pip install newspaper==0.1.0.7

Simplified python article discovery & extraction.

Source
Among top 2% packages on PyPI.
Over 76.7K downloads in the last 90 days.

Commonly used with newspaper

Based on how often these packages appear together in public requirements.txt files on GitHub.

feedfinder2

Find the feed URLs for a website.

jieba

Chinese Words Segmentation Utilities

tldextract

Accurately separate the TLD from the registered domain and subdomains of a URL, using the Public Suffix List. By default, this includes the public ICANN TLDs and their exceptions. You can optionally support the Public Suffix List's private domains as well.

jieba3k

Chinese Words Segementation Utilities

newspaper3k

Simplified python article discovery & extraction.

kipp

Python Utils

nltk

Natural Language Toolkit

Pattern

Web mining module for Python.

extraction

Extract basic info from HTML webpages.

feedparser

Universal feed parser, handles RSS 0.9x, RSS 1.0, RSS 2.0, CDF, Atom 0.3, and Atom 1.0 feeds

jellyfish

a library for doing approximate and phonetic matching of strings.

XlsxWriter

A Python module for creating Excel XLSX files.

jusText

Heuristic based boilerplate removal tool

geograpy

Extract countries, regions and cities from a URL or text

Cython

The Cython compiler for writing C extensions for the Python language.

weibo

Python sina weibo sdk

textblob

Simple, Pythonic text processing. Sentiment analysis, part-of-speech tagging, noun phrase parsing, and more.

goose-extractor

Html Content / Article Extractor, web scrapping

stem

Stem is a Python controller library that allows applications to interact with Tor (https://www.torproject.org/).

Version usage of newspaper

Proportion of downloaded versions in the last 3 months (only versions over 1%).

0.1.0.7

7.92%

0.1.0.6

4.89%

0.1.0.5

4.54%

0.1.0.4

4.52%

0.1.0.3

4.51%

0.0.9.8

4.50%

0.0.9.9

4.49%

0.1.0.2

4.49%

0.0.6

4.48%

0.1.0.1

4.47%

0.1.0.0

4.47%

0.0.9.6

4.41%

0.0.8

4.37%

0.0.9

4.35%

0.0.9.5

4.33%

0.0.9.2

4.33%

0.0.9.1

4.30%

0.0.7

4.26%

0.0.5

4.18%

0.0.4

4.12%

0.0.3

4.09%

0.0.2

3.99%