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Python Pandas DataFrame.merge() 函数合并 DataFrame 或命名的 Series 对象。

pandas.DataFrame.merge() 语法

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

参数

返回值

它返回一个合并给定对象的 DataFrame。

示例代码：DataFrame.merge() 函数合并两个 DataFrame

import pandas as pd

df1 = pd.DataFrame(
    {"Name": ["Suraj", "Zeppy", "Alish", "Sarah"], "Working Hours": [1, 2, 3, 5]}
)
df2 = pd.DataFrame({"Name": ["Suraj", "Zack", "Alish", "Raphel"], "Pay": [5, 6, 7, 8]})

print("1st DataFrame:")
print(df1)
print("2nd DataFrame:")
print(df2)

merged_df = df1.merge(df2)
print("Merged DataFrame:")
print(merged_df)

输出：

1st DataFrame:
    Name  Working Hours
0  Suraj              1
1  Zeppy              2
2  Alish              3
3  Sarah              5
2nd DataFrame:
     Name  Pay
0   Suraj    5
1    Zack    6
2   Alish    7
3  Raphel    8
Merged DataFrame:
    Name  Working Hours  Pay
0  Suraj              1    5
1  Alish              3    7

它使用 SQL 的内连接技术将 df1 和 df2 合并为一个 DataFrame。

对于 inner-join 方法，我们必须确保两个 DataFrame 至少有一列是共同的。

在这里，merge() 函数将把具有相同值的公共列的行连接到两个 DataFrame。

示例代码：在 merge 方法中设置 how 参数，使用各种技术合并 DataFrame

import pandas as pd

df1 = pd.DataFrame(
    {"Name": ["Suraj", "Zeppy", "Alish", "Sarah"], "Working Hours": [1, 2, 3, 5]}
)
df2 = pd.DataFrame({"Name": ["Suraj", "Zack", "Alish", "Raphel"], "Pay": [5, 6, 7, 8]})

print("1st DataFrame:")
print(df1)
print("2nd DataFrame:")
print(df2)

merged_df = df1.merge(df2, how="right")
print("Merged DataFrame:")
print(merged_df)

输出：

1st DataFrame:
    Name  Working Hours
0  Suraj              1
1  Zeppy              2
2  Alish              3
3  Sarah              5
2nd DataFrame:
     Name  Pay
0   Suraj    5
1    Zack    6
2   Alish    7
3  Raphel    8
Merged DataFrame:
     Name  Working Hours  Pay
0   Suraj            1.0    5
1   Alish            3.0    7
2    Zack            NaN    6
3  Raphel            NaN   8 

它使用 SQL 的 right-join 技术将 df1 和 df2 合并为一个 DataFrame。

在这里，merge() 函数从右边的 DataFrame 返回所有的行。然而，只存在于左侧 DataFrame 中的行将得到 NaN 值。

同样，我们也可以使用 how 参数的 left 和 outer 值。

示例代码：在 Pandas 中使用 DataFrame.merge() 函数只合并特定的列

import pandas as pd

df1 = pd.DataFrame(
    {
        "Name": ["Suraj", "Zeppy", "Alish", "Sarah"],
        "Working Hours": [1, 2, 3, 5],
        "Position": ["Salesman", "CEO", "Manager", "Sales Head"],
    }
)
df2 = pd.DataFrame(
    {
        "Name": ["Suraj", "Zack", "Alish", "Raphel"],
        "Pay": [5, 6, 7, 8],
        "Position": ["Salesman", "CEO", "Manager", "Sales Head"],
    }
)

print("1st DataFrame:")
print(df1)
print("2nd DataFrame:")
print(df2)

merged_df = df1.merge(df2, on="Name")
print("Merged DataFrame:")
print(merged_df)

输出：

1st DataFrame:
    Name  Working Hours    Position
0  Suraj              1    Salesman
1  Zeppy              2         CEO
2  Alish              3     Manager
3  Sarah              5  Sales Head
2nd DataFrame:
     Name  Pay    Position
0   Suraj    5    Salesman
1    Zack    6         CEO
2   Alish    7     Manager
3  Raphel    8  Sales Head
Merged DataFrame:
    Name  Working Hours Position_x  Pay Position_y
0  Suraj              1   Salesman    5   Salesman
1  Alish              3    Manager    7    Manager

它只合并 df1 和 df2 的 Name 列。由于默认的连接方法是内连接，因此只有两个 DataFrame 的共同行才会被连接。position 列是两个 DataFrame 共同的，因此有两个位置列，即 Position_x 和 Position_y。

默认情况下，_x 和 _y 后缀被附加到重叠列的名称中。我们可以使用 suffixes 参数指定后缀。

df1 = pd.DataFrame(
    {
        "Name": ["Suraj", "Zeppy", "Alish", "Sarah"],
        "Working Hours": [1, 2, 3, 5],
        "Position": ["Salesman", "CEO", "Manager", "Sales Head"],
    }
)
df2 = pd.DataFrame(
    {
        "Name": ["Suraj", "Zack", "Alish", "Raphel"],
        "Pay": [5, 6, 7, 8],
        "Position": ["Salesman", "CEO", "Manager", "Sales Head"],
    }
)

print("1st DataFrame:")
print(df1)
print("2nd DataFrame:")
print(df2)

merged_df = df1.merge(df2, on="Name", suffixes=("_left", "_right"))
print("Merged DataFrame:")
print(merged_df)

输出：

1st DataFrame:
    Name  Working Hours    Position
0  Suraj              1    Salesman
1  Zeppy              2         CEO
2  Alish              3     Manager
3  Sarah              5  Sales Head
2nd DataFrame:
     Name  Pay    Position
0   Suraj    5    Salesman
1    Zack    6         CEO
2   Alish    7     Manager
3  Raphel    8  Sales Head
Merged DataFrame:
    Name  Working Hours Position_left  Pay Position_right
0  Suraj              1      Salesman    5       Salesman
1  Alish              3       Manager    7        Manager

示例代码:使用索引作为连接键来合并 DataFrame

import pandas as pd

df1 = pd.DataFrame(
    {
        "Name": ["Suraj", "Zeppy", "Alish", "Sarah"],
        "Working Hours": [1, 2, 3, 5],
        "Position": ["Salesman", "CEO", "Manager", "Sales Head"],
    }
)
df2 = pd.DataFrame(
    {
        "Name": ["Suraj", "Zack", "Alish", "Raphel"],
        "Pay": [5, 6, 7, 8],
        "Position": ["Salesman", "CEO", "Manager", "Sales Head"],
    }
)

print("1st DataFrame:")
print(df1)
print("2nd DataFrame:")
print(df2)

merged_df = df1.merge(
    df2, left_index=True, right_index=True, suffixes=("_left", "_right")
)
print("Merged DataFrame:")
print(merged_df)

输出：

1st DataFrame:
    Name  Working Hours    Position
0  Suraj              1    Salesman
1  Zeppy              2         CEO
2  Alish              3     Manager
3  Sarah              5  Sales Head
2nd DataFrame:
     Name  Pay    Position
0   Suraj    5    Salesman
1    Zack    6         CEO
2   Alish    7     Manager
3  Raphel    8  Sales Head
Merged DataFrame:
  Name_left  Working Hours Position_left Name_right  Pay Position_right
0     Suraj              1      Salesman      Suraj    5       Salesman
1     Zeppy              2           CEO       Zack    6            CEO
2     Alish              3       Manager      Alish    7        Manager
3     Sarah              5    Sales Head     Raphel    8     Sales Head

它合并两个 DataFrame 的相应行，不考虑列的相似性。如果两个 DataFrame 上出现相同的列名，则在合并后将后缀附加到列名上，使之成为不同的列。