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Pandas Skill Builder Data
Data Science and Analytics
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About
Data serves as an excellent resource for practising foundational data skills, specifically using Python dictionaries and the Pandas library. This small sample set of global car statistics was created to support educational notebooks and tutorials, helping users quickly improve their data manipulation abilities. It is an ideal, clean starting point for beginner and intermediate programmers.
Columns
- Unnamed: 0: An initial index column for record identification.
- abbreviation: The standard two-letter abbreviation representing the country (seven unique values).
- cars_per_cap: The numerical value indicating the number of cars per capita in the country, ranging from 18 to a maximum of 809. The mean value across all entries is 352.
- country: The full name of the nation, with seven distinct country entries.
- drives_right: A boolean indicator specifying whether traffic drives on the right side of the road. There are 4 instances where this is True and 3 instances where it is False.
Distribution
The data is supplied as a CSV file named
cars.csv, which is extremely lightweight at 201 bytes. It contains 7 total records spread across 4 primary data columns. The structure is notably robust, featuring 100% valid data with zero missing or mismatched values, making it ready for immediate use in programming environments.Usage
The dataset is perfectly suited for numerous educational applications, including:
- Developing familiarity with loading and manipulating CSV data into Pandas DataFrames.
- Practising fundamental Python dictionary operations, such as mapping and key-value extraction.
- Working through assignments focused on data indexing, aggregation, and filtering techniques.
- Creating introductory data visualizations based on the
cars_per_capmetric.
Coverage
The geographic scope includes data points for seven distinct countries. Since this resource is dedicated to skill building and structural practice, there are no specific temporal bounds or demographic notes associated with the information provided. The focus remains strictly on providing variety in data types (numerical, categorical, and boolean).
License
CC0: Public Domain
Who Can Use It
- Beginner Programmers: To gain practical experience using standard Python libraries like Pandas.
- Data Science Students: To master foundational methods for data cleansing and structure checking.
- Tutorial Developers and Instructors: To employ a reliable, clean sample dataset for educational examples and course materials.
Dataset Name Suggestions
- Pandas Skill Builder Data
- Introductory Car Statistics
- Dictionary and DataFrame Practice Set
- Python Data Training Sample
Attributes
Original Data Source: Pandas Skill Builder Data
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