610 lines
18 KiB
Python
610 lines
18 KiB
Python
|
from collections import (
|
||
|
|
Counter,
|
||
|
|
defaultdict,
|
||
|
|
)
|
||
|
|
from decimal import Decimal
|
||
|
|
import math
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import pytest
|
||
|
|
|
||
|
|
import pandas as pd
|
||
|
|
from pandas import (
|
||
|
|
DataFrame,
|
||
|
|
Index,
|
||
|
|
MultiIndex,
|
||
|
|
Series,
|
||
|
|
bdate_range,
|
||
|
|
date_range,
|
||
|
|
isna,
|
||
|
|
timedelta_range,
|
||
|
|
)
|
||
|
|
import pandas._testing as tm
|
||
|
|
|
||
|
|
|
||
|
|
def test_series_map_box_timedelta():
|
||
|
|
# GH#11349
|
||
|
|
ser = Series(timedelta_range("1 day 1 s", periods=5, freq="h"))
|
||
|
|
|
||
|
|
def f(x):
|
||
|
|
return x.total_seconds()
|
||
|
|
|
||
|
|
ser.map(f)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_callable(datetime_series):
|
||
|
|
with np.errstate(all="ignore"):
|
||
|
|
tm.assert_series_equal(datetime_series.map(np.sqrt), np.sqrt(datetime_series))
|
||
|
|
|
||
|
|
# map function element-wise
|
||
|
|
tm.assert_series_equal(datetime_series.map(math.exp), np.exp(datetime_series))
|
||
|
|
|
||
|
|
# empty series
|
||
|
|
s = Series(dtype=object, name="foo", index=Index([], name="bar"))
|
||
|
|
rs = s.map(lambda x: x)
|
||
|
|
tm.assert_series_equal(s, rs)
|
||
|
|
|
||
|
|
# check all metadata (GH 9322)
|
||
|
|
assert s is not rs
|
||
|
|
assert s.index is rs.index
|
||
|
|
assert s.dtype == rs.dtype
|
||
|
|
assert s.name == rs.name
|
||
|
|
|
||
|
|
# index but no data
|
||
|
|
s = Series(index=[1, 2, 3], dtype=np.float64)
|
||
|
|
rs = s.map(lambda x: x)
|
||
|
|
tm.assert_series_equal(s, rs)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_same_length_inference_bug():
|
||
|
|
s = Series([1, 2])
|
||
|
|
|
||
|
|
def f(x):
|
||
|
|
return (x, x + 1)
|
||
|
|
|
||
|
|
s = Series([1, 2, 3])
|
||
|
|
result = s.map(f)
|
||
|
|
expected = Series([(1, 2), (2, 3), (3, 4)])
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
s = Series(["foo,bar"])
|
||
|
|
result = s.map(lambda x: x.split(","))
|
||
|
|
expected = Series([("foo", "bar")])
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_series_map_box_timestamps():
|
||
|
|
# GH#2689, GH#2627
|
||
|
|
ser = Series(date_range("1/1/2000", periods=3))
|
||
|
|
|
||
|
|
def func(x):
|
||
|
|
return (x.hour, x.day, x.month)
|
||
|
|
|
||
|
|
result = ser.map(func)
|
||
|
|
expected = Series([(0, 1, 1), (0, 2, 1), (0, 3, 1)])
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_series_stringdtype(any_string_dtype, using_infer_string):
|
||
|
|
# map test on StringDType, GH#40823
|
||
|
|
ser1 = Series(
|
||
|
|
data=["cat", "dog", "rabbit"],
|
||
|
|
index=["id1", "id2", "id3"],
|
||
|
|
dtype=any_string_dtype,
|
||
|
|
)
|
||
|
|
ser2 = Series(["id3", "id2", "id1", "id7000"], dtype=any_string_dtype)
|
||
|
|
result = ser2.map(ser1)
|
||
|
|
|
||
|
|
item = pd.NA
|
||
|
|
if ser2.dtype == object:
|
||
|
|
item = np.nan
|
||
|
|
|
||
|
|
expected = Series(data=["rabbit", "dog", "cat", item], dtype=any_string_dtype)
|
||
|
|
if using_infer_string and any_string_dtype == "object":
|
||
|
|
expected = expected.astype("string[pyarrow_numpy]")
|
||
|
|
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"data, expected_dtype",
|
||
|
|
[(["1-1", "1-1", np.nan], "category"), (["1-1", "1-2", np.nan], object)],
|
||
|
|
)
|
||
|
|
def test_map_categorical_with_nan_values(data, expected_dtype, using_infer_string):
|
||
|
|
# GH 20714 bug fixed in: GH 24275
|
||
|
|
def func(val):
|
||
|
|
return val.split("-")[0]
|
||
|
|
|
||
|
|
s = Series(data, dtype="category")
|
||
|
|
|
||
|
|
result = s.map(func, na_action="ignore")
|
||
|
|
if using_infer_string and expected_dtype == object:
|
||
|
|
expected_dtype = "string[pyarrow_numpy]"
|
||
|
|
expected = Series(["1", "1", np.nan], dtype=expected_dtype)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_empty_integer_series():
|
||
|
|
# GH52384
|
||
|
|
s = Series([], dtype=int)
|
||
|
|
result = s.map(lambda x: x)
|
||
|
|
tm.assert_series_equal(result, s)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_empty_integer_series_with_datetime_index():
|
||
|
|
# GH 21245
|
||
|
|
s = Series([], index=date_range(start="2018-01-01", periods=0), dtype=int)
|
||
|
|
result = s.map(lambda x: x)
|
||
|
|
tm.assert_series_equal(result, s)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("func", [str, lambda x: str(x)])
|
||
|
|
def test_map_simple_str_callables_same_as_astype(
|
||
|
|
string_series, func, using_infer_string
|
||
|
|
):
|
||
|
|
# test that we are evaluating row-by-row first
|
||
|
|
# before vectorized evaluation
|
||
|
|
result = string_series.map(func)
|
||
|
|
expected = string_series.astype(
|
||
|
|
str if not using_infer_string else "string[pyarrow_numpy]"
|
||
|
|
)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_list_raises(string_series):
|
||
|
|
with pytest.raises(TypeError, match="'list' object is not callable"):
|
||
|
|
string_series.map([lambda x: x])
|
||
|
|
|
||
|
|
|
||
|
|
def test_map():
|
||
|
|
data = {
|
||
|
|
"A": [0.0, 1.0, 2.0, 3.0, 4.0],
|
||
|
|
"B": [0.0, 1.0, 0.0, 1.0, 0.0],
|
||
|
|
"C": ["foo1", "foo2", "foo3", "foo4", "foo5"],
|
||
|
|
"D": bdate_range("1/1/2009", periods=5),
|
||
|
|
}
|
||
|
|
|
||
|
|
source = Series(data["B"], index=data["C"])
|
||
|
|
target = Series(data["C"][:4], index=data["D"][:4])
|
||
|
|
|
||
|
|
merged = target.map(source)
|
||
|
|
|
||
|
|
for k, v in merged.items():
|
||
|
|
assert v == source[target[k]]
|
||
|
|
|
||
|
|
# input could be a dict
|
||
|
|
merged = target.map(source.to_dict())
|
||
|
|
|
||
|
|
for k, v in merged.items():
|
||
|
|
assert v == source[target[k]]
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_datetime(datetime_series):
|
||
|
|
# function
|
||
|
|
result = datetime_series.map(lambda x: x * 2)
|
||
|
|
tm.assert_series_equal(result, datetime_series * 2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_category():
|
||
|
|
# GH 10324
|
||
|
|
a = Series([1, 2, 3, 4])
|
||
|
|
b = Series(["even", "odd", "even", "odd"], dtype="category")
|
||
|
|
c = Series(["even", "odd", "even", "odd"])
|
||
|
|
|
||
|
|
exp = Series(["odd", "even", "odd", np.nan], dtype="category")
|
||
|
|
tm.assert_series_equal(a.map(b), exp)
|
||
|
|
exp = Series(["odd", "even", "odd", np.nan])
|
||
|
|
tm.assert_series_equal(a.map(c), exp)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_category_numeric():
|
||
|
|
a = Series(["a", "b", "c", "d"])
|
||
|
|
b = Series([1, 2, 3, 4], index=pd.CategoricalIndex(["b", "c", "d", "e"]))
|
||
|
|
c = Series([1, 2, 3, 4], index=Index(["b", "c", "d", "e"]))
|
||
|
|
|
||
|
|
exp = Series([np.nan, 1, 2, 3])
|
||
|
|
tm.assert_series_equal(a.map(b), exp)
|
||
|
|
exp = Series([np.nan, 1, 2, 3])
|
||
|
|
tm.assert_series_equal(a.map(c), exp)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_category_string():
|
||
|
|
a = Series(["a", "b", "c", "d"])
|
||
|
|
b = Series(
|
||
|
|
["B", "C", "D", "E"],
|
||
|
|
dtype="category",
|
||
|
|
index=pd.CategoricalIndex(["b", "c", "d", "e"]),
|
||
|
|
)
|
||
|
|
c = Series(["B", "C", "D", "E"], index=Index(["b", "c", "d", "e"]))
|
||
|
|
|
||
|
|
exp = Series(
|
||
|
|
pd.Categorical([np.nan, "B", "C", "D"], categories=["B", "C", "D", "E"])
|
||
|
|
)
|
||
|
|
tm.assert_series_equal(a.map(b), exp)
|
||
|
|
exp = Series([np.nan, "B", "C", "D"])
|
||
|
|
tm.assert_series_equal(a.map(c), exp)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_empty(request, index):
|
||
|
|
if isinstance(index, MultiIndex):
|
||
|
|
request.applymarker(
|
||
|
|
pytest.mark.xfail(
|
||
|
|
reason="Initializing a Series from a MultiIndex is not supported"
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
s = Series(index)
|
||
|
|
result = s.map({})
|
||
|
|
|
||
|
|
expected = Series(np.nan, index=s.index)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_compat():
|
||
|
|
# related GH 8024
|
||
|
|
s = Series([True, True, False], index=[1, 2, 3])
|
||
|
|
result = s.map({True: "foo", False: "bar"})
|
||
|
|
expected = Series(["foo", "foo", "bar"], index=[1, 2, 3])
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_int():
|
||
|
|
left = Series({"a": 1.0, "b": 2.0, "c": 3.0, "d": 4})
|
||
|
|
right = Series({1: 11, 2: 22, 3: 33})
|
||
|
|
|
||
|
|
assert left.dtype == np.float64
|
||
|
|
assert issubclass(right.dtype.type, np.integer)
|
||
|
|
|
||
|
|
merged = left.map(right)
|
||
|
|
assert merged.dtype == np.float64
|
||
|
|
assert isna(merged["d"])
|
||
|
|
assert not isna(merged["c"])
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_type_inference():
|
||
|
|
s = Series(range(3))
|
||
|
|
s2 = s.map(lambda x: np.where(x == 0, 0, 1))
|
||
|
|
assert issubclass(s2.dtype.type, np.integer)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_decimal(string_series):
|
||
|
|
result = string_series.map(lambda x: Decimal(str(x)))
|
||
|
|
assert result.dtype == np.object_
|
||
|
|
assert isinstance(result.iloc[0], Decimal)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_na_exclusion():
|
||
|
|
s = Series([1.5, np.nan, 3, np.nan, 5])
|
||
|
|
|
||
|
|
result = s.map(lambda x: x * 2, na_action="ignore")
|
||
|
|
exp = s * 2
|
||
|
|
tm.assert_series_equal(result, exp)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_dict_with_tuple_keys():
|
||
|
|
"""
|
||
|
|
Due to new MultiIndex-ing behaviour in v0.14.0,
|
||
|
|
dicts with tuple keys passed to map were being
|
||
|
|
converted to a multi-index, preventing tuple values
|
||
|
|
from being mapped properly.
|
||
|
|
"""
|
||
|
|
# GH 18496
|
||
|
|
df = DataFrame({"a": [(1,), (2,), (3, 4), (5, 6)]})
|
||
|
|
label_mappings = {(1,): "A", (2,): "B", (3, 4): "A", (5, 6): "B"}
|
||
|
|
|
||
|
|
df["labels"] = df["a"].map(label_mappings)
|
||
|
|
df["expected_labels"] = Series(["A", "B", "A", "B"], index=df.index)
|
||
|
|
# All labels should be filled now
|
||
|
|
tm.assert_series_equal(df["labels"], df["expected_labels"], check_names=False)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_counter():
|
||
|
|
s = Series(["a", "b", "c"], index=[1, 2, 3])
|
||
|
|
counter = Counter()
|
||
|
|
counter["b"] = 5
|
||
|
|
counter["c"] += 1
|
||
|
|
result = s.map(counter)
|
||
|
|
expected = Series([0, 5, 1], index=[1, 2, 3])
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_defaultdict():
|
||
|
|
s = Series([1, 2, 3], index=["a", "b", "c"])
|
||
|
|
default_dict = defaultdict(lambda: "blank")
|
||
|
|
default_dict[1] = "stuff"
|
||
|
|
result = s.map(default_dict)
|
||
|
|
expected = Series(["stuff", "blank", "blank"], index=["a", "b", "c"])
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_dict_na_key():
|
||
|
|
# https://github.com/pandas-dev/pandas/issues/17648
|
||
|
|
# Checks that np.nan key is appropriately mapped
|
||
|
|
s = Series([1, 2, np.nan])
|
||
|
|
expected = Series(["a", "b", "c"])
|
||
|
|
result = s.map({1: "a", 2: "b", np.nan: "c"})
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("na_action", [None, "ignore"])
|
||
|
|
def test_map_defaultdict_na_key(na_action):
|
||
|
|
# GH 48813
|
||
|
|
s = Series([1, 2, np.nan])
|
||
|
|
default_map = defaultdict(lambda: "missing", {1: "a", 2: "b", np.nan: "c"})
|
||
|
|
result = s.map(default_map, na_action=na_action)
|
||
|
|
expected = Series({0: "a", 1: "b", 2: "c" if na_action is None else np.nan})
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("na_action", [None, "ignore"])
|
||
|
|
def test_map_defaultdict_missing_key(na_action):
|
||
|
|
# GH 48813
|
||
|
|
s = Series([1, 2, np.nan])
|
||
|
|
default_map = defaultdict(lambda: "missing", {1: "a", 2: "b", 3: "c"})
|
||
|
|
result = s.map(default_map, na_action=na_action)
|
||
|
|
expected = Series({0: "a", 1: "b", 2: "missing" if na_action is None else np.nan})
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("na_action", [None, "ignore"])
|
||
|
|
def test_map_defaultdict_unmutated(na_action):
|
||
|
|
# GH 48813
|
||
|
|
s = Series([1, 2, np.nan])
|
||
|
|
default_map = defaultdict(lambda: "missing", {1: "a", 2: "b", np.nan: "c"})
|
||
|
|
expected_default_map = default_map.copy()
|
||
|
|
s.map(default_map, na_action=na_action)
|
||
|
|
assert default_map == expected_default_map
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("arg_func", [dict, Series])
|
||
|
|
def test_map_dict_ignore_na(arg_func):
|
||
|
|
# GH#47527
|
||
|
|
mapping = arg_func({1: 10, np.nan: 42})
|
||
|
|
ser = Series([1, np.nan, 2])
|
||
|
|
result = ser.map(mapping, na_action="ignore")
|
||
|
|
expected = Series([10, np.nan, np.nan])
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_defaultdict_ignore_na():
|
||
|
|
# GH#47527
|
||
|
|
mapping = defaultdict(int, {1: 10, np.nan: 42})
|
||
|
|
ser = Series([1, np.nan, 2])
|
||
|
|
result = ser.map(mapping)
|
||
|
|
expected = Series([10, 42, 0])
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"na_action, expected",
|
||
|
|
[(None, Series([10.0, 42.0, np.nan])), ("ignore", Series([10, np.nan, np.nan]))],
|
||
|
|
)
|
||
|
|
def test_map_categorical_na_ignore(na_action, expected):
|
||
|
|
# GH#47527
|
||
|
|
values = pd.Categorical([1, np.nan, 2], categories=[10, 1, 2])
|
||
|
|
ser = Series(values)
|
||
|
|
result = ser.map({1: 10, np.nan: 42}, na_action=na_action)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_dict_subclass_with_missing():
|
||
|
|
"""
|
||
|
|
Test Series.map with a dictionary subclass that defines __missing__,
|
||
|
|
i.e. sets a default value (GH #15999).
|
||
|
|
"""
|
||
|
|
|
||
|
|
class DictWithMissing(dict):
|
||
|
|
def __missing__(self, key):
|
||
|
|
return "missing"
|
||
|
|
|
||
|
|
s = Series([1, 2, 3])
|
||
|
|
dictionary = DictWithMissing({3: "three"})
|
||
|
|
result = s.map(dictionary)
|
||
|
|
expected = Series(["missing", "missing", "three"])
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_dict_subclass_without_missing():
|
||
|
|
class DictWithoutMissing(dict):
|
||
|
|
pass
|
||
|
|
|
||
|
|
s = Series([1, 2, 3])
|
||
|
|
dictionary = DictWithoutMissing({3: "three"})
|
||
|
|
result = s.map(dictionary)
|
||
|
|
expected = Series([np.nan, np.nan, "three"])
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_abc_mapping(non_dict_mapping_subclass):
|
||
|
|
# https://github.com/pandas-dev/pandas/issues/29733
|
||
|
|
# Check collections.abc.Mapping support as mapper for Series.map
|
||
|
|
s = Series([1, 2, 3])
|
||
|
|
not_a_dictionary = non_dict_mapping_subclass({3: "three"})
|
||
|
|
result = s.map(not_a_dictionary)
|
||
|
|
expected = Series([np.nan, np.nan, "three"])
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_abc_mapping_with_missing(non_dict_mapping_subclass):
|
||
|
|
# https://github.com/pandas-dev/pandas/issues/29733
|
||
|
|
# Check collections.abc.Mapping support as mapper for Series.map
|
||
|
|
class NonDictMappingWithMissing(non_dict_mapping_subclass):
|
||
|
|
def __missing__(self, key):
|
||
|
|
return "missing"
|
||
|
|
|
||
|
|
s = Series([1, 2, 3])
|
||
|
|
not_a_dictionary = NonDictMappingWithMissing({3: "three"})
|
||
|
|
result = s.map(not_a_dictionary)
|
||
|
|
# __missing__ is a dict concept, not a Mapping concept,
|
||
|
|
# so it should not change the result!
|
||
|
|
expected = Series([np.nan, np.nan, "three"])
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_box_dt64(unit):
|
||
|
|
vals = [pd.Timestamp("2011-01-01"), pd.Timestamp("2011-01-02")]
|
||
|
|
ser = Series(vals).dt.as_unit(unit)
|
||
|
|
assert ser.dtype == f"datetime64[{unit}]"
|
||
|
|
# boxed value must be Timestamp instance
|
||
|
|
res = ser.map(lambda x: f"{type(x).__name__}_{x.day}_{x.tz}")
|
||
|
|
exp = Series(["Timestamp_1_None", "Timestamp_2_None"])
|
||
|
|
tm.assert_series_equal(res, exp)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_box_dt64tz(unit):
|
||
|
|
vals = [
|
||
|
|
pd.Timestamp("2011-01-01", tz="US/Eastern"),
|
||
|
|
pd.Timestamp("2011-01-02", tz="US/Eastern"),
|
||
|
|
]
|
||
|
|
ser = Series(vals).dt.as_unit(unit)
|
||
|
|
assert ser.dtype == f"datetime64[{unit}, US/Eastern]"
|
||
|
|
res = ser.map(lambda x: f"{type(x).__name__}_{x.day}_{x.tz}")
|
||
|
|
exp = Series(["Timestamp_1_US/Eastern", "Timestamp_2_US/Eastern"])
|
||
|
|
tm.assert_series_equal(res, exp)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_box_td64(unit):
|
||
|
|
# timedelta
|
||
|
|
vals = [pd.Timedelta("1 days"), pd.Timedelta("2 days")]
|
||
|
|
ser = Series(vals).dt.as_unit(unit)
|
||
|
|
assert ser.dtype == f"timedelta64[{unit}]"
|
||
|
|
res = ser.map(lambda x: f"{type(x).__name__}_{x.days}")
|
||
|
|
exp = Series(["Timedelta_1", "Timedelta_2"])
|
||
|
|
tm.assert_series_equal(res, exp)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_box_period():
|
||
|
|
# period
|
||
|
|
vals = [pd.Period("2011-01-01", freq="M"), pd.Period("2011-01-02", freq="M")]
|
||
|
|
ser = Series(vals)
|
||
|
|
assert ser.dtype == "Period[M]"
|
||
|
|
res = ser.map(lambda x: f"{type(x).__name__}_{x.freqstr}")
|
||
|
|
exp = Series(["Period_M", "Period_M"])
|
||
|
|
tm.assert_series_equal(res, exp)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("na_action", [None, "ignore"])
|
||
|
|
def test_map_categorical(na_action, using_infer_string):
|
||
|
|
values = pd.Categorical(list("ABBABCD"), categories=list("DCBA"), ordered=True)
|
||
|
|
s = Series(values, name="XX", index=list("abcdefg"))
|
||
|
|
|
||
|
|
result = s.map(lambda x: x.lower(), na_action=na_action)
|
||
|
|
exp_values = pd.Categorical(list("abbabcd"), categories=list("dcba"), ordered=True)
|
||
|
|
exp = Series(exp_values, name="XX", index=list("abcdefg"))
|
||
|
|
tm.assert_series_equal(result, exp)
|
||
|
|
tm.assert_categorical_equal(result.values, exp_values)
|
||
|
|
|
||
|
|
result = s.map(lambda x: "A", na_action=na_action)
|
||
|
|
exp = Series(["A"] * 7, name="XX", index=list("abcdefg"))
|
||
|
|
tm.assert_series_equal(result, exp)
|
||
|
|
assert result.dtype == object if not using_infer_string else "string"
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"na_action, expected",
|
||
|
|
(
|
||
|
|
[None, Series(["A", "B", "nan"], name="XX")],
|
||
|
|
[
|
||
|
|
"ignore",
|
||
|
|
Series(
|
||
|
|
["A", "B", np.nan],
|
||
|
|
name="XX",
|
||
|
|
dtype=pd.CategoricalDtype(list("DCBA"), True),
|
||
|
|
),
|
||
|
|
],
|
||
|
|
),
|
||
|
|
)
|
||
|
|
def test_map_categorical_na_action(na_action, expected):
|
||
|
|
dtype = pd.CategoricalDtype(list("DCBA"), ordered=True)
|
||
|
|
values = pd.Categorical(list("AB") + [np.nan], dtype=dtype)
|
||
|
|
s = Series(values, name="XX")
|
||
|
|
result = s.map(str, na_action=na_action)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_datetimetz():
|
||
|
|
values = date_range("2011-01-01", "2011-01-02", freq="h").tz_localize("Asia/Tokyo")
|
||
|
|
s = Series(values, name="XX")
|
||
|
|
|
||
|
|
# keep tz
|
||
|
|
result = s.map(lambda x: x + pd.offsets.Day())
|
||
|
|
exp_values = date_range("2011-01-02", "2011-01-03", freq="h").tz_localize(
|
||
|
|
"Asia/Tokyo"
|
||
|
|
)
|
||
|
|
exp = Series(exp_values, name="XX")
|
||
|
|
tm.assert_series_equal(result, exp)
|
||
|
|
|
||
|
|
result = s.map(lambda x: x.hour)
|
||
|
|
exp = Series(list(range(24)) + [0], name="XX", dtype=np.int64)
|
||
|
|
tm.assert_series_equal(result, exp)
|
||
|
|
|
||
|
|
# not vectorized
|
||
|
|
def f(x):
|
||
|
|
if not isinstance(x, pd.Timestamp):
|
||
|
|
raise ValueError
|
||
|
|
return str(x.tz)
|
||
|
|
|
||
|
|
result = s.map(f)
|
||
|
|
exp = Series(["Asia/Tokyo"] * 25, name="XX")
|
||
|
|
tm.assert_series_equal(result, exp)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"vals,mapping,exp",
|
||
|
|
[
|
||
|
|
(list("abc"), {np.nan: "not NaN"}, [np.nan] * 3 + ["not NaN"]),
|
||
|
|
(list("abc"), {"a": "a letter"}, ["a letter"] + [np.nan] * 3),
|
||
|
|
(list(range(3)), {0: 42}, [42] + [np.nan] * 3),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_map_missing_mixed(vals, mapping, exp, using_infer_string):
|
||
|
|
# GH20495
|
||
|
|
s = Series(vals + [np.nan])
|
||
|
|
result = s.map(mapping)
|
||
|
|
exp = Series(exp)
|
||
|
|
if using_infer_string and mapping == {np.nan: "not NaN"}:
|
||
|
|
exp.iloc[-1] = np.nan
|
||
|
|
tm.assert_series_equal(result, exp)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_scalar_on_date_time_index_aware_series():
|
||
|
|
# GH 25959
|
||
|
|
# Calling map on a localized time series should not cause an error
|
||
|
|
series = Series(
|
||
|
|
np.arange(10, dtype=np.float64),
|
||
|
|
index=date_range("2020-01-01", periods=10, tz="UTC"),
|
||
|
|
name="ts",
|
||
|
|
)
|
||
|
|
result = Series(series.index).map(lambda x: 1)
|
||
|
|
tm.assert_series_equal(result, Series(np.ones(len(series)), dtype="int64"))
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_float_to_string_precision():
|
||
|
|
# GH 13228
|
||
|
|
ser = Series(1 / 3)
|
||
|
|
result = ser.map(lambda val: str(val)).to_dict()
|
||
|
|
expected = {0: "0.3333333333333333"}
|
||
|
|
assert result == expected
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_to_timedelta():
|
||
|
|
list_of_valid_strings = ["00:00:01", "00:00:02"]
|
||
|
|
a = pd.to_timedelta(list_of_valid_strings)
|
||
|
|
b = Series(list_of_valid_strings).map(pd.to_timedelta)
|
||
|
|
tm.assert_series_equal(Series(a), b)
|
||
|
|
|
||
|
|
list_of_strings = ["00:00:01", np.nan, pd.NaT, pd.NaT]
|
||
|
|
|
||
|
|
a = pd.to_timedelta(list_of_strings)
|
||
|
|
ser = Series(list_of_strings)
|
||
|
|
b = ser.map(pd.to_timedelta)
|
||
|
|
tm.assert_series_equal(Series(a), b)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_type():
|
||
|
|
# GH 46719
|
||
|
|
s = Series([3, "string", float], index=["a", "b", "c"])
|
||
|
|
result = s.map(type)
|
||
|
|
expected = Series([int, str, type], index=["a", "b", "c"])
|
||
|
|
tm.assert_series_equal(result, expected)
|