You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
162 lines
5.0 KiB
162 lines
5.0 KiB
import datetime
|
|
|
|
import numpy as np
|
|
|
|
from pandas.compat import (
|
|
IS64,
|
|
is_platform_windows,
|
|
)
|
|
|
|
from pandas import (
|
|
Categorical,
|
|
DataFrame,
|
|
Series,
|
|
date_range,
|
|
)
|
|
import pandas._testing as tm
|
|
|
|
|
|
class TestIteration:
|
|
def test_keys(self, float_frame):
|
|
assert float_frame.keys() is float_frame.columns
|
|
|
|
def test_iteritems(self):
|
|
df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["a", "a", "b"])
|
|
for k, v in df.items():
|
|
assert isinstance(v, DataFrame._constructor_sliced)
|
|
|
|
def test_items(self):
|
|
# GH#17213, GH#13918
|
|
cols = ["a", "b", "c"]
|
|
df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=cols)
|
|
for c, (k, v) in zip(cols, df.items()):
|
|
assert c == k
|
|
assert isinstance(v, Series)
|
|
assert (df[k] == v).all()
|
|
|
|
def test_items_names(self, float_string_frame):
|
|
for k, v in float_string_frame.items():
|
|
assert v.name == k
|
|
|
|
def test_iter(self, float_frame):
|
|
assert tm.equalContents(list(float_frame), float_frame.columns)
|
|
|
|
def test_iterrows(self, float_frame, float_string_frame):
|
|
for k, v in float_frame.iterrows():
|
|
exp = float_frame.loc[k]
|
|
tm.assert_series_equal(v, exp)
|
|
|
|
for k, v in float_string_frame.iterrows():
|
|
exp = float_string_frame.loc[k]
|
|
tm.assert_series_equal(v, exp)
|
|
|
|
def test_iterrows_iso8601(self):
|
|
# GH#19671
|
|
s = DataFrame(
|
|
{
|
|
"non_iso8601": ["M1701", "M1802", "M1903", "M2004"],
|
|
"iso8601": date_range("2000-01-01", periods=4, freq="M"),
|
|
}
|
|
)
|
|
for k, v in s.iterrows():
|
|
exp = s.loc[k]
|
|
tm.assert_series_equal(v, exp)
|
|
|
|
def test_iterrows_corner(self):
|
|
# GH#12222
|
|
df = DataFrame(
|
|
{
|
|
"a": [datetime.datetime(2015, 1, 1)],
|
|
"b": [None],
|
|
"c": [None],
|
|
"d": [""],
|
|
"e": [[]],
|
|
"f": [set()],
|
|
"g": [{}],
|
|
}
|
|
)
|
|
expected = Series(
|
|
[datetime.datetime(2015, 1, 1), None, None, "", [], set(), {}],
|
|
index=list("abcdefg"),
|
|
name=0,
|
|
dtype="object",
|
|
)
|
|
_, result = next(df.iterrows())
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_itertuples(self, float_frame):
|
|
for i, tup in enumerate(float_frame.itertuples()):
|
|
ser = DataFrame._constructor_sliced(tup[1:])
|
|
ser.name = tup[0]
|
|
expected = float_frame.iloc[i, :].reset_index(drop=True)
|
|
tm.assert_series_equal(ser, expected)
|
|
|
|
df = DataFrame(
|
|
{"floats": np.random.randn(5), "ints": range(5)}, columns=["floats", "ints"]
|
|
)
|
|
|
|
for tup in df.itertuples(index=False):
|
|
assert isinstance(tup[1], int)
|
|
|
|
df = DataFrame(data={"a": [1, 2, 3], "b": [4, 5, 6]})
|
|
dfaa = df[["a", "a"]]
|
|
|
|
assert list(dfaa.itertuples()) == [(0, 1, 1), (1, 2, 2), (2, 3, 3)]
|
|
|
|
# repr with int on 32-bit/windows
|
|
if not (is_platform_windows() or not IS64):
|
|
assert (
|
|
repr(list(df.itertuples(name=None)))
|
|
== "[(0, 1, 4), (1, 2, 5), (2, 3, 6)]"
|
|
)
|
|
|
|
tup = next(df.itertuples(name="TestName"))
|
|
assert tup._fields == ("Index", "a", "b")
|
|
assert (tup.Index, tup.a, tup.b) == tup
|
|
assert type(tup).__name__ == "TestName"
|
|
|
|
df.columns = ["def", "return"]
|
|
tup2 = next(df.itertuples(name="TestName"))
|
|
assert tup2 == (0, 1, 4)
|
|
assert tup2._fields == ("Index", "_1", "_2")
|
|
|
|
df3 = DataFrame({"f" + str(i): [i] for i in range(1024)})
|
|
# will raise SyntaxError if trying to create namedtuple
|
|
tup3 = next(df3.itertuples())
|
|
assert isinstance(tup3, tuple)
|
|
assert hasattr(tup3, "_fields")
|
|
|
|
# GH#28282
|
|
df_254_columns = DataFrame([{f"foo_{i}": f"bar_{i}" for i in range(254)}])
|
|
result_254_columns = next(df_254_columns.itertuples(index=False))
|
|
assert isinstance(result_254_columns, tuple)
|
|
assert hasattr(result_254_columns, "_fields")
|
|
|
|
df_255_columns = DataFrame([{f"foo_{i}": f"bar_{i}" for i in range(255)}])
|
|
result_255_columns = next(df_255_columns.itertuples(index=False))
|
|
assert isinstance(result_255_columns, tuple)
|
|
assert hasattr(result_255_columns, "_fields")
|
|
|
|
def test_sequence_like_with_categorical(self):
|
|
|
|
# GH#7839
|
|
# make sure can iterate
|
|
df = DataFrame(
|
|
{"id": [1, 2, 3, 4, 5, 6], "raw_grade": ["a", "b", "b", "a", "a", "e"]}
|
|
)
|
|
df["grade"] = Categorical(df["raw_grade"])
|
|
|
|
# basic sequencing testing
|
|
result = list(df.grade.values)
|
|
expected = np.array(df.grade.values).tolist()
|
|
tm.assert_almost_equal(result, expected)
|
|
|
|
# iteration
|
|
for t in df.itertuples(index=False):
|
|
str(t)
|
|
|
|
for row, s in df.iterrows():
|
|
str(s)
|
|
|
|
for c, col in df.items():
|
|
str(s)
|
|
|