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.

pandas.tar.gz

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

A **comma-separated values (CSV)** file is a plain text file that contains tabular data. The tabular data that we store in these files is separated by commas, with each record represented on a new line. CSV files are known for their simplicity and small size. We use them for storing and transferring data between various programs.

Learn to export data to a CSV file 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

Exporting to a CSV File

Defining a CSV file

Importance of exporting to a CSV file