In [1]:

    

import pandas as pd


    

In [11]:

    

pd.options.display.float_format = 'Rs. {:,.2f}'.format


    

In [12]:

    

data = pd.read_csv('../csvs/ditrict_treasuries/ap/2017-18/krishna.csv')


    

In [13]:

    

data.groupby('MONTH')['AMOUNT'].sum()


    

    
Out[13]:





MONTH
APRIL   Rs. 11,490,450,276.00
AUG      Rs. 8,815,905,174.00
DEC     Rs. 11,219,265,393.00
FEB      Rs. 8,110,764,572.00
JAN      Rs. 8,600,017,738.00
JULY     Rs. 9,658,248,634.00
JUNE    Rs. 14,368,120,194.00
MAR     Rs. 19,584,976,068.00
MAY     Rs. 11,089,880,818.00
NOV      Rs. 8,280,076,233.00
OCT      Rs. 7,125,799,202.00
SEP     Rs. 12,229,282,670.00
Name: AMOUNT, dtype: float64

In [14]:

    

data.columns


    

    
Out[14]:





Index([u'AMOUNT', u'DH', u'DISTRICT', u'GSH', u'MH', u'MH TYPE', u'MINH',
       u'MONTH', u'NPN', u'SDH', u'SH', u'SMH', u'mh_desc', u'smh_desc',
       u'minh_desc', u'gsh_desc', u'sh_desc', u'dh_desc', u'sdh_desc', u'cv',
       u'mhtype', u'hoa'],
      dtype='object')

In [20]:

    

data['mh_desc_filled'] = data['mh_desc'].fillna(value='N/A')
data.groupby(['mh_desc_filled'])['AMOUNT'].sum().to_csv('groupby_mh.csv')


    

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