Web Scraping with Pandas read_html() Function
In today’s digital age, the world wide web has become an abundant source of information, and businesses often rely on it to extract valuable insights from the available data. Web scraping is a technique that allows us to extract data from websites systematically.
In this article, we will explore the basics of web scraping and the popular Pandas library’s read_html() function.
What is Web Scraping?
Web scraping is an automated method of collecting data from websites, which involves extracting the data from the HTML code of a webpage. It is a powerful way of automating data collection that can be used for various applications, such as monitoring competitors, tracking price trends, analyzing social media sentiment, and more.
The process of web scraping involves using Python code to request a webpage, parsing its HTML content, and then extracting the relevant data using various techniques such as string manipulation, regular expressions, and Xpath queries.
Pandas read_html() function:
Pandas is a popular library in the Python programming language that provides data manipulation capabilities. The library’s read_html() function is specifically designed to extract tables from HTML pages and return them as a data frame.
This function can save time and reduce the complexity of web scraping for data analysts and data scientists.
Prerequisites for Using read_html():
To use the Pandas read_html() function, you must have the following prerequisites in place:
- Python: You need to have Python installed on your system.
- Pandas: You must have Pandas installed on your system.
- IDE: You need an integrated development environment (IDE) or text editor to run the Python code.
- Cross-Platform XML and HTML processing Library (lxml): It is a library that handles XML and HTML documents. You should install the “lxml” module in Python using the command “pip install lxml”.
Required software and environment setup:
Python is a general-purpose programming language used for a range of applications across different platforms.
To install Python on your system, you can download it from the official website and follow the installation prompts. Once you have Python installed, you need to install the Pandas library, which can be accomplished by running the command “pip install pandas” in the command prompt.
To write Python code, a text editor is essential, and there are several popular IDEs available for Python development. Some of the most popular options include PyCharm, Jupyter Notebook, Spyder, and Visual Studio Code.
Each IDE has its pros and cons and can be used depending on your comfort level.
Installation of lxml module:
The Pandas read_html() function requires the “lxml” module to handle HTML parsing.
To install this module, run the command “pip install lxml” in the command prompt on your system. In most cases, this process is straightforward and takes a few seconds to complete.
Once the prerequisites are in place, you can move on to use the Pandas read_html() function to extract tables from HTML pages.
Using the Pandas read_html() Function:
To use the Pandas read_html() function, import the Pandas library and pass the URL of the HTML page to the function.
Pandas read_html() function returns a list of data frames that contain the tables available on the HTML page. The function automatically detects and extracts all the tables in the page and returns them as a data frame.
It is also possible to extract specific tables by specifying the HTML tag and attributes that a table contains. For example, you can extract a table by using the following code snippet:
import pandas as pd
url = 'https://www.examplewebsite.com'
tables = pd.read_html(url, attrs={"class":"datatable"})
This code retrieves all tables with the “datatable” class attribute and returns them as a list of data frames.
Conclusion:
Web scraping is a powerful technique of collecting data from the internet automatically. The Pandas read_html() function makes web scraping easy by automatically detecting and parsing HTML content and returning tables as data frames.
By providing a simple and efficient way to extract data from websites, read_html() saves time and reduces the complexity of data collection, making it a go-to tool for data analysts and scientists.
Extracting Tables from Strings of HTML
In our previous article, we discussed how we can extract tables from HTML pages using the Pandas read_html() function. However, what if the HTML table is not present on a webpage, but instead, only in the form of a string?
In this article, we will explore how to extract tables from strings of HTML using Python.
HTML table as a string:
An HTML table can also exist as a string of HTML code.
This string can be either a direct string representation of the table or can be part of an HTML document. In either case, we can still parse the string and extract the table using the Pandas read_html() function.
Reading tables using Pandas read_html() function:
The Pandas library’s read_html() function is used to read HTML tables and convert them into a data frame format. To extract tables from a string of HTML code, we need to pass the string as input to read_html() function.
The function returns a list of data frames that contain the tables available in the HTML string. For example, consider the following HTML string representing a table:
html_string = """
| Name | Age | Gender |
|---|---|---|
| John | 22 | Male |
| Jane | 25 | Female |
"""
We can extract this table and convert it into a data frame using the following code:
import pandas as pd
tables = pd.read_html(html_string)
df = tables[0]
Accessing specific index in the output list:
The Pandas read_html() function returns a list of data frames that contain the tables available in the HTML string.
This list can contain multiple data frames, depending on the number of tables available in the string. To access a specific data frame, we can use indexing.
In the example above, we used index 0 to access the first data frame in the list, which is the only data frame since there is only one table in the HTML string. Similarly, we can access other data frames in the list by using their respective indices.
Checking data type of each column:
After extracting a table from an HTML string, it is useful to check the data type of each column to perform further analysis. We can use the Pandas function “dtypes” to check the data type of each column in the data frame.
For example, consider the following code snippet that checks the data type of each column in the data frame:
dtypes = df.dtypes
print(dtypes)
This code snippet outputs the data type of each column present in the data frame.
Reading tables from a webpage using URL:
We can also extract tables directly from a webpage using its URL.
To extract tables from a webpage, we must read the webpage’s HTML content using Python’s requests library and pass the HTML string to the Pandas read_html() function as discussed earlier. For example, consider the following code that extracts tables from a webpage:
import pandas as pd
import requests
url = "https://www.examplewebsite.com"
html_content = requests.get(url).text
tables = pd.read_html(html_content)
Checking number of tables on the webpage:
Websites can have multiple tables on a single webpage.
When extracting tables from a webpage, it is important to check the number of tables available on the page. If there is more than one table, the Pandas read_html() function returns a list of data frames containing all tables available on the webpage.
We can check the number of tables available on the webpage by checking the length of the list returned by the Pandas read_html() function. For example, consider the following code snippet:
import pandas as pd
import requests
url = "https://www.examplewebsite.com"
html_content = requests.get(url).text
tables = pd.read_html(html_content)
if len(tables) == 0:
print("No tables found on the webpage.")
elif len(tables) == 1:
df = tables[0]
print(df)
else:
print("Multiple tables found on the webpage.")
Conclusion:
Extracting tables from strings of HTML using Python involves reading the string using the Pandas read_html() function and using indexing to access specific data frames and checking data types.
Similarly, we can extract tables from a webpage using its URL and check the number of tables available on the webpage. These techniques are useful for analyzing website data or web scraping for information.
Extracting Tables from Files and Typecasting Columns
In the previous article, we discussed how to extract tables from strings of HTML using the Pandas read_html() function.
In this article, we will explore how to extract tables from files and how to typecast table columns using converters in the Pandas read_html() function.
Reading tables from files using Pandas read_html():
Pandas read_html() function not only extracts tables from HTML web pages but can also extract tables from files like .html, .htm, .xls, .xlsx, etc.
To extract tables from a file, we need to pass the file location as an input to the read_html() function. For example, consider the following code that reads an HTML file containing a table:
import pandas as pd
tables = pd.read_html("example.html")
df = tables[0]
This code reads the first table in the example.html file and converts it to a Pandas data frame.
Handling multiple tables in the output list:
Similar to extracting tables from a web page, if an HTML file contains multiple tables, the read_html() function returns a list of data frames containing all the tables present in the file. In such cases, we can simply access the data frames present in the list by using their respective indices.
For example, consider the following code that reads from an HTML file containing multiple tables:
import pandas as pd
tables = pd.read_html("example.html")
df1 = tables[0] # First table
df2 = tables[1] # Second table
This code reads the first table and second table in example.html file and stores them in data frames df1 and df2.
Typecasting table columns with converters:
Changing the data type of a column in an extracted table is a common requirement in data analysis tasks.
For example, we may need to convert a column of strings to numeric data types or date-time formats. We can achieve this by using “converters” in the read_html() function.
Syntax for typecasting using Pandas read_html() function:
The read_html() function takes multiple optional arguments where the “converters” parameter is used to specify the function that will be used to convert the values in a specific column to a particular data type. The syntax for using converters in the read_html() function is as follows:
import pandas as pd
def converter_func(x):
return pd.to_numeric(x)
tables = pd.read_html(url, converters={"Column_name": converter_func})
df = tables[0]
This code uses the pd.to_numeric() function to convert the values in “Column_name” to numeric integers.
We can also use other functions from the Pandas library like to_datetime() to convert columns to date-time format and so on.
Changing data type of a column in a table:
Let’s consider an example where the prices in a product list are in string format, but we want to convert them to float type.
To achieve this, we can use the following code:
import pandas as pd
def convert_to_float(price):
return float(price.strip('$'))
tables = pd.read_html(url, converters={'Price': convert_to_float})
df = tables[0]
This code defines a converter function “convert_to_float” that converts the price string to float by removing the dollar sign using strip() and then converting it to float using the float() function. The “converters” argument is used to specify which column needs to be converted in the data frame.
Conclusion:
In this article, we have explored how to extract tables from files using the Pandas read_html() function and how to handle multiple tables in the output list. We have also discussed how to typecast columns in a table using converters along with the syntax for using converters in the read_html() function.
These techniques are useful in data analysis tasks where data types need to be changed for further analysis.
Limitations of Pandas read_html() function:
Although the Pandas read_html() function is a useful tool for extracting tables from different sources, it has some limitations. Some of the issues that users may encounter are listed below:
- Performance: The performance of the read_html() function can be negatively impacted while processing large HTML files or web pages with multiple tables. In these cases, the function may take longer to execute or may fail to execute at all.
- Compatibility: The read_html() function may not be compatible with some websites that use dynamic content or require user authentication.
- Table Extraction: Sometimes, read_html() function may not extract the table correctly due to issues with the HTML code’s layout or structure. In such cases, users may have to resort to manual parsing of the HTML code or other scraping techniques like Selenium.
In such cases, users may have to resort to other web scraping techniques like Beautiful Soup or Selenium.
Conclusion and summary of the article:
In this article, we have discussed the basics of web scraping and the Pandas read_html() function, which is used for extracting tables from HTML sources. We have explored how to extract tables from web pages, HTML strings, files, and how to handle multiple tables in the output list.
We have also discussed how to typecast columns in a table using converters. Despite its limitations, the Pandas read_html() function is a powerful tool for web scraping and data analysis.
With the help of this function, we can extract data quickly and easily from a variety of sources. As such, it is a valuable addition to any data analyst or data scientist’s toolkit.
In this article, we have discussed how to extract tables from different HTML sources using the Pandas read_html() function. We covered web scraping basics, how to extract tables from web pages, files, and HTML string, how to handle multiple tables in the output list, and how to typecast columns in a table using converters.
Despite its limitations, the Pandas read_html() function is a powerful tool for web scraping and data analysis, making it a valuable skill for data analysts and data scientists. With these techniques, one can extract data quickly and easily, saving time and reducing the complexity of data collection.