pandas - merge 函数

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我是靠谱客的博主愤怒墨镜，最近开发中收集的这篇文章主要介绍pandas - merge 函数，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

# 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 merge

pd.merge(df1, df2, on="B", how="outer", validate="many_to_one")
#Merge keys are not unique in right dataset; not a many-to-one merge


pd.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