/
datahandler.py
568 lines (503 loc) · 23.1 KB
/
datahandler.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
import pandas as pd
import requests
import json
import mpld3
import matplotlib.pyplot as plt, mpld3
import numpy as np
from datetime import date
import csv
import calendar
import os
# General Methods for organizing Files
def genDate():
today = date.today()
return today.strftime("%Y%m%d")
def modifyFilename(filename):
filename = './data/' + genDate() + '_' + filename
return filename
def writeData(r, filename='data.csv'):
filename = modifyFilename(filename)
with open(filename, 'w') as outfile:
outfile.write(r.text)
print('Daten in ' + filename + ' geschrieben')
def writeCSV(r, filename='data.csv'):
filename = modifyFilename(filename)
r.to_csv(filename, index=False, encoding='utf-8')
print('CSV ' + filename + ' Geschrieben.')
def importData(filename=modifyFilename('yearlyData.csv')):
data = pd.read_csv(filename, sep=',', header=0, index_col=0)
return (data)
def importMonthlyData(filename1='data/monthly_cont.csv', filename2=modifyFilename('monthlyData.csv')):
monthlyDataNew = pd.read_csv(filename2, sep=',', header=0, index_col=False)
monthlyDataOld = pd.read_csv(filename1, sep=',', header=0, index_col=0)
return (monthlyDataNew, monthlyDataOld)
# Methods for requesting Data
def requestdataBFS(url, payload):
r = requests.post(url, data=json.dumps(payload))
return r
def requestdataAS():
url = 'https://www.auto.swiss/fileadmin/3_Statistiken/Autoverkaeufe_nach_Modellen/ModellePW2019.xlsx'
filename = 'ModellePW2019.xlsx'
r = requests.get(url, allow_redirects=True)
open(filename, 'wb').write(r.content)
# Methods for importing and structuring data
def importDataAS(filename='ModellePW2019.xlsx'):
df = pd.read_excel(filename, sheet_name=None, header=15, index_col=None, usecols=[0, 1, 2])
j = 1
for i in df:
writeCSV(df[i], 'zulassungenAS_' + str(j) + '.csv')
j = j + 1
return (df)
def getMonthlyData(filename='monthlyData.csv'):
url = 'https://www.pxweb.bfs.admin.ch/api/v1/de/px-x-1103020200_101/px-x-1103020200_101.px'
payload = {
"query": [
{
"code": "Monat",
"selection": {
"filter": "item",
"values": [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12"
]
}
},
{
"code": "Fahrzeuggruppe / -art",
"selection": {
"filter": "item",
"values": [
"100"
]
}
},
{
"code": "Treibstoff",
"selection": {
"filter": "item",
"values": [
"100",
"200",
"300",
"400",
"500",
"600",
"9900",
"9999"
]
}
}
],
"response": {
"format": "csv"
}
}
r = requestdataBFS(url, payload)
writeData(r, filename)
return r
def getYearlyData(filename='yearlyData.csv'):
payload = {
"query": [
{
"code": "Treibstoff",
"selection": {
"filter": "item",
"values": [
"100",
"200",
"300",
"400",
"500",
"600",
"9900"
]
}
}
],
"response": {
"format": "csv"
}
}
url = 'https://www.pxweb.bfs.admin.ch/api/v1/de/px-x-1103020200_200/px-x-1103020200_200.px'
r = requestdataBFS(url, payload)
writeData(r, filename)
def processMOFIShistory():
getMOFISData(filename='mofis2018.xlsx',
url='https://files.admin.ch/astra_ffr/mofis/Datenlieferungs-Kunden/opendata/1000-Fahrzeuge_IVZ/1200-Neuzulassungen/1220-Neuzlassungsbericht_woechentlich/1222-Vorjahresdaten/NEUZU_W-2018.xlsx')
getMOFISData(filename='mofis2019.xlsx',
url='https://files.admin.ch/astra_ffr/mofis/Datenlieferungs-Kunden/opendata/1000-Fahrzeuge_IVZ/1200-Neuzulassungen/1220-Neuzlassungsbericht_woechentlich/1222-Vorjahresdaten/NEUZU_W-2019.xlsx')
importMOFISdata(filename=('data/20210108_mofis2018.xlsb'), destfilename='mofis2018.csv')
importMOFISdata(filename=modifyFilename('mofis2019.xlsb'), destfilename='mofis2019.csv')
modifyMOFISData(filename=modifyFilename('mofis2018.csv'), destfilename='mofismonthly_2018.csv')
modifyMOFISData(filename=modifyFilename('mofis2019.csv'), destfilename='mofismonthly_2019.csv')
def getMOFISData(filename='mofisData.xlsb',
url='https://files.admin.ch/astra_ffr/mofis/Datenlieferungs-Kunden/opendata/1000-Fahrzeuge_IVZ/1200-Neuzulassungen/1220-Neuzlassungsbericht_woechentlich/NEUZU_W.xlsb'):
if os.path.isfile(modifyFilename(filename)):
print('MOFIS Datei schon vorhanden')
else:
print('MOFIS Datei wird heruntergeladen')
r = requests.get(url, allow_redirects=True)
open(modifyFilename(filename), 'wb').write(r.content)
print('Neuste MOFIS Datei heruntergeladen')
def importMOFISdata(filename=modifyFilename('mofisData.xlsb'), destfilename='mofis_raw_clean.csv'):
if filename[-4:] == 'xlsb':
data = pd.read_excel(filename, engine='pyxlsb', sheet_name='Rohdaten')
if filename[-3:] == 'xls':
data = pd.read_excel(filename, sheet_name='Rohdaten')
data = data.drop(data.columns[0], axis=1)
data.iloc[4] = data.iloc[4].str.replace('\n', '').str.replace('-', '')
data.columns = data.iloc[4]
data = data.iloc[5:]
data.to_csv(modifyFilename(destfilename))
print('rohdaten excel file importiert, csv abgespeichert')
explainations = pd.read_excel(filename, engine='pyxlsb', sheet_name='Erläuterungen')
if filename == modifyFilename('mofisData.xlsb'):
global mofis_latestupdate
mofis_latestupdate = explainations.iat[13, 4]
print('MOFIS Stand: ' + mofis_latestupdate)
with open('latestupdateMOFIS.txt', 'w') as text_file:
text_file.write(mofis_latestupdate)
def modifyMOFISData(filename=modifyFilename('mofis_raw_clean.csv'), destfilename='mofis_monthly_thisyear.csv'):
data = pd.read_csv(filename)
print('Mofis Daten für auswertung geladen')
data = data[data['Fahrzeugart'] == '01 Personenwagen']
count = data.groupby(['Erstinverkehrsetzung_Monat', 'Treibstoff', 'Erstinverkehrsetzung_Jahr']).sum()
countPivot = count['AnzahlFahrzeuge'].unstack(level=['Treibstoff'])
countPivot.reset_index(inplace=True)
countPivot['Erstinverkehrsetzung_Jahr'] = countPivot.Erstinverkehrsetzung_Jahr.astype(int).astype(str)
countPivot['Erstinverkehrsetzung_Monat'] = countPivot.Erstinverkehrsetzung_Monat.astype(int).astype(str)
countPivot['Erstinverkehrsetzung_Monat'] = countPivot['Erstinverkehrsetzung_Monat'].apply(lambda x: x.zfill(2))
countPivot['date'] = countPivot[['Erstinverkehrsetzung_Jahr', 'Erstinverkehrsetzung_Monat']].agg('-'.join, axis=1)
countPivot = countPivot.drop(columns=['Erstinverkehrsetzung_Jahr', 'Erstinverkehrsetzung_Monat'])
# countPivot = countPivot.set_index('date')
writeCSV(countPivot, destfilename)
print('Personenwagen nach Monaten gezählt - MOFIS Tabelle gespeichert')
def aggregate2020Data():
mofisBASEa = pd.read_csv('data/20210108_mofismonthly_2018.csv', index_col=False)
mofisBASEb = pd.read_csv('data/20210108_mofismonthly_2019.csv', index_col=False)
monthlyDataToAdd = pd.read_csv(modifyFilename('mofis_monthly_thisyear.csv'), index_col=False)
aggregated = mofisBASEa.append(mofisBASEb, ignore_index=True)
aggregated = aggregated.append(monthlyDataToAdd, ignore_index=True)
aggregated.fillna(0, inplace=True)
aggregated.encode('utf-8')
aggregated.to_csv(modifyFilename('mofisMonthlyComplete.csv'), index=False)
print('Tabelle 2018-2020 geschrieben')
def aggregateNewData():
mofisBase = pd.read_csv('data/mofis_BASE2020.csv', index_col=False)
monthlyDataToAdd = pd.read_csv(modifyFilename('mofis_monthly_thisyear.csv'), index_col=False)
aggregated = mofisBase.append(monthlyDataToAdd, ignore_index=True)
aggregated.fillna(0, inplace=True)
aggregated.to_csv(modifyFilename('mofisMonthlyComplete.csv'), encoding='utf-8', index=False)
return aggregated
def modifyMonthlyData2021(data):
# Additional Outputs
# - Nur Elektrisch
monthlyElectric = data.filter(['date', 'Elektrisch'])
writeCSV(monthlyElectric, 'monthlyElectric.csv')
# - Nur Nicht-elektrisch
monthlyNonElectric = data.drop(
['Elektrisch'], axis=1)
monthlyNonElectric['Nicht-Elektrisch'] = monthlyNonElectric.sum(axis=1)
monthlyNonElectric = monthlyNonElectric.filter(['date', 'Nicht-Elektrisch'])
writeCSV(monthlyNonElectric, 'monthlyNonElectric.csv')
# - Elektrisch und Nicht-Elektrisch
monthlyElNonEl = pd.concat([monthlyElectric, monthlyNonElectric['Nicht-Elektrisch']], axis=1)
print(monthlyElNonEl)
writeCSV(monthlyElNonEl, 'monthlyElNonEl.csv')
print('Monatsdaten Abgeschlossen')
def modifyYearlyData2021(dataNew):
yearly= pd.DataFrame()
years = [2018,2019,2020,2021]
for i in years:
yearly[i] = dataNew[dataNew['date'].str.startswith(str(i))].sum()
yearly.drop(['date'], inplace=True)
yearly.to_csv(modifyFilename('mofisYearlyComplete.csv'), encoding='utf-8')
yearlyBase = pd.read_csv('data/yearlyBASE.csv', index_col=0)
yearlyElectric = yearlyBase.loc['Elektrisch']
yearlyElectric.loc[2021] = yearly.loc['Elektrisch'][2021]
yearlyElectric.drop(labels=['2005','2006','2007','2008', '2009', '2010'], inplace=True)
yearlyElectric.index.name='date'
yearlyElectric.to_csv(modifyFilename('yearlyElectric.csv'), encoding='utf-8')
yearlyNonElBase = yearlyAddNonElectric(yearlyBase)
yearlyEl = yearly.loc['Elektrisch']
yearlyNonEl = yearlyAddNonElectric(yearly)
yearlyNonElBase= yearlyNonElBase.update(yearlyNonEl)
yearlyElNonEl = pd.concat([yearlyEl, yearlyNonEl], axis=1)
yearlyElNonEl.to_csv(modifyFilename('mofisYearlyElNonEl.csv'), encoding='utf-8')
return yearly
def modifyMonthlyData2020(monthlyNEW, monthlyOLD):
monthlyNEW2020 = monthlyNEW.drop(columns=['2019'])
monthlyNEW2020['Jahr'] = '2020'
unique = pd.DataFrame(monthlyNEW2020.Monat.unique(), columns=['Monat'])
unique.index += 1
unique['monthnumber'] = list(unique.index)
unique.monthnumber = unique.monthnumber.astype(str)
unique['monthnumber'] = unique['monthnumber'].apply(lambda x: x.zfill(2))
monthlyNEW2020 = monthlyNEW2020.merge(unique, on='Monat', how='left')
monthlyNEW2020['datestring'] = 0
monthlyNEW2020['datestring'] = monthlyNEW2020['datestring'].apply(
lambda x: monthlyNEW2020['Monat'] + ' ' + monthlyNEW2020['Jahr'])
# monthlyNEW2020['Jahr'] = monthlyNEW2020['Jahr'].apply(lambda x: str(x) + '-' + str(x))
monthlyNEW2020['Jahr'] = monthlyNEW2020[['Jahr', 'monthnumber']].agg('-'.join, axis=1)
monthlyNEW2020 = monthlyNEW2020.drop(['Fahrzeuggruppe / -art'], axis=1).rename(columns={'2020': 'n'})
cols = ['Jahr', 'Monat', 'Treibstoff', 'n', 'monthnumber', 'datestring']
monthlyNEW2020 = monthlyNEW2020[cols]
monthlyComplete = pd.concat([monthlyOLD, monthlyNEW2020], axis=0, sort=False)
writeCSV(monthlyComplete, 'CompleteData.csv')
# Pivot der Daten
monthlyPivot = monthlyComplete.pivot(index='Jahr', columns='Treibstoff', values='n')
writeCSV(monthlyPivot, 'completeDataPivot.csv')
# Additional Outputs
# - Nur Elektrisch
monthlyElectric = monthlyPivot.drop(
['Andere', 'Benzin', 'Benzin-elektrisch', 'Diesel', 'Diesel-elektrisch', 'Gas (mono- und bivalent)',
'Ohne Motor'], axis=1)
writeCSV(monthlyElectric, 'monthlyElectric.csv')
# - Nur Nicht-elektrisch
monthlyNonElectric = monthlyPivot.drop(
['Elektrisch'], axis=1)
monthlyNonElectric['Nicht-Elektrisch'] = monthlyNonElectric.sum(axis=1)
monthlyNonElectric = monthlyNonElectric.drop(
['Andere', 'Benzin', 'Benzin-elektrisch', 'Diesel', 'Diesel-elektrisch', 'Gas (mono- und bivalent)',
'Ohne Motor'], axis=1
)
writeCSV(monthlyNonElectric, 'monthlyNonElectric.csv')
# - Elektrisch und Nicht-Elektrisch
monthlyElNonEl = pd.concat([monthlyElectric, monthlyNonElectric], axis=1)
print(monthlyElNonEl)
writeCSV(monthlyElNonEl, 'monthlyElNonEl.csv')
return monthlyPivot
print('Monatsdaten Abgeschlossen')
def modifyMonthlyData(data):
data.set_index(['Treibstoff'])
data = data.drop(columns=['Fahrzeuggruppe / -art'])
data = data.reset_index()
data2018 = data.drop(columns=['2019'])
data2019 = data.drop(columns=['2018'])
data2019 = data2019.pivot(index='Treibstoff', columns='Monat', values='2019')
data2018 = data2018.pivot(index='Treibstoff', columns='Monat', values='2018')
# Sortieren der Spalten - keine Benennung
data2018 = data2018[
['Januar', 'Februar', 'März', 'April', 'Mai', 'Juni', 'Juli', 'August', 'September', 'Oktober', 'November',
'Dezember']]
data2019 = data2019[
['Januar', 'Februar', 'März', 'April', 'Mai', 'Juni', 'Juli', 'August', 'September', 'Oktober', 'November',
'Dezember']]
# data2019 = data2019.columns = ['Januar 2019', 'Februar 2019', 'März 2019', 'April 2019', 'Mai 2019', 'Juni 2019', 'Juli 2019', 'August 2019', 'September 2019', 'Oktober 2019', 'November 2019', 'Dezember 2019']
# data2018 = data2018.columns = ['Januar 2018', 'Februar 2018', 'März 2018', 'April 2018', 'Mai 2018', 'Juni 2018', 'Juli 2018', 'August 2018', 'September 2018', 'Oktober 2018', 'November 2018', 'Dezember 2018']
sum2019 = data2019.sum(axis=1)
sum2018 = data2018.sum(axis=1)
writeCSV(sum2018, 'monthlySum2018.csv')
writeCSV(sum2019, 'monthlySum2019.csv')
writeCSV(data2019, 'monthly2019.csv')
writeCSV(data2018, 'monthly2018.csv')
dataNE2019 = yearlyAddNonElectric(data2019)
electric2019 = sum2019.loc['Elektrisch']
ret = {
'data_2019': data2019,
'data_NE_2019': dataNE2019,
'sum_2019': sum2019,
'sum_2018': sum2018,
'electric2019': electric2019
}
return ret
def completeYearly(monthlydata):
sumNew = monthlydata.loc['2020-01': '2020-12']
sumNew = sumNew.sum(axis=0)
yearly['2020'] = sum2020
writeCSV(yearly, 'YearlyData_app.csv')
yearlyelectric = yearly.loc['Elektrisch']
yearlyelectricexport = yearlyelectric.iloc[4:]
writeCSV(yearlyelectricexport, 'yearlyElectric.csv')
yearlynonev = yearlyAddNonElectric(yearly)
yearlycomplete = pd.DataFrame(dict(Elektrisch=yearlyelectric, Andere=yearlynonev))
yearlycompleteexport = yearlycomplete.iloc[4:]
# yearlycomplete = yearlycomplete.rename(columns={'index':'Jahr'})
writeCSV(yearlycompleteexport, 'yearlyComp.csv')
return (yearly)
def yearlyAddNonElectric(data):
dataTemp = data
dataTemp = dataTemp.drop(['Elektrisch'])
dataTemp = dataTemp.sum()
return (dataTemp)
def getTeslaNumbers(data, totalData):
j = 1
teslaNumbers = pd.DataFrame(columns=['Monat', 'MonatID', 'Marke', 'Modell', 'Anzahl', 'Differenz'])
for i in data:
appendd = data[i][data[i]['Marke'] == 'Tesla']
appendd.insert(0, 'Monat', i)
appendd.insert(1, 'MonatID', j)
appendd.insert(5, 'Differenz', 0)
if (j > 1):
for p in appendd['Modell']:
dx = (appendd[appendd['Modell'] == str(p)]['Anzahl'])
if (teslaNumbers[teslaNumbers['MonatID'] == (j - 1)][teslaNumbers['Modell'] == str(p)]['Anzahl'].empty):
dp = pd.Series(data=[0])
else:
dp = (
teslaNumbers[teslaNumbers['MonatID'] == (j - 1)][teslaNumbers['Modell'] == str(p)]['Anzahl'])
diff = dx.iloc[0] - dp.iloc[0]
appendd.loc[appendd['Modell'] == str(p), ['Differenz']] = int(diff)
teslaNumbers = teslaNumbers.append(appendd, ignore_index=True)
j = j + 1
teslaNumbers = teslaNumbers.append(
{'Monat': 'Jan', 'MonatID': 1, 'Marke': 'Tesla', 'Modell': 'Model 3', 'Anzahl': 0, 'Differenz': 0},
ignore_index=True)
teslaNumbers = teslaNumbers.sort_values('MonatID', axis=0, ascending=True)
teslaData = teslaNumbers.reset_index()
# totalData = totalData.transpose().reset_index()
# totalData.insert(0, 'MonatID', range(1, len(totalData) + 1))
# totalData = totalData.set_index('MonatID')
teslaData = teslaData.pivot(index='MonatID', columns='Modell', values=['Anzahl', 'Differenz'])
# totalData = totalData.join(teslaData)
# return totalData
teslaDataDiff = teslaData.drop(columns='Anzahl')
writeCSV(teslaDataDiff, 'TeslaData.csv')
return teslaData
def drawTeslaStats(data):
drawTeslaData(data, query=('Anzahl', 'Model 3'), filename='Model_3', xlab='Monate',
title='Insgesamt zugelassene Tesla Model 3')
drawTeslaData(data, query=('Differenz', 'Model 3'), filename='Model_3_Diff', xlab='Monate',
title='Neu zugelassene Tesla Model 3')
drawTeslaData(data, query=('Differenz', 'Model S'), filename='Model_S_Diff', xlab='Monate',
title='Neu zugelassene Tesla Model S')
print('Teslaplots erfolgreich')
# Methods for drawing plots
def drawTeslaData(data, query='Anzahl', filename='figsingle', xlab='Jahr', recolor=False, title=''):
objects = data[query]
y_pos = np.arange(len(objects))
fig = plt.figure(figsize=[16, 9], dpi=250)
barlist = plt.bar(y_pos, height=objects)
if recolor:
barlist[(len(barlist) - 1)].set_color('r')
plt.xticks(y_pos, data['Monat'])
plt.title(title)
plt.ylabel('Zulassungen')
plt.xlabel(xlab)
plt.savefig('outputs/png/' + genDate() + '_' + filename + '_image.png')
plt.close(fig)
fig2 = plt.figure()
barlist = plt.bar(y_pos, height=objects)
if recolor:
barlist[(len(barlist) - 1)].set_color('r')
plt.xticks(y_pos, data)
plt.ylabel('Zulassungen')
plt.xlabel(xlab)
mpld3.save_html(fig2, 'outputs/mpld3/' + genDate() + '_' + filename + '_code.html')
def drawSinglePlot(data, filename='figsingle', xlab='Jahr', recolor=False):
objects = data.loc['Elektrisch',]
y_pos = np.arange(len(objects))
fig = plt.figure(figsize=[16, 9], dpi=250)
barlist = plt.bar(y_pos, height=objects)
if recolor:
barlist[(len(barlist) - 1)].set_color('r')
plt.xticks(y_pos, data)
plt.ylabel('Zulassungen')
plt.xlabel(xlab)
plt.savefig('outputs/png/' + genDate() + '_' + filename + '_image.png')
plt.close(fig)
fig2 = plt.figure()
barlist = plt.bar(y_pos, height=objects)
if recolor:
barlist[(len(barlist) - 1)].set_color('r')
plt.xticks(y_pos, data)
plt.ylabel('Zulassungen')
plt.xlabel(xlab)
mpld3.save_html(fig2, 'outputs/mpld3/' + genDate() + '_' + filename + '_code.html')
def drawSingleLinePlot(data, filename='lineplot'):
objects = data.loc['Elektrisch',]
y_pos = np.arange(len(objects))
fig = plt.figure(figsize=[13, 6], dpi=250)
plt.plot(objects, 'o-', linewidth=4, markersize=12)
# plt.plot(objects, 'o')
plt.axis(ymin=0)
# plt.xticks(y_pos, data)
plt.ylabel('Zulassungen')
plt.savefig('outputs/png/' + genDate() + '_' + filename + '_image.png')
# mpld3.show()
mpld3.save_html(fig, 'outputs/mpld3/' + genDate() + '_' + filename + '_code.html')
def drawRelativePlot(data, dataSumNE, xlab='Jahr', filename='figboth', recolor=False):
objects = data
y_pos = np.arange(len(objects))
fig = plt.figure(figsize=[16, 9], dpi=72)
p1 = plt.bar(y_pos, height=objects)
p2 = plt.bar(y_pos, height=dataSumNE, bottom=objects)
if recolor:
p1[(len(p1) - 1)].set_color('C3')
p2[(len(p2) - 1)].set_color('#FFC080')
plt.xticks(ticks=y_pos, labels=dataSumNE.index)
plt.ylabel('Anteile Gesamte Inverkehrssetzungen')
plt.xlabel(xlab)
plt.legend(['Elektroautos', 'Andere Antriebe'])
plt.savefig('outputs/png/' + genDate() + '_' + filename + '_legend.png')
plt.close(fig)
fig2 = plt.figure()
p1 = plt.bar(y_pos, height=objects)
p2 = plt.bar(y_pos, height=dataSumNE, bottom=objects)
if recolor:
p1[(len(p1) - 1)].set_color('C3')
p2[(len(p2) - 1)].set_color('#FFC080')
plt.xticks(ticks=y_pos, labels=dataSumNE.index)
plt.ylabel('Anteile Gesamte Inverkehrssetzungen')
plt.xlabel(xlab)
plt.legend(['Elektroautos', 'Andere Antriebe'])
mpld3.save_html(fig2, 'outputs/mpld3/' + genDate() + '_' + filename + '_code.html')
# lplotly_fig = tls.mpl_to_plotly(fig2)
# plo.plot(plotly_fig, filename='outputs/plotly/' + genDate() + '_' + filename + '_code.html')
def drawMultiplePlot(data, dataSumNE, xlab='Jahr', filename='figboth', recolor=False):
objects = data.loc['Elektrisch',]
y_pos = np.arange(len(objects))
fig = plt.figure(figsize=[16, 9], dpi=250)
p1 = plt.bar(y_pos, height=objects)
p2 = plt.bar(y_pos, height=dataSumNE, bottom=objects)
if recolor:
p1[(len(p1) - 1)].set_color('C3')
p2[(len(p2) - 1)].set_color('#FFC080')
plt.xticks(y_pos, data)
plt.ylabel('Zulassungen')
plt.xlabel(xlab)
plt.legend(['Elektroautos', 'Andere Antriebe'])
plt.savefig('outputs/png/' + genDate() + '_' + filename + '_legend.png')
plt.close(fig)
fig2 = plt.figure()
p1 = plt.bar(y_pos, height=objects)
p2 = plt.bar(y_pos, height=dataSumNE, bottom=objects)
if recolor:
p1[(len(p1) - 1)].set_color('C3')
p2[(len(p2) - 1)].set_color('#FFC080')
plt.xticks(y_pos, data)
plt.ylabel('Zulassungen')
plt.xlabel(xlab)
plt.legend(['Elektroautos', 'Andere Antriebe'])
mpld3.save_html(fig, 'outputs/mpld3/' + genDate() + '_' + filename + '_code.html')
def drawTeslaComp(months, teslaData, totalData, xlab='Monat', filename='figboth', recolor=False):
print(teslaData)
objects = pd.concat([months, totalData, teslaData], axis=1)
objects.columns = ['Monat', 'Total Elektrisch', 'Model 3']
objects['NonModel3'] = objects['Total Elektrisch'] - objects['Model 3']
objects['relative'] = (objects['Model 3'] / objects['Total Elektrisch']) * 100
# drawing the figure itself
y_pos = np.arange(len(objects))
fig = plt.figure(figsize=[16, 9], dpi=250)
p1 = plt.bar(y_pos, height=objects['Model 3'])
p2 = plt.bar(y_pos, height=objects['NonModel3'], bottom=objects['Model 3'])
if recolor:
p1[(len(p1) - 1)].set_color('C3')
p2[(len(p2) - 1)].set_color('#FFC080')
plt.xticks(y_pos, objects['Monat'])
plt.ylabel('Zulassungen')
plt.xlabel(xlab)
plt.legend(['Tesla Model 3', 'Übrige Elektroautos'])
plt.savefig('outputs/png/' + genDate() + '_' + filename + '_legend.png')
plt.close(fig)
return objects