Quick Discussion On Python Pandas

Introduction

 

Pandas is an opensource library built on top of Numpy. It allows for fast analysis, data cleansing, data preparation and has data visualization features. It can also work with data from a wide variety of sources.

 

How to install Pandas?

 

Go to your command line or terminal and use:

 

pip install pandas (if you have installed Python by directly going to here)

 

OR

 

conda install pandas (if you have Anaconda distribution of Python)

 

Either of the above should work. :)

 

Let’s start with the first data type while working with Pandas.

 

Series and how they interact with pandas

 

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. It can hold any arbitrary Python object.

 

Let us import Pandas and explore the Series object.

 

Creating a Series

 

 

 

You can convert a list, Numpy array, or dictionary to a Series.

• Using lists

 

 

It looks a lot like Numpy array except it has an index 0 1 2 and corresponding actual data.

 

Now, here we have Index that is labeled meaning we can call these data points using this labeled index. We can also directly use it like this:

 

• Using Numpy arrays.
  
  
  
  
• Using Dictionary.
  
  

Note
A Pandas Series can hold a variety of object types. Such as,

 

 

 

Although we are not going to use it, a Panda Series is this flexible.

 

Grabbing data from Series

 

Here, we are creating two series and storing them. In order to grab data out of a series, we refer to the index such as,

 

 

 

Performing various operations on Series

 

We can also perform various with series such as addition,

 

 

 

Notice, what is happening, it's going to try to match up the operation based off the index. It has got matches for India and USA, so the operation has been performed here. But for other indices, there’s no match, so it has put a null(NaN object). Also, notice that the data type here is converted to float in order to retain all the information possible.

 

For division and multiplication see below,

 

 

 

Thanks for reading!

 

I hope you have enjoyed it throughout.

 

Note

There will be a continuation of this article. We will discuss Data Frame and how it works with Pandas. So keep reading!