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.
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Empfohlene Erfahrung
Was Sie lernen werden
How and when to leverage the Pandas library for your data science projects
Best practices for cleaning, manipulating, and optimizing data with Pandas
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9 Aufgaben
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In diesem Kurs gibt es 4 Module
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.
Das ist alles enthalten
5 Videos7 Lektüren3 Aufgaben3 Programmieraufgaben
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.
Das ist alles enthalten
1 Video9 Lektüren2 Aufgaben3 Unbewertete Labore
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.
Das ist alles enthalten
1 Video13 Lektüren3 Aufgaben4 Unbewertete Labore
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.
Das ist alles enthalten
1 Video11 Lektüren1 Aufgabe5 Unbewertete Labore
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