I am running a certain stoke prediction model code and getting the following error.
I have given below the code.
#import libraries import streamlit as st import numpy as np import plotly.express as px import plotly.io as pio import yfinance as yf import matplotlib.pyplot as plt from keras import optimizers from keras.callbacks import EarlyStopping from keras.layers import Dense, LSTM from keras.models import Sequential from sklearn.preprocessing import MinMaxScaler plt.style.use('fivethirtyeight') # Get price for next days: # Get the last timestep days closing price new_df = stock_data.filter(['Open', 'High', 'Low', 'Close']) last_timestep_days = new_df[-time_step:].values pred_price = np.array([]) for day in range(1, 6): if day != 1: last_day_predicted_data = np.array( [round(pred_price[0][0], 2), round(pred_price[0][1], 2), round(pred_price[0][2], 2), round(pred_price[0][3], 2)]) last_timestep_days = np.concatenate((last_timestep_days, [last_day_predicted_data])) last_timestep_days = np.delete(last_timestep_days, 0, axis=0) # to remove the first row after adding lastest day predicted data
This is the error I am getting:
IndexError Traceback (most recent call last) <ipython-input-42-3623c27dcfbb> in <module> 7 if day != 1: 8 last_day_predicted_data = np.array( ----> 9 [round(pred_price[0][0], 2), round(pred_price[0][1], 2), round(pred_price[0][2], 2), 10 round(pred_price[0][3], 2)]) 11 last_timestep_days = np.concatenate((last_timestep_days, [last_day_predicted_data])) IndexError: index 0 is out of bounds for axis 0 with size 0
Is there anyway to resolve this error? I will share more dependencies information if required.
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Answer
pred_price
is an empty np.array
so trying to index it like you have done on line 9 (“pred_price[0]
“) will give an error. There has to be something in the array first.