def handler(event, context):
print('received event:')
print(event)
df = pd.DataFrame(['first item', 'second item', 'third item'])
return {
'statusCode': 200,
'headers': {
'Access-Control-Allow-Headers': '*',
'Access-Control-Allow-Origin': '*',
'Access-Control-Allow-Methods': 'OPTIONS,POST,GET'
},
'body': pd.to_json()
}
def openFileNameDialog(self):
fileName, _ = QFileDialog.getOpenFileName()
filename = openFileNameDialog()
pd.read_csv(filename)
Filename = filename[:-3]
pd.to_json(filename + '.json')
def _pd_to_sly_table(pd):
return json.loads(pd.to_json(orient='split'))
optimizer=optimizer)
model.load_weights('{}_daily.h5'.format(i))
pred = model.predict(pred_data[-101:]).squeeze()
targets = (max * data[target_col][-window_len:]) + min
# targets.to_csv('{}_100days_actual.csv'.format(i),header=None)
target_json = targets.to_json(orient='split', index=True)
# print(data[i])
# final_data[i]['actual'] = list()
# print(list(target_json['data']))
final_data['actual'] = target_json
# print(final_data)
preds = (max * pred.T[0][:-1]) + min
preds = pd.Series(index=targets.index, data=preds)
# preds.to_csv('{}_100days_predicted.csv'.format(i),header=None)
pred_json = preds.to_json(orient='split', index=True)
final_data['predicted'] = pred_json
# pd.Series(index=pd.date_range(start=date.today() + datetime.timedelta(days=1), end=date.today() + datetime.timedelta(days=10)), data=(max * pred[-1])+min).to_csv('{}_next_10days.csv'.format(i))
pd = pd.Series(index=pd.date_range(
start=date.today() + datetime.timedelta(days=1),
end=date.today() + datetime.timedelta(days=10)),
data=(max * pred[-1]) + min)
pd_json = pd.to_json(orient='split', index=True)
final_data['next_days'] = pd_json
# Serializing json
json_object = json.dumps(final_data, indent=4)
# Writing to sample.json
with open("op.json", "w") as outfile:
outfile.write(json_object)
print(final_data)
self._model = sm.load(self._model_path)
except:
self._model = None
return self
model_path = Path(__file__).parent / "/api/data/finalized_model.pickle"
train_json_path = Path(__file__).parent / "/api/data/train.json"
test_json_path = Path(__file__).parent / "/api/data/test.json"
model = Model(model_path)
def get_model():
return model
if __name__ == "__main__":
jsondf = pd.read_json()
print(jsondf.head())
train_set = pd.to_json(orient='records')
## To do: pull in feature engineering and elements from main.py
## To do: Set up Depends dependency in main.py to call get_model()
## To do: Set up model load, save, train functionality via API
#model.train(X, y)
#model.save()
import pandas as pd
df = pd.read_json('Kickstarter_Kickstarter.json', 'r')
results = df["projects"]
with open('output.json', 'w') as file:
pd.to_json(file)
def panda_to_jsonfile(pd, file_name):
pd.to_json(path_or_buf=file_name, orient='records', date_format='iso', date_unit='s')
