#!/usr/bin/env python
# coding: utf-8

# #GLACINDIA Workshop
# ##Part 7: Time series analysis (Pandas) 2

# Nikolay Koldunov
# 
# koldunovn@gmail.com

# In[1]:


import pandas as pd
import numpy as np
get_ipython().run_line_magic('pylab', 'inline')
import seaborn as sns


# We are going to use data from [Indian Institute Of Tropical Meteorology](http://www.tropmet.res.in/Data%20Archival-51-Page). In particular [Longest Instrumental Rainfall Series of the Indian Regions (1813-2006)](http://www.tropmet.res.in/static_page.php?page_id=52) time series.

# In[2]:


data = np.loadtxt("8-all_ind.txt", skiprows=2)


# Data came in as a table with months and yearly averages as columns:

# In[3]:


data


# We actually need only 12 columns with months:

# In[4]:


data.shape


# In[5]:


data[:,1:13].shape


# One way to make continuous time series is to flatten the array:

# In[6]:


data_flat = data[:,1:13].flatten()


# Now its one dimentional:

# In[7]:


data_flat.shape


# Let's now construct a pandas DataFrame.

# We know the time range of the data, so we can create time array, that can be used as an index:

# In[8]:


dates = pd.period_range('1813-01','2006-12', freq="M")


# In[9]:


dates


# Now we can put together our data and index:

# In[10]:


df = pd.DataFrame({'PRC':data_flat}, index=dates)


# In[12]:


df.plot()


# Now create yearly means:

# In[13]:


df.resample('A', how='sum').plot()


# We actally have information about daily means in the original data. So we can compare out result: 

# In[14]:


plot(data[:,13])


# ##Exersise
# 
# Extract data for JJAS period, put them in to pandas Data frame variable and plot.

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