Create an empty Data frame with date index:

import pandas as pd

def 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 DataFrame
Columns: []
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 pd

def 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 pd

def 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()