Why Gemfury?
Push, build, and install
RubyGems
npm packages
Python packages
Maven artifacts
PHP packages
Go Modules
Bower components
Debian packages
RPM packages
NuGet packages
from datetime import datetime
from io import StringIO
import re
from typing import Dict, List, Union
import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, Index, Series, Timestamp, date_range
import pandas._testing as tm
@pytest.fixture
def mix_ab() -> Dict[str, List[Union[int, str]]]:
return {"a": list(range(4)), "b": list("ab..")}
@pytest.fixture
def mix_abc() -> Dict[str, List[Union[float, str]]]:
return {"a": list(range(4)), "b": list("ab.."), "c": ["a", "b", np.nan, "d"]}
class TestDataFrameReplace:
def test_replace_inplace(self, datetime_frame, float_string_frame):
datetime_frame["A"][:5] = np.nan
datetime_frame["A"][-5:] = np.nan
tsframe = datetime_frame.copy()
return_value = tsframe.replace(np.nan, 0, inplace=True)
assert return_value is None
tm.assert_frame_equal(tsframe, datetime_frame.fillna(0))
# mixed type
mf = float_string_frame
mf.iloc[5:20, mf.columns.get_loc("foo")] = np.nan
mf.iloc[-10:, mf.columns.get_loc("A")] = np.nan
result = float_string_frame.replace(np.nan, 0)
expected = float_string_frame.fillna(value=0)
tm.assert_frame_equal(result, expected)
tsframe = datetime_frame.copy()
return_value = tsframe.replace([np.nan], [0], inplace=True)
assert return_value is None
tm.assert_frame_equal(tsframe, datetime_frame.fillna(0))
def test_regex_replace_scalar(self, mix_ab):
obj = {"a": list("ab.."), "b": list("efgh")}
dfobj = DataFrame(obj)
dfmix = DataFrame(mix_ab)
# simplest cases
# regex -> value
# obj frame
res = dfobj.replace(r"\s*\.\s*", np.nan, regex=True)
tm.assert_frame_equal(dfobj, res.fillna("."))
# mixed
res = dfmix.replace(r"\s*\.\s*", np.nan, regex=True)
tm.assert_frame_equal(dfmix, res.fillna("."))
# regex -> regex
# obj frame
res = dfobj.replace(r"\s*(\.)\s*", r"\1\1\1", regex=True)
objc = obj.copy()
objc["a"] = ["a", "b", "...", "..."]
expec = DataFrame(objc)
tm.assert_frame_equal(res, expec)
# with mixed
res = dfmix.replace(r"\s*(\.)\s*", r"\1\1\1", regex=True)
mixc = mix_ab.copy()
mixc["b"] = ["a", "b", "...", "..."]
expec = DataFrame(mixc)
tm.assert_frame_equal(res, expec)
# everything with compiled regexs as well
res = dfobj.replace(re.compile(r"\s*\.\s*"), np.nan, regex=True)
tm.assert_frame_equal(dfobj, res.fillna("."))
# mixed
res = dfmix.replace(re.compile(r"\s*\.\s*"), np.nan, regex=True)
tm.assert_frame_equal(dfmix, res.fillna("."))
# regex -> regex
# obj frame
res = dfobj.replace(re.compile(r"\s*(\.)\s*"), r"\1\1\1")
objc = obj.copy()
objc["a"] = ["a", "b", "...", "..."]
expec = DataFrame(objc)
tm.assert_frame_equal(res, expec)
# with mixed
res = dfmix.replace(re.compile(r"\s*(\.)\s*"), r"\1\1\1")
mixc = mix_ab.copy()
mixc["b"] = ["a", "b", "...", "..."]
expec = DataFrame(mixc)
tm.assert_frame_equal(res, expec)
res = dfmix.replace(regex=re.compile(r"\s*(\.)\s*"), value=r"\1\1\1")
mixc = mix_ab.copy()
mixc["b"] = ["a", "b", "...", "..."]
expec = DataFrame(mixc)
tm.assert_frame_equal(res, expec)
res = dfmix.replace(regex=r"\s*(\.)\s*", value=r"\1\1\1")
mixc = mix_ab.copy()
mixc["b"] = ["a", "b", "...", "..."]
expec = DataFrame(mixc)
tm.assert_frame_equal(res, expec)
def test_regex_replace_scalar_inplace(self, mix_ab):
obj = {"a": list("ab.."), "b": list("efgh")}
dfobj = DataFrame(obj)
dfmix = DataFrame(mix_ab)
# simplest cases
# regex -> value
# obj frame
res = dfobj.copy()
return_value = res.replace(r"\s*\.\s*", np.nan, regex=True, inplace=True)
assert return_value is None
tm.assert_frame_equal(dfobj, res.fillna("."))
# mixed
res = dfmix.copy()
return_value = res.replace(r"\s*\.\s*", np.nan, regex=True, inplace=True)
assert return_value is None
tm.assert_frame_equal(dfmix, res.fillna("."))
# regex -> regex
# obj frame
res = dfobj.copy()
return_value = res.replace(r"\s*(\.)\s*", r"\1\1\1", regex=True, inplace=True)
assert return_value is None
objc = obj.copy()
objc["a"] = ["a", "b", "...", "..."]
expec = DataFrame(objc)
tm.assert_frame_equal(res, expec)
# with mixed
res = dfmix.copy()
return_value = res.replace(r"\s*(\.)\s*", r"\1\1\1", regex=True, inplace=True)
assert return_value is None
mixc = mix_ab.copy()
mixc["b"] = ["a", "b", "...", "..."]
expec = DataFrame(mixc)
tm.assert_frame_equal(res, expec)
# everything with compiled regexs as well
res = dfobj.copy()
return_value = res.replace(
re.compile(r"\s*\.\s*"), np.nan, regex=True, inplace=True
)
assert return_value is None
tm.assert_frame_equal(dfobj, res.fillna("."))
# mixed
res = dfmix.copy()
return_value = res.replace(
re.compile(r"\s*\.\s*"), np.nan, regex=True, inplace=True
)
assert return_value is None
tm.assert_frame_equal(dfmix, res.fillna("."))
# regex -> regex
# obj frame
res = dfobj.copy()
return_value = res.replace(
re.compile(r"\s*(\.)\s*"), r"\1\1\1", regex=True, inplace=True
)
assert return_value is None
objc = obj.copy()
objc["a"] = ["a", "b", "...", "..."]
expec = DataFrame(objc)
tm.assert_frame_equal(res, expec)
# with mixed
res = dfmix.copy()
return_value = res.replace(
re.compile(r"\s*(\.)\s*"), r"\1\1\1", regex=True, inplace=True
)
assert return_value is None
mixc = mix_ab.copy()
mixc["b"] = ["a", "b", "...", "..."]
expec = DataFrame(mixc)
tm.assert_frame_equal(res, expec)
res = dfobj.copy()
return_value = res.replace(regex=r"\s*\.\s*", value=np.nan, inplace=True)
assert return_value is None
tm.assert_frame_equal(dfobj, res.fillna("."))
# mixed
res = dfmix.copy()
return_value = res.replace(regex=r"\s*\.\s*", value=np.nan, inplace=True)
assert return_value is None
tm.assert_frame_equal(dfmix, res.fillna("."))
# regex -> regex
# obj frame
res = dfobj.copy()
return_value = res.replace(regex=r"\s*(\.)\s*", value=r"\1\1\1", inplace=True)
assert return_value is None
objc = obj.copy()
objc["a"] = ["a", "b", "...", "..."]
expec = DataFrame(objc)
tm.assert_frame_equal(res, expec)
# with mixed
res = dfmix.copy()
return_value = res.replace(regex=r"\s*(\.)\s*", value=r"\1\1\1", inplace=True)
assert return_value is None
mixc = mix_ab.copy()
mixc["b"] = ["a", "b", "...", "..."]
expec = DataFrame(mixc)
tm.assert_frame_equal(res, expec)
# everything with compiled regexs as well
res = dfobj.copy()
return_value = res.replace(
regex=re.compile(r"\s*\.\s*"), value=np.nan, inplace=True
)
assert return_value is None
tm.assert_frame_equal(dfobj, res.fillna("."))
# mixed
res = dfmix.copy()
return_value = res.replace(
regex=re.compile(r"\s*\.\s*"), value=np.nan, inplace=True
)
assert return_value is None
tm.assert_frame_equal(dfmix, res.fillna("."))
# regex -> regex
# obj frame
res = dfobj.copy()
return_value = res.replace(
regex=re.compile(r"\s*(\.)\s*"), value=r"\1\1\1", inplace=True
)
assert return_value is None
objc = obj.copy()
objc["a"] = ["a", "b", "...", "..."]
expec = DataFrame(objc)
tm.assert_frame_equal(res, expec)
# with mixed
res = dfmix.copy()
return_value = res.replace(
regex=re.compile(r"\s*(\.)\s*"), value=r"\1\1\1", inplace=True
)
assert return_value is None
mixc = mix_ab.copy()
mixc["b"] = ["a", "b", "...", "..."]
expec = DataFrame(mixc)
tm.assert_frame_equal(res, expec)
def test_regex_replace_list_obj(self):
obj = {"a": list("ab.."), "b": list("efgh"), "c": list("helo")}
dfobj = DataFrame(obj)
# lists of regexes and values
# list of [re1, re2, ..., reN] -> [v1, v2, ..., vN]
to_replace_res = [r"\s*\.\s*", r"e|f|g"]
values = [np.nan, "crap"]
res = dfobj.replace(to_replace_res, values, regex=True)
expec = DataFrame(
{
"a": ["a", "b", np.nan, np.nan],
"b": ["crap"] * 3 + ["h"],
"c": ["h", "crap", "l", "o"],
}
)
tm.assert_frame_equal(res, expec)
# list of [re1, re2, ..., reN] -> [re1, re2, .., reN]
to_replace_res = [r"\s*(\.)\s*", r"(e|f|g)"]
values = [r"\1\1", r"\1_crap"]
res = dfobj.replace(to_replace_res, values, regex=True)
expec = DataFrame(
{
"a": ["a", "b", "..", ".."],
"b": ["e_crap", "f_crap", "g_crap", "h"],
"c": ["h", "e_crap", "l", "o"],
}
)
tm.assert_frame_equal(res, expec)
# list of [re1, re2, ..., reN] -> [(re1 or v1), (re2 or v2), ..., (reN
# or vN)]
to_replace_res = [r"\s*(\.)\s*", r"e"]
values = [r"\1\1", r"crap"]
res = dfobj.replace(to_replace_res, values, regex=True)
expec = DataFrame(
{
"a": ["a", "b", "..", ".."],
"b": ["crap", "f", "g", "h"],
"c": ["h", "crap", "l", "o"],
}
)
tm.assert_frame_equal(res, expec)
to_replace_res = [r"\s*(\.)\s*", r"e"]
values = [r"\1\1", r"crap"]
res = dfobj.replace(value=values, regex=to_replace_res)
expec = DataFrame(
{
"a": ["a", "b", "..", ".."],
"b": ["crap", "f", "g", "h"],
"c": ["h", "crap", "l", "o"],
}
)
tm.assert_frame_equal(res, expec)
def test_regex_replace_list_obj_inplace(self):
# same as above with inplace=True
# lists of regexes and values
obj = {"a": list("ab.."), "b": list("efgh"), "c": list("helo")}
dfobj = DataFrame(obj)
# lists of regexes and values
# list of [re1, re2, ..., reN] -> [v1, v2, ..., vN]
to_replace_res = [r"\s*\.\s*", r"e|f|g"]
values = [np.nan, "crap"]
res = dfobj.copy()
return_value = res.replace(to_replace_res, values, inplace=True, regex=True)
assert return_value is None
expec = DataFrame(
{
"a": ["a", "b", np.nan, np.nan],
"b": ["crap"] * 3 + ["h"],
"c": ["h", "crap", "l", "o"],
}
)
tm.assert_frame_equal(res, expec)
# list of [re1, re2, ..., reN] -> [re1, re2, .., reN]
to_replace_res = [r"\s*(\.)\s*", r"(e|f|g)"]
values = [r"\1\1", r"\1_crap"]
res = dfobj.copy()
return_value = res.replace(to_replace_res, values, inplace=True, regex=True)
assert return_value is None
expec = DataFrame(
{
"a": ["a", "b", "..", ".."],
"b": ["e_crap", "f_crap", "g_crap", "h"],
"c": ["h", "e_crap", "l", "o"],
}
)
tm.assert_frame_equal(res, expec)
# list of [re1, re2, ..., reN] -> [(re1 or v1), (re2 or v2), ..., (reN
# or vN)]
to_replace_res = [r"\s*(\.)\s*", r"e"]
values = [r"\1\1", r"crap"]
res = dfobj.copy()
return_value = res.replace(to_replace_res, values, inplace=True, regex=True)
assert return_value is None
expec = DataFrame(
{
"a": ["a", "b", "..", ".."],
"b": ["crap", "f", "g", "h"],
"c": ["h", "crap", "l", "o"],
}
)
tm.assert_frame_equal(res, expec)
to_replace_res = [r"\s*(\.)\s*", r"e"]
values = [r"\1\1", r"crap"]
res = dfobj.copy()
return_value = res.replace(value=values, regex=to_replace_res, inplace=True)
assert return_value is None
expec = DataFrame(
{
"a": ["a", "b", "..", ".."],
"b": ["crap", "f", "g", "h"],
"c": ["h", "crap", "l", "o"],
}
)
tm.assert_frame_equal(res, expec)
def test_regex_replace_list_mixed(self, mix_ab):
# mixed frame to make sure this doesn't break things
dfmix = DataFrame(mix_ab)
# lists of regexes and values
# list of [re1, re2, ..., reN] -> [v1, v2, ..., vN]
to_replace_res = [r"\s*\.\s*", r"a"]
values = [np.nan, "crap"]
mix2 = {"a": list(range(4)), "b": list("ab.."), "c": list("halo")}
dfmix2 = DataFrame(mix2)
res = dfmix2.replace(to_replace_res, values, regex=True)
expec = DataFrame(
{
"a": mix2["a"],
"b": ["crap", "b", np.nan, np.nan],
"c": ["h", "crap", "l", "o"],
}
)
tm.assert_frame_equal(res, expec)
# list of [re1, re2, ..., reN] -> [re1, re2, .., reN]
to_replace_res = [r"\s*(\.)\s*", r"(a|b)"]
values = [r"\1\1", r"\1_crap"]
res = dfmix.replace(to_replace_res, values, regex=True)
expec = DataFrame({"a": mix_ab["a"], "b": ["a_crap", "b_crap", "..", ".."]})
tm.assert_frame_equal(res, expec)
# list of [re1, re2, ..., reN] -> [(re1 or v1), (re2 or v2), ..., (reN
# or vN)]
to_replace_res = [r"\s*(\.)\s*", r"a", r"(b)"]
values = [r"\1\1", r"crap", r"\1_crap"]
res = dfmix.replace(to_replace_res, values, regex=True)
expec = DataFrame({"a": mix_ab["a"], "b": ["crap", "b_crap", "..", ".."]})
tm.assert_frame_equal(res, expec)
to_replace_res = [r"\s*(\.)\s*", r"a", r"(b)"]
values = [r"\1\1", r"crap", r"\1_crap"]
res = dfmix.replace(regex=to_replace_res, value=values)
expec = DataFrame({"a": mix_ab["a"], "b": ["crap", "b_crap", "..", ".."]})
tm.assert_frame_equal(res, expec)
def test_regex_replace_list_mixed_inplace(self, mix_ab):
dfmix = DataFrame(mix_ab)
# the same inplace
# lists of regexes and values
# list of [re1, re2, ..., reN] -> [v1, v2, ..., vN]
to_replace_res = [r"\s*\.\s*", r"a"]
values = [np.nan, "crap"]
res = dfmix.copy()
return_value = res.replace(to_replace_res, values, inplace=True, regex=True)
assert return_value is None
expec = DataFrame({"a": mix_ab["a"], "b": ["crap", "b", np.nan, np.nan]})
tm.assert_frame_equal(res, expec)
# list of [re1, re2, ..., reN] -> [re1, re2, .., reN]
to_replace_res = [r"\s*(\.)\s*", r"(a|b)"]
values = [r"\1\1", r"\1_crap"]
res = dfmix.copy()
return_value = res.replace(to_replace_res, values, inplace=True, regex=True)
assert return_value is None
expec = DataFrame({"a": mix_ab["a"], "b": ["a_crap", "b_crap", "..", ".."]})
tm.assert_frame_equal(res, expec)
# list of [re1, re2, ..., reN] -> [(re1 or v1), (re2 or v2), ..., (reN
# or vN)]
to_replace_res = [r"\s*(\.)\s*", r"a", r"(b)"]
values = [r"\1\1", r"crap", r"\1_crap"]
res = dfmix.copy()
return_value = res.replace(to_replace_res, values, inplace=True, regex=True)
assert return_value is None
expec = DataFrame({"a": mix_ab["a"], "b": ["crap", "b_crap", "..", ".."]})
tm.assert_frame_equal(res, expec)
to_replace_res = [r"\s*(\.)\s*", r"a", r"(b)"]
values = [r"\1\1", r"crap", r"\1_crap"]
res = dfmix.copy()
return_value = res.replace(regex=to_replace_res, value=values, inplace=True)
assert return_value is None
expec = DataFrame({"a": mix_ab["a"], "b": ["crap", "b_crap", "..", ".."]})
tm.assert_frame_equal(res, expec)
def test_regex_replace_dict_mixed(self, mix_abc):
dfmix = DataFrame(mix_abc)
# dicts
# single dict {re1: v1}, search the whole frame
# need test for this...
# list of dicts {re1: v1, re2: v2, ..., re3: v3}, search the whole
# frame
res = dfmix.replace({"b": r"\s*\.\s*"}, {"b": np.nan}, regex=True)
res2 = dfmix.copy()
return_value = res2.replace(
{"b": r"\s*\.\s*"}, {"b": np.nan}, inplace=True, regex=True
)
assert return_value is None
expec = DataFrame(
{"a": mix_abc["a"], "b": ["a", "b", np.nan, np.nan], "c": mix_abc["c"]}
)
tm.assert_frame_equal(res, expec)
tm.assert_frame_equal(res2, expec)
# list of dicts {re1: re11, re2: re12, ..., reN: re1N}, search the
# whole frame
res = dfmix.replace({"b": r"\s*(\.)\s*"}, {"b": r"\1ty"}, regex=True)
res2 = dfmix.copy()
return_value = res2.replace(
{"b": r"\s*(\.)\s*"}, {"b": r"\1ty"}, inplace=True, regex=True
)
assert return_value is None
expec = DataFrame(
{"a": mix_abc["a"], "b": ["a", "b", ".ty", ".ty"], "c": mix_abc["c"]}
)
tm.assert_frame_equal(res, expec)
tm.assert_frame_equal(res2, expec)
res = dfmix.replace(regex={"b": r"\s*(\.)\s*"}, value={"b": r"\1ty"})
res2 = dfmix.copy()
return_value = res2.replace(
regex={"b": r"\s*(\.)\s*"}, value={"b": r"\1ty"}, inplace=True
)
assert return_value is None
expec = DataFrame(
{"a": mix_abc["a"], "b": ["a", "b", ".ty", ".ty"], "c": mix_abc["c"]}
)
tm.assert_frame_equal(res, expec)
tm.assert_frame_equal(res2, expec)
# scalar -> dict
# to_replace regex, {value: value}
expec = DataFrame(
{"a": mix_abc["a"], "b": [np.nan, "b", ".", "."], "c": mix_abc["c"]}
)
res = dfmix.replace("a", {"b": np.nan}, regex=True)
res2 = dfmix.copy()
return_value = res2.replace("a", {"b": np.nan}, regex=True, inplace=True)
assert return_value is None
tm.assert_frame_equal(res, expec)
tm.assert_frame_equal(res2, expec)
res = dfmix.replace("a", {"b": np.nan}, regex=True)
res2 = dfmix.copy()
return_value = res2.replace(regex="a", value={"b": np.nan}, inplace=True)
assert return_value is None
expec = DataFrame(
{"a": mix_abc["a"], "b": [np.nan, "b", ".", "."], "c": mix_abc["c"]}
)
tm.assert_frame_equal(res, expec)
tm.assert_frame_equal(res2, expec)
def test_regex_replace_dict_nested(self, mix_abc):
# nested dicts will not work until this is implemented for Series
dfmix = DataFrame(mix_abc)
res = dfmix.replace({"b": {r"\s*\.\s*": np.nan}}, regex=True)
res2 = dfmix.copy()
res4 = dfmix.copy()
return_value = res2.replace(
{"b": {r"\s*\.\s*": np.nan}}, inplace=True, regex=True
)
assert return_value is None
res3 = dfmix.replace(regex={"b": {r"\s*\.\s*": np.nan}})
return_value = res4.replace(regex={"b": {r"\s*\.\s*": np.nan}}, inplace=True)
assert return_value is None
expec = DataFrame(
{"a": mix_abc["a"], "b": ["a", "b", np.nan, np.nan], "c": mix_abc["c"]}
)
tm.assert_frame_equal(res, expec)
tm.assert_frame_equal(res2, expec)
tm.assert_frame_equal(res3, expec)
tm.assert_frame_equal(res4, expec)
def test_regex_replace_dict_nested_non_first_character(self):
# GH 25259
df = pd.DataFrame({"first": ["abc", "bca", "cab"]})
expected = pd.DataFrame({"first": [".bc", "bc.", "c.b"]})
result = df.replace({"a": "."}, regex=True)
tm.assert_frame_equal(result, expected)
def test_regex_replace_dict_nested_gh4115(self):
df = pd.DataFrame({"Type": ["Q", "T", "Q", "Q", "T"], "tmp": 2})
expected = DataFrame({"Type": [0, 1, 0, 0, 1], "tmp": 2})
result = df.replace({"Type": {"Q": 0, "T": 1}})
tm.assert_frame_equal(result, expected)
def test_regex_replace_list_to_scalar(self, mix_abc):
df = DataFrame(mix_abc)
expec = DataFrame(
{
"a": mix_abc["a"],
"b": np.array([np.nan] * 4),
"c": [np.nan, np.nan, np.nan, "d"],
}
)
res = df.replace([r"\s*\.\s*", "a|b"], np.nan, regex=True)
res2 = df.copy()
res3 = df.copy()
return_value = res2.replace(
[r"\s*\.\s*", "a|b"], np.nan, regex=True, inplace=True
)
assert return_value is None
return_value = res3.replace(
regex=[r"\s*\.\s*", "a|b"], value=np.nan, inplace=True
)
assert return_value is None
tm.assert_frame_equal(res, expec)
tm.assert_frame_equal(res2, expec)
tm.assert_frame_equal(res3, expec)
def test_regex_replace_str_to_numeric(self, mix_abc):
# what happens when you try to replace a numeric value with a regex?
df = DataFrame(mix_abc)
res = df.replace(r"\s*\.\s*", 0, regex=True)
res2 = df.copy()
return_value = res2.replace(r"\s*\.\s*", 0, inplace=True, regex=True)
assert return_value is None
res3 = df.copy()
return_value = res3.replace(regex=r"\s*\.\s*", value=0, inplace=True)
assert return_value is None
expec = DataFrame({"a": mix_abc["a"], "b": ["a", "b", 0, 0], "c": mix_abc["c"]})
tm.assert_frame_equal(res, expec)
tm.assert_frame_equal(res2, expec)
tm.assert_frame_equal(res3, expec)
def test_regex_replace_regex_list_to_numeric(self, mix_abc):
df = DataFrame(mix_abc)
res = df.replace([r"\s*\.\s*", "b"], 0, regex=True)
res2 = df.copy()
return_value = res2.replace([r"\s*\.\s*", "b"], 0, regex=True, inplace=True)
assert return_value is None
res3 = df.copy()
return_value = res3.replace(regex=[r"\s*\.\s*", "b"], value=0, inplace=True)
assert return_value is None
expec = DataFrame(
{"a": mix_abc["a"], "b": ["a", 0, 0, 0], "c": ["a", 0, np.nan, "d"]}
)
tm.assert_frame_equal(res, expec)
tm.assert_frame_equal(res2, expec)
tm.assert_frame_equal(res3, expec)
def test_regex_replace_series_of_regexes(self, mix_abc):
df = DataFrame(mix_abc)
s1 = Series({"b": r"\s*\.\s*"})
s2 = Series({"b": np.nan})
res = df.replace(s1, s2, regex=True)
res2 = df.copy()
return_value = res2.replace(s1, s2, inplace=True, regex=True)
assert return_value is None
res3 = df.copy()
return_value = res3.replace(regex=s1, value=s2, inplace=True)
assert return_value is None
expec = DataFrame(
{"a": mix_abc["a"], "b": ["a", "b", np.nan, np.nan], "c": mix_abc["c"]}
)
tm.assert_frame_equal(res, expec)
tm.assert_frame_equal(res2, expec)
tm.assert_frame_equal(res3, expec)
def test_regex_replace_numeric_to_object_conversion(self, mix_abc):
df = DataFrame(mix_abc)
expec = DataFrame({"a": ["a", 1, 2, 3], "b": mix_abc["b"], "c": mix_abc["c"]})
res = df.replace(0, "a")
tm.assert_frame_equal(res, expec)
assert res.a.dtype == np.object_
@pytest.mark.parametrize("metachar", ["[]", "()", r"\d", r"\w", r"\s"])
def test_replace_regex_metachar(self, metachar):
df = DataFrame({"a": [metachar, "else"]})
result = df.replace({"a": {metachar: "paren"}})
expected = DataFrame({"a": ["paren", "else"]})
tm.assert_frame_equal(result, expected)
def test_replace(self, datetime_frame):
datetime_frame["A"][:5] = np.nan
datetime_frame["A"][-5:] = np.nan
zero_filled = datetime_frame.replace(np.nan, -1e8)
tm.assert_frame_equal(zero_filled, datetime_frame.fillna(-1e8))
tm.assert_frame_equal(zero_filled.replace(-1e8, np.nan), datetime_frame)
datetime_frame["A"][:5] = np.nan
datetime_frame["A"][-5:] = np.nan
datetime_frame["B"][:5] = -1e8
# empty
df = DataFrame(index=["a", "b"])
tm.assert_frame_equal(df, df.replace(5, 7))
# GH 11698
# test for mixed data types.
df = pd.DataFrame(
[("-", pd.to_datetime("20150101")), ("a", pd.to_datetime("20150102"))]
)
df1 = df.replace("-", np.nan)
expected_df = pd.DataFrame(
[(np.nan, pd.to_datetime("20150101")), ("a", pd.to_datetime("20150102"))]
)
tm.assert_frame_equal(df1, expected_df)
def test_replace_list(self):
obj = {"a": list("ab.."), "b": list("efgh"), "c": list("helo")}
dfobj = DataFrame(obj)
# lists of regexes and values
# list of [v1, v2, ..., vN] -> [v1, v2, ..., vN]
to_replace_res = [r".", r"e"]
values = [np.nan, "crap"]
res = dfobj.replace(to_replace_res, values)
expec = DataFrame(
{
"a": ["a", "b", np.nan, np.nan],
"b": ["crap", "f", "g", "h"],
"c": ["h", "crap", "l", "o"],
}
)
tm.assert_frame_equal(res, expec)
# list of [v1, v2, ..., vN] -> [v1, v2, .., vN]
to_replace_res = [r".", r"f"]
values = [r"..", r"crap"]
res = dfobj.replace(to_replace_res, values)
expec = DataFrame(
{
"a": ["a", "b", "..", ".."],
"b": ["e", "crap", "g", "h"],
"c": ["h", "e", "l", "o"],
}
)
tm.assert_frame_equal(res, expec)
def test_replace_with_empty_list(self):
# GH 21977
s = pd.Series([["a", "b"], [], np.nan, [1]])
df = pd.DataFrame({"col": s})
expected = df
result = df.replace([], np.nan)
tm.assert_frame_equal(result, expected)
# GH 19266
with pytest.raises(ValueError, match="cannot assign mismatch"):
df.replace({np.nan: []})
with pytest.raises(ValueError, match="cannot assign mismatch"):
df.replace({np.nan: ["dummy", "alt"]})
def test_replace_series_dict(self):
# from GH 3064
df = DataFrame({"zero": {"a": 0.0, "b": 1}, "one": {"a": 2.0, "b": 0}})
result = df.replace(0, {"zero": 0.5, "one": 1.0})
expected = DataFrame({"zero": {"a": 0.5, "b": 1}, "one": {"a": 2.0, "b": 1.0}})
tm.assert_frame_equal(result, expected)
result = df.replace(0, df.mean())
tm.assert_frame_equal(result, expected)
# series to series/dict
df = DataFrame({"zero": {"a": 0.0, "b": 1}, "one": {"a": 2.0, "b": 0}})
s = Series({"zero": 0.0, "one": 2.0})
result = df.replace(s, {"zero": 0.5, "one": 1.0})
expected = DataFrame({"zero": {"a": 0.5, "b": 1}, "one": {"a": 1.0, "b": 0.0}})
tm.assert_frame_equal(result, expected)
result = df.replace(s, df.mean())
tm.assert_frame_equal(result, expected)
def test_replace_convert(self):
# gh 3907
df = DataFrame([["foo", "bar", "bah"], ["bar", "foo", "bah"]])
m = {"foo": 1, "bar": 2, "bah": 3}
rep = df.replace(m)
expec = Series([np.int64] * 3)
res = rep.dtypes
tm.assert_series_equal(expec, res)
def test_replace_mixed(self, float_string_frame):
mf = float_string_frame
mf.iloc[5:20, mf.columns.get_loc("foo")] = np.nan
mf.iloc[-10:, mf.columns.get_loc("A")] = np.nan
result = float_string_frame.replace(np.nan, -18)
expected = float_string_frame.fillna(value=-18)
tm.assert_frame_equal(result, expected)
tm.assert_frame_equal(result.replace(-18, np.nan), float_string_frame)
result = float_string_frame.replace(np.nan, -1e8)
expected = float_string_frame.fillna(value=-1e8)
tm.assert_frame_equal(result, expected)
tm.assert_frame_equal(result.replace(-1e8, np.nan), float_string_frame)
# int block upcasting
df = DataFrame(
{
"A": Series([1.0, 2.0], dtype="float64"),
"B": Series([0, 1], dtype="int64"),
}
)
expected = DataFrame(
{
"A": Series([1.0, 2.0], dtype="float64"),
"B": Series([0.5, 1], dtype="float64"),
}
)
result = df.replace(0, 0.5)
tm.assert_frame_equal(result, expected)
return_value = df.replace(0, 0.5, inplace=True)
assert return_value is None
tm.assert_frame_equal(df, expected)
# int block splitting
df = DataFrame(
{
"A": Series([1.0, 2.0], dtype="float64"),
"B": Series([0, 1], dtype="int64"),
"C": Series([1, 2], dtype="int64"),
}
)
expected = DataFrame(
{
"A": Series([1.0, 2.0], dtype="float64"),
"B": Series([0.5, 1], dtype="float64"),
"C": Series([1, 2], dtype="int64"),
}
)
result = df.replace(0, 0.5)
tm.assert_frame_equal(result, expected)
# to object block upcasting
df = DataFrame(
{
"A": Series([1.0, 2.0], dtype="float64"),
"B": Series([0, 1], dtype="int64"),
}
)
expected = DataFrame(
{
"A": Series([1, "foo"], dtype="object"),
"B": Series([0, 1], dtype="int64"),
}
)
result = df.replace(2, "foo")
tm.assert_frame_equal(result, expected)
expected = DataFrame(
{
"A": Series(["foo", "bar"], dtype="object"),
"B": Series([0, "foo"], dtype="object"),
}
)
result = df.replace([1, 2], ["foo", "bar"])
tm.assert_frame_equal(result, expected)
# test case from
df = DataFrame(
{"A": Series([3, 0], dtype="int64"), "B": Series([0, 3], dtype="int64")}
)
result = df.replace(3, df.mean().to_dict())
expected = df.copy().astype("float64")
m = df.mean()
expected.iloc[0, 0] = m[0]
expected.iloc[1, 1] = m[1]
tm.assert_frame_equal(result, expected)
def test_replace_simple_nested_dict(self):
df = DataFrame({"col": range(1, 5)})
expected = DataFrame({"col": ["a", 2, 3, "b"]})
result = df.replace({"col": {1: "a", 4: "b"}})
tm.assert_frame_equal(expected, result)
# in this case, should be the same as the not nested version
result = df.replace({1: "a", 4: "b"})
tm.assert_frame_equal(expected, result)
def test_replace_simple_nested_dict_with_nonexistent_value(self):
df = DataFrame({"col": range(1, 5)})
expected = DataFrame({"col": ["a", 2, 3, "b"]})
result = df.replace({-1: "-", 1: "a", 4: "b"})
tm.assert_frame_equal(expected, result)
result = df.replace({"col": {-1: "-", 1: "a", 4: "b"}})
tm.assert_frame_equal(expected, result)
def test_replace_value_is_none(self, datetime_frame):
orig_value = datetime_frame.iloc[0, 0]
orig2 = datetime_frame.iloc[1, 0]
datetime_frame.iloc[0, 0] = np.nan
datetime_frame.iloc[1, 0] = 1
result = datetime_frame.replace(to_replace={np.nan: 0})
expected = datetime_frame.T.replace(to_replace={np.nan: 0}).T
tm.assert_frame_equal(result, expected)
result = datetime_frame.replace(to_replace={np.nan: 0, 1: -1e8})
tsframe = datetime_frame.copy()
tsframe.iloc[0, 0] = 0
tsframe.iloc[1, 0] = -1e8
expected = tsframe
tm.assert_frame_equal(expected, result)
datetime_frame.iloc[0, 0] = orig_value
datetime_frame.iloc[1, 0] = orig2
def test_replace_for_new_dtypes(self, datetime_frame):
# dtypes
tsframe = datetime_frame.copy().astype(np.float32)
tsframe["A"][:5] = np.nan
tsframe["A"][-5:] = np.nan
zero_filled = tsframe.replace(np.nan, -1e8)
tm.assert_frame_equal(zero_filled, tsframe.fillna(-1e8))
tm.assert_frame_equal(zero_filled.replace(-1e8, np.nan), tsframe)
tsframe["A"][:5] = np.nan
tsframe["A"][-5:] = np.nan
tsframe["B"][:5] = -1e8
b = tsframe["B"]
b[b == -1e8] = np.nan
tsframe["B"] = b
result = tsframe.fillna(method="bfill")
tm.assert_frame_equal(result, tsframe.fillna(method="bfill"))
@pytest.mark.parametrize(
"frame, to_replace, value, expected",
[
(DataFrame({"ints": [1, 2, 3]}), 1, 0, DataFrame({"ints": [0, 2, 3]})),
(
DataFrame({"ints": [1, 2, 3]}, dtype=np.int32),
1,
0,
DataFrame({"ints": [0, 2, 3]}, dtype=np.int32),
),
(
DataFrame({"ints": [1, 2, 3]}, dtype=np.int16),
1,
0,
DataFrame({"ints": [0, 2, 3]}, dtype=np.int16),
),
(
DataFrame({"bools": [True, False, True]}),
False,
True,
DataFrame({"bools": [True, True, True]}),
),
(
DataFrame({"complex": [1j, 2j, 3j]}),
1j,
0,
DataFrame({"complex": [0j, 2j, 3j]}),
),
(
DataFrame(
{
"datetime64": Index(
[
datetime(2018, 5, 28),
datetime(2018, 7, 28),
datetime(2018, 5, 28),
]
)
}
),
datetime(2018, 5, 28),
datetime(2018, 7, 28),
DataFrame({"datetime64": Index([datetime(2018, 7, 28)] * 3)}),
),
# GH 20380
(
DataFrame({"dt": [datetime(3017, 12, 20)], "str": ["foo"]}),
"foo",
"bar",
DataFrame({"dt": [datetime(3017, 12, 20)], "str": ["bar"]}),
),
(
DataFrame(
{
"A": date_range("20130101", periods=3, tz="US/Eastern"),
"B": [0, np.nan, 2],
}
),
Timestamp("20130102", tz="US/Eastern"),
Timestamp("20130104", tz="US/Eastern"),
DataFrame(
{
"A": [
Timestamp("20130101", tz="US/Eastern"),
Timestamp("20130104", tz="US/Eastern"),
Timestamp("20130103", tz="US/Eastern"),
],
"B": [0, np.nan, 2],
}
),
),
],
)
def test_replace_dtypes(self, frame, to_replace, value, expected):
result = getattr(frame, "replace")(to_replace, value)
tm.assert_frame_equal(result, expected)
def test_replace_input_formats_listlike(self):
# both dicts
to_rep = {"A": np.nan, "B": 0, "C": ""}
values = {"A": 0, "B": -1, "C": "missing"}
df = DataFrame(
{"A": [np.nan, 0, np.inf], "B": [0, 2, 5], "C": ["", "asdf", "fd"]}
)
filled = df.replace(to_rep, values)
expected = {k: v.replace(to_rep[k], values[k]) for k, v in df.items()}
tm.assert_frame_equal(filled, DataFrame(expected))
result = df.replace([0, 2, 5], [5, 2, 0])
expected = DataFrame(
{"A": [np.nan, 5, np.inf], "B": [5, 2, 0], "C": ["", "asdf", "fd"]}
)
tm.assert_frame_equal(result, expected)
# scalar to dict
values = {"A": 0, "B": -1, "C": "missing"}
df = DataFrame(
{"A": [np.nan, 0, np.nan], "B": [0, 2, 5], "C": ["", "asdf", "fd"]}
)
filled = df.replace(np.nan, values)
expected = {k: v.replace(np.nan, values[k]) for k, v in df.items()}
tm.assert_frame_equal(filled, DataFrame(expected))
# list to list
to_rep = [np.nan, 0, ""]
values = [-2, -1, "missing"]
result = df.replace(to_rep, values)
expected = df.copy()
for i in range(len(to_rep)):
return_value = expected.replace(to_rep[i], values[i], inplace=True)
assert return_value is None
tm.assert_frame_equal(result, expected)
msg = r"Replacement lists must match in length\. Expecting 3 got 2"
with pytest.raises(ValueError, match=msg):
df.replace(to_rep, values[1:])
def test_replace_input_formats_scalar(self):
df = DataFrame(
{"A": [np.nan, 0, np.inf], "B": [0, 2, 5], "C": ["", "asdf", "fd"]}
)
# dict to scalar
to_rep = {"A": np.nan, "B": 0, "C": ""}
filled = df.replace(to_rep, 0)
expected = {k: v.replace(to_rep[k], 0) for k, v in df.items()}
tm.assert_frame_equal(filled, DataFrame(expected))
msg = "value argument must be scalar, dict, or Series"
with pytest.raises(TypeError, match=msg):
df.replace(to_rep, [np.nan, 0, ""])
# list to scalar
to_rep = [np.nan, 0, ""]
result = df.replace(to_rep, -1)
expected = df.copy()
for i in range(len(to_rep)):
return_value = expected.replace(to_rep[i], -1, inplace=True)
assert return_value is None
tm.assert_frame_equal(result, expected)
def test_replace_limit(self):
pass
def test_replace_dict_no_regex(self):
answer = Series(
{
0: "Strongly Agree",
1: "Agree",
2: "Neutral",
3: "Disagree",
4: "Strongly Disagree",
}
)
weights = {
"Agree": 4,
"Disagree": 2,
"Neutral": 3,
"Strongly Agree": 5,
"Strongly Disagree": 1,
}
expected = Series({0: 5, 1: 4, 2: 3, 3: 2, 4: 1})
result = answer.replace(weights)
tm.assert_series_equal(result, expected)
def test_replace_series_no_regex(self):
answer = Series(
{
0: "Strongly Agree",
1: "Agree",
2: "Neutral",
3: "Disagree",
4: "Strongly Disagree",
}
)
weights = Series(
{
"Agree": 4,
"Disagree": 2,
"Neutral": 3,
"Strongly Agree": 5,
"Strongly Disagree": 1,
}
)
expected = Series({0: 5, 1: 4, 2: 3, 3: 2, 4: 1})
result = answer.replace(weights)
tm.assert_series_equal(result, expected)
def test_replace_dict_tuple_list_ordering_remains_the_same(self):
df = DataFrame(dict(A=[np.nan, 1]))
res1 = df.replace(to_replace={np.nan: 0, 1: -1e8})
res2 = df.replace(to_replace=(1, np.nan), value=[-1e8, 0])
res3 = df.replace(to_replace=[1, np.nan], value=[-1e8, 0])
expected = DataFrame({"A": [0, -1e8]})
tm.assert_frame_equal(res1, res2)
tm.assert_frame_equal(res2, res3)
tm.assert_frame_equal(res3, expected)
def test_replace_doesnt_replace_without_regex(self):
raw = """fol T_opp T_Dir T_Enh
0 1 0 0 vo
1 2 vr 0 0
2 2 0 0 0
3 3 0 bt 0"""
df = pd.read_csv(StringIO(raw), sep=r"\s+")
res = df.replace({r"\D": 1})
tm.assert_frame_equal(df, res)
def test_replace_bool_with_string(self):
df = DataFrame({"a": [True, False], "b": list("ab")})
result = df.replace(True, "a")
expected = DataFrame({"a": ["a", False], "b": df.b})
tm.assert_frame_equal(result, expected)
def test_replace_pure_bool_with_string_no_op(self):
df = DataFrame(np.random.rand(2, 2) > 0.5)
result = df.replace("asdf", "fdsa")
tm.assert_frame_equal(df, result)
def test_replace_bool_with_bool(self):
df = DataFrame(np.random.rand(2, 2) > 0.5)
result = df.replace(False, True)
expected = DataFrame(np.ones((2, 2), dtype=bool))
tm.assert_frame_equal(result, expected)
def test_replace_with_dict_with_bool_keys(self):
df = DataFrame({0: [True, False], 1: [False, True]})
with pytest.raises(TypeError, match="Cannot compare types .+"):
df.replace({"asdf": "asdb", True: "yes"})
def test_replace_truthy(self):
df = DataFrame({"a": [True, True]})
r = df.replace([np.inf, -np.inf], np.nan)
e = df
tm.assert_frame_equal(r, e)
def test_nested_dict_overlapping_keys_replace_int(self):
# GH 27660 keep behaviour consistent for simple dictionary and
# nested dictionary replacement
df = DataFrame({"a": list(range(1, 5))})
result = df.replace({"a": dict(zip(range(1, 5), range(2, 6)))})
expected = df.replace(dict(zip(range(1, 5), range(2, 6))))
tm.assert_frame_equal(result, expected)
def test_nested_dict_overlapping_keys_replace_str(self):
# GH 27660
a = np.arange(1, 5)
astr = a.astype(str)
bstr = np.arange(2, 6).astype(str)
df = DataFrame({"a": astr})
result = df.replace(dict(zip(astr, bstr)))
expected = df.replace({"a": dict(zip(astr, bstr))})
tm.assert_frame_equal(result, expected)
def test_replace_swapping_bug(self):
df = pd.DataFrame({"a": [True, False, True]})
res = df.replace({"a": {True: "Y", False: "N"}})
expect = pd.DataFrame({"a": ["Y", "N", "Y"]})
tm.assert_frame_equal(res, expect)
df = pd.DataFrame({"a": [0, 1, 0]})
res = df.replace({"a": {0: "Y", 1: "N"}})
expect = pd.DataFrame({"a": ["Y", "N", "Y"]})
tm.assert_frame_equal(res, expect)
def test_replace_period(self):
d = {
"fname": {
"out_augmented_AUG_2011.json": pd.Period(year=2011, month=8, freq="M"),
"out_augmented_JAN_2011.json": pd.Period(year=2011, month=1, freq="M"),
"out_augmented_MAY_2012.json": pd.Period(year=2012, month=5, freq="M"),
"out_augmented_SUBSIDY_WEEK.json": pd.Period(
year=2011, month=4, freq="M"
),
"out_augmented_AUG_2012.json": pd.Period(year=2012, month=8, freq="M"),
"out_augmented_MAY_2011.json": pd.Period(year=2011, month=5, freq="M"),
"out_augmented_SEP_2013.json": pd.Period(year=2013, month=9, freq="M"),
}
}
df = pd.DataFrame(
[
"out_augmented_AUG_2012.json",
"out_augmented_SEP_2013.json",
"out_augmented_SUBSIDY_WEEK.json",
"out_augmented_MAY_2012.json",
"out_augmented_MAY_2011.json",
"out_augmented_AUG_2011.json",
"out_augmented_JAN_2011.json",
],
columns=["fname"],
)
assert set(df.fname.values) == set(d["fname"].keys())
# We don't support converting object -> specialized EA in
# replace yet.
expected = DataFrame(
{"fname": [d["fname"][k] for k in df.fname.values]}, dtype=object
)
result = df.replace(d)
tm.assert_frame_equal(result, expected)
def test_replace_datetime(self):
d = {
"fname": {
"out_augmented_AUG_2011.json": pd.Timestamp("2011-08"),
"out_augmented_JAN_2011.json": pd.Timestamp("2011-01"),
"out_augmented_MAY_2012.json": pd.Timestamp("2012-05"),
"out_augmented_SUBSIDY_WEEK.json": pd.Timestamp("2011-04"),
"out_augmented_AUG_2012.json": pd.Timestamp("2012-08"),
"out_augmented_MAY_2011.json": pd.Timestamp("2011-05"),
"out_augmented_SEP_2013.json": pd.Timestamp("2013-09"),
}
}
df = pd.DataFrame(
[
"out_augmented_AUG_2012.json",
"out_augmented_SEP_2013.json",
"out_augmented_SUBSIDY_WEEK.json",
"out_augmented_MAY_2012.json",
"out_augmented_MAY_2011.json",
"out_augmented_AUG_2011.json",
"out_augmented_JAN_2011.json",
],
columns=["fname"],
)
assert set(df.fname.values) == set(d["fname"].keys())
expected = DataFrame({"fname": [d["fname"][k] for k in df.fname.values]})
result = df.replace(d)
tm.assert_frame_equal(result, expected)
def test_replace_datetimetz(self):
# GH 11326
# behaving poorly when presented with a datetime64[ns, tz]
df = DataFrame(
{
"A": date_range("20130101", periods=3, tz="US/Eastern"),
"B": [0, np.nan, 2],
}
)
result = df.replace(np.nan, 1)
expected = DataFrame(
{
"A": date_range("20130101", periods=3, tz="US/Eastern"),
"B": Series([0, 1, 2], dtype="float64"),
}
)
tm.assert_frame_equal(result, expected)
result = df.fillna(1)
tm.assert_frame_equal(result, expected)
result = df.replace(0, np.nan)
expected = DataFrame(
{
"A": date_range("20130101", periods=3, tz="US/Eastern"),
"B": [np.nan, np.nan, 2],
}
)
tm.assert_frame_equal(result, expected)
result = df.replace(
Timestamp("20130102", tz="US/Eastern"),
Timestamp("20130104", tz="US/Eastern"),
)
expected = DataFrame(
{
"A": [
Timestamp("20130101", tz="US/Eastern"),
Timestamp("20130104", tz="US/Eastern"),
Timestamp("20130103", tz="US/Eastern"),
],
"B": [0, np.nan, 2],
}
)
tm.assert_frame_equal(result, expected)
result = df.copy()
result.iloc[1, 0] = np.nan
result = result.replace({"A": pd.NaT}, Timestamp("20130104", tz="US/Eastern"))
tm.assert_frame_equal(result, expected)
# coerce to object
result = df.copy()
result.iloc[1, 0] = np.nan
result = result.replace({"A": pd.NaT}, Timestamp("20130104", tz="US/Pacific"))
expected = DataFrame(
{
"A": [
Timestamp("20130101", tz="US/Eastern"),
Timestamp("20130104", tz="US/Pacific"),
Timestamp("20130103", tz="US/Eastern"),
],
"B": [0, np.nan, 2],
}
)
tm.assert_frame_equal(result, expected)
result = df.copy()
result.iloc[1, 0] = np.nan
result = result.replace({"A": np.nan}, Timestamp("20130104"))
expected = DataFrame(
{
"A": [
Timestamp("20130101", tz="US/Eastern"),
Timestamp("20130104"),
Timestamp("20130103", tz="US/Eastern"),
],
"B": [0, np.nan, 2],
}
)
tm.assert_frame_equal(result, expected)
def test_replace_with_empty_dictlike(self, mix_abc):
# GH 15289
df = DataFrame(mix_abc)
tm.assert_frame_equal(df, df.replace({}))
tm.assert_frame_equal(df, df.replace(Series([], dtype=object)))
tm.assert_frame_equal(df, df.replace({"b": {}}))
tm.assert_frame_equal(df, df.replace(Series({"b": {}})))
@pytest.mark.parametrize(
"to_replace, method, expected",
[
(0, "bfill", {"A": [1, 1, 2], "B": [5, np.nan, 7], "C": ["a", "b", "c"]}),
(
np.nan,
"bfill",
{"A": [0, 1, 2], "B": [5.0, 7.0, 7.0], "C": ["a", "b", "c"]},
),
("d", "ffill", {"A": [0, 1, 2], "B": [5, np.nan, 7], "C": ["a", "b", "c"]}),
(
[0, 2],
"bfill",
{"A": [1, 1, 2], "B": [5, np.nan, 7], "C": ["a", "b", "c"]},
),
(
[1, 2],
"pad",
{"A": [0, 0, 0], "B": [5, np.nan, 7], "C": ["a", "b", "c"]},
),
(
(1, 2),
"bfill",
{"A": [0, 2, 2], "B": [5, np.nan, 7], "C": ["a", "b", "c"]},
),
(
["b", "c"],
"ffill",
{"A": [0, 1, 2], "B": [5, np.nan, 7], "C": ["a", "a", "a"]},
),
],
)
def test_replace_method(self, to_replace, method, expected):
# GH 19632
df = DataFrame({"A": [0, 1, 2], "B": [5, np.nan, 7], "C": ["a", "b", "c"]})
result = df.replace(to_replace=to_replace, value=None, method=method)
expected = DataFrame(expected)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"replace_dict, final_data",
[({"a": 1, "b": 1}, [[3, 3], [2, 2]]), ({"a": 1, "b": 2}, [[3, 1], [2, 3]])],
)
def test_categorical_replace_with_dict(self, replace_dict, final_data):
# GH 26988
df = DataFrame([[1, 1], [2, 2]], columns=["a", "b"], dtype="category")
final_data = np.array(final_data)
a = pd.Categorical(final_data[:, 0], categories=[3, 2])
excat = [3, 2] if replace_dict["b"] == 1 else [1, 3]
b = pd.Categorical(final_data[:, 1], categories=excat)
expected = DataFrame({"a": a, "b": b})
result = df.replace(replace_dict, 3)
tm.assert_frame_equal(result, expected)
msg = (
r"Attributes of DataFrame.iloc\[:, 0\] \(column name=\"a\"\) are "
"different"
)
with pytest.raises(AssertionError, match=msg):
# ensure non-inplace call does not affect original
tm.assert_frame_equal(df, expected)
return_value = df.replace(replace_dict, 3, inplace=True)
assert return_value is None
tm.assert_frame_equal(df, expected)
@pytest.mark.parametrize(
"df, to_replace, exp",
[
(
{"col1": [1, 2, 3], "col2": [4, 5, 6]},
{4: 5, 5: 6, 6: 7},
{"col1": [1, 2, 3], "col2": [5, 6, 7]},
),
(
{"col1": [1, 2, 3], "col2": ["4", "5", "6"]},
{"4": "5", "5": "6", "6": "7"},
{"col1": [1, 2, 3], "col2": ["5", "6", "7"]},
),
],
)
def test_replace_commutative(self, df, to_replace, exp):
# GH 16051
# DataFrame.replace() overwrites when values are non-numeric
# also added to data frame whilst issue was for series
df = pd.DataFrame(df)
expected = pd.DataFrame(exp)
result = df.replace(to_replace)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"replacer",
[
pd.Timestamp("20170827"),
np.int8(1),
np.int16(1),
np.float32(1),
np.float64(1),
],
)
def test_replace_replacer_dtype(self, replacer):
# GH26632
df = pd.DataFrame(["a"])
result = df.replace({"a": replacer, "b": replacer})
expected = pd.DataFrame([replacer])
tm.assert_frame_equal(result, expected)
def test_replace_after_convert_dtypes(self):
# GH31517
df = pd.DataFrame({"grp": [1, 2, 3, 4, 5]}, dtype="Int64")
result = df.replace(1, 10)
expected = pd.DataFrame({"grp": [10, 2, 3, 4, 5]}, dtype="Int64")
tm.assert_frame_equal(result, expected)
def test_replace_invalid_to_replace(self):
# GH 18634
# API: replace() should raise an exception if invalid argument is given
df = pd.DataFrame({"one": ["a", "b ", "c"], "two": ["d ", "e ", "f "]})
msg = (
r"Expecting 'to_replace' to be either a scalar, array-like, "
r"dict or None, got invalid type.*"
)
with pytest.raises(TypeError, match=msg):
df.replace(lambda x: x.strip())
@pytest.mark.parametrize("dtype", ["float", "float64", "int64", "Int64", "boolean"])
@pytest.mark.parametrize("value", [np.nan, pd.NA])
def test_replace_no_replacement_dtypes(self, dtype, value):
# https://github.com/pandas-dev/pandas/issues/32988
df = pd.DataFrame(np.eye(2), dtype=dtype)
result = df.replace(to_replace=[None, -np.inf, np.inf], value=value)
tm.assert_frame_equal(result, df)
@pytest.mark.parametrize("replacement", [np.nan, 5])
def test_replace_with_duplicate_columns(self, replacement):
# GH 24798
result = pd.DataFrame({"A": [1, 2, 3], "A1": [4, 5, 6], "B": [7, 8, 9]})
result.columns = list("AAB")
expected = pd.DataFrame(
{"A": [1, 2, 3], "A1": [4, 5, 6], "B": [replacement, 8, 9]}
)
expected.columns = list("AAB")
result["B"] = result["B"].replace(7, replacement)
tm.assert_frame_equal(result, expected)
@pytest.mark.xfail(
reason="replace() changes dtype from period to object, see GH34871", strict=True
)
def test_replace_period_ignore_float(self):
"""
Regression test for GH#34871: if df.replace(1.0, 0.0) is called on a df
with a Period column the old, faulty behavior is to raise TypeError.
"""
df = pd.DataFrame({"Per": [pd.Period("2020-01")] * 3})
result = df.replace(1.0, 0.0)
expected = pd.DataFrame({"Per": [pd.Period("2020-01")] * 3})
tm.assert_frame_equal(expected, result)
def test_replace_value_category_type(self):
"""
Test for #23305: to ensure category dtypes are maintained
after replace with direct values
"""
# create input data
input_dict = {
"col1": [1, 2, 3, 4],
"col2": ["a", "b", "c", "d"],
"col3": [1.5, 2.5, 3.5, 4.5],
"col4": ["cat1", "cat2", "cat3", "cat4"],
"col5": ["obj1", "obj2", "obj3", "obj4"],
}
# explicitly cast columns as category and order them
input_df = pd.DataFrame(data=input_dict).astype(
{"col2": "category", "col4": "category"}
)
input_df["col2"] = input_df["col2"].cat.reorder_categories(
["a", "b", "c", "d"], ordered=True
)
input_df["col4"] = input_df["col4"].cat.reorder_categories(
["cat1", "cat2", "cat3", "cat4"], ordered=True
)
# create expected dataframe
expected_dict = {
"col1": [1, 2, 3, 4],
"col2": ["a", "b", "c", "z"],
"col3": [1.5, 2.5, 3.5, 4.5],
"col4": ["cat1", "catX", "cat3", "cat4"],
"col5": ["obj9", "obj2", "obj3", "obj4"],
}
# explicitly cast columns as category and order them
expected = pd.DataFrame(data=expected_dict).astype(
{"col2": "category", "col4": "category"}
)
expected["col2"] = expected["col2"].cat.reorder_categories(
["a", "b", "c", "z"], ordered=True
)
expected["col4"] = expected["col4"].cat.reorder_categories(
["cat1", "catX", "cat3", "cat4"], ordered=True
)
# replace values in input dataframe
input_df = input_df.replace("d", "z")
input_df = input_df.replace("obj1", "obj9")
result = input_df.replace("cat2", "catX")
tm.assert_frame_equal(result, expected)
@pytest.mark.xfail(
reason="category dtype gets changed to object type after replace, see #35268",
strict=True,
)
def test_replace_dict_category_type(self, input_category_df, expected_category_df):
"""
Test to ensure category dtypes are maintained
after replace with dict values
"""
# create input dataframe
input_dict = {"col1": ["a"], "col2": ["obj1"], "col3": ["cat1"]}
# explicitly cast columns as category
input_df = pd.DataFrame(data=input_dict).astype(
{"col1": "category", "col2": "category", "col3": "category"}
)
# create expected dataframe
expected_dict = {"col1": ["z"], "col2": ["obj9"], "col3": ["catX"]}
# explicitly cast columns as category
expected = pd.DataFrame(data=expected_dict).astype(
{"col1": "category", "col2": "category", "col3": "category"}
)
# replace values in input dataframe using a dict
result = input_df.replace({"a": "z", "obj1": "obj9", "cat1": "catX"})
tm.assert_frame_equal(result, expected)
def test_replace_with_compiled_regex(self):
# https://github.com/pandas-dev/pandas/issues/35680
df = pd.DataFrame(["a", "b", "c"])
regex = re.compile("^a$")
result = df.replace({regex: "z"}, regex=True)
expected = pd.DataFrame(["z", "b", "c"])
tm.assert_frame_equal(result, expected)