def test_overflow_coercing():
records = [{'_c0':'12345678901'}]
desired_df = pd.DataFrame(records)
desired_df['_c0'] = pd.to_numeric(desired_df['_c0'])
df = pd.DataFrame(records)
coerce_pandas_df_to_numeric_datetime(df)
assert_frame_equal(desired_df, df)
def test_df_dict_does_not_throw():
json_str = """
[{
"id": 580320,
"name": "COUSIN'S GRILL",
"results": "Fail",
"violations": "37. TOILET area.",
"words": ["37.",
"toilet",
"area."],
"features": {
"type": 0,
"size": 262144,
"indices": [0,
45,
97],
"values": [7.0,
5.0,
1.0]
},
"rawPrediction": {
"type": 1,
"values": [3.640841752791392,
-3.640841752791392]
},
"probability": {
"type": 1,
"values": [0.974440185187647,
0.025559814812352966]
},
"prediction": 0.0
}]
"""
df = pd.read_json(json_str)
coerce_pandas_df_to_numeric_datetime(df)
def test_df_dict_does_not_throw():
json_str = """
[{
"id": 580320,
"name": "COUSIN'S GRILL",
"results": "Fail",
"violations": "37. TOILET area.",
"words": ["37.",
"toilet",
"area."],
"features": {
"type": 0,
"size": 262144,
"indices": [0,
45,
97],
"values": [7.0,
5.0,
1.0]
},
"rawPrediction": {
"type": 1,
"values": [3.640841752791392,
-3.640841752791392]
},
"probability": {
"type": 1,
"values": [0.974440185187647,
0.025559814812352966]
},
"prediction": 0.0
}]
"""
df = pd.read_json(json_str)
coerce_pandas_df_to_numeric_datetime(df)
def _records_to_dataframe(records_text):
strings = records_text.split('\n')
try:
df = pd.DataFrame([json.loads(s) for s in strings])
coerce_pandas_df_to_numeric_datetime(df)
return df
except ValueError:
raise DataFrameParseException("Cannot parse object as JSON: '{}'".format(strings))
def test_no_coercing():
records = [{u'buildingID': 0, u'date': u'6/1/13', u'temp_diff': u'12'},
{u'buildingID': 1, u'date': u'random', u'temp_diff': u'0adsf'}]
desired_df = pd.DataFrame(records)
df = pd.DataFrame(records)
coerce_pandas_df_to_numeric_datetime(df)
assert_frame_equal(desired_df, df)
def _records_to_dataframe(records_text):
strings = records_text.split('\n')
try:
df = pd.DataFrame([json.loads(s) for s in strings])
coerce_pandas_df_to_numeric_datetime(df)
return df
except ValueError:
raise DataFrameParseException(
"Cannot parse object as JSON: '{}'".format(strings))
def test_numeric_coercing_none_values():
records = [{u'buildingID': 0, u'date': u'6/1/13', u'temp_diff': u'12'},
{u'buildingID': 1, u'date': u'asdf', u'temp_diff': None}]
desired_df = pd.DataFrame(records)
desired_df["temp_diff"] = pd.to_numeric(desired_df["temp_diff"])
df = pd.DataFrame(records)
coerce_pandas_df_to_numeric_datetime(df)
assert_frame_equal(desired_df, df)
def test_no_coercing():
records = [{
u'buildingID': 0,
u'date': u'6/1/13',
u'temp_diff': u'12'
}, {
u'buildingID': 1,
u'date': u'random',
u'temp_diff': u'0adsf'
}]
desired_df = pd.DataFrame(records)
df = pd.DataFrame(records)
coerce_pandas_df_to_numeric_datetime(df)
assert_frame_equal(desired_df, df)
def test_date_coercing_none_values():
records = [{
u'buildingID': 0,
u'date': u'6/1/13',
u'temp_diff': u'12'
}, {
u'buildingID': 1,
u'date': None,
u'temp_diff': u'0adsf'
}]
desired_df = pd.DataFrame(records)
desired_df["date"] = pd.to_datetime(desired_df["date"])
df = pd.DataFrame(records)
coerce_pandas_df_to_numeric_datetime(df)
assert_frame_equal(desired_df, df)
