[Python] Normalize the data with Pandas

Posted 2020-10-18 Answer1215

import os
import pandas as pd
import matplotlib.pyplot as plt

def test_run():
    start_date=\'2017-01-01\'
    end_data=\'2017-12-15\'
    dates=pd.date_range(start_date, end_data)

    # Create an empty data frame
    df=pd.DataFrame(index=dates)

    symbols=[\'SPY\', \'AAPL\', \'IBM\', \'GOOG\', \'GLD\']
    for symbol in symbols:
        temp=getAdjCloseForSymbol(symbol)
        df=df.join(temp, how=\'inner\')

    return df   


def normalize_data(df):
    """ Normalize stock prices using the first row of the dataframe """
    df=df/df.ix[0, :]
    return df


def getAdjCloseForSymbol(symbol): 
    # Load csv file
    temp=pd.read_csv("data/{0}.csv".format(symbol), 
        index_col="Date", 
        parse_dates=True,
        usecols=[\'Date\', \'Adj Close\'],
        na_values=[\'nan\'])
    # rename the column
    temp=temp.rename(columns={\'Adj Close\': symbol})
    return temp

def plot_data(df, title="Stock prices"):
    ax=df.plot(title=title, fontsize=10)
    ax.set_xlabel("Date")
    ax.set_ylabel("Price")
    plt.show()


if __name__ == \'__main__\':
    df=test_run()
    # data=data.ix[\'2017-12-01\':\'2017-12-15\', [\'IBM\', \'GOOG\']]    
    df=normalize_data(df)
    plot_data(df)
    """
                       IBM         GOOG
    2017-12-01  154.759995  1010.169983
    2017-12-04  156.460007   998.679993
    2017-12-05  155.350006  1005.150024
    2017-12-06  154.100006  1018.380005
    2017-12-07  153.570007  1030.930054
    2017-12-08  154.809998  1037.050049
    2017-12-11  155.410004  1041.099976
    2017-12-12  156.740005  1040.479980
    2017-12-13  153.910004  1040.609985
    2017-12-15  152.500000  1064.189941
    """

It is easy to compare the data by normalize it.