pandas - merge 函数

# All default
pd.merge(left,right,how="inner",on=None,left_on=None,right_on=None,left_index=False,right_index=False,sort=True,suffixes=("_x", "_y"),copy=True,indicator=False,validate=None,
)

import pandas as pd
#当只加两个dataframe参数，其余参数都为默认的时候，两个dataframe必须要有至少一个相同的两列，否则会报错
#一个相同的key
data1 = pd.DataFrame({"key1": ["K0", "K1", "K2", "K3"],"A": ["A0", "A1", "A2", "A3"],"B": ["B0", "B1", "B2", "B3"],}
)
data2 = pd.DataFrame({"key1": ["K0", "K1", "K2", "K3"],"C": ["C0", "C1", "C2", "C3"],"D": ["D0", "D1", "D2", "D3"],}
)
mergeData = pd.merge(data1,data2)

#两个相同的key
data3 = pd.DataFrame({"key1": ["K0", "K1", "K2", "K3"],"key2": ["K4", "K5", "K6", "K7"],"A": ["A0", "A1", "A2", "A3"],"B": ["B0", "B1", "B2", "B3"],}
)
data4 = pd.DataFrame({"key1": ["K0", "K1", "K2", "K3"],"key2": ["K4", "K5", "K6", "K7"],"C": ["C0", "C1", "C2", "C3"],"D": ["D0", "D1", "D2", "D3"],}
)
mergeData1 = pd.merge(data3,data4)

#没有相同的key
data5 = pd.DataFrame({"A": ["A0", "A1", "A2", "A3"],"B": ["B0", "B1", "B2", "B3"],}
)
data6 = pd.DataFrame({"C": ["C0", "C1", "C2", "C3"],"D": ["D0", "D1", "D2", "D3"],}
)
pd.merge(data5,data6)
#MergeError: No common columns to perform merge on. Merge options: left_on=None, right_on=None, left_index=False, right_index=False

on 指定相同合并的列

pd.merge(data3,data4,on="key1")

pd.merge(data3,data4,on=["key1","key2"])

left_on, right_on 指定不同合并的列

data5 = pd.DataFrame({"key1": ["K0", "K1", "K2"],"A": ["A0", "A1", "A2"],"B": ["B0", "B1", "B2"],}
)
data6 = pd.DataFrame({"key2": ["K1", "K2", "K3"],"C": ["C0", "C1", "C2"],"D": ["D0", "D1", "D2"],}
)
pd.merge(data5,data6,left_on="key1",right_on="key2")

how - left、right、outer、inner(Default)、cross

#注意两个DaraFrame的key1和key2不一样
left = pd.DataFrame({"key1": ["K0", "K0", "K1", "K2"],"key2": ["K0", "K1", "K0", "K1"],"A": ["A0", "A1", "A2", "A3"],"B": ["B0", "B1", "B2", "B3"],}
)
right = pd.DataFrame({"key1": ["K0", "K1", "K1", "K2"],"key2": ["K0", "K0", "K0", "K0"],"C": ["C0", "C1", "C2", "C3"],"D": ["D0", "D1", "D2", "D3"],}
)

left ：只保留左侧dataframe的keys进行合并，右侧dataframe的keys如果左侧dataframe没有的话，则删除。

pd.merge(left, right, how="left", on=["key1", "key2"])

right ：只保留右侧dataframe的keys进行合并，左侧dataframe的keys如果右侧dataframe没有的话，则删除。

pd.merge(left, right, how="right", on=["key1", "key2"])

outer ：并集，即两个dataframe的keys都保留

pd.merge(left, right, how="outer", on=["key1", "key2"])

inner ：交集，即两个dataframe的相同keys才保留

pd.merge(left, right, how="inner", on=["key1", "key2"])

cross ：组合,4*4=16行,但不能再使用"on"参数

pd.merge(left, right, how="cross")

left_index , right_index 通过index进行合并

left = pd.DataFrame({"A": ["A0", "A1", "A2"], "B": ["B0", "B1", "B2"]}, index=["K0", "K1", "K2"]
)right = pd.DataFrame({"C": ["C0", "C2", "C3"], "D": ["D0", "D2", "D3"]}, index=["K0", "K2", "K3"]
)#两个index都必须设置为True，否则报错。
pd.merge(left, right, left_index=True, right_index=True, how="outer")

pd.merge(left, right, left_index=True, right_index=True, how="inner")

suffixes 两个dataframe有相同的列名，并且这列名不做keys时，使用不同的后缀来区别两个dataframe的列

pd.merge(data3, data4, on="key1", how="outer", suffixes=("_first","_second"))

indicator 溯源，判断新dataframe的key分别来自哪个dataframe(both,left_only,right_only)

pd.merge(left, right, left_index=True, right_index=True, how="outer", indicator="indicator_column")

validate

"one_to_one" or "1:1": checks if merge keys are unique in both left and right datasets.
"one_to_many" or "1:m": checks if merge keys are unique in left dataset.
"many_to_one" or "m:1": checks if merge keys are unique in right dataset.
"many_to_many" or "m:m": allowed, but does not result in checks.

df1 = pd.DataFrame({"A": [1, 2], "B": [1, 2]})
df2 = pd.DataFrame({"A": [4, 5, 6], "B": [2, 2, 2]})

#右侧的df2,"B"列里是有重复的
pd.merge(df1, df2, on="B", how="outer", validate="one_to_one")
#MergeError: Merge keys are not unique in right dataset; not a one-to-one mergepd.merge(df1, df2, on="B", how="outer", validate="many_to_one")
#Merge keys are not unique in right dataset; not a many-to-one mergepd.merge(df1, df2, on="B", how="outer", validate="one_to_many")

#换A作为key试试。
pd.merge(df1, df2, on="A", how="outer", validate="one_to_one")
pd.merge(df1, df2, on="A", how="outer", validate="many_to_one")
pd.merge(df1, df2, on="A", how="outer", validate="one_to_many")
#都是同一个结果

merge其他更加复杂的合并方式，请大家移步官网：https://pandas.pydata.org/docs/user_guide/merging.html

pandas - merge 函数相关推荐

Python科学计算之Pandas merge函数操作
文章目录 Merge函数 Merge介绍 Merge操作 Merge函数 Merge介绍 pandas中的merge()函数类似于SQL中join的用法,可以将不同数据集依照某些字段(属性)进行合并操 ...
pandas使用merge函数将多个dataframe数据连接起来、设置how参数为outer指定全连接（outer join）、left_on参数指定左侧dataframe的连接字段
pandas使用merge函数将多个dataframe数据连接起来.设置how参数为outer指定全连接(outer join).left_on参数指定左侧dataframe的连接字段.right_o ...
pandas使用merge函数将多个dataframe数据连接起来、设置how参数为left指定左连接（left join）、on参数指定连接字段
pandas使用merge函数将多个dataframe数据连接起来.设置how参数为left指定左连接(left join).on参数指定连接字段目录
【Python】Pandas宝藏函数-concat()
在数据处理过程中,经常会遇到多个表进行拼接合并的需求,在Pandas中有多个拼接合并的方法,每种方法都有自己擅长的拼接方式,本文对pd.concat()进行详细讲解,希望对你有帮助.pd.concat ...
【Python】精选23个Pandas常用函数
公众号:尤而小屋作者:Peter 编辑:Peter 从26个字母中精选出23个Pandas常用的函数,将它们的使用方法介绍给大家.其中o.y.z没有相应的函数. import pandas as p ...
python中merge函数怎么用_Python Merge函数原理及用法解析
Merge函数的用法简单来说Merge函数相当于Excel中的vlookup函数.当我们对2个表进行数据合并的时候需要通过指定两个表中相同的列作为key,然后通过key匹配到其中要合并在一起的val ...
Python之数据合并——【concat()函数、merge()函数、join()方法、combine_first()方法】
文章目录轴向堆叠数据--concat()函数横向堆叠与外连接纵向堆叠与内连接主键合并数据--merge()函数内连接方式外连接方式左连接方式右连接方式其他根据行索引合并数据--jo ...
pandas 常用函数整理
pandas常用函数整理,作为个人笔记. 仅标记函数大概用途做索引用,具体使用方式请参照pandas官方技术文档. 约定 from pandas import Series, DataFrame im ...
『Python核心技术与实战』pandas.DataFrame()函数介绍
pandas.DataFrame()函数介绍! 文章目录一. 创建DataFrame 1.1. numpy创建 1.2. 直接创建 1.3. 字典创建 1.4. Series和DataFrame 二 ...