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3RNN/Lib/site-packages/pandas/tests/io/json/test_pandas.py

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1.0
2024-05-26 19:49:15 +02:00
import datetime
from datetime import timedelta
from decimal import Decimal
from io import (
BytesIO,
StringIO,
)
import json
import os
import sys
import time
import numpy as np
import pytest
from pandas._config import using_pyarrow_string_dtype
from pandas.compat import IS64
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
NA,
DataFrame,
DatetimeIndex,
Index,
RangeIndex,
Series,
Timestamp,
date_range,
read_json,
)
import pandas._testing as tm
from pandas.core.arrays import (
ArrowStringArray,
StringArray,
)
from pandas.core.arrays.string_arrow import ArrowStringArrayNumpySemantics
from pandas.io.json import ujson_dumps
def test_literal_json_deprecation():
# PR 53409
expected = DataFrame([[1, 2], [1, 2]], columns=["a", "b"])
jsonl = """{"a": 1, "b": 2}
{"a": 3, "b": 4}
{"a": 5, "b": 6}
{"a": 7, "b": 8}"""
msg = (
"Passing literal json to 'read_json' is deprecated and "
"will be removed in a future version. To read from a "
"literal string, wrap it in a 'StringIO' object."
)
with tm.assert_produces_warning(FutureWarning, match=msg):
try:
read_json(jsonl, lines=False)
except ValueError:
pass
with tm.assert_produces_warning(FutureWarning, match=msg):
read_json(expected.to_json(), lines=False)
with tm.assert_produces_warning(FutureWarning, match=msg):
result = read_json('{"a": 1, "b": 2}\n{"b":2, "a" :1}\n', lines=True)
tm.assert_frame_equal(result, expected)
with tm.assert_produces_warning(FutureWarning, match=msg):
try:
result = read_json(
'{"a\\\\":"foo\\\\","b":"bar"}\n{"a\\\\":"foo\\"","b":"bar"}\n',
lines=False,
)
except ValueError:
pass
with tm.assert_produces_warning(FutureWarning, match=msg):
try:
result = read_json('{"a": 1, "b": 2}\n{"b":2, "a" :1}\n', lines=False)
except ValueError:
pass
tm.assert_frame_equal(result, expected)
def assert_json_roundtrip_equal(result, expected, orient):
if orient in ("records", "values"):
expected = expected.reset_index(drop=True)
if orient == "values":
expected.columns = range(len(expected.columns))
tm.assert_frame_equal(result, expected)
class TestPandasContainer:
@pytest.fixture
def categorical_frame(self):
data = {
c: np.random.default_rng(i).standard_normal(30)
for i, c in enumerate(list("ABCD"))
}
cat = ["bah"] * 5 + ["bar"] * 5 + ["baz"] * 5 + ["foo"] * 15
data["E"] = list(reversed(cat))
data["sort"] = np.arange(30, dtype="int64")
return DataFrame(data, index=pd.CategoricalIndex(cat, name="E"))
@pytest.fixture
def datetime_series(self):
# Same as usual datetime_series, but with index freq set to None,
# since that doesn't round-trip, see GH#33711
ser = Series(
1.1 * np.arange(10, dtype=np.float64),
index=date_range("2020-01-01", periods=10),
name="ts",
)
ser.index = ser.index._with_freq(None)
return ser
@pytest.fixture
def datetime_frame(self):
# Same as usual datetime_frame, but with index freq set to None,
# since that doesn't round-trip, see GH#33711
df = DataFrame(
np.random.default_rng(2).standard_normal((30, 4)),
columns=Index(list("ABCD"), dtype=object),
index=date_range("2000-01-01", periods=30, freq="B"),
)
df.index = df.index._with_freq(None)
return df
def test_frame_double_encoded_labels(self, orient):
df = DataFrame(
[["a", "b"], ["c", "d"]],
index=['index " 1', "index / 2"],
columns=["a \\ b", "y / z"],
)
data = StringIO(df.to_json(orient=orient))
result = read_json(data, orient=orient)
expected = df.copy()
assert_json_roundtrip_equal(result, expected, orient)
@pytest.mark.parametrize("orient", ["split", "records", "values"])
def test_frame_non_unique_index(self, orient):
df = DataFrame([["a", "b"], ["c", "d"]], index=[1, 1], columns=["x", "y"])
data = StringIO(df.to_json(orient=orient))
result = read_json(data, orient=orient)
expected = df.copy()
assert_json_roundtrip_equal(result, expected, orient)
@pytest.mark.parametrize("orient", ["index", "columns"])
def test_frame_non_unique_index_raises(self, orient):
df = DataFrame([["a", "b"], ["c", "d"]], index=[1, 1], columns=["x", "y"])
msg = f"DataFrame index must be unique for orient='{orient}'"
with pytest.raises(ValueError, match=msg):
df.to_json(orient=orient)
@pytest.mark.parametrize("orient", ["split", "values"])
@pytest.mark.parametrize(
"data",
[
[["a", "b"], ["c", "d"]],
[[1.5, 2.5], [3.5, 4.5]],
[[1, 2.5], [3, 4.5]],
[[Timestamp("20130101"), 3.5], [Timestamp("20130102"), 4.5]],
],
)
def test_frame_non_unique_columns(self, orient, data):
df = DataFrame(data, index=[1, 2], columns=["x", "x"])
result = read_json(
StringIO(df.to_json(orient=orient)), orient=orient, convert_dates=["x"]
)
if orient == "values":
expected = DataFrame(data)
if expected.iloc[:, 0].dtype == "datetime64[ns]":
# orient == "values" by default will write Timestamp objects out
# in milliseconds; these are internally stored in nanosecond,
# so divide to get where we need
# TODO: a to_epoch method would also solve; see GH 14772
expected.isetitem(0, expected.iloc[:, 0].astype(np.int64) // 1000000)
elif orient == "split":
expected = df
expected.columns = ["x", "x.1"]
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("orient", ["index", "columns", "records"])
def test_frame_non_unique_columns_raises(self, orient):
df = DataFrame([["a", "b"], ["c", "d"]], index=[1, 2], columns=["x", "x"])
msg = f"DataFrame columns must be unique for orient='{orient}'"
with pytest.raises(ValueError, match=msg):
df.to_json(orient=orient)
def test_frame_default_orient(self, float_frame):
assert float_frame.to_json() == float_frame.to_json(orient="columns")
@pytest.mark.parametrize("dtype", [False, float])
@pytest.mark.parametrize("convert_axes", [True, False])
def test_roundtrip_simple(self, orient, convert_axes, dtype, float_frame):
data = StringIO(float_frame.to_json(orient=orient))
result = read_json(data, orient=orient, convert_axes=convert_axes, dtype=dtype)
expected = float_frame
assert_json_roundtrip_equal(result, expected, orient)
@pytest.mark.parametrize("dtype", [False, np.int64])
@pytest.mark.parametrize("convert_axes", [True, False])
def test_roundtrip_intframe(self, orient, convert_axes, dtype, int_frame):
data = StringIO(int_frame.to_json(orient=orient))
result = read_json(data, orient=orient, convert_axes=convert_axes, dtype=dtype)
expected = int_frame
assert_json_roundtrip_equal(result, expected, orient)
@pytest.mark.parametrize("dtype", [None, np.float64, int, "U3"])
@pytest.mark.parametrize("convert_axes", [True, False])
def test_roundtrip_str_axes(self, orient, convert_axes, dtype):
df = DataFrame(
np.zeros((200, 4)),
columns=[str(i) for i in range(4)],
index=[str(i) for i in range(200)],
dtype=dtype,
)
data = StringIO(df.to_json(orient=orient))
result = read_json(data, orient=orient, convert_axes=convert_axes, dtype=dtype)
expected = df.copy()
if not dtype:
expected = expected.astype(np.int64)
# index columns, and records orients cannot fully preserve the string
# dtype for axes as the index and column labels are used as keys in
# JSON objects. JSON keys are by definition strings, so there's no way
# to disambiguate whether those keys actually were strings or numeric
# beforehand and numeric wins out.
if convert_axes and (orient in ("index", "columns")):
expected.columns = expected.columns.astype(np.int64)
expected.index = expected.index.astype(np.int64)
elif orient == "records" and convert_axes:
expected.columns = expected.columns.astype(np.int64)
elif convert_axes and orient == "split":
expected.columns = expected.columns.astype(np.int64)
assert_json_roundtrip_equal(result, expected, orient)
@pytest.mark.parametrize("convert_axes", [True, False])
def test_roundtrip_categorical(
self, request, orient, categorical_frame, convert_axes, using_infer_string
):
# TODO: create a better frame to test with and improve coverage
if orient in ("index", "columns"):
request.applymarker(
pytest.mark.xfail(
reason=f"Can't have duplicate index values for orient '{orient}')"
)
)
data = StringIO(categorical_frame.to_json(orient=orient))
result = read_json(data, orient=orient, convert_axes=convert_axes)
expected = categorical_frame.copy()
expected.index = expected.index.astype(
str if not using_infer_string else "string[pyarrow_numpy]"
) # Categorical not preserved
expected.index.name = None # index names aren't preserved in JSON
assert_json_roundtrip_equal(result, expected, orient)
@pytest.mark.parametrize("convert_axes", [True, False])
def test_roundtrip_empty(self, orient, convert_axes):
empty_frame = DataFrame()
data = StringIO(empty_frame.to_json(orient=orient))
result = read_json(data, orient=orient, convert_axes=convert_axes)
if orient == "split":
idx = Index([], dtype=(float if convert_axes else object))
expected = DataFrame(index=idx, columns=idx)
elif orient in ["index", "columns"]:
expected = DataFrame()
else:
expected = empty_frame.copy()
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("convert_axes", [True, False])
def test_roundtrip_timestamp(self, orient, convert_axes, datetime_frame):
# TODO: improve coverage with date_format parameter
data = StringIO(datetime_frame.to_json(orient=orient))
result = read_json(data, orient=orient, convert_axes=convert_axes)
expected = datetime_frame.copy()
if not convert_axes: # one off for ts handling
# DTI gets converted to epoch values
idx = expected.index.view(np.int64) // 1000000
if orient != "split": # TODO: handle consistently across orients
idx = idx.astype(str)
expected.index = idx
assert_json_roundtrip_equal(result, expected, orient)
@pytest.mark.parametrize("convert_axes", [True, False])
def test_roundtrip_mixed(self, orient, convert_axes):
index = Index(["a", "b", "c", "d", "e"])
values = {
"A": [0.0, 1.0, 2.0, 3.0, 4.0],
"B": [0.0, 1.0, 0.0, 1.0, 0.0],
"C": ["foo1", "foo2", "foo3", "foo4", "foo5"],
"D": [True, False, True, False, True],
}
df = DataFrame(data=values, index=index)
data = StringIO(df.to_json(orient=orient))
result = read_json(data, orient=orient, convert_axes=convert_axes)
expected = df.copy()
expected = expected.assign(**expected.select_dtypes("number").astype(np.int64))
assert_json_roundtrip_equal(result, expected, orient)
@pytest.mark.xfail(
reason="#50456 Column multiindex is stored and loaded differently",
raises=AssertionError,
)
@pytest.mark.parametrize(
"columns",
[
[["2022", "2022"], ["JAN", "FEB"]],
[["2022", "2023"], ["JAN", "JAN"]],
[["2022", "2022"], ["JAN", "JAN"]],
],
)
def test_roundtrip_multiindex(self, columns):
df = DataFrame(
[[1, 2], [3, 4]],
columns=pd.MultiIndex.from_arrays(columns),
)
data = StringIO(df.to_json(orient="split"))
result = read_json(data, orient="split")
tm.assert_frame_equal(result, df)
@pytest.mark.parametrize(
"data,msg,orient",
[
('{"key":b:a:d}', "Expected object or value", "columns"),
# too few indices
(
'{"columns":["A","B"],'
'"index":["2","3"],'
'"data":[[1.0,"1"],[2.0,"2"],[null,"3"]]}',
"|".join(
[
r"Length of values \(3\) does not match length of index \(2\)",
]
),
"split",
),
# too many columns
(
'{"columns":["A","B","C"],'
'"index":["1","2","3"],'
'"data":[[1.0,"1"],[2.0,"2"],[null,"3"]]}',
"3 columns passed, passed data had 2 columns",
"split",
),
# bad key
(
'{"badkey":["A","B"],'
'"index":["2","3"],'
'"data":[[1.0,"1"],[2.0,"2"],[null,"3"]]}',
r"unexpected key\(s\): badkey",
"split",
),
],
)
def test_frame_from_json_bad_data_raises(self, data, msg, orient):
with pytest.raises(ValueError, match=msg):
read_json(StringIO(data), orient=orient)
@pytest.mark.parametrize("dtype", [True, False])
@pytest.mark.parametrize("convert_axes", [True, False])
def test_frame_from_json_missing_data(self, orient, convert_axes, dtype):
num_df = DataFrame([[1, 2], [4, 5, 6]])
result = read_json(
StringIO(num_df.to_json(orient=orient)),
orient=orient,
convert_axes=convert_axes,
dtype=dtype,
)
assert np.isnan(result.iloc[0, 2])
obj_df = DataFrame([["1", "2"], ["4", "5", "6"]])
result = read_json(
StringIO(obj_df.to_json(orient=orient)),
orient=orient,
convert_axes=convert_axes,
dtype=dtype,
)
assert np.isnan(result.iloc[0, 2])
@pytest.mark.parametrize("dtype", [True, False])
def test_frame_read_json_dtype_missing_value(self, dtype):
# GH28501 Parse missing values using read_json with dtype=False
# to NaN instead of None
result = read_json(StringIO("[null]"), dtype=dtype)
expected = DataFrame([np.nan])
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("inf", [np.inf, -np.inf])
@pytest.mark.parametrize("dtype", [True, False])
def test_frame_infinity(self, inf, dtype):
# infinities get mapped to nulls which get mapped to NaNs during
# deserialisation
df = DataFrame([[1, 2], [4, 5, 6]])
df.loc[0, 2] = inf
data = StringIO(df.to_json())
result = read_json(data, dtype=dtype)
assert np.isnan(result.iloc[0, 2])
@pytest.mark.skipif(not IS64, reason="not compliant on 32-bit, xref #15865")
@pytest.mark.parametrize(
"value,precision,expected_val",
[
(0.95, 1, 1.0),
(1.95, 1, 2.0),
(-1.95, 1, -2.0),
(0.995, 2, 1.0),
(0.9995, 3, 1.0),
(0.99999999999999944, 15, 1.0),
],
)
def test_frame_to_json_float_precision(self, value, precision, expected_val):
df = DataFrame([{"a_float": value}])
encoded = df.to_json(double_precision=precision)
assert encoded == f'{{"a_float":{{"0":{expected_val}}}}}'
def test_frame_to_json_except(self):
df = DataFrame([1, 2, 3])
msg = "Invalid value 'garbage' for option 'orient'"
with pytest.raises(ValueError, match=msg):
df.to_json(orient="garbage")
def test_frame_empty(self):
df = DataFrame(columns=["jim", "joe"])
assert not df._is_mixed_type
data = StringIO(df.to_json())
result = read_json(data, dtype=dict(df.dtypes))
tm.assert_frame_equal(result, df, check_index_type=False)
def test_frame_empty_to_json(self):
# GH 7445
df = DataFrame({"test": []}, index=[])
result = df.to_json(orient="columns")
expected = '{"test":{}}'
assert result == expected
def test_frame_empty_mixedtype(self):
# mixed type
df = DataFrame(columns=["jim", "joe"])
df["joe"] = df["joe"].astype("i8")
assert df._is_mixed_type
data = df.to_json()
tm.assert_frame_equal(
read_json(StringIO(data), dtype=dict(df.dtypes)),
df,
check_index_type=False,
)
def test_frame_mixedtype_orient(self): # GH10289
vals = [
[10, 1, "foo", 0.1, 0.01],
[20, 2, "bar", 0.2, 0.02],
[30, 3, "baz", 0.3, 0.03],
[40, 4, "qux", 0.4, 0.04],
]
df = DataFrame(
vals, index=list("abcd"), columns=["1st", "2nd", "3rd", "4th", "5th"]
)
assert df._is_mixed_type
right = df.copy()
for orient in ["split", "index", "columns"]:
inp = StringIO(df.to_json(orient=orient))
left = read_json(inp, orient=orient, convert_axes=False)
tm.assert_frame_equal(left, right)
right.index = RangeIndex(len(df))
inp = StringIO(df.to_json(orient="records"))
left = read_json(inp, orient="records", convert_axes=False)
tm.assert_frame_equal(left, right)
right.columns = RangeIndex(df.shape[1])
inp = StringIO(df.to_json(orient="values"))
left = read_json(inp, orient="values", convert_axes=False)
tm.assert_frame_equal(left, right)
def test_v12_compat(self, datapath):
dti = date_range("2000-01-03", "2000-01-07")
# freq doesn't roundtrip
dti = DatetimeIndex(np.asarray(dti), freq=None)
df = DataFrame(
[
[1.56808523, 0.65727391, 1.81021139, -0.17251653],
[-0.2550111, -0.08072427, -0.03202878, -0.17581665],
[1.51493992, 0.11805825, 1.629455, -1.31506612],
[-0.02765498, 0.44679743, 0.33192641, -0.27885413],
[0.05951614, -2.69652057, 1.28163262, 0.34703478],
],
columns=["A", "B", "C", "D"],
index=dti,
)
df["date"] = Timestamp("19920106 18:21:32.12").as_unit("ns")
df.iloc[3, df.columns.get_loc("date")] = Timestamp("20130101")
df["modified"] = df["date"]
df.iloc[1, df.columns.get_loc("modified")] = pd.NaT
dirpath = datapath("io", "json", "data")
v12_json = os.path.join(dirpath, "tsframe_v012.json")
df_unser = read_json(v12_json)
tm.assert_frame_equal(df, df_unser)
df_iso = df.drop(["modified"], axis=1)
v12_iso_json = os.path.join(dirpath, "tsframe_iso_v012.json")
df_unser_iso = read_json(v12_iso_json)
tm.assert_frame_equal(df_iso, df_unser_iso, check_column_type=False)
def test_blocks_compat_GH9037(self, using_infer_string):
index = date_range("20000101", periods=10, freq="h")
# freq doesn't round-trip
index = DatetimeIndex(list(index), freq=None)
df_mixed = DataFrame(
{
"float_1": [
-0.92077639,
0.77434435,
1.25234727,
0.61485564,
-0.60316077,
0.24653374,
0.28668979,
-2.51969012,
0.95748401,
-1.02970536,
],
"int_1": [
19680418,
75337055,
99973684,
65103179,
79373900,
40314334,
21290235,
4991321,
41903419,
16008365,
],
"str_1": [
"78c608f1",
"64a99743",
"13d2ff52",
"ca7f4af2",
"97236474",
"bde7e214",
"1a6bde47",
"b1190be5",
"7a669144",
"8d64d068",
],
"float_2": [
-0.0428278,
-1.80872357,
3.36042349,
-0.7573685,
-0.48217572,
0.86229683,
1.08935819,
0.93898739,
-0.03030452,
1.43366348,
],
"str_2": [
"14f04af9",
"d085da90",
"4bcfac83",
"81504caf",
"2ffef4a9",
"08e2f5c4",
"07e1af03",
"addbd4a7",
"1f6a09ba",
"4bfc4d87",
],
"int_2": [
86967717,
98098830,
51927505,
20372254,
12601730,
20884027,
34193846,
10561746,
24867120,
76131025,
],
},
index=index,
)
# JSON deserialisation always creates unicode strings
df_mixed.columns = df_mixed.columns.astype(
np.str_ if not using_infer_string else "string[pyarrow_numpy]"
)
data = StringIO(df_mixed.to_json(orient="split"))
df_roundtrip = read_json(data, orient="split")
tm.assert_frame_equal(
df_mixed,
df_roundtrip,
check_index_type=True,
check_column_type=True,
by_blocks=True,
check_exact=True,
)
def test_frame_nonprintable_bytes(self):
# GH14256: failing column caused segfaults, if it is not the last one
class BinaryThing:
def __init__(self, hexed) -> None:
self.hexed = hexed
self.binary = bytes.fromhex(hexed)
def __str__(self) -> str:
return self.hexed
hexed = "574b4454ba8c5eb4f98a8f45"
binthing = BinaryThing(hexed)
# verify the proper conversion of printable content
df_printable = DataFrame({"A": [binthing.hexed]})
assert df_printable.to_json() == f'{{"A":{{"0":"{hexed}"}}}}'
# check if non-printable content throws appropriate Exception
df_nonprintable = DataFrame({"A": [binthing]})
msg = "Unsupported UTF-8 sequence length when encoding string"
with pytest.raises(OverflowError, match=msg):
df_nonprintable.to_json()
# the same with multiple columns threw segfaults
df_mixed = DataFrame({"A": [binthing], "B": [1]}, columns=["A", "B"])
with pytest.raises(OverflowError, match=msg):
df_mixed.to_json()
# default_handler should resolve exceptions for non-string types
result = df_nonprintable.to_json(default_handler=str)
expected = f'{{"A":{{"0":"{hexed}"}}}}'
assert result == expected
assert (
df_mixed.to_json(default_handler=str)
== f'{{"A":{{"0":"{hexed}"}},"B":{{"0":1}}}}'
)
def test_label_overflow(self):
# GH14256: buffer length not checked when writing label
result = DataFrame({"bar" * 100000: [1], "foo": [1337]}).to_json()
expected = f'{{"{"bar" * 100000}":{{"0":1}},"foo":{{"0":1337}}}}'
assert result == expected
def test_series_non_unique_index(self):
s = Series(["a", "b"], index=[1, 1])
msg = "Series index must be unique for orient='index'"
with pytest.raises(ValueError, match=msg):
s.to_json(orient="index")
tm.assert_series_equal(
s,
read_json(
StringIO(s.to_json(orient="split")), orient="split", typ="series"
),
)
unserialized = read_json(
StringIO(s.to_json(orient="records")), orient="records", typ="series"
)
tm.assert_equal(s.values, unserialized.values)
def test_series_default_orient(self, string_series):
assert string_series.to_json() == string_series.to_json(orient="index")
def test_series_roundtrip_simple(self, orient, string_series, using_infer_string):
data = StringIO(string_series.to_json(orient=orient))
result = read_json(data, typ="series", orient=orient)
expected = string_series
if using_infer_string and orient in ("split", "index", "columns"):
# These schemas don't contain dtypes, so we infer string
expected.index = expected.index.astype("string[pyarrow_numpy]")
if orient in ("values", "records"):
expected = expected.reset_index(drop=True)
if orient != "split":
expected.name = None
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("dtype", [False, None])
def test_series_roundtrip_object(self, orient, dtype, object_series):
data = StringIO(object_series.to_json(orient=orient))
result = read_json(data, typ="series", orient=orient, dtype=dtype)
expected = object_series
if orient in ("values", "records"):
expected = expected.reset_index(drop=True)
if orient != "split":
expected.name = None
tm.assert_series_equal(result, expected)
def test_series_roundtrip_empty(self, orient):
empty_series = Series([], index=[], dtype=np.float64)
data = StringIO(empty_series.to_json(orient=orient))
result = read_json(data, typ="series", orient=orient)
expected = empty_series.reset_index(drop=True)
if orient in ("split"):
expected.index = expected.index.astype(np.float64)
tm.assert_series_equal(result, expected)
def test_series_roundtrip_timeseries(self, orient, datetime_series):
data = StringIO(datetime_series.to_json(orient=orient))
result = read_json(data, typ="series", orient=orient)
expected = datetime_series
if orient in ("values", "records"):
expected = expected.reset_index(drop=True)
if orient != "split":
expected.name = None
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("dtype", [np.float64, int])
def test_series_roundtrip_numeric(self, orient, dtype):
s = Series(range(6), index=["a", "b", "c", "d", "e", "f"])
data = StringIO(s.to_json(orient=orient))
result = read_json(data, typ="series", orient=orient)
expected = s.copy()
if orient in ("values", "records"):
expected = expected.reset_index(drop=True)
tm.assert_series_equal(result, expected)
def test_series_to_json_except(self):
s = Series([1, 2, 3])
msg = "Invalid value 'garbage' for option 'orient'"
with pytest.raises(ValueError, match=msg):
s.to_json(orient="garbage")
def test_series_from_json_precise_float(self):
s = Series([4.56, 4.56, 4.56])
result = read_json(StringIO(s.to_json()), typ="series", precise_float=True)
tm.assert_series_equal(result, s, check_index_type=False)
def test_series_with_dtype(self):
# GH 21986
s = Series([4.56, 4.56, 4.56])
result = read_json(StringIO(s.to_json()), typ="series", dtype=np.int64)
expected = Series([4] * 3)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"dtype,expected",
[
(True, Series(["2000-01-01"], dtype="datetime64[ns]")),
(False, Series([946684800000])),
],
)
def test_series_with_dtype_datetime(self, dtype, expected):
s = Series(["2000-01-01"], dtype="datetime64[ns]")
data = StringIO(s.to_json())
result = read_json(data, typ="series", dtype=dtype)
tm.assert_series_equal(result, expected)
def test_frame_from_json_precise_float(self):
df = DataFrame([[4.56, 4.56, 4.56], [4.56, 4.56, 4.56]])
result = read_json(StringIO(df.to_json()), precise_float=True)
tm.assert_frame_equal(result, df)
def test_typ(self):
s = Series(range(6), index=["a", "b", "c", "d", "e", "f"], dtype="int64")
result = read_json(StringIO(s.to_json()), typ=None)
tm.assert_series_equal(result, s)
def test_reconstruction_index(self):
df = DataFrame([[1, 2, 3], [4, 5, 6]])
result = read_json(StringIO(df.to_json()))
tm.assert_frame_equal(result, df)
df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}, index=["A", "B", "C"])
result = read_json(StringIO(df.to_json()))
tm.assert_frame_equal(result, df)
def test_path(self, float_frame, int_frame, datetime_frame):
with tm.ensure_clean("test.json") as path:
for df in [float_frame, int_frame, datetime_frame]:
df.to_json(path)
read_json(path)
def test_axis_dates(self, datetime_series, datetime_frame):
# frame
json = StringIO(datetime_frame.to_json())
result = read_json(json)
tm.assert_frame_equal(result, datetime_frame)
# series
json = StringIO(datetime_series.to_json())
result = read_json(json, typ="series")
tm.assert_series_equal(result, datetime_series, check_names=False)
assert result.name is None
def test_convert_dates(self, datetime_series, datetime_frame):
# frame
df = datetime_frame
df["date"] = Timestamp("20130101").as_unit("ns")
json = StringIO(df.to_json())
result = read_json(json)
tm.assert_frame_equal(result, df)
df["foo"] = 1.0
json = StringIO(df.to_json(date_unit="ns"))
result = read_json(json, convert_dates=False)
expected = df.copy()
expected["date"] = expected["date"].values.view("i8")
expected["foo"] = expected["foo"].astype("int64")
tm.assert_frame_equal(result, expected)
# series
ts = Series(Timestamp("20130101").as_unit("ns"), index=datetime_series.index)
json = StringIO(ts.to_json())
result = read_json(json, typ="series")
tm.assert_series_equal(result, ts)
@pytest.mark.parametrize("date_format", ["epoch", "iso"])
@pytest.mark.parametrize("as_object", [True, False])
@pytest.mark.parametrize("date_typ", [datetime.date, datetime.datetime, Timestamp])
def test_date_index_and_values(self, date_format, as_object, date_typ):
data = [date_typ(year=2020, month=1, day=1), pd.NaT]
if as_object:
data.append("a")
ser = Series(data, index=data)
result = ser.to_json(date_format=date_format)
if date_format == "epoch":
expected = '{"1577836800000":1577836800000,"null":null}'
else:
expected = (
'{"2020-01-01T00:00:00.000":"2020-01-01T00:00:00.000","null":null}'
)
if as_object:
expected = expected.replace("}", ',"a":"a"}')
assert result == expected
@pytest.mark.parametrize(
"infer_word",
[
"trade_time",
"date",
"datetime",
"sold_at",
"modified",
"timestamp",
"timestamps",
],
)
def test_convert_dates_infer(self, infer_word):
# GH10747
data = [{"id": 1, infer_word: 1036713600000}, {"id": 2}]
expected = DataFrame(
[[1, Timestamp("2002-11-08")], [2, pd.NaT]], columns=["id", infer_word]
)
result = read_json(StringIO(ujson_dumps(data)))[["id", infer_word]]
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"date,date_unit",
[
("20130101 20:43:42.123", None),
("20130101 20:43:42", "s"),
("20130101 20:43:42.123", "ms"),
("20130101 20:43:42.123456", "us"),
("20130101 20:43:42.123456789", "ns"),
],
)
def test_date_format_frame(self, date, date_unit, datetime_frame):
df = datetime_frame
df["date"] = Timestamp(date).as_unit("ns")
df.iloc[1, df.columns.get_loc("date")] = pd.NaT
df.iloc[5, df.columns.get_loc("date")] = pd.NaT
if date_unit:
json = df.to_json(date_format="iso", date_unit=date_unit)
else:
json = df.to_json(date_format="iso")
result = read_json(StringIO(json))
expected = df.copy()
tm.assert_frame_equal(result, expected)
def test_date_format_frame_raises(self, datetime_frame):
df = datetime_frame
msg = "Invalid value 'foo' for option 'date_unit'"
with pytest.raises(ValueError, match=msg):
df.to_json(date_format="iso", date_unit="foo")
@pytest.mark.parametrize(
"date,date_unit",
[
("20130101 20:43:42.123", None),
("20130101 20:43:42", "s"),
("20130101 20:43:42.123", "ms"),
("20130101 20:43:42.123456", "us"),
("20130101 20:43:42.123456789", "ns"),
],
)
def test_date_format_series(self, date, date_unit, datetime_series):
ts = Series(Timestamp(date).as_unit("ns"), index=datetime_series.index)
ts.iloc[1] = pd.NaT
ts.iloc[5] = pd.NaT
if date_unit:
json = ts.to_json(date_format="iso", date_unit=date_unit)
else:
json = ts.to_json(date_format="iso")
result = read_json(StringIO(json), typ="series")
expected = ts.copy()
tm.assert_series_equal(result, expected)
def test_date_format_series_raises(self, datetime_series):
ts = Series(Timestamp("20130101 20:43:42.123"), index=datetime_series.index)
msg = "Invalid value 'foo' for option 'date_unit'"
with pytest.raises(ValueError, match=msg):
ts.to_json(date_format="iso", date_unit="foo")
@pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"])
def test_date_unit(self, unit, datetime_frame):
df = datetime_frame
df["date"] = Timestamp("20130101 20:43:42").as_unit("ns")
dl = df.columns.get_loc("date")
df.iloc[1, dl] = Timestamp("19710101 20:43:42")
df.iloc[2, dl] = Timestamp("21460101 20:43:42")
df.iloc[4, dl] = pd.NaT
json = df.to_json(date_format="epoch", date_unit=unit)
# force date unit
result = read_json(StringIO(json), date_unit=unit)
tm.assert_frame_equal(result, df)
# detect date unit
result = read_json(StringIO(json), date_unit=None)
tm.assert_frame_equal(result, df)
@pytest.mark.parametrize("unit", ["s", "ms", "us"])
def test_iso_non_nano_datetimes(self, unit):
# Test that numpy datetimes
# in an Index or a column with non-nano resolution can be serialized
# correctly
# GH53686
index = DatetimeIndex(
[np.datetime64("2023-01-01T11:22:33.123456", unit)],
dtype=f"datetime64[{unit}]",
)
df = DataFrame(
{
"date": Series(
[np.datetime64("2022-01-01T11:22:33.123456", unit)],
dtype=f"datetime64[{unit}]",
index=index,
),
"date_obj": Series(
[np.datetime64("2023-01-01T11:22:33.123456", unit)],
dtype=object,
index=index,
),
},
)
buf = StringIO()
df.to_json(buf, date_format="iso", date_unit=unit)
buf.seek(0)
# read_json always reads datetimes in nanosecond resolution
# TODO: check_dtype/check_index_type should be removable
# once read_json gets non-nano support
tm.assert_frame_equal(
read_json(buf, convert_dates=["date", "date_obj"]),
df,
check_index_type=False,
check_dtype=False,
)
def test_weird_nested_json(self):
# this used to core dump the parser
s = r"""{
"status": "success",
"data": {
"posts": [
{
"id": 1,
"title": "A blog post",
"body": "Some useful content"
},
{
"id": 2,
"title": "Another blog post",
"body": "More content"
}
]
}
}"""
read_json(StringIO(s))
def test_doc_example(self):
dfj2 = DataFrame(
np.random.default_rng(2).standard_normal((5, 2)), columns=list("AB")
)
dfj2["date"] = Timestamp("20130101")
dfj2["ints"] = range(5)
dfj2["bools"] = True
dfj2.index = date_range("20130101", periods=5)
json = StringIO(dfj2.to_json())
result = read_json(json, dtype={"ints": np.int64, "bools": np.bool_})
tm.assert_frame_equal(result, result)
def test_round_trip_exception(self, datapath):
# GH 3867
path = datapath("io", "json", "data", "teams.csv")
df = pd.read_csv(path)
s = df.to_json()
result = read_json(StringIO(s))
res = result.reindex(index=df.index, columns=df.columns)
msg = "The 'downcast' keyword in fillna is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
res = res.fillna(np.nan, downcast=False)
tm.assert_frame_equal(res, df)
@pytest.mark.network
@pytest.mark.single_cpu
@pytest.mark.parametrize(
"field,dtype",
[
["created_at", pd.DatetimeTZDtype(tz="UTC")],
["closed_at", "datetime64[ns]"],
["updated_at", pd.DatetimeTZDtype(tz="UTC")],
],
)
def test_url(self, field, dtype, httpserver):
data = '{"created_at": ["2023-06-23T18:21:36Z"], "closed_at": ["2023-06-23T18:21:36"], "updated_at": ["2023-06-23T18:21:36Z"]}\n' # noqa: E501
httpserver.serve_content(content=data)
result = read_json(httpserver.url, convert_dates=True)
assert result[field].dtype == dtype
def test_timedelta(self):
converter = lambda x: pd.to_timedelta(x, unit="ms")
ser = Series([timedelta(23), timedelta(seconds=5)])
assert ser.dtype == "timedelta64[ns]"
result = read_json(StringIO(ser.to_json()), typ="series").apply(converter)
tm.assert_series_equal(result, ser)
ser = Series([timedelta(23), timedelta(seconds=5)], index=Index([0, 1]))
assert ser.dtype == "timedelta64[ns]"
result = read_json(StringIO(ser.to_json()), typ="series").apply(converter)
tm.assert_series_equal(result, ser)
frame = DataFrame([timedelta(23), timedelta(seconds=5)])
assert frame[0].dtype == "timedelta64[ns]"
tm.assert_frame_equal(
frame, read_json(StringIO(frame.to_json())).apply(converter)
)
def test_timedelta2(self):
frame = DataFrame(
{
"a": [timedelta(days=23), timedelta(seconds=5)],
"b": [1, 2],
"c": date_range(start="20130101", periods=2),
}
)
data = StringIO(frame.to_json(date_unit="ns"))
result = read_json(data)
result["a"] = pd.to_timedelta(result.a, unit="ns")
result["c"] = pd.to_datetime(result.c)
tm.assert_frame_equal(frame, result)
def test_mixed_timedelta_datetime(self):
td = timedelta(23)
ts = Timestamp("20130101")
frame = DataFrame({"a": [td, ts]}, dtype=object)
expected = DataFrame(
{"a": [pd.Timedelta(td).as_unit("ns")._value, ts.as_unit("ns")._value]}
)
data = StringIO(frame.to_json(date_unit="ns"))
result = read_json(data, dtype={"a": "int64"})
tm.assert_frame_equal(result, expected, check_index_type=False)
@pytest.mark.parametrize("as_object", [True, False])
@pytest.mark.parametrize("date_format", ["iso", "epoch"])
@pytest.mark.parametrize("timedelta_typ", [pd.Timedelta, timedelta])
def test_timedelta_to_json(self, as_object, date_format, timedelta_typ):
# GH28156: to_json not correctly formatting Timedelta
data = [timedelta_typ(days=1), timedelta_typ(days=2), pd.NaT]
if as_object:
data.append("a")
ser = Series(data, index=data)
if date_format == "iso":
expected = (
'{"P1DT0H0M0S":"P1DT0H0M0S","P2DT0H0M0S":"P2DT0H0M0S","null":null}'
)
else:
expected = '{"86400000":86400000,"172800000":172800000,"null":null}'
if as_object:
expected = expected.replace("}", ',"a":"a"}')
result = ser.to_json(date_format=date_format)
assert result == expected
@pytest.mark.parametrize("as_object", [True, False])
@pytest.mark.parametrize("timedelta_typ", [pd.Timedelta, timedelta])
def test_timedelta_to_json_fractional_precision(self, as_object, timedelta_typ):
data = [timedelta_typ(milliseconds=42)]
ser = Series(data, index=data)
if as_object:
ser = ser.astype(object)
result = ser.to_json()
expected = '{"42":42}'
assert result == expected
def test_default_handler(self):
value = object()
frame = DataFrame({"a": [7, value]})
expected = DataFrame({"a": [7, str(value)]})
result = read_json(StringIO(frame.to_json(default_handler=str)))
tm.assert_frame_equal(expected, result, check_index_type=False)
def test_default_handler_indirect(self):
def default(obj):
if isinstance(obj, complex):
return [("mathjs", "Complex"), ("re", obj.real), ("im", obj.imag)]
return str(obj)
df_list = [
9,
DataFrame(
{"a": [1, "STR", complex(4, -5)], "b": [float("nan"), None, "N/A"]},
columns=["a", "b"],
),
]
expected = (
'[9,[[1,null],["STR",null],[[["mathjs","Complex"],'
'["re",4.0],["im",-5.0]],"N\\/A"]]]'
)
assert (
ujson_dumps(df_list, default_handler=default, orient="values") == expected
)
def test_default_handler_numpy_unsupported_dtype(self):
# GH12554 to_json raises 'Unhandled numpy dtype 15'
df = DataFrame(
{"a": [1, 2.3, complex(4, -5)], "b": [float("nan"), None, complex(1.2, 0)]},
columns=["a", "b"],
)
expected = (
'[["(1+0j)","(nan+0j)"],'
'["(2.3+0j)","(nan+0j)"],'
'["(4-5j)","(1.2+0j)"]]'
)
assert df.to_json(default_handler=str, orient="values") == expected
def test_default_handler_raises(self):
msg = "raisin"
def my_handler_raises(obj):
raise TypeError(msg)
with pytest.raises(TypeError, match=msg):
DataFrame({"a": [1, 2, object()]}).to_json(
default_handler=my_handler_raises
)
with pytest.raises(TypeError, match=msg):
DataFrame({"a": [1, 2, complex(4, -5)]}).to_json(
default_handler=my_handler_raises
)
def test_categorical(self):
# GH4377 df.to_json segfaults with non-ndarray blocks
df = DataFrame({"A": ["a", "b", "c", "a", "b", "b", "a"]})
df["B"] = df["A"]
expected = df.to_json()
df["B"] = df["A"].astype("category")
assert expected == df.to_json()
s = df["A"]
sc = df["B"]
assert s.to_json() == sc.to_json()
def test_datetime_tz(self):
# GH4377 df.to_json segfaults with non-ndarray blocks
tz_range = date_range("20130101", periods=3, tz="US/Eastern")
tz_naive = tz_range.tz_convert("utc").tz_localize(None)
df = DataFrame({"A": tz_range, "B": date_range("20130101", periods=3)})
df_naive = df.copy()
df_naive["A"] = tz_naive
expected = df_naive.to_json()
assert expected == df.to_json()
stz = Series(tz_range)
s_naive = Series(tz_naive)
assert stz.to_json() == s_naive.to_json()
def test_sparse(self):
# GH4377 df.to_json segfaults with non-ndarray blocks
df = DataFrame(np.random.default_rng(2).standard_normal((10, 4)))
df.loc[:8] = np.nan
sdf = df.astype("Sparse")
expected = df.to_json()
assert expected == sdf.to_json()
s = Series(np.random.default_rng(2).standard_normal(10))
s.loc[:8] = np.nan
ss = s.astype("Sparse")
expected = s.to_json()
assert expected == ss.to_json()
@pytest.mark.parametrize(
"ts",
[
Timestamp("2013-01-10 05:00:00Z"),
Timestamp("2013-01-10 00:00:00", tz="US/Eastern"),
Timestamp("2013-01-10 00:00:00-0500"),
],
)
def test_tz_is_utc(self, ts):
exp = '"2013-01-10T05:00:00.000Z"'
assert ujson_dumps(ts, iso_dates=True) == exp
dt = ts.to_pydatetime()
assert ujson_dumps(dt, iso_dates=True) == exp
def test_tz_is_naive(self):
ts = Timestamp("2013-01-10 05:00:00")
exp = '"2013-01-10T05:00:00.000"'
assert ujson_dumps(ts, iso_dates=True) == exp
dt = ts.to_pydatetime()
assert ujson_dumps(dt, iso_dates=True) == exp
@pytest.mark.parametrize(
"tz_range",
[
date_range("2013-01-01 05:00:00Z", periods=2),
date_range("2013-01-01 00:00:00", periods=2, tz="US/Eastern"),
date_range("2013-01-01 00:00:00-0500", periods=2),
],
)
def test_tz_range_is_utc(self, tz_range):
exp = '["2013-01-01T05:00:00.000Z","2013-01-02T05:00:00.000Z"]'
dfexp = (
'{"DT":{'
'"0":"2013-01-01T05:00:00.000Z",'
'"1":"2013-01-02T05:00:00.000Z"}}'
)
assert ujson_dumps(tz_range, iso_dates=True) == exp
dti = DatetimeIndex(tz_range)
# Ensure datetimes in object array are serialized correctly
# in addition to the normal DTI case
assert ujson_dumps(dti, iso_dates=True) == exp
assert ujson_dumps(dti.astype(object), iso_dates=True) == exp
df = DataFrame({"DT": dti})
result = ujson_dumps(df, iso_dates=True)
assert result == dfexp
assert ujson_dumps(df.astype({"DT": object}), iso_dates=True)
def test_tz_range_is_naive(self):
dti = date_range("2013-01-01 05:00:00", periods=2)
exp = '["2013-01-01T05:00:00.000","2013-01-02T05:00:00.000"]'
dfexp = '{"DT":{"0":"2013-01-01T05:00:00.000","1":"2013-01-02T05:00:00.000"}}'
# Ensure datetimes in object array are serialized correctly
# in addition to the normal DTI case
assert ujson_dumps(dti, iso_dates=True) == exp
assert ujson_dumps(dti.astype(object), iso_dates=True) == exp
df = DataFrame({"DT": dti})
result = ujson_dumps(df, iso_dates=True)
assert result == dfexp
assert ujson_dumps(df.astype({"DT": object}), iso_dates=True)
def test_read_inline_jsonl(self):
# GH9180
result = read_json(StringIO('{"a": 1, "b": 2}\n{"b":2, "a" :1}\n'), lines=True)
expected = DataFrame([[1, 2], [1, 2]], columns=["a", "b"])
tm.assert_frame_equal(result, expected)
@pytest.mark.single_cpu
@td.skip_if_not_us_locale
def test_read_s3_jsonl(self, s3_public_bucket_with_data, s3so):
# GH17200
result = read_json(
f"s3n://{s3_public_bucket_with_data.name}/items.jsonl",
lines=True,
storage_options=s3so,
)
expected = DataFrame([[1, 2], [1, 2]], columns=["a", "b"])
tm.assert_frame_equal(result, expected)
def test_read_local_jsonl(self):
# GH17200
with tm.ensure_clean("tmp_items.json") as path:
with open(path, "w", encoding="utf-8") as infile:
infile.write('{"a": 1, "b": 2}\n{"b":2, "a" :1}\n')
result = read_json(path, lines=True)
expected = DataFrame([[1, 2], [1, 2]], columns=["a", "b"])
tm.assert_frame_equal(result, expected)
def test_read_jsonl_unicode_chars(self):
# GH15132: non-ascii unicode characters
# \u201d == RIGHT DOUBLE QUOTATION MARK
# simulate file handle
json = '{"a": "foo”", "b": "bar"}\n{"a": "foo", "b": "bar"}\n'
json = StringIO(json)
result = read_json(json, lines=True)
expected = DataFrame([["foo\u201d", "bar"], ["foo", "bar"]], columns=["a", "b"])
tm.assert_frame_equal(result, expected)
# simulate string
json = StringIO('{"a": "foo”", "b": "bar"}\n{"a": "foo", "b": "bar"}\n')
result = read_json(json, lines=True)
expected = DataFrame([["foo\u201d", "bar"], ["foo", "bar"]], columns=["a", "b"])
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("bigNum", [sys.maxsize + 1, -(sys.maxsize + 2)])
def test_to_json_large_numbers(self, bigNum):
# GH34473
series = Series(bigNum, dtype=object, index=["articleId"])
json = series.to_json()
expected = '{"articleId":' + str(bigNum) + "}"
assert json == expected
df = DataFrame(bigNum, dtype=object, index=["articleId"], columns=[0])
json = df.to_json()
expected = '{"0":{"articleId":' + str(bigNum) + "}}"
assert json == expected
@pytest.mark.parametrize("bigNum", [-(2**63) - 1, 2**64])
def test_read_json_large_numbers(self, bigNum):
# GH20599, 26068
json = StringIO('{"articleId":' + str(bigNum) + "}")
msg = r"Value is too small|Value is too big"
with pytest.raises(ValueError, match=msg):
read_json(json)
json = StringIO('{"0":{"articleId":' + str(bigNum) + "}}")
with pytest.raises(ValueError, match=msg):
read_json(json)
def test_read_json_large_numbers2(self):
# GH18842
json = '{"articleId": "1404366058080022500245"}'
json = StringIO(json)
result = read_json(json, typ="series")
expected = Series(1.404366e21, index=["articleId"])
tm.assert_series_equal(result, expected)
json = '{"0": {"articleId": "1404366058080022500245"}}'
json = StringIO(json)
result = read_json(json)
expected = DataFrame(1.404366e21, index=["articleId"], columns=[0])
tm.assert_frame_equal(result, expected)
def test_to_jsonl(self):
# GH9180
df = DataFrame([[1, 2], [1, 2]], columns=["a", "b"])
result = df.to_json(orient="records", lines=True)
expected = '{"a":1,"b":2}\n{"a":1,"b":2}\n'
assert result == expected
df = DataFrame([["foo}", "bar"], ['foo"', "bar"]], columns=["a", "b"])
result = df.to_json(orient="records", lines=True)
expected = '{"a":"foo}","b":"bar"}\n{"a":"foo\\"","b":"bar"}\n'
assert result == expected
tm.assert_frame_equal(read_json(StringIO(result), lines=True), df)
# GH15096: escaped characters in columns and data
df = DataFrame([["foo\\", "bar"], ['foo"', "bar"]], columns=["a\\", "b"])
result = df.to_json(orient="records", lines=True)
expected = '{"a\\\\":"foo\\\\","b":"bar"}\n{"a\\\\":"foo\\"","b":"bar"}\n'
assert result == expected
tm.assert_frame_equal(read_json(StringIO(result), lines=True), df)
# TODO: there is a near-identical test for pytables; can we share?
@pytest.mark.xfail(reason="GH#13774 encoding kwarg not supported", raises=TypeError)
@pytest.mark.parametrize(
"val",
[
[b"E\xc9, 17", b"", b"a", b"b", b"c"],
[b"E\xc9, 17", b"a", b"b", b"c"],
[b"EE, 17", b"", b"a", b"b", b"c"],
[b"E\xc9, 17", b"\xf8\xfc", b"a", b"b", b"c"],
[b"", b"a", b"b", b"c"],
[b"\xf8\xfc", b"a", b"b", b"c"],
[b"A\xf8\xfc", b"", b"a", b"b", b"c"],
[np.nan, b"", b"b", b"c"],
[b"A\xf8\xfc", np.nan, b"", b"b", b"c"],
],
)
@pytest.mark.parametrize("dtype", ["category", object])
def test_latin_encoding(self, dtype, val):
# GH 13774
ser = Series(
[x.decode("latin-1") if isinstance(x, bytes) else x for x in val],
dtype=dtype,
)
encoding = "latin-1"
with tm.ensure_clean("test.json") as path:
ser.to_json(path, encoding=encoding)
retr = read_json(StringIO(path), encoding=encoding)
tm.assert_series_equal(ser, retr, check_categorical=False)
def test_data_frame_size_after_to_json(self):
# GH15344
df = DataFrame({"a": [str(1)]})
size_before = df.memory_usage(index=True, deep=True).sum()
df.to_json()
size_after = df.memory_usage(index=True, deep=True).sum()
assert size_before == size_after
@pytest.mark.parametrize(
"index", [None, [1, 2], [1.0, 2.0], ["a", "b"], ["1", "2"], ["1.", "2."]]
)
@pytest.mark.parametrize("columns", [["a", "b"], ["1", "2"], ["1.", "2."]])
def test_from_json_to_json_table_index_and_columns(self, index, columns):
# GH25433 GH25435
expected = DataFrame([[1, 2], [3, 4]], index=index, columns=columns)
dfjson = expected.to_json(orient="table")
result = read_json(StringIO(dfjson), orient="table")
tm.assert_frame_equal(result, expected)
def test_from_json_to_json_table_dtypes(self):
# GH21345
expected = DataFrame({"a": [1, 2], "b": [3.0, 4.0], "c": ["5", "6"]})
dfjson = expected.to_json(orient="table")
result = read_json(StringIO(dfjson), orient="table")
tm.assert_frame_equal(result, expected)
# TODO: We are casting to string which coerces None to NaN before casting back
# to object, ending up with incorrect na values
@pytest.mark.xfail(using_pyarrow_string_dtype(), reason="incorrect na conversion")
@pytest.mark.parametrize("orient", ["split", "records", "index", "columns"])
def test_to_json_from_json_columns_dtypes(self, orient):
# GH21892 GH33205
expected = DataFrame.from_dict(
{
"Integer": Series([1, 2, 3], dtype="int64"),
"Float": Series([None, 2.0, 3.0], dtype="float64"),
"Object": Series([None, "", "c"], dtype="object"),
"Bool": Series([True, False, True], dtype="bool"),
"Category": Series(["a", "b", None], dtype="category"),
"Datetime": Series(
["2020-01-01", None, "2020-01-03"], dtype="datetime64[ns]"
),
}
)
dfjson = expected.to_json(orient=orient)
result = read_json(
StringIO(dfjson),
orient=orient,
dtype={
"Integer": "int64",
"Float": "float64",
"Object": "object",
"Bool": "bool",
"Category": "category",
"Datetime": "datetime64[ns]",
},
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("dtype", [True, {"b": int, "c": int}])
def test_read_json_table_dtype_raises(self, dtype):
# GH21345
df = DataFrame({"a": [1, 2], "b": [3.0, 4.0], "c": ["5", "6"]})
dfjson = df.to_json(orient="table")
msg = "cannot pass both dtype and orient='table'"
with pytest.raises(ValueError, match=msg):
read_json(dfjson, orient="table", dtype=dtype)
@pytest.mark.parametrize("orient", ["index", "columns", "records", "values"])
def test_read_json_table_empty_axes_dtype(self, orient):
# GH28558
expected = DataFrame()
result = read_json(StringIO("{}"), orient=orient, convert_axes=True)
tm.assert_index_equal(result.index, expected.index)
tm.assert_index_equal(result.columns, expected.columns)
def test_read_json_table_convert_axes_raises(self):
# GH25433 GH25435
df = DataFrame([[1, 2], [3, 4]], index=[1.0, 2.0], columns=["1.", "2."])
dfjson = df.to_json(orient="table")
msg = "cannot pass both convert_axes and orient='table'"
with pytest.raises(ValueError, match=msg):
read_json(dfjson, orient="table", convert_axes=True)
@pytest.mark.parametrize(
"data, expected",
[
(
DataFrame([[1, 2], [4, 5]], columns=["a", "b"]),
{"columns": ["a", "b"], "data": [[1, 2], [4, 5]]},
),
(
DataFrame([[1, 2], [4, 5]], columns=["a", "b"]).rename_axis("foo"),
{"columns": ["a", "b"], "data": [[1, 2], [4, 5]]},
),
(
DataFrame(
[[1, 2], [4, 5]], columns=["a", "b"], index=[["a", "b"], ["c", "d"]]
),
{"columns": ["a", "b"], "data": [[1, 2], [4, 5]]},
),
(Series([1, 2, 3], name="A"), {"name": "A", "data": [1, 2, 3]}),
(
Series([1, 2, 3], name="A").rename_axis("foo"),
{"name": "A", "data": [1, 2, 3]},
),
(
Series([1, 2], name="A", index=[["a", "b"], ["c", "d"]]),
{"name": "A", "data": [1, 2]},
),
],
)
def test_index_false_to_json_split(self, data, expected):
# GH 17394
# Testing index=False in to_json with orient='split'
result = data.to_json(orient="split", index=False)
result = json.loads(result)
assert result == expected
@pytest.mark.parametrize(
"data",
[
(DataFrame([[1, 2], [4, 5]], columns=["a", "b"])),
(DataFrame([[1, 2], [4, 5]], columns=["a", "b"]).rename_axis("foo")),
(
DataFrame(
[[1, 2], [4, 5]], columns=["a", "b"], index=[["a", "b"], ["c", "d"]]
)
),
(Series([1, 2, 3], name="A")),
(Series([1, 2, 3], name="A").rename_axis("foo")),
(Series([1, 2], name="A", index=[["a", "b"], ["c", "d"]])),
],
)
def test_index_false_to_json_table(self, data):
# GH 17394
# Testing index=False in to_json with orient='table'
result = data.to_json(orient="table", index=False)
result = json.loads(result)
expected = {
"schema": pd.io.json.build_table_schema(data, index=False),
"data": DataFrame(data).to_dict(orient="records"),
}
assert result == expected
@pytest.mark.parametrize("orient", ["index", "columns"])
def test_index_false_error_to_json(self, orient):
# GH 17394, 25513
# Testing error message from to_json with index=False
df = DataFrame([[1, 2], [4, 5]], columns=["a", "b"])
msg = (
"'index=False' is only valid when 'orient' is 'split', "
"'table', 'records', or 'values'"
)
with pytest.raises(ValueError, match=msg):
df.to_json(orient=orient, index=False)
@pytest.mark.parametrize("orient", ["records", "values"])
def test_index_true_error_to_json(self, orient):
# GH 25513
# Testing error message from to_json with index=True
df = DataFrame([[1, 2], [4, 5]], columns=["a", "b"])
msg = (
"'index=True' is only valid when 'orient' is 'split', "
"'table', 'index', or 'columns'"
)
with pytest.raises(ValueError, match=msg):
df.to_json(orient=orient, index=True)
@pytest.mark.parametrize("orient", ["split", "table"])
@pytest.mark.parametrize("index", [True, False])
def test_index_false_from_json_to_json(self, orient, index):
# GH25170
# Test index=False in from_json to_json
expected = DataFrame({"a": [1, 2], "b": [3, 4]})
dfjson = expected.to_json(orient=orient, index=index)
result = read_json(StringIO(dfjson), orient=orient)
tm.assert_frame_equal(result, expected)
def test_read_timezone_information(self):
# GH 25546
result = read_json(
StringIO('{"2019-01-01T11:00:00.000Z":88}'), typ="series", orient="index"
)
exp_dti = DatetimeIndex(["2019-01-01 11:00:00"], dtype="M8[ns, UTC]")
expected = Series([88], index=exp_dti)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"url",
[
"s3://example-fsspec/",
"gcs://another-fsspec/file.json",
"https://example-site.com/data",
"some-protocol://data.txt",
],
)
def test_read_json_with_url_value(self, url):
# GH 36271
result = read_json(StringIO(f'{{"url":{{"0":"{url}"}}}}'))
expected = DataFrame({"url": [url]})
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"compression",
["", ".gz", ".bz2", ".tar"],
)
def test_read_json_with_very_long_file_path(self, compression):
# GH 46718
long_json_path = f'{"a" * 1000}.json{compression}'
with pytest.raises(
FileNotFoundError, match=f"File {long_json_path} does not exist"
):
# path too long for Windows is handled in file_exists() but raises in
# _get_data_from_filepath()
read_json(long_json_path)
@pytest.mark.parametrize(
"date_format,key", [("epoch", 86400000), ("iso", "P1DT0H0M0S")]
)
def test_timedelta_as_label(self, date_format, key):
df = DataFrame([[1]], columns=[pd.Timedelta("1D")])
expected = f'{{"{key}":{{"0":1}}}}'
result = df.to_json(date_format=date_format)
assert result == expected
@pytest.mark.parametrize(
"orient,expected",
[
("index", "{\"('a', 'b')\":{\"('c', 'd')\":1}}"),
("columns", "{\"('c', 'd')\":{\"('a', 'b')\":1}}"),
# TODO: the below have separate encoding procedures
pytest.param(
"split",
"",
marks=pytest.mark.xfail(
reason="Produces JSON but not in a consistent manner"
),
),
pytest.param(
"table",
"",
marks=pytest.mark.xfail(
reason="Produces JSON but not in a consistent manner"
),
),
],
)
def test_tuple_labels(self, orient, expected):
# GH 20500
df = DataFrame([[1]], index=[("a", "b")], columns=[("c", "d")])
result = df.to_json(orient=orient)
assert result == expected
@pytest.mark.parametrize("indent", [1, 2, 4])
def test_to_json_indent(self, indent):
# GH 12004
df = DataFrame([["foo", "bar"], ["baz", "qux"]], columns=["a", "b"])
result = df.to_json(indent=indent)
spaces = " " * indent
expected = f"""{{
{spaces}"a":{{
{spaces}{spaces}"0":"foo",
{spaces}{spaces}"1":"baz"
{spaces}}},
{spaces}"b":{{
{spaces}{spaces}"0":"bar",
{spaces}{spaces}"1":"qux"
{spaces}}}
}}"""
assert result == expected
@pytest.mark.skipif(
using_pyarrow_string_dtype(),
reason="Adjust expected when infer_string is default, no bug here, "
"just a complicated parametrization",
)
@pytest.mark.parametrize(
"orient,expected",
[
(
"split",
"""{
"columns":[
"a",
"b"
],
"index":[
0,
1
],
"data":[
[
"foo",
"bar"
],
[
"baz",
"qux"
]
]
}""",
),
(
"records",
"""[
{
"a":"foo",
"b":"bar"
},
{
"a":"baz",
"b":"qux"
}
]""",
),
(
"index",
"""{
"0":{
"a":"foo",
"b":"bar"
},
"1":{
"a":"baz",
"b":"qux"
}
}""",
),
(
"columns",
"""{
"a":{
"0":"foo",
"1":"baz"
},
"b":{
"0":"bar",
"1":"qux"
}
}""",
),
(
"values",
"""[
[
"foo",
"bar"
],
[
"baz",
"qux"
]
]""",
),
(
"table",
"""{
"schema":{
"fields":[
{
"name":"index",
"type":"integer"
},
{
"name":"a",
"type":"string"
},
{
"name":"b",
"type":"string"
}
],
"primaryKey":[
"index"
],
"pandas_version":"1.4.0"
},
"data":[
{
"index":0,
"a":"foo",
"b":"bar"
},
{
"index":1,
"a":"baz",
"b":"qux"
}
]
}""",
),
],
)
def test_json_indent_all_orients(self, orient, expected):
# GH 12004
df = DataFrame([["foo", "bar"], ["baz", "qux"]], columns=["a", "b"])
result = df.to_json(orient=orient, indent=4)
assert result == expected
def test_json_negative_indent_raises(self):
with pytest.raises(ValueError, match="must be a nonnegative integer"):
DataFrame().to_json(indent=-1)
def test_emca_262_nan_inf_support(self):
# GH 12213
data = StringIO(
'["a", NaN, "NaN", Infinity, "Infinity", -Infinity, "-Infinity"]'
)
result = read_json(data)
expected = DataFrame(
["a", None, "NaN", np.inf, "Infinity", -np.inf, "-Infinity"]
)
tm.assert_frame_equal(result, expected)
def test_frame_int_overflow(self):
# GH 30320
encoded_json = json.dumps([{"col": "31900441201190696999"}, {"col": "Text"}])
expected = DataFrame({"col": ["31900441201190696999", "Text"]})
result = read_json(StringIO(encoded_json))
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"dataframe,expected",
[
(
DataFrame({"x": [1, 2, 3], "y": ["a", "b", "c"]}),
'{"(0, \'x\')":1,"(0, \'y\')":"a","(1, \'x\')":2,'
'"(1, \'y\')":"b","(2, \'x\')":3,"(2, \'y\')":"c"}',
)
],
)
def test_json_multiindex(self, dataframe, expected):
series = dataframe.stack(future_stack=True)
result = series.to_json(orient="index")
assert result == expected
@pytest.mark.single_cpu
def test_to_s3(self, s3_public_bucket, s3so):
# GH 28375
mock_bucket_name, target_file = s3_public_bucket.name, "test.json"
df = DataFrame({"x": [1, 2, 3], "y": [2, 4, 6]})
df.to_json(f"s3://{mock_bucket_name}/{target_file}", storage_options=s3so)
timeout = 5
while True:
if target_file in (obj.key for obj in s3_public_bucket.objects.all()):
break
time.sleep(0.1)
timeout -= 0.1
assert timeout > 0, "Timed out waiting for file to appear on moto"
def test_json_pandas_nulls(self, nulls_fixture, request):
# GH 31615
if isinstance(nulls_fixture, Decimal):
mark = pytest.mark.xfail(reason="not implemented")
request.applymarker(mark)
result = DataFrame([[nulls_fixture]]).to_json()
assert result == '{"0":{"0":null}}'
def test_readjson_bool_series(self):
# GH31464
result = read_json(StringIO("[true, true, false]"), typ="series")
expected = Series([True, True, False])
tm.assert_series_equal(result, expected)
def test_to_json_multiindex_escape(self):
# GH 15273
df = DataFrame(
True,
index=date_range("2017-01-20", "2017-01-23"),
columns=["foo", "bar"],
).stack(future_stack=True)
result = df.to_json()
expected = (
"{\"(Timestamp('2017-01-20 00:00:00'), 'foo')\":true,"
"\"(Timestamp('2017-01-20 00:00:00'), 'bar')\":true,"
"\"(Timestamp('2017-01-21 00:00:00'), 'foo')\":true,"
"\"(Timestamp('2017-01-21 00:00:00'), 'bar')\":true,"
"\"(Timestamp('2017-01-22 00:00:00'), 'foo')\":true,"
"\"(Timestamp('2017-01-22 00:00:00'), 'bar')\":true,"
"\"(Timestamp('2017-01-23 00:00:00'), 'foo')\":true,"
"\"(Timestamp('2017-01-23 00:00:00'), 'bar')\":true}"
)
assert result == expected
def test_to_json_series_of_objects(self):
class _TestObject:
def __init__(self, a, b, _c, d) -> None:
self.a = a
self.b = b
self._c = _c
self.d = d
def e(self):
return 5
# JSON keys should be all non-callable non-underscore attributes, see GH-42768
series = Series([_TestObject(a=1, b=2, _c=3, d=4)])
assert json.loads(series.to_json()) == {"0": {"a": 1, "b": 2, "d": 4}}
@pytest.mark.parametrize(
"data,expected",
[
(
Series({0: -6 + 8j, 1: 0 + 1j, 2: 9 - 5j}),
'{"0":{"imag":8.0,"real":-6.0},'
'"1":{"imag":1.0,"real":0.0},'
'"2":{"imag":-5.0,"real":9.0}}',
),
(
Series({0: -9.39 + 0.66j, 1: 3.95 + 9.32j, 2: 4.03 - 0.17j}),
'{"0":{"imag":0.66,"real":-9.39},'
'"1":{"imag":9.32,"real":3.95},'
'"2":{"imag":-0.17,"real":4.03}}',
),
(
DataFrame([[-2 + 3j, -1 - 0j], [4 - 3j, -0 - 10j]]),
'{"0":{"0":{"imag":3.0,"real":-2.0},'
'"1":{"imag":-3.0,"real":4.0}},'
'"1":{"0":{"imag":0.0,"real":-1.0},'
'"1":{"imag":-10.0,"real":0.0}}}',
),
(
DataFrame(
[[-0.28 + 0.34j, -1.08 - 0.39j], [0.41 - 0.34j, -0.78 - 1.35j]]
),
'{"0":{"0":{"imag":0.34,"real":-0.28},'
'"1":{"imag":-0.34,"real":0.41}},'
'"1":{"0":{"imag":-0.39,"real":-1.08},'
'"1":{"imag":-1.35,"real":-0.78}}}',
),
],
)
def test_complex_data_tojson(self, data, expected):
# GH41174
result = data.to_json()
assert result == expected
def test_json_uint64(self):
# GH21073
expected = (
'{"columns":["col1"],"index":[0,1],'
'"data":[[13342205958987758245],[12388075603347835679]]}'
)
df = DataFrame(data={"col1": [13342205958987758245, 12388075603347835679]})
result = df.to_json(orient="split")
assert result == expected
@pytest.mark.parametrize(
"orient", ["split", "records", "values", "index", "columns"]
)
def test_read_json_dtype_backend(
self, string_storage, dtype_backend, orient, using_infer_string
):
# GH#50750
pa = pytest.importorskip("pyarrow")
df = DataFrame(
{
"a": Series([1, np.nan, 3], dtype="Int64"),
"b": Series([1, 2, 3], dtype="Int64"),
"c": Series([1.5, np.nan, 2.5], dtype="Float64"),
"d": Series([1.5, 2.0, 2.5], dtype="Float64"),
"e": [True, False, None],
"f": [True, False, True],
"g": ["a", "b", "c"],
"h": ["a", "b", None],
}
)
if using_infer_string:
string_array = ArrowStringArrayNumpySemantics(pa.array(["a", "b", "c"]))
string_array_na = ArrowStringArrayNumpySemantics(pa.array(["a", "b", None]))
elif string_storage == "python":
string_array = StringArray(np.array(["a", "b", "c"], dtype=np.object_))
string_array_na = StringArray(np.array(["a", "b", NA], dtype=np.object_))
elif dtype_backend == "pyarrow":
pa = pytest.importorskip("pyarrow")
from pandas.arrays import ArrowExtensionArray
string_array = ArrowExtensionArray(pa.array(["a", "b", "c"]))
string_array_na = ArrowExtensionArray(pa.array(["a", "b", None]))
else:
string_array = ArrowStringArray(pa.array(["a", "b", "c"]))
string_array_na = ArrowStringArray(pa.array(["a", "b", None]))
out = df.to_json(orient=orient)
with pd.option_context("mode.string_storage", string_storage):
result = read_json(
StringIO(out), dtype_backend=dtype_backend, orient=orient
)
expected = DataFrame(
{
"a": Series([1, np.nan, 3], dtype="Int64"),
"b": Series([1, 2, 3], dtype="Int64"),
"c": Series([1.5, np.nan, 2.5], dtype="Float64"),
"d": Series([1.5, 2.0, 2.5], dtype="Float64"),
"e": Series([True, False, NA], dtype="boolean"),
"f": Series([True, False, True], dtype="boolean"),
"g": string_array,
"h": string_array_na,
}
)
if dtype_backend == "pyarrow":
from pandas.arrays import ArrowExtensionArray
expected = DataFrame(
{
col: ArrowExtensionArray(pa.array(expected[col], from_pandas=True))
for col in expected.columns
}
)
if orient == "values":
expected.columns = list(range(8))
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("orient", ["split", "records", "index"])
def test_read_json_nullable_series(self, string_storage, dtype_backend, orient):
# GH#50750
pa = pytest.importorskip("pyarrow")
ser = Series([1, np.nan, 3], dtype="Int64")
out = ser.to_json(orient=orient)
with pd.option_context("mode.string_storage", string_storage):
result = read_json(
StringIO(out), dtype_backend=dtype_backend, orient=orient, typ="series"
)
expected = Series([1, np.nan, 3], dtype="Int64")
if dtype_backend == "pyarrow":
from pandas.arrays import ArrowExtensionArray
expected = Series(ArrowExtensionArray(pa.array(expected, from_pandas=True)))
tm.assert_series_equal(result, expected)
def test_invalid_dtype_backend(self):
msg = (
"dtype_backend numpy is invalid, only 'numpy_nullable' and "
"'pyarrow' are allowed."
)
with pytest.raises(ValueError, match=msg):
read_json("test", dtype_backend="numpy")
def test_invalid_engine():
# GH 48893
ser = Series(range(1))
out = ser.to_json()
with pytest.raises(ValueError, match="The engine type foo"):
read_json(out, engine="foo")
def test_pyarrow_engine_lines_false():
# GH 48893
ser = Series(range(1))
out = ser.to_json()
with pytest.raises(ValueError, match="currently pyarrow engine only supports"):
read_json(out, engine="pyarrow", lines=False)
def test_json_roundtrip_string_inference(orient):
pytest.importorskip("pyarrow")
df = DataFrame(
[["a", "b"], ["c", "d"]], index=["row 1", "row 2"], columns=["col 1", "col 2"]
)
out = df.to_json()
with pd.option_context("future.infer_string", True):
result = read_json(StringIO(out))
expected = DataFrame(
[["a", "b"], ["c", "d"]],
dtype="string[pyarrow_numpy]",
index=Index(["row 1", "row 2"], dtype="string[pyarrow_numpy]"),
columns=Index(["col 1", "col 2"], dtype="string[pyarrow_numpy]"),
)
tm.assert_frame_equal(result, expected)
def test_json_pos_args_deprecation():
# GH-54229
df = DataFrame({"a": [1, 2, 3]})
msg = (
r"Starting with pandas version 3.0 all arguments of to_json except for the "
r"argument 'path_or_buf' will be keyword-only."
)
with tm.assert_produces_warning(FutureWarning, match=msg):
buf = BytesIO()
df.to_json(buf, "split")
@td.skip_if_no("pyarrow")
def test_to_json_ea_null():
# GH#57224
df = DataFrame(
{
"a": Series([1, NA], dtype="int64[pyarrow]"),
"b": Series([2, NA], dtype="Int64"),
}
)
result = df.to_json(orient="records", lines=True)
expected = """{"a":1,"b":2}
{"a":null,"b":null}
"""
assert result == expected
def test_read_json_lines_rangeindex():
# GH 57429
data = """
{"a": 1, "b": 2}
{"a": 3, "b": 4}
"""
result = read_json(StringIO(data), lines=True).index
expected = RangeIndex(2)
tm.assert_index_equal(result, expected, exact=True)
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