Difference between dictionary and pandas series in Python

Question

I have a requirement to keep data in key value pairs. I search and found 2 ways in python:

1. default data structure dictionary.

   x = {'key':value}
   value = x['key']
   

2. series of pandas data structure.

   x = pandas.Series({'key':value})
   value = x.key
   

I want to know the difference between this two apart from syntax.

Answer 1

Always read the docs first
But since you asked:

• Dictionaries are one of python's default data structures which allow you to store key: value pairs and offer some built-in methods to manipulate your data, which you can read on the docs (here is a good summary to jump start your reading process).
• Panda's Series are one-dimensional ndarrays with axis-labels, which allow you to store array-like, dict, or scalar values and are one of numpy's (a scientific computing python library) built-in data structures.
  If you read the docs provided above (see: Panda's Series link) you will notice that they come with a vast amount of methods and attributes quite different, for the most part, from those of a python dictionary.

So it is not just a syntax difference to say the least.

If you only need to store some key:value pairs, your best and more elegant solution is to use the default dictionary. If you need to make some complex data manipulation on the stored data, then consider using panda's series.

Answer 2

There are 2 important differences.

1) Syntax and associated methods Allows for complex data manipulation in Panda series that would be difficult to achieve using a standard dictionary.

2) Order Standard python dictionaries are unordered sets; values can only be accessed by keys. Data in Panda series can be accessed by keys BUT can also be accessed with a numeric index because they are ordered.

In some ways, Panda series combine the best worlds of standard lists and standard dictionaries in python, but then top it off with some great data manipulation methods.