File size: 2,385 Bytes
2c3c408 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 | # query_condition_datetime.py
#
# LICENSE
#
# The MIT License
#
# Copyright (c) 2021 TileDB, Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
# This example creates an array with one datetime-typed attribute,
# writes sample data to the array, and then prints out a filtered
# dataframe using the TileDB QueryCondition feature to select on
# either equality or ranges of the generated attribute values.
import numpy as np
import pandas as pd
import tiledb
uri = "query_condition_datetime"
data = pd.DataFrame(
np.sort(np.random.randint(438923600, 243892360000, 20, dtype=np.int64)).astype(
"M8[ns]"
),
columns=["dates"],
)
data.sort_values(by="dates")
tiledb.from_pandas(
uri,
data,
column_types={"dates": "datetime64[ns]"},
)
with tiledb.open(uri) as A:
# filter by exact match with the fifth cell
search_date = data["dates"][5].to_numpy().astype(np.int64)
result = A.query(cond=f"dates == {search_date}").df[:]
print()
print("Attribute dates matching index 5:")
print(result)
# filter values between cell index 3 and 8
d1 = data["dates"].iloc[3].to_numpy().astype(np.int64)
d2 = data["dates"].iloc[8].to_numpy().astype(np.int64)
result2 = A.query(cond=f"dates > {d1} and dates < {d2}").df[:]
print()
print("Attribute dates where 'dates[3] < val < dates[8]'")
print(result2)
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