How to create a dataframe from dictionary with details of stocks

I need to create a dataframe with closing prices of the stocks of the companies that I have given in the list.

import pandas_datareader.data as web
from pandas import Series,DataFrame
from datetime import datetime
start = datetime(2017,1,1)
end = datetime(2017,1,12)    
f = web.DataReader(['BP','CVX'], 'iex',start,end)

f in the code is returning a dictionary as shown below. How can I get the closing price of the stocks of listed companies in a DataFrame.

{'BP': open high low close volume date
2017-01-03 38.100 38.1218 37.79 38.00 8779164 2017-01-04 38.045 38.3400 37.94 38.29 6883266 2017-01-05 38.140 38.6800 38.14 38.57 6505685 2017-01-06 38.160 38.1900 37.85 37.91 5800932 2017-01-09 37.580 37.6500 37.31 37.31 5533626 2017-01-10 37.250 37.4500 37.11 37.11 3922015 2017-01-11 37.200 37.6550 37.06 37.55 4422586 2017-01-12 37.990 38.0000 37.66 37.76 4698473, 'CVX': open high low close volume date
2017-01-03 118.38 119.00 116.59 117.85 7404774 2017-01-04 118.41 118.65 117.60 117.82 6679943 2017-01-05 118.00 118.48 116.72 117.31 5928637 2017-01-06 117.45 117.58 116.38 116.84 4762474 2017-01-09 116.29 116.36 115.11 115.84 6891790

Solution

I think you need concat with parameter axis=1 for concatenate along columns and select by xs second level of MultiIndex in columns:

df = pd.concat(f, axis=1).xs('close', axis=1, level=1)
print (df)
               BP     CVX
date                     
2017-01-03  38.00  117.85
2017-01-04  38.29  117.82
2017-01-05  38.57  117.31
2017-01-06  37.91  116.84
2017-01-09  37.31  115.84
2017-01-10  37.11     NaN
2017-01-11  37.55     NaN
2017-01-12  37.76     NaN

Or concatenate along indices, but get MultiIndex in indices:

df = pd.concat(f)['close']
print (df)
     date      
BP   2017-01-03     38.00
     2017-01-04     38.29
     2017-01-05     38.57
     2017-01-06     37.91
     2017-01-09     37.31
     2017-01-10     37.11
     2017-01-11     37.55
     2017-01-12     37.76
CVX  2017-01-03    117.85
     2017-01-04    117.82
     2017-01-05    117.31
     2017-01-06    116.84
     2017-01-09    115.84
Name: close, dtype: float64