Updated script that can be controled by Nodejs web app
This commit is contained in:
mac OS
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from hypothesis import (
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assume,
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example,
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given,
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strategies as st,
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)
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import numpy as np
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import pytest
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from pandas._libs.byteswap import (
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read_double_with_byteswap,
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read_float_with_byteswap,
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read_uint16_with_byteswap,
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read_uint32_with_byteswap,
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read_uint64_with_byteswap,
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)
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import pandas._testing as tm
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@given(read_offset=st.integers(0, 11), number=st.integers(min_value=0))
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@example(number=2**16, read_offset=0)
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@example(number=2**32, read_offset=0)
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@example(number=2**64, read_offset=0)
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@pytest.mark.parametrize("int_type", [np.uint16, np.uint32, np.uint64])
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@pytest.mark.parametrize("should_byteswap", [True, False])
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def test_int_byteswap(read_offset, number, int_type, should_byteswap):
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assume(number < 2 ** (8 * int_type(0).itemsize))
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_test(number, int_type, read_offset, should_byteswap)
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@pytest.mark.filterwarnings("ignore:overflow encountered:RuntimeWarning")
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@given(read_offset=st.integers(0, 11), number=st.floats())
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@pytest.mark.parametrize("float_type", [np.float32, np.float64])
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@pytest.mark.parametrize("should_byteswap", [True, False])
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def test_float_byteswap(read_offset, number, float_type, should_byteswap):
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_test(number, float_type, read_offset, should_byteswap)
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def _test(number, number_type, read_offset, should_byteswap):
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number = number_type(number)
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data = np.random.default_rng(2).integers(0, 256, size=20, dtype="uint8")
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data[read_offset : read_offset + number.itemsize] = number[None].view("uint8")
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swap_func = {
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np.float32: read_float_with_byteswap,
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np.float64: read_double_with_byteswap,
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np.uint16: read_uint16_with_byteswap,
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np.uint32: read_uint32_with_byteswap,
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np.uint64: read_uint64_with_byteswap,
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}[type(number)]
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output_number = number_type(swap_func(bytes(data), read_offset, should_byteswap))
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if should_byteswap:
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tm.assert_equal(output_number, number.byteswap())
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else:
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tm.assert_equal(output_number, number)
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34
lib/python3.13/site-packages/pandas/tests/io/sas/test_sas.py
Normal file
34
lib/python3.13/site-packages/pandas/tests/io/sas/test_sas.py
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from io import StringIO
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import pytest
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from pandas import read_sas
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import pandas._testing as tm
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class TestSas:
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def test_sas_buffer_format(self):
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# see gh-14947
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b = StringIO("")
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msg = (
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"If this is a buffer object rather than a string "
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"name, you must specify a format string"
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)
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with pytest.raises(ValueError, match=msg):
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read_sas(b)
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def test_sas_read_no_format_or_extension(self):
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# see gh-24548
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msg = "unable to infer format of SAS file.+"
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with tm.ensure_clean("test_file_no_extension") as path:
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with pytest.raises(ValueError, match=msg):
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read_sas(path)
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def test_sas_archive(datapath):
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fname_uncompressed = datapath("io", "sas", "data", "airline.sas7bdat")
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df_uncompressed = read_sas(fname_uncompressed)
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fname_compressed = datapath("io", "sas", "data", "airline.sas7bdat.gz")
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df_compressed = read_sas(fname_compressed, format="sas7bdat")
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tm.assert_frame_equal(df_uncompressed, df_compressed)
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import contextlib
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from datetime import datetime
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import io
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import os
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from pathlib import Path
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import numpy as np
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import pytest
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from pandas.compat import IS64
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from pandas.errors import EmptyDataError
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import pandas.util._test_decorators as td
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import pandas as pd
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import pandas._testing as tm
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from pandas.io.sas.sas7bdat import SAS7BDATReader
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@pytest.fixture
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def dirpath(datapath):
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return datapath("io", "sas", "data")
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@pytest.fixture(params=[(1, range(1, 16)), (2, [16])])
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def data_test_ix(request, dirpath):
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i, test_ix = request.param
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fname = os.path.join(dirpath, f"test_sas7bdat_{i}.csv")
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df = pd.read_csv(fname)
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epoch = datetime(1960, 1, 1)
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t1 = pd.to_timedelta(df["Column4"], unit="d")
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df["Column4"] = (epoch + t1).astype("M8[s]")
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t2 = pd.to_timedelta(df["Column12"], unit="d")
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df["Column12"] = (epoch + t2).astype("M8[s]")
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for k in range(df.shape[1]):
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col = df.iloc[:, k]
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if col.dtype == np.int64:
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df.isetitem(k, df.iloc[:, k].astype(np.float64))
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return df, test_ix
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# https://github.com/cython/cython/issues/1720
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class TestSAS7BDAT:
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@pytest.mark.slow
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def test_from_file(self, dirpath, data_test_ix):
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expected, test_ix = data_test_ix
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for k in test_ix:
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fname = os.path.join(dirpath, f"test{k}.sas7bdat")
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df = pd.read_sas(fname, encoding="utf-8")
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tm.assert_frame_equal(df, expected)
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@pytest.mark.slow
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def test_from_buffer(self, dirpath, data_test_ix):
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expected, test_ix = data_test_ix
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for k in test_ix:
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fname = os.path.join(dirpath, f"test{k}.sas7bdat")
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with open(fname, "rb") as f:
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byts = f.read()
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buf = io.BytesIO(byts)
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with pd.read_sas(
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buf, format="sas7bdat", iterator=True, encoding="utf-8"
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) as rdr:
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df = rdr.read()
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tm.assert_frame_equal(df, expected)
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@pytest.mark.slow
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def test_from_iterator(self, dirpath, data_test_ix):
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expected, test_ix = data_test_ix
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for k in test_ix:
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fname = os.path.join(dirpath, f"test{k}.sas7bdat")
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with pd.read_sas(fname, iterator=True, encoding="utf-8") as rdr:
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df = rdr.read(2)
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tm.assert_frame_equal(df, expected.iloc[0:2, :])
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df = rdr.read(3)
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tm.assert_frame_equal(df, expected.iloc[2:5, :])
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@pytest.mark.slow
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def test_path_pathlib(self, dirpath, data_test_ix):
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expected, test_ix = data_test_ix
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for k in test_ix:
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fname = Path(os.path.join(dirpath, f"test{k}.sas7bdat"))
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df = pd.read_sas(fname, encoding="utf-8")
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tm.assert_frame_equal(df, expected)
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@td.skip_if_no("py.path")
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@pytest.mark.slow
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def test_path_localpath(self, dirpath, data_test_ix):
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from py.path import local as LocalPath
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expected, test_ix = data_test_ix
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for k in test_ix:
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fname = LocalPath(os.path.join(dirpath, f"test{k}.sas7bdat"))
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df = pd.read_sas(fname, encoding="utf-8")
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tm.assert_frame_equal(df, expected)
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@pytest.mark.slow
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@pytest.mark.parametrize("chunksize", (3, 5, 10, 11))
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@pytest.mark.parametrize("k", range(1, 17))
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def test_iterator_loop(self, dirpath, k, chunksize):
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# github #13654
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fname = os.path.join(dirpath, f"test{k}.sas7bdat")
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with pd.read_sas(fname, chunksize=chunksize, encoding="utf-8") as rdr:
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y = 0
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for x in rdr:
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y += x.shape[0]
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assert y == rdr.row_count
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def test_iterator_read_too_much(self, dirpath):
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# github #14734
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fname = os.path.join(dirpath, "test1.sas7bdat")
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with pd.read_sas(
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fname, format="sas7bdat", iterator=True, encoding="utf-8"
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) as rdr:
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d1 = rdr.read(rdr.row_count + 20)
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with pd.read_sas(fname, iterator=True, encoding="utf-8") as rdr:
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d2 = rdr.read(rdr.row_count + 20)
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tm.assert_frame_equal(d1, d2)
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def test_encoding_options(datapath):
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fname = datapath("io", "sas", "data", "test1.sas7bdat")
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df1 = pd.read_sas(fname)
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df2 = pd.read_sas(fname, encoding="utf-8")
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for col in df1.columns:
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try:
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df1[col] = df1[col].str.decode("utf-8")
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except AttributeError:
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pass
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tm.assert_frame_equal(df1, df2)
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with contextlib.closing(SAS7BDATReader(fname, convert_header_text=False)) as rdr:
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df3 = rdr.read()
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for x, y in zip(df1.columns, df3.columns):
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assert x == y.decode()
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def test_encoding_infer(datapath):
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fname = datapath("io", "sas", "data", "test1.sas7bdat")
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with pd.read_sas(fname, encoding="infer", iterator=True) as df1_reader:
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# check: is encoding inferred correctly from file
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assert df1_reader.inferred_encoding == "cp1252"
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df1 = df1_reader.read()
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with pd.read_sas(fname, encoding="cp1252", iterator=True) as df2_reader:
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df2 = df2_reader.read()
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# check: reader reads correct information
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tm.assert_frame_equal(df1, df2)
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def test_productsales(datapath):
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fname = datapath("io", "sas", "data", "productsales.sas7bdat")
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df = pd.read_sas(fname, encoding="utf-8")
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fname = datapath("io", "sas", "data", "productsales.csv")
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df0 = pd.read_csv(fname, parse_dates=["MONTH"])
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vn = ["ACTUAL", "PREDICT", "QUARTER", "YEAR"]
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df0[vn] = df0[vn].astype(np.float64)
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df0["MONTH"] = df0["MONTH"].astype("M8[s]")
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tm.assert_frame_equal(df, df0)
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def test_12659(datapath):
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fname = datapath("io", "sas", "data", "test_12659.sas7bdat")
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df = pd.read_sas(fname)
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fname = datapath("io", "sas", "data", "test_12659.csv")
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df0 = pd.read_csv(fname)
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df0 = df0.astype(np.float64)
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tm.assert_frame_equal(df, df0)
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def test_airline(datapath):
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fname = datapath("io", "sas", "data", "airline.sas7bdat")
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df = pd.read_sas(fname)
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fname = datapath("io", "sas", "data", "airline.csv")
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df0 = pd.read_csv(fname)
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df0 = df0.astype(np.float64)
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tm.assert_frame_equal(df, df0)
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def test_date_time(datapath):
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# Support of different SAS date/datetime formats (PR #15871)
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fname = datapath("io", "sas", "data", "datetime.sas7bdat")
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df = pd.read_sas(fname)
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fname = datapath("io", "sas", "data", "datetime.csv")
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df0 = pd.read_csv(
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fname, parse_dates=["Date1", "Date2", "DateTime", "DateTimeHi", "Taiw"]
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)
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# GH 19732: Timestamps imported from sas will incur floating point errors
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# See GH#56014 for discussion of the correct "expected" results
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# We are really just testing that we are "close". This only seems to be
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# an issue near the implementation bounds.
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df[df.columns[3]] = df.iloc[:, 3].dt.round("us")
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df0["Date1"] = df0["Date1"].astype("M8[s]")
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df0["Date2"] = df0["Date2"].astype("M8[s]")
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df0["DateTime"] = df0["DateTime"].astype("M8[ms]")
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df0["Taiw"] = df0["Taiw"].astype("M8[s]")
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res = df0["DateTimeHi"].astype("M8[us]").dt.round("ms")
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df0["DateTimeHi"] = res.astype("M8[ms]")
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if not IS64:
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# No good reason for this, just what we get on the CI
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df0.loc[0, "DateTimeHi"] += np.timedelta64(1, "ms")
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df0.loc[[2, 3], "DateTimeHi"] -= np.timedelta64(1, "ms")
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tm.assert_frame_equal(df, df0)
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@pytest.mark.parametrize("column", ["WGT", "CYL"])
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def test_compact_numerical_values(datapath, column):
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# Regression test for #21616
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fname = datapath("io", "sas", "data", "cars.sas7bdat")
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df = pd.read_sas(fname, encoding="latin-1")
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# The two columns CYL and WGT in cars.sas7bdat have column
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# width < 8 and only contain integral values.
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# Test that pandas doesn't corrupt the numbers by adding
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# decimals.
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result = df[column]
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expected = df[column].round()
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tm.assert_series_equal(result, expected, check_exact=True)
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def test_many_columns(datapath):
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# Test for looking for column information in more places (PR #22628)
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fname = datapath("io", "sas", "data", "many_columns.sas7bdat")
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df = pd.read_sas(fname, encoding="latin-1")
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fname = datapath("io", "sas", "data", "many_columns.csv")
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df0 = pd.read_csv(fname, encoding="latin-1")
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tm.assert_frame_equal(df, df0)
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def test_inconsistent_number_of_rows(datapath):
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# Regression test for issue #16615. (PR #22628)
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fname = datapath("io", "sas", "data", "load_log.sas7bdat")
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df = pd.read_sas(fname, encoding="latin-1")
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assert len(df) == 2097
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def test_zero_variables(datapath):
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# Check if the SAS file has zero variables (PR #18184)
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fname = datapath("io", "sas", "data", "zero_variables.sas7bdat")
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with pytest.raises(EmptyDataError, match="No columns to parse from file"):
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pd.read_sas(fname)
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def test_zero_rows(datapath):
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# GH 18198
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fname = datapath("io", "sas", "data", "zero_rows.sas7bdat")
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result = pd.read_sas(fname)
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expected = pd.DataFrame([{"char_field": "a", "num_field": 1.0}]).iloc[:0]
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tm.assert_frame_equal(result, expected)
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def test_corrupt_read(datapath):
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# We don't really care about the exact failure, the important thing is
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# that the resource should be cleaned up afterwards (BUG #35566)
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fname = datapath("io", "sas", "data", "corrupt.sas7bdat")
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msg = "'SAS7BDATReader' object has no attribute 'row_count'"
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with pytest.raises(AttributeError, match=msg):
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pd.read_sas(fname)
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def test_max_sas_date(datapath):
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# GH 20927
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# NB. max datetime in SAS dataset is 31DEC9999:23:59:59.999
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# but this is read as 29DEC9999:23:59:59.998993 by a buggy
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# sas7bdat module
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# See also GH#56014 for discussion of the correct "expected" results.
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fname = datapath("io", "sas", "data", "max_sas_date.sas7bdat")
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df = pd.read_sas(fname, encoding="iso-8859-1")
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expected = pd.DataFrame(
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{
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"text": ["max", "normal"],
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"dt_as_float": [253717747199.999, 1880323199.999],
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"dt_as_dt": np.array(
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[
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datetime(9999, 12, 29, 23, 59, 59, 999000),
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datetime(2019, 8, 1, 23, 59, 59, 999000),
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],
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dtype="M8[ms]",
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),
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"date_as_float": [2936547.0, 21762.0],
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"date_as_date": np.array(
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[
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datetime(9999, 12, 29),
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datetime(2019, 8, 1),
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],
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dtype="M8[s]",
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),
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},
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columns=["text", "dt_as_float", "dt_as_dt", "date_as_float", "date_as_date"],
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)
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if not IS64:
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# No good reason for this, just what we get on the CI
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expected.loc[:, "dt_as_dt"] -= np.timedelta64(1, "ms")
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tm.assert_frame_equal(df, expected)
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def test_max_sas_date_iterator(datapath):
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# GH 20927
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# when called as an iterator, only those chunks with a date > pd.Timestamp.max
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# are returned as datetime.datetime, if this happens that whole chunk is returned
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# as datetime.datetime
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col_order = ["text", "dt_as_float", "dt_as_dt", "date_as_float", "date_as_date"]
|
||||
fname = datapath("io", "sas", "data", "max_sas_date.sas7bdat")
|
||||
results = []
|
||||
for df in pd.read_sas(fname, encoding="iso-8859-1", chunksize=1):
|
||||
# GH 19732: Timestamps imported from sas will incur floating point errors
|
||||
df.reset_index(inplace=True, drop=True)
|
||||
results.append(df)
|
||||
expected = [
|
||||
pd.DataFrame(
|
||||
{
|
||||
"text": ["max"],
|
||||
"dt_as_float": [253717747199.999],
|
||||
"dt_as_dt": np.array(
|
||||
[datetime(9999, 12, 29, 23, 59, 59, 999000)], dtype="M8[ms]"
|
||||
),
|
||||
"date_as_float": [2936547.0],
|
||||
"date_as_date": np.array([datetime(9999, 12, 29)], dtype="M8[s]"),
|
||||
},
|
||||
columns=col_order,
|
||||
),
|
||||
pd.DataFrame(
|
||||
{
|
||||
"text": ["normal"],
|
||||
"dt_as_float": [1880323199.999],
|
||||
"dt_as_dt": np.array(["2019-08-01 23:59:59.999"], dtype="M8[ms]"),
|
||||
"date_as_float": [21762.0],
|
||||
"date_as_date": np.array(["2019-08-01"], dtype="M8[s]"),
|
||||
},
|
||||
columns=col_order,
|
||||
),
|
||||
]
|
||||
if not IS64:
|
||||
# No good reason for this, just what we get on the CI
|
||||
expected[0].loc[0, "dt_as_dt"] -= np.timedelta64(1, "ms")
|
||||
expected[1].loc[0, "dt_as_dt"] -= np.timedelta64(1, "ms")
|
||||
|
||||
tm.assert_frame_equal(results[0], expected[0])
|
||||
tm.assert_frame_equal(results[1], expected[1])
|
||||
|
||||
|
||||
def test_null_date(datapath):
|
||||
fname = datapath("io", "sas", "data", "dates_null.sas7bdat")
|
||||
df = pd.read_sas(fname, encoding="utf-8")
|
||||
|
||||
expected = pd.DataFrame(
|
||||
{
|
||||
"datecol": np.array(
|
||||
[
|
||||
datetime(9999, 12, 29),
|
||||
np.datetime64("NaT"),
|
||||
],
|
||||
dtype="M8[s]",
|
||||
),
|
||||
"datetimecol": np.array(
|
||||
[
|
||||
datetime(9999, 12, 29, 23, 59, 59, 999000),
|
||||
np.datetime64("NaT"),
|
||||
],
|
||||
dtype="M8[ms]",
|
||||
),
|
||||
},
|
||||
)
|
||||
if not IS64:
|
||||
# No good reason for this, just what we get on the CI
|
||||
expected.loc[0, "datetimecol"] -= np.timedelta64(1, "ms")
|
||||
tm.assert_frame_equal(df, expected)
|
||||
|
||||
|
||||
def test_meta2_page(datapath):
|
||||
# GH 35545
|
||||
fname = datapath("io", "sas", "data", "test_meta2_page.sas7bdat")
|
||||
df = pd.read_sas(fname)
|
||||
assert len(df) == 1000
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"test_file, override_offset, override_value, expected_msg",
|
||||
[
|
||||
("test2.sas7bdat", 0x10000 + 55229, 0x80 | 0x0F, "Out of bounds"),
|
||||
("test2.sas7bdat", 0x10000 + 55229, 0x10, "unknown control byte"),
|
||||
("test3.sas7bdat", 118170, 184, "Out of bounds"),
|
||||
],
|
||||
)
|
||||
def test_rle_rdc_exceptions(
|
||||
datapath, test_file, override_offset, override_value, expected_msg
|
||||
):
|
||||
"""Errors in RLE/RDC decompression should propagate."""
|
||||
with open(datapath("io", "sas", "data", test_file), "rb") as fd:
|
||||
data = bytearray(fd.read())
|
||||
data[override_offset] = override_value
|
||||
with pytest.raises(Exception, match=expected_msg):
|
||||
pd.read_sas(io.BytesIO(data), format="sas7bdat")
|
||||
|
||||
|
||||
def test_0x40_control_byte(datapath):
|
||||
# GH 31243
|
||||
fname = datapath("io", "sas", "data", "0x40controlbyte.sas7bdat")
|
||||
df = pd.read_sas(fname, encoding="ascii")
|
||||
fname = datapath("io", "sas", "data", "0x40controlbyte.csv")
|
||||
df0 = pd.read_csv(fname, dtype="object")
|
||||
tm.assert_frame_equal(df, df0)
|
||||
|
||||
|
||||
def test_0x00_control_byte(datapath):
|
||||
# GH 47099
|
||||
fname = datapath("io", "sas", "data", "0x00controlbyte.sas7bdat.bz2")
|
||||
df = next(pd.read_sas(fname, chunksize=11_000))
|
||||
assert df.shape == (11_000, 20)
|
||||
167
lib/python3.13/site-packages/pandas/tests/io/sas/test_xport.py
Normal file
167
lib/python3.13/site-packages/pandas/tests/io/sas/test_xport.py
Normal file
@ -0,0 +1,167 @@
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
import pandas as pd
|
||||
import pandas._testing as tm
|
||||
|
||||
from pandas.io.sas.sasreader import read_sas
|
||||
|
||||
# CSV versions of test xpt files were obtained using the R foreign library
|
||||
|
||||
# Numbers in a SAS xport file are always float64, so need to convert
|
||||
# before making comparisons.
|
||||
|
||||
|
||||
def numeric_as_float(data):
|
||||
for v in data.columns:
|
||||
if data[v].dtype is np.dtype("int64"):
|
||||
data[v] = data[v].astype(np.float64)
|
||||
|
||||
|
||||
class TestXport:
|
||||
@pytest.fixture
|
||||
def file01(self, datapath):
|
||||
return datapath("io", "sas", "data", "DEMO_G.xpt")
|
||||
|
||||
@pytest.fixture
|
||||
def file02(self, datapath):
|
||||
return datapath("io", "sas", "data", "SSHSV1_A.xpt")
|
||||
|
||||
@pytest.fixture
|
||||
def file03(self, datapath):
|
||||
return datapath("io", "sas", "data", "DRXFCD_G.xpt")
|
||||
|
||||
@pytest.fixture
|
||||
def file04(self, datapath):
|
||||
return datapath("io", "sas", "data", "paxraw_d_short.xpt")
|
||||
|
||||
@pytest.fixture
|
||||
def file05(self, datapath):
|
||||
return datapath("io", "sas", "data", "DEMO_PUF.cpt")
|
||||
|
||||
@pytest.mark.slow
|
||||
def test1_basic(self, file01):
|
||||
# Tests with DEMO_G.xpt (all numeric file)
|
||||
|
||||
# Compare to this
|
||||
data_csv = pd.read_csv(file01.replace(".xpt", ".csv"))
|
||||
numeric_as_float(data_csv)
|
||||
|
||||
# Read full file
|
||||
data = read_sas(file01, format="xport")
|
||||
tm.assert_frame_equal(data, data_csv)
|
||||
num_rows = data.shape[0]
|
||||
|
||||
# Test reading beyond end of file
|
||||
with read_sas(file01, format="xport", iterator=True) as reader:
|
||||
data = reader.read(num_rows + 100)
|
||||
assert data.shape[0] == num_rows
|
||||
|
||||
# Test incremental read with `read` method.
|
||||
with read_sas(file01, format="xport", iterator=True) as reader:
|
||||
data = reader.read(10)
|
||||
tm.assert_frame_equal(data, data_csv.iloc[0:10, :])
|
||||
|
||||
# Test incremental read with `get_chunk` method.
|
||||
with read_sas(file01, format="xport", chunksize=10) as reader:
|
||||
data = reader.get_chunk()
|
||||
tm.assert_frame_equal(data, data_csv.iloc[0:10, :])
|
||||
|
||||
# Test read in loop
|
||||
m = 0
|
||||
with read_sas(file01, format="xport", chunksize=100) as reader:
|
||||
for x in reader:
|
||||
m += x.shape[0]
|
||||
assert m == num_rows
|
||||
|
||||
# Read full file with `read_sas` method
|
||||
data = read_sas(file01)
|
||||
tm.assert_frame_equal(data, data_csv)
|
||||
|
||||
def test1_index(self, file01):
|
||||
# Tests with DEMO_G.xpt using index (all numeric file)
|
||||
|
||||
# Compare to this
|
||||
data_csv = pd.read_csv(file01.replace(".xpt", ".csv"))
|
||||
data_csv = data_csv.set_index("SEQN")
|
||||
numeric_as_float(data_csv)
|
||||
|
||||
# Read full file
|
||||
data = read_sas(file01, index="SEQN", format="xport")
|
||||
tm.assert_frame_equal(data, data_csv, check_index_type=False)
|
||||
|
||||
# Test incremental read with `read` method.
|
||||
with read_sas(file01, index="SEQN", format="xport", iterator=True) as reader:
|
||||
data = reader.read(10)
|
||||
tm.assert_frame_equal(data, data_csv.iloc[0:10, :], check_index_type=False)
|
||||
|
||||
# Test incremental read with `get_chunk` method.
|
||||
with read_sas(file01, index="SEQN", format="xport", chunksize=10) as reader:
|
||||
data = reader.get_chunk()
|
||||
tm.assert_frame_equal(data, data_csv.iloc[0:10, :], check_index_type=False)
|
||||
|
||||
def test1_incremental(self, file01):
|
||||
# Test with DEMO_G.xpt, reading full file incrementally
|
||||
|
||||
data_csv = pd.read_csv(file01.replace(".xpt", ".csv"))
|
||||
data_csv = data_csv.set_index("SEQN")
|
||||
numeric_as_float(data_csv)
|
||||
|
||||
with read_sas(file01, index="SEQN", chunksize=1000) as reader:
|
||||
all_data = list(reader)
|
||||
data = pd.concat(all_data, axis=0)
|
||||
|
||||
tm.assert_frame_equal(data, data_csv, check_index_type=False)
|
||||
|
||||
def test2(self, file02):
|
||||
# Test with SSHSV1_A.xpt
|
||||
|
||||
# Compare to this
|
||||
data_csv = pd.read_csv(file02.replace(".xpt", ".csv"))
|
||||
numeric_as_float(data_csv)
|
||||
|
||||
data = read_sas(file02)
|
||||
tm.assert_frame_equal(data, data_csv)
|
||||
|
||||
def test2_binary(self, file02):
|
||||
# Test with SSHSV1_A.xpt, read as a binary file
|
||||
|
||||
# Compare to this
|
||||
data_csv = pd.read_csv(file02.replace(".xpt", ".csv"))
|
||||
numeric_as_float(data_csv)
|
||||
|
||||
with open(file02, "rb") as fd:
|
||||
# GH#35693 ensure that if we pass an open file, we
|
||||
# dont incorrectly close it in read_sas
|
||||
data = read_sas(fd, format="xport")
|
||||
|
||||
tm.assert_frame_equal(data, data_csv)
|
||||
|
||||
def test_multiple_types(self, file03):
|
||||
# Test with DRXFCD_G.xpt (contains text and numeric variables)
|
||||
|
||||
# Compare to this
|
||||
data_csv = pd.read_csv(file03.replace(".xpt", ".csv"))
|
||||
|
||||
data = read_sas(file03, encoding="utf-8")
|
||||
tm.assert_frame_equal(data, data_csv)
|
||||
|
||||
def test_truncated_float_support(self, file04):
|
||||
# Test with paxraw_d_short.xpt, a shortened version of:
|
||||
# http://wwwn.cdc.gov/Nchs/Nhanes/2005-2006/PAXRAW_D.ZIP
|
||||
# This file has truncated floats (5 bytes in this case).
|
||||
|
||||
# GH 11713
|
||||
|
||||
data_csv = pd.read_csv(file04.replace(".xpt", ".csv"))
|
||||
|
||||
data = read_sas(file04, format="xport")
|
||||
tm.assert_frame_equal(data.astype("int64"), data_csv)
|
||||
|
||||
def test_cport_header_found_raises(self, file05):
|
||||
# Test with DEMO_PUF.cpt, the beginning of puf2019_1_fall.xpt
|
||||
# from https://www.cms.gov/files/zip/puf2019.zip
|
||||
# (despite the extension, it's a cpt file)
|
||||
msg = "Header record indicates a CPORT file, which is not readable."
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
read_sas(file05, format="xport")
|
||||
Reference in New Issue
Block a user