""" test positional based indexing with iloc """
from datetime import datetime
import re
from warnings import catch_warnings, simplefilter
import numpy as np
import pytest
from pandas import (
Categorical,
CategoricalDtype,
DataFrame,
Index,
NaT,
Series,
concat,
date_range,
isna,
)
import pandas._testing as tm
from pandas.api.types import is_scalar
from pandas.core.indexing import IndexingError
from pandas.tests.indexing.common import Base
# We pass through the error message from numpy
_slice_iloc_msg = re.escape(
"only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) "
"and integer or boolean arrays are valid indices"
)
class TestiLoc(Base):
def test_iloc_getitem_int(self):
# integer
self.check_result(
"iloc",
2,
typs=["labels", "mixed", "ts", "floats", "empty"],
fails=IndexError,
)
def test_iloc_getitem_neg_int(self):
# neg integer
self.check_result(
"iloc",
-1,
typs=["labels", "mixed", "ts", "floats", "empty"],
fails=IndexError,
)
def test_iloc_getitem_list_int(self):
self.check_result(
"iloc",
[0, 1, 2],
typs=["labels", "mixed", "ts", "floats", "empty"],
fails=IndexError,
)
# array of ints (GH5006), make sure that a single indexer is returning
# the correct type
class TestiLoc2:
# TODO: better name, just separating out things that dont rely on base class
def test_is_scalar_access(self):
# GH#32085 index with duplicates doesnt matter for _is_scalar_access
index = Index([1, 2, 1])
ser = Series(range(3), index=index)
assert ser.iloc._is_scalar_access((1,))
df = ser.to_frame()
assert df.iloc._is_scalar_access((1, 0))
def test_iloc_exceeds_bounds(self):
# GH6296
# iloc should allow indexers that exceed the bounds
df = DataFrame(np.random.random_sample((20, 5)), columns=list("ABCDE"))
# lists of positions should raise IndexError!
msg = "positional indexers are out-of-bounds"
with pytest.raises(IndexError, match=msg):
df.iloc[:, [0, 1, 2, 3, 4, 5]]
with pytest.raises(IndexError, match=msg):
df.iloc[[1, 30]]
with pytest.raises(IndexError, match=msg):
df.iloc[[1, -30]]
with pytest.raises(IndexError, match=msg):
df.iloc[[100]]
s = df["A"]
with pytest.raises(IndexError, match=msg):
s.iloc[[100]]
with pytest.raises(IndexError, match=msg):
s.iloc[[-100]]
# still raise on a single indexer
msg = "single positional indexer is out-of-bounds"
with pytest.raises(IndexError, match=msg):
df.iloc[30]
with pytest.raises(IndexError, match=msg):
df.iloc[-30]
# GH10779
# single positive/negative indexer exceeding Series bounds should raise
# an IndexError
with pytest.raises(IndexError, match=msg):
s.iloc[30]
with pytest.raises(IndexError, match=msg):
s.iloc[-30]
# slices are ok
result = df.iloc[:, 4:10] # 0 < start < len < stop
expected = df.iloc[:, 4:]
tm.assert_frame_equal(result, expected)
result = df.iloc[:, -4:-10] # stop < 0 < start < len
expected = df.iloc[:, :0]
tm.assert_frame_equal(result, expected)
result = df.iloc[:, 10:4:-1] # 0 < stop < len < start (down)
expected = df.iloc[:, :4:-1]
tm.assert_frame_equal(result, expected)
result = df.iloc[:, 4:-10:-1] # stop < 0 < start < len (down)
expected = df.iloc[:, 4::-1]
tm.assert_frame_equal(result, expected)
result = df.iloc[:, -10:4] # start < 0 < stop < len
expected = df.iloc[:, :4]
tm.assert_frame_equal(result, expected)
result = df.iloc[:, 10:4] # 0 < stop < len < start
expected = df.iloc[:, :0]
tm.assert_frame_equal(result, expected)
result = df.iloc[:, -10:-11:-1] # stop < start < 0 < len (down)
expected = df.iloc[:, :0]
tm.assert_frame_equal(result, expected)
result = df.iloc[:, 10:11] # 0 < len < start < stop
expected = df.iloc[:, :0]
tm.assert_frame_equal(result, expected)
# slice bounds exceeding is ok
result = s.iloc[18:30]
expected = s.iloc[18:]
tm.assert_series_equal(result, expected)
result = s.iloc[30:]
expected = s.iloc[:0]
tm.assert_series_equal(result, expected)
result = s.iloc[30::-1]
expected = s.iloc[::-1]
tm.assert_series_equal(result, expected)
# doc example
def check(result, expected):
str(result)
result.dtypes
tm.assert_frame_equal(result, expected)
dfl = DataFrame(np.random.randn(5, 2), columns=list("AB"))
check(dfl.iloc[:, 2:3], DataFrame(index=dfl.index))
check(dfl.iloc[:, 1:3], dfl.iloc[:, [1]])
check(dfl.iloc[4:6], dfl.iloc[[4]])
msg = "positional indexers are out-of-bounds"
with pytest.raises(IndexError, match=msg):
dfl.iloc[[4, 5, 6]]
msg = "single positional indexer is out-of-bounds"
with pytest.raises(IndexError, match=msg):
dfl.iloc[:, 4]
@pytest.mark.parametrize("index,columns", [(np.arange(20), list("ABCDE"))])
@pytest.mark.parametrize(
"index_vals,column_vals",
[
([slice(None), ["A", "D"]]),
(["1", "2"], slice(None)),
([datetime(2019, 1, 1)], slice(None)),
],
)
def test_iloc_non_integer_raises(self, index, columns, index_vals, column_vals):
# GH 25753
df = DataFrame(
np.random.randn(len(index), len(columns)), index=index, columns=columns
)
msg = ".iloc requires numeric indexers, got"
with pytest.raises(IndexError, match=msg):
df.iloc[index_vals, column_vals]
@pytest.mark.parametrize("dims", [1, 2])
def test_iloc_getitem_invalid_scalar(self, dims):
# GH 21982
if dims == 1:
s = Series(np.arange(10))
else:
s = DataFrame(np.arange(100).reshape(10, 10))
with pytest.raises(TypeError, match="Cannot index by location index"):
s.iloc["a"]
def test_iloc_array_not_mutating_negative_indices(self):
# GH 21867
array_with_neg_numbers = np.array([1, 2, -1])
array_copy = array_with_neg_numbers.copy()
df = DataFrame(
{"A": [100, 101, 102], "B": [103, 104, 105], "C": [106, 107, 108]},
index=[1, 2, 3],
)
df.iloc[array_with_neg_numbers]
tm.assert_numpy_array_equal(array_with_neg_numbers, array_copy)
df.iloc[:, array_with_neg_numbers]
tm.assert_numpy_array_equal(array_with_neg_numbers, array_copy)
def test_iloc_getitem_neg_int_can_reach_first_index(self):
# GH10547 and GH10779
# negative integers should be able to reach index 0
df = DataFrame({"A": [2, 3, 5], "B": [7, 11, 13]})
s = df["A"]
expected = df.iloc[0]
result = df.iloc[-3]
tm.assert_series_equal(result, expected)
expected = df.iloc[[0]]
result = df.iloc[[-3]]
tm.assert_frame_equal(result, expected)
expected = s.iloc[0]
result = s.iloc[-3]
assert result == expected
expected = s.iloc[[0]]
result = s.iloc[[-3]]
tm.assert_series_equal(result, expected)
# check the length 1 Series case highlighted in GH10547
expected = Series(["a"], index=["A"])
result = expected.iloc[[-1]]
tm.assert_series_equal(result, expected)
def test_iloc_getitem_dups(self):
# GH 6766
df1 = DataFrame([{"A": None, "B": 1}, {"A": 2, "B": 2}])
df2 = DataFrame([{"A": 3, "B": 3}, {"A": 4, "B": 4}])
df = concat([df1, df2], axis=1)
# cross-sectional indexing
result = df.iloc[0, 0]
assert isna(result)
result = df.iloc[0, :]
expected = Series([np.nan, 1, 3, 3], index=["A", "B", "A", "B"], name=0)
tm.assert_series_equal(result, expected)
def test_iloc_getitem_array(self):
# TODO: test something here?
pass
def test_iloc_getitem_bool(self):
# TODO: test something here?
pass
@pytest.mark.parametrize("index", [[True, False], [True, False, True, False]])
def test_iloc_getitem_bool_diff_len(self, index):
# GH26658
s = Series([1, 2, 3])
msg = f"Boolean index has wrong length: {len(index)} instead of {len(s)}"
with pytest.raises(IndexError, match=msg):
_ = s.iloc[index]
def test_iloc_getitem_slice(self):
# TODO: test something here?
pass
def test_iloc_getitem_slice_dups(self):
df1 = DataFrame(np.random.randn(10, 4), columns=["A", "A", "B", "B"])
df2 = DataFrame(
np.random.randint(0, 10, size=20).reshape(10, 2), columns=["A", "C"]
)
# axis=1
df = concat([df1, df2], axis=1)
tm.assert_frame_equal(df.iloc[:, :4], df1)
tm.assert_frame_equal(df.iloc[:, 4:], df2)
df = concat([df2, df1], axis=1)
tm.assert_frame_equal(df.iloc[:, :2], df2)
tm.assert_frame_equal(df.iloc[:, 2:], df1)
exp = concat([df2, df1.iloc[:, [0]]], axis=1)
tm.assert_frame_equal(df.iloc[:, 0:3], exp)
# axis=0
df = concat([df, df], axis=0)
tm.assert_frame_equal(df.iloc[0:10, :2], df2)
tm.assert_frame_equal(df.iloc[0:10, 2:], df1)
tm.assert_frame_equal(df.iloc[10:, :2], df2)
tm.assert_frame_equal(df.iloc[10:, 2:], df1)
def test_iloc_setitem(self):
df = DataFrame(
np.random.randn(4, 4), index=np.arange(0, 8, 2), columns=np.arange(0, 12, 3)
)
df.iloc[1, 1] = 1
result = df.iloc[1, 1]
assert result == 1
df.iloc[:, 2:3] = 0
expected = df.iloc[:, 2:3]
result = df.iloc[:, 2:3]
tm.assert_frame_equal(result, expected)
# GH5771
s = Series(0, index=[4, 5, 6])
s.iloc[1:2] += 1
expected = Series([0, 1, 0], index=[4, 5, 6])
tm.assert_series_equal(s, expected)
def test_iloc_setitem_list(self):
# setitem with an iloc list
df = DataFrame(
np.arange(9).reshape((3, 3)), index=["A", "B", "C"], columns=["A", "B", "C"]
)
df.iloc[[0, 1], [1, 2]]
df.iloc[[0, 1], [1, 2]] += 100
expected = DataFrame(
np.array([0, 101, 102, 3, 104, 105, 6, 7, 8]).reshape((3, 3)),
index=["A", "B", "C"],
columns=["A", "B", "C"],
)
tm.assert_frame_equal(df, expected)
def test_iloc_setitem_pandas_object(self):
# GH 17193
s_orig = Series([0, 1, 2, 3])
expected = Series([0, -1, -2, 3])
s = s_orig.copy()
s.iloc[Series([1, 2])] = [-1, -2]
tm.assert_series_equal(s, expected)
s = s_orig.copy()
s.iloc[Index([1, 2])] = [-1, -2]
tm.assert_series_equal(s, expected)
def test_iloc_setitem_dups(self):
# GH 6766
# iloc with a mask aligning from another iloc
df1 = DataFrame([{"A": None, "B": 1}, {"A": 2, "B": 2}])
df2 = DataFrame([{"A": 3, "B": 3}, {"A": 4, "B": 4}])
df = concat([df1, df2], axis=1)
expected = df.fillna(3)
inds = np.isnan(df.iloc[:, 0])
mask = inds[inds].index
df.iloc[mask, 0] = df.iloc[mask, 2]
tm.assert_frame_equal(df, expected)
# del a dup column across blocks
expected = DataFrame({0: [1, 2], 1: [3, 4]})
expected.columns = ["B", "B"]
del df["A"]
tm.assert_frame_equal(df, expected)
# assign back to self
df.iloc[[0, 1], [0, 1]] = df.iloc[[0, 1], [0, 1]]
tm.assert_frame_equal(df, expected)
# reversed x 2
df.iloc[[1, 0], [0, 1]] = df.iloc[[1, 0], [0, 1]].reset_index(drop=True)
df.iloc[[1, 0], [0, 1]] = df.iloc[[1, 0], [0, 1]].reset_index(drop=True)
tm.assert_frame_equal(df, expected)
def test_iloc_setitem_frame_duplicate_columns_multiple_blocks(self):
# Same as the "assign back to self" check in test_iloc_setitem_dups
# but on a DataFrame with multiple blocks
df = DataFrame([[0, 1], [2, 3]], columns=["B", "B"])
df.iloc[:, 0] = df.iloc[:, 0].astype("f8")
assert len(df._mgr.blocks) == 2
expected = df.copy()
# assign back to self
df.iloc[[0, 1], [0, 1]] = df.iloc[[0, 1], [0, 1]]
tm.assert_frame_equal(df, expected)
# TODO: GH#27620 this test used to compare iloc against ix; check if this
# is redundant with another test comparing iloc against loc
def test_iloc_getitem_frame(self):
df = DataFrame(
np.random.randn(10, 4), index=range(0, 20, 2), columns=range(0, 8, 2)
)
result = df.iloc[2]
exp = df.loc[4]
tm.assert_series_equal(result, exp)
result = df.iloc[2, 2]
exp = df.loc[4, 4]
assert result == exp
# slice
result = df.iloc[4:8]
expected = df.loc[8:14]
tm.assert_frame_equal(result, expected)
result = df.iloc[:, 2:3]
expected = df.loc[:, 4:5]
tm.assert_frame_equal(result, expected)
# list of integers
result = df.iloc[[0, 1, 3]]
expected = df.loc[[0, 2, 6]]
tm.assert_frame_equal(result, expected)
result = df.iloc[[0, 1, 3], [0, 1]]
expected = df.loc[[0, 2, 6], [0, 2]]
tm.assert_frame_equal(result, expected)
# neg indices
result = df.iloc[[-1, 1, 3], [-1, 1]]
expected = df.loc[[18, 2, 6], [6, 2]]
tm.assert_frame_equal(result, expected)
# dups indices
result = df.iloc[[-1, -1, 1, 3], [-1, 1]]
expected = df.loc[[18, 18, 2, 6], [6, 2]]
tm.assert_frame_equal(result, expected)
# with index-like
s = Series(index=range(1, 5), dtype=object)
result = df.iloc[s.index]
expected = df.loc[[2, 4, 6, 8]]
tm.assert_frame_equal(result, expected)
def test_iloc_getitem_labelled_frame(self):
# try with labelled frame
df = DataFrame(
np.random.randn(10, 4), index=list("abcdefghij"), columns=list("ABCD")
)
result = df.iloc[1, 1]
exp = df.loc["b", "B"]
assert result == exp
result = df.iloc[:, 2:3]
expected = df.loc[:, ["C"]]
tm.assert_frame_equal(result, expected)
# negative indexing
result = df.iloc[-1, -1]
exp = df.loc["j", "D"]
assert result == exp
# out-of-bounds exception
msg = "single positional indexer is out-of-bounds"
with pytest.raises(IndexError, match=msg):
df.iloc[10, 5]
# trying to use a label
msg = (
r"Location based indexing can only have \[integer, integer "
r"slice \(START point is INCLUDED, END point is EXCLUDED\), "
r"listlike of integers, boolean array\] types"
)
with pytest.raises(ValueError, match=msg):
df.iloc["j", "D"]
def test_iloc_getitem_doc_issue(self):
# multi axis slicing issue with single block
# surfaced in GH 6059
arr = np.random.randn(6, 4)
index = date_range("20130101", periods=6)
columns = list("ABCD")
df = DataFrame(arr, index=index, columns=columns)
# defines ref_locs
df.describe()
result = df.iloc[3:5, 0:2]
str(result)
result.dtypes
expected = DataFrame(arr[3:5, 0:2], index=index[3:5], columns=columns[0:2])
tm.assert_frame_equal(result, expected)
# for dups
df.columns = list("aaaa")
result = df.iloc[3:5, 0:2]
str(result)
result.dtypes
expected = DataFrame(arr[3:5, 0:2], index=index[3:5], columns=list("aa"))
tm.assert_frame_equal(result, expected)
# related
arr = np.random.randn(6, 4)
index = list(range(0, 12, 2))
columns = list(range(0, 8, 2))
df = DataFrame(arr, index=index, columns=columns)
df._mgr.blocks[0].mgr_locs
result = df.iloc[1:5, 2:4]
str(result)
result.dtypes
expected = DataFrame(arr[1:5, 2:4], index=index[1:5], columns=columns[2:4])
tm.assert_frame_equal(result, expected)
def test_iloc_setitem_series(self):
df = DataFrame(
np.random.randn(10, 4), index=list("abcdefghij"), columns=list("ABCD")
)
df.iloc[1, 1] = 1
result = df.iloc[1, 1]
assert result == 1
df.iloc[:, 2:3] = 0
expected = df.iloc[:, 2:3]
result = df.iloc[:, 2:3]
tm.assert_frame_equal(result, expected)
s = Series(np.random.randn(10), index=range(0, 20, 2))
s.iloc[1] = 1
result = s.iloc[1]
assert result == 1
s.iloc[:4] = 0
expected = s.iloc[:4]
result = s.iloc[:4]
tm.assert_series_equal(result, expected)
s = Series([-1] * 6)
s.iloc[0::2] = [0, 2, 4]
s.iloc[1::2] = [1, 3, 5]
result = s
expected = Series([0, 1, 2, 3, 4, 5])
tm.assert_series_equal(result, expected)
def test_iloc_setitem_list_of_lists(self):
# GH 7551
# list-of-list is set incorrectly in mixed vs. single dtyped frames
df = DataFrame(
{"A": np.arange(5, dtype="int64"), "B": np.arange(5, 10, dtype="int64")}
)
df.iloc[2:4] = [[10, 11], [12, 13]]
expected = DataFrame({"A": [0, 1, 10, 12, 4], "B": [5, 6, 11, 13, 9]})
tm.assert_frame_equal(df, expected)
df = DataFrame(
{"A": ["a", "b", "c", "d", "e"], "B": np.arange(5, 10, dtype="int64")}
)
df.iloc[2:4] = [["x", 11], ["y", 13]]
expected = DataFrame({"A": ["a", "b", "x", "y", "e"], "B": [5, 6, 11, 13, 9]})
tm.assert_frame_equal(df, expected)
@pytest.mark.parametrize("indexer", [[0], slice(None, 1, None), np.array([0])])
@pytest.mark.parametrize("value", [["Z"], np.array(["Z"])])
def test_iloc_setitem_with_scalar_index(self, indexer, value):
# GH #19474
# assigning like "df.iloc[0, [0]] = ['Z']" should be evaluated
# elementwisely, not using "setter('A', ['Z'])".
df = DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
df.iloc[0, indexer] = value
result = df.iloc[0, 0]
assert is_scalar(result) and result == "Z"
def test_iloc_mask(self):
# GH 3631, iloc with a mask (of a series) should raise
df = DataFrame(list(range(5)), index=list("ABCDE"), columns=["a"])
mask = df.a % 2 == 0
msg = "iLocation based boolean indexing cannot use an indexable as a mask"
with pytest.raises(ValueError, match=msg):
df.iloc[mask]
mask.index = range(len(mask))
msg = "iLocation based boolean indexing on an integer type is not available"
with pytest.raises(NotImplementedError, match=msg):
df.iloc[mask]
# ndarray ok
result = df.iloc[np.array([True] * len(mask), dtype=bool)]
tm.assert_frame_equal(result, df)
# the possibilities
locs = np.arange(4)
nums = 2 ** locs
reps = [bin(num) for num in nums]
df = DataFrame({"locs": locs, "nums": nums}, reps)
expected = {
(None, ""): "0b1100",
(None, ".loc"): "0b1100",
(None, ".iloc"): "0b1100",
("index", ""): "0b11",
("index", ".loc"): "0b11",
("index", ".iloc"): (
"iLocation based boolean indexing cannot use an indexable as a mask"
),
("locs", ""): "Unalignable boolean Series provided as indexer "
"(index of the boolean Series and of the indexed "
"object do not match).",
("locs", ".loc"): "Unalignable boolean Series provided as indexer "
"(index of the boolean Series and of the "
"indexed object do not match).",
("locs", ".iloc"): (
"iLocation based boolean indexing on an "
"integer type is not available"
),
}
# UserWarnings from reindex of a boolean mask
with catch_warnings(record=True):
simplefilter("ignore", UserWarning)
for idx in [None, "index", "locs"]:
mask = (df.nums > 2).values
if idx:
mask = Series(mask, list(reversed(getattr(df, idx))))
for method in ["", ".loc", ".iloc"]:
try:
if method:
accessor = getattr(df, method[1:])
else:
accessor = df
ans = str(bin(accessor[mask]["nums"].sum()))
except (ValueError, IndexingError, NotImplementedError) as e:
ans = str(e)
key = (
idx,
method,
)
r = expected.get(key)
if r != ans:
raise AssertionError(
f"[{key}] does not match [{ans}], received [{r}]"
)
def test_iloc_non_unique_indexing(self):
# GH 4017, non-unique indexing (on the axis)
df = DataFrame({"A": [0.1] * 3000, "B": [1] * 3000})
idx = np.arange(30) * 99
expected = df.iloc[idx]
df3 = concat([df, 2 * df, 3 * df])
result = df3.iloc[idx]
tm.assert_frame_equal(result, expected)
df2 = DataFrame({"A": [0.1] * 1000, "B": [1] * 1000})
df2 = concat([df2, 2 * df2, 3 * df2])
with pytest.raises(KeyError, match="with any missing labels"):
df2.loc[idx]
def test_iloc_empty_list_indexer_is_ok(self):
df = tm.makeCustomDataframe(5, 2)
# vertical empty
tm.assert_frame_equal(
df.iloc[:, []],
df.iloc[:, :0],
check_index_type=True,
check_column_type=True,
)
# horizontal empty
tm.assert_frame_equal(
df.iloc[[], :],
df.iloc[:0, :],
check_index_type=True,
check_column_type=True,
)
# horizontal empty
tm.assert_frame_equal(
df.iloc[[]], df.iloc[:0, :], check_index_type=True, check_column_type=True
)
def test_identity_slice_returns_new_object(self):
# GH13873
original_df = DataFrame({"a": [1, 2, 3]})
sliced_df = original_df.iloc[:]
assert sliced_df is not original_df
# should be a shallow copy
original_df["a"] = [4, 4, 4]
assert (sliced_df["a"] == 4).all()
original_series = Series([1, 2, 3, 4, 5, 6])
sliced_series = original_series.iloc[:]
assert sliced_series is not original_series
# should also be a shallow copy
original_series[:3] = [7, 8, 9]
assert all(sliced_series[:3] == [7, 8, 9])
def test_indexing_zerodim_np_array(self):
# GH24919
df = DataFrame([[1, 2], [3, 4]])
result = df.iloc[np.array(0)]
s = Series([1, 2], name=0)
tm.assert_series_equal(result, s)
def test_series_indexing_zerodim_np_array(self):
# GH24919
s = Series([1, 2])
result = s.iloc[np.array(0)]
assert result == 1
@pytest.mark.xfail(reason="https://github.com/pandas-dev/pandas/issues/33457")
def test_iloc_setitem_categorical_updates_inplace(self):
# Mixed dtype ensures we go through take_split_path in setitem_with_indexer
cat = Categorical(["A", "B", "C"])
df = DataFrame({1: cat, 2: [1, 2, 3]})
# This should modify our original values in-place
df.iloc[:, 0] = cat[::-1]
expected = Categorical(["C", "B", "A"])
tm.assert_categorical_equal(cat, expected)
def test_iloc_with_boolean_operation(self):
# GH 20627
result = DataFrame([[0, 1], [2, 3], [4, 5], [6, np.nan]])
result.iloc[result.index <= 2] *= 2
expected = DataFrame([[0, 2], [4, 6], [8, 10], [6, np.nan]])
tm.assert_frame_equal(result, expected)
result.iloc[result.index > 2] *= 2
expected = DataFrame([[0, 2], [4, 6], [8, 10], [12, np.nan]])
tm.assert_frame_equal(result, expected)
result.iloc[[True, True, False, False]] *= 2
expected = DataFrame([[0, 4], [8, 12], [8, 10], [12, np.nan]])
tm.assert_frame_equal(result, expected)
result.iloc[[False, False, True, True]] /= 2
expected = DataFrame([[0.0, 4.0], [8.0, 12.0], [4.0, 5.0], [6.0, np.nan]])
tm.assert_frame_equal(result, expected)
def test_iloc_getitem_singlerow_slice_categoricaldtype_gives_series(self):
# GH#29521
df = DataFrame({"x": Categorical("a b c d e".split())})
result = df.iloc[0]
raw_cat = Categorical(["a"], categories=["a", "b", "c", "d", "e"])
expected = Series(raw_cat, index=["x"], name=0, dtype="category")
tm.assert_series_equal(result, expected)
def test_iloc_getitem_categorical_values(self):
# GH#14580
# test iloc() on Series with Categorical data
ser = Series([1, 2, 3]).astype("category")
# get slice
result = ser.iloc[0:2]
expected = Series([1, 2]).astype(CategoricalDtype([1, 2, 3]))
tm.assert_series_equal(result, expected)
# get list of indexes
result = ser.iloc[[0, 1]]
expected = Series([1, 2]).astype(CategoricalDtype([1, 2, 3]))
tm.assert_series_equal(result, expected)
# get boolean array
result = ser.iloc[[True, False, False]]
expected = Series([1]).astype(CategoricalDtype([1, 2, 3]))
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("value", [None, NaT, np.nan])
def test_iloc_setitem_td64_values_cast_na(self, value):
# GH#18586
series = Series([0, 1, 2], dtype="timedelta64[ns]")
series.iloc[0] = value
expected = Series([NaT, 1, 2], dtype="timedelta64[ns]")
tm.assert_series_equal(series, expected)
def test_iloc_setitem_empty_frame_raises_with_3d_ndarray(self):
idx = Index([])
obj = DataFrame(np.random.randn(len(idx), len(idx)), index=idx, columns=idx)
nd3 = np.random.randint(5, size=(2, 2, 2))
msg = f"Cannot set values with ndim > {obj.ndim}"
with pytest.raises(ValueError, match=msg):
obj.iloc[nd3] = 0
@pytest.mark.parametrize("indexer", [lambda x: x.loc, lambda x: x.iloc])
def test_iloc_getitem_read_only_values(self, indexer):
# GH#10043 this is fundamentally a test for iloc, but test loc while
# we're here
rw_array = np.eye(10)
rw_df = DataFrame(rw_array)
ro_array = np.eye(10)
ro_array.setflags(write=False)
ro_df = DataFrame(ro_array)
tm.assert_frame_equal(indexer(rw_df)[[1, 2, 3]], indexer(ro_df)[[1, 2, 3]])
tm.assert_frame_equal(indexer(rw_df)[[1]], indexer(ro_df)[[1]])
tm.assert_series_equal(indexer(rw_df)[1], indexer(ro_df)[1])
tm.assert_frame_equal(indexer(rw_df)[1:3], indexer(ro_df)[1:3])
def test_iloc_getitem_readonly_key(self):
# GH#17192 iloc with read-only array raising TypeError
df = DataFrame({"data": np.ones(100, dtype="float64")})
indices = np.array([1, 3, 6])
indices.flags.writeable = False
result = df.iloc[indices]
expected = df.loc[[1, 3, 6]]
tm.assert_frame_equal(result, expected)
result = df["data"].iloc[indices]
expected = df["data"].loc[[1, 3, 6]]
tm.assert_series_equal(result, expected)
def test_iloc_assign_series_to_df_cell(self):
# GH 37593
df = DataFrame(columns=["a"], index=[0])
df.iloc[0, 0] = Series([1, 2, 3])
expected = DataFrame({"a": [Series([1, 2, 3])]}, columns=["a"], index=[0])
tm.assert_frame_equal(df, expected)
@pytest.mark.parametrize("klass", [list, np.array])
def test_iloc_setitem_bool_indexer(self, klass):
# GH#36741
df = DataFrame({"flag": ["x", "y", "z"], "value": [1, 3, 4]})
indexer = klass([True, False, False])
df.iloc[indexer, 1] = df.iloc[indexer, 1] * 2
expected = DataFrame({"flag": ["x", "y", "z"], "value": [2, 3, 4]})
tm.assert_frame_equal(df, expected)
@pytest.mark.parametrize("indexer", [[1], slice(1, 2)])
def test_iloc_setitem_pure_position_based(self, indexer):
# GH#22046
df1 = DataFrame({"a2": [11, 12, 13], "b2": [14, 15, 16]})
df2 = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
df2.iloc[:, indexer] = df1.iloc[:, [0]]
expected = DataFrame({"a": [1, 2, 3], "b": [11, 12, 13], "c": [7, 8, 9]})
tm.assert_frame_equal(df2, expected)
def test_iloc_setitem_dictionary_value(self):
# GH#37728
df = DataFrame({"x": [1, 2], "y": [2, 2]})
rhs = {"x": 9, "y": 99}
df.iloc[1] = rhs
expected = DataFrame({"x": [1, 9], "y": [2, 99]})
tm.assert_frame_equal(df, expected)
class TestILocErrors:
# NB: this test should work for _any_ Series we can pass as
# series_with_simple_index
def test_iloc_float_raises(self, series_with_simple_index, frame_or_series):
# GH#4892
# float_indexers should raise exceptions
# on appropriate Index types & accessors
# this duplicates the code below
# but is specifically testing for the error
# message
obj = series_with_simple_index
if frame_or_series is DataFrame:
obj = obj.to_frame()
msg = "Cannot index by location index with a non-integer key"
with pytest.raises(TypeError, match=msg):
obj.iloc[3.0]
with pytest.raises(IndexError, match=_slice_iloc_msg):
obj.iloc[3.0] = 0
class TestILocSetItemDuplicateColumns:
def test_iloc_setitem_scalar_duplicate_columns(self):
# GH#15686, duplicate columns and mixed dtype
df1 = DataFrame([{"A": None, "B": 1}, {"A": 2, "B": 2}])
df2 = DataFrame([{"A": 3, "B": 3}, {"A": 4, "B": 4}])
df = concat([df1, df2], axis=1)
df.iloc[0, 0] = -1
assert df.iloc[0, 0] == -1
assert df.iloc[0, 2] == 3
assert df.dtypes.iloc[2] == np.int64
def test_iloc_setitem_list_duplicate_columns(self):
# GH#22036 setting with same-sized list
df = DataFrame([[0, "str", "str2"]], columns=["a", "b", "b"])
df.iloc[:, 2] = ["str3"]
expected = DataFrame([[0, "str", "str3"]], columns=["a", "b", "b"])
tm.assert_frame_equal(df, expected)
def test_iloc_setitem_series_duplicate_columns(self):
df = DataFrame(
np.arange(8, dtype=np.int64).reshape(2, 4), columns=["A", "B", "A", "B"]
)
df.iloc[:, 0] = df.iloc[:, 0].astype(np.float64)
assert df.dtypes.iloc[2] == np.int64
class TestILocCallable:
def test_frame_iloc_getitem_callable(self):
# GH#11485
df = DataFrame({"X": [1, 2, 3, 4], "Y": list("aabb")}, index=list("ABCD"))
# return location
res = df.iloc[lambda x: [1, 3]]
tm.assert_frame_equal(res, df.iloc[[1, 3]])
res = df.iloc[lambda x: [1, 3], :]
tm.assert_frame_equal(res, df.iloc[[1, 3], :])
res = df.iloc[lambda x: [1, 3], lambda x: 0]
tm.assert_series_equal(res, df.iloc[[1, 3], 0])
res = df.iloc[lambda x: [1, 3], lambda x: [0]]
tm.assert_frame_equal(res, df.iloc[[1, 3], [0]])
# mixture
res = df.iloc[[1, 3], lambda x: 0]
tm.assert_series_equal(res, df.iloc[[1, 3], 0])
res = df.iloc[[1, 3], lambda x: [0]]
tm.assert_frame_equal(res, df.iloc[[1, 3], [0]])
res = df.iloc[lambda x: [1, 3], 0]
tm.assert_series_equal(res, df.iloc[[1, 3], 0])
res = df.iloc[lambda x: [1, 3], [0]]
tm.assert_frame_equal(res, df.iloc[[1, 3], [0]])
def test_frame_iloc_setitem_callable(self):
# GH#11485
df = DataFrame({"X": [1, 2, 3, 4], "Y": list("aabb")}, index=list("ABCD"))
# return location
res = df.copy()
res.iloc[lambda x: [1, 3]] = 0
exp = df.copy()
exp.iloc[[1, 3]] = 0
tm.assert_frame_equal(res, exp)
res = df.copy()
res.iloc[lambda x: [1, 3], :] = -1
exp = df.copy()
exp.iloc[[1, 3], :] = -1
tm.assert_frame_equal(res, exp)
res = df.copy()
res.iloc[lambda x: [1, 3], lambda x: 0] = 5
exp = df.copy()
exp.iloc[[1, 3], 0] = 5
tm.assert_frame_equal(res, exp)
res = df.copy()
res.iloc[lambda x: [1, 3], lambda x: [0]] = 25
exp = df.copy()
exp.iloc[[1, 3], [0]] = 25
tm.assert_frame_equal(res, exp)
# mixture
res = df.copy()
res.iloc[[1, 3], lambda x: 0] = -3
exp = df.copy()
exp.iloc[[1, 3], 0] = -3
tm.assert_frame_equal(res, exp)
res = df.copy()
res.iloc[[1, 3], lambda x: [0]] = -5
exp = df.copy()
exp.iloc[[1, 3], [0]] = -5
tm.assert_frame_equal(res, exp)
res = df.copy()
res.iloc[lambda x: [1, 3], 0] = 10
exp = df.copy()
exp.iloc[[1, 3], 0] = 10
tm.assert_frame_equal(res, exp)
res = df.copy()
res.iloc[lambda x: [1, 3], [0]] = [-5, -5]
exp = df.copy()
exp.iloc[[1, 3], [0]] = [-5, -5]
tm.assert_frame_equal(res, exp)
class TestILocSeries:
def test_iloc(self):
ser = Series(np.random.randn(10), index=list(range(0, 20, 2)))
for i in range(len(ser)):
result = ser.iloc[i]
exp = ser[ser.index[i]]
tm.assert_almost_equal(result, exp)
# pass a slice
result = ser.iloc[slice(1, 3)]
expected = ser.loc[2:4]
tm.assert_series_equal(result, expected)
# test slice is a view
result[:] = 0
assert (ser[1:3] == 0).all()
# list of integers
result = ser.iloc[[0, 2, 3, 4, 5]]
expected = ser.reindex(ser.index[[0, 2, 3, 4, 5]])
tm.assert_series_equal(result, expected)
def test_iloc_getitem_nonunique(self):
ser = Series([0, 1, 2], index=[0, 1, 0])
assert ser.iloc[2] == 2
def test_iloc_setitem_pure_position_based(self):
# GH#22046
ser1 = Series([1, 2, 3])
ser2 = Series([4, 5, 6], index=[1, 0, 2])
ser1.iloc[1:3] = ser2.iloc[1:3]
expected = Series([1, 5, 6])
tm.assert_series_equal(ser1, expected)