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TST: test that we work in downstream packages
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xarray | ||
geopandas | ||
dask | ||
seaborn | ||
pandas_gbq | ||
pandas_datareader | ||
statsmodels | ||
scikit-learn |
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#!/bin/bash | ||
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source activate pandas | ||
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echo "install 27" | ||
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conda install -n pandas -c conda-forge pyarrow |
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""" | ||
Testing that we work in the downstream packages | ||
""" | ||
import pytest | ||
from pandas import DataFrame | ||
from pandas.util import testing as tm | ||
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@pytest.fixture | ||
def df(): | ||
return DataFrame({'A': [1, 2, 3]}) | ||
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def test_dask(df): | ||
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dask = pytest.importorskip('dask') # noqa | ||
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import dask.dataframe as dd | ||
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ddf = dd.from_pandas(df) | ||
assert ddf.A is not None | ||
assert ddf.compute() | ||
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def test_xarray(df): | ||
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xarray = pytest.importorskip('xarray') # noqa | ||
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assert df.to_xarray() is not None | ||
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def test_statsmodels(): | ||
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statsmodels = pytest.importorskip('statsmodels') # noqa | ||
import statsmodels.api as sm | ||
import statsmodels.formula.api as smf | ||
df = sm.datasets.get_rdataset("Guerry", "HistData").data | ||
smf.ols('Lottery ~ Literacy + np.log(Pop1831)', data=df).fit() | ||
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def test_scikit_learn(df): | ||
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sklearn = pytest.importorskip('sklearn') # noqa | ||
from sklearn import svm, datasets | ||
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digits = datasets.load_digits() | ||
clf = svm.SVC(gamma=0.001, C=100.) | ||
clf.fit(digits.data[:-1], digits.target[:-1]) | ||
clf.predict(digits.data[-1:]) | ||
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def test_seaborn(): | ||
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seaborn = pytest.importorskip('seaborn') | ||
tips = seaborn.load_dataset("tips") | ||
seaborn.stripplot(x="day", y="total_bill", data=tips) | ||
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def test_pandas_gbq(df): | ||
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pandas_gbq = pytest.importorskip('pandas-gbq') # noqa | ||
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@tm.network | ||
def test_pandas_datareader(): | ||
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pandas_datareader = pytest.importorskip('pandas-datareader') # noqa | ||
pandas_datareader.get_data_yahoo('AAPL') | ||
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def test_geopandas(): | ||
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geopandas = pytest.importorskip('geopandas') # noqa | ||
fp = geopandas.datasets.get_path('naturalearth_lowres') | ||
assert geopandas.read_file(fp) is not None | ||
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def test_pyarrow(df): | ||
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pyarrow = pytest.importorskip('pyarrow') # noqa | ||
table = pyarrow.Table.from_pandas(df) | ||
result = table.to_pandas() | ||
tm.assert_frame_equal(result, df) |