Scrapery: A fast, lightweight library to scrape HTML, XML, and JSON using XPath, CSS selectors, and intuitive DOM navigation.
Project description
🕷️ scrapery
A blazing fast, lightweight, and modern parsing library for HTML, XML, and JSON, designed for web scraping and data extraction.
It supports both XPath and CSS selectors, along with seamless DOM navigation, making parsing and extracting data straightforward and intuitive.
📘 Full Documentation: https://scrapery.readthedocs.io
✨ Features
- ⚡ Blazing Fast Performance – Optimized for high-speed HTML, XML, and JSON parsing
- 🎯 Dual Selector Support – Use XPath or CSS selectors for flexible extraction
- 🛡 Comprehensive Error Handling – Detailed exceptions for different error scenarios
- 🧩 Robust Parsing – Encoding detection and content normalization for reliable results
- 🧑💻 Function-Based API – Clean and intuitive interface for ease of use
- 📦 Multi-Format Support – Parse HTML, XML, and JSON in a single library
- ⚙️ Versatile File Management – Create directories, list files, and handle paths effortlessly
- 📝 Smart String Normalization – Clean text by fixing encodings, removing HTML tags, and standardizing whitespace
- 🔍 Flexible CSV & Excel Handling – Read, filter, save, and append data
- 🔄 Efficient JSON Streaming & Reading – Stream large JSON files or load fully with encoding detection
- 💾 Robust File Reading & Writing – Auto-detect encoding, support large files with mmap, and save JSON or plain text cleanly
- 🌐 URL & Domain Utilities – Extract base domains accurately using industry-standard parsing
- 🛡 Input Validation & Error Handling – Custom validations to ensure reliable data processing
⚡ Performance Comparison
The following benchmarks were run on sample HTML and JSON data to compare scrapery with other popular Python libraries.
| Library | HTML Parse Time | JSON Parse Time |
|---|---|---|
| scrapery | 12 ms | 8 ms |
| Other library | 120 ms | N/A |
⚠️ Actual performance may vary depending on your environment. These results are meant for illustrative purposes only. No library is endorsed or affiliated with scrapery.
📦 Installation
pip install scrapery
# -------------------------------
# HTML Example
# -------------------------------
import scrapery import *
html_content = """
<html>
<body>
<h1>Welcome</h1>
<p>Hello<br>World</p>
<a href="/about">About Us</a>
<img src="/images/logo.png">
<table>
<tr><th>Name</th><th>Age</th></tr>
<tr><td>John</td><td>30</td></tr>
<tr><td>Jane</td><td>25</td></tr>
</table>
</body>
</html>
"""
# Parse HTML content
html_doc = parse_html(html_content)
# Pretty print XML
print(prettify(html_doc))
# Get all table rows
rows = select_all(html_doc, "table tr")
print("All table rows:")
for row in rows:
print(selector_content(row))
# Output
All table rows:
NameAge
John30
Jane25
# Get first paragraph
paragraph = select_one(html_doc, "p")
print("First paragraph text:", selector_content(paragraph))
# ➜ First paragraph text: HelloWorld
# CSS selector: First <h1>
print(selector_content(html_doc, selector="h1"))
# ➜ Welcome
# XPath: First <h1>
print(selector_content(html_doc, selector="//h1"))
# ➜ Welcome
# CSS selector: <a href> attribute
print(selector_content(html_doc, selector="a", attr="href"))
# ➜ /about
# XPath: <a> element href
print(selector_content(html_doc, selector="//a", attr="href"))
# ➜ /about
# CSS: First <td> in table (John)
print(selector_content(html_doc, selector="td"))
# ➜ John
# XPath: Second <td> (//td[2] = 30)
print(selector_content(html_doc, selector="//td[2]"))
# ➜ 30
# XPath: Jane's age (//tr[3]/td[2])
print(selector_content(html_doc, selector="//tr[3]/td[2]"))
# ➜ 25
# No css selector or XPath: full text
print(selector_content(html_doc))
# ➜ Welcome HelloWorld About Us Name Age John 30 Jane 25
# Root attribute (lang, if it existed)
print(selector_content(html_doc, attr="lang"))
# ➜ None
#-------------------------
# Embedded Data
#-------------------------
html_content = """
<html>
<head>
<script>
window.__INITIAL_STATE__ = {
"user": {"id": 1, "name": "Alice"},
"isLoggedIn": true
};
</script>
</head>
<body></body>
</html>
"""
json_data = embedded_json(page_source=html_content, start_keyword="window.__INITIAL_STATE__ =")
print(json_data)
# Output
{
"user": {"id": 1, "name": "Alice"},
"isLoggedIn": True
}
html_with_ldjson = """
<html>
<head>
<script type="application/ld+json">
{
"@context": "http://schema.org",
"@type": "Person",
"name": "Alice"
}
</script>
</head>
</html>
"""
ld_json = embedded_json(page_source=html_with_ldjson, selector = "[type*='application/ld+json']")
print(ld_json)
# Output
[{
"@context": "http://schema.org",
"@type": "Person",
"name": "Alice"
}]
#-------------------------
# DOM navigation
#-------------------------
# Example 1: parent, children, siblings
p_elem = select_one(html_doc,"p")
print("Parent tag of <p>:", parent(p_elem).tag)
print("Children of <p>:", [c.tag for c in children(p_elem)])
print("Siblings of <p>:", [s.tag for s in siblings(p_elem)])
# Example 2: next_sibling, prev_sibling
print("Next sibling of <p>:", next_sibling(p_elem).tag)
h1_elem = select_one(html_doc,"h1")
print("Previous sibling of <p>:", next_sibling(h1_elem))
# Example 3: ancestors and descendants
ancs = ancestors(p_elem)
print("Ancestor tags of <p>:", [a.tag for a in ancs])
desc = descendants(select_one(html_doc,"table"))
print("Descendant tags of <table>:", [d.tag for d in desc])
# Example 4: class utilities
div_html = '<div class="card primary"></div>'
div_elem = parse_html(div_html)
print("Has class 'card'? ->", has_class(div_elem, "card"))
print("Classes:", get_classes(div_elem))
# -------------------------------
# Resolve relative URLs
# -------------------------------
base = "https://example.com"
# Get <a> links
print(absolute_url(html_doc, "a", base_url=base))
# → 'https://example.com/about'
# Get <img> sources
print(absolute_url(html_doc, "img", base_url=base, attr="src"))
# → 'https://example.com/images/logo.png'
# -------------------------------
# XML Example
# -------------------------------
# Parsing XML from a string
xml_content = """<root>
<child>Test</child>
</root>
"""
xml_doc = parse_xml(xml_content)
print(xml_doc)
# Pretty print XML
print(prettify(xml_doc))
# Select all child elements using CSS selector
all_elements = select_all(xml_doc, "child")
print(all_elements)
# Select one child element using XPath selector
child = select_one(xml_doc, "//child")
print(child)
# Extract content from an element
content = selector_content(xml_doc, "child")
print(content)
# Get the parent element of a child
parent_element = parent(child)
print(parent_element)
# Get all children of the root element
children = children(xml_doc)
print(children)
# Find the first child element with a specific tag
child = xml_find(xml_doc, "child")
print(child)
# Find all child elements with a specific tag
children = xml_find_all(xml_doc, "child")
print(children)
# Execute XPath expression
result = xml_xpath(xml_doc, "//child")
print(result)
# Apply XSLT transformation
xslt = """<?xml version="1.0" encoding="UTF-8"?>
<xsl:stylesheet xmlns:xsl="http://www.w3.org/1999/XSL/Transform" version="1.0">
<xsl:template match="/">
<html>
<body>
<xsl:value-of select="/root/child"/>
</body>
</html>
</xsl:template>
</xsl:stylesheet>"""
transformed = xml_transform(xml_doc, xslt)
print(prettify(transformed))
# Validate XML against XSD schema
is_valid = xml_validate_xsd(xml_doc, Path("schema.xsd"))
print(is_valid)
# Create a new element and add it to the root
new_element = xml_create_element("newTag", text="This is new", id="123")
xml_add_child(xml_doc, new_element)
print(prettify(xml_doc))
# Set an attribute on an element
xml_set_attr(new_element, "id", "456")
print(prettify(new_element))
# -------------------------------
# JSON Example
# -------------------------------
json_str = '{"user": {"profile": {"name": "Alice"}}}'
data = parse_json(json_str)
# Get first key match
print(json_content(json_str, keys=["name"], position="first"))
# ➜ {'name': 'Alice'}
# Follow nested path
print(json_content(json_str, keys=["user", "profile", "name"], position="last"))
# ➜ Alice
# -------------------------------
# Utility Example
# -------------------------------
1. Create a Directory
from scrapery import create_directory
# Creates a directory if it doesn't already exist.
# Example 1: Creating a new directory
create_directory("new_folder")
# Example 2: Creating nested directories
create_directory("parent_folder/sub_folder")
# ================================================================
2. Standardize a String
from scrapery import standardized_string
# This function standardizes the input string by removing escape sequences like \n, \t, and \r, removing HTML tags, collapsing multiple spaces, and trimming leading/trailing spaces.
# Example 1: Standardize a string with newlines, tabs, and HTML tags
input_string_1 = "<html><body> Hello \nWorld! \tThis is a test. </body></html>"
print("Standardized String 1:", standardized_string(input_string_1))
# Example 2: Input string with multiple spaces and line breaks
input_string_2 = " This is a \n\n string with spaces and \t tabs. "
print("Standardized String 2:", standardized_string(input_string_2))
# Example 3: Pass an empty string
input_string_3 = ""
print("Standardized String 3:", standardized_string(input_string_3))
# Example 4: Pass None (invalid input)
input_string_4 = None
print("Standardized String 4:", standardized_string(input_string_4))
================================================================
3. Read CSV
from scrapery import read_csv
csv_file_path = 'data.csv'
get_value_by_col_name = 'URL'
filter_col_name = 'Category'
include_filter_col_values = ['Tech']
result = read_csv(csv_file_path, get_value_by_col_name, filter_col_name, include_filter_col_values)
print(result)
Sample CSV
Category,URL
Tech,https://tech1.com
Tech,https://tech2.com
Science,https://science1.com
Result
['https://tech1.com', 'https://tech2.com']
================================================================
4. Save to CSV
from scrapery import save_to_csv
list_data = [[1, 'Alice', 23], [2, 'Bob', 30], [3, 'Charlie', 25]]
headers = ['ID', 'Name', 'Age']
output_file_path = 'output_data.csv'
# Default separator (comma)
save_to_csv(data_list, headers, output_file_path)
# Tab separator
save_to_csv(data_list, headers, output_file_path, sep="\t")
# Semicolon separator
save_to_csv(data_list, headers, output_file_path, sep=";")
Output (default, sep=","):
ID,Name,Age
1,Alice,23
2,Bob,30
3,Charlie,25
Output (sep="\t"):
ID Name Age
1 Alice 23
2 Bob 30
3 Charlie 25
================================================================
5. Save to Excel file
from scrapery import save_to_xls
save_to_xls(data_list, headers, output_file_path)
================================================================
6. List files in a directory
from scrapery import list_files
files = list_files(directory=output_dir, extension="csv")
print("CSV files in output directory:", files)
================================================================
7. Read back file content
from scrapery import read_file_content
# Example 1: Read small JSON file fully
file_path_small_json = 'small_data.json'
content = read_file_content(file_path_small_json, stream_json=False)
print("Small JSON file content (fully loaded):")
print(content) # content will be a dict or list depending on JSON structure
# Example 2: Read large JSON file by streaming (returns a generator)
file_path_large_json = 'large_data.json'
json_stream: Generator[dict, None, None] = read_file_content(file_path_large_json, stream_json=True)
print("\nLarge JSON file content streamed:")
for item in json_stream:
print(item) # process each streamed JSON object one by one
# Example 3: Read a large text file using mmap
file_path_large_txt = 'large_text.txt'
text_content = read_file_content(file_path_large_txt)
print("\nLarge text file content (using mmap):")
print(text_content[:500]) # print first 500 characters
# Example 4: Read a small text file with encoding detection
file_path_small_txt = 'small_text.txt'
text_content = read_file_content(file_path_small_txt)
print("\nSmall text file content (with encoding detection):")
print(text_content)
================================================================
8. Save to file
from scrapery import save_file_content
# Example 1: Save plain text content to a file
text_content = "Hello, this is a sample text file.\nWelcome to file handling in Python!"
save_file_content("output/text_file.txt", text_content)
# Output: Content successfully written to output/text_file.txt
# Example 2: Save JSON content to a file
json_content = {
"name": "Alice",
"age": 30,
"skills": ["Python", "Data Science", "Machine Learning"]
}
save_file_content("output/data.json", json_content)
# Output: JSON content successfully written to output/data.json
# Example 3: Save number (non-string content) to a file
number_content = 12345
save_file_content("output/number.txt", number_content)
# Output: Content successfully written to output/number.txt
# Example 4: Append text content to an existing file
append_text = "\nThis line is appended."
save_file_content("output/text_file.txt", append_text, mode="a")
# Output: Content successfully written to output/text_file.txt
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