Series

The first main data type we will learn about for pandas is the Series data type. Let's import Pandas and explore the Series object.

A Series is very similar to a NumPy array (in fact it is built on top of the NumPy array object). What differentiates the NumPy array from a Series, is that a Series can have axis labels, meaning it can be indexed by a label, instead of just a number location. It also doesn't need to hold numeric data, it can hold any arbitrary Python Object.

Let's explore this concept through some examples:



In [1]:

    
import numpy as np
import pandas as pd

Creating a Series

You can convert a list,numpy array, or dictionary to a Series:



In [2]:

    
labels = ['a', 'b', 'c']
my_list = [10, 20, 30]
arr = np.array([10, 20, 30])
d = {'a': 10,'b': 20,'c': 30}

Using Lists



In [3]:

    
pd.Series(data = my_list)









    Out[3]:





0    10
1    20
2    30
dtype: int64



In [4]:

    
pd.Series(data = my_list,
          index = labels)









    Out[4]:





a    10
b    20
c    30
dtype: int64



In [5]:

    
pd.Series(my_list, labels)









    Out[5]:





a    10
b    20
c    30
dtype: int64

NumPy Arrays



In [6]:

    
pd.Series(arr)









    Out[6]:





0    10
1    20
2    30
dtype: int32



In [7]:

    
pd.Series(arr, labels)









    Out[7]:





a    10
b    20
c    30
dtype: int32

Dictionary



In [8]:

    
pd.Series(d)









    Out[8]:





a    10
b    20
c    30
dtype: int64

Data in a Series

A pandas Series can hold a variety of object types:



In [9]:

    
pd.Series(data = labels)









    Out[9]:





0    a
1    b
2    c
dtype: object



In [10]:

    
# Even functions (although unlikely that you will use this)
pd.Series([sum, print, len])









    Out[10]:





0      <built-in function sum>
1    <built-in function print>
2      <built-in function len>
dtype: object

Using an Index

The key to using a Series is understanding its index. Pandas makes use of these index names or numbers by allowing for fast look ups of information (works like a hash table or dictionary).

Let's see some examples of how to grab information from a Series. Let us create two sereis, ser1 and ser2:



In [11]:

    
ser1 = pd.Series([1, 2, 3, 4], 
                 index = ['USA', 'Germany', 'USSR', 'Japan'])



In [12]:

    
ser1









    Out[12]:





USA        1
Germany    2
USSR       3
Japan      4
dtype: int64



In [13]:

    
ser2 = pd.Series([1, 2, 5, 4], 
                 index = ['USA', 'Germany', 'Italy', 'Japan'])



In [14]:

    
ser2









    Out[14]:





USA        1
Germany    2
Italy      5
Japan      4
dtype: int64



In [15]:

    
ser1['USA']









    Out[15]:





1

Operations are then also done based off of index:



In [16]:

    
ser1 + ser2









    Out[16]:





Germany    4.0
Italy      NaN
Japan      8.0
USA        2.0
USSR       NaN
dtype: float64

Let's stop here for now and move on to DataFrames, which will expand on the concept of Series!

Series

Creating a Series

Data in a Series

Using an Index

Great Job!