<gh_stars>0
from pandas.testing import assert_frame_equal
import pandas as pd

from sparkmagic.utils.utils import coerce_pandas_df_to_numeric_datetime


def test_no_coercing():
    records = [
        {"buildingID": 0, "date": "6/1/13", "temp_diff": "12"},
        {"buildingID": 1, "date": "random", "temp_diff": "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():
    records = [
        {"buildingID": 0, "date": "6/1/13", "temp_diff": "12"},
        {"buildingID": 1, "date": "6/1/13", "temp_diff": "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)


def test_date_coercing_none_values():
    records = [
        {"buildingID": 0, "date": "6/1/13", "temp_diff": "12"},
        {"buildingID": 1, "date": None, "temp_diff": "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)


def test_date_none_values_and_no_coercing():
    records = [
        {"buildingID": 0, "date": "6/1/13", "temp_diff": "12"},
        {"buildingID": 1, "date": None, "temp_diff": "0adsf"},
        {"buildingID": 1, "date": "adsf", "temp_diff": "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_numeric_coercing():
    records = [
        {"buildingID": 0, "date": "6/1/13", "temp_diff": "12"},
        {"buildingID": 1, "date": "adsf", "temp_diff": "0"},
    ]
    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_numeric_coercing_none_values():
    records = [
        {"buildingID": 0, "date": "6/1/13", "temp_diff": "12"},
        {"buildingID": 1, "date": "asdf", "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_numeric_none_values_and_no_coercing():
    records = [
        {"buildingID": 0, "date": "6/1/13", "temp_diff": "12"},
        {"buildingID": 1, "date": "asdf", "temp_diff": None},
        {"buildingID": 1, "date": "adsf", "temp_diff": "0asdf"},
    ]
    desired_df = pd.DataFrame(records)

    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": "<NAME>",
    "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_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_all_null_columns():
    records = [{"_c0": "12345", "nulla": None}, {"_c0": "12345", "nulla": None}]
    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)

from pandas.testing import assert_frame_equal
import pandas as pd

from sparkmagic.utils.utils import coerce_pandas_df_to_numeric_datetime


def test_no_coercing():
    records = [
        {"buildingID": 0, "date": "6/1/13", "temp_diff": "12"},
        {"buildingID": 1, "date": "random", "temp_diff": "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():
    records = [
        {"buildingID": 0, "date": "6/1/13", "temp_diff": "12"},
        {"buildingID": 1, "date": "6/1/13", "temp_diff": "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)


def test_date_coercing_none_values():
    records = [
        {"buildingID": 0, "date": "6/1/13", "temp_diff": "12"},
        {"buildingID": 1, "date": None, "temp_diff": "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)


def test_date_none_values_and_no_coercing():
    records = [
        {"buildingID": 0, "date": "6/1/13", "temp_diff": "12"},
        {"buildingID": 1, "date": None, "temp_diff": "0adsf"},
        {"buildingID": 1, "date": "adsf", "temp_diff": "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_numeric_coercing():
    records = [
        {"buildingID": 0, "date": "6/1/13", "temp_diff": "12"},
        {"buildingID": 1, "date": "adsf", "temp_diff": "0"},
    ]
    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_numeric_coercing_none_values():
    records = [
        {"buildingID": 0, "date": "6/1/13", "temp_diff": "12"},
        {"buildingID": 1, "date": "asdf", "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_numeric_none_values_and_no_coercing():
    records = [
        {"buildingID": 0, "date": "6/1/13", "temp_diff": "12"},
        {"buildingID": 1, "date": "asdf", "temp_diff": None},
        {"buildingID": 1, "date": "adsf", "temp_diff": "0asdf"},
    ]
    desired_df = pd.DataFrame(records)

    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": "<NAME>",
    "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_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_all_null_columns():
    records = [{"_c0": "12345", "nulla": None}, {"_c0": "12345", "nulla": None}]
    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)
[{     "id": 580320,     "name": "<NAME>",     "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 }]