Python uses Pandas to read and write data, including CSV, Excel, SQL, JSON, etc

Environmental preparation: Before using Pandas, it is necessary to first install Pandas and related dependencies. You can install it using the following command: python pip install pandas In addition, other dependent class libraries need to be installed, such as xlrd (for reading Excel files), openpyxl (for writing Excel files), pyodbc (for connecting to SQL Server databases), psycopg2 (for connecting to PostgreSQL databases), etc. You can install these class libraries through the corresponding commands. Dataset introduction: Below is an example dataset, which is a CSV file that contains some basic information about students. The dataset contains fields such as name, age, gender, subject, and score. The website for downloading the dataset is: https://example.com/example.csv The sample code is as follows: python import pandas as pd #Read CSV file df = pd.read_csv('example.csv') #View the first 5 rows of the dataset print(df.head()) #Writing a dataset to an Excel file df.to_excel('example.xlsx', index=False) #Read Excel file df_excel = pd.read_excel('example.xlsx') #Writing a dataset to a SQL Server database import pyodbc #Connect to database conn = pyodbc.connect('DRIVER={SQL Server};SERVER=localhost;DATABASE=mydb;UID=username;PWD=password') #Create Cursor cursor = conn.cursor() #Create Table cursor.execute('CREATE TABLE students (name VARCHAR(255), age INT, gender VARCHAR(255), subject VARCHAR(255), score FLOAT)') #Insert data into a table for index, row in df.iterrows(): cursor.execute('INSERT INTO students (name, age, gender, subject, score) VALUES (?, ?, ?, ?, ?)', row['name'], row['age'], row['gender'], row['subject'], row['score']) #Commit transaction conn.commit() #Close database connection conn.close() #Read SQL Server data conn = pyodbc.connect('DRIVER={SQL Server};SERVER=localhost;DATABASE=mydb;UID=username;PWD=password') df_sql = pd.read_sql('SELECT * FROM students', conn) #Writing a dataset to a JSON file df.to_json('example.json', orient='records') #Reading JSON files df_json = pd.read_json('example.json') The above is an example code for using Pandas to read and write data. The code demonstrates how to read CSV files, write Excel files, write SQL Server databases, read SQL Server data, write JSON files, and read JSON files. Please pay attention to modifying the database connection information and file path according to the actual situation.