Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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.
756 lines
25 KiB
756 lines
25 KiB
4 years ago
|
""" test positional based indexing with iloc """
|
||
|
|
|
||
|
|
from datetime import datetime
|
||
|
|
from warnings import catch_warnings, simplefilter
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import pytest
|
||
|
|
|
||
|
|
import pandas as pd
|
||
|
|
from pandas import DataFrame, 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
|
||
|
|
|
||
|
|
|
||
|
|
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 = pd.Index([1, 2, 1])
|
||
|
|
ser = pd.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 = pd.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[pd.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)
|
||
|
|
|
||
|
|
# 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(
|
||
|
|
dict(A=np.arange(5, dtype="int64"), B=np.arange(5, 10, dtype="int64"))
|
||
|
|
)
|
||
|
|
df.iloc[2:4] = [[10, 11], [12, 13]]
|
||
|
|
expected = DataFrame(dict(A=[0, 1, 10, 12, 4], B=[5, 6, 11, 13, 9]))
|
||
|
|
tm.assert_frame_equal(df, expected)
|
||
|
|
|
||
|
|
df = DataFrame(dict(A=list("abcde"), B=np.arange(5, 10, dtype="int64")))
|
||
|
|
df.iloc[2:4] = [["x", 11], ["y", 13]]
|
||
|
|
expected = DataFrame(dict(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 = pd.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)
|
||
|
|
result = dict()
|
||
|
|
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 = tuple([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 = pd.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 = pd.Categorical(["A", "B", "C"])
|
||
|
|
df = pd.DataFrame({1: cat, 2: [1, 2, 3]})
|
||
|
|
|
||
|
|
# This should modify our original values in-place
|
||
|
|
df.iloc[:, 0] = cat[::-1]
|
||
|
|
|
||
|
|
expected = pd.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)
|
||
|
|
|
||
|
|
|
||
|
|
class TestILocSetItemDuplicateColumns:
|
||
|
|
def test_iloc_setitem_scalar_duplicate_columns(self):
|
||
|
|
# GH#15686, duplicate columns and mixed dtype
|
||
|
|
df1 = pd.DataFrame([{"A": None, "B": 1}, {"A": 2, "B": 2}])
|
||
|
|
df2 = pd.DataFrame([{"A": 3, "B": 3}, {"A": 4, "B": 4}])
|
||
|
|
df = pd.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 = pd.DataFrame([[0, "str", "str2"]], columns=["a", "b", "b"])
|
||
|
|
|
||
|
|
df.iloc[:, 2] = ["str3"]
|
||
|
|
|
||
|
|
expected = pd.DataFrame([[0, "str", "str3"]], columns=["a", "b", "b"])
|
||
|
|
tm.assert_frame_equal(df, expected)
|
||
|
|
|
||
|
|
def test_iloc_setitem_series_duplicate_columns(self):
|
||
|
|
df = pd.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
|