The Best Free Datasets to Use in Python Skill Practice
Python is one of the most popular programming languages used for data analysis. Despite being relatively easy to pick up, it still requires practice to learn. And a great way to improve the skill is by analyzing datasets.
Python is an open-source language used for a variety of cases, from web scraping to software development. By itself, it has limited functions that could be useful for scraping or data analysis, but you can find dozens of Python libraries to increase its flexibility and usability.
However, practicing Python can be tricky if you don’t have a project to work on. If you’re looking to improve your data analysis skills with Python, you should look no further than datasets.
Using Python to examine datasets can help you learn data cleaning, manipulation, handling various types of information (numeric, textual, etc.), and more. Let’s dive into the best datasets you can use to develop your proficiency with Python.
What Is a Dataset?
Datasets are pre-collected records on a specific topic, be it the inventory stock of an e-commerce website or the most popular baby names of this decade.
They’re static organized compilations of important data points prepared for further analysis. Datasets can be used for a variety of cases, including research and business management purposes, as well as personal use, such as finding relevant job postings or product reviews.
Datasets vary not only in size, but also by type – you can encounter numeric, textual, multimedia, mixed, and other types. They will also differ in structure – the way a dataset is organized usually depends on the data type it holds.
Learn all you need to know about datasets, and how they differ from web scrapers.
What to Look for in a Practice Dataset?
When choosing a dataset to practice your Python skills, consider its size, complexity, and structure.
If you’re new to Python, opt for smaller, organized datasets with clear labels and fewer data points – it’ll be easier to navigate Python functions with less data to handle. If you already have some familiarity with Python, you can try exploring larger, unstructured datasets that require cleaning and preprocessing.
In general, a good rule of thumb is to look for datasets that match your learning goals. If you want to practice data visualization, choose datasets with diverse numerical and categorical data. On the other hand, if you’re interested in advanced level problem-solving, opt for datasets with missing values, inconsistencies, or unstructured text.
Lastly, consider availability and documentation. Well-documented datasets, like those from government open data portals, provide descriptions, column explanations, and sample analyses, making them easier to work with. A good dataset challenges your skills while keeping the learning process manageable.
Where to Find Good Datasets for Analysis?
There are a few ways to find datasets to practice Python skills: you can pick free datasets, purchase them from dataset vendors, or make a dataset yourself.
Free Dataset Providers
If you opt for free datasets, there are multiple websites you can get them from. Free providers often have quite high collections of datasets that are often used by professionals and individuals alike.
The key disadvantage of free datasets is their maintenance – since they are provided by courtesy of others, the data might not always be relevant and fresh enough for your project. Nevertheless, it should do the job if you’re just practicing.
- Kaggle. Kaggle is probably one of the most popular dataset providers on the market. It has over 400K datasets for all kinds of projects.
- Google Dataset Search. Google has a specific dataset search engine that will find you relevant datasets from all over the web based on your keyword. Keep in mind that Google Dataset Search will include results with paid datasets, too.
- GitHub. This developer code sharing platform is great for storing, managing, and publicly sharing code, but can be a great place to find free, pre-collected practice datasets, too.
- Public government data websites. Websites like Data.gov or Data.gov.uk are great places to find public datasets on various country-specific topics. They are also often updated.
Paid Dataset Providers
You can also purchase datasets on your topic of interest. These datasets will contain fresh data and will be renewed on your selected frequency. Unfortunately, they don’t come cheap, so might not be the best choice if you’re just learning, but are perfect for business analysis.
- Bright Data. The provider offers over 190 structured datasets on various business niches. The datasets can be refreshed at a chosen frequency, too. Bright Data also offers a few free datasets as well as custom datasets based on your needs.
- Oxylabs. This provider offers ready-to-use business- and development-related datasets, such as job postings, e-commerce, or product review data. Oxylabs can also provide custom datasets on your specific interest.
- Coresignal. The provider has a large collection of datasets on companies, employees, and job postings. It’s a great choice for analyses related to business growth.
Making Your Own Dataset
If you’d like to practice Python for web scraping in addition to data analysis, you can try creating your own dataset by extracting data from relevant websites, structuring, and exporting it in a preferred format.
We have a useful guide on how to start web scraping with Python. It will help you build a scraper and extract web data which you’ll be able to use for building a dataset later on.
An introductory guide to Python web scraping with a step-by-step tutorial.
Python Libraries for Working With Datasets
Being a general-purpose programming language, Python can be used for various projects, but it’s especially popular for web scraping and data analysis tasks due to helpful packages – libraries.
Adding libraries will help you increase Python’s functionality by adding features for data cleaning, filtering, clustering, and more. Here are some of the common Python packages you’ll find helpful for practicing data analysis in Python:
- Pandas. The pandas library can be used for data manipulation and analysis. It makes it easy to clean, filter, and reshape data points as it can handle missing values or formatting issues, group and sort data points.
- NumPy. This library is excellent for working with numerical datasets as it supports fast mathematical operations, such as algebra equations or random number generation.
- Matplotlib. The Matplotlib library can be used for data visualization. It’s very useful for analyzing distributions, correlations, and categorical data, and can assist in creating statistical graphics.
- Scikit-learn. The library is useful for data preprocessing – it has tools to help with data classification, regression, and clustering, and is often used for machine learning tasks. Scikit-learn can be easily used alongside pandas and NumPy.
- BeautifulSoup. The BeautifulSoup library can be useful if you need to extract structured information from a website (i.e., product reviews). Combined with the requests library or a headless browser for dynamic websites, it can scrape and process data.
Free Datasets to Try in Python Skill Training
Using datasets for Python training is one of the simplest ways to learn the language, but it comes with its own set of challenges. You might encounter incomplete, inconsistent, or poorly formatted data, so your challenge is to use Python to solve them before extracting necessary data.
Wine Quality Dataset (Kaggle)
The Wine Quality Dataset on Kaggle is a relatively small dataset (around 15K data points), containing information about the amount of various chemical ingredients in the wine and their effect on its quality.
Based on the given data, your main task would be to use Python to understand the dataset, perform necessary data cleanup (if necessary), and build classification models to predict wine quality.
Electric Vehicle Population Data (Data.gov)
The Electric Vehicle Population Data on Data.gov is a public dataset providing information on various types of electric vehicles currently registered in the State of Washington. This dataset is often updated and has multiple download formats available.
There, you’ll find counties and cities, car models, electric ranges, and more data points to work with. This dataset can be used to learn data clustering, find the average electric car range, discover most popular vehicle models, and more.
IMDb Movie Reviews Dataset (Kaggle)
The IMDB Movie Ratings Dataset on Kaggle has approximately 50K movie reviews that you can use to learn natural language processing or text analytics. It contains two essential data points – a full written review and the sentiment (positive or negative).
This dataset can be used in Python practice for learning how to perform text analysis and predict the rating.
Forest Covertype Dataset (UCI Machine Learning Depository)
The Forest Covertype Dataset on UCI Machine Learning Depository is a small, well-structured dataset on four wilderness areas located in the Roosevelt National Forest of northern Colorado. It’s excellent for predicting forest cover type from cartographic variables only.
The dataset has multiple variables, like soil type, wilderness areas, and hillshades, to work with. What’s great is that there are no missing values, so you won’t need to worry about filling them in manually.
Surface Water Quality Dataset (Open Baltimore)
The Surface Water Quality Dataset on Open Baltimore is a large dataset covering surface water quality in the City of Baltimore from 1995 to 2024. Available in a CSV file, this dataset contains data values like coordinates, tested parameters, and timestamps.
You can use Python to predict the surface level quality by analyzing the given parameters and their results in specific locations of the city.
