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Basics of Python Data Wrangling
Discover the power of Python data wrangling! This guide empowers learners with essential techniques to clean, transform, and prepare data using Pandas, NumPy, and other powerful libraries.
What you learn in Basics of Python Data Wrangling ?
About this Free Certificate Course
Python data wrangling is the process of preparing, cleaning, and transforming raw data into a more structured and usable format for analysis. It is essential in the data analysis workflow, as real-world data is often messy and unorganized. Data wrangling helps ensure that data is accurate, consistent, and suitable for further analysis or modeling.
Key aspects of Python data wrangling include:
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Data Cleaning involves identifying and handling data inconsistencies, errors, and missing values. Typical tasks include removing duplicate records, filling in missing values, and correcting data entry errors.
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Data Transformation: Data often needs to be transformed to be better suited for analysis. This can involve converting data types, aggregating data, and creating new features or variables.
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Data Filtering: Filtering data allows you to extract specific subsets of data that are relevant to your analysis or research. This can be done based on certain conditions or criteria.
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Data Reshaping: Data may need to be reshaped to fit the desired analysis format. This could involve pivoting data, merging datasets, or splitting data into multiple tables.
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Handling Time Series Data: For time series data, Python data wrangling enables tasks like resampling, time-based indexing, and handling time gaps.
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Data Visualization: While not strictly a part of data wrangling, visualizing data can be crucial in understanding its patterns and making informed decisions during the wrangling process.
Python provides powerful libraries such as Pandas, NumPy, and Matplotlib that greatly simplify data-wrangling tasks. Pandas, in particular, are widely used for data manipulation and analysis, offering a range of functions and methods for data cleaning, filtering, grouping, and reshaping.
Mastering Python data wrangling is fundamental for data analysts, data scientists, and anyone working with data, as it ensures data integrity and prepares the foundation for meaningful and accurate data analysis.
Course Outline
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Dr. Bradford Tuckfield
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Frequently Asked Questions
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Yes, you will get a certificate of completion for Basics of Python Data Wrangling after completing all the modules and cracking the assessment. The assessment tests your knowledge of the subject and badges your skills.
How much does this Basics of Python Data Wrangling course cost?
It is an entirely free course from Great Learning Academy. Anyone interested in learning the basics of Basics of Python Data Wrangling can get started with this course.
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Once you enroll in the Basics of Python Data Wrangling course, you have lifetime access to it. So, you can log in anytime and learn it for free online.
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Yes, you can enroll in as many courses as you want from Great Learning Academy. There is no limit to the number of courses you can enroll in at once, but since the courses offered by Great Learning Academy are free, we suggest you learn one by one to get the best out of the subject.
Why choose Great Learning Academy for this free Basics of Python Data Wrangling course?
Great Learning Academy provides this Basics of Python Data Wrangling course for free online. The course is self-paced and helps you understand various topics that fall under the subject with solved problems and demonstrated examples. The course is carefully designed, keeping in mind to cater to both beginners and professionals, and is delivered by subject experts. Great Learning is a global ed-tech platform dedicated to developing competent professionals. Great Learning Academy is an initiative by Great Learning that offers in-demand free online courses to help people advance in their jobs. More than 5 million learners from 140 countries have benefited from Great Learning Academy's free online courses with certificates. It is a one-stop place for all of a learner's goals.
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