In this post, we are going to see How to setup and run Pandas on Google Colab. Pandas is an open source python library that you can use to read, write and analyse the data. Most of the users also make use of it for reading common data file formats.
I mostly use it for reading the CSV files and other files which it allows me to read. Before moving further, if you have not yet followed the Google Colab Tutorial, please do so. I have also covered the library Numpy Google Colab tutorial. You may want to check that too.
Google colab being one of the handy setup that you can use for the data science exercises and executing some of the programs. You would find that if you can do the work properly it can pretty much cover most of your data science work on cloud.
I have created a video to give you an overview on how to use the Google Colab and Pandas Library for the data science work. You should give this one a try.
Open Google Colab
First thing is to open the google colab. You need free account and also make sure to either go from official colab website or you can also use the Google drive for opening the notebook. Either one of the method is good enough to proceed.
You would find the GUI inside the webpage something like this:
Setup and Use Pandas
Pandas library can be installed using the command below. Where you would also get the dependent files installed if you have not yet. You would find this library good enough for simple usage.
!pip install pandas
Now type in the following code into the code editor and then hit run. You would get the output immediately viewable in the next cell.
You can use the pandas library for variety of types of the work. You can use it for the time series functions and the charts. You can use it for plotting the datafrme output. You can use it for analyzing the data.
If you are working with dataset you are going to find the use for the google pandas in your everyday coding. You would work with dataset a lot while you are doing the data science, analytics work. There are some other libraries like scikit and ML libraries which would be useful.
Now next few posts, I wish to cover the libraries like Pytorch and few other libraries. Then maybe take some break and try some other topics before going back with the data science topics. I am sure it would be reasonable and fun to try out these libraries.
If you happen to like this content, do give me feedback over there and that would help me improve my efforts in near future.