1598 lines
47 KiB
Python
1598 lines
47 KiB
Python
"""
|
|
Tests date parsing functionality for all of the
|
|
parsers defined in parsers.py
|
|
"""
|
|
|
|
from datetime import date, datetime
|
|
from io import StringIO
|
|
|
|
from dateutil.parser import parse as du_parse
|
|
from hypothesis import given, settings, strategies as st
|
|
import numpy as np
|
|
import pytest
|
|
import pytz
|
|
|
|
from pandas._libs.tslib import Timestamp
|
|
from pandas._libs.tslibs import parsing
|
|
from pandas._libs.tslibs.parsing import parse_datetime_string
|
|
from pandas.compat import is_platform_windows
|
|
from pandas.compat.numpy import np_array_datetime64_compat
|
|
|
|
import pandas as pd
|
|
from pandas import DataFrame, DatetimeIndex, Index, MultiIndex, Series
|
|
import pandas._testing as tm
|
|
from pandas.core.indexes.datetimes import date_range
|
|
|
|
import pandas.io.date_converters as conv
|
|
|
|
# constant
|
|
_DEFAULT_DATETIME = datetime(1, 1, 1)
|
|
|
|
# Strategy for hypothesis
|
|
if is_platform_windows():
|
|
date_strategy = st.datetimes(min_value=datetime(1900, 1, 1))
|
|
else:
|
|
date_strategy = st.datetimes()
|
|
|
|
|
|
def test_separator_date_conflict(all_parsers):
|
|
# Regression test for gh-4678
|
|
#
|
|
# Make sure thousands separator and
|
|
# date parsing do not conflict.
|
|
parser = all_parsers
|
|
data = "06-02-2013;13:00;1-000.215"
|
|
expected = DataFrame(
|
|
[[datetime(2013, 6, 2, 13, 0, 0), 1000.215]], columns=["Date", 2]
|
|
)
|
|
|
|
df = parser.read_csv(
|
|
StringIO(data),
|
|
sep=";",
|
|
thousands="-",
|
|
parse_dates={"Date": [0, 1]},
|
|
header=None,
|
|
)
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("keep_date_col", [True, False])
|
|
def test_multiple_date_col_custom(all_parsers, keep_date_col):
|
|
data = """\
|
|
KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000
|
|
KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000
|
|
KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000
|
|
KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000
|
|
KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000
|
|
KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000
|
|
"""
|
|
parser = all_parsers
|
|
|
|
def date_parser(*date_cols):
|
|
"""
|
|
Test date parser.
|
|
|
|
Parameters
|
|
----------
|
|
date_cols : args
|
|
The list of data columns to parse.
|
|
|
|
Returns
|
|
-------
|
|
parsed : Series
|
|
"""
|
|
return parsing.try_parse_dates(parsing.concat_date_cols(date_cols))
|
|
|
|
result = parser.read_csv(
|
|
StringIO(data),
|
|
header=None,
|
|
date_parser=date_parser,
|
|
prefix="X",
|
|
parse_dates={"actual": [1, 2], "nominal": [1, 3]},
|
|
keep_date_col=keep_date_col,
|
|
)
|
|
expected = DataFrame(
|
|
[
|
|
[
|
|
datetime(1999, 1, 27, 19, 0),
|
|
datetime(1999, 1, 27, 18, 56),
|
|
"KORD",
|
|
"19990127",
|
|
" 19:00:00",
|
|
" 18:56:00",
|
|
0.81,
|
|
2.81,
|
|
7.2,
|
|
0.0,
|
|
280.0,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 20, 0),
|
|
datetime(1999, 1, 27, 19, 56),
|
|
"KORD",
|
|
"19990127",
|
|
" 20:00:00",
|
|
" 19:56:00",
|
|
0.01,
|
|
2.21,
|
|
7.2,
|
|
0.0,
|
|
260.0,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 21, 0),
|
|
datetime(1999, 1, 27, 20, 56),
|
|
"KORD",
|
|
"19990127",
|
|
" 21:00:00",
|
|
" 20:56:00",
|
|
-0.59,
|
|
2.21,
|
|
5.7,
|
|
0.0,
|
|
280.0,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 21, 0),
|
|
datetime(1999, 1, 27, 21, 18),
|
|
"KORD",
|
|
"19990127",
|
|
" 21:00:00",
|
|
" 21:18:00",
|
|
-0.99,
|
|
2.01,
|
|
3.6,
|
|
0.0,
|
|
270.0,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 22, 0),
|
|
datetime(1999, 1, 27, 21, 56),
|
|
"KORD",
|
|
"19990127",
|
|
" 22:00:00",
|
|
" 21:56:00",
|
|
-0.59,
|
|
1.71,
|
|
5.1,
|
|
0.0,
|
|
290.0,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 23, 0),
|
|
datetime(1999, 1, 27, 22, 56),
|
|
"KORD",
|
|
"19990127",
|
|
" 23:00:00",
|
|
" 22:56:00",
|
|
-0.59,
|
|
1.71,
|
|
4.6,
|
|
0.0,
|
|
280.0,
|
|
],
|
|
],
|
|
columns=[
|
|
"actual",
|
|
"nominal",
|
|
"X0",
|
|
"X1",
|
|
"X2",
|
|
"X3",
|
|
"X4",
|
|
"X5",
|
|
"X6",
|
|
"X7",
|
|
"X8",
|
|
],
|
|
)
|
|
|
|
if not keep_date_col:
|
|
expected = expected.drop(["X1", "X2", "X3"], axis=1)
|
|
elif parser.engine == "python":
|
|
expected["X1"] = expected["X1"].astype(np.int64)
|
|
|
|
# Python can sometimes be flaky about how
|
|
# the aggregated columns are entered, so
|
|
# this standardizes the order.
|
|
result = result[expected.columns]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("container", [list, tuple, Index, Series])
|
|
@pytest.mark.parametrize("dim", [1, 2])
|
|
def test_concat_date_col_fail(container, dim):
|
|
msg = "not all elements from date_cols are numpy arrays"
|
|
value = "19990127"
|
|
|
|
date_cols = tuple(container([value]) for _ in range(dim))
|
|
|
|
with pytest.raises(ValueError, match=msg):
|
|
parsing.concat_date_cols(date_cols)
|
|
|
|
|
|
@pytest.mark.parametrize("keep_date_col", [True, False])
|
|
def test_multiple_date_col(all_parsers, keep_date_col):
|
|
data = """\
|
|
KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000
|
|
KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000
|
|
KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000
|
|
KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000
|
|
KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000
|
|
KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000
|
|
"""
|
|
parser = all_parsers
|
|
result = parser.read_csv(
|
|
StringIO(data),
|
|
header=None,
|
|
prefix="X",
|
|
parse_dates=[[1, 2], [1, 3]],
|
|
keep_date_col=keep_date_col,
|
|
)
|
|
expected = DataFrame(
|
|
[
|
|
[
|
|
datetime(1999, 1, 27, 19, 0),
|
|
datetime(1999, 1, 27, 18, 56),
|
|
"KORD",
|
|
"19990127",
|
|
" 19:00:00",
|
|
" 18:56:00",
|
|
0.81,
|
|
2.81,
|
|
7.2,
|
|
0.0,
|
|
280.0,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 20, 0),
|
|
datetime(1999, 1, 27, 19, 56),
|
|
"KORD",
|
|
"19990127",
|
|
" 20:00:00",
|
|
" 19:56:00",
|
|
0.01,
|
|
2.21,
|
|
7.2,
|
|
0.0,
|
|
260.0,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 21, 0),
|
|
datetime(1999, 1, 27, 20, 56),
|
|
"KORD",
|
|
"19990127",
|
|
" 21:00:00",
|
|
" 20:56:00",
|
|
-0.59,
|
|
2.21,
|
|
5.7,
|
|
0.0,
|
|
280.0,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 21, 0),
|
|
datetime(1999, 1, 27, 21, 18),
|
|
"KORD",
|
|
"19990127",
|
|
" 21:00:00",
|
|
" 21:18:00",
|
|
-0.99,
|
|
2.01,
|
|
3.6,
|
|
0.0,
|
|
270.0,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 22, 0),
|
|
datetime(1999, 1, 27, 21, 56),
|
|
"KORD",
|
|
"19990127",
|
|
" 22:00:00",
|
|
" 21:56:00",
|
|
-0.59,
|
|
1.71,
|
|
5.1,
|
|
0.0,
|
|
290.0,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 23, 0),
|
|
datetime(1999, 1, 27, 22, 56),
|
|
"KORD",
|
|
"19990127",
|
|
" 23:00:00",
|
|
" 22:56:00",
|
|
-0.59,
|
|
1.71,
|
|
4.6,
|
|
0.0,
|
|
280.0,
|
|
],
|
|
],
|
|
columns=[
|
|
"X1_X2",
|
|
"X1_X3",
|
|
"X0",
|
|
"X1",
|
|
"X2",
|
|
"X3",
|
|
"X4",
|
|
"X5",
|
|
"X6",
|
|
"X7",
|
|
"X8",
|
|
],
|
|
)
|
|
|
|
if not keep_date_col:
|
|
expected = expected.drop(["X1", "X2", "X3"], axis=1)
|
|
elif parser.engine == "python":
|
|
expected["X1"] = expected["X1"].astype(np.int64)
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_date_col_as_index_col(all_parsers):
|
|
data = """\
|
|
KORD,19990127 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000
|
|
KORD,19990127 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000
|
|
KORD,19990127 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000
|
|
KORD,19990127 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000
|
|
KORD,19990127 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000
|
|
"""
|
|
parser = all_parsers
|
|
result = parser.read_csv(
|
|
StringIO(data), header=None, prefix="X", parse_dates=[1], index_col=1
|
|
)
|
|
|
|
index = Index(
|
|
[
|
|
datetime(1999, 1, 27, 19, 0),
|
|
datetime(1999, 1, 27, 20, 0),
|
|
datetime(1999, 1, 27, 21, 0),
|
|
datetime(1999, 1, 27, 21, 0),
|
|
datetime(1999, 1, 27, 22, 0),
|
|
],
|
|
name="X1",
|
|
)
|
|
expected = DataFrame(
|
|
[
|
|
["KORD", " 18:56:00", 0.81, 2.81, 7.2, 0.0, 280.0],
|
|
["KORD", " 19:56:00", 0.01, 2.21, 7.2, 0.0, 260.0],
|
|
["KORD", " 20:56:00", -0.59, 2.21, 5.7, 0.0, 280.0],
|
|
["KORD", " 21:18:00", -0.99, 2.01, 3.6, 0.0, 270.0],
|
|
["KORD", " 21:56:00", -0.59, 1.71, 5.1, 0.0, 290.0],
|
|
],
|
|
columns=["X0", "X2", "X3", "X4", "X5", "X6", "X7"],
|
|
index=index,
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"date_parser, warning",
|
|
([conv.parse_date_time, FutureWarning], [pd.to_datetime, None]),
|
|
)
|
|
def test_multiple_date_cols_int_cast(all_parsers, date_parser, warning):
|
|
data = (
|
|
"KORD,19990127, 19:00:00, 18:56:00, 0.8100\n"
|
|
"KORD,19990127, 20:00:00, 19:56:00, 0.0100\n"
|
|
"KORD,19990127, 21:00:00, 20:56:00, -0.5900\n"
|
|
"KORD,19990127, 21:00:00, 21:18:00, -0.9900\n"
|
|
"KORD,19990127, 22:00:00, 21:56:00, -0.5900\n"
|
|
"KORD,19990127, 23:00:00, 22:56:00, -0.5900"
|
|
)
|
|
parse_dates = {"actual": [1, 2], "nominal": [1, 3]}
|
|
parser = all_parsers
|
|
|
|
with tm.assert_produces_warning(warning, check_stacklevel=False):
|
|
result = parser.read_csv(
|
|
StringIO(data),
|
|
header=None,
|
|
date_parser=date_parser,
|
|
parse_dates=parse_dates,
|
|
prefix="X",
|
|
)
|
|
|
|
expected = DataFrame(
|
|
[
|
|
[datetime(1999, 1, 27, 19, 0), datetime(1999, 1, 27, 18, 56), "KORD", 0.81],
|
|
[datetime(1999, 1, 27, 20, 0), datetime(1999, 1, 27, 19, 56), "KORD", 0.01],
|
|
[
|
|
datetime(1999, 1, 27, 21, 0),
|
|
datetime(1999, 1, 27, 20, 56),
|
|
"KORD",
|
|
-0.59,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 21, 0),
|
|
datetime(1999, 1, 27, 21, 18),
|
|
"KORD",
|
|
-0.99,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 22, 0),
|
|
datetime(1999, 1, 27, 21, 56),
|
|
"KORD",
|
|
-0.59,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 23, 0),
|
|
datetime(1999, 1, 27, 22, 56),
|
|
"KORD",
|
|
-0.59,
|
|
],
|
|
],
|
|
columns=["actual", "nominal", "X0", "X4"],
|
|
)
|
|
|
|
# Python can sometimes be flaky about how
|
|
# the aggregated columns are entered, so
|
|
# this standardizes the order.
|
|
result = result[expected.columns]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_multiple_date_col_timestamp_parse(all_parsers):
|
|
parser = all_parsers
|
|
data = """05/31/2012,15:30:00.029,1306.25,1,E,0,,1306.25
|
|
05/31/2012,15:30:00.029,1306.25,8,E,0,,1306.25"""
|
|
|
|
result = parser.read_csv(
|
|
StringIO(data), parse_dates=[[0, 1]], header=None, date_parser=Timestamp
|
|
)
|
|
expected = DataFrame(
|
|
[
|
|
[
|
|
Timestamp("05/31/2012, 15:30:00.029"),
|
|
1306.25,
|
|
1,
|
|
"E",
|
|
0,
|
|
np.nan,
|
|
1306.25,
|
|
],
|
|
[
|
|
Timestamp("05/31/2012, 15:30:00.029"),
|
|
1306.25,
|
|
8,
|
|
"E",
|
|
0,
|
|
np.nan,
|
|
1306.25,
|
|
],
|
|
],
|
|
columns=["0_1", 2, 3, 4, 5, 6, 7],
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_multiple_date_cols_with_header(all_parsers):
|
|
parser = all_parsers
|
|
data = """\
|
|
ID,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir
|
|
KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000
|
|
KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000
|
|
KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000
|
|
KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000
|
|
KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000
|
|
KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000"""
|
|
|
|
result = parser.read_csv(StringIO(data), parse_dates={"nominal": [1, 2]})
|
|
expected = DataFrame(
|
|
[
|
|
[
|
|
datetime(1999, 1, 27, 19, 0),
|
|
"KORD",
|
|
" 18:56:00",
|
|
0.81,
|
|
2.81,
|
|
7.2,
|
|
0.0,
|
|
280.0,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 20, 0),
|
|
"KORD",
|
|
" 19:56:00",
|
|
0.01,
|
|
2.21,
|
|
7.2,
|
|
0.0,
|
|
260.0,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 21, 0),
|
|
"KORD",
|
|
" 20:56:00",
|
|
-0.59,
|
|
2.21,
|
|
5.7,
|
|
0.0,
|
|
280.0,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 21, 0),
|
|
"KORD",
|
|
" 21:18:00",
|
|
-0.99,
|
|
2.01,
|
|
3.6,
|
|
0.0,
|
|
270.0,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 22, 0),
|
|
"KORD",
|
|
" 21:56:00",
|
|
-0.59,
|
|
1.71,
|
|
5.1,
|
|
0.0,
|
|
290.0,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 23, 0),
|
|
"KORD",
|
|
" 22:56:00",
|
|
-0.59,
|
|
1.71,
|
|
4.6,
|
|
0.0,
|
|
280.0,
|
|
],
|
|
],
|
|
columns=[
|
|
"nominal",
|
|
"ID",
|
|
"ActualTime",
|
|
"TDew",
|
|
"TAir",
|
|
"Windspeed",
|
|
"Precip",
|
|
"WindDir",
|
|
],
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"data,parse_dates,msg",
|
|
[
|
|
(
|
|
"""\
|
|
date_NominalTime,date,NominalTime
|
|
KORD1,19990127, 19:00:00
|
|
KORD2,19990127, 20:00:00""",
|
|
[[1, 2]],
|
|
("New date column already in dict date_NominalTime"),
|
|
),
|
|
(
|
|
"""\
|
|
ID,date,nominalTime
|
|
KORD,19990127, 19:00:00
|
|
KORD,19990127, 20:00:00""",
|
|
dict(ID=[1, 2]),
|
|
"Date column ID already in dict",
|
|
),
|
|
],
|
|
)
|
|
def test_multiple_date_col_name_collision(all_parsers, data, parse_dates, msg):
|
|
parser = all_parsers
|
|
|
|
with pytest.raises(ValueError, match=msg):
|
|
parser.read_csv(StringIO(data), parse_dates=parse_dates)
|
|
|
|
|
|
def test_date_parser_int_bug(all_parsers):
|
|
# see gh-3071
|
|
parser = all_parsers
|
|
data = (
|
|
"posix_timestamp,elapsed,sys,user,queries,query_time,rows,"
|
|
"accountid,userid,contactid,level,silo,method\n"
|
|
"1343103150,0.062353,0,4,6,0.01690,3,"
|
|
"12345,1,-1,3,invoice_InvoiceResource,search\n"
|
|
)
|
|
|
|
result = parser.read_csv(
|
|
StringIO(data),
|
|
index_col=0,
|
|
parse_dates=[0],
|
|
date_parser=lambda x: datetime.utcfromtimestamp(int(x)),
|
|
)
|
|
expected = DataFrame(
|
|
[
|
|
[
|
|
0.062353,
|
|
0,
|
|
4,
|
|
6,
|
|
0.01690,
|
|
3,
|
|
12345,
|
|
1,
|
|
-1,
|
|
3,
|
|
"invoice_InvoiceResource",
|
|
"search",
|
|
]
|
|
],
|
|
columns=[
|
|
"elapsed",
|
|
"sys",
|
|
"user",
|
|
"queries",
|
|
"query_time",
|
|
"rows",
|
|
"accountid",
|
|
"userid",
|
|
"contactid",
|
|
"level",
|
|
"silo",
|
|
"method",
|
|
],
|
|
index=Index([Timestamp("2012-07-24 04:12:30")], name="posix_timestamp"),
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_nat_parse(all_parsers):
|
|
# see gh-3062
|
|
parser = all_parsers
|
|
df = DataFrame(
|
|
dict({"A": np.arange(10, dtype="float64"), "B": Timestamp("20010101")})
|
|
)
|
|
df.iloc[3:6, :] = np.nan
|
|
|
|
with tm.ensure_clean("__nat_parse_.csv") as path:
|
|
df.to_csv(path)
|
|
|
|
result = parser.read_csv(path, index_col=0, parse_dates=["B"])
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
|
|
def test_csv_custom_parser(all_parsers):
|
|
data = """A,B,C
|
|
20090101,a,1,2
|
|
20090102,b,3,4
|
|
20090103,c,4,5
|
|
"""
|
|
parser = all_parsers
|
|
result = parser.read_csv(
|
|
StringIO(data), date_parser=lambda x: datetime.strptime(x, "%Y%m%d")
|
|
)
|
|
expected = parser.read_csv(StringIO(data), parse_dates=True)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_parse_dates_implicit_first_col(all_parsers):
|
|
data = """A,B,C
|
|
20090101,a,1,2
|
|
20090102,b,3,4
|
|
20090103,c,4,5
|
|
"""
|
|
parser = all_parsers
|
|
result = parser.read_csv(StringIO(data), parse_dates=True)
|
|
|
|
expected = parser.read_csv(StringIO(data), index_col=0, parse_dates=True)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_parse_dates_string(all_parsers):
|
|
data = """date,A,B,C
|
|
20090101,a,1,2
|
|
20090102,b,3,4
|
|
20090103,c,4,5
|
|
"""
|
|
parser = all_parsers
|
|
result = parser.read_csv(StringIO(data), index_col="date", parse_dates=["date"])
|
|
# freq doesnt round-trip
|
|
index = DatetimeIndex(
|
|
list(date_range("1/1/2009", periods=3)), name="date", freq=None
|
|
)
|
|
|
|
expected = DataFrame(
|
|
{"A": ["a", "b", "c"], "B": [1, 3, 4], "C": [2, 4, 5]}, index=index
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
# Bug in https://github.com/dateutil/dateutil/issues/217
|
|
# has been addressed, but we just don't pass in the `yearfirst`
|
|
@pytest.mark.xfail(reason="yearfirst is not surfaced in read_*")
|
|
@pytest.mark.parametrize("parse_dates", [[["date", "time"]], [[0, 1]]])
|
|
def test_yy_format_with_year_first(all_parsers, parse_dates):
|
|
data = """date,time,B,C
|
|
090131,0010,1,2
|
|
090228,1020,3,4
|
|
090331,0830,5,6
|
|
"""
|
|
parser = all_parsers
|
|
result = parser.read_csv(StringIO(data), index_col=0, parse_dates=parse_dates)
|
|
index = DatetimeIndex(
|
|
[
|
|
datetime(2009, 1, 31, 0, 10, 0),
|
|
datetime(2009, 2, 28, 10, 20, 0),
|
|
datetime(2009, 3, 31, 8, 30, 0),
|
|
],
|
|
dtype=object,
|
|
name="date_time",
|
|
)
|
|
expected = DataFrame({"B": [1, 3, 5], "C": [2, 4, 6]}, index=index)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("parse_dates", [[0, 2], ["a", "c"]])
|
|
def test_parse_dates_column_list(all_parsers, parse_dates):
|
|
data = "a,b,c\n01/01/2010,1,15/02/2010"
|
|
parser = all_parsers
|
|
|
|
expected = DataFrame(
|
|
{"a": [datetime(2010, 1, 1)], "b": [1], "c": [datetime(2010, 2, 15)]}
|
|
)
|
|
expected = expected.set_index(["a", "b"])
|
|
|
|
result = parser.read_csv(
|
|
StringIO(data), index_col=[0, 1], parse_dates=parse_dates, dayfirst=True
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("index_col", [[0, 1], [1, 0]])
|
|
def test_multi_index_parse_dates(all_parsers, index_col):
|
|
data = """index1,index2,A,B,C
|
|
20090101,one,a,1,2
|
|
20090101,two,b,3,4
|
|
20090101,three,c,4,5
|
|
20090102,one,a,1,2
|
|
20090102,two,b,3,4
|
|
20090102,three,c,4,5
|
|
20090103,one,a,1,2
|
|
20090103,two,b,3,4
|
|
20090103,three,c,4,5
|
|
"""
|
|
parser = all_parsers
|
|
index = MultiIndex.from_product(
|
|
[
|
|
(datetime(2009, 1, 1), datetime(2009, 1, 2), datetime(2009, 1, 3)),
|
|
("one", "two", "three"),
|
|
],
|
|
names=["index1", "index2"],
|
|
)
|
|
|
|
# Out of order.
|
|
if index_col == [1, 0]:
|
|
index = index.swaplevel(0, 1)
|
|
|
|
expected = DataFrame(
|
|
[
|
|
["a", 1, 2],
|
|
["b", 3, 4],
|
|
["c", 4, 5],
|
|
["a", 1, 2],
|
|
["b", 3, 4],
|
|
["c", 4, 5],
|
|
["a", 1, 2],
|
|
["b", 3, 4],
|
|
["c", 4, 5],
|
|
],
|
|
columns=["A", "B", "C"],
|
|
index=index,
|
|
)
|
|
result = parser.read_csv(StringIO(data), index_col=index_col, parse_dates=True)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("kwargs", [dict(dayfirst=True), dict(day_first=True)])
|
|
def test_parse_dates_custom_euro_format(all_parsers, kwargs):
|
|
parser = all_parsers
|
|
data = """foo,bar,baz
|
|
31/01/2010,1,2
|
|
01/02/2010,1,NA
|
|
02/02/2010,1,2
|
|
"""
|
|
if "dayfirst" in kwargs:
|
|
df = parser.read_csv(
|
|
StringIO(data),
|
|
names=["time", "Q", "NTU"],
|
|
date_parser=lambda d: du_parse(d, **kwargs),
|
|
header=0,
|
|
index_col=0,
|
|
parse_dates=True,
|
|
na_values=["NA"],
|
|
)
|
|
exp_index = Index(
|
|
[datetime(2010, 1, 31), datetime(2010, 2, 1), datetime(2010, 2, 2)],
|
|
name="time",
|
|
)
|
|
expected = DataFrame(
|
|
{"Q": [1, 1, 1], "NTU": [2, np.nan, 2]},
|
|
index=exp_index,
|
|
columns=["Q", "NTU"],
|
|
)
|
|
tm.assert_frame_equal(df, expected)
|
|
else:
|
|
msg = "got an unexpected keyword argument 'day_first'"
|
|
with pytest.raises(TypeError, match=msg), tm.assert_produces_warning(
|
|
FutureWarning
|
|
):
|
|
parser.read_csv(
|
|
StringIO(data),
|
|
names=["time", "Q", "NTU"],
|
|
date_parser=lambda d: du_parse(d, **kwargs),
|
|
skiprows=[0],
|
|
index_col=0,
|
|
parse_dates=True,
|
|
na_values=["NA"],
|
|
)
|
|
|
|
|
|
def test_parse_tz_aware(all_parsers):
|
|
# See gh-1693
|
|
parser = all_parsers
|
|
data = "Date,x\n2012-06-13T01:39:00Z,0.5"
|
|
|
|
result = parser.read_csv(StringIO(data), index_col=0, parse_dates=True)
|
|
expected = DataFrame(
|
|
{"x": [0.5]}, index=Index([Timestamp("2012-06-13 01:39:00+00:00")], name="Date")
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
assert result.index.tz is pytz.utc
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"parse_dates,index_col",
|
|
[({"nominal": [1, 2]}, "nominal"), ({"nominal": [1, 2]}, 0), ([[1, 2]], 0)],
|
|
)
|
|
def test_multiple_date_cols_index(all_parsers, parse_dates, index_col):
|
|
parser = all_parsers
|
|
data = """
|
|
ID,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir
|
|
KORD1,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000
|
|
KORD2,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000
|
|
KORD3,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000
|
|
KORD4,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000
|
|
KORD5,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000
|
|
KORD6,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000
|
|
"""
|
|
expected = DataFrame(
|
|
[
|
|
[
|
|
datetime(1999, 1, 27, 19, 0),
|
|
"KORD1",
|
|
" 18:56:00",
|
|
0.81,
|
|
2.81,
|
|
7.2,
|
|
0.0,
|
|
280.0,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 20, 0),
|
|
"KORD2",
|
|
" 19:56:00",
|
|
0.01,
|
|
2.21,
|
|
7.2,
|
|
0.0,
|
|
260.0,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 21, 0),
|
|
"KORD3",
|
|
" 20:56:00",
|
|
-0.59,
|
|
2.21,
|
|
5.7,
|
|
0.0,
|
|
280.0,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 21, 0),
|
|
"KORD4",
|
|
" 21:18:00",
|
|
-0.99,
|
|
2.01,
|
|
3.6,
|
|
0.0,
|
|
270.0,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 22, 0),
|
|
"KORD5",
|
|
" 21:56:00",
|
|
-0.59,
|
|
1.71,
|
|
5.1,
|
|
0.0,
|
|
290.0,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 23, 0),
|
|
"KORD6",
|
|
" 22:56:00",
|
|
-0.59,
|
|
1.71,
|
|
4.6,
|
|
0.0,
|
|
280.0,
|
|
],
|
|
],
|
|
columns=[
|
|
"nominal",
|
|
"ID",
|
|
"ActualTime",
|
|
"TDew",
|
|
"TAir",
|
|
"Windspeed",
|
|
"Precip",
|
|
"WindDir",
|
|
],
|
|
)
|
|
expected = expected.set_index("nominal")
|
|
|
|
if not isinstance(parse_dates, dict):
|
|
expected.index.name = "date_NominalTime"
|
|
|
|
result = parser.read_csv(
|
|
StringIO(data), parse_dates=parse_dates, index_col=index_col
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_multiple_date_cols_chunked(all_parsers):
|
|
parser = all_parsers
|
|
data = """\
|
|
ID,date,nominalTime,actualTime,A,B,C,D,E
|
|
KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000
|
|
KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000
|
|
KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000
|
|
KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000
|
|
KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000
|
|
KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000
|
|
"""
|
|
|
|
expected = DataFrame(
|
|
[
|
|
[
|
|
datetime(1999, 1, 27, 19, 0),
|
|
"KORD",
|
|
" 18:56:00",
|
|
0.81,
|
|
2.81,
|
|
7.2,
|
|
0.0,
|
|
280.0,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 20, 0),
|
|
"KORD",
|
|
" 19:56:00",
|
|
0.01,
|
|
2.21,
|
|
7.2,
|
|
0.0,
|
|
260.0,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 21, 0),
|
|
"KORD",
|
|
" 20:56:00",
|
|
-0.59,
|
|
2.21,
|
|
5.7,
|
|
0.0,
|
|
280.0,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 21, 0),
|
|
"KORD",
|
|
" 21:18:00",
|
|
-0.99,
|
|
2.01,
|
|
3.6,
|
|
0.0,
|
|
270.0,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 22, 0),
|
|
"KORD",
|
|
" 21:56:00",
|
|
-0.59,
|
|
1.71,
|
|
5.1,
|
|
0.0,
|
|
290.0,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 23, 0),
|
|
"KORD",
|
|
" 22:56:00",
|
|
-0.59,
|
|
1.71,
|
|
4.6,
|
|
0.0,
|
|
280.0,
|
|
],
|
|
],
|
|
columns=["nominal", "ID", "actualTime", "A", "B", "C", "D", "E"],
|
|
)
|
|
expected = expected.set_index("nominal")
|
|
|
|
with parser.read_csv(
|
|
StringIO(data),
|
|
parse_dates={"nominal": [1, 2]},
|
|
index_col="nominal",
|
|
chunksize=2,
|
|
) as reader:
|
|
chunks = list(reader)
|
|
|
|
tm.assert_frame_equal(chunks[0], expected[:2])
|
|
tm.assert_frame_equal(chunks[1], expected[2:4])
|
|
tm.assert_frame_equal(chunks[2], expected[4:])
|
|
|
|
|
|
def test_multiple_date_col_named_index_compat(all_parsers):
|
|
parser = all_parsers
|
|
data = """\
|
|
ID,date,nominalTime,actualTime,A,B,C,D,E
|
|
KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000
|
|
KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000
|
|
KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000
|
|
KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000
|
|
KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000
|
|
KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000
|
|
"""
|
|
|
|
with_indices = parser.read_csv(
|
|
StringIO(data), parse_dates={"nominal": [1, 2]}, index_col="nominal"
|
|
)
|
|
with_names = parser.read_csv(
|
|
StringIO(data),
|
|
index_col="nominal",
|
|
parse_dates={"nominal": ["date", "nominalTime"]},
|
|
)
|
|
tm.assert_frame_equal(with_indices, with_names)
|
|
|
|
|
|
def test_multiple_date_col_multiple_index_compat(all_parsers):
|
|
parser = all_parsers
|
|
data = """\
|
|
ID,date,nominalTime,actualTime,A,B,C,D,E
|
|
KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000
|
|
KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000
|
|
KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000
|
|
KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000
|
|
KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000
|
|
KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000
|
|
"""
|
|
result = parser.read_csv(
|
|
StringIO(data), index_col=["nominal", "ID"], parse_dates={"nominal": [1, 2]}
|
|
)
|
|
expected = parser.read_csv(StringIO(data), parse_dates={"nominal": [1, 2]})
|
|
|
|
expected = expected.set_index(["nominal", "ID"])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("kwargs", [dict(), dict(index_col="C")])
|
|
def test_read_with_parse_dates_scalar_non_bool(all_parsers, kwargs):
|
|
# see gh-5636
|
|
parser = all_parsers
|
|
msg = (
|
|
"Only booleans, lists, and dictionaries "
|
|
"are accepted for the 'parse_dates' parameter"
|
|
)
|
|
data = """A,B,C
|
|
1,2,2003-11-1"""
|
|
|
|
with pytest.raises(TypeError, match=msg):
|
|
parser.read_csv(StringIO(data), parse_dates="C", **kwargs)
|
|
|
|
|
|
@pytest.mark.parametrize("parse_dates", [(1,), np.array([4, 5]), {1, 3, 3}])
|
|
def test_read_with_parse_dates_invalid_type(all_parsers, parse_dates):
|
|
parser = all_parsers
|
|
msg = (
|
|
"Only booleans, lists, and dictionaries "
|
|
"are accepted for the 'parse_dates' parameter"
|
|
)
|
|
data = """A,B,C
|
|
1,2,2003-11-1"""
|
|
|
|
with pytest.raises(TypeError, match=msg):
|
|
parser.read_csv(StringIO(data), parse_dates=(1,))
|
|
|
|
|
|
@pytest.mark.parametrize("cache_dates", [True, False])
|
|
@pytest.mark.parametrize("value", ["nan", "0", ""])
|
|
def test_bad_date_parse(all_parsers, cache_dates, value):
|
|
# if we have an invalid date make sure that we handle this with
|
|
# and w/o the cache properly
|
|
parser = all_parsers
|
|
s = StringIO((f"{value},\n") * 50000)
|
|
|
|
parser.read_csv(
|
|
s,
|
|
header=None,
|
|
names=["foo", "bar"],
|
|
parse_dates=["foo"],
|
|
infer_datetime_format=False,
|
|
cache_dates=cache_dates,
|
|
)
|
|
|
|
|
|
def test_parse_dates_empty_string(all_parsers):
|
|
# see gh-2263
|
|
parser = all_parsers
|
|
data = "Date,test\n2012-01-01,1\n,2"
|
|
result = parser.read_csv(StringIO(data), parse_dates=["Date"], na_filter=False)
|
|
|
|
expected = DataFrame(
|
|
[[datetime(2012, 1, 1), 1], [pd.NaT, 2]], columns=["Date", "test"]
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"data,kwargs,expected",
|
|
[
|
|
(
|
|
"a\n04.15.2016",
|
|
dict(parse_dates=["a"]),
|
|
DataFrame([datetime(2016, 4, 15)], columns=["a"]),
|
|
),
|
|
(
|
|
"a\n04.15.2016",
|
|
dict(parse_dates=True, index_col=0),
|
|
DataFrame(index=DatetimeIndex(["2016-04-15"], name="a")),
|
|
),
|
|
(
|
|
"a,b\n04.15.2016,09.16.2013",
|
|
dict(parse_dates=["a", "b"]),
|
|
DataFrame(
|
|
[[datetime(2016, 4, 15), datetime(2013, 9, 16)]], columns=["a", "b"]
|
|
),
|
|
),
|
|
(
|
|
"a,b\n04.15.2016,09.16.2013",
|
|
dict(parse_dates=True, index_col=[0, 1]),
|
|
DataFrame(
|
|
index=MultiIndex.from_tuples(
|
|
[(datetime(2016, 4, 15), datetime(2013, 9, 16))], names=["a", "b"]
|
|
)
|
|
),
|
|
),
|
|
],
|
|
)
|
|
def test_parse_dates_no_convert_thousands(all_parsers, data, kwargs, expected):
|
|
# see gh-14066
|
|
parser = all_parsers
|
|
|
|
result = parser.read_csv(StringIO(data), thousands=".", **kwargs)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"date_parser, warning",
|
|
([conv.parse_date_time, FutureWarning], [pd.to_datetime, None]),
|
|
)
|
|
def test_parse_date_time_multi_level_column_name(all_parsers, date_parser, warning):
|
|
data = """\
|
|
D,T,A,B
|
|
date, time,a,b
|
|
2001-01-05, 09:00:00, 0.0, 10.
|
|
2001-01-06, 00:00:00, 1.0, 11.
|
|
"""
|
|
parser = all_parsers
|
|
with tm.assert_produces_warning(warning, check_stacklevel=False):
|
|
result = parser.read_csv(
|
|
StringIO(data),
|
|
header=[0, 1],
|
|
parse_dates={"date_time": [0, 1]},
|
|
date_parser=date_parser,
|
|
)
|
|
|
|
expected_data = [
|
|
[datetime(2001, 1, 5, 9, 0, 0), 0.0, 10.0],
|
|
[datetime(2001, 1, 6, 0, 0, 0), 1.0, 11.0],
|
|
]
|
|
expected = DataFrame(expected_data, columns=["date_time", ("A", "a"), ("B", "b")])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"date_parser, warning",
|
|
([conv.parse_date_time, FutureWarning], [pd.to_datetime, None]),
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"data,kwargs,expected",
|
|
[
|
|
(
|
|
"""\
|
|
date,time,a,b
|
|
2001-01-05, 10:00:00, 0.0, 10.
|
|
2001-01-05, 00:00:00, 1., 11.
|
|
""",
|
|
dict(header=0, parse_dates={"date_time": [0, 1]}),
|
|
DataFrame(
|
|
[
|
|
[datetime(2001, 1, 5, 10, 0, 0), 0.0, 10],
|
|
[datetime(2001, 1, 5, 0, 0, 0), 1.0, 11.0],
|
|
],
|
|
columns=["date_time", "a", "b"],
|
|
),
|
|
),
|
|
(
|
|
(
|
|
"KORD,19990127, 19:00:00, 18:56:00, 0.8100\n"
|
|
"KORD,19990127, 20:00:00, 19:56:00, 0.0100\n"
|
|
"KORD,19990127, 21:00:00, 20:56:00, -0.5900\n"
|
|
"KORD,19990127, 21:00:00, 21:18:00, -0.9900\n"
|
|
"KORD,19990127, 22:00:00, 21:56:00, -0.5900\n"
|
|
"KORD,19990127, 23:00:00, 22:56:00, -0.5900"
|
|
),
|
|
dict(header=None, parse_dates={"actual": [1, 2], "nominal": [1, 3]}),
|
|
DataFrame(
|
|
[
|
|
[
|
|
datetime(1999, 1, 27, 19, 0),
|
|
datetime(1999, 1, 27, 18, 56),
|
|
"KORD",
|
|
0.81,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 20, 0),
|
|
datetime(1999, 1, 27, 19, 56),
|
|
"KORD",
|
|
0.01,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 21, 0),
|
|
datetime(1999, 1, 27, 20, 56),
|
|
"KORD",
|
|
-0.59,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 21, 0),
|
|
datetime(1999, 1, 27, 21, 18),
|
|
"KORD",
|
|
-0.99,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 22, 0),
|
|
datetime(1999, 1, 27, 21, 56),
|
|
"KORD",
|
|
-0.59,
|
|
],
|
|
[
|
|
datetime(1999, 1, 27, 23, 0),
|
|
datetime(1999, 1, 27, 22, 56),
|
|
"KORD",
|
|
-0.59,
|
|
],
|
|
],
|
|
columns=["actual", "nominal", 0, 4],
|
|
),
|
|
),
|
|
],
|
|
)
|
|
def test_parse_date_time(all_parsers, data, kwargs, expected, date_parser, warning):
|
|
parser = all_parsers
|
|
with tm.assert_produces_warning(warning, check_stacklevel=False):
|
|
result = parser.read_csv(StringIO(data), date_parser=date_parser, **kwargs)
|
|
|
|
# Python can sometimes be flaky about how
|
|
# the aggregated columns are entered, so
|
|
# this standardizes the order.
|
|
result = result[expected.columns]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"date_parser, warning",
|
|
([conv.parse_date_fields, FutureWarning], [pd.to_datetime, None]),
|
|
)
|
|
def test_parse_date_fields(all_parsers, date_parser, warning):
|
|
parser = all_parsers
|
|
data = "year,month,day,a\n2001,01,10,10.\n2001,02,1,11."
|
|
with tm.assert_produces_warning(warning, check_stacklevel=False):
|
|
result = parser.read_csv(
|
|
StringIO(data),
|
|
header=0,
|
|
parse_dates={"ymd": [0, 1, 2]},
|
|
date_parser=date_parser,
|
|
)
|
|
|
|
expected = DataFrame(
|
|
[[datetime(2001, 1, 10), 10.0], [datetime(2001, 2, 1), 11.0]],
|
|
columns=["ymd", "a"],
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"date_parser, warning",
|
|
(
|
|
[conv.parse_all_fields, FutureWarning],
|
|
[lambda x: pd.to_datetime(x, format="%Y %m %d %H %M %S"), None],
|
|
),
|
|
)
|
|
def test_parse_date_all_fields(all_parsers, date_parser, warning):
|
|
parser = all_parsers
|
|
data = """\
|
|
year,month,day,hour,minute,second,a,b
|
|
2001,01,05,10,00,0,0.0,10.
|
|
2001,01,5,10,0,00,1.,11.
|
|
"""
|
|
with tm.assert_produces_warning(warning, check_stacklevel=False):
|
|
result = parser.read_csv(
|
|
StringIO(data),
|
|
header=0,
|
|
date_parser=date_parser,
|
|
parse_dates={"ymdHMS": [0, 1, 2, 3, 4, 5]},
|
|
)
|
|
expected = DataFrame(
|
|
[
|
|
[datetime(2001, 1, 5, 10, 0, 0), 0.0, 10.0],
|
|
[datetime(2001, 1, 5, 10, 0, 0), 1.0, 11.0],
|
|
],
|
|
columns=["ymdHMS", "a", "b"],
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"date_parser, warning",
|
|
(
|
|
[conv.parse_all_fields, FutureWarning],
|
|
[lambda x: pd.to_datetime(x, format="%Y %m %d %H %M %S.%f"), None],
|
|
),
|
|
)
|
|
def test_datetime_fractional_seconds(all_parsers, date_parser, warning):
|
|
parser = all_parsers
|
|
data = """\
|
|
year,month,day,hour,minute,second,a,b
|
|
2001,01,05,10,00,0.123456,0.0,10.
|
|
2001,01,5,10,0,0.500000,1.,11.
|
|
"""
|
|
with tm.assert_produces_warning(warning, check_stacklevel=False):
|
|
result = parser.read_csv(
|
|
StringIO(data),
|
|
header=0,
|
|
date_parser=date_parser,
|
|
parse_dates={"ymdHMS": [0, 1, 2, 3, 4, 5]},
|
|
)
|
|
expected = DataFrame(
|
|
[
|
|
[datetime(2001, 1, 5, 10, 0, 0, microsecond=123456), 0.0, 10.0],
|
|
[datetime(2001, 1, 5, 10, 0, 0, microsecond=500000), 1.0, 11.0],
|
|
],
|
|
columns=["ymdHMS", "a", "b"],
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_generic(all_parsers):
|
|
parser = all_parsers
|
|
data = "year,month,day,a\n2001,01,10,10.\n2001,02,1,11."
|
|
|
|
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
|
|
result = parser.read_csv(
|
|
StringIO(data),
|
|
header=0,
|
|
parse_dates={"ym": [0, 1]},
|
|
date_parser=lambda y, m: date(year=int(y), month=int(m), day=1),
|
|
)
|
|
expected = DataFrame(
|
|
[[date(2001, 1, 1), 10, 10.0], [date(2001, 2, 1), 1, 11.0]],
|
|
columns=["ym", "day", "a"],
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_date_parser_resolution_if_not_ns(all_parsers):
|
|
# see gh-10245
|
|
parser = all_parsers
|
|
data = """\
|
|
date,time,prn,rxstatus
|
|
2013-11-03,19:00:00,126,00E80000
|
|
2013-11-03,19:00:00,23,00E80000
|
|
2013-11-03,19:00:00,13,00E80000
|
|
"""
|
|
|
|
def date_parser(dt, time):
|
|
return np_array_datetime64_compat(dt + "T" + time + "Z", dtype="datetime64[s]")
|
|
|
|
result = parser.read_csv(
|
|
StringIO(data),
|
|
date_parser=date_parser,
|
|
parse_dates={"datetime": ["date", "time"]},
|
|
index_col=["datetime", "prn"],
|
|
)
|
|
|
|
datetimes = np_array_datetime64_compat(
|
|
["2013-11-03T19:00:00Z"] * 3, dtype="datetime64[s]"
|
|
)
|
|
expected = DataFrame(
|
|
data={"rxstatus": ["00E80000"] * 3},
|
|
index=MultiIndex.from_tuples(
|
|
[(datetimes[0], 126), (datetimes[1], 23), (datetimes[2], 13)],
|
|
names=["datetime", "prn"],
|
|
),
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_parse_date_column_with_empty_string(all_parsers):
|
|
# see gh-6428
|
|
parser = all_parsers
|
|
data = "case,opdate\n7,10/18/2006\n7,10/18/2008\n621, "
|
|
result = parser.read_csv(StringIO(data), parse_dates=["opdate"])
|
|
|
|
expected_data = [[7, "10/18/2006"], [7, "10/18/2008"], [621, " "]]
|
|
expected = DataFrame(expected_data, columns=["case", "opdate"])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"data,expected",
|
|
[
|
|
(
|
|
"a\n135217135789158401\n1352171357E+5",
|
|
DataFrame({"a": [135217135789158401, 135217135700000]}, dtype="float64"),
|
|
),
|
|
(
|
|
"a\n99999999999\n123456789012345\n1234E+0",
|
|
DataFrame({"a": [99999999999, 123456789012345, 1234]}, dtype="float64"),
|
|
),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize("parse_dates", [True, False])
|
|
def test_parse_date_float(all_parsers, data, expected, parse_dates):
|
|
# see gh-2697
|
|
#
|
|
# Date parsing should fail, so we leave the data untouched
|
|
# (i.e. float precision should remain unchanged).
|
|
parser = all_parsers
|
|
|
|
result = parser.read_csv(StringIO(data), parse_dates=parse_dates)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_parse_timezone(all_parsers):
|
|
# see gh-22256
|
|
parser = all_parsers
|
|
data = """dt,val
|
|
2018-01-04 09:01:00+09:00,23350
|
|
2018-01-04 09:02:00+09:00,23400
|
|
2018-01-04 09:03:00+09:00,23400
|
|
2018-01-04 09:04:00+09:00,23400
|
|
2018-01-04 09:05:00+09:00,23400"""
|
|
result = parser.read_csv(StringIO(data), parse_dates=["dt"])
|
|
|
|
dti = DatetimeIndex(
|
|
list(
|
|
pd.date_range(
|
|
start="2018-01-04 09:01:00",
|
|
end="2018-01-04 09:05:00",
|
|
freq="1min",
|
|
tz=pytz.FixedOffset(540),
|
|
)
|
|
),
|
|
freq=None,
|
|
)
|
|
expected_data = {"dt": dti, "val": [23350, 23400, 23400, 23400, 23400]}
|
|
|
|
expected = DataFrame(expected_data)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"date_string",
|
|
["32/32/2019", "02/30/2019", "13/13/2019", "13/2019", "a3/11/2018", "10/11/2o17"],
|
|
)
|
|
def test_invalid_parse_delimited_date(all_parsers, date_string):
|
|
parser = all_parsers
|
|
expected = DataFrame({0: [date_string]}, dtype="object")
|
|
result = parser.read_csv(StringIO(date_string), header=None, parse_dates=[0])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"date_string,dayfirst,expected",
|
|
[
|
|
# %d/%m/%Y; month > 12 thus replacement
|
|
("13/02/2019", False, datetime(2019, 2, 13)),
|
|
("13/02/2019", True, datetime(2019, 2, 13)),
|
|
# %m/%d/%Y; day > 12 thus there will be no replacement
|
|
("02/13/2019", False, datetime(2019, 2, 13)),
|
|
("02/13/2019", True, datetime(2019, 2, 13)),
|
|
# %d/%m/%Y; dayfirst==True thus replacement
|
|
("04/02/2019", True, datetime(2019, 2, 4)),
|
|
],
|
|
)
|
|
def test_parse_delimited_date_swap(all_parsers, date_string, dayfirst, expected):
|
|
parser = all_parsers
|
|
expected = DataFrame({0: [expected]}, dtype="datetime64[ns]")
|
|
result = parser.read_csv(
|
|
StringIO(date_string), header=None, dayfirst=dayfirst, parse_dates=[0]
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def _helper_hypothesis_delimited_date(call, date_string, **kwargs):
|
|
msg, result = None, None
|
|
try:
|
|
result = call(date_string, **kwargs)
|
|
except ValueError as er:
|
|
msg = str(er)
|
|
pass
|
|
return msg, result
|
|
|
|
|
|
@given(date_strategy)
|
|
@settings(deadline=None)
|
|
@pytest.mark.parametrize("delimiter", list(" -./"))
|
|
@pytest.mark.parametrize("dayfirst", [True, False])
|
|
@pytest.mark.parametrize(
|
|
"date_format",
|
|
["%d %m %Y", "%m %d %Y", "%m %Y", "%Y %m %d", "%y %m %d", "%Y%m%d", "%y%m%d"],
|
|
)
|
|
def test_hypothesis_delimited_date(date_format, dayfirst, delimiter, test_datetime):
|
|
if date_format == "%m %Y" and delimiter == ".":
|
|
pytest.skip(
|
|
"parse_datetime_string cannot reliably tell whether \
|
|
e.g. %m.%Y is a float or a date, thus we skip it"
|
|
)
|
|
result, expected = None, None
|
|
except_in_dateutil, except_out_dateutil = None, None
|
|
date_string = test_datetime.strftime(date_format.replace(" ", delimiter))
|
|
|
|
except_out_dateutil, result = _helper_hypothesis_delimited_date(
|
|
parse_datetime_string, date_string, dayfirst=dayfirst
|
|
)
|
|
except_in_dateutil, expected = _helper_hypothesis_delimited_date(
|
|
du_parse,
|
|
date_string,
|
|
default=_DEFAULT_DATETIME,
|
|
dayfirst=dayfirst,
|
|
yearfirst=False,
|
|
)
|
|
|
|
assert except_out_dateutil == except_in_dateutil
|
|
assert result == expected
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"names, usecols, parse_dates, missing_cols",
|
|
[
|
|
(None, ["val"], ["date", "time"], "date, time"),
|
|
(None, ["val"], [0, "time"], "time"),
|
|
(None, ["val"], [["date", "time"]], "date, time"),
|
|
(None, ["val"], [[0, "time"]], "time"),
|
|
(None, ["val"], {"date": [0, "time"]}, "time"),
|
|
(None, ["val"], {"date": ["date", "time"]}, "date, time"),
|
|
(None, ["val"], [["date", "time"], "date"], "date, time"),
|
|
(["date1", "time1", "temperature"], None, ["date", "time"], "date, time"),
|
|
(
|
|
["date1", "time1", "temperature"],
|
|
["date1", "temperature"],
|
|
["date1", "time"],
|
|
"time",
|
|
),
|
|
],
|
|
)
|
|
def test_missing_parse_dates_column_raises(
|
|
all_parsers, names, usecols, parse_dates, missing_cols
|
|
):
|
|
# gh-31251 column names provided in parse_dates could be missing.
|
|
parser = all_parsers
|
|
content = StringIO("date,time,val\n2020-01-31,04:20:32,32\n")
|
|
msg = f"Missing column provided to 'parse_dates': '{missing_cols}'"
|
|
with pytest.raises(ValueError, match=msg):
|
|
parser.read_csv(
|
|
content, sep=",", names=names, usecols=usecols, parse_dates=parse_dates
|
|
)
|