In [1]:

    

import pandas as pd


    

In [2]:

    

totals = pd.read_csv('totals.csv').set_index(keys=['name'])
counts = pd.read_csv('counts.csv').set_index(keys=['name'])
#totals


    

In [3]:

    

#counts


    

In [4]:

    

print("City with lowest total preciptiation:")
print(totals.sum(axis=1).idxmin())


    

    




City with lowest total preciptiation:
YELLOWKNIFE A

In [5]:

    

print("Average precipitation in each month:")
print(totals.sum()/counts.sum())


    

    




Average precipitation in each month:
2016-01    27.779783
2016-02    30.426295
2016-03    29.410072
2016-04    17.966543
2016-05    21.344569
2016-06    20.694981
2016-07    24.977186
2016-08    19.856618
2016-09    24.065637
2016-10    44.684411
2016-11    34.615686
2016-12    32.366795
dtype: float64

In [6]:

    

print("Average precipitation in each city:")
print(totals.sum(axis=1)/counts.sum(axis=1))


    

    




Average precipitation in each city:
name
BURNABY SIMON FRASER U            47.778598
CALGARY INTL A                    14.333333
GANDER INTL A                     39.912329
HALIFAX INTL A                    41.445055
REVELSTOKE                        23.409972
SHERBROOKE                        23.681440
TORONTO LESTER B. PEARSON INT'    17.521978
VANCOUVER INTL A                  36.522222
YELLOWKNIFE A                      6.154270
dtype: float64