track_data = []
popularity = track["track"]["popularity"]
if popularity == 0:
continue #skip any track with 0 popularity, because I'm unsure if this is a default value
else:
#name = track["track"]["name"]
#track_data.append(name) # will be ignored but could but each track_data_obj should be identifiable
features_dict = sp.audio_features(track["track"]["id"]) #this returns a features dictonary
for key in features_dict[0]: #loop through and add only the attributes we want
if key != "type" and key != "id" and key != "uri" and key != "track_href" and key != "analysis_url" and key != "time_signature" and key != "mode" and key != "key" and key != "loudness":
val = features_dict[0][key]
if key != "tempo" and key != "duration_ms":
val = myutils.percent_to_rating(val)
track_data.append(val)
# if first == True:
# header.append(key)
# first = False
pop_class = myutils.pop_rating(popularity)
track_data.append(pop_class) # popularity will be the y_train
track_data_objs.append(track_data)
# header.append("popularity")
# now we can turn this into an xtrain and ytrain or keep it stitched together
# when dealing with the data we can delete the first col, which is the name identifier
print(len(track_data_objs))
header = ['danceability', 'energy', 'speechiness', 'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo', 'duration_ms', 'popularity']
tracks_mypy = MyPyTable(header, track_data_objs)
tracks_mypy.save_to_file("tracks_data.txt")
from mysklearn.mypytable import MyPyTable
# Object Declaration
table = MyPyTable()
# Trims the Dataset (Gets Data Based on City)
city = "Sydney"
table.load_from_file("weatherAUS.csv")
table.column_names[0] = 'Location'
names, tables = table.group_by("Location")
city_index = names.index(city)
print("\n")
for i in range(10):
print(tables[city_index][i])
table.data = tables[city_index]
table.save_to_file(city+"_weather.csv")