target = pro.daily(ts_code=stock + ".SH")
target = target[["trade_date", "open", "close", "high", "low"]]
target["trade_date"] = pd.to_datetime(target["trade_date"], format="%Y%m%d")
target.index = target["trade_date"]
target.sort_index(ascending=True, inplace=True)
'''
today = ts.get_realtime_quotes(stock) #Single stock symbol
today["date"] = pd.to_datetime(today["date"], format="%Y-%m-%d")
today.index = today["date"]
#添加实时数据到历史列表中
hist.loc[today["date"].iloc[0]] = [today["date"].iloc[0],today["open"].iloc[0],today["price"].iloc[0]]
target.loc[today["date"].iloc[0]] = [today["date"].iloc[0],today["open"].iloc[0],today["price"].iloc[0]]
'''
p = pd.Period(start, freq="B") + 1
csv = pd.DataFrame(columns=['交易类型', '交易价格', '持仓数量', '可用资金', '人民币总资产'])
class Account():
def __init__(self, capital_base, previous_date):
self.capital_base = capital_base
self.current_positions = []
self.target_position = []
self.stock_amounts = {}
self.previous_date = pd.Period(previous_date, freq="B")
self.cash = 10000
def order_to(stock, amounts):
print(1)
def proforma_report(self, apply_inflation_rate_func, fill_forward_func,
results):
""" Calculates the proforma that corresponds to participation in this value stream
Args:
apply_inflation_rate_func:
fill_forward_func:
results (pd.DataFrame):
Returns: A DateFrame of with each year in opt_year as the index and
the corresponding value this stream provided.
"""
pro_forma = super().proforma_report(apply_inflation_rate_func,
fill_forward_func, results)
tech_id = self.unique_tech_id()
if self.variables_df.empty:
return pro_forma
optimization_years = self.variables_df.index.year.unique()
# OM COSTS
om_costs = pd.DataFrame()
cumulative_energy_dispatch_kw = pd.DataFrame()
elec = self.variables_df['elec']
udis = self.variables_df['udis']
dis_column_name = tech_id + ' Cumulative Energy Dispatch (kW)'
variable_column_name = tech_id + ' Variable O&M Costs'
for year in optimization_years:
index_yr = pd.Period(year=year, freq='y')
# add fixed o&m costs
om_costs.loc[index_yr, self.fixed_column_name()] = -self.fixed_om
# add variable costs
elec_sub = elec.loc[elec.index.year == year]
udis_sub = udis.loc[udis.index.year == year]
om_costs.loc[index_yr, variable_column_name] = -self.variable_om
cumulative_energy_dispatch_kw.loc[
index_yr,
dis_column_name] = np.sum(elec_sub) + np.sum(udis_sub)
# fill forward (escalate rates)
om_costs = fill_forward_func(om_costs, None, is_om_cost=True)
# interpolate cumulative energy dispatch between analysis years
# be careful to not include years labeled as Strings (CAPEX)
years_list = list(
filter(lambda x: not (type(x) is str), om_costs.index))
analysis_start_year = min(years_list).year
analysis_end_year = max(years_list).year
cumulative_energy_dispatch_kw = self.interpolate_energy_dispatch(
cumulative_energy_dispatch_kw, analysis_start_year,
analysis_end_year, None)
# calculate om costs in dollars, as rate * energy
# fixed om is already in $
# variable om
om_costs.loc[:,
variable_column_name] = om_costs.loc[:,
variable_column_name] * self.dt * cumulative_energy_dispatch_kw.loc[:,
dis_column_name]
# append with super class's proforma
pro_forma = pd.concat([pro_forma, om_costs], axis=1)
# fuel costs in $/kW
fuel_costs = pd.DataFrame()
fuel_col_name = tech_id + ' Fuel Costs'
for year in optimization_years:
elec_sub = elec.loc[elec.index.year == year]
udis_sub = udis.loc[udis.index.year == year]
# add fuel costs in $/kW
fuel_costs.loc[pd.Period(year=year, freq='y'),
fuel_col_name] = -np.sum(
self.heat_rate * self.fuel_cost * self.dt *
(elec_sub + udis_sub))
# fill forward
fuel_costs = fill_forward_func(fuel_costs, None)
# append with super class's proforma
pro_forma = pd.concat([pro_forma, fuel_costs], axis=1)
return pro_forma
def test_constructor_period_incompatible_frequency(self):
data = [pd.Period('2000', 'D'), pd.Period('2001', 'A')]
result = pd.Series(data)
assert result.dtype == object
assert result.tolist() == data
def dashboard_sourceData_temp(request):
if request.method == 'GET':
json_out = {}
main_out = {}
days_num = 0
data = {}
try:
# sourcedata
days_list = []
topic_list = Topic.objects
day = pd.Period(datetime.datetime.now(),freq='D')
# logger.info(type(Topic.objects)) # Topic.objects.all()Topic.objects()返回类型相同
datatype_list = Datatype_name.objects
post_7days = Post.objects(Q(pt_time__gte=datetime.datetime.combine\
(date.today()-datetime.timedelta(6),datetime.time.min)) &
Q(pt_time__lte=datetime.datetime.combine(date.today(), datetime.time.max)))
while days_num < 7:
day_dict = {}
day_str = day.strftime('%Y%m%d')
day_dict['time'] = day_str
post = post_7days(Q(pt_time__gte=\
datetime.datetime.combine(date.today()-\
datetime.timedelta(days=days_num), \
datetime.time.min)) & \
Q(pt_time__lte=datetime.datetime.combine\
(date.today()-datetime.timedelta(days=days_num), \
datetime.time.max)))
for topic in topic_list:
for data in datatype_list:
day_dict = {}
day_dict['time'] = day_str
day_dict['topic_id'] = topic._id
day_dict['topic_name'] = topic.topic_name
day_dict['dataTypeName'] = data.datatype_name
day_dict['data_type'] = data.data_type
post_datatype = post(Q(data_type=data.data_type) & Q(topic_id=topic._id))
# logger.info('post_num = ' + str(len(post_datatype)))
day_dict['post_num'] = post_datatype.count()
days_list.append(day_dict)
# for data in datatype_list:
# day_dict['dataTypeName'] = data.datatype_name
# day_dict['data_type'] = data.data_type
# post_datatype = post(Q(data_type=data.data_type))
# # logger.info('post_num = ' + str(len(post_datatype)))
# day_dict['post_num'] = len(post_datatype)
# day_dict_ = dict(day_dict)
# days_list.append(day_dict_)
day -= 1
days_num += 1
####### Hot for all host posts
hot_dict = {}
hot_posts_temp = post_7days(Q(data_type=3))
hot_posts = hot_posts_temp.order_by \
('-comm_num')[:10].only("_id", "url", \
"board", "title", "content", "pt_time", \
"img_url","comm_num","repost_num")
hot_weibo_temp = post_7days(Q(data_type=2))
hot_weibo = hot_weibo_temp.order_by \
('-comm_num')[:10].only("_id", "url", \
"board", "title", "content", "pt_time", \
"img_url","comm_num","repost_num")
hot_poster = hot_posts_temp.order_by('-poster.post_num')[:10].only("poster")
hot_weibouser_post_num = hot_weibo_temp.order_by('-poster.post_num').only("poster")
hot_weibouser = []
hot_weibouser_id = []
num_ten=0
for item in hot_weibouser_post_num:
if num_ten ==10:
break
else:
if item.poster.id not in hot_weibouser_id:
hot_weibouser.append(item)
hot_weibouser_id.append(item.poster.id)
num_ten=num_ten+1
hot_dict['hotPost'] = handle_post_list1(hot_posts)
hot_dict['hotPoster'] = handle_poster_list1(hot_poster)
hot_dict['hotWeibo'] = handle_post_list1(hot_weibo)
# hot_dict['hotPoster'] = handle_poster_list(hot_posts)
hot_dict['hotWeiboUser'] = handle_weibouser_list1(hot_weibouser)
####### map data
mapData_list = [{'id':'001',
'pro':"陕西",
'nums':52
},
{
'id':'002',
'pro':"北京",
'nums':100
},
{
'id':'003',
'pro':"上海",
'nums':60
},
{
'id':'004',
'pro':"杭州",
'nums':48
},
{
'id':'005',
'pro':"南京",
'nums':50
}
]
wordlist = []
wordres=Cloud_formain.objects(Q(topic_id=999)).only("word", "frequency")
for worditem in wordres:
temp={}
temp['word']=worditem.word
temp['weight']=worditem.frequency
wordlist.append(temp)
main_out['mapData'] = mapData_list
main_out['sourceData'] = days_list
main_out['Hot'] = hot_dict
main_out['word_cloud'] = wordlist
json_out['code'] = 0
json_out['success'] = True
json_out['data'] = main_out
except:
traceback.print_exc()
json_out['code'] = 1
json_out['data'] = {}
json_out['success'] = False
return HttpResponse(json.dumps(json_out, cls=MyEncoder),content_type="application/json")
# Since right now, by default MI will drop NA from levels when we create MI
# via `from_*`, so we need to add NA for level manually afterwards.
if not dropna:
mi = mi.set_levels(["A", "B", np.nan], level="b")
expected = pd.DataFrame(outputs, index=mi)
tm.assert_frame_equal(grouped, expected)
@pytest.mark.parametrize(
"datetime1, datetime2",
[
(pd.Timestamp("2020-01-01"), pd.Timestamp("2020-02-01")),
(pd.Timedelta("-2 days"), pd.Timedelta("-1 days")),
(pd.Period("2020-01-01"), pd.Period("2020-02-01")),
],
)
@pytest.mark.parametrize("dropna, values", [(True, [12, 3]),
(False, [12, 3, 6])])
def test_groupby_dropna_datetime_like_data(dropna, values, datetime1,
datetime2, unique_nulls_fixture,
unique_nulls_fixture2):
# 3729
df = pd.DataFrame({
"values": [1, 2, 3, 4, 5, 6],
"dt": [
datetime1,
unique_nulls_fixture,
datetime2,
unique_nulls_fixture2,
class TestSeriesReplace:
def test_replace_explicit_none(self):
# GH#36984 if the user explicitly passes value=None, give it to them
ser = pd.Series([0, 0, ""], dtype=object)
result = ser.replace("", None)
expected = pd.Series([0, 0, None], dtype=object)
tm.assert_series_equal(result, expected)
df = pd.DataFrame(np.zeros((3, 3)))
df.iloc[2, 2] = ""
result = df.replace("", None)
expected = pd.DataFrame(
{
0: np.zeros(3),
1: np.zeros(3),
2: np.array([0.0, 0.0, None], dtype=object),
}
)
assert expected.iloc[2, 2] is None
tm.assert_frame_equal(result, expected)
# GH#19998 same thing with object dtype
ser = pd.Series([10, 20, 30, "a", "a", "b", "a"])
result = ser.replace("a", None)
expected = pd.Series([10, 20, 30, None, None, "b", None])
assert expected.iloc[-1] is None
tm.assert_series_equal(result, expected)
def test_replace_numpy_nan(self, nulls_fixture):
# GH#45725 ensure numpy.nan can be replaced with all other null types
to_replace = np.nan
value = nulls_fixture
dtype = object
ser = pd.Series([to_replace], dtype=dtype)
expected = pd.Series([value], dtype=dtype)
result = ser.replace({to_replace: value}).astype(dtype=dtype)
tm.assert_series_equal(result, expected)
assert result.dtype == dtype
# same thing but different calling convention
result = ser.replace(to_replace, value).astype(dtype=dtype)
tm.assert_series_equal(result, expected)
assert result.dtype == dtype
def test_replace_noop_doesnt_downcast(self):
# GH#44498
ser = pd.Series([None, None, pd.Timestamp("2021-12-16 17:31")], dtype=object)
res = ser.replace({np.nan: None}) # should be a no-op
tm.assert_series_equal(res, ser)
assert res.dtype == object
# same thing but different calling convention
res = ser.replace(np.nan, None)
tm.assert_series_equal(res, ser)
assert res.dtype == object
def test_replace(self):
N = 100
ser = pd.Series(np.random.randn(N))
ser[0:4] = np.nan
ser[6:10] = 0
# replace list with a single value
return_value = ser.replace([np.nan], -1, inplace=True)
assert return_value is None
exp = ser.fillna(-1)
tm.assert_series_equal(ser, exp)
rs = ser.replace(0.0, np.nan)
ser[ser == 0.0] = np.nan
tm.assert_series_equal(rs, ser)
ser = pd.Series(np.fabs(np.random.randn(N)), tm.makeDateIndex(N), dtype=object)
ser[:5] = np.nan
ser[6:10] = "foo"
ser[20:30] = "bar"
# replace list with a single value
rs = ser.replace([np.nan, "foo", "bar"], -1)
assert (rs[:5] == -1).all()
assert (rs[6:10] == -1).all()
assert (rs[20:30] == -1).all()
assert (pd.isna(ser[:5])).all()
# replace with different values
rs = ser.replace({np.nan: -1, "foo": -2, "bar": -3})
assert (rs[:5] == -1).all()
assert (rs[6:10] == -2).all()
assert (rs[20:30] == -3).all()
assert (pd.isna(ser[:5])).all()
# replace with different values with 2 lists
rs2 = ser.replace([np.nan, "foo", "bar"], [-1, -2, -3])
tm.assert_series_equal(rs, rs2)
# replace inplace
return_value = ser.replace([np.nan, "foo", "bar"], -1, inplace=True)
assert return_value is None
assert (ser[:5] == -1).all()
assert (ser[6:10] == -1).all()
assert (ser[20:30] == -1).all()
def test_replace_nan_with_inf(self):
ser = pd.Series([np.nan, 0, np.inf])
tm.assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0))
ser = pd.Series([np.nan, 0, "foo", "bar", np.inf, None, pd.NaT])
tm.assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0))
filled = ser.copy()
filled[4] = 0
tm.assert_series_equal(ser.replace(np.inf, 0), filled)
def test_replace_listlike_value_listlike_target(self, datetime_series):
ser = pd.Series(datetime_series.index)
tm.assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0))
# malformed
msg = r"Replacement lists must match in length\. Expecting 3 got 2"
with pytest.raises(ValueError, match=msg):
ser.replace([1, 2, 3], [np.nan, 0])
# ser is dt64 so can't hold 1 or 2, so this replace is a no-op
result = ser.replace([1, 2], [np.nan, 0])
tm.assert_series_equal(result, ser)
ser = pd.Series([0, 1, 2, 3, 4])
result = ser.replace([0, 1, 2, 3, 4], [4, 3, 2, 1, 0])
tm.assert_series_equal(result, pd.Series([4, 3, 2, 1, 0]))
def test_replace_gh5319(self):
# API change from 0.12?
# GH 5319
ser = pd.Series([0, np.nan, 2, 3, 4])
expected = ser.ffill()
result = ser.replace([np.nan])
tm.assert_series_equal(result, expected)
ser = pd.Series([0, np.nan, 2, 3, 4])
expected = ser.ffill()
result = ser.replace(np.nan)
tm.assert_series_equal(result, expected)
def test_replace_datetime64(self):
# GH 5797
ser = pd.Series(pd.date_range("20130101", periods=5))
expected = ser.copy()
expected.loc[2] = pd.Timestamp("20120101")
result = ser.replace({pd.Timestamp("20130103"): pd.Timestamp("20120101")})
tm.assert_series_equal(result, expected)
result = ser.replace(pd.Timestamp("20130103"), pd.Timestamp("20120101"))
tm.assert_series_equal(result, expected)
def test_replace_nat_with_tz(self):
# GH 11792: Test with replacing NaT in a list with tz data
ts = pd.Timestamp("2015/01/01", tz="UTC")
s = pd.Series([pd.NaT, pd.Timestamp("2015/01/01", tz="UTC")])
result = s.replace([np.nan, pd.NaT], pd.Timestamp.min)
expected = pd.Series([pd.Timestamp.min, ts], dtype=object)
tm.assert_series_equal(expected, result)
def test_replace_timedelta_td64(self):
tdi = pd.timedelta_range(0, periods=5)
ser = pd.Series(tdi)
# Using a single dict argument means we go through replace_list
result = ser.replace({ser[1]: ser[3]})
expected = pd.Series([ser[0], ser[3], ser[2], ser[3], ser[4]])
tm.assert_series_equal(result, expected)
def test_replace_with_single_list(self):
ser = pd.Series([0, 1, 2, 3, 4])
result = ser.replace([1, 2, 3])
tm.assert_series_equal(result, pd.Series([0, 0, 0, 0, 4]))
s = ser.copy()
return_value = s.replace([1, 2, 3], inplace=True)
assert return_value is None
tm.assert_series_equal(s, pd.Series([0, 0, 0, 0, 4]))
# make sure things don't get corrupted when fillna call fails
s = ser.copy()
msg = (
r"Invalid fill method\. Expecting pad \(ffill\) or backfill "
r"\(bfill\)\. Got crash_cymbal"
)
with pytest.raises(ValueError, match=msg):
return_value = s.replace([1, 2, 3], inplace=True, method="crash_cymbal")
assert return_value is None
tm.assert_series_equal(s, ser)
def test_replace_mixed_types(self):
ser = pd.Series(np.arange(5), dtype="int64")
def check_replace(to_rep, val, expected):
sc = ser.copy()
result = ser.replace(to_rep, val)
return_value = sc.replace(to_rep, val, inplace=True)
assert return_value is None
tm.assert_series_equal(expected, result)
tm.assert_series_equal(expected, sc)
# 3.0 can still be held in our int64 series, so we do not upcast GH#44940
tr, v = [3], [3.0]
check_replace(tr, v, ser)
# Note this matches what we get with the scalars 3 and 3.0
check_replace(tr[0], v[0], ser)
# MUST upcast to float
e = pd.Series([0, 1, 2, 3.5, 4])
tr, v = [3], [3.5]
check_replace(tr, v, e)
# casts to object
e = pd.Series([0, 1, 2, 3.5, "a"])
tr, v = [3, 4], [3.5, "a"]
check_replace(tr, v, e)
# again casts to object
e = pd.Series([0, 1, 2, 3.5, pd.Timestamp("20130101")])
tr, v = [3, 4], [3.5, pd.Timestamp("20130101")]
check_replace(tr, v, e)
# casts to object
e = pd.Series([0, 1, 2, 3.5, True], dtype="object")
tr, v = [3, 4], [3.5, True]
check_replace(tr, v, e)
# test an object with dates + floats + integers + strings
dr = pd.Series(pd.date_range("1/1/2001", "1/10/2001", freq="D"))
result = dr.astype(object).replace([dr[0], dr[1], dr[2]], [1.0, 2, "a"])
expected = pd.Series([1.0, 2, "a"] + dr[3:].tolist(), dtype=object)
tm.assert_series_equal(result, expected)
def test_replace_bool_with_string_no_op(self):
s = pd.Series([True, False, True])
result = s.replace("fun", "in-the-sun")
tm.assert_series_equal(s, result)
def test_replace_bool_with_string(self):
# nonexistent elements
s = pd.Series([True, False, True])
result = s.replace(True, "2u")
expected = pd.Series(["2u", False, "2u"])
tm.assert_series_equal(expected, result)
def test_replace_bool_with_bool(self):
s = pd.Series([True, False, True])
result = s.replace(True, False)
expected = pd.Series([False] * len(s))
tm.assert_series_equal(expected, result)
def test_replace_with_dict_with_bool_keys(self):
s = pd.Series([True, False, True])
result = s.replace({"asdf": "asdb", True: "yes"})
expected = pd.Series(["yes", False, "yes"])
tm.assert_series_equal(result, expected)
def test_replace_Int_with_na(self, any_int_ea_dtype):
# GH 38267
result = pd.Series([0, None], dtype=any_int_ea_dtype).replace(0, pd.NA)
expected = pd.Series([pd.NA, pd.NA], dtype=any_int_ea_dtype)
tm.assert_series_equal(result, expected)
result = pd.Series([0, 1], dtype=any_int_ea_dtype).replace(0, pd.NA)
result.replace(1, pd.NA, inplace=True)
tm.assert_series_equal(result, expected)
def test_replace2(self):
N = 100
ser = pd.Series(np.fabs(np.random.randn(N)), tm.makeDateIndex(N), dtype=object)
ser[:5] = np.nan
ser[6:10] = "foo"
ser[20:30] = "bar"
# replace list with a single value
rs = ser.replace([np.nan, "foo", "bar"], -1)
assert (rs[:5] == -1).all()
assert (rs[6:10] == -1).all()
assert (rs[20:30] == -1).all()
assert (pd.isna(ser[:5])).all()
# replace with different values
rs = ser.replace({np.nan: -1, "foo": -2, "bar": -3})
assert (rs[:5] == -1).all()
assert (rs[6:10] == -2).all()
assert (rs[20:30] == -3).all()
assert (pd.isna(ser[:5])).all()
# replace with different values with 2 lists
rs2 = ser.replace([np.nan, "foo", "bar"], [-1, -2, -3])
tm.assert_series_equal(rs, rs2)
# replace inplace
return_value = ser.replace([np.nan, "foo", "bar"], -1, inplace=True)
assert return_value is None
assert (ser[:5] == -1).all()
assert (ser[6:10] == -1).all()
assert (ser[20:30] == -1).all()
def test_replace_with_dictlike_and_string_dtype(self, nullable_string_dtype):
# GH 32621, GH#44940
ser = pd.Series(["one", "two", np.nan], dtype=nullable_string_dtype)
expected = pd.Series(["1", "2", np.nan], dtype=nullable_string_dtype)
result = ser.replace({"one": "1", "two": "2"})
tm.assert_series_equal(expected, result)
def test_replace_with_empty_dictlike(self):
# GH 15289
s = pd.Series(list("abcd"))
tm.assert_series_equal(s, s.replace({}))
with tm.assert_produces_warning(FutureWarning):
empty_series = pd.Series([])
tm.assert_series_equal(s, s.replace(empty_series))
def test_replace_string_with_number(self):
# GH 15743
s = pd.Series([1, 2, 3])
result = s.replace("2", np.nan)
expected = pd.Series([1, 2, 3])
tm.assert_series_equal(expected, result)
def test_replace_replacer_equals_replacement(self):
# GH 20656
# make sure all replacers are matching against original values
s = pd.Series(["a", "b"])
expected = pd.Series(["b", "a"])
result = s.replace({"a": "b", "b": "a"})
tm.assert_series_equal(expected, result)
def test_replace_unicode_with_number(self):
# GH 15743
s = pd.Series([1, 2, 3])
result = s.replace("2", np.nan)
expected = pd.Series([1, 2, 3])
tm.assert_series_equal(expected, result)
def test_replace_mixed_types_with_string(self):
# Testing mixed
s = pd.Series([1, 2, 3, "4", 4, 5])
result = s.replace([2, "4"], np.nan)
expected = pd.Series([1, np.nan, 3, np.nan, 4, 5])
tm.assert_series_equal(expected, result)
@pytest.mark.parametrize(
"categorical, numeric",
[
(pd.Categorical(["A"], categories=["A", "B"]), [1]),
(pd.Categorical(["A", "B"], categories=["A", "B"]), [1, 2]),
],
)
def test_replace_categorical(self, categorical, numeric):
# GH 24971, GH#23305
ser = pd.Series(categorical)
result = ser.replace({"A": 1, "B": 2})
expected = pd.Series(numeric).astype("category")
if 2 not in expected.cat.categories:
# i.e. categories should be [1, 2] even if there are no "B"s present
# GH#44940
expected = expected.cat.add_categories(2)
tm.assert_series_equal(expected, result)
def test_replace_categorical_single(self):
# GH 26988
dti = pd.date_range("2016-01-01", periods=3, tz="US/Pacific")
s = pd.Series(dti)
c = s.astype("category")
expected = c.copy()
expected = expected.cat.add_categories("foo")
expected[2] = "foo"
expected = expected.cat.remove_unused_categories()
assert c[2] != "foo"
result = c.replace(c[2], "foo")
tm.assert_series_equal(expected, result)
assert c[2] != "foo" # ensure non-inplace call does not alter original
return_value = c.replace(c[2], "foo", inplace=True)
assert return_value is None
tm.assert_series_equal(expected, c)
first_value = c[0]
return_value = c.replace(c[1], c[0], inplace=True)
assert return_value is None
assert c[0] == c[1] == first_value # test replacing with existing value
def test_replace_with_no_overflowerror(self):
# GH 25616
# casts to object without Exception from OverflowError
s = pd.Series([0, 1, 2, 3, 4])
result = s.replace([3], ["100000000000000000000"])
expected = pd.Series([0, 1, 2, "100000000000000000000", 4])
tm.assert_series_equal(result, expected)
s = pd.Series([0, "100000000000000000000", "100000000000000000001"])
result = s.replace(["100000000000000000000"], [1])
expected = pd.Series([0, 1, "100000000000000000001"])
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"ser, to_replace, exp",
[
([1, 2, 3], {1: 2, 2: 3, 3: 4}, [2, 3, 4]),
(["1", "2", "3"], {"1": "2", "2": "3", "3": "4"}, ["2", "3", "4"]),
],
)
def test_replace_commutative(self, ser, to_replace, exp):
# GH 16051
# DataFrame.replace() overwrites when values are non-numeric
series = pd.Series(ser)
expected = pd.Series(exp)
result = series.replace(to_replace)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"ser, exp", [([1, 2, 3], [1, True, 3]), (["x", 2, 3], ["x", True, 3])]
)
def test_replace_no_cast(self, ser, exp):
# GH 9113
# BUG: replace int64 dtype with bool coerces to int64
series = pd.Series(ser)
result = series.replace(2, True)
expected = pd.Series(exp)
tm.assert_series_equal(result, expected)
def test_replace_invalid_to_replace(self):
# GH 18634
# API: replace() should raise an exception if invalid argument is given
series = pd.Series(["a", "b", "c "])
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):
series.replace(lambda x: x.strip())
@pytest.mark.parametrize("frame", [False, True])
def test_replace_nonbool_regex(self, frame):
obj = pd.Series(["a", "b", "c "])
if frame:
obj = obj.to_frame()
msg = "'to_replace' must be 'None' if 'regex' is not a bool"
with pytest.raises(ValueError, match=msg):
obj.replace(to_replace=["a"], regex="foo")
@pytest.mark.parametrize("frame", [False, True])
def test_replace_empty_copy(self, frame):
obj = pd.Series([], dtype=np.float64)
if frame:
obj = obj.to_frame()
res = obj.replace(4, 5, inplace=True)
assert res is None
res = obj.replace(4, 5, inplace=False)
tm.assert_equal(res, obj)
assert res is not obj
def test_replace_only_one_dictlike_arg(self, fixed_now_ts):
# GH#33340
ser = pd.Series([1, 2, "A", fixed_now_ts, True])
to_replace = {0: 1, 2: "A"}
value = "foo"
msg = "Series.replace cannot use dict-like to_replace and non-None value"
with pytest.raises(ValueError, match=msg):
ser.replace(to_replace, value)
to_replace = 1
value = {0: "foo", 2: "bar"}
msg = "Series.replace cannot use dict-value and non-None to_replace"
with pytest.raises(ValueError, match=msg):
ser.replace(to_replace, value)
def test_replace_extension_other(self, frame_or_series):
# https://github.com/pandas-dev/pandas/issues/34530
obj = frame_or_series(pd.array([1, 2, 3], dtype="Int64"))
result = obj.replace("", "") # no exception
# should not have changed dtype
tm.assert_equal(obj, result)
def _check_replace_with_method(self, ser: pd.Series):
df = ser.to_frame()
res = ser.replace(ser[1], method="pad")
expected = pd.Series([ser[0], ser[0]] + list(ser[2:]), dtype=ser.dtype)
tm.assert_series_equal(res, expected)
res_df = df.replace(ser[1], method="pad")
tm.assert_frame_equal(res_df, expected.to_frame())
ser2 = ser.copy()
res2 = ser2.replace(ser[1], method="pad", inplace=True)
assert res2 is None
tm.assert_series_equal(ser2, expected)
res_df2 = df.replace(ser[1], method="pad", inplace=True)
assert res_df2 is None
tm.assert_frame_equal(df, expected.to_frame())
def test_replace_ea_dtype_with_method(self, any_numeric_ea_dtype):
arr = pd.array([1, 2, pd.NA, 4], dtype=any_numeric_ea_dtype)
ser = pd.Series(arr)
self._check_replace_with_method(ser)
@pytest.mark.parametrize("as_categorical", [True, False])
def test_replace_interval_with_method(self, as_categorical):
# in particular interval that can't hold NA
idx = pd.IntervalIndex.from_breaks(range(4))
ser = pd.Series(idx)
if as_categorical:
ser = ser.astype("category")
self._check_replace_with_method(ser)
@pytest.mark.parametrize("as_period", [True, False])
@pytest.mark.parametrize("as_categorical", [True, False])
def test_replace_datetimelike_with_method(self, as_period, as_categorical):
idx = pd.date_range("2016-01-01", periods=5, tz="US/Pacific")
if as_period:
idx = idx.tz_localize(None).to_period("D")
ser = pd.Series(idx)
ser.iloc[-2] = pd.NaT
if as_categorical:
ser = ser.astype("category")
self._check_replace_with_method(ser)
def test_replace_with_compiled_regex(self):
# https://github.com/pandas-dev/pandas/issues/35680
s = pd.Series(["a", "b", "c"])
regex = re.compile("^a$")
result = s.replace({regex: "z"}, regex=True)
expected = pd.Series(["z", "b", "c"])
tm.assert_series_equal(result, expected)
def test_pandas_replace_na(self):
# GH#43344
ser = pd.Series(["AA", "BB", "CC", "DD", "EE", "", pd.NA], dtype="string")
regex_mapping = {
"AA": "CC",
"BB": "CC",
"EE": "CC",
"CC": "CC-REPL",
}
result = ser.replace(regex_mapping, regex=True)
exp = pd.Series(["CC", "CC", "CC-REPL", "DD", "CC", "", pd.NA], dtype="string")
tm.assert_series_equal(result, exp)
@pytest.mark.parametrize(
"dtype, input_data, to_replace, expected_data",
[
("bool", [True, False], {True: False}, [False, False]),
("int64", [1, 2], {1: 10, 2: 20}, [10, 20]),
("Int64", [1, 2], {1: 10, 2: 20}, [10, 20]),
("float64", [1.1, 2.2], {1.1: 10.1, 2.2: 20.5}, [10.1, 20.5]),
("Float64", [1.1, 2.2], {1.1: 10.1, 2.2: 20.5}, [10.1, 20.5]),
("string", ["one", "two"], {"one": "1", "two": "2"}, ["1", "2"]),
(
pd.IntervalDtype("int64"),
IntervalArray([pd.Interval(1, 2), pd.Interval(2, 3)]),
{pd.Interval(1, 2): pd.Interval(10, 20)},
IntervalArray([pd.Interval(10, 20), pd.Interval(2, 3)]),
),
(
pd.IntervalDtype("float64"),
IntervalArray([pd.Interval(1.0, 2.7), pd.Interval(2.8, 3.1)]),
{pd.Interval(1.0, 2.7): pd.Interval(10.6, 20.8)},
IntervalArray([pd.Interval(10.6, 20.8), pd.Interval(2.8, 3.1)]),
),
(
pd.PeriodDtype("M"),
[pd.Period("2020-05", freq="M")],
{pd.Period("2020-05", freq="M"): pd.Period("2020-06", freq="M")},
[pd.Period("2020-06", freq="M")],
),
],
)
def test_replace_dtype(self, dtype, input_data, to_replace, expected_data):
# GH#33484
ser = pd.Series(input_data, dtype=dtype)
result = ser.replace(to_replace)
expected = pd.Series(expected_data, dtype=dtype)
tm.assert_series_equal(result, expected)
def test_replace_string_dtype(self):
# GH#40732, GH#44940
ser = pd.Series(["one", "two", np.nan], dtype="string")
res = ser.replace({"one": "1", "two": "2"})
expected = pd.Series(["1", "2", np.nan], dtype="string")
tm.assert_series_equal(res, expected)
# GH#31644
ser2 = pd.Series(["A", np.nan], dtype="string")
res2 = ser2.replace("A", "B")
expected2 = pd.Series(["B", np.nan], dtype="string")
tm.assert_series_equal(res2, expected2)
ser3 = pd.Series(["A", "B"], dtype="string")
res3 = ser3.replace("A", pd.NA)
expected3 = pd.Series([pd.NA, "B"], dtype="string")
tm.assert_series_equal(res3, expected3)
def test_replace_string_dtype_list_to_replace(self):
# GH#41215, GH#44940
ser = pd.Series(["abc", "def"], dtype="string")
res = ser.replace(["abc", "any other string"], "xyz")
expected = pd.Series(["xyz", "def"], dtype="string")
tm.assert_series_equal(res, expected)
def test_replace_string_dtype_regex(self):
# GH#31644
ser = pd.Series(["A", "B"], dtype="string")
res = ser.replace(r".", "C", regex=True)
expected = pd.Series(["C", "C"], dtype="string")
tm.assert_series_equal(res, expected)
def test_replace_nullable_numeric(self):
# GH#40732, GH#44940
floats = pd.Series([1.0, 2.0, 3.999, 4.4], dtype=pd.Float64Dtype())
assert floats.replace({1.0: 9}).dtype == floats.dtype
assert floats.replace(1.0, 9).dtype == floats.dtype
assert floats.replace({1.0: 9.0}).dtype == floats.dtype
assert floats.replace(1.0, 9.0).dtype == floats.dtype
res = floats.replace(to_replace=[1.0, 2.0], value=[9.0, 10.0])
assert res.dtype == floats.dtype
ints = pd.Series([1, 2, 3, 4], dtype=pd.Int64Dtype())
assert ints.replace({1: 9}).dtype == ints.dtype
assert ints.replace(1, 9).dtype == ints.dtype
assert ints.replace({1: 9.0}).dtype == ints.dtype
assert ints.replace(1, 9.0).dtype == ints.dtype
# FIXME: ints.replace({1: 9.5}) raises bc of incorrect _can_hold_element
@pytest.mark.parametrize("regex", [False, True])
def test_replace_regex_dtype_series(self, regex):
# GH-48644
series = pd.Series(["0"])
expected = pd.Series([1])
result = series.replace(to_replace="0", value=1, regex=regex)
tm.assert_series_equal(result, expected)
def test_replace_different_int_types(self, any_int_numpy_dtype):
# GH#45311
labs = pd.Series([1, 1, 1, 0, 0, 2, 2, 2], dtype=any_int_numpy_dtype)
maps = pd.Series([0, 2, 1], dtype=any_int_numpy_dtype)
map_dict = {old: new for (old, new) in zip(maps.values, maps.index)}
result = labs.replace(map_dict)
expected = labs.replace({0: 0, 2: 1, 1: 2})
tm.assert_series_equal(result, expected)
class TestInsertIndexCoercion(CoercionBase):
klasses = ["index"]
method = "insert"
def _assert_insert_conversion(self, original, value, expected,
expected_dtype):
"""test coercion triggered by insert"""
target = original.copy()
res = target.insert(1, value)
tm.assert_index_equal(res, expected)
assert res.dtype == expected_dtype
@pytest.mark.parametrize(
"insert, coerced_val, coerced_dtype",
[
(1, 1, object),
(1.1, 1.1, object),
(False, False, object),
("x", "x", object),
],
)
def test_insert_index_object(self, insert, coerced_val, coerced_dtype):
obj = pd.Index(list("abcd"))
assert obj.dtype == object
exp = pd.Index(["a", coerced_val, "b", "c", "d"])
self._assert_insert_conversion(obj, insert, exp, coerced_dtype)
@pytest.mark.parametrize(
"insert, coerced_val, coerced_dtype",
[
(1, 1, np.int64),
(1.1, 1.1, np.float64),
(False, False, object), # GH#36319
("x", "x", object),
],
)
def test_insert_index_int64(self, insert, coerced_val, coerced_dtype):
obj = Int64Index([1, 2, 3, 4])
assert obj.dtype == np.int64
exp = pd.Index([1, coerced_val, 2, 3, 4])
self._assert_insert_conversion(obj, insert, exp, coerced_dtype)
@pytest.mark.parametrize(
"insert, coerced_val, coerced_dtype",
[
(1, 1.0, np.float64),
(1.1, 1.1, np.float64),
(False, False, object), # GH#36319
("x", "x", object),
],
)
def test_insert_index_float64(self, insert, coerced_val, coerced_dtype):
obj = Float64Index([1.0, 2.0, 3.0, 4.0])
assert obj.dtype == np.float64
exp = pd.Index([1.0, coerced_val, 2.0, 3.0, 4.0])
self._assert_insert_conversion(obj, insert, exp, coerced_dtype)
@pytest.mark.parametrize(
"fill_val,exp_dtype",
[
(pd.Timestamp("2012-01-01"), "datetime64[ns]"),
(pd.Timestamp("2012-01-01",
tz="US/Eastern"), "datetime64[ns, US/Eastern]"),
],
ids=["datetime64", "datetime64tz"],
)
@pytest.mark.parametrize(
"insert_value",
[
pd.Timestamp("2012-01-01"),
pd.Timestamp("2012-01-01", tz="Asia/Tokyo"), 1
],
)
def test_insert_index_datetimes(self, request, fill_val, exp_dtype,
insert_value):
obj = pd.DatetimeIndex(
["2011-01-01", "2011-01-02", "2011-01-03", "2011-01-04"],
tz=fill_val.tz)
assert obj.dtype == exp_dtype
exp = pd.DatetimeIndex(
[
"2011-01-01",
fill_val.date(), "2011-01-02", "2011-01-03", "2011-01-04"
],
tz=fill_val.tz,
)
self._assert_insert_conversion(obj, fill_val, exp, exp_dtype)
if fill_val.tz:
# mismatched tzawareness
ts = pd.Timestamp("2012-01-01")
result = obj.insert(1, ts)
expected = obj.astype(object).insert(1, ts)
assert expected.dtype == object
tm.assert_index_equal(result, expected)
# mismatched tz --> cast to object (could reasonably cast to common tz)
ts = pd.Timestamp("2012-01-01", tz="Asia/Tokyo")
with tm.assert_produces_warning(FutureWarning,
match="mismatched timezone"):
result = obj.insert(1, ts)
# once deprecation is enforced:
# expected = obj.insert(1, ts.tz_convert(obj.dtype.tz))
# assert expected.dtype == obj.dtype
expected = obj.astype(object).insert(1, ts)
tm.assert_index_equal(result, expected)
else:
# mismatched tzawareness
ts = pd.Timestamp("2012-01-01", tz="Asia/Tokyo")
result = obj.insert(1, ts)
expected = obj.astype(object).insert(1, ts)
assert expected.dtype == object
tm.assert_index_equal(result, expected)
item = 1
result = obj.insert(1, item)
expected = obj.astype(object).insert(1, item)
assert expected[1] == item
assert expected.dtype == object
tm.assert_index_equal(result, expected)
def test_insert_index_timedelta64(self):
obj = pd.TimedeltaIndex(["1 day", "2 day", "3 day", "4 day"])
assert obj.dtype == "timedelta64[ns]"
# timedelta64 + timedelta64 => timedelta64
exp = pd.TimedeltaIndex(["1 day", "10 day", "2 day", "3 day", "4 day"])
self._assert_insert_conversion(obj, pd.Timedelta("10 day"), exp,
"timedelta64[ns]")
for item in [pd.Timestamp("2012-01-01"), 1]:
result = obj.insert(1, item)
expected = obj.astype(object).insert(1, item)
assert expected.dtype == object
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize(
"insert, coerced_val, coerced_dtype",
[
(pd.Period("2012-01", freq="M"), "2012-01", "period[M]"),
(pd.Timestamp("2012-01-01"), pd.Timestamp("2012-01-01"), object),
(1, 1, object),
("x", "x", object),
],
)
def test_insert_index_period(self, insert, coerced_val, coerced_dtype):
obj = pd.PeriodIndex(["2011-01", "2011-02", "2011-03", "2011-04"],
freq="M")
assert obj.dtype == "period[M]"
data = [
pd.Period("2011-01", freq="M"),
coerced_val,
pd.Period("2011-02", freq="M"),
pd.Period("2011-03", freq="M"),
pd.Period("2011-04", freq="M"),
]
if isinstance(insert, pd.Period):
exp = pd.PeriodIndex(data, freq="M")
self._assert_insert_conversion(obj, insert, exp, coerced_dtype)
# string that can be parsed to appropriate PeriodDtype
self._assert_insert_conversion(obj, str(insert), exp,
coerced_dtype)
else:
result = obj.insert(0, insert)
expected = obj.astype(object).insert(0, insert)
tm.assert_index_equal(result, expected)
# TODO: ATM inserting '2012-01-01 00:00:00' when we have obj.freq=="M"
# casts that string to Period[M], not clear that is desirable
if not isinstance(insert, pd.Timestamp):
# non-castable string
result = obj.insert(0, str(insert))
expected = obj.astype(object).insert(0, str(insert))
tm.assert_index_equal(result, expected)
msg = r"Unexpected keyword arguments {'freq'}"
with pytest.raises(TypeError, match=msg):
with tm.assert_produces_warning(FutureWarning):
# passing keywords to pd.Index
pd.Index(data, freq="M")
@pytest.mark.xfail(reason="Test not implemented")
def test_insert_index_complex128(self):
raise NotImplementedError
@pytest.mark.xfail(reason="Test not implemented")
def test_insert_index_bool(self):
raise NotImplementedError
assert result is array
def test_array_multiindex_raises():
idx = pd.MultiIndex.from_product([['A'], ['a', 'b']])
with pytest.raises(ValueError, match='MultiIndex'):
idx.array
@pytest.mark.parametrize(
'array, expected',
[
(np.array([1, 2], dtype=np.int64), np.array([1, 2], dtype=np.int64)),
(pd.Categorical(['a', 'b']), np.array(['a', 'b'], dtype=object)),
(pd.core.arrays.period_array(['2000', '2001'], freq='D'),
np.array([pd.Period('2000', freq="D"),
pd.Period('2001', freq='D')])),
(pd.core.arrays.integer_array(
[0, np.nan]), np.array([0, np.nan], dtype=object)),
(pd.core.arrays.IntervalArray.from_breaks([0, 1, 2]),
np.array([pd.Interval(0, 1), pd.Interval(1, 2)], dtype=object)),
(pd.SparseArray([0, 1]), np.array([0, 1], dtype=np.int64)),
# TODO: DatetimeArray(add)
])
@pytest.mark.parametrize('box', [pd.Series, pd.Index])
def test_to_numpy(array, expected, box):
thing = box(array)
if array.dtype.name in ('Int64', 'Sparse[int64, 0]') and box is pd.Index:
pytest.skip("No index type for {}".format(array.dtype))
def main():
#read in data
indPath = "Data/30_Industry_Portfolios.CSV"
rfPath = "Data/rf.csv"
begDate = "195912"
endDate = "201612"
(ind, rf) = loadData(indPath, rfPath, begDate, endDate)
exsReturns = excessReturns(ind, rf)
nrow = rf.count()[0]
#create summary table (table 1 in paper)
sumTable = summaryStat(exsReturns.iloc[:, 1:]) # no date
#OLS post Lasso with entire time period
indNames = list(exsReturns.iloc[:, 1:])
df = exsReturns #with date
(inter, fullPeriodResult) = OLSlassoRegression(df) # use aic
# expanding period L/S portfolio construction
startRow = 0
endRow = df.loc[df["Date"] == 196912].index[
0] #start before the first prediction date (paper uses 196912)
lastRow = df.loc[df["Date"] == 201612].index[0]
periodR = pd.DataFrame(np.zeros(lastRow - endRow))
# start with first predicting row
dateIndex = pd.period_range(start=pd.Period(str(df.loc[endRow + 1,
"Date"]),
freq="M"),
end=pd.Period(str(df.loc[lastRow, "Date"]),
freq="M"),
freq="M")
indBetaList = [
pd.DataFrame(np.zeros((lastRow - endRow, len(indNames))),
index=dateIndex,
columns=indNames) for i in range(len(indNames))
]
for e in range(endRow, lastRow):
#change this to OLSlassoRegression(df, endRow = e, mode="predict")
(yPred, betas) = OLSlassoRegression(df, endRow=e, mode="predict")
yPred.sort_values(by=["yPred"], ascending=True, inplace=True)
#print("yPred = ", yPred)
#after sorted returns, long top quintile, and short bottom quintile
bottomInd = yPred.iloc[:5, :].index #find the industries
topInd = yPred.iloc[-5:, :].index
bottomR = df.loc[endRow + 1, bottomInd] #get the realized returns
topR = df.loc[endRow + 1, topInd]
print(indBetaList[0].index[e - endRow], np.round(np.average(topR)),
np.round(np.average(bottomR)),
np.round(np.average(topR) - np.average(bottomR)))
periodR.iloc[e - endRow, :] = np.mean(topR) - np.mean(bottomR)
for i in range(len(indNames)):
indBeta = indBetaList[i]
indBeta.iloc[e - endRow, :] = betas.loc[betas.index[i], :]
print(np.mean(periodR) * 12)
# print(indBeta)
# for i in range(len(indNames)):
# indBeta = indBetaList[i]
# writer = pd.ExcelWriter(indNames[i] + " betas over time.xlsx")
# indBeta.to_excel(writer, "Sheet1")
# writer.save()
# lineplot(indBeta.index, indBeta, "Date", "OLS post Lasso Coefficient", indNames[i] + " Betas Over Time")
# indBeta = indBetaList[0]
# writer = pd.ExcelWriter(indNames[0] + " betas over time.xlsx")
# indBeta.to_excel(writer, "Sheet1")
# writer.save()
lineplot(indBeta.index, indBeta, "Date", "OLS post Lasso Coefficient",
indNames[0] + " Betas Over Time")
'X4_Mon_logerror6std',
'X4_Mon_logerror3mean',
'X1_Mon_logerror6mean']
x = train.yrmonth
train = train[cols]
y = joblib.load("../input/y.pkl")
#################################################
# Val Split
#################################################
#x = pd.read_csv('../input/train_2016_v2.csv')
#x["transactiondate"] = pd.to_datetime(x["transactiondate"])
#x["yrmonth"] = x["transactiondate"].apply(lambda x: x.strftime('%Y%m')).astype(int)
y_logit = x
valindex = y_logit > pd.Period('2017-05')
trainindex = y_logit <= pd.Period('2017-05')
valid = train[valindex]
yval = y[valindex]
#train = train[trainindex]#
#y = y[trainindex]
#################################################
lbound = np.mean(y) - 3 * np.std(y)
ubound = np.mean(y) + 3 * np.std(y)
test = joblib.load("../input/teststat.pkl")
test = test[cols]
#test = valid.copy()
gc.collect()
('decimal', [Decimal(1), np.nan, Decimal(2)]),
('boolean', [True, np.nan, False]),
('datetime64',
[np.datetime64('2013-01-01'), np.nan,
np.datetime64('2018-01-01')]),
('datetime', [pd.Timestamp('20130101'), np.nan,
pd.Timestamp('20180101')]),
('date', [date(2013, 1, 1), np.nan,
date(2018, 1, 1)]),
# The following two dtypes are commented out due to GH 23554
# ('complex', [1 + 1j, np.nan, 2 + 2j]),
# ('timedelta64', [np.timedelta64(1, 'D'),
# np.nan, np.timedelta64(2, 'D')]),
('timedelta', [timedelta(1), np.nan, timedelta(2)]),
('time', [time(1), np.nan, time(2)]),
('period', [pd.Period(2013), pd.NaT,
pd.Period(2018)]),
('interval', [pd.Interval(0, 1), np.nan,
pd.Interval(0, 2)])
]
ids, _ = zip(*_any_skipna_inferred_dtype) # use inferred type as fixture-id
@pytest.fixture(params=_any_skipna_inferred_dtype, ids=ids)
def any_skipna_inferred_dtype(request):
"""
Fixture for all inferred dtypes from _libs.lib.infer_dtype
The covered (inferred) types are:
* 'string'
* 'empty'
def test_pandas_period_index(self):
v = self.cls(['x'], pd.period_range(start='2000', periods=20,
freq='B'))
self.assertEqual(v[0], pd.Period('2000', freq='B'))
assert "Period('2000-01-03', 'B')" in repr(v)
import itertools
import numpy as np
import pandas as pd
import pytest
from hamcrest import assert_that, none, not_none, calling, raises, close_to
import cifrum as lib
from conftest import decimal_places, delta
from cifrum._portfolio.currency import PortfolioCurrencyFactory
from cifrum.common.enums import Currency
__end_period = pd.Period('2018-12', freq='M')
@pytest.fixture
def pcf():
return lib.obj_graph.provide(PortfolioCurrencyFactory)
@pytest.mark.parametrize(
'currency, inflation_kind',
itertools.product(Currency, ['values', 'cumulative', 'a_mean', 'g_mean']))
def test__exists_for_all_currencies(pcf: PortfolioCurrencyFactory,
currency: Currency, inflation_kind: str):
pc = pcf.new(currency=currency)
infl = pc.inflation(kind=inflation_kind,
end_period=__end_period,
years_ago=4)
assert_that(infl, not_none())
def net_time_period_to_pandas_period(net_time_period, freq):
start_datetime = net_datetime_to_py_datetime(net_time_period.Start)
return pd.Period(start_datetime, freq=freq)
def export_dataset(db, dataset):
"""Export all series for one Dataset
Return array - one line by serie
"""
#TODO: Utiliser une queue Redis car trop de code en RAM ?
start = time.time()
headers = ['key'] + dataset['dimension_keys']
#['key', 'freq', 'geo', 'na_item', 'nace_r2', 'unit']
# revient à 0 et -1 ?
dmin = float('inf')
dmax = -float('inf')
query = {
'provider_name': dataset['provider_name'],
"dataset_code": dataset['dataset_code']
}
series_list = db[constants.COL_SERIES].find(query)
for s in series_list:
#collect la première et dernière date trouvé
"""
Permet d'avoir ensuite une plage de date la plus ancienne à la plus récente
car chaque série n'a pas toujours les mêmes dates
"""
if s['start_date'] < dmin:
dmin = s['start_date']
if s['end_date'] > dmax:
dmax = s['end_date']
freq = s['frequency']
series_list.rewind()
pDmin = pandas.Period(ordinal=dmin, freq=freq)
pDmax = pandas.Period(ordinal=dmax, freq=freq)
headers += list(
pandas.period_range(pDmin, pDmax, freq=freq).to_native_types())
#['key', 'freq', 'geo', 'na_item', 'nace_r2', 'unit', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014']
elements = [headers]
series_list.rewind()
def row_process(s):
row = [s['key']]
for c in dataset['dimension_keys']:
if c in s['dimensions']:
row.append(s['dimensions'][c])
else:
row.append('')
p_start_date = pandas.Period(ordinal=s['start_date'], freq=freq)
p_end_date = pandas.Period(ordinal=s['end_date'], freq=freq)
"""
pDmin : pandas.Period() la plus ancienne
p_start_date-1 : périod en cours -1
>>> p_start_date -1
Period('1994', 'A-DEC')
Bug: ne renvoi rien si
p_start_date -1 devient identique à pDmin
"""
# Les None sont pour les périodes qui n'ont pas de valeur correspondantes
_row = [
None
for d in pandas.period_range(pDmin, p_start_date - 1, freq=freq)
]
row.extend(_row)
_row = [val["value"] for val in s['values']]
row.extend(_row)
_row = [
None for d in pandas.period_range(p_end_date + 1, pDmax, freq=freq)
]
row.extend(_row)
return row
for s in series_list:
elements.append(row_process(s))
end = time.time() - start
logger.info("export_dataset - %s : %.3f" % (dataset['dataset_code'], end))
return elements
def test_is_period(self):
self.assertTrue(lib.is_period(pd.Period('2011-01', freq='M')))
self.assertFalse(lib.is_period(pd.PeriodIndex(['2011-01'], freq='M')))
self.assertFalse(lib.is_period(pd.Timestamp('2011-01')))
self.assertFalse(lib.is_period(1))
self.assertFalse(lib.is_period(np.nan))
marks=not_implemented_mark,
),
pytest.param(
(pd.DataFrame, frame_data,
operator.methodcaller("quantile", q=[0.25, 0.75])),
marks=not_implemented_mark,
),
pytest.param(
(pd.DataFrame, frame_data, operator.methodcaller("quantile")),
marks=not_implemented_mark,
),
(
pd.DataFrame,
({
"A": [1]
}, [pd.Period("2000", "D")]),
operator.methodcaller("to_timestamp"),
),
(
pd.DataFrame,
({
"A": [1]
}, [pd.Timestamp("2000")]),
operator.methodcaller("to_period", freq="D"),
),
pytest.param(
(pd.DataFrame, frame_mi_data, operator.methodcaller("isin", [1])),
marks=not_implemented_mark,
),
pytest.param(
(pd.DataFrame, frame_mi_data,
def test_count_nonnumeric_types(self):
# GH12541
cols = [
"int",
"float",
"string",
"datetime",
"timedelta",
"periods",
"fl_inf",
"fl_nan",
"str_nan",
"dt_nat",
"periods_nat",
]
df = DataFrame(
{
"int": [1, 2, 3],
"float": [4.0, 5.0, 6.0],
"string":
list("abc"),
"datetime":
pd.date_range("20170101", periods=3),
"timedelta":
pd.timedelta_range("1 s", periods=3, freq="s"),
"periods": [
pd.Period("2012-01"),
pd.Period("2012-02"),
pd.Period("2012-03"),
],
"fl_inf": [1.0, 2.0, np.Inf],
"fl_nan": [1.0, 2.0, np.NaN],
"str_nan": ["aa", "bb", np.NaN],
"dt_nat": [
Timestamp("20170101"),
Timestamp("20170203"),
Timestamp(None),
],
"periods_nat": [
pd.Period("2012-01"),
pd.Period("2012-02"),
pd.Period(None),
],
},
columns=cols,
)
expected = DataFrame(
{
"int": [1.0, 2.0, 2.0],
"float": [1.0, 2.0, 2.0],
"string": [1.0, 2.0, 2.0],
"datetime": [1.0, 2.0, 2.0],
"timedelta": [1.0, 2.0, 2.0],
"periods": [1.0, 2.0, 2.0],
"fl_inf": [1.0, 2.0, 2.0],
"fl_nan": [1.0, 2.0, 1.0],
"str_nan": [1.0, 2.0, 1.0],
"dt_nat": [1.0, 2.0, 1.0],
"periods_nat": [1.0, 2.0, 1.0],
},
columns=cols,
)
result = df.rolling(window=2).count()
tm.assert_frame_equal(result, expected)
result = df.rolling(1).count()
expected = df.notna().astype(float)
tm.assert_frame_equal(result, expected)
def test_timestep_greater_than_zero_days():
"""Test trying to create zero length Timestep."""
with pytest.raises(ValueError):
# Test setting days <= 0 raises an error
Timestep(pandas.Period('2019-01-01', freq='D'), 0, 0)
def date_to_period(row, freq='D'):
""" 'Date' is a string. Create a Period. Default is year, month and day. """
date = row[common.DATE_COL]
period = pd.Period(date, freq=freq)
return period
def dashboard_sourceData(request):
if request.method == 'GET':
json_out = {}
main_out = {}
days_num = 0
data = {}
try:
# sourcedata
days_list = []
day = pd.Period(datetime.datetime.now(),freq='D')
# logger.info(type(Topic.objects)) # Topic.objects.all()Topic.objects()返回类型相同
topic_list = Topic.objects
datatype_list = Datatype_name.objects
today = date.today()
post_7days = Post.objects(Q(pt_time__gte=datetime.datetime.combine\
(today-datetime.timedelta(7),datetime.time.min)) &
Q(pt_time__lte=datetime.datetime.combine(today, datetime.time.max)))
while days_num < 7:
day_str = day.strftime('%Y%m%d')
day_change = today - datetime.timedelta(days=days_num)
post = post_7days(Q(pt_time__gte=\
datetime.datetime.combine(day_change,datetime.time.min)) & \
Q(pt_time__lte=datetime.datetime.combine\
(day_change, datetime.time.max)))
for topic in topic_list:
for data in datatype_list:
day_dict = {}
day_dict['time'] = day_str
day_dict['topic_id'] = topic._id
day_dict['topic_name'] = topic.topic_name
day_dict['dataTypeName'] = data.datatype_name
day_dict['data_type'] = data.data_type
post_datatype = post(Q(data_type=data.data_type) & Q(topic_id=topic._id))
# logger.info('post_num = ' + str(len(post_datatype)))
day_dict['post_num'] = post_datatype.count()
days_list.append(day_dict)
for data in datatype_list:
day_dict = {}
day_dict['time'] = day_str
day_dict['topic_id'] = 0
day_dict['topic_name'] = ''
day_dict['dataTypeName'] = data.datatype_name
day_dict['data_type'] = data.data_type
post_datatype = post(Q(data_type=data.data_type) & Q(topic_id=0))
# logger.info('post_num = ' + str(len(post_datatype)))
day_dict['post_num'] = post_datatype.count()
days_list.append(day_dict)
day -= 1
days_num += 1
####### Hot for all host posts
hot_dict = {}
hot_posts = post_7days(Q(topic_id__ne=0) & (Q(data_type=3) | Q(data_type=2))).order_by \
('-comm_num')[:10].only("_id", "url", \
"board", "title", "content", "pt_time", \
"img_url", "poster")
# hot_poster = post_7days.only('poster').all()
# logger.info("hot_poster = " + str(hot_poster.count()))
hot_weibo = post_7days(Q(topic_id__ne=0) & Q(data_type=2)).order_by \
('-comm_num')[:10].only("_id", "url", \
"board", "title", "content", "pt_time", \
"img_url")
hot_dict['hotPost'] = handle_post_list(hot_posts)
hot_dict['hotPoster'] = handle_poster_list(hot_posts)
hot_dict['hotWeibo'] = handle_post_list(hot_weibo)
# wordlist = []
# wordres=Cloud_formain.objects.only("word", "frequency")
# for worditem in wordres:
# temp={}
# temp['word']=worditem.word
# temp['weight']=worditem.frequency
# wordlist.append(temp)
####### map data
mapData_list = [{'id':'001',
'pro':"陕西",
'nums':52
},
{
'id':'002',
'pro':"北京",
'nums':100
},
{
'id':'003',
'pro':"上海",
'nums':60
},
{
'id':'004',
'pro':"杭州",
'nums':48
},
{
'id':'005',
'pro':"南京",
'nums':50
}
]
main_out['mapData'] = mapData_list
main_out['sourceData'] = days_list
main_out['Hot'] = hot_dict
# main_out['word_cloud'] = wordlist
json_out['code'] = 0
json_out['success'] = True
json_out['data'] = main_out
except:
traceback.print_exc()
json_out['code'] = 1
json_out['data'] = {}
json_out['success'] = False
return HttpResponse(json.dumps(json_out, cls=MyEncoder),content_type="application/json")
def _to_timestamp(year):
return pd.Period(year=year, freq='Y').to_timestamp()
class TestDataFrameAppend:
@pytest.mark.filterwarnings(
"ignore:.*append method is deprecated.*:FutureWarning")
def test_append_multiindex(self, multiindex_dataframe_random_data,
frame_or_series):
obj = multiindex_dataframe_random_data
obj = tm.get_obj(obj, frame_or_series)
a = obj[:5]
b = obj[5:]
result = a.append(b)
tm.assert_equal(result, obj)
def test_append_empty_list(self):
# GH 28769
df = DataFrame()
result = df._append([])
expected = df
tm.assert_frame_equal(result, expected)
assert result is not df
df = DataFrame(np.random.randn(5, 4),
columns=["foo", "bar", "baz", "qux"])
result = df._append([])
expected = df
tm.assert_frame_equal(result, expected)
assert result is not df # ._append() should return a new object
def test_append_series_dict(self):
df = DataFrame(np.random.randn(5, 4),
columns=["foo", "bar", "baz", "qux"])
series = df.loc[4]
msg = "Indexes have overlapping values"
with pytest.raises(ValueError, match=msg):
df._append(series, verify_integrity=True)
series.name = None
msg = "Can only append a Series if ignore_index=True"
with pytest.raises(TypeError, match=msg):
df._append(series, verify_integrity=True)
result = df._append(series[::-1], ignore_index=True)
expected = df._append(DataFrame({
0: series[::-1]
}, index=df.columns).T,
ignore_index=True)
tm.assert_frame_equal(result, expected)
# dict
result = df._append(series.to_dict(), ignore_index=True)
tm.assert_frame_equal(result, expected)
result = df._append(series[::-1][:3], ignore_index=True)
expected = df._append(DataFrame({
0: series[::-1][:3]
}).T,
ignore_index=True,
sort=True)
tm.assert_frame_equal(result, expected.loc[:, result.columns])
msg = "Can only append a dict if ignore_index=True"
with pytest.raises(TypeError, match=msg):
df._append(series.to_dict())
# can append when name set
row = df.loc[4]
row.name = 5
result = df._append(row)
expected = df._append(df[-1:], ignore_index=True)
tm.assert_frame_equal(result, expected)
def test_append_list_of_series_dicts(self):
df = DataFrame(np.random.randn(5, 4),
columns=["foo", "bar", "baz", "qux"])
dicts = [x.to_dict() for idx, x in df.iterrows()]
result = df._append(dicts, ignore_index=True)
expected = df._append(df, ignore_index=True)
tm.assert_frame_equal(result, expected)
# different columns
dicts = [
{
"foo": 1,
"bar": 2,
"baz": 3,
"peekaboo": 4
},
{
"foo": 5,
"bar": 6,
"baz": 7,
"peekaboo": 8
},
]
result = df._append(dicts, ignore_index=True, sort=True)
expected = df._append(DataFrame(dicts), ignore_index=True, sort=True)
tm.assert_frame_equal(result, expected)
def test_append_list_retain_index_name(self):
df = DataFrame([[1, 2], [3, 4]],
index=pd.Index(["a", "b"], name="keepthisname"))
serc = Series([5, 6], name="c")
expected = DataFrame(
[[1, 2], [3, 4], [5, 6]],
index=pd.Index(["a", "b", "c"], name="keepthisname"),
)
# append series
result = df._append(serc)
tm.assert_frame_equal(result, expected)
# append list of series
result = df._append([serc])
tm.assert_frame_equal(result, expected)
def test_append_missing_cols(self):
# GH22252
# exercise the conditional branch in append method where the data
# to be appended is a list and does not contain all columns that are in
# the target DataFrame
df = DataFrame(np.random.randn(5, 4),
columns=["foo", "bar", "baz", "qux"])
dicts = [{"foo": 9}, {"bar": 10}]
result = df._append(dicts, ignore_index=True, sort=True)
expected = df._append(DataFrame(dicts), ignore_index=True, sort=True)
tm.assert_frame_equal(result, expected)
def test_append_empty_dataframe(self):
# Empty df append empty df
df1 = DataFrame()
df2 = DataFrame()
result = df1._append(df2)
expected = df1.copy()
tm.assert_frame_equal(result, expected)
# Non-empty df append empty df
df1 = DataFrame(np.random.randn(5, 2))
df2 = DataFrame()
result = df1._append(df2)
expected = df1.copy()
tm.assert_frame_equal(result, expected)
# Empty df with columns append empty df
df1 = DataFrame(columns=["bar", "foo"])
df2 = DataFrame()
result = df1._append(df2)
expected = df1.copy()
tm.assert_frame_equal(result, expected)
# Non-Empty df with columns append empty df
df1 = DataFrame(np.random.randn(5, 2), columns=["bar", "foo"])
df2 = DataFrame()
result = df1._append(df2)
expected = df1.copy()
tm.assert_frame_equal(result, expected)
def test_append_dtypes(self):
# GH 5754
# row appends of different dtypes (so need to do by-item)
# can sometimes infer the correct type
df1 = DataFrame({"bar": Timestamp("20130101")}, index=range(5))
df2 = DataFrame()
result = df1._append(df2)
expected = df1.copy()
tm.assert_frame_equal(result, expected)
df1 = DataFrame({"bar": Timestamp("20130101")}, index=range(1))
df2 = DataFrame({"bar": "foo"}, index=range(1, 2))
result = df1._append(df2)
expected = DataFrame({"bar": [Timestamp("20130101"), "foo"]})
tm.assert_frame_equal(result, expected)
df1 = DataFrame({"bar": Timestamp("20130101")}, index=range(1))
df2 = DataFrame({"bar": np.nan}, index=range(1, 2))
result = df1._append(df2)
expected = DataFrame(
{"bar": Series([Timestamp("20130101"), np.nan], dtype="M8[ns]")})
expected = expected.astype(object)
tm.assert_frame_equal(result, expected)
df1 = DataFrame({"bar": Timestamp("20130101")}, index=range(1))
df2 = DataFrame({"bar": np.nan}, index=range(1, 2), dtype=object)
result = df1._append(df2)
expected = DataFrame(
{"bar": Series([Timestamp("20130101"), np.nan], dtype="M8[ns]")})
expected = expected.astype(object)
tm.assert_frame_equal(result, expected)
df1 = DataFrame({"bar": np.nan}, index=range(1))
df2 = DataFrame({"bar": Timestamp("20130101")}, index=range(1, 2))
result = df1._append(df2)
expected = DataFrame(
{"bar": Series([np.nan, Timestamp("20130101")], dtype="M8[ns]")})
expected = expected.astype(object)
tm.assert_frame_equal(result, expected)
df1 = DataFrame({"bar": Timestamp("20130101")}, index=range(1))
df2 = DataFrame({"bar": 1}, index=range(1, 2), dtype=object)
result = df1._append(df2)
expected = DataFrame({"bar": Series([Timestamp("20130101"), 1])})
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"timestamp", ["2019-07-19 07:04:57+0100", "2019-07-19 07:04:57"])
def test_append_timestamps_aware_or_naive(self, tz_naive_fixture,
timestamp):
# GH 30238
tz = tz_naive_fixture
df = DataFrame([Timestamp(timestamp, tz=tz)])
result = df._append(df.iloc[0]).iloc[-1]
expected = Series(Timestamp(timestamp, tz=tz), name=0)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"data, dtype",
[
([1], pd.Int64Dtype()),
([1], pd.CategoricalDtype()),
([pd.Interval(left=0, right=5)], pd.IntervalDtype()),
([pd.Period("2000-03", freq="M")], pd.PeriodDtype("M")),
([1], pd.SparseDtype()),
],
)
def test_other_dtypes(self, data, dtype):
df = DataFrame(data, dtype=dtype)
result = df._append(df.iloc[0]).iloc[-1]
expected = Series(data, name=0, dtype=dtype)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("dtype", ["datetime64[ns]", "timedelta64[ns]"])
def test_append_numpy_bug_1681(self, dtype):
# another datetime64 bug
if dtype == "datetime64[ns]":
index = date_range("2011/1/1", "2012/1/1", freq="W-FRI")
else:
index = timedelta_range("1 days", "10 days", freq="2D")
df = DataFrame()
other = DataFrame({"A": "foo", "B": index}, index=index)
result = df._append(other)
assert (result["B"] == index).all()
@pytest.mark.filterwarnings("ignore:The values in the array:RuntimeWarning"
)
def test_multiindex_column_append_multiple(self):
# GH 29699
df = DataFrame(
[[1, 11], [2, 12], [3, 13]],
columns=pd.MultiIndex.from_tuples([("multi", "col1"),
("multi", "col2")],
names=["level1", None]),
)
df2 = df.copy()
for i in range(1, 10):
df[i, "colA"] = 10
df = df._append(df2, ignore_index=True)
result = df["multi"]
expected = DataFrame({
"col1": [1, 2, 3] * (i + 1),
"col2": [11, 12, 13] * (i + 1)
})
tm.assert_frame_equal(result, expected)
def test_append_raises_future_warning(self):
# GH#35407
df1 = DataFrame([[1, 2], [3, 4]])
df2 = DataFrame([[5, 6], [7, 8]])
with tm.assert_produces_warning(FutureWarning):
df1.append(df2)
import numpy as np
import pandas as pd
from hamcrest import assert_that, close_to, calling, raises
import yapo as y
from conftest import decimal_places, delta
from yapo._settings import _MONTHS_PER_YEAR
from yapo.common.time_series import TimeSeriesKind
__asset_name = 'mut_ru/0890-94127385'
__portfolio_period_start = pd.Period('2011-1', freq='M')
__portfolio_period_end = pd.Period('2017-2', freq='M')
__asset = y.portfolio_asset(name=__asset_name,
start_period=str(__portfolio_period_start),
end_period=str(__portfolio_period_end),
currency='USD')
def test__cumulative_get_return():
arors = __asset.get_return(kind='cumulative').values
assert_that(arors.max(), close_to(.0924, delta))
assert_that(arors.min(), close_to(-.5464, delta))
arors_real = __asset.get_return(kind='cumulative', real=True).values
assert_that(arors_real.max(), close_to(.0765, delta))
assert_that(arors_real.min(), close_to(-.5725, delta))
def test__ytd_get_return():
ror_ytd = __asset.get_return(kind='ytd')
assert ror_ytd.start_period == pd.Period('2012-1', freq='M')
[1, 2],
np.dtype("float32"),
PandasArray(np.array([1.0, 2.0], dtype=np.dtype("float32"))),
),
(np.array([1, 2],
dtype="int64"), None, IntegerArray._from_sequence([1, 2])),
(
np.array([1.0, 2.0], dtype="float64"),
None,
FloatingArray._from_sequence([1.0, 2.0]),
),
# String alias passes through to NumPy
([1, 2], "float32", PandasArray(np.array([1, 2], dtype="float32"))),
# Period alias
(
[pd.Period("2000", "D"),
pd.Period("2001", "D")],
"Period[D]",
period_array(["2000", "2001"], freq="D"),
),
# Period dtype
(
[pd.Period("2000", "D")],
pd.PeriodDtype("D"),
period_array(["2000"], freq="D"),
),
# Datetime (naive)
(
[1, 2],
np.dtype("datetime64[ns]"),
DatetimeArray._from_sequence(
class TestInsertIndexCoercion(CoercionBase):
klasses = ['index']
method = 'insert'
def _assert_insert_conversion(self, original, value, expected,
expected_dtype):
""" test coercion triggered by insert """
target = original.copy()
res = target.insert(1, value)
tm.assert_index_equal(res, expected)
assert res.dtype == expected_dtype
@pytest.mark.parametrize("insert, coerced_val, coerced_dtype",
[(1, 1, np.object), (1.1, 1.1, np.object),
(False, False, np.object),
('x', 'x', np.object)])
def test_insert_index_object(self, insert, coerced_val, coerced_dtype):
obj = pd.Index(list('abcd'))
assert obj.dtype == np.object
exp = pd.Index(['a', coerced_val, 'b', 'c', 'd'])
self._assert_insert_conversion(obj, insert, exp, coerced_dtype)
@pytest.mark.parametrize("insert, coerced_val, coerced_dtype",
[(1, 1, np.int64), (1.1, 1.1, np.float64),
(False, 0, np.int64), ('x', 'x', np.object)])
def test_insert_index_int64(self, insert, coerced_val, coerced_dtype):
obj = pd.Int64Index([1, 2, 3, 4])
assert obj.dtype == np.int64
exp = pd.Index([1, coerced_val, 2, 3, 4])
self._assert_insert_conversion(obj, insert, exp, coerced_dtype)
@pytest.mark.parametrize("insert, coerced_val, coerced_dtype",
[(1, 1., np.float64), (1.1, 1.1, np.float64),
(False, 0., np.float64), ('x', 'x', np.object)])
def test_insert_index_float64(self, insert, coerced_val, coerced_dtype):
obj = pd.Float64Index([1., 2., 3., 4.])
assert obj.dtype == np.float64
exp = pd.Index([1., coerced_val, 2., 3., 4.])
self._assert_insert_conversion(obj, insert, exp, coerced_dtype)
@pytest.mark.parametrize('fill_val,exp_dtype',
[(pd.Timestamp('2012-01-01'), 'datetime64[ns]'),
(pd.Timestamp('2012-01-01', tz='US/Eastern'),
'datetime64[ns, US/Eastern]')],
ids=['datetime64', 'datetime64tz'])
def test_insert_index_datetimes(self, fill_val, exp_dtype):
obj = pd.DatetimeIndex(
['2011-01-01', '2011-01-02', '2011-01-03', '2011-01-04'],
tz=fill_val.tz)
assert obj.dtype == exp_dtype
exp = pd.DatetimeIndex([
'2011-01-01',
fill_val.date(), '2011-01-02', '2011-01-03', '2011-01-04'
],
tz=fill_val.tz)
self._assert_insert_conversion(obj, fill_val, exp, exp_dtype)
msg = "Passed item and index have different timezone"
if fill_val.tz:
with tm.assert_raises_regex(ValueError, msg):
obj.insert(1, pd.Timestamp('2012-01-01'))
with tm.assert_raises_regex(ValueError, msg):
obj.insert(1, pd.Timestamp('2012-01-01', tz='Asia/Tokyo'))
msg = "cannot insert DatetimeIndex with incompatible label"
with tm.assert_raises_regex(TypeError, msg):
obj.insert(1, 1)
pytest.xfail("ToDo: must coerce to object")
def test_insert_index_timedelta64(self):
obj = pd.TimedeltaIndex(['1 day', '2 day', '3 day', '4 day'])
assert obj.dtype == 'timedelta64[ns]'
# timedelta64 + timedelta64 => timedelta64
exp = pd.TimedeltaIndex(['1 day', '10 day', '2 day', '3 day', '4 day'])
self._assert_insert_conversion(obj, pd.Timedelta('10 day'), exp,
'timedelta64[ns]')
# ToDo: must coerce to object
msg = "cannot insert TimedeltaIndex with incompatible label"
with tm.assert_raises_regex(TypeError, msg):
obj.insert(1, pd.Timestamp('2012-01-01'))
# ToDo: must coerce to object
msg = "cannot insert TimedeltaIndex with incompatible label"
with tm.assert_raises_regex(TypeError, msg):
obj.insert(1, 1)
@pytest.mark.parametrize(
"insert, coerced_val, coerced_dtype",
[(pd.Period('2012-01', freq='M'), '2012-01', 'period[M]'),
(pd.Timestamp('2012-01-01'), pd.Timestamp('2012-01-01'), np.object),
(1, 1, np.object), ('x', 'x', np.object)])
def test_insert_index_period(self, insert, coerced_val, coerced_dtype):
obj = pd.PeriodIndex(['2011-01', '2011-02', '2011-03', '2011-04'],
freq='M')
assert obj.dtype == 'period[M]'
if isinstance(insert, pd.Period):
index_type = pd.PeriodIndex
else:
index_type = pd.Index
exp = index_type([
pd.Period('2011-01', freq='M'), coerced_val,
pd.Period('2011-02', freq='M'),
pd.Period('2011-03', freq='M'),
pd.Period('2011-04', freq='M')
],
freq='M')
self._assert_insert_conversion(obj, insert, exp, coerced_dtype)
def test_insert_index_complex128(self):
pass
def test_insert_index_bool(self):
pass
####################################################################
# basic date parse (string to date):
df = pd.read_csv("file.csv", parse_dates["column_name"])
# Use the DATE column as INDEX (first convert the value to date with parse)
df = pd.read_csv("file.csv", parse_dates["column_name"], index_col["column_name"])
# now you can use partial index to retrieve all of the month etc.
df["2020-01"]
# can also add metrics:
df["2020-01"].mean()
# can use range of dates:
df['2020-01-01':'2020-01-20']
# Define Datetime periods:
m = pd.Period('2020-1',freq='M')
# print out the starttime/endtime
m.start_time / m.end_time
# operations:
m+1 (adds febuary)
# TIMEZONES:
from pytz import all_timezones
print(all_timezones) #this will show all the timezones available
# python has 2 datetime objects: 'Naive' (no timezone) and 'Timezone Aware'
# assign timezone to date index:
df = df.tz_localize(tz='US/Eastern')
df.index # now it will show timezone with UTC-4
# Convert to Berlin:
df = df.tz_convert(tz='Europe/Berlin')
def test_isscalar_pandas_scalars(self):
self.assertTrue(isscalar(pd.Timestamp('2014-01-01')))
self.assertTrue(isscalar(pd.Timedelta(hours=1)))
self.assertTrue(isscalar(pd.Period('2014-01-01')))
# <a id="371"></a> <br>
# ### 3-7-1 Timestamp
# In[ ]:
pd.Timestamp('9/1/2016 10:05AM')
# <a id="372"></a> <br>
# ### 3-7-2 Period
# In[ ]:
pd.Period('1/2016')
# In[ ]:
pd.Period('3/5/2016')
# <a id="373"></a> <br>
# ### 3-7-3 DatetimeIndex
# In[ ]:
t1 = pd.Series(list('abc'), [pd.Timestamp('2016-09-01'), pd.Timestamp('2016-09-02'), pd.Timestamp('2016-09-03')])
t1
("floating", [1.0, np.nan, 2.0]),
("integer", [1, np.nan, 2]),
("mixed-integer-float", [1, np.nan, 2.0]),
("decimal", [Decimal(1), np.nan, Decimal(2)]),
("boolean", [True, np.nan, False]),
("boolean", [True, pd.NA, False]),
("datetime64", [np.datetime64("2013-01-01"), np.nan, np.datetime64("2018-01-01")]),
("datetime", [pd.Timestamp("20130101"), np.nan, pd.Timestamp("20180101")]),
("date", [date(2013, 1, 1), np.nan, date(2018, 1, 1)]),
# The following two dtypes are commented out due to GH 23554
# ('complex', [1 + 1j, np.nan, 2 + 2j]),
# ('timedelta64', [np.timedelta64(1, 'D'),
# np.nan, np.timedelta64(2, 'D')]),
("timedelta", [timedelta(1), np.nan, timedelta(2)]),
("time", [time(1), np.nan, time(2)]),
("period", [pd.Period(2013), pd.NaT, pd.Period(2018)]),
("interval", [pd.Interval(0, 1), np.nan, pd.Interval(0, 2)]),
]
ids, _ = zip(*_any_skipna_inferred_dtype) # use inferred type as fixture-id
@pytest.fixture(params=_any_skipna_inferred_dtype, ids=ids)
def any_skipna_inferred_dtype(request):
"""
Fixture for all inferred dtypes from _libs.lib.infer_dtype
The covered (inferred) types are:
* 'string'
* 'empty'
* 'bytes'
* 'mixed'