Python Advanced- Series

Today we will learn about one of the most important data structure in pandas library- Series.
It is similar to a NumPy 1-dimensional array.
In addition to the values that are specified by the programmer, pandas attaches a label to each of the values. If the labels are not provided by the programmer, then pandas assigns labels ( 0 for first element, 1 for second element and so on).
A benefit of assigning labels to data values is that it becomes easier to perform manipulations on the dataset as the whole dataset becomes more of a dictionary where each value is associated with a label.
For more details about Series please visit Series in pandas
Let's understand Series and some operations by below code snippets-

Series example:-

Knowing values and indexing of Series:-

Defining custom indexing in your Series:-

Accessing your Series is as same as we saw in NumPy:-

Let's do some mathematical operations in our series-

If you have a dictionary, you can create a Series data structure from that dictionary. Suppose you are interested in EPS values for firms and the values come from different sources and is not clean. In that case you don't have to worry about cleaning and aligning those values-

If any index don't have the value matching the key then it will show as NaN(not a number):-

Make use of isnull() function to find out if there are any missing values in the data structure-

Key feature of Series Data  is that you don't have to worry about data alignment.
Understand this key feature with below example- if we have run a word count program on two different files and we have the following data structures-

Now if we want to calculate the sum of common words in combined files, then we don't have to worry about data alignment. If we want to include all words, then we can take care of NaN values and compute the sum. By default, Series data structure ignores NaN values-

So keep practicing by your own with above examples in your notebook and comment if you face any issue.