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')