use actual row number rather than # restarting at 0 for each chunk. if index_col is None: data.index = RangeIndex( self._lines_read - read_lines, self._lines_read ) # set attr instead of set_index to avoid copy if columns is not None: data = self._do_select_columns(data, columns) # Decode strings for col, typ in zip(data, self._typlist): if isinstance(typ, int): data[col] = data[col].apply(self._decode) data = self._insert_strls(data) # Convert columns (if needed) to match input type valid_dtypes = [i for i, dtyp in enumerate(self._dtyplist) if dtyp is not None] object_type = np.dtype(object) for idx in valid_dtypes: dtype = data.iloc[:, idx].dtype if dtype not in (object_type, self._dtyplist[idx]): data.isetitem(idx, data.iloc[:, idx].astype(dtype)) data = self._do_convert_missing(data, convert_missing) if convert_dates: for i, fmt in enumerate(self._fmtlist): if any(fmt.startswith(date_fmt) for date_fmt in _date_formats): data.isetitem( i, _stata_elapsed_date_to_datetime_vec(data.iloc[:, i], fmt) ) if convert_categoricals and self._format_version > 108: data = self._do_convert_categoricals( data, self._value_label_dict, self._lbllist, order_categoricals ) if not preserve_dtypes: retyped_data = [] convert = False for col in data: dtype = data[col].dtype if dtype in (np.dtype(np.float16), np.dtype(np.float32)): dtype = np.dtype(np.float64) convert = True elif dtype in ( np.dtype(np.int8), np.dtype(np.int16), np.dtype(np.int32), ): dtype = np.dtype(np.int64) convert = True retyped_data.append((col, data[col].astype(dtype))) if convert: data = DataFrame.from_dict(dict(retyped_data)) if index_col is not None: data = data.set_index(data.pop(index_col)) return data def _do_convert_missing(self, data: DataFrame, convert_missing: bool) -> DataFrame: # Check for missing values, and replace if found replacements = {} for i in range(len(data.columns)): fmt = self._typlist[i] if fmt not in self.VALID_RANGE: continue fmt = cast(str, fmt) # only strs in VALID_RANGE nmin, nmax = self.VALID_RANGE[fmt] series = data.iloc[:, i] # appreciably faster to do this with ndarray instead of Series svals = series._values missing = (svals < nmin) | (svals > nmax) if not missing.any(): continue if convert_missing: # Replacement follows Stata notation missing_loc = np.nonzero(np.asarray(missing))[0] umissing, umissing_loc = np.unique(series[missing], return_inverse=True) replacement = Series(series, dtype=object) for j, um in enumerate(umissing): missing_value = StataMissingValue(um) loc = missing_loc[umissing_loc == j] replacement.iloc[loc] = missing_value else: # All replacements are identical dtype = series.dtype if dtype not in (np.float32, np.float64): dtype = np.float64 replacement = Series(series, dtype=dtype) if not replacement._values.flags["WRITEABLE"]: # only relevant for ArrayManager; construction # path for BlockManager ensures writeability replacement = replacement.copy() # Note: operating on ._values is much faster than directly # TODO: can we fix that? replacement._values[missing] = np.nan replacements[i] = replacement if replacements: for idx, value in replacements.items(): data.isetitem(idx, value) return data def _insert_strls(self, data: DataFrame) -> DataFrame: if not hasattr(self, "GSO") or len(self.GSO) == 0: return data for i, typ in enumerate(self._typlist): if typ != "Q": continue # Wrap v_o in a string to allow uint64 values as keys on 32bit OS data.isetitem(i, [self.GSO[str(k)] for k in data.iloc[:, i]]) return data def _do_select_columns(self, data: DataFrame, columns: Sequence[str]) -> DataFrame: if not self._column_selector_set: column_set = set(columns) if len(column_set) != len(columns): raise ValueError("columns contains duplicate entries") unmatched = column_set.difference(data.columns) if unmatched: joined = ", ".join(list(unmatched)) raise ValueError( "The following columns were not " f"found in the Stata data set: {joined}" ) # Copy information for retained columns for later processing dtyplist = [] typlist = [] fmtlist = [] lbllist = [] for col in columns: i = data.columns.get_loc(col) dtyplist.append(self._dtyplist[i]) typlist.append(self._typlist[i]) fmtlist.append(self._fmtlist[i]) lbllist.append(self._lbllist[i]) self._dtyplist = dtyplist self._typlist = typlist self._fmtlist = fmtlist self._lbllist = lbllist self._column_selector_set = True return data[columns] def _do_convert_categoricals( self, data: DataFrame, value_label_dict: dict[str, dict[float, str]], lbllist: Sequence[str], order_categoricals: bool, ) -> DataFrame: """ Converts categorical columns to Categorical type. """ if not value_label_dict: return data cat_converted_data = [] for col, label in zip(data, lbllist): if label in value_label_dict: # Explicit call with ordered=True vl = value_label_dict[label] keys = np.array(list(vl.keys())) column = data[col] key_matches = column.isin(keys) if self._using_iterator and key_matches.all(): initial_categories: np.ndarray | None = keys # If all categories are in the keys and we are iterating, # use the same keys for all chunks. If some are missing # value labels, then we will fall back to the categories # varying across chunks. else: if self._using_iterator: # warn is using an iterator warnings.warn( categorical_conversion_warning, CategoricalConversionWarning, stacklevel=find_stack_level(), ) initial_categories = None cat_data = Categorical( column, categories=initial_categories, ordered=order_categoricals ) if initial_categories is None: # If None here, then we need to match the cats in the Categorical categories = [] for category in cat_data.categories: if category in vl: categories.append(vl[category]) else: categories.append(category) else: # If all cats are matched, we can use the values categories = list(vl.values()) try: # Try to catch duplicate categories # TODO: if we get a non-copying rename_categories, use that cat_data = cat_data.rename_categories(categories) except ValueError as err: vc = Series(categories, copy=False).value_counts() repeated_cats = list(vc.index[vc > 1]) repeats = "-" * 80 + "\n" + "\n".join(repeated_cats) # GH 25772 msg = f""" Value labels for column {col} are not unique. These cannot be converted to pandas categoricals. Either read the file with `convert_categoricals` set to False or use the low level interface in `StataReader` to separately read the values and the value_labels. The repeated labels are: {repeats} """ raise ValueError(msg) from err # TODO: is the next line needed above in the data(...) method? cat_series = Series(cat_data, index=data.index, copy=False) cat_converted_data.append((col, cat_series)) else: cat_converted_data.append((col, data[col])) data = DataFrame(dict(cat_converted_data), copy=False) return data @property def data_label(self) -> str: """ Return data label of Stata file. Examples -------- >>> df = pd.DataFrame([(1,)], columns=["variable"]) >>> time_stamp = pd.Timestamp(2000, 2, 29, 14, 21) >>> data_label = "This is a data file." >>> path = "/My_path/filename.dta" >>> df.to_stata(path, time_stamp=time_stamp, # doctest: +SKIP ... data_label=data_label, # doctest: +SKIP ... version=None) # doctest: +SKIP >>> with pd.io.stata.StataReader(path) as reader: # doctest: +SKIP ... print(reader.data_label) # doctest: +SKIP This is a data file. """ self._ensure_open() return self._data_label @property def time_stamp(self) -> str: """ Return time stamp of Stata file. """ self._ensure_open() return self._time_stamp def variable_labels(self) -> dict[str, str]: """ Return a dict associating each variable name with corresponding label. Returns ------- dict Examples -------- >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=["col_1", "col_2"]) >>> time_stamp = pd.Timestamp(2000, 2, 29, 14, 21) >>> path = "/My_path/filename.dta" >>> variable_labels = {"col_1": "This is an example"} >>> df.to_stata(path, time_stamp=time_stamp, # doctest: +SKIP ... variable_labels=variable_labels, version=None) # doctest: +SKIP >>> with pd.io.stata.StataReader(path) as reader: # doctest: +SKIP ... print(reader.variable_labels()) # doctest: +SKIP {'index': '', 'col_1': 'This is an example', 'col_2': ''} >>> pd.read_stata(path) # doctest: +SKIP index col_1 col_2 0 0 1 2 1 1 3 4 """ self._ensure_open() return dict(zip(self._varlist, self._variable_labels)) def value_labels(self) -> dict[str, dict[float, str]]: """ Return a nested dict associating each variable name to its value and label. Returns ------- dict Examples -------- >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=["col_1", "col_2"]) >>> time_stamp = pd.Timestamp(2000, 2, 29, 14, 21) >>> path = "/My_path/filename.dta" >>> value_labels = {"col_1": {3: "x"}} >>> df.to_stata(path, time_stamp=time_stamp, # doctest: +SKIP ... value_labels=value_labels, version=None) # doctest: +SKIP >>> with pd.io.stata.StataReader(path) as reader: # doctest: +SKIP ... print(reader.value_labels()) # doctest: +SKIP {'col_1': {3: 'x'}} >>> pd.read_stata(path) # doctest: +SKIP index col_1 col_2 0 0 1 2 1 1 x 4 """ if not self._value_labels_read: self._read_value_labels() return self._value_label_dict @Appender(_read_stata_doc) def read_stata( filepath_or_buffer: FilePath | ReadBuffer[bytes], *, convert_dates: bool = True, convert_categoricals: bool = True, index_col: str | None = None, convert_missing: bool = False, preserve_dtypes: bool = True, columns: Sequence[str] | None = None, order_categoricals: bool = True, chunksize: int | None = None, iterator: bool = False, compression: CompressionOptions = "infer", storage_options: StorageOptions | None = None, ) -> DataFrame | StataReader: reader = StataReader( filepath_or_buffer, convert_dates=convert_dates, convert_categoricals=convert_categoricals, index_col=index_col, convert_missing=convert_missing, preserve_dtypes=preserve_dtypes, columns=columns, order_categoricals=order_categoricals, chunksize=chunksize, storage_options=storage_options, compression=compression, ) if iterator or chunksize: return reader with reader: return reader.read() def _set_endianness(endianness: str) -> str: if endianness.lower() in ["<", "little"]: return "<" elif endianness.lower() in [">", "big"]: return ">" else: # pragma : no cover raise ValueError(f"Endianness {endianness} not understood") def _pad_bytes(name: AnyStr, length: int) -> AnyStr: """ Take a char string and pads it with null bytes until it's length chars. """ if isinstance(name, bytes): return name + b"\x00" * (length - len(name)) return name + "\x00" * (length - len(name)) def _convert_datetime_to_stata_type(fmt: str) -> np.dtype: """ Convert from one of the stata date formats to a type in TYPE_MAP. """ if fmt in [ "tc", "%tc", "td", "%td", "tw", "%tw", "tm", "%tm", "tq", "%tq", "th", "%th", "ty", "%ty", ]: return np.dtype(np.float64) # Stata expects doubles for SIFs else: raise NotImplementedError(f"Format {fmt} not implemented") def _maybe_convert_to_int_keys(convert_dates: dict, varlist: list[Hashable]) -> dict: new_dict = {} for key in convert_dates: if not convert_dates[key].startswith("%"): # make sure proper fmts convert_dates[key] = "%" + convert_dates[key] if key in varlist: new_dict.update({varlist.index(key): convert_dates[key]}) else: if not isinstance(key, int): raise ValueError("convert_dates key must be a column or an integer") new_dict.update({key: convert_dates[key]}) return new_dict def _dtype_to_stata_type(dtype: np.dtype, column: Series) -> int: """ Convert dtype types to stata types. Returns the byte of the given ordinal. See TYPE_MAP and comments for an explanation. This is also explained in the dta spec. 1 - 244 are strings of this length Pandas Stata 251 - for int8 byte 252 - for int16 int 253 - for int32 long 254 - for float32 float 255 - for double double If there are dates to convert, then dtype will already have the correct type inserted. """ # TODO: expand to handle datetime to integer conversion if dtype.type is np.object_: # try to coerce it to the biggest string # not memory efficient, what else could we # do? itemsize = max_len_string_array(ensure_object(column._values)) return max(itemsize, 1) elif dtype.type is np.float64: return 255 elif dtype.type is np.float32: return 254 elif dtype.type is np.int32: return 253 elif dtype.type is np.int16: return 252 elif dtype.type is np.int8: return 251 else: # pragma : no cover raise NotImplementedError(f"Data type {dtype} not supported.") def _dtype_to_default_stata_fmt( dtype, column: Series, dta_version: int = 114, force_strl: bool = False ) -> str: """ Map numpy dtype to stata's default format for this type. Not terribly important since users can change this in Stata. Semantics are object -> "%DDs" where DD is the length of the string. If not a string, raise ValueError float64 -> "%10.0g" float32 -> "%9.0g" int64 -> "%9.0g" int32 -> "%12.0g" int16 -> "%8.0g" int8 -> "%8.0g" strl -> "%9s" """ # TODO: Refactor to combine type with format # TODO: expand this to handle a default datetime format? if dta_version < 117: max_str_len = 244 else: max_str_len = 2045 if force_strl: return "%9s" if dtype.type is np.object_: itemsize = max_len_string_array(ensure_object(column._values)) if itemsize > max_str_len: if dta_version >= 117: return "%9s" else: raise ValueError(excessive_string_length_error.format(column.name)) return "%" + str(max(itemsize, 1)) + "s" elif dtype == np.float64: return "%10.0g" elif dtype == np.float32: return "%9.0g" elif dtype == np.int32: return "%12.0g" elif dtype in (np.int8, np.int16): return "%8.0g" else: # pragma : no cover raise NotImplementedError(f"Data type {dtype} not supported.") @doc( storage_options=_shared_docs["storage_options"], compression_options=_shared_docs["compression_options"] % "fname", ) class StataWriter(StataParser): """ A class for writing Stata binary dta files Parameters ---------- fname : path (string), buffer or path object string, path object (pathlib.Path or py._path.local.LocalPath) or object implementing a binary write() functions. If using a buffer then the buffer will not be automatically closed after the file is written. data : DataFrame Input to save convert_dates : dict Dictionary mapping columns containing datetime types to stata internal format to use when writing the dates. Options are 'tc', 'td', 'tm', 'tw', 'th', 'tq', 'ty'. Column can be either an integer or a name. Datetime columns that do not have a conversion type specified will be converted to 'tc'. Raises NotImplementedError if a datetime column has timezone information write_index : bool Write the index to Stata dataset. byteorder : str Can be ">", "<", "little", or "big". default is `sys.byteorder` time_stamp : datetime A datetime to use as file creation date. Default is the current time data_label : str A label for the data set. Must be 80 characters or smaller. variable_labels : dict Dictionary containing columns as keys and variable labels as values. Each label must be 80 characters or smaller. {compression_options} .. versionchanged:: 1.4.0 Zstandard support. {storage_options} value_labels : dict of dicts Dictionary containing columns as keys and dictionaries of column value to labels as values. The combined length of all labels for a single variable must be 32,000 characters or smaller. .. versionadded:: 1.4.0 Returns ------- writer : StataWriter instance The StataWriter instance has a write_file method, which will write the file to the given `fname`. Raises ------ NotImplementedError * If datetimes contain timezone information ValueError * Columns listed in convert_dates are neither datetime64[ns] or datetime * Column dtype is not representable in Stata * Column listed in convert_dates is not in DataFrame * Categorical label contains more than 32,000 characters Examples -------- >>> data = pd.DataFrame([[1.0, 1]], columns=['a', 'b']) >>> writer = StataWriter('./data_file.dta', data) >>> writer.write_file() Directly write a zip file >>> compression = {{"method": "zip", "archive_name": "data_file.dta"}} >>> writer = StataWriter('./data_file.zip', data, compression=compression) >>> writer.write_file() Save a DataFrame with dates >>> from datetime import datetime >>> data = pd.DataFrame([[datetime(2000,1,1)]], columns=['date']) >>> writer = StataWriter('./date_data_file.dta', data, {{'date' : 'tw'}}) >>> writer.write_file() """ _max_string_length = 244 _encoding: Literal["latin-1", "utf-8"] = "latin-1" def __init__( self, fname: FilePath | WriteBuffer[bytes], data: DataFrame, convert_dates: dict[Hashable, str] | None = None, write_index: bool = True, byteorder: str | None = None, time_stamp: datetime | None = None, data_label: str | None = None, variable_labels: dict[Hashable, str] | None = None, compression: CompressionOptions = "infer", storage_options: StorageOptions | None = None, *, value_labels: dict[Hashable, dict[float, str]] | None = None, ) -> None: super().__init__() self.data = data self._convert_dates = {} if convert_dates is None else convert_dates self._write_index = write_index self._time_stamp = time_stamp self._data_label = data_label self._variable_labels = variable_labels self._non_cat_value_labels = value_labels self._value_labels: list[StataValueLabel] = [] self._has_value_labels = np.array([], dtype=bool) self._compression = compression self._output_file: IO[bytes] | None = None self._converted_names: dict[Hashable, str] = {} # attach nobs, nvars, data, varlist, typlist self._prepare_pandas(data) self.storage_options = storage_options if byteorder is None: byteorder = sys.byteorder self._byteorder = _set_endianness(byteorder) self._fname = fname self.type_converters = {253: np.int32, 252: np.int16, 251: np.int8} def _write(self, to_write: str) -> None: """ Helper to call encode before writing to file for Python 3 compat. """ self.handles.handle.write(to_write.encode(self._encoding)) def _write_bytes(self, value: bytes) -> None: """ Helper to assert file is open before writing. """ self.handles.handle.write(value) def _prepare_non_cat_value_labels( self, data: DataFrame ) -> list[StataNonCatValueLabel]: """ Check for value labels provided for non-categorical columns. Value labels """ non_cat_value_labels: list[StataNonCatValueLabel] = [] if self._non_cat_value_labels is None: return non_cat_value_labels for labname, labels in self._non_cat_value_labels.items(): if labname in self._converted_names: colname = self._converted_names[labname] elif labname in data.columns: colname = str(labname) else: raise KeyError( f"Can't create value labels for {labname}, it wasn't " "found in the dataset." ) if not is_numeric_dtype(data[colname].dtype): # Labels should not be passed explicitly for categorical # columns that will be converted to int raise ValueError( f"Can't create value labels for {labname}, value labels " "can only be applied to numeric columns." ) svl = StataNonCatValueLabel(colname, labels, self._encoding) non_cat_value_labels.append(svl) return non_cat_value_labels def _prepare_categoricals(self, data: DataFrame) -> DataFrame: """ Check for categorical columns, retain categorical information for Stata file and convert categorical data to int """ is_cat = [isinstance(dtype, CategoricalDtype) for dtype in data.dtypes] if not any(is_cat): return data self._has_value_labels |= np.array(is_cat) get_base_missing_value = StataMissingValue.get_base_missing_value data_formatted = [] for col, col_is_cat in zip(data, is_cat): if col_is_cat: svl = StataValueLabel(data[col], encoding=self._encoding) self._value_labels.append(svl) dtype = data[col].cat.codes.dtype if dtype == np.int64: raise ValueError( "It is not possible to export " "int64-based categorical data to Stata." ) values = data[col].cat.codes._values.copy() # Upcast if needed so that correct missing values can be set if values.max() >= get_base_missing_value(dtype): if dtype == np.int8: dtype = np.dtype(np.int16) elif dtype == np.int16: dtype = np.dtype(np.int32) else: dtype = np.dtype(np.float64) values = np.array(values, dtype=dtype) # Replace missing values with Stata missing value for type values[values == -1] = get_base_missing_value(dtype) data_formatted.append((col, values)) else: data_formatted.append((col, data[col])) return DataFrame.from_dict(dict(data_formatted)) def _replace_nans(self, data: DataFrame) -> DataFrame: # return data """ Checks floating point data columns for nans, and replaces these with the generic Stata for missing value (.) """ for c in data: dtype = data[c].dtype if dtype in (np.float32, np.float64): if dtype == np.float32: replacement = self.MISSING_VALUES["f"] else: replacement = self.MISSING_VALUES["d"] data[c] = data[c].fillna(replacement) return data def _update_strl_names(self) -> None: """No-op, forward compatibility""" def _validate_variable_name(self, name: str) -> str: """ Validate variable names for Stata export. Parameters ---------- name : str Variable name Returns ------- str The validated name with invalid characters replaced with underscores. Notes ----- Stata 114 and 117 support ascii characters in a-z, A-Z, 0-9 and _. """ for c in name: if ( (c < "A" or c > "Z") and (c < "a" or c > "z") and (c < "0" or c > "9") and c != "_" ): name = name.replace(c, "_") return name def _check_column_names(self, data: DataFrame) -> DataFrame: """ Checks column names to ensure that they are valid Stata column names. This includes checks for: * Non-string names * Stata keywords * Variables that start with numbers * Variables with names that are too long When an illegal variable name is detected, it is converted, and if dates are exported, the variable name is propagated to the date conversion dictionary """ converted_names: dict[Hashable, str] = {} columns = list(data.columns) original_columns = columns[:] duplicate_var_id = 0 for j, name in enumerate(columns): orig_name = name if not isinstance(name, str): name = str(name) name = self._validate_variable_name(name) # Variable name must not be a reserved word if name in self.RESERVED_WORDS: name = "_" + name # Variable name may not start with a number if "0" <= name[0] <= "9": name = "_" + name name = name[: min(len(name), 32)] if not name == orig_name: # check for duplicates while columns.count(name) > 0: # prepend ascending number to avoid duplicates name = "_" + str(duplicate_var_id) + name name = name[: min(len(name), 32)] duplicate_var_id += 1 converted_names[orig_name] = name columns[j] = name data.columns = Index(columns) # Check date conversion, and fix key if needed if self._convert_dates: for c, o in zip(columns, original_columns): if c != o: self._convert_dates[c] = self._convert_dates[o] del self._convert_dates[o] if converted_names: conversion_warning = [] for orig_name, name in converted_names.items(): msg = f"{orig_name} -> {name}" conversion_warning.append(msg) ws = invalid_name_doc.format("\n ".join(conversion_warning)) warnings.warn( ws, InvalidColumnName, stacklevel=find_stack_level(), ) self._converted_names = converted_names self._update_strl_names() return data def _set_formats_and_types(self, dtypes: Series) -> None: self.fmtlist: list[str] = [] self.typlist: list[int] = [] for col, dtype in dtypes.items(): self.fmtlist.append(_dtype_to_default_stata_fmt(dtype, self.data[col])) self.typlist.append(_dtype_to_stata_type(dtype, self.data[col])) def _prepare_pandas(self, data: DataFrame) -> None: # NOTE: we might need a different API / class for pandas objects so # we can set different semantics - handle this with a PR to pandas.io data = data.copy() if self._write_index: temp = data.reset_index() if isinstance(temp, DataFrame): data = temp # Ensure column names are strings data = self._check_column_names(data) # Check columns for compatibility with stata, upcast if necessary # Raise if outside the supported range data = _cast_to_stata_types(data) # Replace NaNs with Stata missing values data = self._replace_nans(data) # Set all columns to initially unlabelled self._has_value_labels = np.repeat(False, data.shape[1]) # Create value labels for non-categorical data non_cat_value_labels = self._prepare_non_cat_value_labels(data) non_cat_columns = [svl.labname for svl in non_cat_value_labels] has_non_cat_val_labels = data.columns.isin(non_cat_columns) self._has_value_labels |= has_non_cat_val_labels self._value_labels.extend(non_cat_value_labels) # Convert categoricals to int data, and strip labels data = self._prepare_categoricals(data) self.nobs, self.nvar = data.shape self.data = data self.varlist = data.columns.tolist() dtypes = data.dtypes # Ensure all date columns are converted for col in data: if col in self._convert_dates: continue if lib.is_np_dtype(data[col].dtype, "M"): self._convert_dates[col] = "tc" self._convert_dates = _maybe_convert_to_int_keys( self._convert_dates, self.varlist ) for key in self._convert_dates: new_type = _convert_datetime_to_stata_type(self._convert_dates[key]) dtypes.iloc[key] = np.dtype(new_type) # Verify object arrays are strings and encode to bytes self._encode_strings() self._set_formats_and_types(dtypes) # set the given format for the datetime cols if self._convert_dates is not None: for key in self._convert_dates: if isinstance(key, int): self.fmtlist[key] = self._convert_dates[key] def _encode_strings(self) -> None: """ Encode strings in dta-specific encoding Do not encode columns marked for date conversion or for strL conversion. The strL converter independently handles conversion and also accepts empty string arrays. """ convert_dates = self._convert_dates # _convert_strl is not available in dta 114 convert_strl = getattr(self, "_convert_strl", []) for i, col in enumerate(self.data): # Skip columns marked for date conversion or strl conversion if i in convert_dates or col in convert_strl: continue column = self.data[col] dtype = column.dtype if dtype.type is np.object_: inferred_dtype = infer_dtype(column, skipna=True) if not ((inferred_dtype == "string") or len(column) == 0): col = column.name raise ValueError( f"""\ Column `{col}` cannot be exported.\n\nOnly string-like object arrays containing all strings or a mix of strings and None can be exported. Object arrays containing only null values are prohibited. Other object types cannot be exported and must first be converted to one of the supported types.""" ) encoded = self.data[col].str.encode(self._encoding) # If larger than _max_string_length do nothing if ( max_len_string_array(ensure_object(encoded._values)) <= self._max_string_length ): self.data[col] = encoded def write_file(self) -> None: """ Export DataFrame object to Stata dta format. Examples -------- >>> df = pd.DataFrame({"fully_labelled": [1, 2, 3, 3, 1], ... "partially_labelled": [1.0, 2.0, np.nan, 9.0, np.nan], ... "Y": [7, 7, 9, 8, 10], ... "Z": pd.Categorical(["j", "k", "l", "k", "j"]), ... }) >>> path = "/My_path/filename.dta" >>> labels = {"fully_labelled": {1: "one", 2: "two", 3: "three"}, ... "partially_labelled": {1.0: "one", 2.0: "two"}, ... } >>> writer = pd.io.stata.StataWriter(path, ... df, ... value_labels=labels) # doctest: +SKIP >>> writer.write_file() # doctest: +SKIP >>> df = pd.read_stata(path) # doctest: +SKIP >>> df # doctest: +SKIP index fully_labelled partially_labeled Y Z 0 0 one one 7 j 1 1 two two 7 k 2 2 three NaN 9 l 3 3 three 9.0 8 k 4 4 one NaN 10 j """ with get_handle( self._fname, "wb", compression=self._compression, is_text=False, storage_options=self.storage_options, ) as self.handles: if self.handles.compression["method"] is not None: # ZipFile creates a file (with the same name) for each write call. # Write it first into a buffer and then write the buffer to the ZipFile. self._output_file, self.handles.handle = self.handles.handle, BytesIO() self.handles.created_handles.append(self.handles.handle) try: self._write_header( data_label=self._data_label, time_stamp=self._time_stamp ) self._write_map() self._write_variable_types() self._write_varnames() self._write_sortlist() self._write_formats() self._write_value_label_names() self._write_variable_labels() self._write_expansion_fields() self._write_characteristics() records = self._prepare_data() self._write_data(records) self._write_strls() self._write_value_labels() self._write_file_close_tag() self._write_map() self._close() except Exception as exc: self.handles.close() if isinstance(self._fname, (str, os.PathLike)) and os.path.isfile( self._fname ): try: os.unlink(self._fname) except OSError: warnings.warn( f"This save was not successful but {self._fname} could not " "be deleted. This file is not valid.", ResourceWarning, stacklevel=find_stack_level(), ) raise exc def _close(self) -> None: """ Close the file if it was created by the writer. If a buffer or file-like object was passed in, for example a GzipFile, then leave this file open for the caller to close. """ # write compression if self._output_file is not None: assert isinstance(self.handles.handle, BytesIO) bio, self.handles.handle = self.handles.handle, self._output_file self.handles.handle.write(bio.getvalue()) def _write_map(self) -> None: """No-op, future compatibility""" def _write_file_close_tag(self) -> None: """No-op, future compatibility""" def _write_characteristics(self) -> None: """No-op, future compatibility""" def _write_strls(self) -> None: """No-op, future compatibility""" def _write_expansion_fields(self) -> None: """Write 5 zeros for expansion fields""" self._write(_pad_bytes("", 5)) def _write_value_labels(self) -> None: for vl in self._value_labels: self._write_bytes(vl.generate_value_label(self._byteorder)) def _write_header( self, data_label: str | None = None, time_stamp: datetime | None = None, ) -> None: byteorder = self._byteorder # ds_format - just use 114 self._write_bytes(struct.pack("b", 114)) # byteorder self._write(byteorder == ">" and "\x01" or "\x02") # filetype self._write("\x01") # unused self._write("\x00") # number of vars, 2 bytes self._write_bytes(struct.pack(byteorder + "h", self.nvar)[:2]) # number of obs, 4 bytes self._write_bytes(struct.pack(byteorder + "i", self.nobs)[:4]) # data label 81 bytes, char, null terminated if data_label is None: self._write_bytes(self._null_terminate_bytes(_pad_bytes("", 80))) else: self._write_bytes( self._null_terminate_bytes(_pad_bytes(data_label[:80], 80)) ) # time stamp, 18 bytes, char, null terminated # format dd Mon yyyy hh:mm if time_stamp is None: time_stamp = datetime.now() elif not isinstance(time_stamp, datetime): raise ValueError("time_stamp should be datetime type") # GH #13856 # Avoid locale-specific month conversion months = [ "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec", ] month_lookup = {i + 1: month for i, month in enumerate(months)} ts = ( time_stamp.strftime("%d ") + month_lookup[time_stamp.month] + time_stamp.strftime(" %Y %H:%M") ) self._write_bytes(self._null_terminate_bytes(ts)) def _write_variable_types(self) -> None: for typ in self.typlist: self._write_bytes(struct.pack("B", typ)) def _write_varnames(self) -> None: # varlist names are checked by _check_column_names # varlist, requires null terminated for name in self.varlist: name = self._null_terminate_str(name) name = _pad_bytes(name[:32], 33) self._write(name) def _write_sortlist(self) -> None: # srtlist, 2*(nvar+1), int array, encoded by byteorder srtlist = _pad_bytes("", 2 * (self.nvar + 1)) self._write(srtlist) def _write_formats(self) -> None: # fmtlist, 49*nvar, char array for fmt in self.fmtlist: self._write(_pad_bytes(fmt, 49)) def _write_value_label_names(self) -> None: # lbllist, 33*nvar, char array for i in range(self.nvar): # Use variable name when categorical if self._has_value_labels[i]: name = self.varlist[i] name = self._null_terminate_str(name) name = _pad_bytes(name[:32], 33) self._write(name) else: # Default is empty label self._write(_pad_bytes("", 33)) def _write_variable_labels(self) -> None: # Missing labels are 80 blank characters plus null termination blank = _pad_bytes("", 81) if self._variable_labels is None: for i in range(self.nvar): self._write(blank) return for col in self.data: if col in self._variable_labels: label = self._variable_labels[col] if len(label) > 80: raise ValueError("Variable labels must be 80 characters or fewer") is_latin1 = all(ord(c) < 256 for c in label) if not is_latin1: raise ValueError( "Variable labels must contain only characters that " "can be encoded in Latin-1" ) self._write(_pad_bytes(label, 81)) else: self._write(blank) def _convert_strls(self, data: DataFrame) -> DataFrame: """No-op, future compatibility""" return data def _prepare_data(self) -> np.rec.recarray: data = self.data typlist = self.typlist convert_dates = self._convert_dates # 1. Convert dates if self._convert_dates is not None: for i, col in enumerate(data): if i in convert_dates: data[col] = _datetime_to_stata_elapsed_vec( data[col], self.fmtlist[i] ) # 2. Convert strls data = self._convert_strls(data) # 3. Convert bad string data to '' and pad to correct length dtypes = {} native_byteorder = self._byteorder == _set_endianness(sys.byteorder) for i, col in enumerate(data): typ = typlist[i] if typ <= self._max_string_length: with warnings.catch_warnings(): warnings.filterwarnings( "ignore", "Downcasting object dtype arrays", category=FutureWarning, ) dc = data[col].fillna("") data[col] = dc.apply(_pad_bytes, args=(typ,)) stype = f"S{typ}" dtypes[col] = stype data[col] = data[col].astype(stype) else: dtype = data[col].dtype if not native_byteorder: dtype = dtype.newbyteorder(self._byteorder) dtypes[col] = dtype return data.to_records(index=False, column_dtypes=dtypes) def _write_data(self, records: np.rec.recarray) -> None: self._write_bytes(records.tobytes()) @staticmethod def _null_terminate_str(s: str) -> str: s += "\x00" return s def _null_terminate_bytes(self, s: str) -> bytes: return self._null_terminate_str(s).encode(self._encoding) def _dtype_to_stata_type_117(dtype: np.dtype, column: Series, force_strl: bool) -> int: """ Converts dtype types to stata types. Returns the byte of the given ordinal. See TYPE_MAP and comments for an explanation. This is also explained in the dta spec. 1 - 2045 are strings of this length Pandas Stata 32768 - for object strL 65526 - for int8 byte 65527 - for int16 int 65528 - for int32 long 65529 - for float32 float 65530 - for double double If there are dates to convert, then dtype will already have the correct type inserted. """ # TODO: expand to handle datetime to integer conversion if force_strl: return 32768 if dtype.type is np.object_: # try to coerce it to the biggest string # not memory efficient, what else could we # do? itemsize = max_len_string_array(ensure_object(column._values)) itemsize = max(itemsize, 1) if itemsize <= 2045: return itemsize return 32768 elif dtype.type is np.float64: return 65526 elif dtype.type is np.float32: return 65527 elif dtype.type is np.int32: return 65528 elif dtype.type is np.int16: return 65529 elif dtype.type is np.int8: return 65530 else: # pragma : no cover raise NotImplementedError(f"Data type {dtype} not supported.") def _pad_bytes_new(name: str | bytes, length: int) -> bytes: """ Takes a bytes instance and pads it with null bytes until it's length chars. """ if isinstance(name, str): name = bytes(name, "utf-8") return name + b"\x00" * (length - len(name)) class StataStrLWriter: """ Converter for Stata StrLs Stata StrLs map 8 byte values to strings which are stored using a dictionary-like format where strings are keyed to two values. Parameters ---------- df : DataFrame DataFrame to convert columns : Sequence[str] List of columns names to convert to StrL version : int, optional dta version. Currently supports 117, 118 and 119 byteorder : str, optional Can be ">", "<", "little", or "big". default is `sys.byteorder` Notes ----- Supports creation of the StrL block of a dta file for dta versions 117, 118 and 119. These differ in how the GSO is stored. 118 and 119 store the GSO lookup value as a uint32 and a uint64, while 117 uses two uint32s. 118 and 119 also encode all strings as unicode which is required by the format. 117 uses 'latin-1' a fixed width encoding that extends the 7-bit ascii table with an additional 128 characters. """ def __init__( self, df: DataFrame, columns: Sequence[str], version: int = 117, byteorder: str | None = None, ) -> None: if version not in (117, 118, 119): raise ValueError("Only dta versions 117, 118 and 119 supported") self._dta_ver = version self.df = df self.columns = columns self._gso_table = {"": (0, 0)} if byteorder is None: byteorder = sys.byteorder self._byteorder = _set_endianness(byteorder) gso_v_type = "I" # uint32 gso_o_type = "Q" # uint64 self._encoding = "utf-8" if version == 117: o_size = 4 gso_o_type = "I" # 117 used uint32 self._encoding = "latin-1" elif version == 118: o_size = 6 else: # version == 119 o_size = 5 self._o_offet = 2 ** (8 * (8 - o_size)) self._gso_o_type = gso_o_type self._gso_v_type = gso_v_type def _convert_key(self, key: tuple[int, int]) -> int: v, o = key return v + self._o_offet * o def generate_table(self) -> tuple[dict[str, tuple[int, int]], DataFrame]: """ Generates the GSO lookup table for the DataFrame Returns ------- gso_table : dict Ordered dictionary using the string found as keys and their lookup position (v,o) as values gso_df : DataFrame DataFrame where strl columns have been converted to (v,o) values Notes ----- Modifies the DataFrame in-place. The DataFrame returned encodes the (v,o) values as uint64s. The encoding depends on the dta version, and can be expressed as enc = v + o * 2 ** (o_size * 8) so that v is stored in the lower bits and o is in the upper bits. o_size is * 117: 4 * 118: 6 * 119: 5 """ gso_table = self._gso_table gso_df = self.df columns = list(gso_df.columns) selected = gso_df[self.columns] col_index = [(col, columns.index(col)) for col in self.columns] keys = np.empty(selected.shape, dtype=np.uint64) for o, (idx, row) in enumerate(selected.iterrows()): for j, (col, v) in enumerate(col_index): val = row[col] # Allow columns with mixed str and None (GH 23633) val = "" if val is None else val key = gso_table.get(val, None) if key is None: # Stata prefers human numbers key = (v + 1, o + 1) gso_table[val] = key keys[o, j] = self._convert_key(key) for i, col in enumerate(self.columns): gso_df[col] = keys[:, i] return gso_table, gso_df def generate_blob(self, gso_table: dict[str, tuple[int, int]]) -> bytes: """ Generates the binary blob of GSOs that is written to the dta file. Parameters ---------- gso_table : dict Ordered dictionary (str, vo) Returns ------- gso : bytes Binary content of dta file to be placed between strl tags Notes ----- Output format depends on dta version. 117 uses two uint32s to express v and o while 118+ uses a uint32 for v and a uint64 for o. """ # Format information # Length includes null term # 117 # GSOvvvvooootllllxxxxxxxxxxxxxxx...x # 3 u4 u4 u1 u4 string + null term # # 118, 119 # GSOvvvvooooooootllllxxxxxxxxxxxxxxx...x # 3 u4 u8 u1 u4 string + null term bio = BytesIO() gso = bytes("GSO", "ascii") gso_type = struct.pack(self._byteorder + "B", 130) null = struct.pack(self._byteorder + "B", 0) v_type = self._byteorder + self._gso_v_type o_type = self._byteorder + self._gso_o_type len_type = self._byteorder + "I" for strl, vo in gso_table.items(): if vo == (0, 0): continue v, o = vo # GSO bio.write(gso) # vvvv bio.write(struct.pack(v_type, v)) # oooo / oooooooo bio.write(struct.pack(o_type, o)) # t bio.write(gso_type) # llll utf8_string = bytes(strl, "utf-8") bio.write(struct.pack(len_type, len(utf8_string) + 1)) # xxx...xxx bio.write(utf8_string) bio.write(null) return bio.getvalue() class StataWriter117(StataWriter): """ A class for writing Stata binary dta files in Stata 13 format (117) Parameters ---------- fname : path (string), buffer or path object string, path object (pathlib.Path or py._path.local.LocalPath) or object implementing a binary write() functions. If using a buffer then the buffer will not be automatically closed after the file is written. data : DataFrame Input to save convert_dates : dict Dictionary mapping columns containing datetime types to stata internal format to use when writing the dates. Options are 'tc', 'td', 'tm', 'tw', 'th', 'tq', 'ty'. Column can be either an integer or a name. Datetime columns that do not have a conversion type specified will be converted to 'tc'. Raises NotImplementedError if a datetime column has timezone information write_index : bool Write the index to Stata dataset. byteorder : str Can be ">", "<", "little", or "big". default is `sys.byteorder` time_stamp : datetime A datetime to use as file creation date. Default is the current time data_label : str A label for the data set. Must be 80 characters or smaller. variable_labels : dict Dictionary containing columns as keys and variable labels as values. Each label must be 80 characters or smaller. convert_strl : list List of columns names to convert to Stata StrL format. Columns with more than 2045 characters are automatically written as StrL. Smaller columns can be converted by including the column name. Using StrLs can reduce output file size when strings are longer than 8 characters, and either frequently repeated or sparse. {compression_options} .. versionchanged:: 1.4.0 Zstandard support. value_labels : dict of dicts Dictionary containing columns as keys and dictionaries of column value to labels as values. The combined length of all labels for a single variable must be 32,000 characters or smaller. .. versionadded:: 1.4.0 Returns ------- writer : StataWriter117 instance The StataWriter117 instance has a write_file method, which will write the file to the given `fname`. Raises ------ NotImplementedError * If datetimes contain timezone information ValueError * Columns listed in convert_dates are neither datetime64[ns] or datetime * Column dtype is not representable in Stata * Column listed in convert_dates is not in DataFrame * Categorical label contains more than 32,000 characters Examples -------- >>> data = pd.DataFrame([[1.0, 1, 'a']], columns=['a', 'b', 'c']) >>> writer = pd.io.stata.StataWriter117('./data_file.dta', data) >>> writer.write_file() Directly write a zip file >>> compression = {"method": "zip", "archive_name": "data_file.dta"} >>> writer = pd.io.stata.StataWriter117( ... './data_file.zip', data, compression=compression ... ) >>> writer.write_file() Or with long strings stored in strl format >>> data = pd.DataFrame([['A relatively long string'], [''], ['']], ... columns=['strls']) >>> writer = pd.io.stata.StataWriter117( ... './data_file_with_long_strings.dta', data, convert_strl=['strls']) >>> writer.write_file() """ _max_string_length = 2045 _dta_version = 117 def __init__( self, fname: FilePath | WriteBuffer[bytes], data: DataFrame, convert_dates: dict[Hashable, str] | None = None, write_index: bool = True, byteorder: str | None = None, time_stamp: datetime | None = None, data_label: str | None = None, variable_labels: dict[Hashable, str] | None = None, convert_strl: Sequence[Hashable] | None = None, compression: CompressionOptions = "infer", storage_options: StorageOptions | None = None, *, value_labels: dict[Hashable, dict[float, str]] | None = None, ) -> None: # Copy to new list since convert_strl might be modified later self._convert_strl: list[Hashable] = [] if convert_strl is not None: self._convert_strl.extend(convert_strl) super().__init__( fname, data, convert_dates, write_index, byteorder=byteorder, time_stamp=time_stamp, data_label=data_label, variable_labels=variable_labels, value_labels=value_labels, compression=compression, storage_options=storage_options, ) self._map: dict[str, int] = {} self._strl_blob = b"" @staticmethod def _tag(val: str | bytes, tag: str) -> bytes: """Surround val with """ if isinstance(val, str): val = bytes(val, "utf-8") return bytes("<" + tag + ">", "utf-8") + val + bytes("", "utf-8") def _update_map(self, tag: str) -> None: """Update map location for tag with file position""" assert self.handles.handle is not None self._map[tag] = self.handles.handle.tell() def _write_header( self, data_label: str | None = None, time_stamp: datetime | None = None, ) -> None: """Write the file header""" byteorder = self._byteorder self._write_bytes(bytes("", "utf-8")) bio = BytesIO() # ds_format - 117 bio.write(self._tag(bytes(str(self._dta_version), "utf-8"), "release")) # byteorder bio.write(self._tag(byteorder == ">" and "MSF" or "LSF", "byteorder")) # number of vars, 2 bytes in 117 and 118, 4 byte in 119 nvar_type = "H" if self._dta_version <= 118 else "I" bio.write(self._tag(struct.pack(byteorder + nvar_type, self.nvar), "K")) # 117 uses 4 bytes, 118 uses 8 nobs_size = "I" if self._dta_version == 117 else "Q" bio.write(self._tag(struct.pack(byteorder + nobs_size, self.nobs), "N")) # data label 81 bytes, char, null terminated label = data_label[:80] if data_label is not None else "" encoded_label = label.encode(self._encoding) label_size = "B" if self._dta_version == 117 else "H" label_len = struct.pack(byteorder + label_size, len(encoded_label)) encoded_label = label_len + encoded_label bio.write(self._tag(encoded_label, "label")) # time stamp, 18 bytes, char, null terminated # format dd Mon yyyy hh:mm if time_stamp is None: time_stamp = datetime.now() elif not isinstance(time_stamp, datetime): raise ValueError("time_stamp should be datetime type") # Avoid locale-specific month conversion months = [ "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec", ] month_lookup = {i + 1: month for i, month in enumerate(months)} ts = ( time_stamp.strftime("%d ") + month_lookup[time_stamp.month] + time_stamp.strftime(" %Y %H:%M") ) # '\x11' added due to inspection of Stata file stata_ts = b"\x11" + bytes(ts, "utf-8") bio.write(self._tag(stata_ts, "timestamp")) self._write_bytes(self._tag(bio.getvalue(), "header")) def _write_map(self) -> None: """ Called twice during file write. The first populates the values in the map with 0s. The second call writes the final map locations when all blocks have been written. """ if not self._map: self._map = { "stata_data": 0, "map": self.handles.handle.tell(), "variable_types": 0, "varnames": 0, "sortlist": 0, "formats": 0, "value_label_names": 0, "variable_labels": 0, "characteristics": 0, "data": 0, "strls": 0, "value_labels": 0, "stata_data_close": 0, "end-of-file": 0, } # Move to start of map self.handles.handle.seek(self._map["map"]) bio = BytesIO() for val in self._map.values(): bio.write(struct.pack(self._byteorder + "Q", val)) self._write_bytes(self._tag(bio.getvalue(), "map")) def _write_variable_types(self) -> None: self._update_map("variable_types") bio = BytesIO() for typ in self.typlist: bio.write(struct.pack(self._byteorder + "H", typ)) self._write_bytes(self._tag(bio.getvalue(), "variable_types")) def _write_varnames(self) -> None: self._update_map("varnames") bio = BytesIO() # 118 scales by 4 to accommodate utf-8 data worst case encoding vn_len = 32 if self._dta_version == 117 else 128 for name in self.varlist: name = self._null_terminate_str(name) name = _pad_bytes_new(name[:32].encode(self._encoding), vn_len + 1) bio.write(name) self._write_bytes(self._tag(bio.getvalue(), "varnames")) def _write_sortlist(self) -> None: self._update_map("sortlist") sort_size = 2 if self._dta_version < 119 else 4 self._write_bytes(self._tag(b"\x00" * sort_size * (self.nvar + 1), "sortlist")) def _write_formats(self) -> None: self._update_map("formats") bio = BytesIO() fmt_len = 49 if self._dta_version == 117 else 57 for fmt in self.fmtlist: bio.write(_pad_bytes_new(fmt.encode(self._encoding), fmt_len)) self._write_bytes(self._tag(bio.getvalue(), "formats")) def _write_value_label_names(self) -> None: self._update_map("value_label_names") bio = BytesIO() # 118 scales by 4 to accommodate utf-8 data worst case encoding vl_len = 32 if self._dta_version == 117 else 128 for i in range(self.nvar): # Use variable name when categorical name = "" # default name if self._has_value_labels[i]: name = self.varlist[i] name = self._null_terminate_str(name) encoded_name = _pad_bytes_new(name[:32].encode(self._encoding), vl_len + 1) bio.write(encoded_name) self._write_bytes(self._tag(bio.getvalue(), "value_label_names")) def _write_variable_labels(self) -> None: # Missing labels are 80 blank characters plus null termination self._update_map("variable_labels") bio = BytesIO() # 118 scales by 4 to accommodate utf-8 data worst case encoding vl_len = 80 if self._dta_version == 117 else 320 blank = _pad_bytes_new("", vl_len + 1) if self._variable_labels is None: for _ in range(self.nvar): bio.write(blank) self._write_bytes(self._tag(bio.getvalue(), "variable_labels")) return for col in self.data: if col in self._variable_labels: label = self._variable_labels[col] if len(label) > 80: raise ValueError("Variable labels must be 80 characters or fewer") try: encoded = label.encode(self._encoding) except UnicodeEncodeError as err: raise ValueError( "Variable labels must contain only characters that " f"can be encoded in {self._encoding}" ) from err bio.write(_pad_bytes_new(encoded, vl_len + 1)) else: bio.write(blank) self._write_bytes(self._tag(bio.getvalue(), "variable_labels")) def _write_characteristics(self) -> None: self._update_map("characteristics") self._write_bytes(self._tag(b"", "characteristics")) def _write_data(self, records) -> None: self._update_map("data") self._write_bytes(b"") self._write_bytes(records.tobytes()) self._write_bytes(b"") def _write_strls(self) -> None: self._update_map("strls") self._write_bytes(self._tag(self._strl_blob, "strls")) def _write_expansion_fields(self) -> None: """No-op in dta 117+""" def _write_value_labels(self) -> None: self._update_map("value_labels") bio = BytesIO() for vl in self._value_labels: lab = vl.generate_value_label(self._byteorder) lab = self._tag(lab, "lbl") bio.write(lab) self._write_bytes(self._tag(bio.getvalue(), "value_labels")) def _write_file_close_tag(self) -> None: self._update_map("stata_data_close") self._write_bytes(bytes("", "utf-8")) self._update_map("end-of-file") def _update_strl_names(self) -> None: """ Update column names for conversion to strl if they might have been changed to comply with Stata naming rules """ # Update convert_strl if names changed for orig, new in self._converted_names.items(): if orig in self._convert_strl: idx = self._convert_strl.index(orig) self._convert_strl[idx] = new def _convert_strls(self, data: DataFrame) -> DataFrame: """ Convert columns to StrLs if either very large or in the convert_strl variable """ convert_cols = [ col for i, col in enumerate(data) if self.typlist[i] == 32768 or col in self._convert_strl ] if convert_cols: ssw = StataStrLWriter(data, convert_cols, version=self._dta_version) tab, new_data = ssw.generate_table() data = new_data self._strl_blob = ssw.generate_blob(tab) return data def _set_formats_and_types(self, dtypes: Series) -> None: self.typlist = [] self.fmtlist = [] for col, dtype in dtypes.items(): force_strl = col in self._convert_strl fmt = _dtype_to_default_stata_fmt( dtype, self.data[col], dta_version=self._dta_version, force_strl=force_strl, ) self.fmtlist.append(fmt) self.typlist.append( _dtype_to_stata_type_117(dtype, self.data[col], force_strl) ) class StataWriterUTF8(StataWriter117): """ Stata binary dta file writing in Stata 15 (118) and 16 (119) formats DTA 118 and 119 format files support unicode string data (both fixed and strL) format. Unicode is also supported in value labels, variable labels and the dataset label. Format 119 is automatically used if the file contains more than 32,767 variables. Parameters ---------- fname : path (string), buffer or path object string, path object (pathlib.Path or py._path.local.LocalPath) or object implementing a binary write() functions. If using a buffer then the buffer will not be automatically closed after the file is written. data : DataFrame Input to save convert_dates : dict, default None Dictionary mapping columns containing datetime types to stata internal format to use when writing the dates. Options are 'tc', 'td', 'tm', 'tw', 'th', 'tq', 'ty'. Column can be either an integer or a name. Datetime columns that do not have a conversion type specified will be converted to 'tc'. Raises NotImplementedError if a datetime column has timezone information write_index : bool, default True Write the index to Stata dataset. byteorder : str, default None Can be ">", "<", "little", or "big". default is `sys.byteorder` time_stamp : datetime, default None A datetime to use as file creation date. Default is the current time data_label : str, default None A label for the data set. Must be 80 characters or smaller. variable_labels : dict, default None Dictionary containing columns as keys and variable labels as values. Each label must be 80 characters or smaller. convert_strl : list, default None List of columns names to convert to Stata StrL format. Columns with more than 2045 characters are automatically written as StrL. Smaller columns can be converted by including the column name. Using StrLs can reduce output file size when strings are longer than 8 characters, and either frequently repeated or sparse. version : int, default None The dta version to use. By default, uses the size of data to determine the version. 118 is used if data.shape[1] <= 32767, and 119 is used for storing larger DataFrames. {compression_options} .. versionchanged:: 1.4.0 Zstandard support. value_labels : dict of dicts Dictionary containing columns as keys and dictionaries of column value to labels as values. The combined length of all labels for a single variable must be 32,000 characters or smaller. .. versionadded:: 1.4.0 Returns ------- StataWriterUTF8 The instance has a write_file method, which will write the file to the given `fname`. Raises ------ NotImplementedError * If datetimes contain timezone information ValueError * Columns listed in convert_dates are neither datetime64[ns] or datetime * Column dtype is not representable in Stata * Column listed in convert_dates is not in DataFrame * Categorical label contains more than 32,000 characters Examples -------- Using Unicode data and column names >>> from pandas.io.stata import StataWriterUTF8 >>> data = pd.DataFrame([[1.0, 1, 'ᴬ']], columns=['a', 'β', 'ĉ']) >>> writer = StataWriterUTF8('./data_file.dta', data) >>> writer.write_file() Directly write a zip file >>> compression = {"method": "zip", "archive_name": "data_file.dta"} >>> writer = StataWriterUTF8('./data_file.zip', data, compression=compression) >>> writer.write_file() Or with long strings stored in strl format >>> data = pd.DataFrame([['ᴀ relatively long ŝtring'], [''], ['']], ... columns=['strls']) >>> writer = StataWriterUTF8('./data_file_with_long_strings.dta', data, ... convert_strl=['strls']) >>> writer.write_file() """ _encoding: Literal["utf-8"] = "utf-8" def __init__( self, fname: FilePath | WriteBuffer[bytes], data: DataFrame, convert_dates: dict[Hashable, str] | None = None, write_index: bool = True, byteorder: str | None = None, time_stamp: datetime | None = None, data_label: str | None = None, variable_labels: dict[Hashable, str] | None = None, convert_strl: Sequence[Hashable] | None = None, version: int | None = None, compression: CompressionOptions = "infer", storage_options: StorageOptions | None = None, *, value_labels: dict[Hashable, dict[float, str]] | None = None, ) -> None: if version is None: version = 118 if data.shape[1] <= 32767 else 119 elif version not in (118, 119): raise ValueError("version must be either 118 or 119.") elif version == 118 and data.shape[1] > 32767: raise ValueError( "You must use version 119 for data sets containing more than" "32,767 variables" ) super().__init__( fname, data, convert_dates=convert_dates, write_index=write_index, byteorder=byteorder, time_stamp=time_stamp, data_label=data_label, variable_labels=variable_labels, value_labels=value_labels, convert_strl=convert_strl, compression=compression, storage_options=storage_options, ) # Override version set in StataWriter117 init self._dta_version = version def _validate_variable_name(self, name: str) -> str: """ Validate variable names for Stata export. Parameters ---------- name : str Variable name Returns ------- str The validated name with invalid characters replaced with underscores. Notes ----- Stata 118+ support most unicode characters. The only limitation is in the ascii range where the characters supported are a-z, A-Z, 0-9 and _. """ # High code points appear to be acceptable for c in name: if ( ( ord(c) < 128 and (c < "A" or c > "Z") and (c < "a" or c > "z") and (c < "0" or c > "9") and c != "_" ) or 128 <= ord(c) < 192 or c in {"×", "÷"} # noqa: RUF001 ): name = name.replace(c, "_") return name