265 lines
6.9 KiB
Python
265 lines
6.9 KiB
Python
|
from datetime import datetime
|
||
|
|
from io import StringIO
|
||
|
|
from textwrap import dedent
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import pytest
|
||
|
|
|
||
|
|
from pandas import DataFrame, Series, option_context, to_datetime
|
||
|
|
|
||
|
|
|
||
|
|
def test_repr_embedded_ndarray():
|
||
|
|
arr = np.empty(10, dtype=[("err", object)])
|
||
|
|
for i in range(len(arr)):
|
||
|
|
arr["err"][i] = np.random.randn(i)
|
||
|
|
|
||
|
|
df = DataFrame(arr)
|
||
|
|
repr(df["err"])
|
||
|
|
repr(df)
|
||
|
|
df.to_string()
|
||
|
|
|
||
|
|
|
||
|
|
def test_repr_tuples():
|
||
|
|
buf = StringIO()
|
||
|
|
|
||
|
|
df = DataFrame({"tups": list(zip(range(10), range(10)))})
|
||
|
|
repr(df)
|
||
|
|
df.to_string(col_space=10, buf=buf)
|
||
|
|
|
||
|
|
|
||
|
|
def test_to_string_truncate():
|
||
|
|
# GH 9784 - dont truncate when calling DataFrame.to_string
|
||
|
|
df = DataFrame(
|
||
|
|
[
|
||
|
|
{
|
||
|
|
"a": "foo",
|
||
|
|
"b": "bar",
|
||
|
|
"c": "let's make this a very VERY long line that is longer "
|
||
|
|
"than the default 50 character limit",
|
||
|
|
"d": 1,
|
||
|
|
},
|
||
|
|
{"a": "foo", "b": "bar", "c": "stuff", "d": 1},
|
||
|
|
]
|
||
|
|
)
|
||
|
|
df.set_index(["a", "b", "c"])
|
||
|
|
assert df.to_string() == (
|
||
|
|
" a b "
|
||
|
|
" c d\n"
|
||
|
|
"0 foo bar let's make this a very VERY long line t"
|
||
|
|
"hat is longer than the default 50 character limit 1\n"
|
||
|
|
"1 foo bar "
|
||
|
|
" stuff 1"
|
||
|
|
)
|
||
|
|
with option_context("max_colwidth", 20):
|
||
|
|
# the display option has no effect on the to_string method
|
||
|
|
assert df.to_string() == (
|
||
|
|
" a b "
|
||
|
|
" c d\n"
|
||
|
|
"0 foo bar let's make this a very VERY long line t"
|
||
|
|
"hat is longer than the default 50 character limit 1\n"
|
||
|
|
"1 foo bar "
|
||
|
|
" stuff 1"
|
||
|
|
)
|
||
|
|
assert df.to_string(max_colwidth=20) == (
|
||
|
|
" a b c d\n"
|
||
|
|
"0 foo bar let's make this ... 1\n"
|
||
|
|
"1 foo bar stuff 1"
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"input_array, expected",
|
||
|
|
[
|
||
|
|
("a", "a"),
|
||
|
|
(["a", "b"], "a\nb"),
|
||
|
|
([1, "a"], "1\na"),
|
||
|
|
(1, "1"),
|
||
|
|
([0, -1], " 0\n-1"),
|
||
|
|
(1.0, "1.0"),
|
||
|
|
([" a", " b"], " a\n b"),
|
||
|
|
([".1", "1"], ".1\n 1"),
|
||
|
|
(["10", "-10"], " 10\n-10"),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_format_remove_leading_space_series(input_array, expected):
|
||
|
|
# GH: 24980
|
||
|
|
s = Series(input_array).to_string(index=False)
|
||
|
|
assert s == expected
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"input_array, expected",
|
||
|
|
[
|
||
|
|
({"A": ["a"]}, "A\na"),
|
||
|
|
({"A": ["a", "b"], "B": ["c", "dd"]}, "A B\na c\nb dd"),
|
||
|
|
({"A": ["a", 1], "B": ["aa", 1]}, "A B\na aa\n1 1"),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_format_remove_leading_space_dataframe(input_array, expected):
|
||
|
|
# GH: 24980
|
||
|
|
df = DataFrame(input_array).to_string(index=False)
|
||
|
|
assert df == expected
|
||
|
|
|
||
|
|
|
||
|
|
def test_to_string_unicode_columns(float_frame):
|
||
|
|
df = DataFrame({"\u03c3": np.arange(10.0)})
|
||
|
|
|
||
|
|
buf = StringIO()
|
||
|
|
df.to_string(buf=buf)
|
||
|
|
buf.getvalue()
|
||
|
|
|
||
|
|
buf = StringIO()
|
||
|
|
df.info(buf=buf)
|
||
|
|
buf.getvalue()
|
||
|
|
|
||
|
|
result = float_frame.to_string()
|
||
|
|
assert isinstance(result, str)
|
||
|
|
|
||
|
|
|
||
|
|
def test_to_string_utf8_columns():
|
||
|
|
n = "\u05d0".encode()
|
||
|
|
|
||
|
|
with option_context("display.max_rows", 1):
|
||
|
|
df = DataFrame([1, 2], columns=[n])
|
||
|
|
repr(df)
|
||
|
|
|
||
|
|
|
||
|
|
def test_to_string_unicode_two():
|
||
|
|
dm = DataFrame({"c/\u03c3": []})
|
||
|
|
buf = StringIO()
|
||
|
|
dm.to_string(buf)
|
||
|
|
|
||
|
|
|
||
|
|
def test_to_string_unicode_three():
|
||
|
|
dm = DataFrame(["\xc2"])
|
||
|
|
buf = StringIO()
|
||
|
|
dm.to_string(buf)
|
||
|
|
|
||
|
|
|
||
|
|
def test_to_string_with_formatters():
|
||
|
|
df = DataFrame(
|
||
|
|
{
|
||
|
|
"int": [1, 2, 3],
|
||
|
|
"float": [1.0, 2.0, 3.0],
|
||
|
|
"object": [(1, 2), True, False],
|
||
|
|
},
|
||
|
|
columns=["int", "float", "object"],
|
||
|
|
)
|
||
|
|
|
||
|
|
formatters = [
|
||
|
|
("int", lambda x: f"0x{x:x}"),
|
||
|
|
("float", lambda x: f"[{x: 4.1f}]"),
|
||
|
|
("object", lambda x: f"-{x!s}-"),
|
||
|
|
]
|
||
|
|
result = df.to_string(formatters=dict(formatters))
|
||
|
|
result2 = df.to_string(formatters=list(zip(*formatters))[1])
|
||
|
|
assert result == (
|
||
|
|
" int float object\n"
|
||
|
|
"0 0x1 [ 1.0] -(1, 2)-\n"
|
||
|
|
"1 0x2 [ 2.0] -True-\n"
|
||
|
|
"2 0x3 [ 3.0] -False-"
|
||
|
|
)
|
||
|
|
assert result == result2
|
||
|
|
|
||
|
|
|
||
|
|
def test_to_string_with_datetime64_monthformatter():
|
||
|
|
months = [datetime(2016, 1, 1), datetime(2016, 2, 2)]
|
||
|
|
x = DataFrame({"months": months})
|
||
|
|
|
||
|
|
def format_func(x):
|
||
|
|
return x.strftime("%Y-%m")
|
||
|
|
|
||
|
|
result = x.to_string(formatters={"months": format_func})
|
||
|
|
expected = dedent(
|
||
|
|
"""\
|
||
|
|
months
|
||
|
|
0 2016-01
|
||
|
|
1 2016-02"""
|
||
|
|
)
|
||
|
|
assert result.strip() == expected
|
||
|
|
|
||
|
|
|
||
|
|
def test_to_string_with_datetime64_hourformatter():
|
||
|
|
x = DataFrame(
|
||
|
|
{"hod": to_datetime(["10:10:10.100", "12:12:12.120"], format="%H:%M:%S.%f")}
|
||
|
|
)
|
||
|
|
|
||
|
|
def format_func(x):
|
||
|
|
return x.strftime("%H:%M")
|
||
|
|
|
||
|
|
result = x.to_string(formatters={"hod": format_func})
|
||
|
|
expected = dedent(
|
||
|
|
"""\
|
||
|
|
hod
|
||
|
|
0 10:10
|
||
|
|
1 12:12"""
|
||
|
|
)
|
||
|
|
assert result.strip() == expected
|
||
|
|
|
||
|
|
|
||
|
|
def test_to_string_with_formatters_unicode():
|
||
|
|
df = DataFrame({"c/\u03c3": [1, 2, 3]})
|
||
|
|
result = df.to_string(formatters={"c/\u03c3": str})
|
||
|
|
expected = dedent(
|
||
|
|
"""\
|
||
|
|
c/\u03c3
|
||
|
|
0 1
|
||
|
|
1 2
|
||
|
|
2 3"""
|
||
|
|
)
|
||
|
|
assert result == expected
|
||
|
|
|
||
|
|
|
||
|
|
def test_to_string_complex_number_trims_zeros():
|
||
|
|
s = Series([1.000000 + 1.000000j, 1.0 + 1.0j, 1.05 + 1.0j])
|
||
|
|
result = s.to_string()
|
||
|
|
expected = dedent(
|
||
|
|
"""\
|
||
|
|
0 1.00+1.00j
|
||
|
|
1 1.00+1.00j
|
||
|
|
2 1.05+1.00j"""
|
||
|
|
)
|
||
|
|
assert result == expected
|
||
|
|
|
||
|
|
|
||
|
|
def test_nullable_float_to_string(float_ea_dtype):
|
||
|
|
# https://github.com/pandas-dev/pandas/issues/36775
|
||
|
|
dtype = float_ea_dtype
|
||
|
|
s = Series([0.0, 1.0, None], dtype=dtype)
|
||
|
|
result = s.to_string()
|
||
|
|
expected = dedent(
|
||
|
|
"""\
|
||
|
|
0 0.0
|
||
|
|
1 1.0
|
||
|
|
2 <NA>"""
|
||
|
|
)
|
||
|
|
assert result == expected
|
||
|
|
|
||
|
|
|
||
|
|
def test_nullable_int_to_string(any_nullable_int_dtype):
|
||
|
|
# https://github.com/pandas-dev/pandas/issues/36775
|
||
|
|
dtype = any_nullable_int_dtype
|
||
|
|
s = Series([0, 1, None], dtype=dtype)
|
||
|
|
result = s.to_string()
|
||
|
|
expected = dedent(
|
||
|
|
"""\
|
||
|
|
0 0
|
||
|
|
1 1
|
||
|
|
2 <NA>"""
|
||
|
|
)
|
||
|
|
assert result == expected
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("na_rep", ["NaN", "Ted"])
|
||
|
|
def test_to_string_na_rep_and_float_format(na_rep):
|
||
|
|
# GH 13828
|
||
|
|
df = DataFrame([["A", 1.2225], ["A", None]], columns=["Group", "Data"])
|
||
|
|
result = df.to_string(na_rep=na_rep, float_format="{:.2f}".format)
|
||
|
|
expected = dedent(
|
||
|
|
f"""\
|
||
|
|
Group Data
|
||
|
|
0 A 1.22
|
||
|
|
1 A {na_rep}"""
|
||
|
|
)
|
||
|
|
assert result == expected
|