Python的数据类型集合：由不同元素组成的集合，集合中是一组无序排列的可 Hash 的值（不可变类型），可以作为字典的Key
Pandas中的DataFrame：DataFrame是一个表格型的数据结构，可以理解为带有标签的二维数组。
常用的集合操作如下图所示：
pandas的 merge 功能默认为 inner 连接，可以实现取交集set 可以直接用 & 取交集import pandas as pd
print("CSDN叶庭云：https://yetingyun.blog.csdn.net/")
set1 = {"Python", "Go", "C++", "Java"}
set2 = {"Go", "C++", "JavaScript", "C"}
set1 & set2
df1 = pd.DataFrame([
['1', 'Python'],
['2', 'Go'],
['3', 'C++'],
['4', 'Java'],
], columns=['id','name'])
df2 = pd.DataFrame([
['2','Go'],
['3','C++'],
['5','JavaScript'],
['6','C'],
], columns=['id','name'])
pd.merge(df1, df2, on=['id','name'])操作如下所示：
set1 = {"Python", "Go", "C++", "Java"}
set2 = {"Go", "C++", "JavaScript", "C"}
set1 | set2
print("CSDN叶庭云：https://yetingyun.blog.csdn.net/")
df1 = pd.DataFrame([
['1', 'Python'],
['2', 'Go'],
['3', 'C++'],
['4', 'Java'],
], columns=['id','name'])
df2 = pd.DataFrame([
['2','Go'],
['3','C++'],
['5','JavaScript'],
['6','C'],
], columns=['id','name'])
pd.merge(df1, df2,
on=['id','name'],
how='outer')
df3 = df1.append(df2)
df3.drop_duplicates(subset=['id'], keep="first")
set1 = {"Python", "Go", "C++", "Java"}
set2 = {"Go", "C++", "JavaScript", "C"}
set1 - set2
print("CSDN叶庭云：https://yetingyun.blog.csdn.net/")
set1 = {"Python", "Go", "C++", "Java"}
set2 = {"Go", "C++", "JavaScript", "C"}
set2 - set1
# df1-df2
df1 = pd.DataFrame([
['1', 'Python'],
['2', 'Go'],
['3', 'C++'],
['4', 'Java'],
], columns=['id','name'])
df2 = pd.DataFrame([
['2','Go'],
['3','C++'],
['5','JavaScript'],
['6','C'],
], columns=['id','name'])
df1 = df1.append(df2)
df1 = df1.append(df2)
set_diff_df = df1.drop_duplicates(subset=df1.columns,
keep=False)
set_diff_df
# df2-df1
df1 = pd.DataFrame([
['1', 'Python'],
['2', 'Go'],
['3', 'C++'],
['4', 'Java'],
], columns=['id','name'])
df2 = pd.DataFrame([
['2','Go'],
['3','C++'],
['5','JavaScript'],
['6','C'],
], columns=['id','name'])
print("CSDN叶庭云：https://yetingyun.blog.csdn.net/")
df2 = df2.append(df1)
df2 = df2.append(df1)
set_diff_df = df2.drop_duplicates(subset=df2.columns,
keep=False)
set_diff_df
# df1-df2
df1 = pd.DataFrame([
['1', 'Python'],
['2', 'Go'],
['3', 'C++'],
['4', 'Java'],
], columns=['id','name'])
df2 = pd.DataFrame([
['2','Go'],
['3','C++'],
['5','JavaScript'],
['6','C'],
], columns=['id','name'])
pd.concat([df1, df2, df2]).drop_duplicates(keep=False)
# df2-df1
df1 = pd.DataFrame([
['1', 'Python'],
['2', 'Go'],
['3', 'C++'],
['4', 'Java'],
], columns=['id','name'])
df2 = pd.DataFrame([
['2','Go'],
['3','C++'],
['5','JavaScript'],
['6','C'],
], columns=['id','name'])
pd.concat([df2, df1, df1]).drop_duplicates(keep=False)
print("CSDN叶庭云：https://yetingyun.blog.csdn.net/")
set1 = {"Python", "Go", "C++", "Java"}
set2 = {"Go", "C++", "JavaScript", "C"}
set1 ^ set2 # 对称差集
# 去重 不保留重复的：即可实现取对称差集
df3 = df1.append(df2)
df3.drop_duplicates(subset=['id'], keep=False)
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