Data Wrangling in Python
Data is often messy, requiring cleaning and restructuring before it can be reliably used in a program or project. In this course, learners will augment their understanding of Python using two of the most popular libraries for data cleaning and wrangling, NumPy and Pandas. First, learners will practice working with NumPy objects, transforming data into efficient arrays for ease of analysis. Then, learners will clean and structure arrays into readable tabular DataFrames using Pandas, allowing them to profile a dataset for key answers and values.
8 hours of instruction
Data is often messy, requiring cleaning and restructuring before it can be reliably used in a program or project. In this course, learners will augment their understanding of Python using two of the most popular libraries for data cleaning and wrangling, NumPy and Pandas. First, learners will practice working with NumPy objects, transforming data into efficient arrays for ease of analysis. Then, learners will clean and structure arrays into readable tabular DataFrames using Pandas, allowing them to profile a dataset for key answers and values.
OBJECTIVES
- Perform data processing using NumPy & Pandas
- Clean unstructured data sets using python so that they can be explored and analyzed more effectively
- Explore the power of dataframes
PREREQUISITES
Learners should be comfortable implementing conditional statements, for loops, and while loops in Python.
SYLLABUS & TOPICS COVERED
- Data Wrangling With Numpy
- NumPy use cases and object types
- NumPy array manipulation
- Data Wrangling With Pandas
- Pandas use cases and basic operations
- Dataframe definition and manipulation
SOFTWARE REQUIREMENTS
You will have access to a Python-based JupyterHub environment for this course. No additional download or installation is required.
