CH5 Getting Started With Pandas



In [2]:

    
from pandas import Series, DataFrame
import pandas as pd



In [4]:

    
obj = Series([4,7,-5,3])
obj









    Out[4]:





0    4
1    7
2   -5
3    3
dtype: int64



In [5]:

    
obj.values









    Out[5]:





array([ 4,  7, -5,  3])



In [6]:

    
obj.index









    Out[6]:





Int64Index([0, 1, 2, 3], dtype='int64')



In [9]:

    
obj2 = Series([4,6,8,9], index = ['a','d','t','y'])



In [10]:

    
obj2









    Out[10]:





a    4
d    6
t    8
y    9
dtype: int64



In [11]:

    
obj2.index









    Out[11]:





Index([u'a', u'd', u't', u'y'], dtype='object')



In [12]:

    
obj2['y']









    Out[12]:





9



In [13]:

    
obj2['y']=3



In [14]:

    
obj2['y']









    Out[14]:





3



In [18]:

    
obj2[obj2 >= 6] # note that not y =3









    Out[18]:





d    6
t    8
dtype: int64



In [23]:

    
obj2*2









    Out[23]:





a     8
d    12
t    16
y     6
dtype: int64

Should you have data contained in a Python dict, you can create a Series from it by passing the dict



In [19]:

    
sdata = {'Ohio':30000, 'Texas': 71000, 'Oregon': 16000, 'Utah': 5000}



In [20]:

    
obj3 = Series(sdata) # treats the first colomn as the index



In [21]:

    
obj3









    Out[21]:





Ohio      30000
Oregon    16000
Texas     71000
Utah       5000
dtype: int64



In [22]:

    
obj3.index









    Out[22]:





Index([u'Ohio', u'Oregon', u'Texas', u'Utah'], dtype='object')



In [24]:

    
states = ['California', 'Ohio', 'Oregon', 'Texas']



In [25]:

    
obj4 = Series(sdata, index = states)



In [26]:

    
obj4









    Out[26]:





California      NaN
Ohio          30000
Oregon        16000
Texas         71000
dtype: float64



In [27]:

    
pd.isnull(obj4)









    Out[27]:





California     True
Ohio          False
Oregon        False
Texas         False
dtype: bool

You can also do this:



In [28]:

    
obj4.isnull()









    Out[28]:





California     True
Ohio          False
Oregon        False
Texas         False
dtype: bool

A critical Series feature for many applications is that it automatically aligns differently- indexed data in arithmetic operations:



In [29]:

    
obj3 + obj4









    Out[29]:





California       NaN
Ohio           60000
Oregon         32000
Texas         142000
Utah             NaN
dtype: float64



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