Cheat Sheet: APIs and Data Collection

-

 

Cheat Sheet: API's and Data Collection

Package/MethodDescriptionCode Example
Accessing element attributeAccess the value of a specific attribute of an HTML element.Syntax:
  1. 1
  1. attribute = element[(attribute)]

Example:

  1. 1
  1. href = link_element[(href)]
BeautifulSoup()Parse the HTML content of a web page using BeautifulSoup. The parser type can vary based on the project.Syntax:
  1. 1
  1. soup = BeautifulSoup(html, (html.parser))

Example:

  1. 1
  1. html = (https://api.example.com/data) soup = BeautifulSoup(html, (html.parser))
delete()Send a DELETE request to remove data or a resource from the server. DELETE requests delete a specified resource on the server.Syntax:
  1. 1
  1. response = requests.delete(url)

Example:

  1. 1
  1. response = requests.delete((https://api.example.com/delete))
find()Find the first HTML element that matches the specified tag and attributes.Syntax:
  1. 1
  1. element = soup.find(tag, attrs)

Example:

  1. 1
  1. first_link = soup.find((a), {(class): (link)})
find_all()Find all HTML elements that match the specified tag and attributes.Syntax:
  1. 1
  1. elements = soup.find_all(tag, attrs)

Example:

  1. 1
  1. all_links = soup.find_all((a), {(class): (link)})</td>
findChildren()Find all child elements of an HTML element.Syntax:
  1. 1
  1. children = element.findChildren()

Example:

  1. 1
  1. child_elements = parent_div.findChildren()
get()Perform a GET request to retrieve data from a specified URL. GET requests are typically used for reading data from an API. The response variable will contain the server's response, which you can process further.Syntax:
  1. 1
  1. response = requests.get(url)

Example:

  1. 1
  1. response = requests.get((https://api.example.com/data))
HeadersInclude custom headers in the request. Headers can provide additional information to the server, such as authentication tokens or content types.Syntax:
  1. 1
  1. headers = {(HeaderName): (Value)}

Example:

  1. 1
  1. base_url = (https://api.example.com/data) headers = {(Authorization): (Bearer YOUR_TOKEN)} response = requests.get(base_url, headers=headers)
Import LibrariesImport the necessary Python libraries for web scraping.Syntax:
  1. 1
  1. from bs4 import BeautifulSoup
json()Parse JSON data from the response. This extracts and works with the data returned by the API. The response.json() method converts the JSON response into a Python data structure (usually a dictionary or list).Syntax:
  1. 1
  1. data = response.json()

Example:

  1. 1
  2. 2
  1. response = requests.get((https://api.example.com/data)) 
  2. data = response.json()
next_sibling()Find the next sibling element in the DOM.Syntax:
  1. 1
  1. sibling = element.find_next_sibling()

Example:

  1. 1
  1. next_sibling = current_element.find_next_sibling()
parentAccess the parent element in the Document Object Model (DOM).Syntax:
  1. 1
  1. parent = element.parent

Example:

  1. 1
  1. parent_div = paragraph.parent
post()Send a POST request to a specified URL with data. Create or update POST requests using resources on the server. The data parameter contains the data to send to the server, often in JSON format.Syntax:
  1. 1
  1. response = requests.post(url, data)

Example:

  1. 1
  1. response = requests.post((https://api.example.com/submit), data={(key): (value)})
put()Send a PUT request to update data on the server. PUT requests are used to update an existing resource on the server with the data provided in the data parameter, typically in JSON format.Syntax:
  1. 1
  1. response = requests.put(url, data)

Example:

  1. 1
  1. response = requests.put((https://api.example.com/update), data={(key): (value)})
Query parametersPass query parameters in the URL to filter or customize the request. Query parameters specify conditions or limits for the requested data.Syntax:
  1. 1
  1. params = {(param_name): (value)}

Example:

  1. 1
  2. 2
  3. 3
  1. base_url = "https://api.example.com/data"
  2. params = {"page": 1, "per_page": 10}
  3. response = requests.get(base_url, params=params)
select()Select HTML elements from the parsed HTML using a CSS selector.Syntax:
  1. 1
  1. element = soup.select(selector)

Example:

  1. 1
  1. titles = soup.select((h1))
status_codeCheck the HTTP status code of the response. The HTTP status code indicates the result of the request (success, error, redirection). Use the HTTP status codeIt can be used for error handling and decision-making in your code.Syntax:
  1. 1
  1. response.status_code

Example:

  1. 1
  2. 2
  3. 3
  1. url = "https://api.example.com/data"
  2. response = requests.get(url)
  3. status_code = response.status_code
tags for find() and find_all()Specify any valid HTML tag as the tag parameter to search for elements of that type. Here are some common HTML tags that you can use with the tag parameter.Tag Example:
  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9
  10. 10
  1. - (a): Find anchor () tags.
  2. - (p): Find paragraph ((p)) tags.
  3. - (h1), (h2), (h3), (h4), (h5), (h6): Find heading tags from level 1 to 6 ( (h1),n (h2)).
  4. - (table): Find table () tags.
  5. - (tr): Find table row () tags.
  6. - (td): Find table cell ((td)) tags.
  7. - (th): Find table header cell ((td))tags.
  8. - (img): Find image ((img)) tags.
  9. - (form): Find form ((form)) tags.
  10. - (button): Find button ((button)) tags.
textRetrieve the text content of an HTML element.Syntax:
  1. 1
  1. text = element.text

Example:

  1. 1
  1. title_text = title_element.text

Comments

Post a Comment

Popular posts from this blog

Lila's Journey to Becoming a Data Scientist: Her Working Approach on the First Task

-
  Education and Skill Acquisition: Lila, with an economics background, decides to transition to data science. Enrolls in the IBM Data Science Professional Certificate for comprehensive training. Gains proficiency in statistics, machine learning, and programming languages. Building a Strong Foundation: Develops a deep understanding of data manipulation and visualization using Python libraries. Visualization for Storytelling: Learns to create informative visualizations, aiding in effective communication of findings. Hands-On Experience: Engages in Kaggle competitions and personal data projects for practical experience. Establishes a GitHub profile to showcase her projects and skills. Data Wrangling and Preprocessing: Focuses on data cleaning, preprocessing, and handling missing data and outliers. Communication and Storytelling: Hones data storytelling skills using tools like Matplotlib and Plotly. Networking and Collaboration: Actively participates in data science communities, attend...
Read more

Notes on Hiring for Data Science Teams

-
  Unicorn Myth in Hiring: Companies often seek individuals with a myriad of skills for data science roles. Ideal candidates possess domain-specific knowledge, excel in data analysis, and have strong presentation and storytelling skills. However, finding such a "unicorn" is rare, and the focus should shift to other essential qualities. Resonance with Company DNA: The key is to identify applicants who resonate with the company's values and business domain. While analytics skills can be taught, passion for the business is intrinsic and contributes significantly to productivity. Curiosity Matters: Curiosity is a top priority in hiring for a data science team. Candidates should demonstrate a broad curiosity, showing interest in various aspects of their surroundings beyond just data science. Sense of Humor: A lighthearted approach is crucial, as being too serious may hinder collaboration and creativity. A sense of humor aids in navigating challenges and fosters a positive work ...
Read more

switch functions

-
  Nested IF Function : The  nested IF  function allows you to perform tests with multiple conditions and return different values based on the results of those tests. It is called “nested” because one IF function is embedded inside another IF function. Syntax of a generic nested IF formula: =IF(condition1, result1, IF(condition2, result2, IF(condition3, result3, result4) ) ) Each subsequent IF function is embedded into the  value_if_false  argument of the previous function. SWITCH Function : The  SWITCH  function is a more compact alternative to nested IF statements. It compares an expression against a list of values and returns the result according to the first matching value. Syntax of the SWITCH function: =SWITCH(expression, value1, result1, [default or value2, result2], ... [default or valueN, resultN]) It has four arguments: expression : The value to compare against the list of values. valueN : A value com...
Read more