for index in range(len(values_names)):
name = values_names[index]
df = values_dfs[index]
cols = df.columns.values
new_cols = [
x for x in cols if x not in
['Date', 'USD', 'Value', 'Open', 'Close', 'High', 'Low', 'Volume']
]
new_cols2 = [x for x in cols if x not in ['Date']]
dict_dfs_cols[name] = new_cols
dict_dfs_cols2[name] = new_cols2
dataset = ml_dataset.generate_df_dataset(values_names, values_dfs,
dict_dfs_cols)
dataset_all = ml_dataset.generate_df_dataset(values_names, values_dfs,
dict_dfs_cols2)
#First 30 row
dataset = dataset[31:]
dataset = dataset.reset_index(drop=True)
#colsToShift = [col for col in dataset.columns if 'HSI' in col or'N225' in col or'AXJO' in col]
#dataset[colsToShift] = dataset[colsToShift].shift(-1)
#last_row = dataset.shape[0]-1
#dataset = dataset.drop(last_row, axis=0)
dataset_all = dataset_all[31:]
dataset_all = dataset_all.reset_index(drop=True)
#dataset_all[colsToShift] = dataset_all[colsToShift].shift(-1)
#last_row = dataset_all.shape[0]-1
values_cols = ['USD', 'Value', 'Open', 'Close', 'High', 'Low', 'Volume']
dict_dfs_cols = {}
dict_dfs_cols2 = {}
for index in range(len(values_names)):
name = values_names[index]
df = values_dfs[index]
cols = df.columns.values
new_cols = [x for x in cols if x not in ['Date', 'USD', 'Value', 'Open', 'Close', 'High', 'Low', 'Volume']]
new_cols2 = [x for x in cols if x not in ['Date']]
dict_dfs_cols[name] = new_cols
dict_dfs_cols2[name] = new_cols2
dataset = ml_dataset.generate_df_dataset(values_names, values_dfs, dict_dfs_cols)
dataset_all = ml_dataset.generate_df_dataset(values_names, values_dfs, dict_dfs_cols2)
#First 30 row
dataset = dataset[31:]
dataset = dataset.reset_index(drop=True)
#colsToShift = [col for col in dataset.columns if 'HSI' in col or'N225' in col or'AXJO' in col]
#dataset[colsToShift] = dataset[colsToShift].shift(-1)
#last_row = dataset.shape[0]-1
#dataset = dataset.drop(last_row, axis=0)
dataset_all = dataset_all[31:]
dataset_all = dataset_all.reset_index(drop=True)
#dataset_all[colsToShift] = dataset_all[colsToShift].shift(-1)