Gain insights into data wrangling using Python. Learn about cleaning, transforming, and organizing data with libraries like NumPy, pandas, and scikit-learn for data science and machine learning projects.

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Data Wrangling

Data wrangling is cleaning, transforming, and organizing data for further analysis. In this course, you will learn how to use Python to effectively wrangle and prepare data for use in data science and machine learning projects.

Throughout the course, you will learn about the common challenges that arise when working with data and how to overcome them. You will use Python and several libraries commonly used in data wrangling, including NumPy and pandas. Then, you will learn how to use pandas to clean, transform, and aggregate data. Moreover, you will also use scikit-learn, a library for machine learning, to identify outliers in our data.

By the end of the course, you will be able to use Python to effectively wrangle and prepare data for use in data science and machine learning projects. With these tools at your disposal, you can efficiently apply machine learning models and get realistic predictions after applying various wrangling techniques to the dataset.

Data Wrangling With Python

* **Deduplication:** This involves identifying and removing duplicate records from the dataset so that only a single, unique copy of each record is retained. This can be done manually by reviewing the data and identifying duplicate records or automatically using algorithms or tools to detect and remove duplicates. For example, a company with a customer database that contains multiple entries for some customers with slightly different information (e.g., different spellings of their name or address) can use Python to identify and remove duplicate records.

* **Data consolidation:** This involves combining duplicate records into a single record so that the data is consistent and accurate. This can be done by selecting a single record as the "golden" record and merging the other duplicate records into it. For example, a hospital can streamline its data management processes and ensure that all information is accurate and up to date if the hospital decides to consolidate all of its databases into a single, unified database. This would help eliminate duplicate records and make it easier to access and update all data in one place.

* **Data reconciliation:** This involves reconciling or resolving any inconsistencies or errors in the duplicate records so that the data is consistent and accurate. This can be done by comparing the values in the duplicate records and selecting the most accurate or up-to-date value for each field. For example, a company with a customer order database can ensure that all orders are accurately tracked and fulfilled by reconciling data from all different sales channels. This would help resolve discrepancies and duplicates.

Learn how to deal with duplicate data using Python.

About This Course

Introduction to Data Wrangling

Reading Data

Standardization

Syntax Errors and Irrelevant Data

Duplicate and Missing Data

Filtering and Sorting

Splitting, Combining, and Merging

Handling Outliers

Exporting Data

Humanitarian Aid Project

Conclusion

Handling Duplicate Data

Strategies for handling duplicate data