How can you effectively use Python to clean, sort, and store data? What are the benefits of using the Pandas library for data science? What best practices can data scientists leverage to better work with multiple types of datasets? In the third course of Data Science Python Foundations Specialization from Duke University, Python users will learn about how Pandas — a common library in Python used for data science — can ease their workflow.
Pandas for Data Science
Pandas for Data Science
This course is part of Programming for Python Data Science: Principles to Practice Specialization
Instructors: Genevieve M. Lipp
Sponsored by InternMart, Inc
Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
41 hours to complete
3 weeks at 13 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
How and when to leverage the Pandas library for your data science projects
Best practices for cleaning, manipulating, and optimizing data with Pandas
Skills you'll gain
- Statistical Analysis
- Extract, Transform, Load
- Computer Science
- Data Wrangling
- Python Programming
- Data Import/Export
- Data Transformation
- Data Analysis
- Data Integration
- Pandas (Python Package)
- Data Storage
- Computer Programming
- Data Manipulation
- File Systems
- Data Processing
- Exploratory Data Analysis
- Data Mapping
- NumPy
- Data Science
- Data Engineering
Details to know
Shareable certificate
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Assessments
9 assignments
Taught in English
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This course is part of the Programming for Python Data Science: Principles to Practice Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
This module, you will learn how to read data from files into your python program, and write that corresponding data to a file. We’ll be working primarily with string-type data in this unit and will give special attention to the way that python handles strings. Additionally we’ll go over some basic debugging in python using exception traces, and you’ll leverage these to create your own python program that is capable of reading and writing to a file.
What's included
5 videos7 readings3 assignments3 programming assignments
This module, you’ll learn how to begin to utilize Pandas, one of the most commonly used libraries in Data Science with python. Pandas is predominantly used for working with tabular data. By the end of this module you’ll be able to identify the hallmarks and quirks of working with tabular data, describe the benefits and limitations of using Pandas, and be able to perform some basic data manipulation techniques in Pandas.
What's included
1 video9 readings2 assignments3 ungraded labs
This Module, you will learn how to perform basic file operations in Pandas, as well as how to clean up large datasets. You’ll learn to read and write from common tabular file formats, and Pandas-specific intricacies for working with that data. Additionally, you’ll learn best practices for cleaning your data.
What's included
1 video13 readings3 assignments4 ungraded labs
This module you will learn how to combine datasets from different sources. Pandas has different methods of combining data depending on your preferred outcome, and you’ll be able to differentiate between when to use each kind. Additionally, we’ll go over computationally efficient ways of querying your data, which, while similar to selecting data via subsetting in its outcomes, has a distinct set of advantages.
What's included
1 video11 readings1 assignment5 ungraded labs
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