Python Seminar of Data analytics using python
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The document provides an overview of a seminar on web scraping and numerical analysis hosted by the Department of Master of Computer Applications at Visvesvaraya Technological University. It highlights the importance of web scraping in Python for data analytics, introducing key libraries such as Beautiful Soup, lxml, and Scrapy. Additionally, it includes a sample program for extracting and processing XML data using lxml and pandas.
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from lxml import objectify
import pandas as pd
# Parse XML data
xml_data = objectify.parse('properties.xml')
root = xml_data.getroot() # Root element
# Extract data and column names
data = []
cols = []
for child in root.getchildren():
data.append([subchild.text for subchild in child.getchildren()])
cols.append(child.tag)
# Create DataFrame
df = pd.DataFrame(data).T # Create DataFrame and transpose it
# Set column names
df.columns = cols
# Print DataFrame
print(df)](https://image.slidesharecdn.com/pythonseminar-240615093943-a3fb36cb/85/Python-Seminar-of-Data-analytics-using-python-5-320.jpg)
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Python Seminar of Data analytics using python
- 1. Visvesvaraya Technological University “jnana Sangam” Belagavi Karnataka India SECAB Institute of Engineering & Technology, Vijayapur Department Of Master Of Computer Applications 2022-2023 A Seminar On Web Scraping And Numerical Analysis By Course Co-Ordinator Prof.Nazeera Madabhavi Mohammad Azeem Maniyar 2SA22MC013
- 2. Web Scraping Web scraping in Python is a technique used to extract data from websites. It's a valuable skill in data analytics as it allows you to collect large amounts of data from the web for analysis
- 3. Beautiful Soup lxml Requests Scrapy Selenium html5lib Python libraries are commonly used. Here are some most Popular libraries
- 4. Parsing XML with lxml.objectify <?xml version="1.0" encoding="UTF-8"?> <root> <room> <n35237 type="number">1.0</n35237> <n32238 type="number">3.0</n32238> <n44699 type="number">nan</n44699> </room> <price> <n35237 type="number">7020000.0</n35237> <n32238 type="number">10000000.0</n32238> <n44699 type="number">4128000.0</n44699> </price> <property_id> <n35237 type="number">35237.0</n35237> <n32238 type="number">32238.0</n32238> <n44699 type="number">44699.0</n44699> </property_id> </root>
- 5. Program from lxml import objectify import pandas as pd # Parse XML data xml_data = objectify.parse('properties.xml') root = xml_data.getroot() # Root element # Extract data and column names data = [] cols = [] for child in root.getchildren(): data.append([subchild.text for subchild in child.getchildren()]) cols.append(child.tag) # Create DataFrame df = pd.DataFrame(data).T # Create DataFrame and transpose it # Set column names df.columns = cols # Print DataFrame print(df)
- 6. Output