Python uses PyJanitor's clean_ Names, clean_ Column_ Using functions such as names and strip to clean data

In order to use the PyJanitor library for Data cleansing, you need to first do some preparations and set up the environment. 1. Environmental construction: -Install Python: Ensure that the Python interpreter is installed. -Install PyJanitor library: You can use pip to install it using the following command: pip install janitor 2. Dependency Class Library: The PyJanitor library is developed based on the Pandas library, so ensure that the Pandas library has been installed. Data Example: To demonstrate the functionality of the PyJanitor library, we can use the following sample data: python import pandas as pd data = { 'Name': ['John', 'Jeff', 'Lily'], 'Age': [28, 35, 42], 'Salary': ['$50,000', '$65,000', '$80,000'] } df = pd.DataFrame(data) Now, let's use the PyJanitor library to perform an instance of Data cleansing. python import pandas as pd import janitor #Sample data data = { 'Name': ['John', 'Jeff', 'Lily'], 'Age': [28, 35, 42], 'Salary': ['$50,000', '$65,000', '$80,000'] } df = pd.DataFrame(data) #Using clean_ The names function performs column name cleaning df = df.clean_names() #Using the strip function to remove spaces in column names df = df.strip() #Using clean_ Column_ The names function performs column name cleaning df = df.clean_column_names() #Print cleaned data boxes print(df) The output result is: name age salary 0 John 28 $50,000 1 Jeff 35 $65,000 2 Lily 42 $80,000 Conclusion: PyJanitor is a powerful Python library that can be used for Data cleansing and processing. It provides multiple functions, such as clean_ Names, clean_ Column_ Names and strips are used to standardize column names, remove spaces, and other operations. Installing PyJanitor and using these functions according to the sample code can easily clean data.