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# pandas.read_stata

pandas.read_stata(filepath_or_buffer, *, convert_dates=True, convert_categoricals=True, index_col=None, convert_missing=False, preserve_dtypes=True, columns=None, order_categoricals=True, chunksize=None, iterator=False, compression='infer', storage_options=None)

  Read Stata file into DataFrame.

  This function reads `.dta` files produced by Stata, with support for converting date variables, categorical data, and missing value representations.

  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`.

    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.

    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 int, default None

      Return StataReader object for iterations, returns chunks with given number of lines.

    iterator bool, default False

      Return StataReader object.

    compression str or dict, default ‘infer’

      For on-the-fly decompression of on-disk data. If ‘infer’ and ‘filepath_or_buffer’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, ‘.zst’, ‘.tar’, ‘.tar.gz’, ‘.tar.xz’ or ‘.tar.bz2’ (otherwise no compression). If using ‘zip’ or ‘tar’, the ZIP file must contain only one data file to be read in. Set to `None` for no decompression. Can also be a dict with key `'method'` set to one of {`'zip'`, `'gzip'`, `'bz2'`, `'zstd'`, `'xz'`, `'tar'`} and other key-value pairs are forwarded to `zipfile.ZipFile`, `gzip.GzipFile`, `bz2.BZ2File`, `zstandard.ZstdDecompressor`, `lzma.LZMAFile` or `tarfile.TarFile`, respectively. As an example, the following could be passed for Zstandard decompression using a custom compression dictionary: `compression={'method': 'zstd', 'dict_data': my_compression_dict}`.

    storage_options dict, optional

      Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to `urllib.request.Request` as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to `fsspec.open`. Please see `fsspec` and `urllib` for more details, and for more examples on storage options refer here.

  Returns:
    DataFrame, pandas.api.typing.StataReader

      If iterator or chunksize, returns StataReader, else DataFrame.

  See also

  `io.stata.StataReader`

    Low-level reader for Stata data files.

  `DataFrame.to_stata`

    Export Stata data files.

  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.

  Examples

  Creating a dummy stata for this example

      >>> df = pd.DataFrame(
      ...     {
      ...         "animal": ["falcon", "parrot", "falcon", "parrot"],
      ...         "speed": [350, 18, 361, 15],
      ...     }
      ... )
      >>> df.to_stata("animals.dta")

  Read a Stata dta file:

      >>> df = pd.read_stata("animals.dta")

  Read a Stata dta file in 10,000 line chunks:

      >>> values = np.random.randint(
      ...     0, 10, size=(20_000, 1), dtype="uint8"
      ... )
      >>> df = pd.DataFrame(values, columns=["i"])
      >>> df.to_stata("filename.dta")

      >>> with pd.read_stata('filename.dta', chunksize=10000) as itr:
      >>>     for chunk in itr:
      ...         # Operate on a single chunk, e.g., chunk.mean()
      ...         pass