1.1. Introduction to Pandas#

1.1.1. What is Pandas?#

Pandas is a Python library in Python that allows for you to easily work with tabular data that is often stored in Excel files or .csv files. It is often considered one of the most vital Python libraries for data analysis because of its robust capabilities. There are other Python libraries that allow you to work with Excel data, such as XLSX and XLRD, which are leveraged by Pandas on the back end to interact with Excel spreadsheets. However, most tabular data is stored in .csv files, or Comma Separated Values files. There are other variations of this same structure (such as .tsv), but they all work the same. They use a special character to denote change in the table to the next column and/or row.

1.1.2. Why use Pandas?#

If you work with data at all or plan to in the future, becoming comfortable with Pandas early on will make your life a lot easier. There are other Python libraries that allow you to work with .csv files, such as the CSV library, but these are not the same as Pandas. Pandas has one large advantage over the CSV library. It not only allows you to input .csv files into Python, it allows you to easily load them as DataFrames.

DataFrames are special data structures that contain not only the raw data in a table, but preserve the structure and hierarchy of that table. By loading .csv files as a DataFrame, Pandas not only allows you easy access to your data, but a powerful way to analyze it within a script. In addition to that, Pandas also has robust built-in features that we will explore throughout this part of the textbook.

Finally, many resources for data analysis are built on top of Pandas. Being able to understand tutorials and documentation of these resources, therefore, often requires a basic understanding of Pandas.

So, why use Pandas? Because it is the best library for importing and working with tabular data. It allows you to easily read files as DataFrames. And, it is a required library for most data analysis resources.

1.1.3. How to Install Pandas#

Installing pandas is as easy as installing any other Python library. If you are working within a Jupyter notebook like this one, you can execute the following command within a cell:

!pip install pandas

In Jupyter notebooks the ! indicates that you want to perform a command in the terminal. We then specify what command we want to run. In this case, pip install. Finally, we specify the library we want to install, pandas. If you are not working within a Jupyter notebook, you can do the same thing by opening up your terminal, such as Command Prompt on Windows, and executing the same command.

1.1.4. How to Import Pandas#

Once you have installed Pandas, it is time to import it. It is Pythonic, or good Python practice, to use import pandas as pd. By importing a library as something, you give it that specific variable as a name. This has a few benefits. First, it makes Pandas easier to call in your script, because you can call the library with “pd” rather than “pandas”. Second, all Pandas tutorials and posts on Stackoverflow will use “pd”. This means that your script will conform to traditional convention.

import pandas as pd

After executing the above command, you will have successfully imported pandas into your Python script. In the next notebook, we will start working with Pandas