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import pytest
import warnings
import json
import numpy as np
import pandas as pd
from .. import infer_vegalite_type, sanitize_dataframe
def test_infer_vegalite_type():
def _check(arr, typ):
assert infer_vegalite_type(arr) == typ
_check(np.arange(5, dtype=float), "quantitative")
_check(np.arange(5, dtype=int), "quantitative")
_check(np.zeros(5, dtype=bool), "nominal")
_check(pd.date_range("2012", "2013"), "temporal")
_check(pd.timedelta_range(365, periods=12), "temporal")
nulled = pd.Series(np.random.randint(10, size=10))
nulled[0] = None
_check(nulled, "quantitative")
_check(["a", "b", "c"], "nominal")
if hasattr(pytest, "warns"): # added in pytest 2.8
with pytest.warns(UserWarning):
_check([], "nominal")
else:
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
_check([], "nominal")
def test_sanitize_dataframe():
# create a dataframe with various types
df = pd.DataFrame(
{
"s": list("abcde"),
"f": np.arange(5, dtype=float),
"i": np.arange(5, dtype=int),
"b": np.array([True, False, True, True, False]),
"d": pd.date_range("2012-01-01", periods=5, freq="H"),
"c": pd.Series(list("ababc"), dtype="category"),
"c2": pd.Series([1, "A", 2.5, "B", None], dtype="category"),
"o": pd.Series([np.array(i) for i in range(5)]),
"p": pd.date_range("2012-01-01", periods=5, freq="H").tz_localize("UTC"),
}
)
# add some nulls
df.iloc[0, df.columns.get_loc("s")] = None
df.iloc[0, df.columns.get_loc("f")] = np.nan
df.iloc[0, df.columns.get_loc("d")] = pd.NaT
df.iloc[0, df.columns.get_loc("o")] = np.array(np.nan)
# JSON serialize. This will fail on non-sanitized dataframes
print(df[["s", "c2"]])
df_clean = sanitize_dataframe(df)
print(df_clean[["s", "c2"]])
print(df_clean[["s", "c2"]].to_dict())
s = json.dumps(df_clean.to_dict(orient="records"))
print(s)
# Re-construct pandas dataframe
df2 = pd.read_json(s)
# Re-order the columns to match df
df2 = df2[df.columns]
# Re-apply original types
for col in df:
if str(df[col].dtype).startswith("datetime"):
# astype(datetime) introduces time-zone issues:
# to_datetime() does not.
utc = isinstance(df[col].dtype, pd.core.dtypes.dtypes.DatetimeTZDtype)
df2[col] = pd.to_datetime(df2[col], utc=utc)
else:
df2[col] = df2[col].astype(df[col].dtype)
# pandas doesn't properly recognize np.array(np.nan), so change it here
df.iloc[0, df.columns.get_loc("o")] = np.nan
assert df.equals(df2)
def test_sanitize_dataframe_colnames():
df = pd.DataFrame(np.arange(12).reshape(4, 3))
# Test that RangeIndex is converted to strings
df = sanitize_dataframe(df)
assert [isinstance(col, str) for col in df.columns]
# Test that non-string columns result in an error
df.columns = [4, "foo", "bar"]
with pytest.raises(ValueError) as err:
sanitize_dataframe(df)
assert str(err.value).startswith("Dataframe contains invalid column name: 4.")
def test_sanitize_dataframe_timedelta():
df = pd.DataFrame({"r": pd.timedelta_range(start="1 day", periods=4)})
with pytest.raises(ValueError) as err:
sanitize_dataframe(df)
assert str(err.value).startswith('Field "r" has type "timedelta')
def test_sanitize_dataframe_infs():
df = pd.DataFrame({"x": [0, 1, 2, np.inf, -np.inf, np.nan]})
df_clean = sanitize_dataframe(df)
assert list(df_clean.dtypes) == [object]
assert list(df_clean["x"]) == [0, 1, 2, None, None, None]
@pytest.mark.skipif(
not hasattr(pd, "Int64Dtype"),
reason="Nullable integers not supported in pandas v{}".format(pd.__version__),
)
def test_sanitize_nullable_integers():
df = pd.DataFrame(
{
"int_np": [1, 2, 3, 4, 5],
"int64": pd.Series([1, 2, 3, None, 5], dtype="UInt8"),
"int64_nan": pd.Series([1, 2, 3, float("nan"), 5], dtype="Int64"),
"float": [1.0, 2.0, 3.0, 4, 5.0],
"float_null": [1, 2, None, 4, 5],
"float_inf": [1, 2, None, 4, (float("inf"))],
}
)
df_clean = sanitize_dataframe(df)
assert {col.dtype.name for _, col in df_clean.iteritems()} == {"object"}
result_python = {col_name: list(col) for col_name, col in df_clean.iteritems()}
assert result_python == {
"int_np": [1, 2, 3, 4, 5],
"int64": [1, 2, 3, None, 5],
"int64_nan": [1, 2, 3, None, 5],
"float": [1.0, 2.0, 3.0, 4.0, 5.0],
"float_null": [1.0, 2.0, None, 4.0, 5.0],
"float_inf": [1.0, 2.0, None, 4.0, None],
}
@pytest.mark.skipif(
not hasattr(pd, "StringDtype"),
reason="dedicated String dtype not supported in pandas v{}".format(pd.__version__),
)
def test_sanitize_string_dtype():
df = pd.DataFrame(
{
"string_object": ["a", "b", "c", "d"],
"string_string": pd.array(["a", "b", "c", "d"], dtype="string"),
"string_object_null": ["a", "b", None, "d"],
"string_string_null": pd.array(["a", "b", None, "d"], dtype="string"),
}
)
df_clean = sanitize_dataframe(df)
assert {col.dtype.name for _, col in df_clean.iteritems()} == {"object"}
result_python = {col_name: list(col) for col_name, col in df_clean.iteritems()}
assert result_python == {
"string_object": ["a", "b", "c", "d"],
"string_string": ["a", "b", "c", "d"],
"string_object_null": ["a", "b", None, "d"],
"string_string_null": ["a", "b", None, "d"],
}
@pytest.mark.skipif(
not hasattr(pd, "BooleanDtype"),
reason="Nullable boolean dtype not supported in pandas v{}".format(pd.__version__),
)
def test_sanitize_boolean_dtype():
df = pd.DataFrame(
{
"bool_none": pd.array([True, False, None], dtype="boolean"),
"none": pd.array([None, None, None], dtype="boolean"),
"bool": pd.array([True, False, True], dtype="boolean"),
}
)
df_clean = sanitize_dataframe(df)
assert {col.dtype.name for _, col in df_clean.iteritems()} == {"object"}
result_python = {col_name: list(col) for col_name, col in df_clean.iteritems()}
assert result_python == {
"bool_none": [True, False, None],
"none": [None, None, None],
"bool": [True, False, True],
}
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