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

3,328 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

15 reviews

Beginner level

Recommended experience

4 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

15 reviews

Beginner level

Recommended experience

4 weeks to complete

at 10 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

Tools you'll learn

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

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.

We recommend you should take this course after the first two courses of the specialization. However, if you hold a prerequisite knowledge of basic algebra, Python programming, and NumPy, you should be able to complete the material in this course. In the first week, we’ll discuss Python file concepts, including the programming syntax that allows you to read and write to a file. Then in the following weeks, we’ll transition into discussing Pandas more specifically and the pros and cons of using this library for specific data projects. By the end of this course, you should be able to know when to use Pandas, how to load and clean data in Pandas, and how to use Pandas for data manipulation. This will prepare you to take the next step in your data scientist journey using Python; creating larger software programs.

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 videos8 readings3 assignments3 programming assignments

5 videos Total 25 minutes

Course Introduction 3 minutes
Paths and Filesystems 4 minutes
Exceptions 8 minutes
Using an Exception Traceback to Debug 4 minutes
File IO 6 minutes

8 readings Total 64 minutes

Report a problem with the course 10 minutes
Reading and Writing Data 10 minutes
Strings 5 minutes
File System Concepts 10 minutes
Exceptions 2 minutes
Understanding Exception Traces 15 minutes
You Might Need Data Other than Pandas 10 minutes
CSV Files 2 minutes

3 assignments Total 375 minutes

Exception Concepts 15 minutes
Understanding Exception Traces 180 minutes
File Concepts 180 minutes

3 programming assignments Total 540 minutes

Roman Numerals 180 minutes
Get Parenthesized Regions 180 minutes
Sort File 180 minutes

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

1 video Total 32 minutes

Live Coding - Indexing and Subsetting 32 minutes

9 readings Total 90 minutes

Introduction to the Module: Tabular Data 10 minutes
Pandas Series 10 minutes
Manipulating Series: Subsetting and Indexing Series 10 minutes
(OPTIONAL) Indexing with Brackets 10 minutes
The object Data Type 10 minutes
Tabular Data through Dataframes 10 minutes
Subsetting DataFrames: Tips and Pitfalls 10 minutes
The Categorical Data Type 10 minutes
PyArrow: An Alternative to Numpy as Pandas Backend 10 minutes

2 assignments Total 60 minutes

Module 2 Wrap Up Graded Quiz (For Submitting Answers Generated in Lab) 30 minutes
Module 2 Pandas Series Self-Check 30 minutes

3 ungraded labs Total 180 minutes

Module 2 Pandas Series Exercises 60 minutes
Live Coding Lab 60 minutes
Module 2 Wrap Up Lab (For Graded Quiz) 60 minutes

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

1 video Total 50 minutes

Live Coding: Cleaning Data 50 minutes

13 readings Total 130 minutes

Intro to Module 3 10 minutes
Storing and Reading Data: Plain Text 10 minutes
Storing and Reading Data: Binary Files 10 minutes
Pandas Indices 10 minutes
Views and Copies: NumPy Review 10 minutes
Views and Copies: Pandas 10 minutes
Views and Copies: Copy on Write 10 minutes
Indexing Lab Discussion 10 minutes
Cleaning Data: Identifying 10 minutes
Cleaning Data: Editing Globally 10 minutes
Cleaning Data: Editing Specific Data 10 minutes
Cleaning Data: Datatypes 10 minutes
Cleaning Data: Missing Data 10 minutes

3 assignments Total 90 minutes

Module 3 Wrap Up Graded Quiz (For Submitting Answers Generated in Lab) 30 minutes
Module 3 Missing Data Exercises Self-Check 30 minutes
Module 3 Missing Data Exercises Self-Check 30 minutes

4 ungraded labs Total 240 minutes

Indexing Lab 60 minutes
Module 3 Missing Data Exercises (Ungraded) 60 minutes
Module 3 Live Coding Lab 60 minutes
Module 3 Wrap Up Lab (for Graded Quiz) 60 minutes

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.