Open a Jupyter Notebook and import the Pandas library as shown in the steps below. If you you have never used or even heard about the Jupyter Notebook, please go to my Jupyter/IPython Notebook tutorial before continuing.
Click the Windows button and start typing command prompt as shown below.
Please click on the icon as shown below to start the Command Prompt.
Using the Command Prompt, type jupyter notebook as shown below to start the Jupyter application. Note that you need to have Python and all associated Jupyter libraries installed for this to work. Again, if you are confused, please go to my Jupyter/IPython Notebook tutorial before continuing.
If all goes well, your browser will open the Jupyter application and you can then proceed to start a new notebook (starting a new notebook was covered in a previous tutorial). If you have made it this far, you are already way ahead of the game, good job! Import the Pandas library as shown below.
Now that we are ready to go and we have the Pandas library loaded, let's talk about the basic building block of Pandas. The Pandas dataframe is the basic and most likely the most common data structure you will use while working with Pandas. As a Data Scientist, your main goal is to get your data into a Pandas dataframe. When you get your data into a dataframe, you can then make use of the vast features the library has to offer. Just like you need to get your data into an Excel file to actually do something with Excel, this is the same reasoning for getting the data into Pandas.
The code below shows you how to create a very basic dataframe consisting of two columns using some made up data. Congrats! your very first dataframe.