Python is kind of like the frozen yogurt of programming languages — it’s extremely popular and versatile on its own, but it’s even better when you add toppings. Of course, by “toppings,” we’re talking about the many Python libraries and tools that level-up what you can do with the language.
With data science, in particular, there are lots of pre-written Python code packages and extra tools that allow you to work with data in more advanced ways, explains Ada Morse, Codecademy Curriculum Developer in Data Science. In the new free course Getting Started with Python for Data Science, you’ll get to use Pandas, a Python module that’s used for data manipulation.
“Pandas is really helpful because instead of having to reinvent how to work with tables of data, a lot of the basic code has already been written,” Ada says. “Now your job is just to apply that to the dataset that you want to work with.” Pandas is just a taste of what you can do with Python, and there are thousands of additional libraries you can choose from. Curious which data science libraries and tools you should try first? Here are the most common, beginner-friendly Python libraries and tools that you can use for data science.
This is the standard data science library that’s used for data manipulation in Python. “Anyone who does data science in Python works in Pandas — and actually, a lot of the time the vast majority of the code will be Pandas as opposed to Python,” Ada says. Pandas comes with pre-packaged code for working with tables of data that’s organized into rows and columns.
In Getting Started with Python for Data Science, you’ll start working with Pandas right away to import datasets, summarize the data, identify problems, and explore possible outcomes.
In our new course Getting Started with Python for Data Science, you’ll get hands-on practice using Jupyter Notebooks, an interactive workspace for developing data science code and visualizations, Ada says. With Jupyter Notebooks, you can execute Python code, review the output quickly, and record your results just like you would in an analog notebook.
Jupyter Notebooks is an essential tool for data analysis, because you can test a bunch of hypotheses and keep a running log of your results. “Most working data scientists do their work in Jupyter Notebook,” Ada says.
If you’re a beginner who’s just learning how to code, using Jupyter Notebooks to test lines of code one at a time is super helpful. Jupyter Notebooks supports other programming languages besides Python, like R and Java. “If then you want to learn something else, chances are you can do that in Jupyter Notebooks and feel at home,” Ada says.
If you want to make compelling data visualization and graphical plots, you’ll want to use MatPlotLib. With this Python package, you can make all kinds of interactive visualizations including pie charts, heat maps, histograms, and 3D bar charts. (Take a look at this gallery to see all the gorgeous MatPlotLib data visualizations you can use in your work.) In the course Learn Data Visualization with Python, you’ll turn data into impactful line, bar, and pie graphs.
Another Python add-on for data visualizations is Seaborn, which enables you to give your charts some style and flair. Using Seaborn you can adjust the background color, grids, borders, and fonts within a chart. Colors and aesthetics might seem superfluous, but when you’re trying to communicate insights with data, style can greatly affect how well your audience perceives your message. In the skill path Learn Data Visualization with Python, you’ll work with Seaborn to style a MatPlotLib graph.
NumPy (short for “NumericalPython”), is the standard library for working with numbers in Python, and is frequently used in science and engineering. With NumPy, you can quickly complete numerical operations and create multi-dimensional arrays and matrices. Want to understand how to use this Python library for statistical analysis? Check out the course Learn Statistics with NumPy.
BeautifulSoup is a quirky name for a highly practical package that allows you to scrape data from the web in a format that’s suitable for Python. Once you’ve scraped your data with BeautifulSoup, you can do all kinds of things with Python, like make visualizations with MatPlotLib or analyze it with Pandas. You can learn how to use BeautifulSoup in our course Learn Web Scraping with BeautifulSoup.
Ready to start learning Python? Try our free introductory course Getting Started with Python for Data Science! You’ll get hands-on practice working with real datasets using industry-standard data science tools: Python, Pandas, and Jupyter Notebooks. Once you get familiar with Python, be sure to explore the rest of our Python courses to explore all the other cool things you can make with Python.