pynytimes 0.6.0

A Python wrapper for (most) New York Times APIs

pynytimes

Use all (actually most) New York Times APIs, get all the data you need from the Times!

Installation

There are multiple options to install and ugprade pynytimes, but the easiest is by just installing it using pip (or pip3). You can also optionally install orjson for faster json parsing.

Linux and Mac

pip install --upgrade pynytimes

Windows

python -m pip install --upgrade pynytimes

Development

You can also install pynytimes manually from GitHub itself. This can be done by cloning this repository first, and then installing it using Python. This might install an unreleased version, installation using this method is only advised if you want to modify the code or help maintain this library.

git clone https://github.com/michadenheijer/pynytimes.git
cd pynytimes
python setup.py install

Usage

You can easily import this library using:

from pynytimes import NYTAPI

Then you can simply add your API key (get your API key from The New York Times Dev Portal):

nyt = NYTAPI("Your API key", parse_dates=True)

Optionally you can also set to use http instead of https.

nyt = NYTAPI("Your API key", https=False)

Variables	Description	Data type	Required	Default
key	The API key from The New York Times	str	True	None
https	Use HTTPS	bool	False	True
session	Optionally set your own request.session	requests.sessions.Session	False	requests.Session()
backoff	Enable exponential backoff	bool	False	True
user_agent	Set the User Agent	str	False	pynytimes/[version]
parse_dates	Enable the parsing of dates into datetime.datetime or datetime.date objects	bool	False	False

Supported APIs

When you have imported this library you can use the following features from the New York Times API.

• Top stories
• Most viewed articles
• Most shared articles
• Article search
• Book reviews
• Movie reviews
• Best sellers lists
• Article metadata (Times Wire)
• Load latest articles (Times Wire)
• Tag query (TimesTags)
• Archive metadata

Top stories

You can request the top stories from the New York Times. You can also get the top stories from a specific section.

top_stories = nyt.top_stories()

# Get all the top stories from a specific category
top_science_stories = nyt.top_stories(section = "science")

Variables	Description	Data type	Required
section	Get Top Stories from a specific section	str	False

The possible sections are: arts, automobiles, books, business, fashion, food, health, home, insider, magazine, movies, national, nyregion, obituaries, opinion, politics, realestate, science, sports, sundayreview, technology, theater, tmagazine, travel, upshot, and world.

Most viewed articles

The New York Times API can provide the most popular articles from the last day, week or month.

most_viewed = nyt.most_viewed()

# Get most viewed articles of last 7 or 30 days
most_viewed = nyt.most_viewed(days = 7)
most_viewed = nyt.most_viewed(days = 30)

Variables	Description	Data type	Required	Default
days	Get most viewed articles over the last 1, 7 or 30 days	int	False	1

Most shared articles

Not only can you request the most viewed articles from the New York Times API, you can also request the most shared articles. You can even request the articles that are most shared by email and Facebook. You can get the most shared articles per day, week or month.

most_shared = nyt.most_shared()

# Get most emailed articles of the last day
most_shared = myt.most_shared(
    days = 1,
    method = "email"
)

# Get most shared articles to Facebook of the last 7 days
most_shared = nyt.most_shared(
    days = 7,
    method = "facebook"    
)

# Get most shared articles to Facebook of the last 30 days
most_shared = nyt.most_shared(
    days = 30,
    method = "facebook"
)

Variables	Description	Data type	Required	Default
days	Get most viewed articles over the last 1, 7 or 30 days	int	False	1
method	Method of sharing (email or facebook)	str	False	"email"

Article search

You can also search all New York Times articles. Optionally you can define your search query (using the query option), the amount of results (using results) and the amount of results you'd like. You can even add more options so you can filter the results.

import datetime

articles = nyt.article_search(
    query = "Obama",
    results = 30,
    dates = {
        "begin": datetime.datetime(2019, 1, 31),
        "end": datetime.datetime(2019, 2, 28)
    },
    options = {
        "sort": "oldest",
        "sources": [
            "New York Times",
            "AP",
            "Reuters",
            "International Herald Tribune"
        ],
        "news_desk": [
            "Politics"
        ],
        "type_of_material": [
            "News Analysis"
        ]
    }
)

Variables	Description	Data type	Required
query	What you want to search for	str	False
results	The amount of results that you want to receive (returns a multiple of 10)	int	False
dates	A dictionary of the dates you'd like the results to be between	dict	False
options	A dictionary of additional options	dict	False

dates

Variables	Description	Data type	Required
begin	Results should be published at or after this date	datetime.datetime	False
end	Results should be published at or before this date	datetime.datetime	False

options

Variables	Description	Data type	Required
sort	How you want the results to be sorted (oldest, newest or relevance)	str	False
sources	Results should be from one of these sources	list	False
news_desk	Results should be from one of these news desks (valid options)	list	False
type_of_material	Results should be from this type of material (valid options)	list	False
section_name	Results should be from this section (valid options)	list	False

Book reviews

You can easily find book reviews for every book you've read. You can find those reviews by searching for the author, ISBN or title of the book.

# Get reviews by author (first and last name)
reviews = nyt.book_reviews(author = "George Orwell")

# Get reviews by ISBN
reviews = nyt.book_reviews(isbn = 9780062963673)

# Get book reviews by title
reviews = nyt.book_reviews(title = "Becoming")

Variables	Description	Data type	Required
author	Reviews of books from this author	str	One of these three
isbn	Reviews of books with this ISBN	str	One of these three
title	Reviews of books with this title	str	One of these three

Movie reviews

You can not only get the book reviews, but the movie reviews too.

import datetime

reviews = nyt.movie_reviews(
    keyword = "Green Book",
    options = {
        "order": "by-opening-date",
        "reviewer": "A.O. Scott",
        "critics_pick": False
    },
    dates = {
        "opening_date_start": datetime.datetime(2017, 1, 1),
        "opening_date_end": datetime.datetime(2019, 1, 1),
        "publication_date_start": datetime.datetime(2017, 1, 1),
        "publication_date_end": datetime.datetime(2019, 1, 1)
})

Variables	Description	Data type	Required
keyword	Reviews of movies with this keyword	str	False
options	Dictionary of search options	dict	False
dates	Dictionary of dates about the review	dict	False

options

Variables	Description	Data type	Required
order	How to sort the results (by-title, by-publication-dateor by-opening-date)	str	False
reviewer	Name of the reviewer	str	False
critics_pick	Only return critics' pick if True	bool	False

dates

Variables	Description	Data type	Required
opening_date_start	Reviews about movies released at or after this date	datetime.datetime	False
opening_date_end	Reviews about movies released at or before this date	datetime.datetime	False
publication_date_start	Reviews released at or after this date	datetime.datetime	False
publication_date_end	Reviews released at or before this date	datetime.datetime	False

Best sellers lists

The New York Times has multiple best sellers lists. You can easily request those lists using this library.

# Get all the available New York Times best sellers lists
lists = nyt.best_sellers_lists()

# Get fiction best sellers list
books = nyt.best_sellers_list()

# Get non-fiction best sellers list
books = nyt.best_sellers_list(
    name = "combined-print-and-e-book-nonfiction"
)

# Get best sellers lists from other date
import datetime

books = nyt.best_sellers_list(
    name = "combined-print-and-e-book-nonfiction",
    date = datetime.datetime(2019, 1, 1)
)

Variables	Description	Data type	Required	Default
name	Name of best sellers list	str	False	"combined-print-and-e-book-fiction"
date	Date of best sellers list	datetime.datetime	False	Today

Article metadata

With an URL from a New York Times article you can easily get all the metadata you need from it.

metadata = nyt.article_metadata(
    url = "https://www.nytimes.com/2019/10/20/world/middleeast/erdogan-turkey-nuclear-weapons-trump.html"
)

Variables	Description	Data type	Required
url	URL of the article	str	True

Load latest articles

You can easily load the latest articles published by the New York Times.

latest = nyt.latest_articles(
    source = "nyt",
    section = "books"
)

Variables	Description	Data type	Required	Default
source	Source of article (all, nyt and inyt)	str	False	"all"
section	Section of articles	str	False

You can find all possible sections using:

sections = nyt.section_list()

Tag query

The New York Times has their own tags library. You can query this library with this API.

tags = nyt.tag_query(
    "pentagon",
    max_results = 20
)

Variables	Description	Data type	Required	Default
query	Tags you're looking for	str	True
max_results	Maximum results you'd like	int	False	20
filter_options	Filter options	list	False

Archive metadata

If you want to load all the metadata from a specific month, then this API makes that possible. Be aware you'll download a big JSON file (about 20 Mb), so it can take a while.

import datetime

data = nyt.archive_metadata(
    date = datetime.datetime(2019, 1, 1)
)

Variables	Description	Data type	Required
date	Date of month of all the metadata	datetime.datetime	True

Close session

Optionally you close the requests.Session() connection with the New York Times server.

nyt.close()

License

Disclaimer: This project is not made by anyone from the New York Times, nor is it affiliated with The New York Times Company.