wehub-resource-sync
212 lines
8.5 KiB
Python
212 lines
8.5 KiB
Python
# ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
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# ┃ ██████ ██████ ██████ █ █ █ █ █ █▄ ▀███ █ ┃
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# ┃ ▄▄▄▄▄█ █▄▄▄▄▄ ▄▄▄▄▄█ ▀▀▀▀▀█▀▀▀▀▀ █ ▀▀▀▀▀█ ████████▌▐███ ███▄ ▀█ █ ▀▀▀▀▀ ┃
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# ┃ █▀▀▀▀▀ █▀▀▀▀▀ █▀██▀▀ ▄▄▄▄▄ █ ▄▄▄▄▄█ ▄▄▄▄▄█ ████████▌▐███ █████▄ █ ▄▄▄▄▄ ┃
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# ┃ █ ██████ █ ▀█▄ █ ██████ █ ███▌▐███ ███████▄ █ ┃
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# ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┫
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# ┃ Copyright (c) 2017, the Perspective Authors. ┃
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# ┃ ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌ ┃
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# ┃ This file is part of the Perspective library, distributed under the terms ┃
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# ┃ of the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). ┃
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# ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
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import numpy as np
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import pandas as pd
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from datetime import date, datetime
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from pytest import mark
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import pytest
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import perspective as psp
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client = psp.Server().new_local_client()
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Table = client.table
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class TestUpdatePandas(object):
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def test_update_df(self):
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tbl = Table({"a": [1, 2, 3, 4]})
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update_data = pd.DataFrame({"a": [5, 6, 7, 8]})
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tbl.update(update_data)
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assert tbl.view().to_columns() == {"a": [1, 2, 3, 4, 5, 6, 7, 8]}
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def test_update_df_i32_vs_i64(self):
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tbl = Table({"a": "integer"})
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update_data = pd.DataFrame({"a": np.array([5, 6, 7, 8], dtype="int64")})
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tbl.update(update_data)
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assert tbl.view().to_columns() == {"a": [5, 6, 7, 8]}
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def test_update_df_bool(self):
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tbl = Table({"a": [True, False, True, False]})
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update_data = pd.DataFrame({"a": [True, False, True, False]})
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tbl.update(update_data)
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assert tbl.view().to_columns() == {
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"a": [True, False, True, False, True, False, True, False]
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}
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def test_update_df_str(self):
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tbl = Table({"a": ["a", "b", "c", "d"]})
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update_data = pd.DataFrame({"a": ["a", "b", "c", "d"]})
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tbl.update(update_data)
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assert tbl.view().to_columns() == {
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"a": ["a", "b", "c", "d", "a", "b", "c", "d"]
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}
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def test_update_df_date(self, util):
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# set_global_serializer(serializer)
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tbl = Table({"a": [date(2019, 7, 11)]})
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assert tbl.schema() == {"a": "date"}
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update_data = pd.DataFrame({"a": [date(2019, 7, 12)]})
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tbl.update(update_data)
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assert tbl.view().to_columns() == {
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"a": [
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util.to_timestamp(d)
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for d in [datetime(2019, 7, 11), datetime(2019, 7, 12)]
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]
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}
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@mark.skip(reason="this test relies on a lossy conversion from datetime -> date.")
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def test_update_df_date_timestamp(self, util):
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tbl = Table({"a": [date(2019, 7, 11)]})
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assert tbl.schema() == {"a": "date"}
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update_data = pd.DataFrame({"a": [datetime(2019, 7, 12, 0, 0, 0)]})
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tbl.update(update_data)
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assert tbl.view().to_columns() == {
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"a": [
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util.to_timestamp(datetime(2019, 7, 11)),
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util.to_timestamp(datetime(2019, 7, 12)),
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]
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}
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def test_update_df_datetime(self, util):
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tbl = Table({"a": [datetime(2019, 7, 11, 11, 0)]})
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update_data = pd.DataFrame({"a": [datetime(2019, 7, 12, 11, 0)]})
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tbl.update(update_data)
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assert tbl.view().to_columns() == {
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"a": [
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util.to_timestamp(datetime(2019, 7, 11, 11, 0)),
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util.to_timestamp(datetime(2019, 7, 12, 11, 0)),
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]
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}
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def test_update_df_datetime_timestamp_seconds(self, util):
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tbl = Table({"a": [datetime(2019, 7, 11, 11, 0)]})
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update_data = pd.DataFrame({"a": [datetime(2019, 7, 12, 11, 0)]})
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tbl.update(update_data)
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assert tbl.view().to_columns() == {
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"a": [
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util.to_timestamp(datetime(2019, 7, 11, 11, 0)),
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util.to_timestamp(datetime(2019, 7, 12, 11, 0)),
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]
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}
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def test_update_df_datetime_timestamp_ms(self, util):
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tbl = Table({"a": [datetime(2019, 7, 11, 11, 0)]})
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update_data = pd.DataFrame({"a": [datetime(2019, 7, 12, 11, 0)]})
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tbl.update(update_data)
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assert tbl.view().to_columns() == {
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"a": [
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util.to_timestamp(datetime(2019, 7, 11, 11, 0)),
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util.to_timestamp(datetime(2019, 7, 12, 11, 0)),
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]
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}
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def test_update_df_partial(self):
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tbl = Table({"a": [1, 2, 3, 4], "b": ["a", "b", "c", "d"]}, index="b")
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update_data = pd.DataFrame({"a": [5, 6, 7, 8], "b": ["a", "b", "c", "d"]})
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tbl.update(update_data)
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assert tbl.view().to_columns() == {"a": [5, 6, 7, 8], "b": ["a", "b", "c", "d"]}
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def test_df_update_index(self):
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tbl = Table(pd.DataFrame({"a": [1, 2, 3, 4]}))
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update_data = pd.DataFrame({"a": [5, 6, 7, 8], "__INDEX__": [0, 1, 2, 3]})
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tbl.update(update_data)
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assert tbl.view().to_columns() == {"a": [5, 6, 7, 8], "index": [0, 1, 2, 3]}
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def test_update_df_partial_implicit(self):
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tbl = Table({"a": [1, 2, 3, 4]})
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update_data = pd.DataFrame({"a": [5, 6, 7, 8], "__INDEX__": [0, 1, 2, 3]})
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tbl.update(update_data)
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assert tbl.view().to_columns() == {"a": [5, 6, 7, 8]}
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def test_update_df_datetime_partial(self, util):
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tbl = Table({"a": [datetime(2019, 7, 11, 11, 0)], "b": [1]}, index="b")
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update_data = pd.DataFrame({"a": [datetime(2019, 7, 12, 11, 0)], "b": [1]})
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tbl.update(update_data)
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assert tbl.view().to_columns() == {
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"a": [util.to_timestamp(datetime(2019, 7, 12, 11, 0))],
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"b": [1],
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}
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def test_update_df_one_col(self):
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tbl = Table({"a": [1, 2, 3, 4], "b": ["a", "b", "c", "d"]})
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update_data = pd.DataFrame({"a": [5, 6, 7]})
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tbl.update(update_data)
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assert tbl.view().to_columns() == {
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"a": [1, 2, 3, 4, 5, 6, 7],
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"b": ["a", "b", "c", "d", None, None, None],
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}
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def test_update_df_nonseq_partial(self):
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tbl = Table({"a": [1, 2, 3, 4], "b": ["a", "b", "c", "d"]}, index="b")
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update_data = pd.DataFrame({"a": [5, 6, 7], "b": ["a", "c", "d"]})
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tbl.update(update_data)
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assert tbl.view().to_columns() == {"a": [5, 2, 6, 7], "b": ["a", "b", "c", "d"]}
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@pytest.mark.skip
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def test_update_df_with_none_partial(self):
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tbl = Table({"a": [1, float("nan"), 3], "b": ["a", None, "d"]}, index="b")
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update_data = pd.DataFrame({"a": [4, 5], "b": ["a", "d"]})
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tbl.update(update_data)
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assert tbl.view().to_columns() == {
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"a": [None, 4, 5],
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"b": [None, "a", "d"],
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} # pkeys are ordered
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def test_update_df_unset_partial(self):
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tbl = Table({"a": [1, 2, 3], "b": ["a", "b", "c"]}, index="b")
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update_data = pd.DataFrame(
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{"a": pd.Series([None, None], dtype=pd.Int32Dtype()), "b": ["a", "c"]}
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)
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tbl.update(update_data)
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assert tbl.view().to_columns() == {"a": [None, 2, None], "b": ["a", "b", "c"]}
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def test_update_df_nan_partial(self):
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tbl = Table({"a": [1, 2, 3], "b": ["a", "b", "c"]}, index="b")
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update_data = pd.DataFrame(
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{"a": pd.Series([None, None], dtype=pd.Int32Dtype()), "b": ["a", "c"]}
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)
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tbl.update(update_data)
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assert tbl.view().to_columns() == {"a": [None, 2, None], "b": ["a", "b", "c"]}
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