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