# 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)