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@@ -61,11 +61,11 @@ def create_orders_df(
df["order_is_success"] = np.random.randint(0, 2, size=order_count).astype(np.int32)
df[DEFAULT_ENTITY_DF_EVENT_TIMESTAMP_COL] = [
_convert_event_timestamp(
pd.Timestamp(dt, unit="ms", tz="UTC").round("ms"),
pd.Timestamp(dt, unit="ms").round("ms"),
EventTimestampType(idx % 4),
)
for idx, dt in enumerate(
pd.date_range(start=start_date, end=end_date, periods=order_count)
pd.date_range(start=start_date, end=end_date, periods=order_count, tz="UTC")
)
]
df.sort_values(
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@@ -101,9 +101,13 @@ def create_driver_hourly_stats_df(drivers, start_date, end_date) -> pd.DataFrame
df_hourly = pd.DataFrame(
{
"event_timestamp": [
pd.Timestamp(dt, unit="ms", tz="UTC").round("ms")
pd.Timestamp(dt, unit="ms").round("ms")
for dt in pd.date_range(
start=start_date, end=end_date, freq="1h", inclusive="left"
start=start_date,
end=end_date,
freq="1h",
inclusive="left",
tz="UTC",
)
]
# include a fixed timestamp for get_historical_features in the quickstart
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@@ -162,9 +166,13 @@ def create_customer_daily_profile_df(customers, start_date, end_date) -> pd.Data
df_daily = pd.DataFrame(
{
"event_timestamp": [
pd.Timestamp(dt, unit="ms", tz="UTC").round("ms")
pd.Timestamp(dt, unit="ms").round("ms")
for dt in pd.date_range(
start=start_date, end=end_date, freq="1D", inclusive="left"
start=start_date,
end=end_date,
freq="1D",
inclusive="left",
tz="UTC",
)
]
}
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@@ -207,9 +215,13 @@ def create_location_stats_df(locations, start_date, end_date) -> pd.DataFrame:
df_hourly = pd.DataFrame(
{
"event_timestamp": [
pd.Timestamp(dt, unit="ms", tz="UTC").round("ms")
pd.Timestamp(dt, unit="ms").round("ms")
for dt in pd.date_range(
start=start_date, end=end_date, freq="1h", inclusive="left"
start=start_date,
end=end_date,
freq="1h",
inclusive="left",
tz="UTC",
)
]
}
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@@ -254,9 +266,16 @@ def create_global_daily_stats_df(start_date, end_date) -> pd.DataFrame:
df_daily = pd.DataFrame(
{
"event_timestamp": [
pd.Timestamp(dt, unit="ms", tz="UTC").round("ms")
pd.Timestamp(
dt,
unit="ms",
).round("ms")
for dt in pd.date_range(
start=start_date, end=end_date, freq="1D", inclusive="left"
start=start_date,
end=end_date,
freq="1D",
inclusive="left",
tz="UTC",
)
]
}
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@@ -286,11 +305,11 @@ def create_field_mapping_df(start_date, end_date) -> pd.DataFrame:
df["column_name"] = np.random.randint(1, 100, size=size).astype(np.int32)
df[DEFAULT_ENTITY_DF_EVENT_TIMESTAMP_COL] = [
_convert_event_timestamp(
pd.Timestamp(dt, unit="ms", tz="UTC").round("ms"),
pd.Timestamp(dt, unit="ms").round("ms"),
EventTimestampType(idx % 4),
)
for idx, dt in enumerate(
pd.date_range(start=start_date, end=end_date, periods=size)
pd.date_range(start=start_date, end=end_date, periods=size, tz="UTC")
)
]
df["created"] = pd.to_datetime(pd.Timestamp.now(tz=None).round("ms"))
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