# -*- coding: utf-8 -*-
# pylint: disable=E1101
import datetime as dt
import os
import struct
import sys
import warnings
from datetime import datetime
from distutils.version import LooseVersion
import numpy as np
import pandas as pd
import pandas.util.testing as tm
import pytest
from pandas import compat
from pandas._libs.tslib import NaT
from pandas.compat import iterkeys
from pandas.core.dtypes.common import is_categorical_dtype
from pandas.core.frame import DataFrame, Series
from pandas.io.parsers import read_csv
from pandas.io.stata import (read_stata, StataReader, InvalidColumnName,
PossiblePrecisionLoss, StataMissingValue)
@pytest.fixture
def dirpath():
return tm.get_data_path()
@pytest.fixture
def parsed_114(dirpath):
dta14_114 = os.path.join(dirpath, 'stata5_114.dta')
parsed_114 = read_stata(dta14_114, convert_dates=True)
parsed_114.index.name = 'index'
return parsed_114
class TestStata(object):
def setup_method(self, method):
self.dirpath = tm.get_data_path()
self.dta1_114 = os.path.join(self.dirpath, 'stata1_114.dta')
self.dta1_117 = os.path.join(self.dirpath, 'stata1_117.dta')
self.dta2_113 = os.path.join(self.dirpath, 'stata2_113.dta')
self.dta2_114 = os.path.join(self.dirpath, 'stata2_114.dta')
self.dta2_115 = os.path.join(self.dirpath, 'stata2_115.dta')
self.dta2_117 = os.path.join(self.dirpath, 'stata2_117.dta')
self.dta3_113 = os.path.join(self.dirpath, 'stata3_113.dta')
self.dta3_114 = os.path.join(self.dirpath, 'stata3_114.dta')
self.dta3_115 = os.path.join(self.dirpath, 'stata3_115.dta')
self.dta3_117 = os.path.join(self.dirpath, 'stata3_117.dta')
self.csv3 = os.path.join(self.dirpath, 'stata3.csv')
self.dta4_113 = os.path.join(self.dirpath, 'stata4_113.dta')
self.dta4_114 = os.path.join(self.dirpath, 'stata4_114.dta')
self.dta4_115 = os.path.join(self.dirpath, 'stata4_115.dta')
self.dta4_117 = os.path.join(self.dirpath, 'stata4_117.dta')
self.dta_encoding = os.path.join(self.dirpath, 'stata1_encoding.dta')
self.csv14 = os.path.join(self.dirpath, 'stata5.csv')
self.dta14_113 = os.path.join(self.dirpath, 'stata5_113.dta')
self.dta14_114 = os.path.join(self.dirpath, 'stata5_114.dta')
self.dta14_115 = os.path.join(self.dirpath, 'stata5_115.dta')
self.dta14_117 = os.path.join(self.dirpath, 'stata5_117.dta')
self.csv15 = os.path.join(self.dirpath, 'stata6.csv')
self.dta15_113 = os.path.join(self.dirpath, 'stata6_113.dta')
self.dta15_114 = os.path.join(self.dirpath, 'stata6_114.dta')
self.dta15_115 = os.path.join(self.dirpath, 'stata6_115.dta')
self.dta15_117 = os.path.join(self.dirpath, 'stata6_117.dta')
self.dta16_115 = os.path.join(self.dirpath, 'stata7_115.dta')
self.dta16_117 = os.path.join(self.dirpath, 'stata7_117.dta')
self.dta17_113 = os.path.join(self.dirpath, 'stata8_113.dta')
self.dta17_115 = os.path.join(self.dirpath, 'stata8_115.dta')
self.dta17_117 = os.path.join(self.dirpath, 'stata8_117.dta')
self.dta18_115 = os.path.join(self.dirpath, 'stata9_115.dta')
self.dta18_117 = os.path.join(self.dirpath, 'stata9_117.dta')
self.dta19_115 = os.path.join(self.dirpath, 'stata10_115.dta')
self.dta19_117 = os.path.join(self.dirpath, 'stata10_117.dta')
self.dta20_115 = os.path.join(self.dirpath, 'stata11_115.dta')
self.dta20_117 = os.path.join(self.dirpath, 'stata11_117.dta')
self.dta21_117 = os.path.join(self.dirpath, 'stata12_117.dta')
self.dta22_118 = os.path.join(self.dirpath, 'stata14_118.dta')
self.dta23 = os.path.join(self.dirpath, 'stata15.dta')
self.dta24_111 = os.path.join(self.dirpath, 'stata7_111.dta')
self.stata_dates = os.path.join(self.dirpath, 'stata13_dates.dta')
def read_dta(self, file):
# Legacy default reader configuration
return read_stata(file, convert_dates=True)
def read_csv(self, file):
return read_csv(file, parse_dates=True)
def test_read_empty_dta(self):
empty_ds = DataFrame(columns=['unit'])
# GH 7369, make sure can read a 0-obs dta file
with tm.ensure_clean() as path:
empty_ds.to_stata(path, write_index=False)
empty_ds2 = read_stata(path)
tm.assert_frame_equal(empty_ds, empty_ds2)
def test_data_method(self):
# Minimal testing of legacy data method
with StataReader(self.dta1_114) as rdr:
with warnings.catch_warnings(record=True) as w: # noqa
parsed_114_data = rdr.data()
with StataReader(self.dta1_114) as rdr:
parsed_114_read = rdr.read()
tm.assert_frame_equal(parsed_114_data, parsed_114_read)
@pytest.mark.parametrize(
'file', ['dta1_114', 'dta1_117'])
def test_read_dta1(self, file):
file = getattr(self, file)
parsed = self.read_dta(file)
# Pandas uses np.nan as missing value.
# Thus, all columns will be of type float, regardless of their name.
expected = DataFrame([(np.nan, np.nan, np.nan, np.nan, np.nan)],
columns=['float_miss', 'double_miss', 'byte_miss',
'int_miss', 'long_miss'])
# this is an oddity as really the nan should be float64, but
# the casting doesn't fail so need to match stata here
expected['float_miss'] = expected['float_miss'].astype(np.float32)
tm.assert_frame_equal(parsed, expected)
def test_read_dta2(self):
if LooseVersion(sys.version) < '2.7':
pytest.skip('datetime interp under 2.6 is faulty')
expected = DataFrame.from_records(
[
(
datetime(2006, 11, 19, 23, 13, 20),
1479596223000,
datetime(2010, 1, 20),
datetime(2010, 1, 8),
datetime(2010, 1, 1),
datetime(1974, 7, 1),
datetime(2010, 1, 1),
datetime(2010, 1, 1)
),
(
datetime(1959, 12, 31, 20, 3, 20),
-1479590,
datetime(1953, 10, 2),
datetime(1948, 6, 10),
datetime(1955, 1, 1),
datetime(1955, 7, 1),
datetime(1955, 1, 1),
datetime(2, 1, 1)
),
(
pd.NaT,
pd.NaT,
pd.NaT,
pd.NaT,
pd.NaT,
pd.NaT,
pd.NaT,
pd.NaT,
)
],
columns=['datetime_c', 'datetime_big_c', 'date', 'weekly_date',
'monthly_date', 'quarterly_date', 'half_yearly_date',
'yearly_date']
)
expected['yearly_date'] = expected['yearly_date'].astype('O')
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
parsed_114 = self.read_dta(self.dta2_114)
parsed_115 = self.read_dta(self.dta2_115)
parsed_117 = self.read_dta(self.dta2_117)
# 113 is buggy due to limits of date format support in Stata
# parsed_113 = self.read_dta(self.dta2_113)
# Remove resource warnings
w = [x for x in w if x.category is UserWarning]
# should get warning for each call to read_dta
assert len(w) == 3
# buggy test because of the NaT comparison on certain platforms
# Format 113 test fails since it does not support tc and tC formats
# tm.assert_frame_equal(parsed_113, expected)
tm.assert_frame_equal(parsed_114, expected,
check_datetimelike_compat=True)
tm.assert_frame_equal(parsed_115, expected,
check_datetimelike_compat=True)
tm.assert_frame_equal(parsed_117, expected,
check_datetimelike_compat=True)
@pytest.mark.parametrize(
'file', ['dta3_113', 'dta3_114', 'dta3_115', 'dta3_117'])
def test_read_dta3(self, file):
file = getattr(self, file)
parsed = self.read_dta(file)
# match stata here
expected = self.read_csv(self.csv3)
expected = expected.astype(np.float32)
expected['year'] = expected['year'].astype(np.int16)
expected['quarter'] = expected['quarter'].astype(np.int8)
tm.assert_frame_equal(parsed, expected)
@pytest.mark.parametrize(
'file', ['dta4_113', 'dta4_114', 'dta4_115', 'dta4_117'])
def test_read_dta4(self, file):
file = getattr(self, file)
parsed = self.read_dta(file)
expected = DataFrame.from_records(
[
["one", "ten", "one", "one", "one"],
["two", "nine", "two", "two", "two"],
["three", "eight", "three", "three", "three"],
["four", "seven", 4, "four", "four"],
["five", "six", 5, np.nan, "five"],
["six", "five", 6, np.nan, "six"],
["seven", "four", 7, np.nan, "seven"],
["eight", "three", 8, np.nan, "eight"],
["nine", "two", 9, np.nan, "nine"],
["ten", "one", "ten", np.nan, "ten"]
],
columns=['fully_labeled', 'fully_labeled2', 'incompletely_labeled',
'labeled_with_missings', 'float_labelled'])
# these are all categoricals
expected = pd.concat([expected[col].astype('category')
for col in expected], axis=1)
# stata doesn't save .category metadata
tm.assert_frame_equal(parsed, expected, check_categorical=False)
# File containing strls
def test_read_dta12(self):
parsed_117 = self.read_dta(self.dta21_117)
expected = DataFrame.from_records(
[
[1, "abc", "abcdefghi"],
[3, "cba", "qwertywertyqwerty"],
[93, "", "strl"],
],
columns=['x', 'y', 'z'])
tm.assert_frame_equal(parsed_117, expected, check_dtype=False)
def test_read_dta18(self):
parsed_118 = self.read_dta(self.dta22_118)
parsed_118["Bytes"] = parsed_118["Bytes"].astype('O')
expected = DataFrame.from_records(
[['Cat', 'Bogota', u'Bogotá', 1, 1.0, u'option b Ünicode', 1.0],
['Dog', 'Boston', u'Uzunköprü', np.nan, np.nan, np.nan, np.nan],
['Plane', 'Rome', u'Tromsø', 0, 0.0, 'option a', 0.0],
['Potato', 'Tokyo', u'Elâzığ', -4, 4.0, 4, 4],
['', '', '', 0, 0.3332999, 'option a', 1 / 3.]
],
columns=['Things', 'Cities', 'Unicode_Cities_Strl',
'Ints', 'Floats', 'Bytes', 'Longs'])
expected["Floats"] = expected["Floats"].astype(np.float32)
for col in parsed_118.columns:
tm.assert_almost_equal(parsed_118[col], expected[col])
with StataReader(self.dta22_118) as rdr:
vl = rdr.variable_labels()
vl_expected = {u'Unicode_Cities_Strl':
u'Here are some strls with Ünicode chars',
u'Longs': u'long data',
u'Things': u'Here are some things',
u'Bytes': u'byte data',
u'Ints': u'int data',
u'Cities': u'Here are some cities',
u'Floats': u'float data'}
tm.assert_dict_equal(vl, vl_expected)
assert rdr.data_label == u'This is a Ünicode data label'
def test_read_write_dta5(self):
original = DataFrame([(np.nan, np.nan, np.nan, np.nan, np.nan)],
columns=['float_miss', 'double_miss', 'byte_miss',
'int_miss', 'long_miss'])
original.index.name = 'index'
with tm.ensure_clean() as path:
original.to_stata(path, None)
written_and_read_again = self.read_dta(path)
tm.assert_frame_equal(written_and_read_again.set_index('index'),
original)
def test_write_dta6(self):
original = self.read_csv(self.csv3)
original.index.name = 'index'
original.index = original.index.astype(np.int32)
original['year'] = original['year'].astype(np.int32)
original['quarter'] = original['quarter'].astype(np.int32)
with tm.ensure_clean() as path:
original.to_stata(path, None)
written_and_read_again = self.read_dta(path)
tm.assert_frame_equal(written_and_read_again.set_index('index'),
original, check_index_type=False)
def test_read_write_dta10(self):
original = DataFrame(data=[["string", "object", 1, 1.1,
np.datetime64('2003-12-25')]],
columns=['string', 'object', 'integer',
'floating', 'datetime'])
original["object"] = Series(original["object"], dtype=object)
original.index.name = 'index'
original.index = original.index.astype(np.int32)
original['integer'] = original['integer'].astype(np.int32)
with tm.ensure_clean() as path:
original.to_stata(path, {'datetime': 'tc'})
written_and_read_again = self.read_dta(path)
# original.index is np.int32, readed index is np.int64
tm.assert_frame_equal(written_and_read_again.set_index('index'),
original, check_index_type=False)
def test_stata_doc_examples(self):
with tm.ensure_clean() as path:
df = DataFrame(np.random.randn(10, 2), columns=list('AB'))
df.to_stata(path)
def test_write_preserves_original(self):
# 9795
np.random.seed(423)
df = pd.DataFrame(np.random.randn(5, 4), columns=list('abcd'))
df.loc[2, 'a':'c'] = np.nan
df_copy = df.copy()
with tm.ensure_clean() as path:
df.to_stata(path, write_index=False)
tm.assert_frame_equal(df, df_copy)
def test_encoding(self):
# GH 4626, proper encoding handling
raw = read_stata(self.dta_encoding)
encoded = read_stata(self.dta_encoding, encoding="latin-1")
result = encoded.kreis1849[0]
if compat.PY3:
expected = raw.kreis1849[0]
assert result == expected
assert isinstance(result, compat.string_types)
else:
expected = raw.kreis1849.str.decode("latin-1")[0]
assert result == expected
assert isinstance(result, unicode) # noqa
with tm.ensure_clean() as path:
encoded.to_stata(path, encoding='latin-1', write_index=False)
reread_encoded = read_stata(path, encoding='latin-1')
tm.assert_frame_equal(encoded, reread_encoded)
def test_read_write_dta11(self):
original = DataFrame([(1, 2, 3, 4)],
columns=['good', compat.u('b\u00E4d'), '8number',
'astringwithmorethan32characters______'])
formatted = DataFrame([(1, 2, 3, 4)],
columns=['good', 'b_d', '_8number',
'astringwithmorethan32characters_'])
formatted.index.name = 'index'
formatted = formatted.astype(np.int32)
with tm.ensure_clean() as path:
with warnings.catch_warnings(record=True) as w:
original.to_stata(path, None)
# should get a warning for that format.
assert len(w) == 1
written_and_read_again = self.read_dta(path)
tm.assert_frame_equal(
written_and_read_again.set_index('index'), formatted)
def test_read_write_dta12(self):
original = DataFrame([(1, 2, 3, 4, 5, 6)],
columns=['astringwithmorethan32characters_1',
'astringwithmorethan32characters_2',
'+',
'-',
'short',
'delete'])
formatted = DataFrame([(1, 2, 3, 4, 5, 6)],
columns=['astringwithmorethan32characters_',
'_0astringwithmorethan32character',
'_',
'_1_',
'_short',
'_delete'])
formatted.index.name = 'index'
formatted = formatted.astype(np.int32)
with tm.ensure_clean() as path:
with warnings.catch_warnings(record=True) as w:
original.to_stata(path, None)
# should get a warning for that format.
assert len(w) == 1
written_and_read_again = self.read_dta(path)
tm.assert_frame_equal(
written_and_read_again.set_index('index'), formatted)
def test_read_write_dta13(self):
s1 = Series(2 ** 9, dtype=np.int16)
s2 = Series(2 ** 17, dtype=np.int32)
s3 = Series(2 ** 33, dtype=np.int64)
original = DataFrame({'int16': s1, 'int32': s2, 'int64': s3})
original.index.name = 'index'
formatted = original
formatted['int64'] = formatted['int64'].astype(np.float64)
with tm.ensure_clean() as path:
original.to_stata(path)
written_and_read_again = self.read_dta(path)
tm.assert_frame_equal(written_and_read_again.set_index('index'),
formatted)
@pytest.mark.parametrize(
'file', ['dta14_113', 'dta14_114', 'dta14_115', 'dta14_117'])
def test_read_write_reread_dta14(self, file, parsed_114):
file = getattr(self, file)
parsed = self.read_dta(file)
parsed.index.name = 'index'
expected = self.read_csv(self.csv14)
cols = ['byte_', 'int_', 'long_', 'float_', 'double_']
for col in cols:
expected[col] = expected[col]._convert(datetime=True, numeric=True)
expected['float_'] = expected['float_'].astype(np.float32)
expected['date_td'] = pd.to_datetime(
expected['date_td'], errors='coerce')
tm.assert_frame_equal(parsed_114, parsed)
with tm.ensure_clean() as path:
parsed_114.to_stata(path, {'date_td': 'td'})
written_and_read_again = self.read_dta(path)
tm.assert_frame_equal(
written_and_read_again.set_index('index'), parsed_114)
@pytest.mark.parametrize(
'file', ['dta15_113', 'dta15_114', 'dta15_115', 'dta15_117'])
def test_read_write_reread_dta15(self, file):
expected = self.read_csv(self.csv15)
expected['byte_'] = expected['byte_'].astype(np.int8)
expected['int_'] = expected['int_'].astype(np.int16)
expected['long_'] = expected['long_'].astype(np.int32)
expected['float_'] = expected['float_'].astype(np.float32)
expected['double_'] = expected['double_'].astype(np.float64)
expected['date_td'] = expected['date_td'].apply(
datetime.strptime, args=('%Y-%m-%d',))
file = getattr(self, file)
parsed = self.read_dta(file)
tm.assert_frame_equal(expected, parsed)
def test_timestamp_and_label(self):
original = DataFrame([(1,)], columns=['variable'])
time_stamp = datetime(2000, 2, 29, 14, 21)
data_label = 'This is a data file.'
with tm.ensure_clean() as path:
original.to_stata(path, time_stamp=time_stamp,
data_label=data_label)
with StataReader(path) as reader:
assert reader.time_stamp == '29 Feb 2000 14:21'
assert reader.data_label == data_label
def test_numeric_column_names(self):
original = DataFrame(np.reshape(np.arange(25.0), (5, 5)))
original.index.name = 'index'
with tm.ensure_clean() as path:
# should get a warning for that format.
with tm.assert_produces_warning(InvalidColumnName):
original.to_stata(path)
written_and_read_again = self.read_dta(path)
written_and_read_again = written_and_read_again.set_index('index')
columns = list(written_and_read_again.columns)
convert_col_name = lambda x: int(x[1])
written_and_read_again.columns = map(convert_col_name, columns)
tm.assert_frame_equal(original, written_and_read_again)
def test_nan_to_missing_value(self):
s1 = Series(np.arange(4.0), dtype=np.float32)
s2 = Series(np.arange(4.0), dtype=np.float64)
s1[::2] = np.nan
s2[1::2] = np.nan
original = DataFrame({'s1': s1, 's2': s2})
original.index.name = 'index'
with tm.ensure_clean() as path:
original.to_stata(path)
written_and_read_again = self.read_dta(path)
written_and_read_again = written_and_read_again.set_index('index')
tm.assert_frame_equal(written_and_read_again, original)
def test_no_index(self):
columns = ['x', 'y']
original = DataFrame(np.reshape(np.arange(10.0), (5, 2)),
columns=columns)
original.index.name = 'index_not_written'
with tm.ensure_clean() as path:
original.to_stata(path, write_index=False)
written_and_read_again = self.read_dta(path)
pytest.raises(
KeyError, lambda: written_and_read_again['index_not_written'])
def test_string_no_dates(self):
s1 = Series(['a', 'A longer string'])
s2 = Series([1.0, 2.0], dtype=np.float64)
original = DataFrame({'s1': s1, 's2': s2})
original.index.name = 'index'
with tm.ensure_clean() as path:
original.to_stata(path)
written_and_read_again = self.read_dta(path)
tm.assert_frame_equal(written_and_read_again.set_index('index'),
original)
def test_large_value_conversion(self):
s0 = Series([1, 99], dtype=np.int8)
s1 = Series([1, 127], dtype=np.int8)
s2 = Series([1, 2 ** 15 - 1], dtype=np.int16)
s3 = Series([1, 2 ** 63 - 1], dtype=np.int64)
original = DataFrame({'s0': s0, 's1': s1, 's2': s2, 's3': s3})
original.index.name = 'index'
with tm.ensure_clean() as path:
with tm.assert_produces_warning(PossiblePrecisionLoss):
original.to_stata(path)
written_and_read_again = self.read_dta(path)
modified = original.copy()
modified['s1'] = Series(modified['s1'], dtype=np.int16)
modified['s2'] = Series(modified['s2'], dtype=np.int32)
modified['s3'] = Series(modified['s3'], dtype=np.float64)
tm.assert_frame_equal(written_and_read_again.set_index('index'),
modified)
def test_dates_invalid_column(self):
original = DataFrame([datetime(2006, 11, 19, 23, 13, 20)])
original.index.name = 'index'
with tm.ensure_clean() as path:
with tm.assert_produces_warning(InvalidColumnName):
original.to_stata(path, {0: 'tc'})
written_and_read_again = self.read_dta(path)
modified = original.copy()
modified.columns = ['_0']
tm.assert_frame_equal(written_and_read_again.set_index('index'),
modified)
def test_105(self):
# Data obtained from:
# http://go.worldbank.org/ZXY29PVJ21
dpath = os.path.join(self.dirpath, 'S4_EDUC1.dta')
df = pd.read_stata(dpath)
df0 = [[1, 1, 3, -2], [2, 1, 2, -2], [4, 1, 1, -2]]
df0 = pd.DataFrame(df0)
df0.columns = ["clustnum", "pri_schl", "psch_num", "psch_dis"]
df0['clustnum'] = df0["clustnum"].astype(np.int16)
df0['pri_schl'] = df0["pri_schl"].astype(np.int8)
df0['psch_num'] = df0["psch_num"].astype(np.int8)
df0['psch_dis'] = df0["psch_dis"].astype(np.float32)
tm.assert_frame_equal(df.head(3), df0)
def test_date_export_formats(self):
columns = ['tc', 'td', 'tw', 'tm', 'tq', 'th', 'ty']
conversions = dict(((c, c) for c in columns))
data = [datetime(2006, 11, 20, 23, 13, 20)] * len(columns)
original = DataFrame([data], columns=columns)
original.index.name = 'index'
expected_values = [datetime(2006, 11, 20, 23, 13, 20), # Time
datetime(2006, 11, 20), # Day
datetime(2006, 11, 19), # Week
datetime(2006, 11, 1), # Month
datetime(2006, 10, 1), # Quarter year
datetime(2006, 7, 1), # Half year
datetime(2006, 1, 1)] # Year
expected = DataFrame([expected_values], columns=columns)
expected.index.name = 'index'
with tm.ensure_clean() as path:
original.to_stata(path, conversions)
written_and_read_again = self.read_dta(path)
tm.assert_frame_equal(written_and_read_again.set_index('index'),
expected)
def test_write_missing_strings(self):
original = DataFrame([["1"], [None]], columns=["foo"])
expected = DataFrame([["1"], [""]], columns=["foo"])
expected.index.name = 'index'
with tm.ensure_clean() as path:
original.to_stata(path)
written_and_read_again = self.read_dta(path)
tm.assert_frame_equal(written_and_read_again.set_index('index'),
expected)
def test_bool_uint(self):
s0 = Series([0, 1, True], dtype=np.bool)
s1 = Series([0, 1, 100], dtype=np.uint8)
s2 = Series([0, 1, 255], dtype=np.uint8)
s3 = Series([0, 1, 2 ** 15 - 100], dtype=np.uint16)
s4 = Series([0, 1, 2 ** 16 - 1], dtype=np.uint16)
s5 = Series([0, 1, 2 ** 31 - 100], dtype=np.uint32)
s6 = Series([0, 1, 2 ** 32 - 1], dtype=np.uint32)
original = DataFrame({'s0': s0, 's1': s1, 's2': s2, 's3': s3,
's4': s4, 's5': s5, 's6': s6})
original.index.name = 'index'
expected = original.copy()
expected_types = (np.int8, np.int8, np.int16, np.int16, np.int32,
np.int32, np.float64)
for c, t in zip(expected.columns, expected_types):
expected[c] = expected[c].astype(t)
with tm.ensure_clean() as path:
original.to_stata(path)
written_and_read_again = self.read_dta(path)
written_and_read_again = written_and_read_again.set_index('index')
tm.assert_frame_equal(written_and_read_again, expected)
def test_variable_labels(self):
with StataReader(self.dta16_115) as rdr:
sr_115 = rdr.variable_labels()
with StataReader(self.dta16_117) as rdr:
sr_117 = rdr.variable_labels()
keys = ('var1', 'var2', 'var3')
labels = ('label1', 'label2', 'label3')
for k, v in compat.iteritems(sr_115):
assert k in sr_117
assert v == sr_117[k]
assert k in keys
assert v in labels
def test_minimal_size_col(self):
str_lens = (1, 100, 244)
s = {}
for str_len in str_lens:
s['s' + str(str_len)] = Series(['a' * str_len,
'b' * str_len, 'c' * str_len])
original = DataFrame(s)
with tm.ensure_clean() as path:
original.to_stata(path, write_index=False)
with StataReader(path) as sr:
typlist = sr.typlist
variables = sr.varlist
formats = sr.fmtlist
for variable, fmt, typ in zip(variables, formats, typlist):
assert int(variable[1:]) == int(fmt[1:-1])
assert int(variable[1:]) == typ
def test_excessively_long_string(self):
str_lens = (1, 244, 500)
s = {}
for str_len in str_lens:
s['s' + str(str_len)] = Series(['a' * str_len,
'b' * str_len, 'c' * str_len])
original = DataFrame(s)
with pytest.raises(ValueError):
with tm.ensure_clean() as path:
original.to_stata(path)
def test_missing_value_generator(self):
types = ('b', 'h', 'l')
df = DataFrame([[0.0]], columns=['float_'])
with tm.ensure_clean() as path:
df.to_stata(path)
with StataReader(path) as rdr:
valid_range = rdr.VALID_RANGE
expected_values = ['.' + chr(97 + i) for i in range(26)]
expected_values.insert(0, '.')
for t in types:
offset = valid_range[t][1]
for i in range(0, 27):
val = StataMissingValue(offset + 1 + i)
assert val.string == expected_values[i]
# Test extremes for floats
val = StataMissingValue(struct.unpack('<f', b'\x00\x00\x00\x7f')[0])
assert val.string == '.'
val = StataMissingValue(struct.unpack('<f', b'\x00\xd0\x00\x7f')[0])
assert val.string == '.z'
# Test extremes for floats
val = StataMissingValue(struct.unpack(
'<d', b'\x00\x00\x00\x00\x00\x00\xe0\x7f')[0])
assert val.string == '.'
val = StataMissingValue(struct.unpack(
'<d', b'\x00\x00\x00\x00\x00\x1a\xe0\x7f')[0])
assert val.string == '.z'
@pytest.mark.parametrize(
'file', ['dta17_113', 'dta17_115', 'dta17_117'])
def test_missing_value_conversion(self, file):
columns = ['int8_', 'int16_', 'int32_', 'float32_', 'float64_']
smv = StataMissingValue(101)
keys = [key for key in iterkeys(smv.MISSING_VALUES)]
keys.sort()
data = []
for i in range(27):
row = [StataMissingValue(keys[i + (j * 27)]) for j in range(5)]
data.append(row)
expected = DataFrame(data, columns=columns)
parsed = read_stata(getattr(self, file), convert_missing=True)
tm.assert_frame_equal(parsed, expected)
def test_big_dates(self):
yr = [1960, 2000, 9999, 100, 2262, 1677]
mo = [1, 1, 12, 1, 4, 9]
dd = [1, 1, 31, 1, 22, 23]
hr = [0, 0, 23, 0, 0, 0]
mm = [0, 0, 59, 0, 0, 0]
ss = [0, 0, 59, 0, 0, 0]
expected = []
for i in range(len(yr)):
row = []
for j in range(7):
if j == 0:
row.append(
datetime(yr[i], mo[i], dd[i], hr[i], mm[i], ss[i]))
elif j == 6:
row.append(datetime(yr[i], 1, 1))
else:
row.append(datetime(yr[i], mo[i], dd[i]))
expected.append(row)
expected.append([NaT] * 7)
columns = ['date_tc', 'date_td', 'date_tw', 'date_tm', 'date_tq',
'date_th', 'date_ty']
# Fixes for weekly, quarterly,half,year
expected[2][2] = datetime(9999, 12, 24)
expected[2][3] = datetime(9999, 12, 1)
expected[2][4] = datetime(9999, 10, 1)
expected[2][5] = datetime(9999, 7, 1)
expected[4][2] = datetime(2262, 4, 16)
expected[4][3] = expected[4][4] = datetime(2262, 4, 1)
expected[4][5] = expected[4][6] = datetime(2262, 1, 1)
expected[5][2] = expected[5][3] = expected[
5][4] = datetime(1677, 10, 1)
expected[5][5] = expected[5][6] = datetime(1678, 1, 1)
expected = DataFrame(expected, columns=columns, dtype=np.object)
parsed_115 = read_stata(self.dta18_115)
parsed_117 = read_stata(self.dta18_117)
tm.assert_frame_equal(expected, parsed_115,
check_datetimelike_compat=True)
tm.assert_frame_equal(expected, parsed_117,
check_datetimelike_compat=True)
date_conversion = dict((c, c[-2:]) for c in columns)
# {c : c[-2:] for c in columns}
with tm.ensure_clean() as path:
expected.index.name = 'index'
expected.to_stata(path, date_conversion)
written_and_read_again = self.read_dta(path)
tm.assert_frame_equal(written_and_read_again.set_index('index'),
expected,
check_datetimelike_compat=True)
def test_dtype_conversion(self):
expected = self.read_csv(self.csv15)
expected['byte_'] = expected['byte_'].astype(np.int8)
expected['int_'] = expected['int_'].astype(np.int16)
expected['long_'] = expected['long_'].astype(np.int32)
expected['float_'] = expected['float_'].astype(np.float32)
expected['double_'] = expected['double_'].astype(np.float64)
expected['date_td'] = expected['date_td'].apply(datetime.strptime,
args=('%Y-%m-%d',))
no_conversion = read_stata(self.dta15_117,
convert_dates=True)
tm.assert_frame_equal(expected, no_conversion)
conversion = read_stata(self.dta15_117,
convert_dates=True,
preserve_dtypes=False)
# read_csv types are the same
expected = self.read_csv(self.csv15)
expected['date_td'] = expected['date_td'].apply(datetime.strptime,
args=('%Y-%m-%d',))
tm.assert_frame_equal(expected, conversion)
def test_drop_column(self):
expected = self.read_csv(self.csv15)
expected['byte_'] = expected['byte_'].astype(np.int8)
expected['int_'] = expected['int_'].astype(np.int16)
expected['long_'] = expected['long_'].astype(np.int32)
expected['float_'] = expected['float_'].astype(np.float32)
expected['double_'] = expected['double_'].astype(np.float64)
expected['date_td'] = expected['date_td'].apply(datetime.strptime,
args=('%Y-%m-%d',))
columns = ['byte_', 'int_', 'long_']
expected = expected[columns]
dropped = read_stata(self.dta15_117, convert_dates=True,
columns=columns)
tm.assert_frame_equal(expected, dropped)
# See PR 10757
columns = ['int_', 'long_', 'byte_']
expected = expected[columns]
reordered = read_stata(self.dta15_117, convert_dates=True,
columns=columns)
tm.assert_frame_equal(expected, reordered)
with pytest.raises(ValueError):
columns = ['byte_', 'byte_']
read_stata(self.dta15_117, convert_dates=True, columns=columns)
with pytest.raises(ValueError):
columns = ['byte_', 'int_', 'long_', 'not_found']
read_stata(self.dta15_117, convert_dates=True, columns=columns)
def test_categorical_writing(self):
original = DataFrame.from_records(
[
["one", "ten", "one", "one", "one", 1],
["two", "nine", "two", "two", "two", 2],
["three", "eight", "three", "three", "three", 3],
["four", "seven", 4, "four", "four", 4],
["five", "six", 5, np.nan, "five", 5],
["six", "five", 6, np.nan, "six", 6],
["seven", "four", 7, np.nan, "seven", 7],
["eight", "three", 8, np.nan, "eight", 8],
["nine", "two", 9, np.nan, "nine", 9],
["ten", "one", "ten", np.nan, "ten", 10]
],
columns=['fully_labeled', 'fully_labeled2', 'incompletely_labeled',
'labeled_with_missings', 'float_labelled', 'unlabeled'])
expected = original.copy()
# these are all categoricals
original = pd.concat([original[col].astype('category')
for col in original], axis=1)
expected['incompletely_labeled'] = expected[
'incompletely_labeled'].apply(str)
expected['unlabeled'] = expected['unlabeled'].apply(str)
expected = pd.concat([expected[col].astype('category')
for col in expected], axis=1)
expected.index.name = 'index'
with tm.ensure_clean() as path:
with warnings.catch_warnings(record=True) as w: # noqa
# Silence warnings
original.to_stata(path)
written_and_read_again = self.read_dta(path)
res = written_and_read_again.set_index('index')
tm.assert_frame_equal(res, expected, check_categorical=False)
def test_categorical_warnings_and_errors(self):
# Warning for non-string labels
# Error for labels too long
original = pd.DataFrame.from_records(
[['a' * 10000],
['b' * 10000],
['c' * 10000],
['d' * 10000]],
columns=['Too_long'])
original = pd.concat([original[col].astype('category')
for col in original], axis=1)
with tm.ensure_clean() as path:
pytest.raises(ValueError, original.to_stata, path)
original = pd.DataFrame.from_records(
[['a'],
['b'],
['c'],
['d'],
[1]],
columns=['Too_long'])
original = pd.concat([original[col].astype('category')
for col in original], axis=1)
with warnings.catch_warnings(record=True) as w:
original.to_stata(path)
# should get a warning for mixed content
assert len(w) == 1
def test_categorical_with_stata_missing_values(self):
values = [['a' + str(i)] for i in range(120)]
values.append([np.nan])
original = pd.DataFrame.from_records(values, columns=['many_labels'])
original = pd.concat([original[col].astype('category')
for col in original], axis=1)
original.index.name = 'index'
with tm.ensure_clean() as path:
original.to_stata(path)
written_and_read_again = self.read_dta(path)
res = written_and_read_again.set_index('index')
tm.assert_frame_equal(res, original, check_categorical=False)
@pytest.mark.parametrize(
'file', ['dta19_115', 'dta19_117'])
def test_categorical_order(self, file):
# Directly construct using expected codes
# Format is is_cat, col_name, labels (in order), underlying data
expected = [(True, 'ordered', ['a', 'b', 'c', 'd', 'e'], np.arange(5)),
(True, 'reverse', ['a', 'b', 'c',
'd', 'e'], np.arange(5)[::-1]),
(True, 'noorder', ['a', 'b', 'c', 'd',
'e'], np.array([2, 1, 4, 0, 3])),
(True, 'floating', [
'a', 'b', 'c', 'd', 'e'], np.arange(0, 5)),
(True, 'float_missing', [
'a', 'd', 'e'], np.array([0, 1, 2, -1, -1])),
(False, 'nolabel', [
1.0, 2.0, 3.0, 4.0, 5.0], np.arange(5)),
(True, 'int32_mixed', ['d', 2, 'e', 'b', 'a'],
np.arange(5))]
cols = []
for is_cat, col, labels, codes in expected:
if is_cat:
cols.append((col, pd.Categorical.from_codes(codes, labels)))
else:
cols.append((col, pd.Series(labels, dtype=np.float32)))
expected = DataFrame.from_items(cols)
# Read with and with out categoricals, ensure order is identical
file = getattr(self, file)
parsed = read_stata(file)
tm.assert_frame_equal(expected, parsed, check_categorical=False)
# Check identity of codes
for col in expected:
if is_categorical_dtype(expected[col]):
tm.assert_series_equal(expected[col].cat.codes,
parsed[col].cat.codes)
tm.assert_index_equal(expected[col].cat.categories,
parsed[col].cat.categories)
@pytest.mark.parametrize(
'file', ['dta20_115', 'dta20_117'])
def test_categorical_sorting(self, file):
parsed = read_stata(getattr(self, file))
# Sort based on codes, not strings
parsed = parsed.sort_values("srh")
# Don't sort index
parsed.index = np.arange(parsed.shape[0])
codes = [-1, -1, 0, 1, 1, 1, 2, 2, 3, 4]
categories = ["Poor", "Fair", "Good", "Very good", "Excellent"]
cat = pd.Categorical.from_codes(codes=codes, categories=categories)
expected = pd.Series(cat, name='srh')
tm.assert_series_equal(expected, parsed["srh"],
check_categorical=False)
@pytest.mark.parametrize(
'file', ['dta19_115', 'dta19_117'])
def test_categorical_ordering(self, file):
file = getattr(self, file)
parsed = read_stata(file)
parsed_unordered = read_stata(file,
order_categoricals=False)
for col in parsed:
if not is_categorical_dtype(parsed[col]):
continue
assert parsed[col].cat.ordered
assert not parsed_unordered[col].cat.ordered
@pytest.mark.parametrize(
'file', ['dta1_117', 'dta2_117', 'dta3_117',
'dta4_117', 'dta14_117', 'dta15_117',
'dta16_117', 'dta17_117', 'dta18_117',
'dta19_117', 'dta20_117'])
@pytest.mark.parametrize(
'chunksize', [1, 2])
@pytest.mark.parametrize(
'convert_categoricals', [False, True])
@pytest.mark.parametrize(
'convert_dates', [False, True])
def test_read_chunks_117(self, file, chunksize,
convert_categoricals, convert_dates):
fname = getattr(self, file)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
parsed = read_stata(
fname,
convert_categoricals=convert_categoricals,
convert_dates=convert_dates)
itr = read_stata(
fname, iterator=True,
convert_categoricals=convert_categoricals,
convert_dates=convert_dates)
pos = 0
for j in range(5):
with warnings.catch_warnings(record=True) as w: # noqa
warnings.simplefilter("always")
try:
chunk = itr.read(chunksize)
except StopIteration:
break
from_frame = parsed.iloc[pos:pos + chunksize, :]
tm.assert_frame_equal(
from_frame, chunk, check_dtype=False,
check_datetimelike_compat=True,
check_categorical=False)
pos += chunksize
itr.close()
def test_iterator(self):
fname = self.dta3_117
parsed = read_stata(fname)
with read_stata(fname, iterator=True) as itr:
chunk = itr.read(5)
tm.assert_frame_equal(parsed.iloc[0:5, :], chunk)
with read_stata(fname, chunksize=5) as itr:
chunk = list(itr)
tm.assert_frame_equal(parsed.iloc[0:5, :], chunk[0])
with read_stata(fname, iterator=True) as itr:
chunk = itr.get_chunk(5)
tm.assert_frame_equal(parsed.iloc[0:5, :], chunk)
with read_stata(fname, chunksize=5) as itr:
chunk = itr.get_chunk()
tm.assert_frame_equal(parsed.iloc[0:5, :], chunk)
# GH12153
with read_stata(fname, chunksize=4) as itr:
from_chunks = pd.concat(itr)
tm.assert_frame_equal(parsed, from_chunks)
@pytest.mark.parametrize(
'file', ['dta2_115', 'dta3_115', 'dta4_115',
'dta14_115', 'dta15_115', 'dta16_115',
'dta17_115', 'dta18_115', 'dta19_115',
'dta20_115'])
@pytest.mark.parametrize(
'chunksize', [1, 2])
@pytest.mark.parametrize(
'convert_categoricals', [False, True])
@pytest.mark.parametrize(
'convert_dates', [False, True])
def test_read_chunks_115(self, file, chunksize,
convert_categoricals, convert_dates):
fname = getattr(self, file)
# Read the whole file
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
parsed = read_stata(
fname,
convert_categoricals=convert_categoricals,
convert_dates=convert_dates)
# Compare to what we get when reading by chunk
itr = read_stata(
fname, iterator=True,
convert_dates=convert_dates,
convert_categoricals=convert_categoricals)
pos = 0
for j in range(5):
with warnings.catch_warnings(record=True) as w: # noqa
warnings.simplefilter("always")
try:
chunk = itr.read(chunksize)
except StopIteration:
break
from_frame = parsed.iloc[pos:pos + chunksize, :]
tm.assert_frame_equal(
from_frame, chunk, check_dtype=False,
check_datetimelike_compat=True,
check_categorical=False)
pos += chunksize
itr.close()
def test_read_chunks_columns(self):
fname = self.dta3_117
columns = ['quarter', 'cpi', 'm1']
chunksize = 2
parsed = read_stata(fname, columns=columns)
with read_stata(fname, iterator=True) as itr:
pos = 0
for j in range(5):
chunk = itr.read(chunksize, columns=columns)
if chunk is None:
break
from_frame = parsed.iloc[pos:pos + chunksize, :]
tm.assert_frame_equal(from_frame, chunk, check_dtype=False)
pos += chunksize
def test_write_variable_labels(self):
# GH 13631, add support for writing variable labels
original = pd.DataFrame({'a': [1, 2, 3, 4],
'b': [1.0, 3.0, 27.0, 81.0],
'c': ['Atlanta', 'Birmingham',
'Cincinnati', 'Detroit']})
original.index.name = 'index'
variable_labels = {'a': 'City Rank', 'b': 'City Exponent', 'c': 'City'}
with tm.ensure_clean() as path:
original.to_stata(path, variable_labels=variable_labels)
with StataReader(path) as sr:
read_labels = sr.variable_labels()
expected_labels = {'index': '',
'a': 'City Rank',
'b': 'City Exponent',
'c': 'City'}
assert read_labels == expected_labels
variable_labels['index'] = 'The Index'
with tm.ensure_clean() as path:
original.to_stata(path, variable_labels=variable_labels)
with StataReader(path) as sr:
read_labels = sr.variable_labels()
assert read_labels == variable_labels
def test_write_variable_label_errors(self):
original = pd.DataFrame({'a': [1, 2, 3, 4],
'b': [1.0, 3.0, 27.0, 81.0],
'c': ['Atlanta', 'Birmingham',
'Cincinnati', 'Detroit']})
values = [u'\u03A1', u'\u0391',
u'\u039D', u'\u0394',
u'\u0391', u'\u03A3']
variable_labels_utf8 = {'a': 'City Rank',
'b': 'City Exponent',
'c': u''.join(values)}
with pytest.raises(ValueError):
with tm.ensure_clean() as path:
original.to_stata(path, variable_labels=variable_labels_utf8)
variable_labels_long = {'a': 'City Rank',
'b': 'City Exponent',
'c': 'A very, very, very long variable label '
'that is too long for Stata which means '
'that it has more than 80 characters'}
with pytest.raises(ValueError):
with tm.ensure_clean() as path:
original.to_stata(path, variable_labels=variable_labels_long)
def test_default_date_conversion(self):
# GH 12259
dates = [dt.datetime(1999, 12, 31, 12, 12, 12, 12000),
dt.datetime(2012, 12, 21, 12, 21, 12, 21000),
dt.datetime(1776, 7, 4, 7, 4, 7, 4000)]
original = pd.DataFrame({'nums': [1.0, 2.0, 3.0],
'strs': ['apple', 'banana', 'cherry'],
'dates': dates})
with tm.ensure_clean() as path:
original.to_stata(path, write_index=False)
reread = read_stata(path, convert_dates=True)
tm.assert_frame_equal(original, reread)
original.to_stata(path,
write_index=False,
convert_dates={'dates': 'tc'})
direct = read_stata(path, convert_dates=True)
tm.assert_frame_equal(reread, direct)
def test_unsupported_type(self):
original = pd.DataFrame({'a': [1 + 2j, 2 + 4j]})
with pytest.raises(NotImplementedError):
with tm.ensure_clean() as path:
original.to_stata(path)
def test_unsupported_datetype(self):
dates = [dt.datetime(1999, 12, 31, 12, 12, 12, 12000),
dt.datetime(2012, 12, 21, 12, 21, 12, 21000),
dt.datetime(1776, 7, 4, 7, 4, 7, 4000)]
original = pd.DataFrame({'nums': [1.0, 2.0, 3.0],
'strs': ['apple', 'banana', 'cherry'],
'dates': dates})
with pytest.raises(NotImplementedError):
with tm.ensure_clean() as path:
original.to_stata(path, convert_dates={'dates': 'tC'})
dates = pd.date_range('1-1-1990', periods=3, tz='Asia/Hong_Kong')
original = pd.DataFrame({'nums': [1.0, 2.0, 3.0],
'strs': ['apple', 'banana', 'cherry'],
'dates': dates})
with pytest.raises(NotImplementedError):
with tm.ensure_clean() as path:
original.to_stata(path)
def test_repeated_column_labels(self):
# GH 13923
with pytest.raises(ValueError) as cm:
read_stata(self.dta23, convert_categoricals=True)
assert 'wolof' in cm.exception
def test_stata_111(self):
# 111 is an old version but still used by current versions of
# SAS when exporting to Stata format. We do not know of any
# on-line documentation for this version.
df = read_stata(self.dta24_111)
original = pd.DataFrame({'y': [1, 1, 1, 1, 1, 0, 0, np.NaN, 0, 0],
'x': [1, 2, 1, 3, np.NaN, 4, 3, 5, 1, 6],
'w': [2, np.NaN, 5, 2, 4, 4, 3, 1, 2, 3],
'z': ['a', 'b', 'c', 'd', 'e', '', 'g', 'h',
'i', 'j']})
original = original[['y', 'x', 'w', 'z']]
tm.assert_frame_equal(original, df)
def test_out_of_range_double(self):
# GH 14618
df = DataFrame({'ColumnOk': [0.0,
np.finfo(np.double).eps,
4.49423283715579e+307],
'ColumnTooBig': [0.0,
np.finfo(np.double).eps,
np.finfo(np.double).max]})
with pytest.raises(ValueError) as cm:
with tm.ensure_clean() as path:
df.to_stata(path)
assert 'ColumnTooBig' in cm.exception
df.loc[2, 'ColumnTooBig'] = np.inf
with pytest.raises(ValueError) as cm:
with tm.ensure_clean() as path:
df.to_stata(path)
assert 'ColumnTooBig' in cm.exception
assert 'infinity' in cm.exception
def test_out_of_range_float(self):
original = DataFrame({'ColumnOk': [0.0,
np.finfo(np.float32).eps,
np.finfo(np.float32).max / 10.0],
'ColumnTooBig': [0.0,
np.finfo(np.float32).eps,
np.finfo(np.float32).max]})
original.index.name = 'index'
for col in original:
original[col] = original[col].astype(np.float32)
with tm.ensure_clean() as path:
original.to_stata(path)
reread = read_stata(path)
original['ColumnTooBig'] = original['ColumnTooBig'].astype(
np.float64)
tm.assert_frame_equal(original,
reread.set_index('index'))
original.loc[2, 'ColumnTooBig'] = np.inf
with pytest.raises(ValueError) as cm:
with tm.ensure_clean() as path:
original.to_stata(path)
assert 'ColumnTooBig' in cm.exception
assert 'infinity' in cm.exception
def test_invalid_encoding(self):
# GH15723, validate encoding
original = self.read_csv(self.csv3)
with pytest.raises(ValueError):
with tm.ensure_clean() as path:
original.to_stata(path, encoding='utf-8')
def test_path_pathlib(self):
df = tm.makeDataFrame()
df.index.name = 'index'
reader = lambda x: read_stata(x).set_index('index')
result = tm.round_trip_pathlib(df.to_stata, reader)
tm.assert_frame_equal(df, result)
def test_pickle_path_localpath(self):
df = tm.makeDataFrame()
df.index.name = 'index'
reader = lambda x: read_stata(x).set_index('index')
result = tm.round_trip_localpath(df.to_stata, reader)
tm.assert_frame_equal(df, result)
@pytest.mark.parametrize(
'write_index', [True, False])
def test_value_labels_iterator(self, write_index):
# GH 16923
d = {'A': ['B', 'E', 'C', 'A', 'E']}
df = pd.DataFrame(data=d)
df['A'] = df['A'].astype('category')
with tm.ensure_clean() as path:
df.to_stata(path, write_index=write_index)
with pd.read_stata(path, iterator=True) as dta_iter:
value_labels = dta_iter.value_labels()
assert value_labels == {'A': {0: 'A', 1: 'B', 2: 'C', 3: 'E'}}
def test_set_index(self):
# GH 17328
df = tm.makeDataFrame()
df.index.name = 'index'
with tm.ensure_clean() as path:
df.to_stata(path)
reread = pd.read_stata(path, index_col='index')
tm.assert_frame_equal(df, reread)
@pytest.mark.parametrize(
'column', ['ms', 'day', 'week', 'month', 'qtr', 'half', 'yr'])
def test_date_parsing_ignores_format_details(self, column):
# GH 17797
#
# Test that display formats are ignored when determining if a numeric
# column is a date value.
#
# All date types are stored as numbers and format associated with the
# column denotes both the type of the date and the display format.
#
# STATA supports 9 date types which each have distinct units. We test 7
# of the 9 types, ignoring %tC and %tb. %tC is a variant of %tc that
# accounts for leap seconds and %tb relies on STATAs business calendar.
df = read_stata(self.stata_dates)
unformatted = df.loc[0, column]
formatted = df.loc[0, column + "_fmt"]
assert unformatted == formatted