-
Notifications
You must be signed in to change notification settings - Fork 816
/
test_request.py
654 lines (588 loc) · 23.1 KB
/
test_request.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
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
from dataclasses import dataclass
from unittest.mock import ANY
import re
import pandas as pd
import numpy as np
import pytest
import responses
from pandas.testing import assert_frame_equal
from pytrends.request import TrendReq, BASE_TRENDS_URL
@dataclass
class ExpectedResult:
"""
Small class to reduce boilerplate code in the tests that compares a `pd.DataFrame`.
Instead of compare against a whole `pd.DataFrame` compares it with a given length, head
and tail.
The length of head and tail is 3 by default but you can change it.
"""
length: int
df_head: pd.DataFrame
df_tail: pd.DataFrame
head_tail_length: int = 3
def assert_equals(self, df_result: pd.DataFrame):
assert len(df_result) == self.length
assert_frame_equal(df_result.head(self.head_tail_length), self.df_head)
assert_frame_equal(df_result.tail(self.head_tail_length), self.df_tail)
def build_interest_over_time_df(records, dates):
"""
Creates a `pd.DataFrame` with the same shape as a pytrends `interest_over_time` result.
`records` is a dict of keyword -> results.
If `records` doesn't include the key 'isPartial' then `[False]` will be used.
Example:
In [6]: build_interest_over_time_df(
...: {'kw1': [1, 2, 3], 'kw2': [10, 20, 30]},
...: dates=['2021-01-01', '2021-01-02', '2021-01-03']
...: )
Out[6]:
kw1 kw2 isPartial
date
2021-01-01 1 10 False
2021-01-02 2 20 False
2021-01-03 3 30 False
"""
records = records.copy()
if 'isPartial' not in records:
records['isPartial'] = [False]
return pd.DataFrame(
records,
index=pd.Index(
data=pd.to_datetime(dates),
name='date'
)
)
@pytest.mark.vcr
def test_initial_data():
"""Should use same values as in the documentation."""
pytrend = TrendReq()
result = {
"hl": pytrend.hl,
"tz": pytrend.tz,
"geo": pytrend.geo,
"cookie_NID": bool(pytrend.cookies["NID"]),
}
expected = {"hl": "en-US", "tz": 360, "geo": "", "cookie_NID": True}
assert result == expected
@pytest.mark.vcr
def test_build_payload():
"""Should return the widgets to get data."""
pytrend = TrendReq()
pytrend.build_payload(kw_list=["pizza", "bagel"])
expected = {
"hl": "en-US",
"tz": 360,
"req": (
'{"comparisonItem": ['
'{"keyword": "pizza", "time": "today 5-y", "geo": ""}, '
'{"keyword": "bagel", "time": "today 5-y", "geo": ""}'
'], "category": 0, "property": ""}'
),
}
assert pytrend.token_payload == expected
@pytest.mark.vcr
def test_tokens():
pytrend = TrendReq()
pytrend.build_payload(kw_list=["pizza", "bagel"], timeframe='2021-01-01 2021-12-31')
expected = [
{
"request": {
"restriction": {
"geo": {},
"time": "2021-01-01 2021-12-31",
"originalTimeRangeForExploreUrl": "2021-01-01 2021-12-31",
"complexKeywordsRestriction": {
"keyword": [{"type": "BROAD", "value": "pizza"}]
},
},
"keywordType": "QUERY",
"metric": ["TOP", "RISING"],
"trendinessSettings": {"compareTime": "2020-01-02 2020-12-31"},
"requestOptions": {"property": "", "backend": "IZG", "category": 0},
"language": "en",
"userCountryCode": "ES",
"userConfig": {"userType": "USER_TYPE_SCRAPER"},
},
# We don't care if the help dialog changes.
"helpDialog": ANY,
"color": "PALETTE_COLOR_1",
"keywordName": "pizza",
# The token will change in every request.
"token": ANY,
"id": "RELATED_QUERIES_0",
"type": "fe_related_searches",
"title": "Related queries",
"template": "fe",
"embedTemplate": "fe_embed",
"version": "1",
"isLong": False,
"isCurated": False,
},
{
"request": {
"restriction": {
"geo": {},
"time": "2021-01-01 2021-12-31",
"originalTimeRangeForExploreUrl": "2021-01-01 2021-12-31",
"complexKeywordsRestriction": {
"keyword": [{"type": "BROAD", "value": "bagel"}]
},
},
"keywordType": "QUERY",
"metric": ["TOP", "RISING"],
"trendinessSettings": {"compareTime": "2020-01-02 2020-12-31"},
"requestOptions": {"property": "", "backend": "IZG", "category": 0},
"language": "en",
"userCountryCode": "ES",
"userConfig": {"userType": "USER_TYPE_SCRAPER"},
},
# We don't care if the help dialog changes.
"helpDialog": ANY,
"color": "PALETTE_COLOR_2",
"keywordName": "bagel",
# The token will change in every request.
"token": ANY,
"id": "RELATED_QUERIES_1",
"type": "fe_related_searches",
"title": "Related queries",
"template": "fe",
"embedTemplate": "fe_embed",
"version": "1",
"isLong": False,
"isCurated": False,
},
]
assert pytrend.related_queries_widget_list == expected
@pytest.mark.vcr
def test_interest_over_time_ok():
pytrend = TrendReq()
pytrend.build_payload(kw_list=['pizza', 'bagel'], timeframe='2021-01-01 2021-01-05')
df_result = pytrend.interest_over_time()
df_expected = build_interest_over_time_df({
'pizza': [100, 83, 78, 49, 50],
'bagel': [2, 2, 2, 1, 1]
}, dates=['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04', '2021-01-05'])
assert_frame_equal(df_result, df_expected)
@pytest.mark.vcr
def test_interest_over_time_images():
pytrend = TrendReq()
pytrend.build_payload(
kw_list=['pizza', 'bagel'],
gprop='images',
timeframe='2021-01-01 2021-01-05'
)
df_result = pytrend.interest_over_time()
df_expected = build_interest_over_time_df({
'pizza': [85, 100, 93, 93, 93],
'bagel': [3, 2, 9, 4, 4]
}, dates=['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04', '2021-01-05'])
assert_frame_equal(df_result, df_expected)
@pytest.mark.vcr
def test_interest_over_time_news():
pytrend = TrendReq()
pytrend.build_payload(
kw_list=['pizza', 'bagel'],
gprop='news',
timeframe='2021-01-01 2021-01-05'
)
df_result = pytrend.interest_over_time()
df_expected = build_interest_over_time_df({
'pizza': [100, 67, 78, 32, 75],
'bagel': [0, 0, 0, 20, 0]
}, dates=['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04', '2021-01-05'])
assert_frame_equal(df_result, df_expected)
@pytest.mark.vcr
def test_interest_over_time_youtube():
pytrend = TrendReq()
pytrend.build_payload(
kw_list=['pizza', 'bagel'],
gprop='youtube',
timeframe='2021-01-01 2021-01-05'
)
df_result = pytrend.interest_over_time()
df_expected = build_interest_over_time_df({
'pizza': [88, 100, 100, 92, 95],
'bagel': [1, 1, 1, 2, 1]
}, dates=['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04', '2021-01-05'])
assert_frame_equal(df_result, df_expected)
@pytest.mark.vcr
def test_interest_over_time_froogle():
pytrend = TrendReq()
pytrend.build_payload(
kw_list=['pizza', 'bagel'],
gprop='froogle',
timeframe='2021-01-01 2021-01-05'
)
df_result = pytrend.interest_over_time()
df_expected = build_interest_over_time_df({
'pizza': [94, 99, 94, 62, 100],
'bagel': [0, 0, 0, 0, 8]
}, dates=['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04', '2021-01-05'])
assert_frame_equal(df_result, df_expected)
@pytest.mark.vcr
def test_interest_over_time_bad_gprop():
pytrend = TrendReq()
expected_message = re.compile(r'^gprop must be.+$')
with pytest.raises(ValueError, match=expected_message):
pytrend.build_payload(kw_list=['pizza', 'bagel'], gprop=' ')
@pytest.mark.vcr
def test_multirange_interest_over_time_ok():
pytrend = TrendReq()
pytrend.build_payload(kw_list=['pizza', 'bagel'], timeframe=['2021-01-01 2021-01-05', '2021-01-06 2021-01-10'])
df_result = pytrend.multirange_interest_over_time()
expected_result = ExpectedResult(
length=6,
df_head=pd.DataFrame({
'[0] pizza date': ['Average', 'Jan 1, 2021', 'Jan 2, 2021'],
'[0] pizza value': [72, 100, 83],
'[1] bagel date': ['Average', 'Jan 6, 2021', 'Jan 7, 2021'],
'[1] bagel value': [1, 1, 1]
}),
df_tail=pd.DataFrame({
'[0] pizza date': ['Jan 3, 2021', 'Jan 4, 2021', 'Jan 5, 2021'],
'[0] pizza value': [78, 49, 50],
'[1] bagel date': ['Jan 8, 2021', 'Jan 9, 2021', 'Jan 10, 2021'],
'[1] bagel value': [1, 2, 2]
}, index=pd.Index([3, 4, 5]))
)
expected_result.assert_equals(df_result)
@pytest.mark.vcr
def test_multirange_interest_over_time_same_keyword_ok():
pytrend = TrendReq()
pytrend.build_payload(kw_list=['pizza', 'pizza'], timeframe=['2021-01-01 2021-01-05', '2021-01-06 2021-01-10'])
df_result = pytrend.multirange_interest_over_time()
expected_result = ExpectedResult(
length=6,
df_head=pd.DataFrame({
'[0] pizza date': ['Average', 'Jan 1, 2021', 'Jan 2, 2021'],
'[0] pizza value': [72, 100, 83],
'[1] pizza date': ['Average', 'Jan 6, 2021', 'Jan 7, 2021'],
'[1] pizza value': [68, 52, 52]
}),
df_tail=pd.DataFrame({
'[0] pizza date': ['Jan 3, 2021', 'Jan 4, 2021', 'Jan 5, 2021'],
'[0] pizza value': [78, 49, 50],
'[1] pizza date': ['Jan 8, 2021', 'Jan 9, 2021', 'Jan 10, 2021'],
'[1] pizza value': [70, 89, 74]
}, index=pd.Index([3, 4, 5]))
)
expected_result.assert_equals(df_result)
@pytest.mark.vcr
def test_interest_by_region_ok():
pytrend = TrendReq()
pytrend.build_payload(kw_list=['pizza', 'bagel'], timeframe='2021-01-01 2021-12-31')
df_result = pytrend.interest_by_region()
# Both head and tail have all 0's in both values, sort the result to test more meaningful values
df_result = df_result.sort_values(by=['bagel', 'pizza', 'geoName'], ascending=False)
expected_result = ExpectedResult(
length=250,
df_head=pd.DataFrame({
'pizza': [92, 94, 96],
'bagel': [8, 6, 4],
}, index=pd.Index(['Singapore', 'Hong Kong', 'Taiwan'], name='geoName')),
df_tail=pd.DataFrame({
'pizza': [0, 0, 0],
'bagel': [0, 0, 0],
}, index=pd.Index(['Algeria', 'Albania', 'Afghanistan'], name='geoName'))
)
expected_result.assert_equals(df_result)
@pytest.mark.vcr
def test_interest_by_region_city_resolution():
pytrend = TrendReq()
pytrend.build_payload(kw_list=['pizza', 'bagel'], timeframe='2021-01-01 2021-12-31')
df_result = pytrend.interest_by_region(resolution='CITY')
# Both head and tail have all 0's in both values, sort the result to test more meaningful values
df_result = df_result.sort_values(by=['bagel', 'pizza', 'geoName'], ascending=False)
expected_result = ExpectedResult(
length=250,
df_head=pd.DataFrame({
'pizza': [93, 94, 95],
'bagel': [7, 6, 5],
}, index=pd.Index(['Singapore', 'Hong Kong', 'Japan'], name='geoName')),
df_tail=pd.DataFrame({
'pizza': [0, 0, 0],
'bagel': [0, 0, 0],
}, index=pd.Index(['Algeria', 'Albania', 'Afghanistan'], name='geoName'))
)
expected_result.assert_equals(df_result)
# FIXME: With more than one term the result is always an empty dict.
# In the web we can get related topics using ['Torvalds', 'Dijkstra'] but not here, something's wrong.
@pytest.mark.vcr
def test_related_topics_result_keys():
pytrend = TrendReq()
pytrend.build_payload(kw_list=['pizza'], timeframe='2021-01-01 2021-12-31')
df_result = pytrend.related_topics()
# Since the result dict contains pd.DataFrame's we can't create an expected dict and compare
# because PAndas raises an error "the truth value of a DataFrame is ambiguous".
assert set(df_result.keys()) == {'pizza'}
df_result = df_result['pizza']
assert set(df_result.keys()) == {'top', 'rising'}
@pytest.mark.vcr
def test_related_topics_result_top():
pytrend = TrendReq()
pytrend.build_payload(kw_list=['pizza'], timeframe='2021-01-01 2021-12-31')
df_result = pytrend.related_topics()['pizza']['top']
expected_result = ExpectedResult(
length=21,
df_head=pd.DataFrame({
'value': [100, 23, 12],
'formattedValue': ['100', '23', '12'],
'hasData': [True, True, True],
'link': [
'/trends/explore?q=/m/0663v&date=2021-01-01+2021-12-31',
'/trends/explore?q=/m/09cfq&date=2021-01-01+2021-12-31',
'/trends/explore?q=/m/03clwm&date=2021-01-01+2021-12-31'
],
'topic_mid': ['/m/0663v', '/m/09cfq', '/m/03clwm'],
'topic_title': ['Pizza', 'Pizza Hut', "Domino's"],
'topic_type': ['Dish', 'Restaurant chain', 'Restaurant chain'],
}),
df_tail=pd.DataFrame({
'value': [0, 0, 0],
'formattedValue': ['<1', '<1', '<1'],
'hasData': [True, True, True],
'link': [
'/trends/explore?q=/g/11b7c9w1y6&date=2021-01-01+2021-12-31',
'/trends/explore?q=/m/09nghg&date=2021-01-01+2021-12-31',
'/trends/explore?q=/m/0gwh_4&date=2021-01-01+2021-12-31',
],
'topic_mid': ['/g/11b7c9w1y6', '/m/09nghg', '/m/0gwh_4'],
'topic_title': ["Roman's Pizza", 'Sam Goody', 'Detroit-style pizza'],
'topic_type': ['Topic', 'Retail company', 'Food']
}, index=pd.Index([18, 19, 20]))
)
expected_result.assert_equals(df_result)
@pytest.mark.vcr
def test_related_topics_result_rising():
pytrend = TrendReq()
pytrend.build_payload(kw_list=['pizza'], timeframe='2021-01-01 2021-12-31')
df_result = pytrend.related_topics()['pizza']['rising']
df_expected = pd.DataFrame({
'value': [12600, 160, 80, 70],
'formattedValue': ['Breakout', '+160%', '+80%', '+70%'],
'link': [
'/trends/explore?q=/m/09nghg&date=2021-01-01+2021-12-31',
'/trends/explore?q=/m/0gwh_4&date=2021-01-01+2021-12-31',
'/trends/explore?q=/g/11g6qhxwmd&date=2021-01-01+2021-12-31',
'/trends/explore?q=/m/02hvyj&date=2021-01-01+2021-12-31',
],
'topic_mid': ['/m/09nghg', '/m/0gwh_4', '/g/11g6qhxwmd', '/m/02hvyj'],
'topic_title': ['Sam Goody', 'Detroit-style pizza', 'Ooni', 'Mystic Pizza'],
'topic_type': ['Retail company', 'Food', 'Topic', '1988 film'],
})
assert_frame_equal(df_result, df_expected)
@pytest.mark.vcr
def test_related_queries_result_keys():
pytrend = TrendReq()
pytrend.build_payload(kw_list=['pizza', 'bagel'], timeframe='2021-01-01 2021-12-31')
df_result = pytrend.related_queries()
assert set(df_result.keys()) == {'pizza', 'bagel'}
df_result_pizza = df_result['pizza']
assert set(df_result_pizza.keys()) == {'top', 'rising'}
df_result_bagel = df_result['bagel']
assert set(df_result_bagel.keys()) == {'top', 'rising'}
@pytest.mark.vcr
def test_related_queries_result_top():
pytrend = TrendReq()
pytrend.build_payload(kw_list=['pizza', 'bagel'], timeframe='2021-01-01 2021-12-31')
df_result = pytrend.related_queries()
expected_pizza = ExpectedResult(
length=25,
df_head=pd.DataFrame({
'query': ['pizza hut', 'pizza near me', 'pizza pizza near me'],
'value': [100, 68, 65],
}),
df_tail=pd.DataFrame({
'query': ['pizza little caesars', 'cheese pizza', 'new york pizza'],
'value': [5, 5, 5],
}, index=pd.Index([22, 23, 24]))
)
expected_pizza.assert_equals(df_result['pizza']['top'])
expected_bagel = ExpectedResult(
length=25,
df_head=pd.DataFrame({
'query': ['the bagel', 'bagel me', 'everything bagel'],
'value': [100, 97, 92],
}),
df_tail=pd.DataFrame({
'query': ['what a bagel', 'bagel sandwich', 'coffee meets bagel'],
'value': [22, 22, 22],
}, index=pd.Index([22, 23, 24]))
)
expected_bagel.assert_equals(df_result['bagel']['top'])
@pytest.mark.vcr
def test_related_queries_result_rising():
pytrend = TrendReq()
pytrend.build_payload(kw_list=['pizza', 'bagel'], timeframe='2021-01-01 2021-12-31')
df_result = pytrend.related_queries()
expected_pizza = ExpectedResult(
length=11,
df_head=pd.DataFrame({
'query': ['licorice pizza', 'history of pizza', 'stoned pizza'],
'value': [8550, 400, 300],
}),
df_tail=pd.DataFrame({
'query': ['incredible pizza', 'mountain mike pizza', 'angels pizza'],
'value': [60, 50, 40],
}, index=pd.Index([8, 9, 10]))
)
expected_pizza.assert_equals(df_result['pizza']['rising'])
expected_bagel = ExpectedResult(
length=18,
df_head=pd.DataFrame({
'query': ['rover bagel', 'kettlemans bagel', 'bagel karen'],
'value': [350, 250, 180],
}),
df_tail=pd.DataFrame({
'query': ['bagel street deli', 'best bagel near me', 'the bagel nook'],
'value': [50, 50, 40],
}, index=pd.Index([15, 16, 17]))
)
expected_bagel.assert_equals(df_result['bagel']['rising'])
@pytest.mark.vcr
def test_trending_searches_ok():
pytrend = TrendReq()
# trending_searches doesn't need to call build_payload.
df_result = pytrend.trending_searches()
# NOTE: This expected result needs to be rebuilt from scratch every time the cassette is rewritten.
# They're time-dependent.
expected_result = ExpectedResult(
length=20,
df_head=pd.DataFrame({0: ['Chabelo', 'Jonathan Majors', 'Benavidez vs Plant']}),
df_tail=pd.DataFrame(
{0: ['Gonzaga', 'Reese Witherspoon', 'France vs Netherlands']},
index=pd.Index([17, 18, 19])
)
)
expected_result.assert_equals(df_result)
@pytest.mark.vcr
def test_realtime_trending_searches_ok():
pytrend = TrendReq()
# realtime_trending_searches doesn't need to call build_payload.
df_result = pytrend.realtime_trending_searches()
# NOTE: This expected result needs to be rebuilt from scratch every time the cassette is rewritten.
# They're time-dependent.
# NOTE: The result returned by this function is a monster.
# Strings of almost 200 characters, lists of strings, lists of lists of strings...
# Rebuilding a full 3-head-tail result from scratch is a chore, with a single record for head
# and tail is more than enough.
expected_result = ExpectedResult(
length=111,
head_tail_length=1,
df_head=pd.DataFrame({
'title': [
('Caleb Plant, '
'Boxing, Canelo Álvarez, '
'David Benavidez, '
'Anthony Dirrell')
],
'entityNames': [
[
'Caleb Plant',
'Boxing',
'Canelo Álvarez',
'David Benavidez',
'Anthony Dirrell',
]
]
}),
df_tail=pd.DataFrame({
'title': [
('Ron DeSantis, '
'Casey DeSantis, '
'Republican Party, '
'Christina DeSantis')
],
'entityNames': [
[
'Ron DeSantis',
'Casey DeSantis',
'Republican Party',
'Christina DeSantis',
]
]
}, index=pd.Index([110]))
)
expected_result.assert_equals(df_result)
@pytest.mark.vcr
def test_top_charts_ok():
pytrend = TrendReq()
# top_chars doesn't need to call build_payload.
df_result = pytrend.top_charts(date=2021)
df_expected = pd.DataFrame({
'title': [
'Australia vs India',
'India vs England',
'IPL',
'NBA',
'Euro 2021',
'Copa América',
'India vs New Zealand',
'T20 World Cup',
'Squid Game',
'DMX'
],
'exploreQuery': ['', '', '', '', '', 'Copa America', '', '', '', '']
})
assert_frame_equal(df_result, df_expected)
@pytest.mark.vcr
def test_suggestions_ok():
pytrend = TrendReq()
# suggestions doesn't need to call build_payload.
result = pytrend.suggestions(keyword='pizza')
expected = [
{'mid': '/m/0663v', 'title': 'Pizza', 'type': 'Dish'},
{'mid': '/g/11k19hmrkk', 'title': 'Licorice Pizza', 'type': '2021 film'},
{'mid': '/m/0rg8s1t', 'title': 'Dominos Pizza', 'type': 'Topic'},
{'mid': '/m/0dfxdnc', 'title': 'Pizza dough', 'type': 'Food'},
{'mid': '/g/11hdxfw7c0', 'title': 'history of pizza', 'type': 'Food'},
]
assert result == expected
@pytest.mark.vcr
def test_interest_over_time_partial():
# NOTE: This test may fails if regenerate the cassette on Mondays because the last week will not be partial
pytrend = TrendReq()
pytrend.build_payload(kw_list=['pizza', 'bagel'])
df_result = pytrend.interest_over_time()
s_last_row = df_result.iloc[-1]
assert s_last_row.isPartial is np.bool_(True)
def test_request_args_passing(mocked_responses):
mocked_responses.add(
url=f'{BASE_TRENDS_URL}/explore/?geo=US',
method='GET',
match=[responses.matchers.header_matcher({'User-Agent': 'pytrends'})]
)
mocked_responses.add(
url=TrendReq.TRENDING_SEARCHES_URL,
method='GET',
match=[responses.matchers.header_matcher({'User-Agent': 'pytrends'})],
json={
'united_states': ['term 1', 'term 2']
}
)
requests_args = {'headers': {
'User-Agent': 'pytrends',
}}
pytrend = TrendReq(requests_args=requests_args)
pytrend.trending_searches()
@pytest.mark.vcr
def test_interest_over_time_multiple_regions():
pytrend = TrendReq()
pytrend.build_payload(kw_list=['pizza', 'bagel'], geo=['US-NY', 'US-IL'])
df = pytrend.interest_over_time()
assert df is not None
pd.testing.assert_index_equal(
df.columns,
pd.MultiIndex.from_tuples(
[
('pizza', 'US-NY'),
('pizza', 'US-IL'),
('bagel', 'US-NY'),
('bagel', 'US-IL'),
('isPartial', ),
],
names=['keyword', 'region']
)
)
assert df[('pizza', 'US-NY')].notna().all()
assert df[('pizza', 'US-IL')].notna().all()
assert (df[('pizza', 'US-NY')] >= 0).all()
assert (df[('pizza', 'US-IL')] >= 0).all()