def get_input(type, nrows, ncols, dtype, order='C', out_dtype=False):
rand_mat = (cp.random.rand(nrows, ncols) * 10)
rand_mat = cp.array(rand_mat, dtype=dtype, order=order)
if type == 'numpy':
result = np.array(cp.asnumpy(rand_mat), order=order)
if type == 'cupy':
result = rand_mat
if type == 'numba':
result = nbcuda.as_cuda_array(rand_mat)
if type == 'cudf':
result = cudf.DataFrame(rand_mat)
if type == 'pandas':
result = pdDF(cp.asnumpy(rand_mat))
if type == 'cuml':
result = CumlArray(data=rand_mat)
if out_dtype:
return result, np.array(cp.asnumpy(rand_mat).astype(out_dtype),
order=order)
else:
return result, np.array(cp.asnumpy(rand_mat), order=order)
def makeDataFrame(self):
dataGather = {'제목': [], '일시': [], '내용': [], '링크': []}
for item in self.allItems:
dataGather['제목'].append(
BeautifulSoup(item.title.get_text(strip=True),
'lxml').get_text())
pubDate = item.pubdate.get_text(strip=True)
pubDate = dt.strptime(pubDate, '%a, %d %b %Y %H:%M:%S %z')
pubDate = dt.strftime(pubDate, '%Y-%m-%d %H:%M:%S')
dataGather['일시'].append(pubDate)
dataGather['내용'].append(
BeautifulSoup(item.description.get_text(strip=True),
'lxml').get_text())
dataGather['링크'].append(item.originallink.get_text(strip=True))
self.resData = pdDF(dataGather)
self.resData.index += 1
self.fileWrite()
def liked_songs_to_csv(self, *args):
from pandas import DataFrame as pdDF
columns = ['title', 'artist(s)']
keys = {
"added_at": lambda it: it['added_at'],
"album": lambda it: it['track']['album']['name'],
"album_type": lambda it: it['track']['album']['album_type'],
"release_date": lambda it: it['track']['album']['release_date'],
"total_tracks": lambda it: it['track']['album']['total_tracks'],
"disc_number": lambda it: it['track']['disc_number'],
"duration_ms": lambda it: it['track']['duration_ms'],
"explicit": lambda it: it['track']['explicit'],
"popularity": lambda it: it['track']['popularity'],
"track_number": lambda it: it['track']['track_number'],
"id": lambda it: it['track']['id'],
"is_local": lambda it: it['track']['is_local']
}
for kw in args:
if kw.lower() in keys:
columns.append(kw)
df = pdDF(columns=columns)
off = 0
i = 0
while True:
results = self.sp.current_user_saved_tracks(limit=50, offset=off)
off += 50
for item in results['items']:
row = [
item['track']['name'], item['track']['artists'][0]['name']
]
if len(columns) > 2:
for kw in columns[2:]:
row.append(keys[kw](item))
df.loc[i] = row
i += 1
if results["next"] is None:
break
df.to_csv('MySpotipyLikedSongs.csv')