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

# In[1]:


# IMPORT
import pandas as pd


# ### Pandas Data Structure - Series

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sr = pd.Series([1,2,3,-3,4,0])
print(sr)


# In[3]:


# get values frm Series
print(sr.values)


# In[4]:


# get indexes from Series
print(sr.index)


# In[5]:


# some calc on Seires
print("2nd indexed value:",sr[2],
      "\nadding 2nd + 4rd index:", sr[2]+sr[4])


# In[6]:


# creating Series with named index
sr1 = pd.Series([1,2,0,-3,4,-6], index=['a','b','c','d','e','f'])
print(sr1)


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for e in sr1.index:
    print(e, "\t", sr1[e])


# In[8]:


# getting only positive values from Series
print(sr1[sr1>=0])   


# In[9]:


# we can convert a dictionary into Series
sdata = {'Ohio': 35000, 'Texas': 71000, 'Oregon': 16000, 'Utah': 5000}
print(sdata, type(sdata))


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sr2 = pd.Series(sdata)
print(sr2)


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ind = ['California', 'Alabama', 'Oregon', 'Texas']
sr3 = pd.Series(sdata, index=ind)
print(sr3)


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# Californica and Alabama's value is NaN (Not a Number) cause there is no value for these index in sdata
# let's check for NULLability
print(pd.isnull(sr3))


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print(pd.notnull(sr3))


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print(sr2+sr3)


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# Series object can have Name and index Name
sr3.name = "States"
sr3.index.name = "Population"
print(sr3)


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# Stats about Series
print(sr3.name,"\t",sr3.shape,"\t",len(sr3))  #print name, shape and length


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print(sr3.index,"\t",sr3.index.name)  #print indexes and index name


# In[18]:


print(sr3.dtype)  #print data type


# In[19]:


print(sr3.values) #print Series values


#