提交代码

Author: zyn1233

qingxiangke/README.md

@@ -13,4 +13,6 @@ Python技术 公众号文章代码库
 
 [爬虫](https://github.com/JustDoPython/python-examples/tree/master/qingxiangke/Crawler) : 用爬虫代替工作的魅力
 
-[pandas](https://github.com/JustDoPython/python-examples/tree/master/qingxiangke/PandasSift) : pandas的多条件筛选
+[pandas条件筛选](https://github.com/JustDoPython/python-examples/tree/master/qingxiangke/PandasSift) : pandas的多条件筛选
+
+[pandas的多表连接](https://github.com/JustDoPython/python-examples/tree/master/qingxiangke/pandasMerge) : pandas的多表连接

qingxiangke/pandasMerge/main.py

@@ -0,0 +1,33 @@
+import pandas as pd
+
+df1 = pd.DataFrame({
+    '姓名': ['张三', '李四', '王五', '刘六', '齐四'],
+    '号码': ['123', '456', '789', '987', '654']
+})
+
+df2 = pd.DataFrame({
+    '姓名': ['张三', '张三', '张三', '李四', '李四', '李四', '李四', '王五', '王五', '刘玉', '胡军', '刘玉', '刘六', '刘六', '刘六', '刘六', '刘克', '刘玉', '齐七', '齐七', '齐七', '齐七', '冯亮', '刘玉', '王云'],
+
+    '号码': ['123', '123', '123', '123', '123', '456', '456', '456', '456', '456', '741', '741', '741', '741', '741', '789', '789', '789', '789', '789', '852', '852', '852', '852', '852'],
+
+    '日期': ['2022-03-13', '2022-03-06', '2022-01-30', '2022-01-04', '2022-02-26', '2022-03-26', '2022-03-06', '2022-01-30', '2022-01-29', '2022-03-13', '2022-03-06', '2022-02-19', '2022-02-04', '2022-03-10', '2022-04-19', '2022-03-10', '2022-01-29', '2022-02-19', '2022-03-06', '2022-03-26', '2022-01-04', '2022-02-04', '2022-04-19', '2022-02-26', '2022-03-06'],
+
+    '方案': ['G1012', 'G1022', 'G1002', 'G1007', 'G1017', 'G1023', 'G1018', 'G1003', 'G1008', 'G1013', 'G1020', 'G1015', 'G1010', 'G1005', 'G1025', 'G1004', 'G1009', 'G1014', 'G1019', 'G1024', 'G1006', 'G1011', 'G1026', 'G1016', 'G1021']
+})
+
+# how默认为”inner"：内连接查询特点是有匹配的才显示，不匹配的不显示
+df = pd.merge(left=df1, right=df2, on="姓名", how="inner")
+
+# how="outer"为外连接：查询特点是无论匹不匹配都显示，对应的值没有则显示空
+df = pd.merge(left=df1, right=df2, on="姓名", how="outer")
+
+# how="left"为左连接：查询表示左边的值全部显示，如右边无匹配则显示空。但是右边有的值匹配不了左边则不显示
+df = pd.merge(left=df1, right=df2, on="姓名", how="left")
+
+# how="right"为右连接：与左连接相反
+df = pd.merge(left=df1, right=df2, on="姓名", how="right")
+
+# 如果和表合并的过程中遇到有一列两个表都同名，但是值不同，合并的时候又都想保留下来，就可以用suffixes给每个表的重复列名增加后缀。suffixes=['_1','_r']
+df = pd.merge(left=df1, right=df2, on="姓名", how="right")
+
+print(df)