Compute daily climatology using pandas python

Question

I am trying to use pandas to compute daily climatology. My code is:

import pandas as pd

dates      = pd.date_range(\'1950-01-01\', \'1953-12-31\', freq=\'D\         


        

Answer 1

@joris. Thanks. Your answer was just what I needed to use pandas to calculate daily climatologies, but you stopped short of the final step. Re-mapping the month,day index back to an index of day of the year for all years, including leap years, i.e. 1 thru 366. So I thought I'd share my solution for other users. 1950 thru 1953 is 4 years with one leap year, 1952. Note since random values are used each run will give different results.

...   
from datetime import date
doy = []
doy_mean = []
doy_size = []
for name, group in cum_data.groupby([cum_data.index.month, cum_data.index.day]):
  (mo, dy) = name
  # Note: can use any leap year here.
  yrday = (date(1952, mo, dy)).timetuple().tm_yday
  doy.append(yrday)
  doy_mean.append(group.mean())
  doy_size.append(group.count())
  # Note: useful climatology stats are also available via group.describe() returned as dict
  #desc = group.describe()
  # desc["mean"], desc["min"], desc["max"], std,quartiles, etc.

# we lose the counts here.
new_cum_data  = pd.Series(doy_mean, index=doy)
print new_cum_data.ix[366]
>> 634.5

pd_dict = {}
pd_dict["mean"] = doy_mean
pd_dict["size"] = doy_size
cum_data_df = pd.DataFrame(data=pd_dict, index=doy)

print cum_data_df.ix[366]
>> mean    634.5
>> size      4.0
>> Name: 366, dtype: float64
# and just to check Feb 29
print cum_data_df.ix[60]
>> mean    343
>> size      1
>> Name: 60, dtype: float64