Python uses Pandas to implement missing value processing, Outlier processing, data Type conversion, duplicate value processing, standardization, normalization, etc

Environmental construction and preparation work: 1. Install the Python environment: from the official Python website（ https://www.python.org/ ）Download and install Python version, it is recommended to install Python 3. x. 2. Install the Pandas library: You can use the pip command to install it from the command line, and run the following command to install the Pandas library: pip install pandas Dependency Class Library: -Pandas: A powerful library for processing and analyzing data. Dataset download: In this example, we will use a CSV file named 'students. csv' as the sample dataset. This dataset contains information about students in a class, including fields such as name, age, gender, and grades. You can download the dataset from the following website: https://example.com/students.csv Example code: python import pandas as pd #Read Dataset data = pd.read_csv('students.csv') #View the first few rows of the dataset print(data.head()) #Handling missing values data.fillna(0, inplace=True) #Handling Outlier (for example, replacing values with scores greater than 100 with 100) Data ['Grade ']=data ['Grade']. apply (lambda x: min (x, 100)) #Data Type conversion (for example, converting age from string to integer) Data ['Age ']=data ['Age']. astype (int) #Handling duplicate values data.drop_duplicates(inplace=True) #Standardization (for example, standardizing grades to values between 0 and 1) Data ['Grade ']=(data ['Grade'] - data ['Grade ']. min())/(data ['Grade']. max() - data ['Grade ']. min()) #Normalization (e.g. normalizing age to values between 0 and 1) Data ['Age ']=(data ['Age'] - data ['Age ']. min())/(data ['Age']. max() - data ['Age ']. min()) #Output processed dataset print(data) Please note that the file path 'students. csv' in the above example code should be replaced with your own file path.