Vectorized Operations

not necessary to write loops for element-by-element operations
pandas' Series objects can be passed to MOST NumPy functions

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



In [7]:

    
import pandas as pd
import numpy as np



In [8]:

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









    Out[8]:





a      45.0
b     -19.5
c    4444.0
dtype: float64

add Series without loop



In [9]:

    
my_series + my_series









    Out[9]:





a      90.0
b     -39.0
c    8888.0
dtype: float64



In [10]:

    
my_series









    Out[10]:





a      45.0
b     -19.5
c    4444.0
dtype: float64

Series within arithmetic expression



In [11]:

    
my_series + 5









    Out[11]:





a      50.0
b     -14.5
c    4449.0
dtype: float64

Series used as argument to NumPy function



In [12]:

    
np.exp(my_series)









    Out[12]:





a    3.493427e+19
b    3.398268e-09
c             inf
dtype: float64

A key difference between Series and ndarray is that operations between Series automatically align the data based on label. Thus, you can write computations without giving consideration to whether the Series involved have the same labels.



In [13]:

    
my_series[1:]









    Out[13]:





b     -19.5
c    4444.0
dtype: float64



In [14]:

    
my_series[:-1]









    Out[14]:





a    45.0
b   -19.5
dtype: float64



In [15]:

    
my_series[1:] + my_series[:-1]









    Out[15]:





a     NaN
b   -39.0
c     NaN
dtype: float64

Apply Python functions on an element-by-element basis



In [16]:

    
def multiply_by_ten (input_element):
    return input_element * 10.0



In [17]:

    
my_series.map(multiply_by_ten)









    Out[17]:





a      450.0
b     -195.0
c    44440.0
dtype: float64

Vectorized string methods

Series is equipped with a set of string processing methods that make it easy to operate on each element of the array. Perhaps most importantly, these methods exclude missing/NA values automatically.



In [18]:

    
series_of_strings = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])



In [19]:

    
series_of_strings.str.lower()









    Out[19]:





0       a
1       b
2       c
3    aaba
4    baca
5     NaN
6    caba
7     dog
8     cat
dtype: object



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