Python Pandas Basics: Load and Export Data

Offered By
Coursera Project Network
In this Guided Project, you will:

Load data from CSV files, identify the data frame shape, and apply some operations to validate the dataset.

Manipulate and filter the dataset, rename and delete columns, clean the data to apply some aggregate functions on it and export it into a CSV file.

Clock1 hour
BeginnerBeginner
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

By the end of this project, you will be able to load data from CSV files, identify the data frame shape, apply some operations to validate the dataset, manipulate and filter the dataset. Moreover, you will be able to rename and delete columns, clean the data to apply some aggregate functions, and finally, export it into CSV files using Pandas library which is an open-source Python package that provides numerous tools for data analysis. The package comes with several data structures that can be used for many different data manipulation tasks. It also has a variety of methods that can be invoked for data analysis, which comes in handy when working on data science and machine learning problems in Python. This guided project is for people in the field of business and data analysis. And also people who want to learn more about python and Pandas library. It provides you with the important steps to be a data analyst. Moreover, it equips you with the knowledge of python's native data structures Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

Data ScienceData AnalysisPython ProgrammingPandas

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. You will be able to load data from CSV files and identify the data frame shape.

  2. You will be able to apply some operations to validate the dataset.

  3. You will be able to manipulate and filter the dataset.

  4. You will be able to rename and delete columns, clean the data to apply some aggregate functions on it and export it into a CSV file.

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

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