paid course

Importing Data in Python (Part 1)

Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.

  • 3 hours
  • 15 Videos
  • 54 Exercises
  • 80,689 Participants
  • 4,150 XP

Course Description

As a Data Scientist, on a daily basis you will need to clean data, wrangle and munge it, visualize it, build predictive models and interpret these models. Before doing any of these, however, you will need to know how to get data into Python. In this course, you'll learn the many ways to import data into Python: (i) from flat files such as .txts and .csvs; (ii) from files native to other software such as Excel spreadsheets, Stata, SAS and MATLAB files; (iii) from relational databases such as SQLite & PostgreSQL.

Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.

Course Outline

  1. 1

    Introduction and flat files

    Free

    In this chapter, you'll learn how to import data into Python from all types of flat files, a simple and prevalent form of data storage. You've previously learned how to use NumPy and pandas - you will learn how to use these packages to import flat files, as well as how to customize your imports.

  2. Working with relational databases in Python

    In this chapter, you'll learn how to extract meaningful data from relational databases, an essential element of any data scientist's toolkit. You will be learning about the relational model, creating SQL queries, filtering and ordering your SQL records, and advanced querying by JOINing database tables.

  1. 1

    Introduction and flat files

    Free

    In this chapter, you'll learn how to import data into Python from all types of flat files, a simple and prevalent form of data storage. You've previously learned how to use NumPy and pandas - you will learn how to use these packages to import flat files, as well as how to customize your imports.

  2. Importing data from other file types

    You've learned how to import flat files, but there are many other file types you will potentially have to work with as a data scientist. In this chapter, you'll learn how to import data into Python from a wide array of important file types. You will be importing file types such as pickled files, Excel spreadsheets, SAS and Stata files, HDF5 files, a file type for storing large quantities of numerical data, and MATLAB files.

  3. Working with relational databases in Python

    In this chapter, you'll learn how to extract meaningful data from relational databases, an essential element of any data scientist's toolkit. You will be learning about the relational model, creating SQL queries, filtering and ordering your SQL records, and advanced querying by JOINing database tables.

Hugo Bowne-Anderson
Hugo Bowne-Anderson

Data Scientist at DataCamp

Hugo hearts all things Pythonic and is charged with building out DataCamp’s Python curriculum. He can be found at hackathons, meetups & code sprints, primarily in NYC. Before joining the ranks of DataCamp, he worked in applied mathematics (biology) research at Yale University.

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Course Instructor

Hugo Bowne-Anderson
Hugo Bowne-Anderson

Data Scientist at DataCamp

Hugo hearts all things Pythonic and is charged with building out DataCamp’s Python curriculum. He can be found at hackathons, meetups & code sprints, primarily in NYC. Before joining the ranks of DataCamp, he worked in applied mathematics (biology) research at Yale University.

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Collaborator(s)
  • Francisco Castro

    Francisco Castro

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