Pandas 多列合併
Suraj Joshi
2023年1月30日
本教程介紹瞭如何在 Pandas 中使用 DataFrame.merge() 方法合併兩個 DataFrame。
import pandas as pd
roll_no = [501, 502, 503, 504, 505]
student_df = pd.DataFrame(
{
"Roll No": [500, 501, 503, 504, 505, 506],
"Name": ["Jennifer", "Travis", "Bob", "Emma", "Luna", "Anish"],
"Gender": ["Female", "Male", "Male", "Female", "Female", "Male"],
"Age": [17, 18, 17, 16, 18, 16],
}
)
grades_df = pd.DataFrame(
{
"Roll No": [501, 502, 503, 504, 505, 506],
"Name": ["Jennifer", "Travis", "Bob", "Emma", "Luna", "Anish"],
"Grades": ["A", "B+", "A-", "A", "B", "A+"],
}
)
print("1st DataFrame:")
print(student_df, "\n")
print("2nd DataFrame:")
print(grades_df, "\n")
print("Merged df:")
print(merged_df)
輸出：
1st DataFrame:
Roll No Name Gender Age
0 500 Jennifer Female 17
1 501 Travis Male 18
2 503 Bob Male 17
3 504 Emma Female 16
4 505 Luna Female 18
5 506 Anish Male 16
2nd DataFrame:
Roll No Name Grades
0 501 Jennifer A
1 502 Travis B+
2 503 Bob A-
3 504 Emma A
4 505 Luna B
5 506 Anish A+
我們將使用 DataFrame student_df 和 grades_df 來演示 DataFrame.merge() 的工作。
Pandas DataFrame 不含任何鍵列的預設合併
如果我們只使用傳遞兩個 DataFrames 來合併到 merge() 方法，該方法將收集兩個 DataFrame 中的所有公共列，並將兩個 DataFrame 中的每個公共列替換為一個。
import pandas as pd
roll_no = [501, 502, 503, 504, 505]
student_df = pd.DataFrame(
{
"Roll No": [500, 501, 503, 504, 505, 506],
"Name": ["Jennifer", "Travis", "Bob", "Emma", "Luna", "Anish"],
"Gender": ["Female", "Male", "Male", "Female", "Female", "Male"],
"Age": [17, 18, 17, 16, 18, 16],
}
)
grades_df = pd.DataFrame(
{
"Roll No": [501, 502, 503, 504, 505, 506],
"Name": ["Jennifer", "Travis", "Bob", "Emma", "Luna", "Anish"],
"Grades": ["A", "B+", "A-", "A", "B", "A+"],
}
)
merged_df = pd.merge(student_df, grades_df)
print("1st DataFrame:")
print(student_df, "\n")
print("2nd DataFrame:")
print(grades_df, "\n")
print("Merged df:")
print(merged_df)
輸出：
1st DataFrame:
Roll No Name Gender Age
0 500 Jennifer Female 17
1 501 Travis Male 18
2 503 Bob Male 17
3 504 Emma Female 16
4 505 Luna Female 18
5 506 Anish Male 16
2nd DataFrame:
Roll No Name Grades
0 501 Jennifer A
1 502 Travis B+
2 503 Bob A-
3 504 Emma A
4 505 Luna B
5 506 Anish A+
Merged df:
Roll No Name Gender Age Grades
0 503 Bob Male 17 A-
1 504 Emma Female 16 A
2 505 Luna Female 18 B
3 506 Anish Male 16 A+
它將合併 DataFrame student_df 和 grades_df，並分配給 merged_df。我們有兩列 Roll No 和 Name 是兩個 DataFrame 共有的，但 merge() 函式會將每個通用列合併為一列。
Pandas 設定 on 引數的值來指定合併的鍵值
import pandas as pd
roll_no = [501, 502, 503, 504, 505]
student_df = pd.DataFrame(
{
"Roll No": [500, 501, 503, 504, 505, 506],
"Name": ["Jennifer", "Travis", "Bob", "Emma", "Luna", "Anish"],
"Gender": ["Female", "Male", "Male", "Female", "Female", "Male"],
"Age": [17, 18, 17, 16, 18, 16],
}
)
grades_df = pd.DataFrame(
{
"Roll No": [501, 502, 503, 504, 505, 506],
"Name": ["Jennifer", "Travis", "Bob", "Emma", "Luna", "Anish"],
"Grades": ["A", "B+", "A-", "A", "B", "A+"],
}
)
merged_df = pd.merge(student_df, grades_df, on="Roll No")
print("1st DataFrame:")
print(student_df, "\n")
print("2nd DataFrame:")
print(grades_df, "\n")
print("Merged df:")
print(merged_df)
輸出：
1st DataFrame:
Roll No Name Gender Age
0 500 Jennifer Female 17
1 501 Travis Male 18
2 503 Bob Male 17
3 504 Emma Female 16
4 505 Luna Female 18
5 506 Anish Male 16
2nd DataFrame:
Roll No Name Grades
0 501 Jennifer A
1 502 Travis B+
2 503 Bob A-
3 504 Emma A
4 505 Luna B
5 506 Anish A+
Merged df:
Roll No Name_x Gender Age Name_y Grades
0 501 Travis Male 18 Jennifer A
1 503 Bob Male 17 Bob A-
2 504 Emma Female 16 Emma A
3 505 Luna Female 18 Luna B
4 506 Anish Male 16 Anish A+
這裡，我們設定 on="Roll No"，merge() 函式將在兩個 DataFrame 中找到 Roll No 命名的列，我們在 merged_df 將會只有一個 Roll No 列。雖然 Name 列在兩個 DataFrames 中也是通用的，但由於 Name 不作為 on 引數傳遞，所以我們為左右 DataFrame 的 Name 列單獨設定了一列，分別由 Name_x 和 Name_y 表示。
使用 left_on 和 right_on 合併 DataFrame
import pandas as pd
roll_no = [501, 502, 503, 504, 505]
student_df = pd.DataFrame(
{
"Roll No": [500, 501, 503, 504, 505, 506],
"Name": ["Jennifer", "Travis", "Bob", "Emma", "Luna", "Anish"],
"Gender": ["Female", "Male", "Male", "Female", "Female", "Male"],
"Age": [17, 18, 17, 16, 18, 16],
}
)
grades_df = pd.DataFrame(
{"Id": [501, 502, 503, 504, 505, 506], "Grades": ["A", "B+", "A-", "A", "B", "A+"]}
)
merged_df = pd.merge(student_df, grades_df, left_on="Roll No", right_on="Id")
print("1st DataFrame:")
print(student_df, "\n")
print("2nd DataFrame:")
print(grades_df, "\n")
print("Merged df:")
print(merged_df)
輸出：
1st DataFrame:
Roll No Name Gender Age
0 500 Jennifer Female 17
1 501 Travis Male 18
2 503 Bob Male 17
3 504 Emma Female 16
4 505 Luna Female 18
5 506 Anish Male 16
2nd DataFrame:
Id Grades
0 501 A
1 502 B+
2 503 A-
3 504 A
4 505 B
5 506 A+
Merged df:
Roll No Name Gender Age Id Grades
0 501 Travis Male 18 501 A
1 503 Bob Male 17 503 A-
2 504 Emma Female 16 504 A
3 505 Luna Female 18 505 B
4 506 Anish Male 16 506 A+
如果我們要合併的一列在 DataFrames 中有不同的列名，我們可以使用 left_on 和 right_on 引數。left_on 將被設定為左邊 DataFrame 中的列名，right_on 將被設定為右邊 DataFrame 中的列名。
作者： Suraj Joshi
Suraj Joshi is a backend software engineer at Matrice.ai.
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