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dataframes.py
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125 lines (115 loc) · 3.91 KB
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from datetime import datetime
import numpy as np
import pandas as pd
import pytz
GOOD = pd.DataFrame(
{
"datetime": [datetime.utcnow().replace(tzinfo=pytz.utc) for _ in range(3)],
"entity_id": [1001, 1002, 1004],
"feature_1": [0.2, 0.4, 0.5],
"feature_2": ["string1", "string2", "string3"],
"feature_3": [1, 2, 5],
}
)
GOOD_FIVE_FEATURES = pd.DataFrame(
{
"datetime": [datetime.utcnow().replace(tzinfo=pytz.utc) for _ in range(3)],
"entity_id": [1001, 1002, 1004],
"feature_1": [0.2, 0.4, 0.5],
"feature_2": ["string1", "string2", "string3"],
"feature_3": [1, 2, 5],
"feature_4": [1, 2, 5],
"feature_5": [1, 2, 5],
}
)
GOOD_FIVE_FEATURES_TWO_ENTITIES = pd.DataFrame(
{
"datetime": [datetime.utcnow().replace(tzinfo=pytz.utc) for _ in range(3)],
"entity_1_id": [1001, 1002, 1004],
"entity_2_id": ["1001", "1002", "1003"],
"feature_1": [0.2, 0.4, 0.5],
"feature_2": ["string1", "string2", "string3"],
"feature_3": [1, 2, 5],
"feature_4": [1, 2, 5],
"feature_5": [1.3, 1.3, 1.3],
}
)
BAD_NO_ENTITY = pd.DataFrame(
{
"datetime": [datetime.utcnow().replace(tzinfo=pytz.utc) for _ in range(3)],
"feature_1": [0.2, 0.4, 0.5],
"feature_2": [0.3, 0.3, 0.34],
"feature_3": [1, 2, 5],
}
)
NO_FEATURES = pd.DataFrame(
{
"datetime": [datetime.utcnow().replace(tzinfo=pytz.utc) for _ in range(3)],
"entity_id": [1001, 1002, 1004],
}
)
BAD_NO_DATETIME = pd.DataFrame(
{
"entity_id": [1001, 1002, 1004],
"feature_1": [0.2, 0.4, 0.5],
"feature_2": [0.3, 0.3, 0.34],
"feature_3": [1, 2, 5],
}
)
BAD_INCORRECT_DATETIME_TYPE = pd.DataFrame(
{
"datetime": [1.23, 3.23, 1.23],
"entity_id": [1001, 1002, 1004],
"feature_1": [0.2, 0.4, 0.5],
"feature_2": [0.3, 0.3, 0.34],
"feature_3": [1, 2, 5],
}
)
ALL_TYPES = pd.DataFrame(
{
"datetime": [datetime.utcnow().replace(tzinfo=pytz.utc) for _ in range(3)],
"user_id": [1001, 1002, 1004],
"int32_feature": [np.int32(1), np.int32(2), np.int32(3)],
"int64_feature": [np.int64(1), np.int64(2), np.int64(3)],
"float_feature": [np.float32(0.1), np.float32(0.2), np.float32(0.3)],
"double_feature": [np.float64(0.1), np.float64(0.2), np.float64(0.3)],
"string_feature": ["one", "two", "three"],
"bytes_feature": [b"one", b"two", b"three"],
"bool_feature": [True, False, False],
"int32_list_feature": [
np.array([1, 2, 3, 4], dtype=np.int32),
np.array([1, 2, 3, 4], dtype=np.int32),
np.array([1, 2, 3, 4], dtype=np.int32),
],
"int64_list_feature": [
np.array([1, 2, 3, 4], dtype=np.int64),
np.array([1, 2, 3, 4], dtype=np.int64),
np.array([1, 2, 3, 4], dtype=np.int64),
],
"float_list_feature": [
np.array([1.1, 1.2, 1.3, 1.4], dtype=np.float32),
np.array([1.1, 1.2, 1.3, 1.4], dtype=np.float32),
np.array([1.1, 1.2, 1.3, 1.4], dtype=np.float32),
],
"double_list_feature": [
np.array([1.1, 1.2, 1.3, 1.4], dtype=np.float64),
np.array([1.1, 1.2, 1.3, 1.4], dtype=np.float64),
np.array([1.1, 1.2, 1.3, 1.4], dtype=np.float64),
],
"string_list_feature": [
np.array(["one", "two", "three"]),
np.array(["one", "two", "three"]),
np.array(["one", "two", "three"]),
],
"bytes_list_feature": [
np.array([b"one", b"two", b"three"]),
np.array([b"one", b"two", b"three"]),
np.array([b"one", b"two", b"three"]),
],
"bool_list_feature": [
[True, False, True],
[True, False, True],
[True, False, True],
],
}
)