Pandas is a Python library that allows you to work with data that is organized using rows and columns sometimes called “tabular data”. You’ve probably used a spreadsheet like Excel to manipulate tabular data. The beauty of Pandas is that you can use it for similar tasks that you would use Excel for, like simple data manipulation, but you can also use it to power complex data science tasks such as machine learning.
Pandas is a popular tool in data science. It can be used to drive business decisions across industries, helping people make decisions in areas like marketing, sales, product creation, finance, and health.
What is Pandas?
Pandas is a Python library with data analysis tools. Using this library allows you to manipulate data to get insights from it. Wes McKinney created Pandas and it was originally developed for performing quantitative analysis on financial data. In 2009, Pandas was released and since then it has grown in popularity as a tool for data analysis.
With Pandas, you can import data from databases such as Excel. Pandas lets you clean your data before analyzing it. “Cleaning” your data, often called “data wrangling” or “data munging”, is a process of removing erroneous data from your dataset prior to processing it and drawing any insights from it. It’s important to have clean and accurate data. Otherwise, the results of your analysis process will be skewed at best and useless at worst.
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When using Pandas, you’ll likely use a platform called Jupyter Notebook, a tool often used for data science projects. Jupyter Notebook lets you clean and transform data. With it, you can also perform tasks like statistical modeling and machine learning. It is similar to a code editor: You can type and run code inside it.
If you are familiar with Python, you know about data structures like arrays and dictionaries. In Pandas, the central data structure is a DataFrame, a 2D labeled data structure with columns, similar to a spreadsheet. A spreadsheet typically has a row across the top that contains the title of each row. It also has a column along the side that contains the title of each column. In Pandas, each of these “title” sections is called an index. Just as with an Excel spreadsheet, you can modify this data structure.
What is Pandas used for?
Pandas is used for data analysis in the field of data science. Data science is simply the study of data, with the goal of getting insights from sets of data. A dataset could include just a few entries or millions of single pieces of information. The data scientist’s goal is to extract meaning from that data through a process of refinement and analysis. Once the analysis has occurred, the results can be visualized with tools like Matplotlib, another Python library.
If you are interested in data science, you’ll definitely need to learn Pandas. Even if you don’t want to be a data scientist but are still interested in the process of analyzing data, you should still understand this valuable technology.
Specifically then, what can Pandas do?
- Make changes to an existing file. For example, say you have an Excel spreadsheet. You want to perform some calculations using the existing data and add some columns containing the results of those calculations. With Pandas, you can import the original spreadsheet, make the calculations using a few lines of code, and then save the spreadsheet so that it contains the results.
- Help you visualize data. Once you have cleaned your data, you can visually represent it with Matplotlib.
- Build machine learning projects. Just as you can pair Pandas with Matplotlib to serve your data visualization needs, you can also combine Pandas with Scikit-Learn to do machine learning tasks.
More and more business roles require an understanding of data. Data powers decisions made in areas like sales, marketing, and product development, meaning that even if you aren’t currently a data scientist at your company, you may be expected to extract meaning from data. Learning to use Python libraries like Pandas can help you make decisions based on data. Learn more about why everyone should be data literate.
That said, there are many job opportunities for people who want to focus on using Pandas and other Python libraries. As of this writing, on LinkedIn there are nearly 2,000 job postings for positions in the United States that mention Pandas. Other job boards where you’ll find a demand for Pandas and other data analytics/data science skills include Built In, Data Jobs, and Glassdoor. Hired is a website that shakes up the job search process for candidates looking for jobs in tech: Set up a profile, and Hired will “match” you with companies.
People with data analysis and data science skills can earn good salaries. According to Glassdoor, the average annual salary in the US for data analysts is $62,453. For companies like Google and Facebook, that number is in the $90-100k range. Indeed lists the average data analyst salary at $75,091. For data scientists, the numbers are higher: According to Glassdoor, the average data science salary is $113,309, and Indeed records the average at $122,525.
Pandas is an important skill to learn whether you want to get better at understanding data at your current job or want to be a data analyst or data scientist.
How Long Does It Take to Learn Pandas?
Assuming that you already know Python, it should take you about two weeks to get started with Pandas. Focus on basic data manipulation when you are starting your Pandas projects. As your skills improve, experiment with more complex uses, like data visualization and machine learning. Using Pandas for machine learning will require you to be familiar with additional tools like Scikit-Learn, so you’ll want to learn those skills as well.
Is It Hard to Learn Pandas?
You should know Python before learning Pandas. Fortunately, Python is a highly readable language and is suitable for programmers who are just getting started with learning web development. There are many resources to help you learn Python, including this comprehensive guide on how to learn Python.
Once you’ve become proficient at Python, you’ll be ready to try your hand at data analysis with Python libraries like Pandas.
You should also note that Pandas is built on top of NumPy, a Python library used for mathematical operations, so if you are familiar with NumPy it may be easier for you to learn Pandas.
One thing to keep in mind as you’re learning Pandas is that you can install it as part of the data science platform called Anaconda. When you install Anaconda on your machine, you are installing all the Python libraries, packages, and other tools that can be used for data science purposes, including Pandas, Matplotlib, and Jupyter Notebook.
How to Learn Pandas: Step-by-Step
Here are some general guidelines to use as you begin learning Pandas.
- Decide why you want to learn Pandas. Do you want to be a data analysis ninja in your current job as a marketer, salesperson, or project manager? Or do you want to transition into a full-fledged data analytics or data science role?
- Know Python. As mentioned above, you should already have basic Python skills before getting started with Pandas.
- Get familiar with the functionalities of Pandas. Apply your learning style to acquiring Pandas skills: Watch online tutorial videos, take a course, or read a book about Pandas. Doing this before installing and using Pandas will give you a better idea of how to best leverage Pandas.
- Install Pandas. The simplest way to install Pandas is to download Anaconda, which includes Pandas and other Python libraries and packages for data science. If you don’t want to download Anaconda, you can install Pandas here.
- Start with basic Excel/Pandas projects. One way to get the hang of Pandas is to use it along with Excel. Check out this tutorial on using Excel with Python and Pandas.
- As your skills grow, try more advanced projects. Move on from Excel with Pandas projects like this one, where you make a teacher gradebook with Python and Pandas.
- Keep learning and join the community. Continue fine-tuning your skills by building projects and learning from others. You can interact with others in the Pandas and larger data analytics/data science community on sites like Kaggle and StackOverflow.
The Best Pandas Courses
One of the best ways to increase your Pandas knowledge is to take a course. Courses allow you to dig deeper into a topic and usually include activities to help you cement your understanding. Here are some of the best courses for learning Pandas.
Udemy: Data Analysis with Pandas and Python
This course guides you from setup and installation to using Pandas like a pro. You’ll understand data manipulation concepts like visualizing, sorting and filtering, aggregating and grouping. Learn about data types like strings, booleans, and datetimes. With this course you’ll get 20.5 hours of video content and a certificate upon completion.
edX: Analyzing Data with Python
Cost: Free (Certificate is $99)
This course teaches you how to use several tools for data analysis. These include NumPy, which stands for “Numerical Python” and is a Python library used for mathematical operations; Pandas; SciPy, which stands for “Scientific Python” and is an ecosystem of software for mathematics, science, and engineering; and scikit-learn, a Python library used for machine learning.
Codecademy: Learn Data Analysis with Pandas
Cost: Codecademy Pro Membership ($19.99/month)
This course teaches you how to use Pandas to clean and aggregate large amounts of data and pair that data with Matplotlib, a Python library for data visualization, and SciPy, a Python library for mathematics, science, and engineering. The course takes just six hours to complete and includes a certificate of completion at the end.
Reading books about Python will give you the chance to digest content written by experts in the field. In addition to taking courses, books can help you get started with your own Pandas projects.
‘Python Data Science Handbook’ by Jake VanderPlas
This book is available online for free on Github. It’s a great introduction to Python’s data science libraries, including Pandas. If you’re also interested in some of Python’s other libraries, like Numpy, Matplotlib, and Sci-kit-learn, this is a great book for you.
‘Learning the Pandas Library: Python Tools for Data Munging, Data Analysis, and Visualization’ by Matt Harrison
This book allows you to learn about Pandas through examples, code samples, and graphics. It takes you from installation to handling DataFrames. It’s best read once you know Python, so be sure to have a proficient understanding of the programming language to get the most out of it. By the way, “data munging”, or “data wrangling”, is the process of refining data before it is analyzed.
‘Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython’ by Wes McKinney
This book is written by the creator of Pandas. Know that Pandas is just one skill you’ll want to have in your data analysis toolkit: You should also be familiar with other technologies like Matplotlib, NumPy, and Jupyter. This book offers you a fantastic introduction to all of these skills.
As you continue your journey of learning Pandas, you’ll want to draw on available online resources. While you’re working on projects, questions will arise, so you need to know where you can look to find the answers.
The official Pandas website allows you to download Pandas, get the Python for Data Analysis book, and get involved with the Pandas community.
One of the best resources for learning any new technology is its documentation. This resource, available for free online, contains helpful guides and information about different aspects of Pandas. You can learn how to get started with Pandas, try out tutorials, and read about all the tasks you can perform with Pandas in the user guide.
Kaggle is a data science platform that offers free data science courses in addition to other resources. One of these courses is their Pandas course. It takes about four hours to complete and helps you learn how to get insights from your data, how to perform grouping and sorting tasks. Kaggle has a repository of datasets that you can use to power your data analysis projects. Also, there are forums you can join. If you’re interested in data science, check out Kaggle.
Ready to see Pandas in action? With this interactive tutorial, you can run code examples in your browser without installing Pandas or any other technologies. This website is a great resource to help you see how Pandas works.
Should You Study Pandas?
Pandas is a Python library used for data manipulation, refining, and analysis. If you have worked with Excel before, you know that getting insights from tabular data can help drive business decisions. Pandas also works with tabular data, but offers more sophisticated functionality than Excel.
You can combine Pandas with a data visualization library like Matplotlib to create shareable findings. When you combine Pandas with Scikit-Learn, another Python library, you can perform machine learning tasks.
Pandas is a popular tool used in the data analytics and data science fields. Jobs as data analysts or data scientists usually earn great salaries, so this can be a smart career move if you have the skills necessary for this kind of work. Even if you don’t want to be a data analyst or data scientist, learning Pandas can still help you with your daily work.
So, should you study Pandas? If you’re looking for a tool that lets you analyze data in interesting ways, then yes. And if you want to pursue a career in data analysis or data science, you should definitely learn Pandas, along with other Python libraries for data science.
Start exploring the exciting world of data today with Pandas!
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