def test_all_any_params(self):
# Check skipna, with implicit 'object' dtype.
s1 = Series([np.nan, True])
s2 = Series([np.nan, False])
assert s1.all(skipna=False) # nan && True => True
assert s1.all(skipna=True)
assert np.isnan(s2.any(skipna=False)) # nan || False => nan
assert not s2.any(skipna=True)
# Check level.
s = pd.Series([False, False, True, True, False, True],
index=[0, 0, 1, 1, 2, 2])
tm.assert_series_equal(s.all(level=0), Series([False, True, False]))
tm.assert_series_equal(s.any(level=0), Series([False, True, True]))
# bool_only is not implemented with level option.
with pytest.raises(NotImplementedError):
s.any(bool_only=True, level=0)
with pytest.raises(NotImplementedError):
s.all(bool_only=True, level=0)
# bool_only is not implemented alone.
with pytest.raises(NotImplementedError):
s.any(bool_only=True)
with pytest.raises(NotImplementedError):
s.all(bool_only=True)
def test_all_any_params(self):
# Check skipna, with implicit 'object' dtype.
s1 = Series([np.nan, True])
s2 = Series([np.nan, False])
assert s1.all(skipna=False) # nan && True => True
assert s1.all(skipna=True)
assert np.isnan(s2.any(skipna=False)) # nan || False => nan
assert not s2.any(skipna=True)
# Check level.
s = pd.Series([False, False, True, True, False, True],
index=[0, 0, 1, 1, 2, 2])
tm.assert_series_equal(s.all(level=0), Series([False, True, False]))
tm.assert_series_equal(s.any(level=0), Series([False, True, True]))
# bool_only is not implemented with level option.
with pytest.raises(NotImplementedError):
s.any(bool_only=True, level=0)
with pytest.raises(NotImplementedError):
s.all(bool_only=True, level=0)
# bool_only is not implemented alone.
with pytest.raises(NotImplementedError):
s.any(bool_only=True,)
with pytest.raises(NotImplementedError):
s.all(bool_only=True)
def test_all_any_boolean(self):
# Check skipna, with boolean type
s1 = Series([pd.NA, True], dtype="boolean")
s2 = Series([pd.NA, False], dtype="boolean")
assert s1.all(skipna=False) is pd.NA # NA && True => NA
assert s1.all(skipna=True)
assert s2.any(skipna=False) is pd.NA # NA || False => NA
assert not s2.any(skipna=True)
# GH-33253: all True / all False values buggy with skipna=False
s3 = Series([True, True], dtype="boolean")
s4 = Series([False, False], dtype="boolean")
assert s3.all(skipna=False)
assert not s4.any(skipna=False)
# Check level TODO(GH-33449) result should also be boolean
s = Series(
[False, False, True, True, False, True],
index=[0, 0, 1, 1, 2, 2],
dtype="boolean",
)
with tm.assert_produces_warning(FutureWarning):
tm.assert_series_equal(s.all(level=0), Series([False, True,
False]))
with tm.assert_produces_warning(FutureWarning):
tm.assert_series_equal(s.any(level=0), Series([False, True, True]))
class Any:
params = [[10**3, 10**6], ["fast", "slow"]]
param_names = ["N", "case"]
def setup(self, N, case):
val = case == "fast"
self.s = Series([val] * N)
def time_any(self, N, case):
self.s.any()
class Any(object):
params = [[10**3, 10**6], ['fast', 'slow']]
param_names = ['N', 'case']
def setup(self, N, case):
val = case == 'fast'
self.s = Series([val] * N)
def time_any(self, N, case):
self.s.any()
def _fill_res_dict(col: pd.Series, col_oob: pd.Series, res_dict: dict) -> dict:
valid = not col_oob.any()
res_dict["valid"] = valid
if not valid:
col_oob = col_oob.fillna(False)
n = global_log_verbosity
# get the unexpected values
unexpected_index = col_oob.index[col_oob]
unexpected_values = col[unexpected_index].astype(str)
res_dict["percentage_of_column_is_error"] = (len(unexpected_index) /
len(col) * 100)
if n is not None:
# if the global_log_verbosity is not 0, sample
if n != 0:
# asking for a higher sample than is there?
if global_log_verbosity > len(unexpected_values):
n = len(unexpected_values)
# sample the requested amount
unexpected_values = unexpected_values.sample(n=n)
unexpected_index = unexpected_values[unexpected_values.index]
# log the required unexpected values
res_dict["unexpected_index_sample"] = unexpected_index.tolist()
res_dict["unexpected_values_sample"] = unexpected_values.tolist()
return res_dict
def test_all_any_params(self):
# Check skipna, with implicit 'object' dtype.
s1 = Series([np.nan, True])
s2 = Series([np.nan, False])
assert s1.all(skipna=False) # nan && True => True
assert s1.all(skipna=True)
assert s2.any(skipna=False)
assert not s2.any(skipna=True)
# Check level.
s = Series([False, False, True, True, False, True], index=[0, 0, 1, 1, 2, 2])
with tm.assert_produces_warning(FutureWarning):
tm.assert_series_equal(s.all(level=0), Series([False, True, False]))
with tm.assert_produces_warning(FutureWarning):
tm.assert_series_equal(s.any(level=0), Series([False, True, True]))
msg = "Option bool_only is not implemented with option level"
with pytest.raises(NotImplementedError, match=msg):
with tm.assert_produces_warning(FutureWarning):
s.any(bool_only=True, level=0)
with pytest.raises(NotImplementedError, match=msg):
with tm.assert_produces_warning(FutureWarning):
s.all(bool_only=True, level=0)
# GH#38810 bool_only is not implemented alone.
msg = "Series.any does not implement bool_only"
with pytest.raises(NotImplementedError, match=msg):
s.any(bool_only=True)
msg = "Series.all does not implement bool_only."
with pytest.raises(NotImplementedError, match=msg):
s.all(bool_only=True)
def test_all_any_params(self):
# Check skipna, with implicit 'object' dtype.
s1 = Series([np.nan, True])
s2 = Series([np.nan, False])
assert s1.all(skipna=False) # nan && True => True
assert s1.all(skipna=True)
assert np.isnan(s2.any(skipna=False)) # nan || False => nan
assert not s2.any(skipna=True)
# Check level.
s = Series([False, False, True, True, False, True],
index=[0, 0, 1, 1, 2, 2])
tm.assert_series_equal(s.all(level=0), Series([False, True, False]))
tm.assert_series_equal(s.any(level=0), Series([False, True, True]))
msg = "Option bool_only is not implemented with option level"
with pytest.raises(NotImplementedError, match=msg):
s.any(bool_only=True, level=0)
with pytest.raises(NotImplementedError, match=msg):
s.all(bool_only=True, level=0)
# bool_only is not implemented alone.
# TODO GH38810 change this error message to:
# "Series.any does not implement bool_only"
msg = "Series.any does not implement numeric_only"
with pytest.raises(NotImplementedError, match=msg):
s.any(bool_only=True)
msg = "Series.all does not implement numeric_only."
with pytest.raises(NotImplementedError, match=msg):
s.all(bool_only=True)
def test_all_any(self):
ts = tm.makeTimeSeries()
bool_series = ts > 0
assert not bool_series.all()
assert bool_series.any()
# Alternative types, with implicit 'object' dtype.
s = Series(["abc", True])
assert "abc" == s.any() # 'abc' || True => 'abc'
def test_all_any(self):
ts = tm.makeTimeSeries()
bool_series = ts > 0
assert not bool_series.all()
assert bool_series.any()
# Alternative types, with implicit 'object' dtype.
s = Series(['abc', True])
assert 'abc' == s.any() # 'abc' || True => 'abc'
def test_any_all_datetimelike(self):
# GH#38723 these may not be the desired long-term behavior (GH#34479)
# but in the interim should be internally consistent
dta = date_range("1995-01-02", periods=3)._data
ser = Series(dta)
df = DataFrame(ser)
assert dta.all()
assert dta.any()
assert ser.all()
assert ser.any()
assert df.any().all()
assert df.all().all()
dta = dta.tz_localize("UTC")
ser = Series(dta)
df = DataFrame(ser)
assert dta.all()
assert dta.any()
assert ser.all()
assert ser.any()
assert df.any().all()
assert df.all().all()
tda = dta - dta[0]
ser = Series(tda)
df = DataFrame(ser)
assert tda.any()
assert not tda.all()
assert ser.any()
assert not ser.all()
assert df.any().all()
assert not df.all().any()
def test_any_non_keyword_deprecation():
df = DataFrame({"A": [1, 2], "B": [0, 2], "C": [0, 0]})
msg = (
"In a future version of pandas all arguments of "
"DataFrame.any and Series.any will be keyword-only."
)
with tm.assert_produces_warning(FutureWarning, match=msg):
result = df.any("index", None)
expected = Series({"A": True, "B": True, "C": False})
tm.assert_series_equal(result, expected)
s = Series([False, False, False])
msg = (
"In a future version of pandas all arguments of "
"DataFrame.any and Series.any will be keyword-only."
)
with tm.assert_produces_warning(FutureWarning, match=msg):
result = s.any("index")
expected = False
tm.assert_equal(result, expected)
def threshold_cluster(Data_set,threshold):
stand_array=np.asarray(Data_set).ravel('C')
stand_Data=Series(stand_array)
index_list,class_k=[],[]
while stand_Data.any():
if len(stand_Data)==1:
index_list.append(list(stand_Data.index))
class_k.append(list(stand_Data))
stand_Data=stand_Data.drop(stand_Data.index)
else:
class_data_index=stand_Data.index[0]
class_data=stand_Data[class_data_index]
stand_Data=stand_Data.drop(class_data_index)
if (abs(stand_Data-class_data)<=threshold).any():
args_data=stand_Data[abs(stand_Data-class_data)<=threshold]
stand_Data=stand_Data.drop(args_data.index)
index_list.append([class_data_index]+list(args_data.index))
class_k.append([class_data]+list(args_data))
else:
index_list.append([class_data_index])
class_k.append([class_data])
return index_list,class_k
def load(self, datelist, tracklist=[], path=''):
"""
load all JCapper Race files (aka past performance files) for the specified dates
:param datelist: list of dates
:param tracklist: list of x8 track symbols e.g. DMR, etc
:param path: str, if given, load data from given directory, otherwise, load data directly from s3
"""
schema_filepath = os.path.join(os.path.dirname(__file__),
'schema_pastperformance.csv')
columnDict = read_csv(schema_filepath)['field_name'].to_dict()
# convert tracklist symbols to jcp track symbols
tracklist = Series(tracklist).map(self.map_track_x8_to_jcp)
if tracklist.isnull().any():
raise ValueError(
'tracklist must be list of x8 track symbols in track_detail: \n%s'
% tracklist)
# raw data
self.dfraw = DataFrame()
if not path:
# load each date and concat to the master raw df
for d in datelist:
# load the DataFrame for this date, e.g. DMR0831F.TXT for 2017-08-31
year = d.strftime('%Y')
month = d.strftime('%m')
day = d.strftime('%d')
# skip Christmas, no jcapper file
if month == '12' and day in ['24', '25']:
continue
key = 'x8-bucket/jcapper/%s/%s/%s/' % (year, month, day)
s3_files = self.s3.ls(
key
) # list of all files in a given direcetory - in this case, all files for a single day
# filter for .jcp files, in this case for non Chart Files
s3_files = [
os.path.basename(fp) for fp in s3_files if fp[-5] != 'F'
]
# filter tracks
if len(tracklist) > 0:
s3_files = [
fp for fp in s3_files if fp[:3] in list(tracklist)
]
idx_s3files = Series([n[:3] for n in s3_files
]).drop_duplicates().index
if self.verbose:
print('pp.load(%s) loading %s race cards..' %
(d.strftime('%Y-%m-%d'), len(idx_s3files)))
# load all past performance files for given date, track is no longer a condition
for i in idx_s3files:
fp = os.path.join(key, s3_files[i])
if fp[-3:] == 'jcp':
df = read_csv(self.s3.open(fp, mode='rb'),
header=None,
encoding='ISO-8859-1')
else:
df = read_csv(self.s3.open(fp, mode='rb'),
header=None,
compression='zip',
encoding='ISO-8859-1')
# concat in the master df
self.dfraw = concat([self.dfraw, df])
else:
# load each date and concat to the master raw df
for d in datelist:
# load the DataFrame for this date, e.g. DMR0831F.TXT for 2017-08-31
year = d.strftime('%Y')
month = d.strftime('%m')
day = d.strftime('%d')
# skip Christmas, no jcapper file
if month == '12' and day in ['24', '25']:
continue
path_day = os.path.join(path, 'jcapper', year, month, day)
files = os.listdir(
path_day
) # list of all files in a given direcetory - in this case, all files for a single day
# filter tracks
if tracklist.any():
files = [
fp for fp in files
if os.path.basename(fp)[:3] in list(tracklist)
]
if self.verbose:
print('pp.load(%s) loading %s race cards..' %
(d.strftime('%Y-%m-%d'), len(files)))
# load all past performance files for given date, track is no longer a condition
for fp in files:
# filter for .jcp files, in this case for non Chart Files
if fp[-5] != 'F':
fp = os.path.join(path_day, fp)
if fp[-3:] == 'jcp':
df = read_csv(fp,
header=None,
encoding='ISO-8859-1')
else:
df = read_csv(fp,
header=None,
compression='zip',
encoding='ISO-8859-1')
# concat in the master df
self.dfraw = concat([self.dfraw, df])
try:
# copy a subset of columns and replace the header names (numbers to text)
cols = list(columnDict.keys())
self.df = self.dfraw[cols].copy()
except KeyError:
raise Exception(
'No files available for given datelist and tracklist.')
# column names
self.df.rename(columns=columnDict, inplace=True)
# normalize track sym and make race_id
self.df['x8_track_sym'] = self.df['jcp_track_sym'].map(
self.map_track_jcp_to_x8)
# adding itsp_track_sym here so that we can filter for bettable tracks in daily races
self.df['itsp_track_sym'] = self.df['x8_track_sym'].map(
self.map_track_x8_to_itsp)
# drop rows where we are missing jcp symbol mapping in track detail if any
x8_isnull = self.df['x8_track_sym'].isnull()
if x8_isnull.any():
missing_jcp_symbols = self.df[x8_isnull]['jcp_track_sym'].unique()
warn(
'pp.load() track_detail.csv is missing jcp symbols: %s\nDropping all rows with missing symbols'
% missing_jcp_symbols)
self.df = self.df[~x8_isnull]
print('pp.load() dropping %s rows' % x8_isnull.sum())
# convert dates and validate
self.df['race_time_flag'] = self.df['race_time'].isnull(
) # flag bad race_time values (sometimes is null)
self.df['race_time'] = to_datetime(
self.df['date'].astype(str) +
self.df['race_time'].fillna(1000.0).astype(int).astype(str),
format='%Y%m%d%H%M')
self.df['race_time_utc'] = self.df['race_time'].map(
lambda x: x + timedelta(hours=8))
self.df['race_time_toronto'] = self.df['race_time'].map(
lambda x: x + timedelta(hours=3))
self.df['date'] = to_datetime(self.df['date'], format='%Y%m%d')
self.df['date_str'] = self.df['date'].dt.strftime('%Y%m%d')
self._birthdate_columns()
# clean nans in wk date cols
fields_wk_date = [
c for c in self.df.columns if c.startswith('wk_date')
]
self.df['wk_date_1'].fillna(self.df['date_str'], inplace=True)
self.df[fields_wk_date] = self.df[fields_wk_date].fillna(
method='ffill', axis=1)
self.df[fields_wk_date] = self.df[fields_wk_date].applymap(
lambda x: to_datetime(str(int(x)), format='%Y%m%d'))
# clean nans in pp date cols
fields_pp_date = [
c for c in self.df.columns if c.startswith('pp_date')
]
self.df['pp_date_0'].fillna(self.df['date_str'], inplace=True)
self.df[fields_pp_date] = self.df[fields_pp_date].fillna(
method='ffill', axis=1)
self.df[fields_pp_date] = self.df[fields_pp_date].applymap(
lambda x: to_datetime(str(int(x)), format='%Y%m%d'))
self.df['race_id'] = self.df['x8_track_sym'] + '_' + self.df[
'date_str'] + '_' + self.df['race_race_num'].astype(str)
self.df['runner_program_number'] = self.df[
'runner_program_number'].map(str)
self.df['betting_interest'] = self.df[
'runner_program_number'].str.strip('A')
self.df['coupled'] = self.df['runner_program_number'].str.count(
'A').astype(bool)
self.df['coupled_race'] = self.df.groupby(
'race_id')['coupled'].transform('any')
self.df['runner_id'] = self.df['race_id'] + '_' + self.df[
'runner_program_number']
# additional time index data and day of week for seasonality
self.df['month'] = self.df['date'].map(lambda x: x.month)
self.df['weekday'] = self.df['date'].map(lambda x: x.strftime('%A'))
self.df['year'] = self.df['date'].map(lambda x: x.year)
self.df['weeknum'] = self.df['date'].map(lambda x: x.strftime('%w'))
# normalize horse name
self.df['x8name'] = self.df['name'].map(self._normalize_name)
self.df['x8country'] = self.df['name'].map(self._country_from_name)
# convert pp_track and wk_track columns to x8 symbol
fields_pp_track = [
c for c in self.df.columns if c.startswith('pp_track_')
]
self.df[fields_pp_track] = self.df[fields_pp_track].applymap(
lambda x: self.map_track_chart_to_x8.get(x))
fields_wk_track = [
c for c in self.df.columns if c.startswith('wk_track_')
]
self.df[fields_wk_track] = self.df[fields_wk_track].applymap(
lambda x: self.map_track_chart_to_x8.get(x))
# make dataframes for historical pp columns and wk columns that are multiindexed by date
self._index_pp_columns()
self._index_wk_columns()
# validate df
self._validate(datelist)
