Fun Datasets for Python Practice

Fun and Public Datasets for Python Practice

datasets to practice python pandas                                                             image

What are datasets?

In the world of data analysis, datasets are the foundation upon which insights and conclusions are built. A dataset is a collection of structured data that is organized in a specific format, allowing for easy manipulation and analysis. These datasets can be sourced from various places such as surveys, experiments, or even online databases. Understanding the fundamentals of datasets and how they are handled is crucial for data analysts, developers or software engineers.

Basics of Python and its role in data manipulation

Python has several libraries and tools that aid in data manipulation, and one such library is Pandas. A powerful data analysis library with built-in data structures and functions to efficiently work with datasets.

Pandas and its capabilities as a data analysis library

Pandas is an open-source library built on top of Python and Numpy. It offers a plethora of functionalities that help in data manipulation, exploration, and cleaning. Pandas introduces two primary data structures – Series and DataFrame. A Series is a one-dimensional labeled array while a DataFrame is a two-dimensional labeled data structure with columns of potentially different types. These data structures, combined with Pandas’ extensive functions, make it an indispensable tool for any data analysis task.
Dataframes are the heart and soul of Pandas. They are 2D structures that organize data in a tabular format, similar to a spreadsheet. Each column in a dataframe represents a specific variable, while each row represents an observation or record. Dataframes allow for easy manipulation, analysis, and visualization of data. With the power of dataframes, analysts can perform operations like filtering, sorting, aggregating, and merging data effortlessly.

Structure of a dataframe: columns, rows, and values

To fully comprehend dataframes, it is essential to understand their anatomy. A dataframe consists of three main elements: columns, rows, and values. Columns represent the variables or attributes of the dataset, and each column has a unique name. Rows, also known as records or observations, represent individual instances or data points. The values within the dataframe are the actual data entries corresponding to each variable and observation.  Understanding the structure of dataframes and their elements is neccessary to effectively navigate and analyze datasets.

Importing and exporting datasets in different file formats using Pandas

In Pandas there are various methods to import and export datasets in different file formats. It supports file formats such as CSV, Excel, SQL databases, and more. Importing datasets into Pandas is as simple as using the "read_csv()" function for CSV files or "read_excel()" function for Excel files. Similarly, to export datasets,  functions like "to_csv()" or “to_excel()" are also available.

Data cleaning and preprocessing techniques using Pandas dataframes

Data cleaning and preprocessing are critical steps in any data project. Analysts can handle missing values, remove duplicates, handle outliers, and perform various data transformations using Pandas dataframes. Techniques such as data imputation, normalization, and scaling are easily implemented using Pandas’ intuitive syntax. To learn more about data cleaning routines, visit Practity data projects.

Compilation of Free Datasets to Practice data analysis

Kaggle: Platform for data science projects. Hundreds of datasets of a wide range of industries.

Data.gov: site to download multiple files from US government agencies.

Standford University: compilation of datasets from multiple sources about different subjects.

Capital Bikeshare: data about riders of bike sharing company of DC.

Heathrow: Excel spreadsheets about air traffic data.

Sexualitics: metadata of porn videos published on xhamster from its creation in 2007 – until february 2013.

Wolfrang Data Repository: compilation of datasets of many sources about different topics: education, politics, transportation.

Inside Airbnb: database of Airbnb listings and reviews of different regions.

Academic Torrents: list of datasets used by universitites.

Eurocontrol: European data about flights, flight trajectories, airspace structure and so on.

Mendeley : Dataset with road images collected from India, Japan, and the Czech Republic with more than 31000 instances of road damage.

Eurostat: Database of the statistical office of the European union.

IMDb: geatest database of films and cinema related data.

Plane Crash : database of aviation accidents from 1921.

MoMA: Research dataset (140,848 records), representing all of the works that have been accessioned into MoMA’s collection and cataloged in the database of the Museum of Modern Art (NY).

FiveThirtyEight: data from the popular interactive news and sports site: drugs, bad drivers, marriage, etc.

The Million Song Dataset : Freely-available collection of audio features and metadata for a million contemporary popular music tracks.

Basketball: Stats about world-wide basket leagues.

New York City Open Data: thousands of datasets about New York.

 

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