Learning Pandas

Dhanya Arumughom -

What is Pandas and what is it used for?

Python, a very widely used general purpose programming language, has lately gained a lot of popularity in the data science area and has become one of the most loved programming languages for building data models. This is mainly due to its large ecosystem consisting of many dedicated libraries for data analysis and predictive modeling. So, here we introduce to you another cute animal from the Python’s ecosystem for data analysis, Pandas.

The name Panda is derived from “Panel Data” which is a term for data sets that include time series and cross sectional data. Pandas is the Python library for data manipulation and analysis. Data manipulation is the process of changing data to make it more readable and organized. It involves data wrangling or data munging which is cleaning of the raw dataset, preparing the data and making it more structured to make analysis easier.
An overview of How Pandas manipulates data

Pandas is extremely useful in manipulating all kinds of data sets. Panda’s functionalities revolve around the two key data structures in Pandas – Series and DataFrames. While Series is a one dimensional labeled indexed array, DataFrame is a two dimensional array. DataFrame can be considered to be the python version spreadsheet, stored in memory. It consists of rows and columns which can be accessed via row and column indexes. So basically in Pandas, data sets are read as Series/DataFrame objects and various operations are applied to the columns. There is also a third type of data structure available in Pandas, the Panel which is a three dimensional array. But Panel is used less frequently for data analysis.

The Pandas data structures are built on top of NumPy array. Numpy is another Python library which is a general purpose array processing package to efficiently manipulate large multi-dimensional arrays. Since Pandas is built on top of NumPy, installation of pandas on your system requires NumPy to be installed. Indeed, Pandas provides high level data manipulation tools built on top of NumPy. NumPy by itself is a low level tool for data manipulation, while Pandas provides a more streamlined way of working with numerical and tabular data and offers added functionalities like attaching labels to data, working with missing data, grouping data, pivoting data, plotting graphs, etc.

Why Pandas

Pandas is one Python library which has been instrumental in boosting Python’s usage in the area of Data Science. It is one of the most preferred tool for data munging, exploring and making them ready for modeling. It provides fast, flexible and expressive data structures and makes working with many different kinds of data easy and intuitive. It can deal with tabular data with heterogeneously typed columns, ordered and unordered time series data, or any other form of statistical data sets.

Another cool thing about Pandas is, it can take the data set from various sources and different file formats like CSV or TSV file, SQL database, Excel and import them as dataframes. A pandas DataFrame provides a plethora of utilities which make manipulating data a lot easier.

Pandas can handle missing data easily, align data automatically, has got powerful and flexible group by functionalities, merge and join data sets, reshape and pivot data sets and has time series specific functionalities for date range generation, frequency conversion, etc. These are just a few of the things that Pandas can do, there are many more. To, summarize Pandas comes handy to a data scientist and simplifies things for the most important and time consuming phases of data analysis- data exploration and munging. Many of the tasks would be tedious if you don’t use Pandas.

Learn Pandas

Now that you have seen, what Pandas is and why is it used for data analysis, I hope you have enough reasons to learn Pandas if you want to work with data cleansing and analysis. Here are a few courses and tutorials to start with!

Pre requisites: Pandas being a Python library built on top of NumPy library, you should know the basics of Python programming language and the NumPy library before you start with Pandas. Many courses and tutorials have NumPy and Pandas together.

Best Tutorials for Pandas

We have collected a list of some of the best beginner level tutorials on AWS.

Best Courses For Pandas

However, if you already decided to take a deep dive, here are some of the best courses we found on Pandas.

For Beginners
For Intermediate Level