def _init_dict(self, data, axes, dtype=None):
haxis = axes.pop(self._info_axis_number)
# prefilter if haxis passed
if haxis is not None:
haxis = _ensure_index(haxis)
data = OrderedDict(
(k, v) for k, v in compat.iteritems(data) if k in haxis)
else:
ks = list(data.keys())
if not isinstance(data, OrderedDict):
ks = _try_sort(ks)
haxis = Index(ks)
for k, v in compat.iteritems(data):
if isinstance(v, dict):
data[k] = self._constructor_sliced(v)
# extract axis for remaining axes & create the slicemap
raxes = [
self._extract_axis(self, data, axis=i) if a is None else a
for i, a in enumerate(axes)
]
raxes_sm = self._extract_axes_for_slice(self, raxes)
# shallow copy
arrays = []
haxis_shape = [len(a) for a in raxes]
for h in haxis:
v = values = data.get(h)
if v is None:
values = np.empty(haxis_shape, dtype=dtype)
values.fill(np.nan)
elif isinstance(v, self._constructor_sliced):
d = raxes_sm.copy()
d['copy'] = False
v = v.reindex(**d)
if dtype is not None:
v = v.astype(dtype)
values = v.values
arrays.append(values)
return self._init_arrays(arrays, haxis, [haxis] + raxes)
def _init_dict(self, data, axes, dtype=None):
haxis = axes.pop(self._info_axis_number)
# prefilter if haxis passed
if haxis is not None:
haxis = _ensure_index(haxis)
data = OrderedDict((k, v) for k, v
in compat.iteritems(data) if k in haxis)
else:
ks = list(data.keys())
if not isinstance(data, OrderedDict):
ks = _try_sort(ks)
haxis = Index(ks)
for k, v in compat.iteritems(data):
if isinstance(v, dict):
data[k] = self._constructor_sliced(v)
# extract axis for remaining axes & create the slicemap
raxes = [self._extract_axis(self, data, axis=i)
if a is None else a for i, a in enumerate(axes)]
raxes_sm = self._extract_axes_for_slice(self, raxes)
# shallow copy
arrays = []
haxis_shape = [len(a) for a in raxes]
for h in haxis:
v = values = data.get(h)
if v is None:
values = np.empty(haxis_shape, dtype=dtype)
values.fill(np.nan)
elif isinstance(v, self._constructor_sliced):
d = raxes_sm.copy()
d['copy'] = False
v = v.reindex(**d)
if dtype is not None:
v = v.astype(dtype)
values = v.values
arrays.append(values)
return self._init_arrays(arrays, haxis, [haxis] + raxes)
# Group by the hours, so we have one average value for each hour of the day
df['hour'] = df['FZ_AB'].str[0:2].astype(int)
df_grouped = df.groupby(['ID_Abschnitt', 'hour']).mean()
# Loop over the data and create a entry with the necessary info for each section
data_json = OrderedDict()
for row in df_grouped.itertuples():
From = stops_joined.loc[stops_joined.Haltestellen_Id ==
row.Haltestellen_Id].index[0]
To = stops_joined.loc[stops_joined.Haltestellen_Id ==
row.Nach_Hst_Id].index[0]
id_fromTo = str(min(From, To)) + '_' + str(max(From, To))
# If it does not exist yet (first time this section comes up), create a new entry
if (id_fromTo not in data_json.keys()):
data_json[id_fromTo] = OrderedDict()
data_json[id_fromTo]['Fr'] = From
data_json[id_fromTo]['To'] = To
data_json[id_fromTo]['x_from'] = stops_joined.loc[
stops_joined.Haltestellen_Id == row.Haltestellen_Id].x.iloc[0]
data_json[id_fromTo]['y_from'] = stops_joined.loc[
stops_joined.Haltestellen_Id == row.Haltestellen_Id].y.iloc[0]
data_json[id_fromTo]['x_to'] = stops_joined.loc[
stops_joined.Haltestellen_Id == row.Nach_Hst_Id].x.iloc[0]
data_json[id_fromTo]['y_to'] = stops_joined.loc[
stops_joined.Haltestellen_Id == row.Nach_Hst_Id].y.iloc[0]
# Else just update the values
if ('h_' + str(row.Index[1]) not in data_json[id_fromTo].keys()):
data_json[id_fromTo]['h_' + str(row.Index[1])] = row.Besetzung
'''
data = pd.read_csv("../../data/traffic/verkehr_filtered.csv", sep=",", parse_dates=['MessungDatZeit'], date_parser=dateparse)
data['hour'] = data['MessungDatZeit'].dt.hour
#data['day'] = data['MessungDatZeit'].day
df_grouped = data.groupby(['ZSID','hour', 'Richtung']).mean()
print df_grouped.head(30)
df_grouped_all = df_grouped.groupby(['ZSID', 'Richtung']).sum()
print df_grouped_all.head(30)
data_json = OrderedDict()
for row in df_grouped.itertuples():
key = row.Index[0] + '_' + row.Index[2]
if (key not in data_json.keys()):
data_json[key] = OrderedDict()
#data_json[row.Index[0]]['ZSName'] = row.ZSName
data_json[key]['Direction'] = row.Index[2]
data_json[key]['EKoord'] = row.EKoord
data_json[key]['NKoord'] = row.NKoord
data_json[key]['all'] = df_grouped_all.loc[row.Index[0], row.Index[2]].AnzFahrzeuge
data_json[key]['h_' + str(row.Index[1])] = row.AnzFahrzeuge
final = pd.DataFrame.from_dict(data_json, orient='index')
final.to_csv('../../data/traffic/traffic_direction.csv', sep=',')
