Python Pandas Tutorial: An Introduction for Beginners

If you鈥檙e learning Python for data analytics, odds are you鈥檝e heard of the pandas library. Back when I first started to learn data analytics, the first tool I used was spreadsheets since I didn鈥檛 know how to code.聽

Although tools like Excel and Google Sheets are powerful, spreadsheets become difficult to use when handling large datasets and can quickly feel cumbersome or run out of memory. If you have to work with big data containing millions or billions of records, Python is a much better tool for the job.聽

Pandas is one of the most popular Python libraries for handling data, and is widely used in analytics, data science, and finance because of its robust functionality and ability to process data quickly. Created by Wes McKinney, the Pandas library has remained open source and has a solid community that is regularly updating the package.聽

In this article we鈥檙e going to walk through a Python pandas tutorial so you have a better understanding of how and when to use it. We鈥檒l cover the following topics in this article:

This article assumes you already have a basic understanding of the Python programming language. Check out this article if you鈥檙e new to Python and want to learn more about it.聽

Before getting into the Python pandas tutorial code examples, let鈥檚 review how data analysts use the Pandas library. Pandas is a powerful library that provides easy-to-use data structures and data analysis tools for handling and manipulating numerical tables and time series data. The library uses Cython under the hood, so it loads your data into memory efficiently.

One of the main benefits of using pandas is its ability to read in and work with a wide range of data formats, like CSV, Excel, databases, and JSON. It is a single library that allows you to聽 import data from various sources, clean and transform the data, and then analyze it and visualize it using a variety of functionality. Because of these reasons, the pandas library is often the first library you鈥檒l explore when learning data analytics with Python.

In pandas, one of the primary data structures is the DataFrame, which makes it easy to work with data structured into rows and columns. Once the data is in a DataFrame, it鈥檚 possible to group the data and apply aggregate functions such as mean() or sum() to calculate statistics. It even has a pivot_table() function to create pivot tables, which are a useful way to summarize data. We鈥檒l cover these functions in depth in the following sections.

In addition to these basic functions, pandas also provides a range of more advanced tools for data analysis, such as time series analysis, statistical modeling, and machine learning. I can use these tools to perform complex data analysis tasks and extract valuable insights from my data. Ultimately, the Python pandas library is an essential tool for making sense of your data.

When using the Pandas library, most of the functionality revolves around two data structures: Series and DataFrame. Many of the operations in the pandas library鈥攍ike aggregating, slicing, and transforming data鈥攃an be done on both a Series and a DataFrame.聽

Think of a Series as a single column in a spreadsheet. It is a 1-dimensional object, similar to an array. It can hold any data type and has a labeled axis, referred to as the index. Although similar, a Series has differences from a numpy array.

聽For example, a series of numbers would look like this:

Notice the index starts at 0. Both DataFrames and Series use an index that starts at 0, which is important to know when iterating through the values in loops.聽

If a series is similar to a single column in a spreadsheet, think of a DataFrame as the complete spreadsheet. A DataFrame is a 2-dimensional array-like object with an index that is used to represent tabular data. Here is an example of a DataFrame:

The Index is created by default starting with 0, but we could also create our own when we initialize the DataFrame by passing a value to the index parameter. We鈥檒l cover this more in depth when looking at some code in the following sections.

Before we can create data structures in our Python pandas tutorial, we need to make sure we have the pandas package installed. Installing it is simple and can be done a couple different ways. The documentation recommends using pip or conda to install the pandas package.

OR

When importing pandas as a dependency in our code, we鈥檒l follow best practice and give it an alias of pd since that is most commonly used throughout pandas documentation.

Now that the package has been installed, let鈥檚 begin exploring the pandas Series. The Series is an essential part of the pandas library, and can be constructed from different Python objects like lists and dictionaries. In this section, we鈥檒l review how to create a Series and explore the index. We鈥檒l also cover how to select elements from a Series.

Creating a Series in pandas can be done by using the available Series constructor. The syntax looks like this:

pandas.Series(data, index, dtype, copy)

Let鈥檚 start our Python pandas tutorial by learning how to transform a Python list into a pandas Series.聽