def pivot_simple(index, columns, values):
"""
Produce 'pivot' table based on 3 columns of this DataFrame.
Uses unique values from index / columns and fills with values.
Parameters
----------
index : ndarray
Labels to use to make new frame's index
columns : ndarray
Labels to use to make new frame's columns
values : ndarray
Values to use for populating new frame's values
Note
----
Obviously, all 3 of the input arguments must have the same length
Returns
-------
DataFrame
"""
if (len(index) != len(columns)) or (len(columns) != len(values)):
raise AssertionError('Length of index, columns, and values must be the'
' same')
if len(index) == 0:
return DataFrame(index=[])
hindex = MultiIndex.from_arrays([index, columns])
series = Series(values.ravel(), index=hindex)
series = series.sortlevel(0)
return series.unstack()
def value_counts(values, sort=True, ascending=False):
"""
Compute a histogram of the counts of non-null values
Parameters
----------
values : ndarray (1-d)
sort : boolean, default True
Sort by values
ascending : boolean, default False
Sort in ascending order
Returns
-------
value_counts : Series
"""
from pandas.core.series import Series
from collections import defaultdict
if com.is_integer_dtype(values.dtype):
values = com._ensure_int64(values)
keys, counts = lib.value_count_int64(values)
result = Series(counts, index=keys)
else:
counter = defaultdict(lambda: 0)
values = values[com.notnull(values)]
for value in values:
counter[value] += 1
result = Series(counter)
if sort:
result.sort()
if not ascending:
result = result[::-1]
return result
def get_sysprice_list(start_time, end_time, frequency='hourly'):
'''Wrapper function for creating pandas Series object from data
received from the database.
Returns: pandas Series object with Elspot daily system prices and
corresponding dates for predefined time period.
Parameters:
start_time - string representing the start of the time period,
format must be 'yyyy-mm-dd'.
end_time - string representing the end of the time period,
format must be 'yyyy-mm-dd'.
frequency - string representing the frequency of the output pandas
Series object. Currently must be one of ['hourly', 'daily']
'''
#Retrieve hourly system prices and timestamps from database as lists
_ , sys_prices, times = get_system_price_volume(start_time, end_time)
ts = Series(sys_prices, index=times)
if frequency == 'daily':
resampling_frequency = 'D'
if frequency == 'hourly':
#Resampling is not necessary
return ts
else:
return ts.resample(resampling_frequency, how='mean',
kind='timestamp')
def xs(self, key):
"""
Returns a row from the DataMatrix as a Series object.
Parameters
----------
key : some index contained in the index
Returns
-------
Series
"""
if key not in self.index:
raise Exception('No cross-section for %s' % key)
loc = self.index.indexMap[key]
theSlice = self.values[loc, :].copy()
xsIndex = self.columns
result = Series(theSlice, index=xsIndex)
if self.objects is not None and len(self.objects.columns) > 0:
result = result.append(self.objects.getXS(key))
return result
def _init_spmatrix(self, data, index, columns, dtype=None,
fill_value=None):
""" Init self from scipy.sparse matrix """
index, columns = self._prep_index(data, index, columns)
data = data.tocoo()
N = len(index)
# Construct a dict of SparseSeries
sdict = {}
values = Series(data.data, index=data.row, copy=False)
for col, rowvals in values.groupby(data.col):
# get_blocks expects int32 row indices in sorted order
rowvals = rowvals.sort_index()
rows = rowvals.index.values.astype(np.int32)
blocs, blens = get_blocks(rows)
sdict[columns[col]] = SparseSeries(
rowvals.values, index=index,
fill_value=fill_value,
sparse_index=BlockIndex(N, blocs, blens))
# Add any columns that were empty and thus not grouped on above
sdict.update({column: SparseSeries(index=index,
fill_value=fill_value,
sparse_index=BlockIndex(N, [], []))
for column in columns
if column not in sdict})
return self._init_dict(sdict, index, columns, dtype)
def value_counts(values, sort=True, ascending=False):
"""
Compute a histogram of the counts of non-null values
Returns
-------
value_counts : Series
"""
from collections import defaultdict
if com.is_integer_dtype(values.dtype):
values = com._ensure_int64(values)
keys, counts = lib.value_count_int64(values)
result = Series(counts, index=keys)
else:
counter = defaultdict(lambda: 0)
values = values[com.notnull(values)]
for value in values:
counter[value] += 1
result = Series(counter)
if sort:
result.sort()
if not ascending:
result = result[::-1]
return result
def pivot(index, columns, values):
"""
Produce 'pivot' table based on 3 columns of this DataFrame.
Uses unique values from index / columns and fills with values.
Parameters
----------
index : ndarray
Labels to use to make new frame's index
columns : ndarray
Labels to use to make new frame's columns
values : ndarray
Values to use for populating new frame's values
Note
----
Obviously, all 3 of the input arguments must have the same length
Returns
-------
DataFrame
"""
assert(len(index) == len(columns) == len(values))
if len(index) == 0:
return DataFrame(index=[])
hindex = _make_long_index(index, columns)
series = Series(values.ravel(), index=hindex)
series = series.sortlevel(0)
return series.unstack()
def match(to_match, values, na_sentinel=-1):
"""
Compute locations of to_match into values
Parameters
----------
to_match : array-like
values to find positions of
values : array-like
Unique set of values
na_sentinel : int, default -1
Value to mark "not found"
Examples
--------
Returns
-------
match : ndarray of integers
"""
values = com._asarray_tuplesafe(values)
if issubclass(values.dtype.type, string_types):
values = np.array(values, dtype='O')
f = lambda htype, caster: _match_generic(to_match, values, htype, caster)
result = _hashtable_algo(f, values.dtype)
if na_sentinel != -1:
# replace but return a numpy array
# use a Series because it handles dtype conversions properly
from pandas.core.series import Series
result = Series(result.ravel()).replace(-1,na_sentinel).values.reshape(result.shape)
return result
def test_groupby_categorical_unequal_len(self):
# GH3011
series = Series([np.nan, np.nan, 1, 1, 2, 2, 3, 3, 4, 4])
# The raises only happens with categorical, not with series of types
# category
bins = pd.cut(series.dropna().values, 4)
# len(bins) != len(series) here
self.assertRaises(ValueError, lambda: series.groupby(bins).mean())
def _value_counts_arraylike(values, dropna=True):
is_datetimetz_type = is_datetimetz(values)
is_period_type = (is_period_dtype(values) or
is_period_arraylike(values))
orig = values
from pandas.core.series import Series
values = Series(values).values
dtype = values.dtype
if needs_i8_conversion(dtype) or is_period_type:
from pandas.tseries.index import DatetimeIndex
from pandas.tseries.period import PeriodIndex
if is_period_type:
# values may be an object
values = PeriodIndex(values)
freq = values.freq
values = values.view(np.int64)
keys, counts = htable.value_count_int64(values, dropna)
if dropna:
msk = keys != iNaT
keys, counts = keys[msk], counts[msk]
# convert the keys back to the dtype we came in
keys = keys.astype(dtype)
# dtype handling
if is_datetimetz_type:
keys = DatetimeIndex._simple_new(keys, tz=orig.dtype.tz)
if is_period_type:
keys = PeriodIndex._simple_new(keys, freq=freq)
elif is_signed_integer_dtype(dtype):
values = _ensure_int64(values)
keys, counts = htable.value_count_int64(values, dropna)
elif is_unsigned_integer_dtype(dtype):
values = _ensure_uint64(values)
keys, counts = htable.value_count_uint64(values, dropna)
elif is_float_dtype(dtype):
values = _ensure_float64(values)
keys, counts = htable.value_count_float64(values, dropna)
else:
values = _ensure_object(values)
keys, counts = htable.value_count_object(values, dropna)
mask = isnull(values)
if not dropna and mask.any():
keys = np.insert(keys, 0, np.NaN)
counts = np.insert(counts, 0, mask.sum())
return keys, counts
def pivot(self, index=None, columns=None, values=None):
"""
See DataFrame.pivot
"""
if values is None:
indexed = self.set_index([index, columns])
return indexed.unstack(columns)
else:
indexed = Series(self[values], index=[self[index], self[columns]])
return indexed.unstack(columns)
def size(self):
"""
Compute group sizes
"""
# TODO: better impl
labels, _, ngroups = self.group_info
bin_counts = Series(labels).value_counts()
bin_counts = bin_counts.reindex(np.arange(ngroups))
bin_counts.index = self.result_index
return bin_counts
def _value_counts_arraylike(values, dropna=True):
is_datetimetz = com.is_datetimetz(values)
is_period = (isinstance(values, gt.ABCPeriodIndex) or
com.is_period_arraylike(values))
orig = values
from pandas.core.series import Series
values = Series(values).values
dtype = values.dtype
if com.is_datetime_or_timedelta_dtype(dtype) or is_period:
from pandas.tseries.index import DatetimeIndex
from pandas.tseries.period import PeriodIndex
if is_period:
values = PeriodIndex(values)
freq = values.freq
values = values.view(np.int64)
keys, counts = htable.value_count_scalar64(values, dropna)
if dropna:
msk = keys != iNaT
keys, counts = keys[msk], counts[msk]
# convert the keys back to the dtype we came in
keys = keys.astype(dtype)
# dtype handling
if is_datetimetz:
if isinstance(orig, gt.ABCDatetimeIndex):
tz = orig.tz
else:
tz = orig.dt.tz
keys = DatetimeIndex._simple_new(keys, tz=tz)
if is_period:
keys = PeriodIndex._simple_new(keys, freq=freq)
elif com.is_integer_dtype(dtype):
values = com._ensure_int64(values)
keys, counts = htable.value_count_scalar64(values, dropna)
elif com.is_float_dtype(dtype):
values = com._ensure_float64(values)
keys, counts = htable.value_count_scalar64(values, dropna)
else:
values = com._ensure_object(values)
mask = com.isnull(values)
keys, counts = htable.value_count_object(values, mask)
if not dropna and mask.any():
keys = np.insert(keys, 0, np.NaN)
counts = np.insert(counts, 0, mask.sum())
return keys, counts
def _coo_to_sparse_series(A, dense_index=False):
""" Convert a scipy.sparse.coo_matrix to a SparseSeries.
Use the defaults given in the SparseSeries constructor. """
s = Series(A.data, MultiIndex.from_arrays((A.row, A.col)))
s = s.sort_index()
s = s.to_sparse() # TODO: specify kind?
if dense_index:
# is there a better constructor method to use here?
i = range(A.shape[0])
j = range(A.shape[1])
ind = MultiIndex.from_product([i, j])
s = s.reindex_axis(ind)
return s
def test_filter_against_workaround(self):
np.random.seed(0)
# Series of ints
s = Series(np.random.randint(0, 100, 1000))
grouper = s.apply(lambda x: np.round(x, -1))
grouped = s.groupby(grouper)
f = lambda x: x.mean() > 10
old_way = s[grouped.transform(f).astype('bool')]
new_way = grouped.filter(f)
assert_series_equal(new_way.sort_values(), old_way.sort_values())
# Series of floats
s = 100 * Series(np.random.random(1000))
grouper = s.apply(lambda x: np.round(x, -1))
grouped = s.groupby(grouper)
f = lambda x: x.mean() > 10
old_way = s[grouped.transform(f).astype('bool')]
new_way = grouped.filter(f)
assert_series_equal(new_way.sort_values(), old_way.sort_values())
# Set up DataFrame of ints, floats, strings.
from string import ascii_lowercase
letters = np.array(list(ascii_lowercase))
N = 1000
random_letters = letters.take(np.random.randint(0, 26, N))
df = DataFrame({'ints': Series(np.random.randint(0, 100, N)),
'floats': N / 10 * Series(np.random.random(N)),
'letters': Series(random_letters)})
# Group by ints; filter on floats.
grouped = df.groupby('ints')
old_way = df[grouped.floats.
transform(lambda x: x.mean() > N / 20).astype('bool')]
new_way = grouped.filter(lambda x: x['floats'].mean() > N / 20)
assert_frame_equal(new_way, old_way)
# Group by floats (rounded); filter on strings.
grouper = df.floats.apply(lambda x: np.round(x, -1))
grouped = df.groupby(grouper)
old_way = df[grouped.letters.
transform(lambda x: len(x) < N / 10).astype('bool')]
new_way = grouped.filter(lambda x: len(x.letters) < N / 10)
assert_frame_equal(new_way, old_way)
# Group by strings; filter on ints.
grouped = df.groupby('letters')
old_way = df[grouped.ints.
transform(lambda x: x.mean() > N / 20).astype('bool')]
new_way = grouped.filter(lambda x: x['ints'].mean() > N / 20)
assert_frame_equal(new_way, old_way)
def _get_index_subset_to_coord_dict(index, subset, sort_labels=False):
ilabels = list(zip(*[index._get_level_values(i) for i in subset]))
labels_to_i = _get_label_to_i_dict(ilabels,
sort_labels=sort_labels)
labels_to_i = Series(labels_to_i)
if len(subset) > 1:
labels_to_i.index = MultiIndex.from_tuples(labels_to_i.index)
labels_to_i.index.names = [index.names[i] for i in subset]
else:
labels_to_i.index = Index(x[0] for x in labels_to_i.index)
labels_to_i.index.name = index.names[subset[0]]
labels_to_i.name = 'value'
return (labels_to_i)
def value_counts(values, sort=True, ascending=False, normalize=False):
"""
Compute a histogram of the counts of non-null values
Parameters
----------
values : ndarray (1-d)
sort : boolean, default True
Sort by values
ascending : boolean, default False
Sort in ascending order
normalize: boolean, default False
If True then compute a relative histogram
Returns
-------
value_counts : Series
"""
from pandas.core.series import Series
values = np.asarray(values)
if com.is_integer_dtype(values.dtype):
values = com._ensure_int64(values)
keys, counts = htable.value_count_int64(values)
elif issubclass(values.dtype.type, (np.datetime64,np.timedelta64)):
dtype = values.dtype
values = values.view(np.int64)
keys, counts = htable.value_count_int64(values)
# convert the keys back to the dtype we came in
keys = Series(keys,dtype=dtype)
else:
mask = com.isnull(values)
values = com._ensure_object(values)
keys, counts = htable.value_count_object(values, mask)
result = Series(counts, index=keys)
if sort:
result.sort()
if not ascending:
result = result[::-1]
if normalize:
result = result / float(values.size)
return result
def __repr__(self):
with warnings.catch_warnings():
warnings.filterwarnings("ignore", "Sparse")
series_rep = Series.__repr__(self)
rep = '{series}\n{index!r}'.format(series=series_rep,
index=self.sp_index)
return rep
def sort_group_labels(ids, labels, counts):
n = len(ids)
rng = np.arange(n)
values = Series(ids, index=rng, dtype=object).values
indexer = values.argsort()
reverse_indexer = np.empty(n, dtype=np.int32)
reverse_indexer.put(indexer, np.arange(n))
new_labels = reverse_indexer.take(labels)
np.putmask(new_labels, labels == -1, -1)
new_ids = dict(izip(rng, values.take(indexer)))
new_counts = counts.take(indexer)
return new_ids, new_labels, new_counts
def pivot(self, index=None, columns=None, values=None):
"""
See DataFrame.pivot
"""
if values is None:
cols = [columns] if index is None else [index, columns]
append = index is None
indexed = self.set_index(cols, append=append)
return indexed.unstack(columns)
else:
if index is None:
index = self.index
else:
index = self[index]
indexed = Series(self[values].values, index=MultiIndex.from_arrays([index, self[columns]]))
return indexed.unstack(columns)
def _get_index_subset_to_coord_dict(index, subset, sort_labels=False):
def robust_get_level_values(i):
# if index has labels (that are not None) use those,
# else use the level location
try:
return(index.get_level_values(index.names[i]))
except KeyError:
return(index.get_level_values(i))
ilabels = list(
zip(*[robust_get_level_values(i) for i in subset]))
labels_to_i = _get_label_to_i_dict(
ilabels, sort_labels=sort_labels)
labels_to_i = Series(labels_to_i)
labels_to_i.index = MultiIndex.from_tuples(labels_to_i.index)
labels_to_i.index.names = [index.names[i] for i in subset]
labels_to_i.name = 'value'
return(labels_to_i)
def test_groupby_categorical_no_compress(self):
data = Series(np.random.randn(9))
codes = np.array([0, 0, 0, 1, 1, 1, 2, 2, 2])
cats = Categorical.from_codes(codes, [0, 1, 2], ordered=True)
result = data.groupby(cats).mean()
exp = data.groupby(codes).mean()
exp.index = CategoricalIndex(exp.index, categories=cats.categories,
ordered=cats.ordered)
assert_series_equal(result, exp)
codes = np.array([0, 0, 0, 1, 1, 1, 3, 3, 3])
cats = Categorical.from_codes(codes, [0, 1, 2, 3], ordered=True)
result = data.groupby(cats).mean()
exp = data.groupby(codes).mean().reindex(cats.categories)
exp.index = CategoricalIndex(exp.index, categories=cats.categories,
ordered=cats.ordered)
assert_series_equal(result, exp)
cats = Categorical(["a", "a", "a", "b", "b", "b", "c", "c", "c"],
categories=["a", "b", "c", "d"], ordered=True)
data = DataFrame({"a": [1, 1, 1, 2, 2, 2, 3, 4, 5], "b": cats})
result = data.groupby("b").mean()
result = result["a"].values
exp = np.array([1, 2, 4, np.nan])
self.assert_numpy_array_equal(result, exp)
def init_dict(data, index, columns, dtype=None):
"""
Segregate Series based on type and coerce into matrices.
Needs to handle a lot of exceptional cases.
"""
if columns is not None:
from pandas.core.series import Series
arrays = Series(data, index=columns, dtype=object)
data_names = arrays.index
missing = arrays.isnull()
if index is None:
# GH10856
# raise ValueError if only scalars in dict
index = extract_index(arrays[~missing])
else:
index = ensure_index(index)
# no obvious "empty" int column
if missing.any() and not is_integer_dtype(dtype):
if dtype is None or np.issubdtype(dtype, np.flexible):
# GH#1783
nan_dtype = object
else:
nan_dtype = dtype
val = construct_1d_arraylike_from_scalar(np.nan, len(index),
nan_dtype)
arrays.loc[missing] = [val] * missing.sum()
else:
for key in data:
if (isinstance(data[key], ABCDatetimeIndex) and
data[key].tz is not None):
# GH#24096 need copy to be deep for datetime64tz case
# TODO: See if we can avoid these copies
data[key] = data[key].copy(deep=True)
keys = com.dict_keys_to_ordered_list(data)
columns = data_names = Index(keys)
arrays = [data[k] for k in keys]
return arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)
def _coo_to_sparse_series(A, dense_index: bool = False,
sparse_series: bool = True):
"""
Convert a scipy.sparse.coo_matrix to a SparseSeries.
Parameters
----------
A : scipy.sparse.coo.coo_matrix
dense_index : bool, default False
sparse_series : bool, default True
Returns
-------
Series or SparseSeries
Raises
------
TypeError if A is not a coo_matrix
"""
from pandas import SparseDtype
try:
s = Series(A.data, MultiIndex.from_arrays((A.row, A.col)))
except AttributeError:
raise TypeError('Expected coo_matrix. Got {} instead.'
.format(type(A).__name__))
s = s.sort_index()
if sparse_series:
# TODO(SparseSeries): remove this and the sparse_series keyword.
# This is just here to avoid a DeprecationWarning when
# _coo_to_sparse_series is called via Series.sparse.from_coo
s = s.to_sparse() # TODO: specify kind?
else:
s = s.astype(SparseDtype(s.dtype))
if dense_index:
# is there a better constructor method to use here?
i = range(A.shape[0])
j = range(A.shape[1])
ind = MultiIndex.from_product([i, j])
s = s.reindex(ind)
return s
def plot(self):
"""
Plots 2 graphs. One for N-period moving average, lower and upper bands.
One for P/N and position.
"""
columns = {"Upper Bands": self.upper_bands,
"Lower Bands": self.lower_bands,
"Moving Means": self.moving_means,
"Opening Prices": self.prices}
df = DataFrame(columns, index=self.dates)
df.plot()
fig = plt.figure(num=None, figsize=(18, 10), dpi=80, facecolor='w', edgecolor='k')
fig.add_subplot(121)
trans_dates = [tran.date for tran in self.transactions]
# we negate the value here to show profit/loss
trans = Series([-tran.value() for tran in self.transactions], index=trans_dates)
position = Series([tran.units for tran in self.transactions], index=trans_dates)
position.cumsum().plot(label="Position")
plt.xlabel("Date")
plt.ylabel("Position")
plt.title("Position over Time")
plt.legend(loc="best")
fig.add_subplot(122)
trans.cumsum().plot(label="P/L")
plt.xlabel("Date")
plt.ylabel("Profit/Loss")
plt.title("Profit and Loss over Time")
plt.legend(loc="best")
plt.show()
def get_sysprice_list(start_time, end_time, frequency='hourly'):
'''Wrapper function for creating pandas Series object from data
received from the database.
Returns: pandas Series object with Elspot daily system prices and
corresponding dates for predefined time period.
Parameters:
start_time - string representing the start of the time period,
format must be 'yyyy-mm-dd'.
end_time - string representing the end of the time period,
format must be 'yyyy-mm-dd'.
frequency - string representing the frequency of the output pandas
Series object. Currently must be one of ['hourly', 'daily']
'''
#Retrieve hourly system prices and timestamps from database as lists
_ , sys_prices, times = get_system_price_volume(start_time, end_time)
ts = Series(sys_prices, index=times)
if frequency == 'daily':
resampling_frequency = 'D'
'''Weekly functionality not needed for now'''
'''elif frequency == 'weekly':
start_time = datetime.datetime.strptime(start_time, '%Y-%m-%d %H:%M:%S')
end_time = datetime.datetime.strptime(end_time, '%Y-%m-%d %H:%M:%S')
if start_time.date().weekday() != 0:
raise ValueError(str(start_time.date())+ " is a " + start_time.date().strftime('%A') + ". start_date must be a Monday.")
if end_time.date().weekday() != 6:
raise ValueError(str(end_time.date())+ " is a " + end_time.date().strftime('%A') + ". end_date must be a Sunday.")
resampling_frequency = 'W' '''
if frequency == 'monthly':
resampling_frequency = 'M'
if frequency == 'hourly':
#Resampling is not necessary
return ts
else:
return ts.resample(resampling_frequency, how='mean',
kind='timestamp')
def value_counts(values, sort=True, ascending=False):
"""
Compute a histogram of the counts of non-null values
Parameters
----------
values : ndarray (1-d)
sort : boolean, default True
Sort by values
ascending : boolean, default False
Sort in ascending order
Returns
-------
value_counts : Series
"""
from pandas.core.series import Series
values = np.asarray(values)
if com.is_integer_dtype(values.dtype):
values = com._ensure_int64(values)
keys, counts = htable.value_count_int64(values)
else:
mask = com.isnull(values)
values = com._ensure_object(values)
keys, counts = htable.value_count_object(values, mask)
result = Series(counts, index=keys)
if sort:
result.sort()
if not ascending:
result = result[::-1]
return result
def __unicode__(self):
# currently, unicode is same as repr...fixes infinite loop
series_rep = Series.__unicode__(self)
rep = '{series}\n{index!r}'.format(series=series_rep,
index=self.sp_index)
return rep
def value_counts(values,
sort=True,
ascending=False,
normalize=False,
bins=None,
dropna=True):
"""
Compute a histogram of the counts of non-null values.
Parameters
----------
values : ndarray (1-d)
sort : boolean, default True
Sort by values
ascending : boolean, default False
Sort in ascending order
normalize: boolean, default False
If True then compute a relative histogram
bins : integer, optional
Rather than count values, group them into half-open bins,
convenience for pd.cut, only works with numeric data
dropna : boolean, default True
Don't include counts of NaN
Returns
-------
value_counts : Series
"""
from pandas.core.series import Series
name = getattr(values, 'name', None)
if bins is not None:
try:
from pandas.tools.tile import cut
values = Series(values).values
cat, bins = cut(values, bins, retbins=True)
except TypeError:
raise TypeError("bins argument only works with numeric data.")
values = cat.codes
if is_extension_type(values) and not is_datetimetz(values):
# handle Categorical and sparse,
# datetime tz can be handeled in ndarray path
result = Series(values).values.value_counts(dropna=dropna)
result.name = name
counts = result.values
else:
# ndarray path. pass original to handle DatetimeTzBlock
keys, counts = _value_counts_arraylike(values, dropna=dropna)
from pandas import Index, Series
if not isinstance(keys, Index):
keys = Index(keys)
result = Series(counts, index=keys, name=name)
if bins is not None:
# TODO: This next line should be more efficient
result = result.reindex(np.arange(len(cat.categories)), fill_value=0)
result.index = bins[:-1]
if sort:
result = result.sort_values(ascending=ascending)
if normalize:
result = result / float(counts.sum())
return result
def aggregate(self, func_or_funcs, *args, **kwargs):
"""
Apply aggregation function or functions to groups, yielding most likely
Series but in some cases DataFrame depending on the output of the
aggregation function
Parameters
----------
func_or_funcs : function or list / dict of functions
List/dict of functions will produce DataFrame with column names
determined by the function names themselves (list) or the keys in
the dict
Notes
-----
agg is an alias for aggregate. Use it.
Example
-------
>>> series
bar 1.0
baz 2.0
qot 3.0
qux 4.0
>>> mapper = lambda x: x[0] # first letter
>>> grouped = series.groupby(mapper)
>>> grouped.aggregate(np.sum)
b 3.0
q 7.0
>>> grouped.aggregate([np.sum, np.mean, np.std])
mean std sum
b 1.5 0.5 3
q 3.5 0.5 7
>>> grouped.agg({'result' : lambda x: x.mean() / x.std(),
... 'total' : np.sum})
result total
b 2.121 3
q 4.95 7
See also
--------
apply, transform
Returns
-------
Series or DataFrame
"""
if isinstance(func_or_funcs, basestring):
return getattr(self, func_or_funcs)(*args, **kwargs)
if hasattr(func_or_funcs, '__iter__'):
ret = self._aggregate_multiple_funcs(func_or_funcs)
else:
if len(self.grouper.groupings) > 1:
return self._python_agg_general(func_or_funcs, *args, **kwargs)
try:
return self._python_agg_general(func_or_funcs, *args, **kwargs)
except Exception:
result = self._aggregate_named(func_or_funcs, *args, **kwargs)
index = Index(sorted(result), name=self.grouper.names[0])
ret = Series(result, index=index)
if not self.as_index: # pragma: no cover
print 'Warning, ignoring as_index=True'
return ret
def __init__(self, data, index=None, sparse_index=None, kind='block',
fill_value=None, name=None, dtype=None, copy=False,
fastpath=False):
# we are called internally, so short-circuit
if fastpath:
# data is an ndarray, index is defined
data = SingleBlockManager(data, index, fastpath=True)
if copy:
data = data.copy()
else:
is_sparse_array = isinstance(data, SparseArray)
if fill_value is None:
if is_sparse_array:
fill_value = data.fill_value
else:
fill_value = nan
if is_sparse_array:
if isinstance(data, SparseSeries) and index is None:
index = data.index
elif index is not None:
assert(len(index) == len(data))
sparse_index = data.sp_index
data = np.asarray(data)
elif isinstance(data, SparseSeries):
if index is None:
index = data.index
# extract the SingleBlockManager
data = data._data
elif isinstance(data, (Series, dict)):
if index is None:
index = data.index
data = Series(data)
data, sparse_index = make_sparse(data, kind=kind,
fill_value=fill_value)
elif isinstance(data, (tuple, list, np.ndarray)):
# array-like
if sparse_index is None:
data, sparse_index = make_sparse(data, kind=kind,
fill_value=fill_value)
else:
assert(len(data) == sparse_index.npoints)
elif isinstance(data, SingleBlockManager):
if dtype is not None:
data = data.astype(dtype)
if index is None:
index = data.index
else:
data = data.reindex(index, copy=False)
else:
length = len(index)
if data == fill_value or (isnull(data)
and isnull(fill_value)):
if kind == 'block':
sparse_index = BlockIndex(length, [], [])
else:
sparse_index = IntIndex(length, [])
data = np.array([])
else:
if kind == 'block':
locs, lens = ([0], [length]) if length else ([], [])
sparse_index = BlockIndex(length, locs, lens)
else:
sparse_index = IntIndex(length, index)
v = data
data = np.empty(length)
data.fill(v)
if index is None:
index = com._default_index(sparse_index.length)
index = _ensure_index(index)
# create/copy the manager
if isinstance(data, SingleBlockManager):
if copy:
data = data.copy()
else:
# create a sparse array
if not isinstance(data, SparseArray):
data = SparseArray(
data, sparse_index=sparse_index, fill_value=fill_value, dtype=dtype, copy=copy)
data = SingleBlockManager(data, index)
generic.NDFrame.__init__(self, data)
self.index = index
self.name = name
def _get_empty_meta(self,
columns,
index_col,
index_names,
dtype: DtypeArg | None = None):
columns = list(columns)
# Convert `dtype` to a defaultdict of some kind.
# This will enable us to write `dtype[col_name]`
# without worrying about KeyError issues later on.
if not is_dict_like(dtype):
# if dtype == None, default will be object.
default_dtype = dtype or object
# error: Argument 1 to "defaultdict" has incompatible type "Callable[[],
# Union[ExtensionDtype, str, dtype[Any], Type[object], Dict[Hashable,
# Union[ExtensionDtype, Union[str, dtype[Any]], Type[str], Type[float],
# Type[int], Type[complex], Type[bool], Type[object]]]]]"; expected
# "Optional[Callable[[], Union[ExtensionDtype, str, dtype[Any],
# Type[object]]]]"
# error: Incompatible return value type (got "Union[ExtensionDtype, str,
# dtype[Any], Type[object], Dict[Hashable, Union[ExtensionDtype, Union[str,
# dtype[Any]], Type[str], Type[float], Type[int], Type[complex], Type[bool],
# Type[object]]]]", expected "Union[ExtensionDtype, str, dtype[Any],
# Type[object]]")
dtype = defaultdict(
lambda: default_dtype # type: ignore[arg-type, return-value]
)
else:
dtype = cast(dict, dtype)
dtype = defaultdict(
lambda: object,
{
columns[k] if is_integer(k) else k: v
for k, v in dtype.items()
},
)
# Even though we have no data, the "index" of the empty DataFrame
# could for example still be an empty MultiIndex. Thus, we need to
# check whether we have any index columns specified, via either:
#
# 1) index_col (column indices)
# 2) index_names (column names)
#
# Both must be non-null to ensure a successful construction. Otherwise,
# we have to create a generic empty Index.
if (index_col is None or index_col is False) or index_names is None:
index = Index([])
else:
data = [Series([], dtype=dtype[name]) for name in index_names]
index = ensure_index_from_sequences(data, names=index_names)
index_col.sort()
for i, n in enumerate(index_col):
columns.pop(n - i)
col_dict = {
col_name: Series([], dtype=dtype[col_name])
for col_name in columns
}
return index, columns, col_dict
def value_counts(values,
sort=True,
ascending=False,
normalize=False,
bins=None):
"""
Compute a histogram of the counts of non-null values
Parameters
----------
values : ndarray (1-d)
sort : boolean, default True
Sort by values
ascending : boolean, default False
Sort in ascending order
normalize: boolean, default False
If True then compute a relative histogram
bins : integer, optional
Rather than count values, group them into half-open bins,
convenience for pd.cut, only works with numeric data
Returns
-------
value_counts : Series
"""
from pandas.core.series import Series
from pandas.tools.tile import cut
values = Series(values).values
if bins is not None:
try:
cat, bins = cut(values, bins, retbins=True)
except TypeError:
raise TypeError("bins argument only works with numeric data.")
values = cat.labels
if com.is_integer_dtype(values.dtype):
values = com._ensure_int64(values)
keys, counts = htable.value_count_int64(values)
elif issubclass(values.dtype.type, (np.datetime64, np.timedelta64)):
dtype = values.dtype
values = values.view(np.int64)
keys, counts = htable.value_count_int64(values)
# convert the keys back to the dtype we came in
keys = Series(keys, dtype=dtype)
else:
mask = com.isnull(values)
values = com._ensure_object(values)
keys, counts = htable.value_count_object(values, mask)
result = Series(counts, index=com._values_from_object(keys))
if bins is not None:
# TODO: This next line should be more efficient
result = result.reindex(np.arange(len(cat.levels)), fill_value=0)
result.index = bins[:-1]
if sort:
result.sort()
if not ascending:
result = result[::-1]
if normalize:
result = result / float(values.size)
return result
def str_extract(arr, pat, flags=0):
"""
Find groups in each string using passed regular expression
Parameters
----------
pat : string
Pattern or regular expression
flags : int, default 0 (no flags)
re module flags, e.g. re.IGNORECASE
Returns
-------
extracted groups : Series (one group) or DataFrame (multiple groups)
Note that dtype of the result is always object, even when no match is
found and the result is a Series or DataFrame containing only NaN
values.
Examples
--------
A pattern with one group will return a Series. Non-matches will be NaN.
>>> Series(['a1', 'b2', 'c3']).str.extract('[ab](\d)')
0 1
1 2
2 NaN
dtype: object
A pattern with more than one group will return a DataFrame.
>>> Series(['a1', 'b2', 'c3']).str.extract('([ab])(\d)')
0 1
0 a 1
1 b 2
2 NaN NaN
A pattern may contain optional groups.
>>> Series(['a1', 'b2', 'c3']).str.extract('([ab])?(\d)')
0 1
0 a 1
1 b 2
2 NaN 3
Named groups will become column names in the result.
>>> Series(['a1', 'b2', 'c3']).str.extract('(?P<letter>[ab])(?P<digit>\d)')
letter digit
0 a 1
1 b 2
2 NaN NaN
"""
from pandas.core.series import Series
from pandas.core.frame import DataFrame
regex = re.compile(pat, flags=flags)
# just to be safe, check this
if regex.groups == 0:
raise ValueError("This pattern contains no groups to capture.")
empty_row = [np.nan]*regex.groups
def f(x):
if not isinstance(x, compat.string_types):
return empty_row
m = regex.search(x)
if m:
return [np.nan if item is None else item for item in m.groups()]
else:
return empty_row
if regex.groups == 1:
result = Series([f(val)[0] for val in arr],
name=_get_single_group_name(regex),
index=arr.index, dtype=object)
else:
names = dict(zip(regex.groupindex.values(), regex.groupindex.keys()))
columns = [names.get(1 + i, i) for i in range(regex.groups)]
if arr.empty:
result = DataFrame(columns=columns, dtype=object)
else:
result = DataFrame([f(val) for val in arr],
columns=columns,
index=arr.index,
dtype=object)
return result
def evaluate_model(self, x_test: pdSeries.Series, y_test: pdSeries.Series, pipe:Pipeline):
predicted = pipe.predict(x_test)
logger.info("model accuracy is: %.3f \n" % pipe.score(x_test, y_test) )
logger.info(metrics.classification_report(y_test, predicted, target_names=y_test.unique()))
def _get_dummies_1d(
data,
prefix,
prefix_sep="_",
dummy_na=False,
sparse=False,
drop_first=False,
dtype=None,
):
from pandas.core.reshape.concat import concat
# Series avoids inconsistent NaN handling
codes, levels = _factorize_from_iterable(Series(data))
if dtype is None:
dtype = np.uint8
dtype = np.dtype(dtype)
if is_object_dtype(dtype):
raise ValueError("dtype=object is not a valid dtype for get_dummies")
def get_empty_frame(data):
if isinstance(data, Series):
index = data.index
else:
index = np.arange(len(data))
return DataFrame(index=index)
# if all NaN
if not dummy_na and len(levels) == 0:
return get_empty_frame(data)
codes = codes.copy()
if dummy_na:
codes[codes == -1] = len(levels)
levels = np.append(levels, np.nan)
# if dummy_na, we just fake a nan level. drop_first will drop it again
if drop_first and len(levels) == 1:
return get_empty_frame(data)
number_of_cols = len(levels)
if prefix is None:
dummy_cols = levels
else:
# PY2 embedded unicode, gh-22084
def _make_col_name(prefix, prefix_sep, level):
fstr = "{prefix}{prefix_sep}{level}"
return fstr.format(prefix=prefix,
prefix_sep=prefix_sep,
level=level)
dummy_cols = [
_make_col_name(prefix, prefix_sep, level) for level in levels
]
if isinstance(data, Series):
index = data.index
else:
index = None
if sparse:
if is_integer_dtype(dtype):
fill_value = 0
elif dtype == bool:
fill_value = False
else:
fill_value = 0.0
sparse_series = []
N = len(data)
sp_indices = [[] for _ in range(len(dummy_cols))]
mask = codes != -1
codes = codes[mask]
n_idx = np.arange(N)[mask]
for ndx, code in zip(n_idx, codes):
sp_indices[code].append(ndx)
if drop_first:
# remove first categorical level to avoid perfect collinearity
# GH12042
sp_indices = sp_indices[1:]
dummy_cols = dummy_cols[1:]
for col, ixs in zip(dummy_cols, sp_indices):
sarr = SparseArray(
np.ones(len(ixs), dtype=dtype),
sparse_index=IntIndex(N, ixs),
fill_value=fill_value,
dtype=dtype,
)
sparse_series.append(Series(data=sarr, index=index, name=col))
out = concat(sparse_series, axis=1, copy=False)
return out
else:
dummy_mat = np.eye(number_of_cols, dtype=dtype).take(codes, axis=0)
if not dummy_na:
# reset NaN GH4446
dummy_mat[codes == -1] = 0
if drop_first:
# remove first GH12042
dummy_mat = dummy_mat[:, 1:]
dummy_cols = dummy_cols[1:]
return DataFrame(dummy_mat, index=index, columns=dummy_cols)
def beta(self):
return Series(self._beta_raw, index=self._x.columns)
def _unstack_multiple(data, clocs):
if len(clocs) == 0:
return data
# NOTE: This doesn't deal with hierarchical columns yet
index = data.index
clocs = [index._get_level_number(i) for i in clocs]
rlocs = [i for i in range(index.nlevels) if i not in clocs]
clevels = [index.levels[i] for i in clocs]
clabels = [index.labels[i] for i in clocs]
cnames = [index.names[i] for i in clocs]
rlevels = [index.levels[i] for i in rlocs]
rlabels = [index.labels[i] for i in rlocs]
rnames = [index.names[i] for i in rlocs]
shape = [len(x) for x in clevels]
group_index = get_group_index(clabels, shape)
comp_ids, obs_ids = _compress_group_index(group_index, sort=False)
recons_labels = decons_group_index(obs_ids, shape)
dummy_index = MultiIndex(levels=rlevels + [obs_ids],
labels=rlabels + [comp_ids],
names=rnames + ['__placeholder__'])
if isinstance(data, Series):
dummy = Series(data.values, index=dummy_index)
unstacked = dummy.unstack('__placeholder__')
new_levels = clevels
new_names = cnames
new_labels = recons_labels
else:
if isinstance(data.columns, MultiIndex):
result = data
for i in range(len(clocs)):
val = clocs[i]
result = result.unstack(val)
clocs = [val if i > val else val - 1 for val in clocs]
return result
dummy = DataFrame(data.values, index=dummy_index,
columns=data.columns)
unstacked = dummy.unstack('__placeholder__')
if isinstance(unstacked, Series):
unstcols = unstacked.index
else:
unstcols = unstacked.columns
new_levels = [unstcols.levels[0]] + clevels
new_names = [data.columns.name] + cnames
new_labels = [unstcols.labels[0]]
for rec in recons_labels:
new_labels.append(rec.take(unstcols.labels[-1]))
new_columns = MultiIndex(levels=new_levels, labels=new_labels,
names=new_names)
if isinstance(unstacked, Series):
unstacked.index = new_columns
else:
unstacked.columns = new_columns
return unstacked
def _chop(self, sdata: Series, slice_obj: slice) -> Series:
# fastpath equivalent to `sdata.iloc[slice_obj]`
mgr = sdata._mgr.get_slice(slice_obj)
# __finalize__ not called here, must be applied by caller if applicable
return sdata._constructor(mgr, name=sdata.name, fastpath=True)
def _generate_marginal_results(table,
data,
values,
rows,
cols,
aggfunc,
grand_margin,
margins_name='All'):
if len(cols) > 0:
# need to "interleave" the margins
table_pieces = []
margin_keys = []
def _all_key(key):
return (key, margins_name) + ('', ) * (len(cols) - 1)
if len(rows) > 0:
margin = data[rows + values].groupby(rows).agg(aggfunc)
cat_axis = 1
for key, piece in table.groupby(level=0, axis=cat_axis):
all_key = _all_key(key)
# we are going to mutate this, so need to copy!
piece = piece.copy()
try:
piece[all_key] = margin[key]
except TypeError:
# we cannot reshape, so coerce the axis
piece.set_axis(
piece._get_axis(cat_axis)._to_safe_for_reshape(),
axis=cat_axis,
inplace=True)
piece[all_key] = margin[key]
table_pieces.append(piece)
margin_keys.append(all_key)
else:
margin = grand_margin
cat_axis = 0
for key, piece in table.groupby(level=0, axis=cat_axis):
all_key = _all_key(key)
table_pieces.append(piece)
table_pieces.append(Series(margin[key], index=[all_key]))
margin_keys.append(all_key)
result = concat(table_pieces, axis=cat_axis)
if len(rows) == 0:
return result
else:
result = table
margin_keys = table.columns
if len(cols) > 0:
row_margin = data[cols + values].groupby(cols).agg(aggfunc)
row_margin = row_margin.stack()
# slight hack
new_order = [len(cols)] + lrange(len(cols))
row_margin.index = row_margin.index.reorder_levels(new_order)
else:
row_margin = Series(np.nan, index=result.columns)
return result, margin_keys, row_margin
def _add_margins(table,
data,
values,
rows,
cols,
aggfunc,
margins_name='All',
fill_value=None):
if not isinstance(margins_name, compat.string_types):
raise ValueError('margins_name argument must be a string')
msg = u'Conflicting name "{name}" in margins'.format(name=margins_name)
for level in table.index.names:
if margins_name in table.index.get_level_values(level):
raise ValueError(msg)
grand_margin = _compute_grand_margin(data, values, aggfunc, margins_name)
# could be passed a Series object with no 'columns'
if hasattr(table, 'columns'):
for level in table.columns.names[1:]:
if margins_name in table.columns.get_level_values(level):
raise ValueError(msg)
if len(rows) > 1:
key = (margins_name, ) + ('', ) * (len(rows) - 1)
else:
key = margins_name
if not values and isinstance(table, ABCSeries):
# If there are no values and the table is a series, then there is only
# one column in the data. Compute grand margin and return it.
return table.append(Series({key: grand_margin[margins_name]}))
if values:
marginal_result_set = _generate_marginal_results(
table, data, values, rows, cols, aggfunc, grand_margin,
margins_name)
if not isinstance(marginal_result_set, tuple):
return marginal_result_set
result, margin_keys, row_margin = marginal_result_set
else:
marginal_result_set = _generate_marginal_results_without_values(
table, data, rows, cols, aggfunc, margins_name)
if not isinstance(marginal_result_set, tuple):
return marginal_result_set
result, margin_keys, row_margin = marginal_result_set
row_margin = row_margin.reindex(result.columns, fill_value=fill_value)
# populate grand margin
for k in margin_keys:
if isinstance(k, compat.string_types):
row_margin[k] = grand_margin[k]
else:
row_margin[k] = grand_margin[k[0]]
from pandas import DataFrame
margin_dummy = DataFrame(row_margin, columns=[key]).T
row_names = result.index.names
try:
for dtype in set(result.dtypes):
cols = result.select_dtypes([dtype]).columns
margin_dummy[cols] = margin_dummy[cols].astype(dtype)
result = result.append(margin_dummy)
except TypeError:
# we cannot reshape, so coerce the axis
result.index = result.index._to_safe_for_reshape()
result = result.append(margin_dummy)
result.index.names = row_names
return result
def _add_margins(
table: DataFrame | Series,
data: DataFrame,
values,
rows,
cols,
aggfunc,
observed=None,
margins_name: str = "All",
fill_value=None,
):
if not isinstance(margins_name, str):
raise ValueError("margins_name argument must be a string")
msg = f'Conflicting name "{margins_name}" in margins'
for level in table.index.names:
if margins_name in table.index.get_level_values(level):
raise ValueError(msg)
grand_margin = _compute_grand_margin(data, values, aggfunc, margins_name)
if table.ndim == 2:
# i.e. DataFrame
for level in table.columns.names[1:]:
if margins_name in table.columns.get_level_values(level):
raise ValueError(msg)
key: str | tuple[str, ...]
if len(rows) > 1:
key = (margins_name,) + ("",) * (len(rows) - 1)
else:
key = margins_name
if not values and isinstance(table, ABCSeries):
# If there are no values and the table is a series, then there is only
# one column in the data. Compute grand margin and return it.
return table._append(Series({key: grand_margin[margins_name]}))
elif values:
marginal_result_set = _generate_marginal_results(
table, data, values, rows, cols, aggfunc, observed, margins_name
)
if not isinstance(marginal_result_set, tuple):
return marginal_result_set
result, margin_keys, row_margin = marginal_result_set
else:
# no values, and table is a DataFrame
assert isinstance(table, ABCDataFrame)
marginal_result_set = _generate_marginal_results_without_values(
table, data, rows, cols, aggfunc, observed, margins_name
)
if not isinstance(marginal_result_set, tuple):
return marginal_result_set
result, margin_keys, row_margin = marginal_result_set
row_margin = row_margin.reindex(result.columns, fill_value=fill_value)
# populate grand margin
for k in margin_keys:
if isinstance(k, str):
row_margin[k] = grand_margin[k]
else:
row_margin[k] = grand_margin[k[0]]
from pandas import DataFrame
margin_dummy = DataFrame(row_margin, columns=[key]).T
row_names = result.index.names
# check the result column and leave floats
for dtype in set(result.dtypes):
cols = result.select_dtypes([dtype]).columns
margin_dummy[cols] = margin_dummy[cols].apply(
maybe_downcast_to_dtype, args=(dtype,)
)
result = result._append(margin_dummy)
result.index.names = row_names
return result
def dict_to_mgr(
data: dict,
index,
columns,
*,
dtype: DtypeObj | None = None,
typ: str = "block",
copy: bool = True,
) -> Manager:
"""
Segregate Series based on type and coerce into matrices.
Needs to handle a lot of exceptional cases.
Used in DataFrame.__init__
"""
arrays: Sequence[Any] | Series
if columns is not None:
from pandas.core.series import Series
arrays = Series(data, index=columns, dtype=object)
data_names = arrays.index
missing = arrays.isna()
if index is None:
# GH10856
# raise ValueError if only scalars in dict
index = extract_index(arrays[~missing])
else:
index = ensure_index(index)
# no obvious "empty" int column
if missing.any() and not is_integer_dtype(dtype):
nan_dtype: DtypeObj
if dtype is None or (isinstance(dtype, np.dtype)
and np.issubdtype(dtype, np.flexible)):
# GH#1783
nan_dtype = np.dtype("object")
else:
nan_dtype = dtype
val = construct_1d_arraylike_from_scalar(np.nan, len(index),
nan_dtype)
arrays.loc[missing] = [val] * missing.sum()
arrays = list(arrays)
else:
keys = list(data.keys())
columns = data_names = Index(keys)
arrays = [com.maybe_iterable_to_list(data[k]) for k in keys]
# GH#24096 need copy to be deep for datetime64tz case
# TODO: See if we can avoid these copies
arrays = [
arr if not isinstance(arr, ABCIndex) else arr._data
for arr in arrays
]
arrays = [
arr if not is_datetime64tz_dtype(arr) else arr.copy()
for arr in arrays
]
if copy:
# arrays_to_mgr (via form_blocks) won't make copies for EAs
# dtype attr check to exclude EADtype-castable strs
arrays = [
x if not hasattr(x, "dtype")
or not isinstance(x.dtype, ExtensionDtype) else x.copy()
for x in arrays
]
# TODO: can we get rid of the dt64tz special case above?
return arrays_to_mgr(arrays,
data_names,
index,
columns,
dtype=dtype,
typ=typ,
consolidate=copy)
def _generate_marginal_results(
table, data, values, rows, cols, aggfunc, observed, margins_name: str = "All"
):
if len(cols) > 0:
# need to "interleave" the margins
table_pieces = []
margin_keys = []
def _all_key(key):
return (key, margins_name) + ("",) * (len(cols) - 1)
if len(rows) > 0:
margin = data[rows + values].groupby(rows, observed=observed).agg(aggfunc)
cat_axis = 1
for key, piece in table.groupby(level=0, axis=cat_axis, observed=observed):
all_key = _all_key(key)
# we are going to mutate this, so need to copy!
piece = piece.copy()
piece[all_key] = margin[key]
table_pieces.append(piece)
margin_keys.append(all_key)
else:
from pandas import DataFrame
cat_axis = 0
for key, piece in table.groupby(level=0, axis=cat_axis, observed=observed):
if len(cols) > 1:
all_key = _all_key(key)
else:
all_key = margins_name
table_pieces.append(piece)
# GH31016 this is to calculate margin for each group, and assign
# corresponded key as index
transformed_piece = DataFrame(piece.apply(aggfunc)).T
transformed_piece.index = Index([all_key], name=piece.index.name)
# append piece for margin into table_piece
table_pieces.append(transformed_piece)
margin_keys.append(all_key)
result = concat(table_pieces, axis=cat_axis)
if len(rows) == 0:
return result
else:
result = table
margin_keys = table.columns
if len(cols) > 0:
row_margin = data[cols + values].groupby(cols, observed=observed).agg(aggfunc)
row_margin = row_margin.stack()
# slight hack
new_order = [len(cols)] + list(range(len(cols)))
row_margin.index = row_margin.index.reorder_levels(new_order)
else:
row_margin = Series(np.nan, index=result.columns)
return result, margin_keys, row_margin
def to_datetime(arg, errors='ignore', dayfirst=False, utc=None, box=True,
format=None, coerce=False, unit='ns',
infer_datetime_format=False):
"""
Convert argument to datetime
Parameters
----------
arg : string, datetime, array of strings (with possible NAs)
errors : {'ignore', 'raise'}, default 'ignore'
Errors are ignored by default (values left untouched)
dayfirst : boolean, default False
If True parses dates with the day first, eg 20/01/2005
Warning: dayfirst=True is not strict, but will prefer to parse
with day first (this is a known bug).
utc : boolean, default None
Return UTC DatetimeIndex if True (converting any tz-aware
datetime.datetime objects as well)
box : boolean, default True
If True returns a DatetimeIndex, if False returns ndarray of values
format : string, default None
strftime to parse time, eg "%d/%m/%Y"
coerce : force errors to NaT (False by default)
unit : unit of the arg (D,s,ms,us,ns) denote the unit in epoch
(e.g. a unix timestamp), which is an integer/float number
infer_datetime_format: boolean, default False
If no `format` is given, try to infer the format based on the first
datetime string. Provides a large speed-up in many cases.
Returns
-------
ret : datetime if parsing succeeded
Examples
--------
Take separate series and convert to datetime
>>> import pandas as pd
>>> i = pd.date_range('20000101',periods=100)
>>> df = pd.DataFrame(dict(year = i.year, month = i.month, day = i.day))
>>> pd.to_datetime(df.year*10000 + df.month*100 + df.day, format='%Y%m%d')
Or from strings
>>> df = df.astype(str)
>>> pd.to_datetime(df.day + df.month + df.year, format="%d%m%Y")
"""
from pandas import Timestamp
from pandas.core.series import Series
from pandas.tseries.index import DatetimeIndex
def _convert_listlike(arg, box, format):
if isinstance(arg, (list,tuple)):
arg = np.array(arg, dtype='O')
if com.is_datetime64_ns_dtype(arg):
if box and not isinstance(arg, DatetimeIndex):
try:
return DatetimeIndex(arg, tz='utc' if utc else None)
except ValueError:
pass
return arg
arg = com._ensure_object(arg)
if infer_datetime_format and format is None:
format = _guess_datetime_format_for_array(arg, dayfirst=dayfirst)
if format is not None:
# There is a special fast-path for iso8601 formatted
# datetime strings, so in those cases don't use the inferred
# format because this path makes process slower in this
# special case
format_is_iso8601 = (
'%Y-%m-%dT%H:%M:%S.%f'.startswith(format) or
'%Y-%m-%d %H:%M:%S.%f'.startswith(format)
)
if format_is_iso8601:
format = None
try:
result = None
if format is not None:
# shortcut formatting here
if format == '%Y%m%d':
try:
result = _attempt_YYYYMMDD(arg)
except:
raise ValueError("cannot convert the input to '%Y%m%d' date format")
# fallback
if result is None:
try:
result = tslib.array_strptime(
arg, format, coerce=coerce
)
except (tslib.OutOfBoundsDatetime):
if errors == 'raise':
raise
result = arg
except ValueError:
# Only raise this error if the user provided the
# datetime format, and not when it was inferred
if not infer_datetime_format:
raise
if result is None and (format is None or infer_datetime_format):
result = tslib.array_to_datetime(arg, raise_=errors == 'raise',
utc=utc, dayfirst=dayfirst,
coerce=coerce, unit=unit)
if com.is_datetime64_dtype(result) and box:
result = DatetimeIndex(result, tz='utc' if utc else None)
return result
except ValueError as e:
try:
values, tz = tslib.datetime_to_datetime64(arg)
return DatetimeIndex._simple_new(values, None, tz=tz)
except (ValueError, TypeError):
raise e
if arg is None:
return arg
elif isinstance(arg, Timestamp):
return arg
elif isinstance(arg, Series):
values = _convert_listlike(arg.values, False, format)
return Series(values, index=arg.index, name=arg.name)
elif com.is_list_like(arg):
return _convert_listlike(arg, box, format)
return _convert_listlike(np.array([ arg ]), box, format)[0]
def granger_causality(self):
"""Returns the f-stats and p-values from the Granger Causality Test.
If the data consists of columns x1, x2, x3, then we perform the
following regressions:
x1 ~ L(x2, x3)
x1 ~ L(x1, x3)
x1 ~ L(x1, x2)
The f-stats of these results are placed in the 'x1' column of the
returned DataFrame. We then repeat for x2, x3.
Returns
-------
Dict, where 'f-stat' returns the DataFrame containing the f-stats,
and 'p-value' returns the DataFrame containing the corresponding
p-values of the f-stats.
"""
from pandas.stats.api import ols
from scipy.stats import f
d = {}
for col in self._columns:
d[col] = {}
for i in xrange(1, 1 + self._p):
lagged_data = self._lagged_data[i].filter(self._columns -
[col])
for key, value in lagged_data.iteritems():
d[col][_make_param_name(i, key)] = value
f_stat_dict = {}
p_value_dict = {}
for col, y in self._data.iteritems():
ssr_full = (self.resid[col]**2).sum()
f_stats = []
p_values = []
for col2 in self._columns:
result = ols(y=y, x=d[col2])
resid = result.resid
ssr_reduced = (resid**2).sum()
M = self._p
N = self._nobs
K = self._k * self._p + 1
f_stat = ((ssr_reduced - ssr_full) / M) / (ssr_full / (N - K))
f_stats.append(f_stat)
p_value = f.sf(f_stat, M, N - K)
p_values.append(p_value)
f_stat_dict[col] = Series(f_stats, self._columns)
p_value_dict[col] = Series(p_values, self._columns)
f_stat_mat = DataFrame(f_stat_dict)
p_value_mat = DataFrame(p_value_dict)
return {
'f-stat': f_stat_mat,
'p-value': p_value_mat,
}
def _wrap_aggregated_output(self, output):
# sort of a kludge
output = output[self.name]
index = self.grouper.result_index
return Series(output, index=index, name=self.name)
def agg_dict_like(
obj: AggObjType,
arg: AggFuncTypeDict,
_axis: int,
) -> FrameOrSeriesUnion:
"""
Compute aggregation in the case of a dict-like argument.
Parameters
----------
obj : Pandas object to compute aggregation on.
arg : dict
label-aggregation pairs to compute.
_axis : int, 0 or 1
Axis to compute aggregation on.
Returns
-------
Result of aggregation.
"""
is_aggregator = lambda x: isinstance(x, (list, tuple, dict))
if _axis != 0: # pragma: no cover
raise ValueError("Can only pass dict with axis=0")
selected_obj = obj._selected_obj
# if we have a dict of any non-scalars
# eg. {'A' : ['mean']}, normalize all to
# be list-likes
# Cannot use arg.values() because arg may be a Series
if any(is_aggregator(x) for _, x in arg.items()):
new_arg: AggFuncTypeDict = {}
for k, v in arg.items():
if not isinstance(v, (tuple, list, dict)):
new_arg[k] = [v]
else:
new_arg[k] = v
# the keys must be in the columns
# for ndim=2, or renamers for ndim=1
# ok for now, but deprecated
# {'A': { 'ra': 'mean' }}
# {'A': { 'ra': ['mean'] }}
# {'ra': ['mean']}
# not ok
# {'ra' : { 'A' : 'mean' }}
if isinstance(v, dict):
raise SpecificationError("nested renamer is not supported")
elif isinstance(selected_obj, ABCSeries):
raise SpecificationError("nested renamer is not supported")
elif (isinstance(selected_obj, ABCDataFrame)
and k not in selected_obj.columns):
raise KeyError(f"Column '{k}' does not exist!")
arg = new_arg
else:
# deprecation of renaming keys
# GH 15931
keys = list(arg.keys())
if isinstance(selected_obj, ABCDataFrame) and len(
selected_obj.columns.intersection(keys)) != len(keys):
cols = list(
safe_sort(
list(
set(keys) -
set(selected_obj.columns.intersection(keys))), ))
raise SpecificationError(f"Column(s) {cols} do not exist")
from pandas.core.reshape.concat import concat
if selected_obj.ndim == 1:
# key only used for output
colg = obj._gotitem(obj._selection, ndim=1)
results = {key: colg.agg(how) for key, how in arg.items()}
else:
# key used for column selection and output
results = {
key: obj._gotitem(key, ndim=1).agg(how)
for key, how in arg.items()
}
# set the final keys
keys = list(arg.keys())
# Avoid making two isinstance calls in all and any below
is_ndframe = [isinstance(r, ABCNDFrame) for r in results.values()]
# combine results
if all(is_ndframe):
keys_to_use = [k for k in keys if not results[k].empty]
# Have to check, if at least one DataFrame is not empty.
keys_to_use = keys_to_use if keys_to_use != [] else keys
axis = 0 if isinstance(obj, ABCSeries) else 1
result = concat({k: results[k] for k in keys_to_use}, axis=axis)
elif any(is_ndframe):
# There is a mix of NDFrames and scalars
raise ValueError("cannot perform both aggregation "
"and transformation operations "
"simultaneously")
else:
from pandas import Series
# we have a dict of scalars
# GH 36212 use name only if obj is a series
if obj.ndim == 1:
obj = cast("Series", obj)
name = obj.name
else:
name = None
result = Series(results, name=name)
return result
def _count_generic(values, table_type, type_caster):
values = type_caster(values)
table = table_type(len(values))
uniques, labels, counts = table.factorize(values)
return Series(counts, index=uniques)
def agg_list_like(
obj: AggObjType,
arg: List[AggFuncTypeBase],
_axis: int,
) -> FrameOrSeriesUnion:
"""
Compute aggregation in the case of a list-like argument.
Parameters
----------
obj : Pandas object to compute aggregation on.
arg : list
Aggregations to compute.
_axis : int, 0 or 1
Axis to compute aggregation on.
Returns
-------
Result of aggregation.
"""
from pandas.core.reshape.concat import concat
if _axis != 0:
raise NotImplementedError("axis other than 0 is not supported")
if obj._selected_obj.ndim == 1:
selected_obj = obj._selected_obj
else:
selected_obj = obj._obj_with_exclusions
results = []
keys = []
# degenerate case
if selected_obj.ndim == 1:
for a in arg:
colg = obj._gotitem(selected_obj.name, ndim=1, subset=selected_obj)
try:
new_res = colg.aggregate(a)
except TypeError:
pass
else:
results.append(new_res)
# make sure we find a good name
name = com.get_callable_name(a) or a
keys.append(name)
# multiples
else:
for index, col in enumerate(selected_obj):
colg = obj._gotitem(col,
ndim=1,
subset=selected_obj.iloc[:, index])
try:
new_res = colg.aggregate(arg)
except (TypeError, DataError):
pass
except ValueError as err:
# cannot aggregate
if "Must produce aggregated value" in str(err):
# raised directly in _aggregate_named
pass
elif "no results" in str(err):
# raised directly in _aggregate_multiple_funcs
pass
else:
raise
else:
results.append(new_res)
keys.append(col)
# if we are empty
if not len(results):
raise ValueError("no results")
try:
return concat(results, keys=keys, axis=1, sort=False)
except TypeError as err:
# we are concatting non-NDFrame objects,
# e.g. a list of scalars
from pandas import Series
result = Series(results, index=keys, name=obj.name)
if is_nested_object(result):
raise ValueError(
"cannot combine transform and aggregation operations") from err
return result
def __unicode__(self):
# currently, unicode is same as repr...fixes infinite loop
series_rep = Series.__unicode__(self)
rep = '%s\n%s' % (series_rep, repr(self.sp_index))
return rep
def __init__(self,
data=None,
index=None,
sparse_index=None,
kind='block',
fill_value=None,
name=None,
dtype=None,
copy=False,
fastpath=False):
# we are called internally, so short-circuit
if fastpath:
# data is an ndarray, index is defined
if not isinstance(data, SingleBlockManager):
data = SingleBlockManager(data, index, fastpath=True)
if copy:
data = data.copy()
else:
if data is None:
data = []
if isinstance(data, Series) and name is None:
name = data.name
if isinstance(data, SparseArray):
if index is not None:
assert (len(index) == len(data))
sparse_index = data.sp_index
if fill_value is None:
fill_value = data.fill_value
data = np.asarray(data)
elif isinstance(data, SparseSeries):
if index is None:
index = data.index.view()
if fill_value is None:
fill_value = data.fill_value
# extract the SingleBlockManager
data = data._data
elif isinstance(data, (Series, dict)):
data = Series(data, index=index)
index = data.index.view()
res = make_sparse(data, kind=kind, fill_value=fill_value)
data, sparse_index, fill_value = res
elif isinstance(data, (tuple, list, np.ndarray)):
# array-like
if sparse_index is None:
res = make_sparse(data, kind=kind, fill_value=fill_value)
data, sparse_index, fill_value = res
else:
assert (len(data) == sparse_index.npoints)
elif isinstance(data, SingleBlockManager):
if dtype is not None:
data = data.astype(dtype)
if index is None:
index = data.index.view()
elif not data.index.equals(index) or copy: # pragma: no cover
# GH#19275 SingleBlockManager input should only be called
# internally
raise AssertionError('Cannot pass both SingleBlockManager '
'`data` argument and a different '
'`index` argument. `copy` must '
'be False.')
else:
length = len(index)
if data == fill_value or (isna(data) and isna(fill_value)):
if kind == 'block':
sparse_index = BlockIndex(length, [], [])
else:
sparse_index = IntIndex(length, [])
data = np.array([])
else:
if kind == 'block':
locs, lens = ([0], [length]) if length else ([], [])
sparse_index = BlockIndex(length, locs, lens)
else:
sparse_index = IntIndex(length, index)
v = data
data = np.empty(length)
data.fill(v)
if index is None:
index = com._default_index(sparse_index.length)
index = _ensure_index(index)
# create/copy the manager
if isinstance(data, SingleBlockManager):
if copy:
data = data.copy()
else:
# create a sparse array
if not isinstance(data, SparseArray):
data = SparseArray(data,
sparse_index=sparse_index,
fill_value=fill_value,
dtype=dtype,
copy=copy)
data = SingleBlockManager(data, index)
generic.NDFrame.__init__(self, data)
self.index = index
self.name = name
def test01():
print("Test 01")
userID: int = 1
history: AHistory = HistoryHierDF("databse1")
# userID, itemID, position, observation, clicked
# userID, itemID, position, observation, clicked
history.insertRecommendation(userID, 45, 1, False)
history.insertRecommendation(userID, 45, 2, False)
history.insertRecommendation(userID, 78, 3, False)
history.insertRecommendation(userID, 68, 4, False)
history.insertRecommendation(userID, 50, 5, False)
history.insertRecommendation(userID, 50, 6, False)
history.insertRecommendation(userID, 50, 7, False)
history.insertRecommendation(userID, 50, 8, False)
history.insertRecommendation(userID, 50, 9, False)
history.insertRecommendation(userID, 68, 4, False)
history.insertRecommendation(userID, 50, 5, False)
history.insertRecommendation(userID, 50, 6, False)
history.insertRecommendation(userID, 50, 7, False)
history.insertRecommendation(userID, 50, 8, False)
history.insertRecommendation(userID, 50, 9, False)
history.insertRecommendation(userID, 68, 4, False)
history.insertRecommendation(userID, 50, 5, False)
history.insertRecommendation(userID, 50, 6, False)
history.insertRecommendation(userID, 50, 7, False)
history.insertRecommendation(userID, 50, 8, False)
history.insertRecommendation(userID, 50, 9, False)
history.insertRecommendation(userID, 68, 4, False)
history.insertRecommendation(userID, 50, 5, False)
history.insertRecommendation(userID, 50, 6, False)
history.insertRecommendation(userID, 50, 7, False)
history.insertRecommendation(userID, 50, 8, False)
history.insertRecommendation(userID, 50, 9, False)
history.insertRecommendation(userID, 68, 4, False)
history.insertRecommendation(userID, 50, 5, False)
history.insertRecommendation(userID, 50, 6, False)
history.insertRecommendation(userID, 50, 7, False)
history.insertRecommendation(userID, 50, 8, False)
history.insertRecommendation(userID, 50, 9, False)
history.insertRecommendation(userID, 68, 4, False)
history.insertRecommendation(userID, 50, 5, False)
history.insertRecommendation(userID, 50, 6, False)
history.insertRecommendation(userID, 50, 7, False)
history.insertRecommendation(userID, 50, 8, False)
history.insertRecommendation(userID, 50, 9, False)
history.insertRecommendation(userID, 68, 4, False)
history.insertRecommendation(userID, 50, 5, False)
history.insertRecommendation(userID, 50, 6, False)
history.insertRecommendation(userID, 50, 7, False)
history.insertRecommendation(userID, 50, 8, False)
history.insertRecommendation(userID, 50, 9, False)
history.insertRecommendation(userID, 68, 4, False)
history.insertRecommendation(userID, 50, 5, False)
history.insertRecommendation(userID, 50, 6, False)
history.insertRecommendation(userID, 50, 7, False)
history.insertRecommendation(userID, 50, 8, False)
history.insertRecommendation(userID, 50, 9, False)
history.insertRecommendation(userID, 68, 4, False)
history.insertRecommendation(userID, 50, 5, False)
history.insertRecommendation(userID, 50, 6, False)
history.insertRecommendation(userID, 50, 7, False)
history.insertRecommendation(userID, 50, 8, False)
history.insertRecommendation(userID, 50, 9, False)
history.insertRecommendation(userID, 68, 4, False)
history.insertRecommendation(userID, 50, 5, False)
history.insertRecommendation(userID, 50, 6, False)
history.insertRecommendation(userID, 50, 7, False)
history.insertRecommendation(userID, 50, 8, False)
history.insertRecommendation(userID, 50, 9, False)
history.insertRecommendation(userID, 68, 4, False)
history.insertRecommendation(userID, 50, 5, False)
history.insertRecommendation(userID, 50, 6, False)
history.insertRecommendation(userID, 50, 7, False)
history.insertRecommendation(userID, 50, 8, False)
history.insertRecommendation(userID, 50, 9, False)
history.insertRecommendation(userID, 68, 4, False)
history.insertRecommendation(userID, 50, 5, False)
history.insertRecommendation(userID, 50, 6, False)
history.insertRecommendation(userID, 50, 7, False)
history.insertRecommendation(userID, 50, 8, False)
history.insertRecommendation(userID, 50, 9, False)
history.insertRecommendation(userID, 68, 4, False)
history.insertRecommendation(userID, 50, 5, False)
history.insertRecommendation(userID, 50, 6, False)
history.insertRecommendation(userID, 50, 7, False)
history.insertRecommendation(userID, 50, 8, False)
history.insertRecommendation(userID, 50, 9, False)
history.insertRecommendation(userID, 68, 4, False)
history.insertRecommendation(userID, 50, 5, False)
history.insertRecommendation(userID, 50, 6, False)
history.insertRecommendation(userID, 50, 7, False)
history.insertRecommendation(userID, 50, 8, False)
history.insertRecommendation(userID, 50, 9, False)
history.insertRecommendation(userID, 50, 10, False)
history.insertRecommendation(userID, 100, 1, False)
history.insertRecommendation(userID, 6, 2, True)
history.insertRecommendation(userID, 100, 5, True)
history.insertRecommendation(userID, 100, 15, True)
recommendationDict: dict = {
100: 0.35,
125: 0.25,
95: 0.15,
45: 0.1,
78: 0.05,
68: 0.05,
32: 0.02,
6: 0.01,
18: 0.01,
47: 0.01
}
recommendationSrs: Series = Series(recommendationDict)
penalty: APenalization = PenalUsingReduceRelevance(penaltyLinear,
[0.8, 0.2, 100],
penaltyLinear,
[1.0, 0.2, 100], 100)
pRecommendationSrs: Series = penalty.runOneMethodPenalization(
userID, recommendationSrs, history)
print(pRecommendationSrs)
def __unicode__(self):
# currently, unicode is same as repr...fixes infinite loop
series_rep = Series.__unicode__(self)
rep = '{series}\n{index!r}'.format(series=series_rep,
index=self.sp_index)
return rep
def get_dtype_counts(self):
from collections import defaultdict
d = defaultdict(int)
for k, v in self.iteritems():
d[v.dtype.name] += 1
return Series(d)
def _chop(self, sdata: Series, slice_obj: slice) -> Series:
return sdata._get_values(slice_obj)
def __unicode__(self):
# currently, unicode is same as repr...fixes infinite loop
series_rep = Series.__unicode__(self)
rep = '%s\n%s' % (series_rep, repr(self.sp_index))
return rep
def _to_datetime(arg,
errors='raise',
dayfirst=False,
yearfirst=False,
utc=None,
box=True,
format=None,
exact=True,
unit='ns',
freq=None,
infer_datetime_format=False):
"""
Same as to_datetime, but accept freq for
DatetimeIndex internal construction
"""
from pandas.core.series import Series
from pandas.tseries.index import DatetimeIndex
def _convert_listlike(arg, box, format, name=None):
if isinstance(arg, (list, tuple)):
arg = np.array(arg, dtype='O')
# these are shortcutable
if com.is_datetime64_ns_dtype(arg):
if box and not isinstance(arg, DatetimeIndex):
try:
return DatetimeIndex(arg,
tz='utc' if utc else None,
name=name)
except ValueError:
pass
return arg
elif com.is_datetime64tz_dtype(arg):
if not isinstance(arg, DatetimeIndex):
return DatetimeIndex(arg, tz='utc' if utc else None)
if utc:
arg = arg.tz_convert(None)
return arg
elif format is None and com.is_integer_dtype(arg) and unit == 'ns':
result = arg.astype('datetime64[ns]')
if box:
return DatetimeIndex(result,
tz='utc' if utc else None,
name=name)
return result
arg = com._ensure_object(arg)
require_iso8601 = False
if infer_datetime_format and format is None:
format = _guess_datetime_format_for_array(arg, dayfirst=dayfirst)
if format is not None:
# There is a special fast-path for iso8601 formatted
# datetime strings, so in those cases don't use the inferred
# format because this path makes process slower in this
# special case
format_is_iso8601 = (('%Y-%m-%dT%H:%M:%S.%f'.startswith(format)
or '%Y-%m-%d %H:%M:%S.%f'.startswith(format))
and format != '%Y')
if format_is_iso8601:
require_iso8601 = not infer_datetime_format
format = None
try:
result = None
if format is not None:
# shortcut formatting here
if format == '%Y%m%d':
try:
result = _attempt_YYYYMMDD(arg, errors=errors)
except:
raise ValueError(
"cannot convert the input to '%Y%m%d' date format")
# fallback
if result is None:
try:
result = tslib.array_strptime(arg,
format,
exact=exact,
errors=errors)
except (tslib.OutOfBoundsDatetime):
if errors == 'raise':
raise
result = arg
except ValueError:
# if format was inferred, try falling back
# to array_to_datetime - terminate here
# for specified formats
if not infer_datetime_format:
if errors == 'raise':
raise
result = arg
if result is None and (format is None or infer_datetime_format):
result = tslib.array_to_datetime(
arg,
errors=errors,
utc=utc,
dayfirst=dayfirst,
yearfirst=yearfirst,
freq=freq,
unit=unit,
require_iso8601=require_iso8601)
if com.is_datetime64_dtype(result) and box:
result = DatetimeIndex(result,
tz='utc' if utc else None,
name=name)
return result
except ValueError as e:
try:
values, tz = tslib.datetime_to_datetime64(arg)
return DatetimeIndex._simple_new(values, name=name, tz=tz)
except (ValueError, TypeError):
raise e
if arg is None:
return arg
elif isinstance(arg, tslib.Timestamp):
return arg
elif isinstance(arg, Series):
values = _convert_listlike(arg._values, False, format)
return Series(values, index=arg.index, name=arg.name)
elif isinstance(arg, ABCIndexClass):
return _convert_listlike(arg, box, format, name=arg.name)
elif com.is_list_like(arg):
return _convert_listlike(arg, box, format)
return _convert_listlike(np.array([arg]), box, format)[0]
except ValueError, e:
try:
values, tz = tslib.datetime_to_datetime64(arg)
return DatetimeIndex._simple_new(values, None, tz=tz)
except (ValueError, TypeError):
raise e
if arg is None:
return arg
elif isinstance(arg, datetime):
return arg
elif isinstance(arg, Series):
values = arg.values
if not com.is_datetime64_dtype(values):
values = _convert_f(values)
return Series(values, index=arg.index, name=arg.name)
elif isinstance(arg, (np.ndarray, list)):
if isinstance(arg, list):
arg = np.array(arg, dtype='O')
if com.is_datetime64_dtype(arg):
if box and not isinstance(arg, DatetimeIndex):
try:
return DatetimeIndex(arg, tz='utc' if utc else None)
except ValueError, e:
try:
values, tz = tslib.datetime_to_datetime64(arg)
return DatetimeIndex._simple_new(values, None, tz=tz)
except (ValueError, TypeError):
raise e
return arg
def __repr__(self):
series_rep = Series.__repr__(self)
rep = '{series}\n{index!r}'.format(series=series_rep,
index=self.sp_index)
return rep