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TST: parametrize test_info #37887

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Merged
merged 11 commits into from
Nov 25, 2020
Merged

TST: parametrize test_info #37887

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41db5fb
TST: rename, parametrize test_info
ivanovmg Nov 16, 2020
4baed2c
REF: use option_context
ivanovmg Nov 16, 2020
1c27dc0
TST: parametrize smoke test
ivanovmg Nov 17, 2020
f91d17d
TST: revert test_info_memory_usage_qualified
ivanovmg Nov 17, 2020
1ffba54
TST: move duplicate_just_works into smoke_test
ivanovmg Nov 17, 2020
db0fa5e
TST: de-duplicate datetime_frame using import
ivanovmg Nov 17, 2020
429387d
FIX: add missing test due to incomplete revert
ivanovmg Nov 17, 2020
baf5c3d
Merge branch 'master' into refactor/test-info
ivanovmg Nov 18, 2020
a8056ab
TST: assert output length for smoke test
ivanovmg Nov 18, 2020
84263e3
TST: avoid fixture imports
ivanovmg Nov 22, 2020
173c0ac
Merge branch 'master' into refactor/test-info
ivanovmg Nov 22, 2020
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112 changes: 51 additions & 61 deletions pandas/tests/io/formats/test_info.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,39 +16,14 @@
Series,
date_range,
option_context,
reset_option,
set_option,
)
import pandas._testing as tm
from pandas.conftest import datetime_frame, float_frame, int_frame


@pytest.fixture
def datetime_frame():
"""
Fixture for DataFrame of floats with DatetimeIndex

Columns are ['A', 'B', 'C', 'D']

A B C D
2000-01-03 -1.122153 0.468535 0.122226 1.693711
2000-01-04 0.189378 0.486100 0.007864 -1.216052
2000-01-05 0.041401 -0.835752 -0.035279 -0.414357
2000-01-06 0.430050 0.894352 0.090719 0.036939
2000-01-07 -0.620982 -0.668211 -0.706153 1.466335
2000-01-10 -0.752633 0.328434 -0.815325 0.699674
2000-01-11 -2.236969 0.615737 -0.829076 -1.196106
... ... ... ... ...
2000-02-03 1.642618 -0.579288 0.046005 1.385249
2000-02-04 -0.544873 -1.160962 -0.284071 -1.418351
2000-02-07 -2.656149 -0.601387 1.410148 0.444150
2000-02-08 -1.201881 -1.289040 0.772992 -1.445300
2000-02-09 1.377373 0.398619 1.008453 -0.928207
2000-02-10 0.473194 -0.636677 0.984058 0.511519
2000-02-11 -0.965556 0.408313 -1.312844 -0.381948

[30 rows x 4 columns]
"""
return DataFrame(tm.getTimeSeriesData())
def duplicate_columns_frame():
"""Dataframe with duplicate column names."""
return DataFrame(np.random.randn(1500, 4), columns=["a", "a", "b", "b"])


def test_info_empty():
Expand All @@ -65,9 +40,7 @@ def test_info_empty():
assert result == expected


def test_info_categorical_column():

# make sure it works
def test_info_categorical_column_smoke_test():
n = 2500
df = DataFrame({"int64": np.random.randint(100, size=n)})
df["category"] = Series(
Expand All @@ -82,18 +55,46 @@ def test_info_categorical_column():
df2.info(buf=buf)


def test_info(float_frame, datetime_frame):
@pytest.mark.parametrize(
"fixture_func",
[
int_frame,
float_frame,
datetime_frame,
duplicate_columns_frame,
],
)
def test_info_smoke_test(fixture_func, request):
frame = request.getfixturevalue(fixture_func.__name__)
io = StringIO()
float_frame.info(buf=io)
datetime_frame.info(buf=io)
frame.info(buf=io)
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can you do result = io.getvalue() and assert somthing about the result (not the whole thing)

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e.g. assert len(result.splitlines()) > 100 or something

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Done.



@pytest.mark.parametrize(
"num_columns, max_info_columns, verbose",
[
(10, 100, True),
(10, 11, True),
(10, 10, True),
(10, 9, False),
(10, 1, False),
],
)
def test_info_default_verbose_selection(num_columns, max_info_columns, verbose):
frame = DataFrame(np.random.randn(5, num_columns))
with option_context("display.max_info_columns", max_info_columns):
io_default = StringIO()
frame.info(buf=io_default)
result = io_default.getvalue()

frame = DataFrame(np.random.randn(5, 3))
io_explicit = StringIO()
frame.info(buf=io_explicit, verbose=verbose)
expected = io_explicit.getvalue()

frame.info()
frame.info(verbose=False)
assert result == expected


def test_info_verbose():
def test_info_verbose_check_header_separator_body():
buf = StringIO()
size = 1001
start = 5
Expand Down Expand Up @@ -202,33 +203,23 @@ def test_info_wide():

io = StringIO()
df.info(buf=io, max_cols=101)
rs = io.getvalue()
assert len(rs.splitlines()) > 100
xp = rs

set_option("display.max_info_columns", 101)
io = StringIO()
df.info(buf=io)
assert rs == xp
reset_option("display.max_info_columns")

result = io.getvalue()
assert len(result.splitlines()) > 100

def test_info_duplicate_columns():
io = StringIO()

# it works!
frame = DataFrame(np.random.randn(1500, 4), columns=["a", "a", "b", "b"])
frame.info(buf=io)
expected = result
with option_context("display.max_info_columns", 101):
io = StringIO()
df.info(buf=io)
result = io.getvalue()
assert result == expected


def test_info_duplicate_columns_shows_correct_dtypes():
# GH11761
io = StringIO()

frame = DataFrame([[1, 2.0]], columns=["a", "a"])
frame.info(buf=io)
io.seek(0)
lines = io.readlines()
lines = io.getvalue().splitlines(True)
assert " 0 a 1 non-null int64 \n" == lines[5]
assert " 1 a 1 non-null float64\n" == lines[6]

Expand Down Expand Up @@ -272,7 +263,6 @@ def test_info_max_cols():
assert len(res.strip().split("\n")) == len_

for len_, verbose in [(12, None), (5, False), (12, True)]:

# max_cols not exceeded
with option_context("max_info_columns", 5):
buf = StringIO()
Expand Down Expand Up @@ -418,7 +408,6 @@ def test_usage_via_getsizeof():


def test_info_memory_usage_qualified():

buf = StringIO()
df = DataFrame(1, columns=list("ab"), index=[1, 2, 3])
df.info(buf=buf)
Expand Down Expand Up @@ -454,7 +443,8 @@ def memory_usage(f):
N = 100
M = len(uppercase)
index = MultiIndex.from_product(
[list(uppercase), date_range("20160101", periods=N)], names=["id", "date"]
[list(uppercase), date_range("20160101", periods=N)],
names=["id", "date"],
)
df = DataFrame({"value": np.random.randn(N * M)}, index=index)

Expand Down