快速加入两个DataFrames

import pandas as pd

# 创建两个示例DataFrames
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df2 = pd.DataFrame({'A': [7, 8, 9], 'B': [10, 11, 12]})

# 按行合并两个DataFrames
result = pd.concat([df1, df2])

# 按列合并两个DataFrames
result = pd.concat([df1, df2], axis=1)

from pyspark.sql import SparkSession

# 创建SparkSession
spark = SparkSession.builder.getOrCreate()

# 创建两个示例DataFrames
df1 = spark.createDataFrame([(1, 4), (2, 5), (3, 6)], ['A', 'B'])
df2 = spark.createDataFrame([(7, 10), (8, 11), (9, 12)], ['A', 'B'])

# 按行合并两个DataFrames
result = df1.union(df2)

# 按列合并两个DataFrames
result = df1.join(df2, on='A', how='inner')

import sqlite3

# 连接到SQLite数据库
conn = sqlite3.connect('data.db')

# 创建两个示例DataFrames
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df2 = pd.DataFrame({'A': [7, 8, 9], 'B': [10, 11, 12]})

# 将DataFrames写入SQLite数据库
df1.to_sql('table1', conn, if_exists='replace')
df2.to_sql('table2', conn, if_exists='replace')

# 使用SQL语句合并两个DataFrames
result = pd.read_sql_query('SELECT * FROM table1 UNION ALL SELECT * FROM table2', conn)