def test_pandas_parser_nan_with_field_type_information_1143():
descriptor = {
"dialect": {
"delimiter": ","
},
"name": "issue-1109",
"path": "data/issue-1109.csv",
"schema": {
"fields": [
{
"name": "int",
"type": "integer"
},
{
"name": "number",
"type": "number"
},
{
"name": "string",
"type": "string"
},
]
},
}
res = Resource(descriptor)
df = res.to_pandas()
assert all(df.dtypes.values == pd.array([pd.Int64Dtype(), float, object]))
def test_pandas_parser_test_issue_sample_data_1138():
descriptor = {
"path": "data/issue-1138.csv",
"name": "pegeldaten-schleswig-holstein-114515",
"profile": "tabular-data-resource",
"format": "csv",
"encoding": "iso8859-1",
"dialect": {
"delimiter": ";"
},
"schema": {
"fields": [
{
"type": "date",
"format": "%d.%m.%Y",
"name": "Zeit [MEZ]"
},
{
"type": "integer",
"name": "Wasserstand"
},
{
"type": "string",
"name": "Status",
"constraints": {
"enum":
["qualitätsgesichert", "nicht qualitätsgesichert"]
},
},
]
},
}
resource = Resource(descriptor)
df = resource.to_pandas()
assert is_datetime64_ns_dtype(df.dtypes.values[0])
def test_pandas_parser_preserve_datetime_field_type_1138():
descriptor = {
"name":
"article",
"schema": {
"fields": [
{
"name": "id",
"type": "integer"
},
{
"name": "datetime",
"type": "date"
},
]
},
"data": [
["id", "datetime"],
["1", "2020-01-01 15:00:00"],
["2", "2020-01-01 15:00:00"],
],
}
resource = Resource(descriptor)
df = resource.to_pandas()
assert is_datetime64_ns_dtype(df.dtypes.values[1])
def test_pandas_storage_write_resource_existent_error():
resource = Resource(path="data/table.csv")
storage = resource.to_pandas()
with pytest.raises(FrictionlessException) as excinfo:
storage.write_resource(resource)
error = excinfo.value.error
assert error.code == "storage-error"
assert error.note.count("already exists")
# Cleanup storage
storage.delete_package(list(storage))
def test_pandas_parser_nan_in_integer_resource_column():
# see issue 1109
res = Resource([
["int", "number", "string"],
["1", "2.3", "string"],
["", "4.3", "string"],
["3", "3.14", "string"],
])
df = res.to_pandas()
assert all(df.dtypes.values == pd.array([pd.Int64Dtype(), float, object]))
def test_pandas_parser_nan_in_integer_csv_column():
# see issue 1109
res = Resource("data/issue-1109.csv")
df = res.to_pandas()
assert all(df.dtypes.values == pd.array([pd.Int64Dtype(), float, object]))