课程

Streamlined Data Ingestion with pandas

中级技能水平

更新时间 2024年11月

Learn to acquire data from common file formats and systems such as CSV files, spreadsheets, JSON, SQL databases, and APIs.

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PythonData Preparation4 小时16 视频53 练习4,500 经验值61,980成就声明

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课程描述

Before you can analyze data, you first have to acquire it. This course teaches you how to build pipelines to import data kept in common storage formats. You’ll use pandas, a major Python library for analytics, to get data from a variety of sources, from spreadsheets of survey responses, to a database of public service requests, to an API for a popular review site. Along the way, you’ll learn how to fine-tune imports to get only what you need and to address issues like incorrect data types. Finally, you’ll assemble a custom dataset from a mix of sources.

先决条件

Intermediate Python Intermediate SQL

1

Importing Data from Flat Files

Practice using pandas to get just the data you want from flat files, learn how to wrangle data types and handle errors, and look into some U.S. tax data along the way.

Introduction to flat files

50 经验值

Get data from CSVs

100 经验值

Get data from other flat files

100 经验值

Modifying flat file imports

50 经验值

Import a subset of columns

100 经验值

Import a file in chunks

100 经验值

Handling errors and missing data

50 经验值

Specify data types

100 经验值

Set custom NA values

100 经验值

Skip bad data

100 经验值

2

Importing Data From Excel Files

Automate data imports from that staple of office life, Excel files. Import part or all of a workbook and ensure boolean and datetime data are properly loaded, all while learning about how other people are learning to code.

Introduction to spreadsheets

50 经验值

Get data from a spreadsheet

100 经验值

Load a portion of a spreadsheet

100 经验值

Getting data from multiple worksheets

50 经验值

Select a single sheet

100 经验值

Select multiple sheets

100 经验值

Work with multiple spreadsheets

100 经验值

Modifying imports: true/false data

50 经验值

Set Boolean columns

100 经验值

Set custom true/false values

100 经验值

Modifying imports: parsing dates

50 经验值

Parse simple dates

100 经验值

Get datetimes from multiple columns

100 经验值

Parse non-standard date formats

100 经验值

3

Importing Data from Databases

Combine pandas with the powers of SQL to find out just how many problems New Yorkers have with their housing. This chapter features introductory SQL topics like WHERE clauses, aggregate functions, and basic joins.

Introduction to databases

50 经验值

Connect to a database

100 经验值

Load entire tables

100 经验值

Refining imports with SQL queries

50 经验值

Selecting columns with SQL

100 经验值

Selecting rows

100 经验值

Filtering on multiple conditions

100 经验值

More complex SQL queries

50 经验值

Getting distinct values

100 经验值

Counting in groups

100 经验值

Working with aggregate functions

100 经验值

Loading multiple tables with joins

50 经验值

Joining tables

100 经验值

Joining and filtering

100 经验值

Joining, filtering, and aggregating

100 经验值

4

Importing JSON Data and Working with APIs

Learn how to work with JSON data and web APIs by exploring a public dataset and getting cafe recommendations from Yelp. End by learning some techniques to combine datasets once they have been loaded into data frames.

Introduction to JSON

50 经验值

Load JSON data

100 经验值

Work with JSON orientations

100 经验值

Introduction to APIs

50 经验值

Get data from an API

100 经验值

Set API parameters

100 经验值

Set request headers

100 经验值

Working with nested JSONs

50 经验值

Flatten nested JSONs

100 经验值

Handle deeply nested data

100 经验值

Combining multiple datasets

50 经验值

Concatenate dataframes

100 经验值

Merge dataframes

100 经验值

50 经验值

Streamlined Data Ingestion with pandas

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