In this chapter, you'll learn how to import data into Python from all types of flat files, which are 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 and customize your imports.

Welcome to the course!

Importing entire text files

Importing text files line by line

The importance of flat files in data science

Pop quiz: what exactly are flat files?

Why we like flat files and the Zen of Python

Importing flat files using NumPy

Using NumPy to import flat files

Customizing your NumPy import

Importing different datatypes

Importing flat files using pandas

Using pandas to import flat files as DataFrames (1)

Using pandas to import flat files as DataFrames (2)

Customizing your pandas import

Final thoughts on data import

Introduction and flat files

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. These include pickled files, Excel spreadsheets, SAS and Stata files, HDF5 files, a file type for storing large quantities of numerical data, and MATLAB files.

Introduction to other file types

Not so flat any more

Loading a pickled file

Listing sheets in Excel files

Importing sheets from Excel files

Customizing your spreadsheet import

Importing SAS/Stata files using pandas

How to import SAS7BDAT

Importing SAS files

Using read_stata to import Stata files

Importing Stata files

Importing HDF5 files

Using File to import HDF5 files

Using h5py to import HDF5 files

Extracting data from your HDF5 file

Importing MATLAB files

Loading .mat files

The structure of .mat in Python

Importing data from other file types

In this chapter, you'll learn how to extract meaningful data from relational databases, an essential skill for any data scientist. You will learn about relational models, how to create SQL queries, how to filter and order your SQL records, and how to perform advanced queries by joining database tables.

Introduction to relational databases

Pop quiz: The relational model

Creating a database engine in Python

Creating a database engine

What are the tables in the database?

Querying relational databases in Python

The Hello World of SQL Queries!

Customizing the Hello World of SQL Queries

Filtering your database records using SQL's WHERE

Ordering your SQL records with ORDER BY

Querying relational databases directly with pandas

Pandas and The Hello World of SQL Queries!

Pandas for more complex querying

Advanced querying: exploiting table relationships

The power of SQL lies in relationships between tables: INNER JOIN

Filtering your INNER JOIN

Final Thoughts

Working with relational databases in Python

Chinook (SQLite)

LIGO (HDF5)

Battledeath (XLSX)

Extent of infectious diseases (DTA)

Gene expressions (MATLAB)

MNIST

Sales (SAS7BDAT)

Seaslugs

Titanic

As a data scientist, you will need to clean data, wrangle and munge it, visualize it, build predictive models, and interpret these models. Before you can do so, 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: from flat files such as .txt and .csv; from files native to other software such as Excel spreadsheets, Stata, SAS, and MATLAB files; and from relational databases such as SQLite and PostgreSQL.

Intermediate Python

You'll learn the many ways to read data into Python: from flat files such as CSVs to Excel spreadsheets and relational databases in SQLite & PostgreSQL.

Introduction to Importing Data in Python

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

Associate Data Scientist  in Python

Data Engineer in Python

Importing & Cleaning Data  in Python

Pop quiz: The relational model

Introduction to Importing Data in Python

Hands-on interactive exercise