3.9. Other Python Sequence Types¶

There are number of modules that add specialized sequence data structures often useful in certain situations.

• The collections module from the standard module, which includes

  • deque, a list-like structure which can efficiently append objects to either ends.

    In [1]: from collections import deque
    
    In [2]: d = deque([1,2,3])
    
    In [3]: d.append(4)
    
    In [4]: d.appendleft(0)
    
    In [5]: d
    Out[5]: deque([0, 1, 2, 3, 4])
    

  • nametuple, a tuple that allows efficient accessing records by name.

• The numpy numerical library provides

  • arrays and matrices for efficient numerical computations

    In [6]: import numpy as np
    
    In [7]: v = np.array([1,2,3])
    
    In [8]: v
    Out[8]: array([1, 2, 3])
    
    In [9]: m = np.matrix([[1,2],[3,4]])
    
    In [10]: m
    Out[10]: 
    matrix([[1, 2],
            [3, 4]])
    

  • functions that can be applied to all elements of a numpy array.

    In [11]: np.cos(v)
    Out[11]: array([ 0.54030231, -0.41614684, -0.9899925 ])
    

• The pandas library provides

  • data frames similar in construction and usage to the data.frame from R

    In [12]: import pandas as pd
    
    In [13]: d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),
       ....:          'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}
       ....: 
    
    In [14]: df = pd.DataFrame(d)
    
    In [15]: df
    Out[15]: 
       one  two
    a  1.0  1.0
    b  2.0  2.0
    c  3.0  3.0
    d  NaN  4.0
    
    In [16]: pd.DataFrame(d, index=['d', 'b', 'a'])
    Out[16]: 
       one  two
    d  NaN  4.0
    b  2.0  2.0
    a  1.0  1.0
    
    In [17]: pd.DataFrame(d, index=['d', 'b', 'a'], columns=['two', 'three'])
    Out[17]: 
       two three
    d  4.0   NaN
    b  2.0   NaN
    a  1.0   NaN
    

    Note

    This example was copied from the pandas documentation.

• The pyrsistent library provides

  • persistent and immutable data structures for use in functional programming.