PCR (Dollar-weighted Put Call Ratio) calculation issues

I am trying to generate a Put / Call Ratio calculation program for my own purpose. Here is the code: problem is I am stuck in some places.

1. I need to generate a summation of all strikes volume * all strikes prices
2. and final one is generating the ratio i.e. summation (all strikes PUT volume * all strikes PUT prices) / summation(all strikes Call volume * all strikes Call prices).

So far I tried a lot and ended up having buggy program. However I am providing the code for now for further so that I can complete the rest of the part get the OutPut properly. (nse does not provide PCR as CBO does provide a solution.)

from nsepy import get_history
from datetime import date
import pandas as pd
import requests
from io import BytesIO 
import certifi
from scipy import stats
from dateutil.relativedelta import relativedelta
import numpy as np
#import matplotlib.pyplot as plt
import datetime
import numpy as np
import matplotlib.colors as colors
import matplotlib.finance as finance
import matplotlib.dates as mdates
import matplotlib.ticker as mticker
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import matplotlib.font_manager as font_manager
import talib as ta
from talib import MA_Type
import statsmodels as sm



nf_calls=[]
nf_puts=[]


#nf_calls[['VolumeCalls']]=np.nan
#nf_puts[['VolumeCalls']]=np.nan
i=min_avalable_strikes=4850
max_avalable_strike=9400
wPCR=nf_opt_CE=nf_opt_PE=pd.DataFrame()

while i in range(min_avalable_strikes,max_avalable_strike):
    temp_CE = get_history(symbol="NIFTY",
                         start=date(2016,2,1), 
                         end=date(2016,4,24),
                         index=True,
                         option_type="CE",
                         strike_price=i,
                         expiry_date=date(2016,4,28))

    #print(nf_opt_CE.head())
    #if nf_opt_CE['Number of Contracts'].values >0 :
    '''if nf_opt_CE.empty :
        nf_opt_CE.append(0)
    '''

    temp_PE = get_history(symbol="NIFTY",
                         start=date(2016,2,1), 
                         end=date(2016,4,22),
                         index=True,
                         option_type="PE",
                         strike_price=i,
                         expiry_date=date(2016,4,28))
    #nf_opt_PE.rename(columns = {'Number of Contracts':'Volume'}, inplace = True)
    #nf_opt_CE.rename(columns = {'Number of Contracts':'Volume'}, inplace = True)

    #temp_CE=temp_CE.drop(temp_CE[temp_CE['Number of Contracts']>0.0].index)
    #temp_PE=temp_PE.drop(temp_CE[temp_CE['Number of Contracts']>0.0].index)
    nf_opt_CE=pd.concat([nf_opt_CE,temp_CE]).drop_duplicates()
    nf_opt_PE=pd.concat([nf_opt_PE,temp_PE]).drop_duplicates()
    nf_opt_CE.index=pd.to_datetime(nf_opt_CE.index)
    nf_opt_PE.index=pd.to_datetime(nf_opt_PE.index)
    i=i+50
    #print(i)

#print(nf_opt_PE.head())
nf_opt_PE.drop_duplicates(inplace=True)
nf_opt_CE.drop_duplicates(inplace=True)
#print(nf_opt_PE.head(100))

nf_opt_PE.rename(columns = {'Number of Contracts':'Volume'}, inplace = True)
nf_opt_CE.rename(columns = {'Number of Contracts':'Volume'}, inplace = True)

nf_opt_PE.drop(['Symbol','Expiry','Open','High' ,'Low','Last','Settle Price','Turnover','Premium Turnover','Open Interest'  ,'Change in OI','Underlying'],axis=1,inplace=True)

nf_opt_CE.drop(['Symbol','Expiry','Open','High' ,'Low','Last','Settle Price','Turnover','Open Interest','Premium Turnover' ,'Change in OI','Underlying'],axis=1,inplace=True)

nf_opt_PE = nf_opt_PE[nf_opt_PE.Volume > 0]
nf_opt_CE = nf_opt_CE[nf_opt_CE.Volume > 0]
#print(nf_opt_PE.tail())

##priceCrossVolume###
nf_opt_PE['VolCrossPrice']=nf_opt_PE.apply(lambda x: (x['Volume']*x['Close']),axis=1)
nf_opt_CE['VolCrossPrice']=nf_opt_CE.apply(lambda x: (x['Volume']*x['Close']),axis=1)

#nf_puts= nf_opt_CE['Number of Contracts']*nf_opt_CE['Close']
#print(nf_calls.head())


nf_opt_PE.drop(['Volume','Close'],axis=1,inplace=True)
nf_opt_CE.drop(['Volume','Close'],axis=1,inplace=True)
#print(nf_opt_PE.index.Date)
#nf_opt_PE['Summation']=nf_opt_PE.groupby(nf_opt_PE.columns[[0]])['VolCrossPrice'].sum()
wPCR=pd.concat([nf_opt_PE,nf_opt_CE])
#wPCR.drop(['Volume','Close'],axis=1,inplace=True)
#wPCR['Summation']=wPCR.groupby(wPCR.columns[[0,1]]).sum()
wPCR['Summation']=wPCR[['Option Type'] == wPCR['Option Type']].groupby(level=0).sum()

print(nf_opt_PE.head(500))
print(wPCR.tail(500))

Encountered Error:

Traceback (most recent call last):
  File "PCRForNF.py", line 101, in <module>
    wPCR['Summation']=wPCR[['Option Type'] == wPCR['Option Type']].groupby(level=0).sum()
  File "/usr/local/lib/python2.7/dist-packages/pandas/core/ops.py", line 761, in wrapper
    res = na_op(values, other)
  File "/usr/local/lib/python2.7/dist-packages/pandas/core/ops.py", line 677, in na_op
    result = lib.vec_compare(x, y.astype(np.object_), op)
  File "pandas/lib.pyx", line 828, in pandas.lib.vec_compare (pandas/lib.c:14760)
ValueError: Arrays were different lengths: 3030 vs 1

I am assuming the error is in:

wPCR['Summation']=wPCR[['Option Type'] == wPCR['Option Type']].groupby(level=0).sum()

Can anyone suggest a simple way to find the proper weighted PCR from here?