pandas - concat函数

最新推荐文章于 2025-11-09 09:25:46 发布

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本文详细介绍了如何使用pandas在Python中进行DataFrame的纵向（axis=0）和横向（axis=1）合并，以及通过join参数实现外连接和内连接。此外，还讲解了ignore_index和keys选项的作用，以及在keys和levels使用场景。

import pandas as pd

df1 = pd.DataFrame(
    {
        "A": ["A0", "A1", "A2", "A3"],
        "B": ["B0", "B1", "B2", "B3"],
        "C": ["C0", "C1", "C2", "C3"],
        "D": ["D0", "D1", "D2", "D3"],
    },
    index=[0, 1, 2, 3],
)
df2 = pd.DataFrame(
    {
        "A": ["A4", "A5", "A6", "A7"],
        "B": ["B4", "B5", "B6", "B7"],
        "C": ["C4", "C5", "C6", "C7"],
        "D": ["D4", "D5", "D6", "D7"],
    },
    index=[4, 5, 6, 7],
)
df3 = pd.DataFrame(
    {
        "A": ["A8", "A9", "A10", "A11"],
        "B": ["B8", "B9", "B10", "B11"],
        "C": ["C8", "C9", "C10", "C11"],
        "D": ["D8", "D9", "D10", "D11"],
    },
    index=[8, 9, 10, 11],
)
df4 = pd.DataFrame(
    {
        "B": ["B2", "B3", "B6", "B7"],
        "D": ["D2", "D3", "D6", "D7"],
        "F": ["F2", "F3", "F6", "F7"],
    },
    index=[2, 3, 6, 7],
)

参数

#ALL default
data = pd.concat([df1, df2], 
                 axis = 0, 
                 join = "outer",
                 ignore_index = False,
                 keys = None,
                 levels = None,
                 names = None,
                 verify_integrity = False,
                 copy = True
                )

axis = 0 纵向合并

df4 = pd.DataFrame(
    {
        "B": ["B2", "B3", "B6", "B7"],
        "D": ["D2", "D3", "D6", "D7"],
        "F": ["F2", "F3", "F6", "F7"],
    },
    index=[2, 3, 6, 7],
)
pd.concat([df1, df4], axis=0)

axis = 1 横向合并

df4 = pd.DataFrame(
    {
        "B": ["B2", "B3", "B6", "B7"],
        "D": ["D2", "D3", "D6", "D7"],
        "F": ["F2", "F3", "F6", "F7"],
    },
    index=[2, 3, 6, 7],
)
pd.concat([df1, df4], axis=1)

join = "outer" 纵向取并集

pd.concat([df1, df4], axis=0, join="outer")

join = "inner" 纵向取交集

#保留相同的列索引。
pd.concat([df1, df4], axis=0, join="inner")

ignore_index=True

False可查看join = "outer" 纵向取并集

# True代表，两个dataframe合并之后，index不再使用各自的，而是相当于全体重置
pd.concat([df1,df4], axis=0, join="outer",ignore_index=True)

keys 嗯，有点难以用语言表达,找个头头？

#可以用list
pd.concat([df1, df4], axis=0, join="outer",\
        ignore_index=False,keys=["DataFrame1","DataFrame2"])

#也可以用tuple
data2 = pd.concat([df1, df2, df4], axis=1, \
        join="outer",ignore_index=False,keys=\
        ("DataFrame1","DataFrame2","DataFrame4"))

data2.DataFrame1

#当keys有重复时，使用verify_interity 就护报错。
pd.concat([df1, df2, df4], axis=1, \
        join="outer",keys=("DataFrame1","DataFrame2",\
        'DataFrame2'),verify_integrity = True)

levels 我感觉没什么大用处,就没测（实际是测了也不知道有啥用）

names 用他前提是用levels，我跳

#当keys有重复时，使用verify_interity 就护报错。
pd.concat([df1, df2, df4], axis=1, \
        join="outer",keys=("DataFrame1","DataFrame2",\
        'DataFrame2'),verify_integrity = True)