521 lines
18 KiB
Python
521 lines
18 KiB
Python
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from io import StringIO
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from pathlib import Path
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from typing import Iterator
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import pytest
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import pandas as pd
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from pandas import (
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DataFrame,
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read_json,
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)
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import pandas._testing as tm
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from pandas.io.json._json import JsonReader
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@pytest.fixture
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def lines_json_df():
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df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
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return df.to_json(lines=True, orient="records")
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def test_read_jsonl():
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# GH9180
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result = read_json('{"a": 1, "b": 2}\n{"b":2, "a" :1}\n', lines=True)
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expected = DataFrame([[1, 2], [1, 2]], columns=["a", "b"])
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tm.assert_frame_equal(result, expected)
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def test_read_jsonl_engine_pyarrow(datapath, engine):
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result = read_json(
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datapath("io", "json", "data", "line_delimited.json"),
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lines=True,
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engine=engine,
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)
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expected = DataFrame({"a": [1, 3, 5], "b": [2, 4, 6]})
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tm.assert_frame_equal(result, expected)
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def test_read_datetime(request, engine):
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# GH33787
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if engine == "pyarrow":
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# GH 48893
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reason = "Pyarrow only supports a file path as an input and line delimited json"
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request.node.add_marker(pytest.mark.xfail(reason=reason, raises=ValueError))
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df = DataFrame(
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[([1, 2], ["2020-03-05", "2020-04-08T09:58:49+00:00"], "hector")],
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columns=["accounts", "date", "name"],
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)
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json_line = df.to_json(lines=True, orient="records")
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result = read_json(json_line, engine=engine)
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expected = DataFrame(
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[[1, "2020-03-05", "hector"], [2, "2020-04-08T09:58:49+00:00", "hector"]],
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columns=["accounts", "date", "name"],
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)
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tm.assert_frame_equal(result, expected)
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def test_read_jsonl_unicode_chars():
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# GH15132: non-ascii unicode characters
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# \u201d == RIGHT DOUBLE QUOTATION MARK
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# simulate file handle
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json = '{"a": "foo”", "b": "bar"}\n{"a": "foo", "b": "bar"}\n'
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json = StringIO(json)
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result = read_json(json, lines=True)
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expected = DataFrame([["foo\u201d", "bar"], ["foo", "bar"]], columns=["a", "b"])
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tm.assert_frame_equal(result, expected)
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# simulate string
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json = '{"a": "foo”", "b": "bar"}\n{"a": "foo", "b": "bar"}\n'
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result = read_json(json, lines=True)
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expected = DataFrame([["foo\u201d", "bar"], ["foo", "bar"]], columns=["a", "b"])
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tm.assert_frame_equal(result, expected)
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def test_to_jsonl():
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# GH9180
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df = DataFrame([[1, 2], [1, 2]], columns=["a", "b"])
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result = df.to_json(orient="records", lines=True)
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expected = '{"a":1,"b":2}\n{"a":1,"b":2}\n'
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assert result == expected
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df = DataFrame([["foo}", "bar"], ['foo"', "bar"]], columns=["a", "b"])
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result = df.to_json(orient="records", lines=True)
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expected = '{"a":"foo}","b":"bar"}\n{"a":"foo\\"","b":"bar"}\n'
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assert result == expected
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tm.assert_frame_equal(read_json(result, lines=True), df)
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# GH15096: escaped characters in columns and data
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df = DataFrame([["foo\\", "bar"], ['foo"', "bar"]], columns=["a\\", "b"])
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result = df.to_json(orient="records", lines=True)
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expected = '{"a\\\\":"foo\\\\","b":"bar"}\n{"a\\\\":"foo\\"","b":"bar"}\n'
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assert result == expected
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tm.assert_frame_equal(read_json(result, lines=True), df)
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def test_to_jsonl_count_new_lines():
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# GH36888
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df = DataFrame([[1, 2], [1, 2]], columns=["a", "b"])
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actual_new_lines_count = df.to_json(orient="records", lines=True).count("\n")
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expected_new_lines_count = 2
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assert actual_new_lines_count == expected_new_lines_count
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@pytest.mark.parametrize("chunksize", [1, 1.0])
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def test_readjson_chunks(request, lines_json_df, chunksize, engine):
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# Basic test that read_json(chunks=True) gives the same result as
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# read_json(chunks=False)
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# GH17048: memory usage when lines=True
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if engine == "pyarrow":
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# GH 48893
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reason = (
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"Pyarrow only supports a file path as an input and line delimited json"
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"and doesn't support chunksize parameter."
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)
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request.node.add_marker(pytest.mark.xfail(reason=reason, raises=ValueError))
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unchunked = read_json(StringIO(lines_json_df), lines=True)
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with read_json(
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StringIO(lines_json_df), lines=True, chunksize=chunksize, engine=engine
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) as reader:
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chunked = pd.concat(reader)
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tm.assert_frame_equal(chunked, unchunked)
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def test_readjson_chunksize_requires_lines(lines_json_df, engine):
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msg = "chunksize can only be passed if lines=True"
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with pytest.raises(ValueError, match=msg):
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with read_json(
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StringIO(lines_json_df), lines=False, chunksize=2, engine=engine
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) as _:
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pass
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def test_readjson_chunks_series(request, engine):
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if engine == "pyarrow":
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# GH 48893
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reason = (
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"Pyarrow only supports a file path as an input and line delimited json"
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"and doesn't support chunksize parameter."
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)
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request.node.add_marker(pytest.mark.xfail(reason=reason))
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# Test reading line-format JSON to Series with chunksize param
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s = pd.Series({"A": 1, "B": 2})
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strio = StringIO(s.to_json(lines=True, orient="records"))
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unchunked = read_json(strio, lines=True, typ="Series", engine=engine)
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strio = StringIO(s.to_json(lines=True, orient="records"))
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with read_json(
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strio, lines=True, typ="Series", chunksize=1, engine=engine
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) as reader:
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chunked = pd.concat(reader)
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tm.assert_series_equal(chunked, unchunked)
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def test_readjson_each_chunk(request, lines_json_df, engine):
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if engine == "pyarrow":
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# GH 48893
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reason = (
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"Pyarrow only supports a file path as an input and line delimited json"
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"and doesn't support chunksize parameter."
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)
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request.node.add_marker(pytest.mark.xfail(reason=reason, raises=ValueError))
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# Other tests check that the final result of read_json(chunksize=True)
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# is correct. This checks the intermediate chunks.
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with read_json(
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StringIO(lines_json_df), lines=True, chunksize=2, engine=engine
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) as reader:
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chunks = list(reader)
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assert chunks[0].shape == (2, 2)
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assert chunks[1].shape == (1, 2)
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def test_readjson_chunks_from_file(request, engine):
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if engine == "pyarrow":
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# GH 48893
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reason = (
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"Pyarrow only supports a file path as an input and line delimited json"
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"and doesn't support chunksize parameter."
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)
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request.node.add_marker(pytest.mark.xfail(reason=reason, raises=ValueError))
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with tm.ensure_clean("test.json") as path:
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df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
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df.to_json(path, lines=True, orient="records")
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with read_json(path, lines=True, chunksize=1, engine=engine) as reader:
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chunked = pd.concat(reader)
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unchunked = read_json(path, lines=True, engine=engine)
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tm.assert_frame_equal(unchunked, chunked)
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@pytest.mark.parametrize("chunksize", [None, 1])
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def test_readjson_chunks_closes(chunksize):
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with tm.ensure_clean("test.json") as path:
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df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
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df.to_json(path, lines=True, orient="records")
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reader = JsonReader(
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path,
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orient=None,
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typ="frame",
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dtype=True,
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convert_axes=True,
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convert_dates=True,
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keep_default_dates=True,
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precise_float=False,
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date_unit=None,
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encoding=None,
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lines=True,
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chunksize=chunksize,
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compression=None,
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nrows=None,
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)
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with reader:
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reader.read()
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assert (
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reader.handles.handle.closed
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), f"didn't close stream with chunksize = {chunksize}"
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@pytest.mark.parametrize("chunksize", [0, -1, 2.2, "foo"])
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def test_readjson_invalid_chunksize(lines_json_df, chunksize, engine):
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msg = r"'chunksize' must be an integer >=1"
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with pytest.raises(ValueError, match=msg):
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with read_json(
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StringIO(lines_json_df), lines=True, chunksize=chunksize, engine=engine
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) as _:
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pass
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@pytest.mark.parametrize("chunksize", [None, 1, 2])
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def test_readjson_chunks_multiple_empty_lines(chunksize):
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j = """
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{"A":1,"B":4}
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{"A":2,"B":5}
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{"A":3,"B":6}
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"""
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orig = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
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test = read_json(j, lines=True, chunksize=chunksize)
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if chunksize is not None:
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with test:
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test = pd.concat(test)
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tm.assert_frame_equal(orig, test, obj=f"chunksize: {chunksize}")
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def test_readjson_unicode(request, monkeypatch, engine):
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if engine == "pyarrow":
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# GH 48893
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reason = (
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"Pyarrow only supports a file path as an input and line delimited json"
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"and doesn't support chunksize parameter."
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)
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request.node.add_marker(pytest.mark.xfail(reason=reason, raises=ValueError))
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with tm.ensure_clean("test.json") as path:
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monkeypatch.setattr("locale.getpreferredencoding", lambda do_setlocale: "cp949")
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with open(path, "w", encoding="utf-8") as f:
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f.write('{"£©µÀÆÖÞßéöÿ":["АБВГДабвгд가"]}')
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result = read_json(path, engine=engine)
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expected = DataFrame({"£©µÀÆÖÞßéöÿ": ["АБВГДабвгд가"]})
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("nrows", [1, 2])
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def test_readjson_nrows(nrows, engine):
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# GH 33916
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# Test reading line-format JSON to Series with nrows param
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jsonl = """{"a": 1, "b": 2}
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{"a": 3, "b": 4}
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{"a": 5, "b": 6}
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{"a": 7, "b": 8}"""
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result = read_json(jsonl, lines=True, nrows=nrows)
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expected = DataFrame({"a": [1, 3, 5, 7], "b": [2, 4, 6, 8]}).iloc[:nrows]
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("nrows,chunksize", [(2, 2), (4, 2)])
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def test_readjson_nrows_chunks(request, nrows, chunksize, engine):
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# GH 33916
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# Test reading line-format JSON to Series with nrows and chunksize param
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if engine == "pyarrow":
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# GH 48893
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reason = (
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"Pyarrow only supports a file path as an input and line delimited json"
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"and doesn't support chunksize parameter."
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)
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request.node.add_marker(pytest.mark.xfail(reason=reason, raises=ValueError))
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jsonl = """{"a": 1, "b": 2}
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{"a": 3, "b": 4}
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{"a": 5, "b": 6}
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{"a": 7, "b": 8}"""
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with read_json(
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jsonl, lines=True, nrows=nrows, chunksize=chunksize, engine=engine
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) as reader:
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chunked = pd.concat(reader)
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expected = DataFrame({"a": [1, 3, 5, 7], "b": [2, 4, 6, 8]}).iloc[:nrows]
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tm.assert_frame_equal(chunked, expected)
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def test_readjson_nrows_requires_lines(engine):
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# GH 33916
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# Test ValuError raised if nrows is set without setting lines in read_json
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jsonl = """{"a": 1, "b": 2}
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{"a": 3, "b": 4}
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{"a": 5, "b": 6}
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{"a": 7, "b": 8}"""
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msg = "nrows can only be passed if lines=True"
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with pytest.raises(ValueError, match=msg):
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read_json(jsonl, lines=False, nrows=2, engine=engine)
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def test_readjson_lines_chunks_fileurl(request, datapath, engine):
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# GH 27135
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# Test reading line-format JSON from file url
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if engine == "pyarrow":
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# GH 48893
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reason = (
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"Pyarrow only supports a file path as an input and line delimited json"
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"and doesn't support chunksize parameter."
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)
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request.node.add_marker(pytest.mark.xfail(reason=reason, raises=ValueError))
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df_list_expected = [
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DataFrame([[1, 2]], columns=["a", "b"], index=[0]),
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DataFrame([[3, 4]], columns=["a", "b"], index=[1]),
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DataFrame([[5, 6]], columns=["a", "b"], index=[2]),
|
||
|
|
]
|
||
|
|
os_path = datapath("io", "json", "data", "line_delimited.json")
|
||
|
|
file_url = Path(os_path).as_uri()
|
||
|
|
with read_json(file_url, lines=True, chunksize=1, engine=engine) as url_reader:
|
||
|
|
for index, chuck in enumerate(url_reader):
|
||
|
|
tm.assert_frame_equal(chuck, df_list_expected[index])
|
||
|
|
|
||
|
|
|
||
|
|
def test_chunksize_is_incremental():
|
||
|
|
# See https://github.com/pandas-dev/pandas/issues/34548
|
||
|
|
jsonl = (
|
||
|
|
"""{"a": 1, "b": 2}
|
||
|
|
{"a": 3, "b": 4}
|
||
|
|
{"a": 5, "b": 6}
|
||
|
|
{"a": 7, "b": 8}\n"""
|
||
|
|
* 1000
|
||
|
|
)
|
||
|
|
|
||
|
|
class MyReader:
|
||
|
|
def __init__(self, contents) -> None:
|
||
|
|
self.read_count = 0
|
||
|
|
self.stringio = StringIO(contents)
|
||
|
|
|
||
|
|
def read(self, *args):
|
||
|
|
self.read_count += 1
|
||
|
|
return self.stringio.read(*args)
|
||
|
|
|
||
|
|
def __iter__(self) -> Iterator:
|
||
|
|
self.read_count += 1
|
||
|
|
return iter(self.stringio)
|
||
|
|
|
||
|
|
reader = MyReader(jsonl)
|
||
|
|
assert len(list(read_json(reader, lines=True, chunksize=100))) > 1
|
||
|
|
assert reader.read_count > 10
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("orient_", ["split", "index", "table"])
|
||
|
|
def test_to_json_append_orient(orient_):
|
||
|
|
# GH 35849
|
||
|
|
# Test ValueError when orient is not 'records'
|
||
|
|
df = DataFrame({"col1": [1, 2], "col2": ["a", "b"]})
|
||
|
|
msg = (
|
||
|
|
r"mode='a' \(append\) is only supported when"
|
||
|
|
"lines is True and orient is 'records'"
|
||
|
|
)
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
df.to_json(mode="a", orient=orient_)
|
||
|
|
|
||
|
|
|
||
|
|
def test_to_json_append_lines():
|
||
|
|
# GH 35849
|
||
|
|
# Test ValueError when lines is not True
|
||
|
|
df = DataFrame({"col1": [1, 2], "col2": ["a", "b"]})
|
||
|
|
msg = (
|
||
|
|
r"mode='a' \(append\) is only supported when"
|
||
|
|
"lines is True and orient is 'records'"
|
||
|
|
)
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
df.to_json(mode="a", lines=False, orient="records")
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("mode_", ["r", "x"])
|
||
|
|
def test_to_json_append_mode(mode_):
|
||
|
|
# GH 35849
|
||
|
|
# Test ValueError when mode is not supported option
|
||
|
|
df = DataFrame({"col1": [1, 2], "col2": ["a", "b"]})
|
||
|
|
msg = (
|
||
|
|
f"mode={mode_} is not a valid option."
|
||
|
|
"Only 'w' and 'a' are currently supported."
|
||
|
|
)
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
df.to_json(mode=mode_, lines=False, orient="records")
|
||
|
|
|
||
|
|
|
||
|
|
def test_to_json_append_output_consistent_columns():
|
||
|
|
# GH 35849
|
||
|
|
# Testing that resulting output reads in as expected.
|
||
|
|
# Testing same columns, new rows
|
||
|
|
df1 = DataFrame({"col1": [1, 2], "col2": ["a", "b"]})
|
||
|
|
df2 = DataFrame({"col1": [3, 4], "col2": ["c", "d"]})
|
||
|
|
|
||
|
|
expected = DataFrame({"col1": [1, 2, 3, 4], "col2": ["a", "b", "c", "d"]})
|
||
|
|
with tm.ensure_clean("test.json") as path:
|
||
|
|
# Save dataframes to the same file
|
||
|
|
df1.to_json(path, lines=True, orient="records")
|
||
|
|
df2.to_json(path, mode="a", lines=True, orient="records")
|
||
|
|
|
||
|
|
# Read path file
|
||
|
|
result = read_json(path, lines=True)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_to_json_append_output_inconsistent_columns():
|
||
|
|
# GH 35849
|
||
|
|
# Testing that resulting output reads in as expected.
|
||
|
|
# Testing one new column, one old column, new rows
|
||
|
|
df1 = DataFrame({"col1": [1, 2], "col2": ["a", "b"]})
|
||
|
|
df3 = DataFrame({"col2": ["e", "f"], "col3": ["!", "#"]})
|
||
|
|
|
||
|
|
expected = DataFrame(
|
||
|
|
{
|
||
|
|
"col1": [1, 2, None, None],
|
||
|
|
"col2": ["a", "b", "e", "f"],
|
||
|
|
"col3": [None, None, "!", "#"],
|
||
|
|
}
|
||
|
|
)
|
||
|
|
with tm.ensure_clean("test.json") as path:
|
||
|
|
# Save dataframes to the same file
|
||
|
|
df1.to_json(path, mode="a", lines=True, orient="records")
|
||
|
|
df3.to_json(path, mode="a", lines=True, orient="records")
|
||
|
|
|
||
|
|
# Read path file
|
||
|
|
result = read_json(path, lines=True)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_to_json_append_output_different_columns():
|
||
|
|
# GH 35849
|
||
|
|
# Testing that resulting output reads in as expected.
|
||
|
|
# Testing same, differing and new columns
|
||
|
|
df1 = DataFrame({"col1": [1, 2], "col2": ["a", "b"]})
|
||
|
|
df2 = DataFrame({"col1": [3, 4], "col2": ["c", "d"]})
|
||
|
|
df3 = DataFrame({"col2": ["e", "f"], "col3": ["!", "#"]})
|
||
|
|
df4 = DataFrame({"col4": [True, False]})
|
||
|
|
|
||
|
|
expected = DataFrame(
|
||
|
|
{
|
||
|
|
"col1": [1, 2, 3, 4, None, None, None, None],
|
||
|
|
"col2": ["a", "b", "c", "d", "e", "f", None, None],
|
||
|
|
"col3": [None, None, None, None, "!", "#", None, None],
|
||
|
|
"col4": [None, None, None, None, None, None, True, False],
|
||
|
|
}
|
||
|
|
).astype({"col4": "float"})
|
||
|
|
with tm.ensure_clean("test.json") as path:
|
||
|
|
# Save dataframes to the same file
|
||
|
|
df1.to_json(path, mode="a", lines=True, orient="records")
|
||
|
|
df2.to_json(path, mode="a", lines=True, orient="records")
|
||
|
|
df3.to_json(path, mode="a", lines=True, orient="records")
|
||
|
|
df4.to_json(path, mode="a", lines=True, orient="records")
|
||
|
|
|
||
|
|
# Read path file
|
||
|
|
result = read_json(path, lines=True)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_to_json_append_output_different_columns_reordered():
|
||
|
|
# GH 35849
|
||
|
|
# Testing that resulting output reads in as expected.
|
||
|
|
# Testing specific result column order.
|
||
|
|
df1 = DataFrame({"col1": [1, 2], "col2": ["a", "b"]})
|
||
|
|
df2 = DataFrame({"col1": [3, 4], "col2": ["c", "d"]})
|
||
|
|
df3 = DataFrame({"col2": ["e", "f"], "col3": ["!", "#"]})
|
||
|
|
df4 = DataFrame({"col4": [True, False]})
|
||
|
|
|
||
|
|
# df4, df3, df2, df1 (in that order)
|
||
|
|
expected = DataFrame(
|
||
|
|
{
|
||
|
|
"col4": [True, False, None, None, None, None, None, None],
|
||
|
|
"col2": [None, None, "e", "f", "c", "d", "a", "b"],
|
||
|
|
"col3": [None, None, "!", "#", None, None, None, None],
|
||
|
|
"col1": [None, None, None, None, 3, 4, 1, 2],
|
||
|
|
}
|
||
|
|
).astype({"col4": "float"})
|
||
|
|
with tm.ensure_clean("test.json") as path:
|
||
|
|
# Save dataframes to the same file
|
||
|
|
df4.to_json(path, mode="a", lines=True, orient="records")
|
||
|
|
df3.to_json(path, mode="a", lines=True, orient="records")
|
||
|
|
df2.to_json(path, mode="a", lines=True, orient="records")
|
||
|
|
df1.to_json(path, mode="a", lines=True, orient="records")
|
||
|
|
|
||
|
|
# Read path file
|
||
|
|
result = read_json(path, lines=True)
|
||
|
|
tm.assert_frame_equal(result, expected)
|