pandas.read_stata

pandas.read_stata(filepath_or_buffer, convert_dates=True, convert_categoricals=True, encoding=None, index=None, convert_missing=False, preserve_dtypes=True, columns=None, order_categoricals=True, chunksize=None, iterator=False) [source]

Read Stata file into DataFrame

Parameters:

filepath_or_buffer : string or file-like object Path to .dta file or object implementing a binary read() functions convert_dates : boolean, defaults to True Convert date variables to DataFrame time values convert_categoricals : boolean, defaults to True Read value labels and convert columns to Categorical/Factor variables encoding : string, None or encoding Encoding used to parse the files. None defaults to latin-1. index : identifier of index column identifier of column that should be used as index of the DataFrame convert_missing : boolean, defaults to False Flag indicating whether to convert missing values to their Stata representations. If False, missing values are replaced with nans. If True, columns containing missing values are returned with object data types and missing values are represented by StataMissingValue objects. preserve_dtypes : boolean, defaults to 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 : boolean, defaults to 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 : boolean, default False Return StataReader object

Returns:

DataFrame or StataReader

Examples

Read a Stata dta file:

>>> df = pandas.read_stata('filename.dta')

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

>>> itr = pandas.read_stata('filename.dta', chunksize=10000)
>>> for chunk in itr:
>>>     do_something(chunk)