<jupyter_start><jupyter_text>We have been working with data obtained from [GHCN (Global Historical Climatology Network)-Daily](http://www.ncdc.noaa.gov/oa/climate/ghcn-daily/) data. Convinient way to select data from there is to use [KNMI Climatological Service](http://climexp.knmi.nl/selectdailyseries.cgi?id)<jupyter_code>%matplotlib inline
import pandas as pd
import numpy as np
msk = pd.read_table('xgdcnRSM00027612.dat.txt', sep='\s*', skiprows=5, \
        parse_dates={'dates':[0, 1, 2]}, header=None, index_col=0, squeeze=True )
msk.plot()<jupyter_output><empty_output><jupyter_text>Exercise- Select TMAX data set for your home city or nearby place- Open it with pandas- Plot data for 2000-2010- Find maximum and minimum TMAX for all observational period- Find mean temperature- Plot monthly means- Plot maximum/minimum temperatures for each month- Plot seasonal mean for one of the seasons- Plot overall monthly means (use groupby(msk.index.month))- Plot daily season cycle ( use index.dayofyear )- Plot daily seasonal cycle and +- standard deviation<jupyter_code>msk['2000':'2010'].plot()
msk.min()
msk.max()
msk.mean()
msk.resample('M').plot()
msk.resample('M',how=['max','min']).plot()
msk_s = msk.resample('Q-NOV')
msk_s[msk_s.index.quarter==1].mean()
msk.groupby(msk.index.month).mean().plot(kind='bar')
msk.groupby(msk.index.dayofyear).mean().plot()
seas = msk.groupby(msk.index.dayofyear).mean()
seas_plus = seas + msk.groupby(msk.index.dayofyear).std()
seas_minus = seas - msk.groupby(msk.index.dayofyear).std()
seas.plot()
seas_plus.plot()<jupyter_output><empty_output>
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