A PyQT GUI application for converting InfoLease report outputs into Excel files. Handles parsing and summarizing. Learns where files are meant to be store and compiles monthly and yearly summaries.
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InfoLeaseExtract/venv/Lib/site-packages/pandas/tests/io/test_spss.py

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2.7 KiB

from pathlib import Path
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
pyreadstat = pytest.importorskip("pyreadstat")
@pytest.mark.parametrize("path_klass", [lambda p: p, Path])
def test_spss_labelled_num(path_klass, datapath):
# test file from the Haven project (https://haven.tidyverse.org/)
fname = path_klass(datapath("io", "data", "spss", "labelled-num.sav"))
df = pd.read_spss(fname, convert_categoricals=True)
expected = pd.DataFrame({"VAR00002": "This is one"}, index=[0])
expected["VAR00002"] = pd.Categorical(expected["VAR00002"])
tm.assert_frame_equal(df, expected)
df = pd.read_spss(fname, convert_categoricals=False)
expected = pd.DataFrame({"VAR00002": 1.0}, index=[0])
tm.assert_frame_equal(df, expected)
def test_spss_labelled_num_na(datapath):
# test file from the Haven project (https://haven.tidyverse.org/)
fname = datapath("io", "data", "spss", "labelled-num-na.sav")
df = pd.read_spss(fname, convert_categoricals=True)
expected = pd.DataFrame({"VAR00002": ["This is one", None]})
expected["VAR00002"] = pd.Categorical(expected["VAR00002"])
tm.assert_frame_equal(df, expected)
df = pd.read_spss(fname, convert_categoricals=False)
expected = pd.DataFrame({"VAR00002": [1.0, np.nan]})
tm.assert_frame_equal(df, expected)
def test_spss_labelled_str(datapath):
# test file from the Haven project (https://haven.tidyverse.org/)
fname = datapath("io", "data", "spss", "labelled-str.sav")
df = pd.read_spss(fname, convert_categoricals=True)
expected = pd.DataFrame({"gender": ["Male", "Female"]})
expected["gender"] = pd.Categorical(expected["gender"])
tm.assert_frame_equal(df, expected)
df = pd.read_spss(fname, convert_categoricals=False)
expected = pd.DataFrame({"gender": ["M", "F"]})
tm.assert_frame_equal(df, expected)
def test_spss_umlauts(datapath):
# test file from the Haven project (https://haven.tidyverse.org/)
fname = datapath("io", "data", "spss", "umlauts.sav")
df = pd.read_spss(fname, convert_categoricals=True)
expected = pd.DataFrame(
{"var1": ["the ä umlaut", "the ü umlaut", "the ä umlaut", "the ö umlaut"]}
)
expected["var1"] = pd.Categorical(expected["var1"])
tm.assert_frame_equal(df, expected)
df = pd.read_spss(fname, convert_categoricals=False)
expected = pd.DataFrame({"var1": [1.0, 2.0, 1.0, 3.0]})
tm.assert_frame_equal(df, expected)
def test_spss_usecols(datapath):
# usecols must be list-like
fname = datapath("io", "data", "spss", "labelled-num.sav")
with pytest.raises(TypeError, match="usecols must be list-like."):
pd.read_spss(fname, usecols="VAR00002")