MLA_Learn_Periodic_Functions/code/wilshire_5000/wilshire.py

import numpy as np
import tensorflow as tf
import pandas as pd
import matplotlib.pyplot as plt


def parser(path):
    df = pd.read_csv(path,na_values='.')
    print(df.shape)
    df = df.interpolate()
    #df = df.dropna()
    print(df.shape)
    #df = df.drop(labels=np.arange(1825)) ### To obtain the same graph than in the article
    print(df.shape)
    return(df)

def preprocess(path):
    df = parser(path)
    print(df)
    df_normalized = df[:]
    df_normalized["WILL5000INDFC"]=df_normalized["WILL5000INDFC"]/np.max(df_normalized["WILL5000INDFC"])

    # df.plot()
    # plt.show()
    df_train = df_normalized[:6544]
    df_test = df_normalized[6545:6629]

    # df_train.plot()
    # df_test.plot()
    # plt.show()
    return(df_train,df_test)