Pandas DataFrame DataFrame.merge() 函数
Suraj Joshi
2023年1月30日
-
pandas.DataFrame.merge()语法 -
示例代码：
DataFrame.merge()函数合并两个DataFrame -
示例代码：在
merge方法中设置how参数，使用各种技术合并DataFrame -
示例代码：在 Pandas 中使用
DataFrame.merge()函数只合并特定的列 -
示例代码:使用索引作为连接键来合并
DataFrame
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
right |
DataFrame 或命名的 Series。要合并的对象 |
how |
left, right,inner 或 outer。如何进行合并操作 |
on |
标签或列表。要合并的列或索引名称 |
left_on |
标签或列表。要合并在左侧 DataFrame 中的列名或索引名。 |
right_on |
标签或列表。列名或索引名要合并到右边的 DataFrame 中。 |
left_index |
布尔型。使用左侧 DataFrame 的索引作为连接键(left_index=True) |
right_index |
布尔型。使用右侧 DataFrame 的索引作为连接键(right_index=True) |
sort |
布尔型。在输出中按字母顺序对连接键进行排序(sort=True) |
suffixes |
后缀分别应用于左侧和右侧重叠的列名 |
copy |
布尔型。避免复制 copy=False。 |
indicator |
在输出的 DataFrame 中增加一列名为 _merge 的列，其中包含每行的来源信息(indicator=True)，并在输出的 DataFrame 中增加一列名为 string 的列(indicator=string) |
validate |
检查合并是否为指定类型的参数 |
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。
作者： Suraj Joshi
Suraj Joshi is a backend software engineer at Matrice.ai.
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