[Python] Pandas load DataFrames

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Create an empty Data frame with date index:

import pandas as pddef test_run():    start_date=‘2017-11-24‘    end_data=‘2017-11-28‘    dates=pd.date_range(start_date, end_data)    df1=pd.DataFrame(index=dates)    print(df1)"""Empty DataFrameColumns: []Index: [2010-01-22 00:00:00, 2010-01-23 00:00:00, 2010-01-24 00:00:00, 2010-01-25 00:00:00, 2010-01-26 00:00:00]"""

 

 

Now we want to load SPY.csv and get ‘Adj Close‘ column value and copy the range (11-21, 11-28) data to the empty data frame:

import pandas as pddef test_run():    start_date=‘2017-11-24‘    end_data=‘2017-11-28‘    dates=pd.date_range(start_date, end_data)    # Create an empty data frame    df1=pd.DataFrame(index=dates)    # Load csv file    dspy=pd.read_csv(‘data/SPY.csv‘,     index_col="Date",     parse_dates=True,    usecols=[‘Date‘, ‘Adj Close‘],    na_values=[‘nan‘])    # print(dspy)     """             Adj Close    Date    2017-11-16  258.619995    2017-11-17  257.859985    2017-11-20  258.299988    """    # join the table    df1=df1.join(dspy)    #print(df1)    """                 Adj Close    2017-11-24  260.359985    2017-11-25         NaN    2017-11-26         NaN    2017-11-27  260.230011    """    # drop the nan row    df1=df1.dropna()    print(df1)    """                 Adj Close    2017-11-24  260.359985    2017-11-27  260.230011    2017-11-28  262.869995    """if __name__ == ‘__main__‘:    test_run()    

 

 

There is a simpy way to drop the data which index is not present in dspy:

df1=df1.join(dspy, how=‘inner‘)

 

We can also rename the ‘Adj Close‘ to prevent conflicts:

    # rename the column    dspy=dspy.rename(columns={‘Adj Close‘: ‘SPY‘})

 

Load more stocks:

import pandas as pddef test_run():    start_date=‘2017-11-24‘    end_data=‘2017-11-28‘    dates=pd.date_range(start_date, end_data)    # Create an empty data frame    df1=pd.DataFrame(index=dates)    # Load csv file    dspy=pd.read_csv(‘data/spy.csv‘,     index_col="Date",     parse_dates=True,    usecols=[‘Date‘, ‘Adj Close‘],    na_values=[‘nan‘])    # print(dspy)     """             Adj Close    Date    2017-11-16  258.619995    2017-11-17  257.859985    2017-11-20  258.299988    """    # rename the column    dspy=dspy.rename(columns={‘Adj Close‘: ‘spy‘})    # join the table    df1=df1.join(dspy, how=‘inner‘)    # print(df1)    """                 Adj Close    2017-11-24  260.359985    2017-11-27  260.230011    2017-11-28  262.869995    """    symbols=[‘aapl‘, ‘ibm‘]    for symbol in symbols:        temp=pd.read_csv(‘data/{0}.csv‘.format(symbol), index_col="Date", parse_dates=True, usecols=[‘Date‘, ‘Adj Close‘], na_values=[‘nan‘])                temp=temp.rename(columns={‘Adj Close‘: symbol})                df1=df1.join(temp)    print(df1)    """                       spy        aapl         ibm    2017-11-24  260.359985  174.970001  151.839996    2017-11-27  260.230011  174.089996  151.979996    2017-11-28  262.869995  173.070007  152.470001    """if __name__ == ‘__main__‘:    test_run()    

 

[Python] Pandas load DataFrames