Series

Series is a one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.). The axis labels are collectively referred to as the index.

documentation: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html



In [2]:

    
import pandas as pd
import numpy as np

Create series from NumPy array

number of labels in 'index' must be the same as the number of elements in array



In [3]:

    
my_simple_series = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])
my_simple_series









    Out[3]:





a    0.591872
b   -1.381102
c    2.354536
d   -1.339378
e   -1.888910
dtype: float64



In [4]:

    
my_simple_series.index









    Out[4]:





Index(['a', 'b', 'c', 'd', 'e'], dtype='object')

Create series from NumPy array, without explicit index



In [5]:

    
my_simple_series = pd.Series(np.random.randn(5))
my_simple_series









    Out[5]:





0   -0.839923
1    0.310805
2   -0.491671
3   -0.418519
4   -1.785353
dtype: float64

Access a series like a NumPy array



In [6]:

    
my_simple_series[:3]









    Out[6]:





0   -0.839923
1    0.310805
2   -0.491671
dtype: float64

Create series from Python dictionary



In [7]:

    
my_dictionary = {'a' : 45., 'b' : -19.5, 'c' : 4444}
my_second_series = pd.Series(my_dictionary)
my_second_series









    Out[7]:





a      45.0
b     -19.5
c    4444.0
dtype: float64

Access a series like a dictionary



In [8]:

    
my_second_series['b']









    Out[8]:





-19.5

note order in display; same as order in "index"

note NaN



In [9]:

    
pd.Series(my_dictionary, index=['b', 'c', 'd', 'a'])









    Out[9]:





b     -19.5
c    4444.0
d       NaN
a      45.0
dtype: float64



In [10]:

    
my_second_series.get('a')









    Out[10]:





45.0



In [11]:

    
unknown = my_second_series.get('f')
type(unknown)









    Out[11]:





NoneType

Create series from scalar

If data is a scalar value, an index must be provided. The value will be repeated to match the length of index



In [12]:

    
pd.Series(5., index=['a', 'b', 'c', 'd', 'e'])









    Out[12]:





a    5.0
b    5.0
c    5.0
d    5.0
e    5.0
dtype: float64



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