课程

Python 数据导入入门

基础技能水平

更新时间 2025年12月

学习如何将来自 Excel、SQL、SAS 以及网页等各种来源的数据导入 Python。

免费开始课程

PythonData Preparation

3小时

15 视频

50 道练习

3,900 XP

330K+

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课程描述

作为一名数据科学家，您需要清洗数据、整理与变换数据、可视化、构建预测模型，并解释这些模型。在此之前，您需要先学会如何将数据导入到 Python 中。本课程将带您学习多种在 Python 中导入数据的方法：从 .txt、.csv 等平面文件；从其它软件的原生文件，如 Excel 电子表格、Stata、SAS 和 MATLAB 文件；以及从 SQLite、PostgreSQL 等关系型数据库导入数据。视频包含实时字幕，您可以点击视频左下角的 "Show transcript" 展开查看。课程术语表位于右侧的资源部分。若要获得 CPE 学分，您需要完成课程并在合格测评中达到 70% 的得分。您可以点击右侧的 CPE 学分提示进入测评。

先决条件

Intermediate Python

1

Introduction and flat files

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

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. 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

3

Working with relational databases in Python

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

Python 数据导入入门

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